international conference

ISBN: 978-93-84007-77-5

19th-20th, November 2018

9 789384 007775

IMPACT OF GLOBAL ATMOSPHERIC CHANGES ON NATURAL RESOURCES (IGACNR)

Impact of Climate Change is seriously increasing and is evident across the Globe. Professional Environmental Engineers are fully aware of the fact that the impact or effect of Climate Change must be a part of planning and management of all projects involving resources such as water, air and soil. It is high time to take steps to scale up the clean energy production and to initiateat International Climate negotiation. As a part of Global Natural Resources, keeping in view of climate initiation, is required to analyze the stress on various natural resources and identify various challenges, appropriate solution, feasible action and responsibilities involved in research, awareness, education and management. The major action or activities identified for focused attention from the Government, Academic and Private sectors are mainly to strengthen sustainability and resilience of natural and engineered systems, including reduced risk. Thus greater knowledge of climate in the environment and institutional responses by synchronizing the power of multidisciplinary approaches employing Atmospheric Sciences, Statistics, Chemistry, Hydrology and Geology are required. Thus the multidisciplinary knowledge application between natural resources and climate related challenges work towards the safe environment and save the general environment from clutches of devastating impact due to climate change. About the Conference Our Supporters 19th-20th, November 2018 The conference will provide opportunity to bring together scientists, engineers and practicing professionals from Government Departments, Private Institutions, Consulting Establishments, Research Institutes and University Organizations to discuss emerging technologies and scientific advancements in these themes during the conference.

NAAC Accredited with A Grade

INTERNATIONAL CONFERENCE IMPACT OF GLOBAL ATMOSPHERIC CHANGES ON NATURAL RESOURCES (IGACNR)

19th-20th, November 2018 EDITORS Dr. Usha N. Murthy & Dr . H.B. Rekha

ORGANISED BY DEPARTMENT OF CIVIL ENGINEERING, UVCE, BANGALORE UNIVERSITY, BENGALURU

INTERNATIONAL CONFERENCE

IMPACT OF GLOBAL ATMOSPHERIC CHANGES ON NATURAL RESOURCES IGACNR-2018 19th-20th November 2018

Organized by Department of Civil Engineering, UVCE, Bangalore University, JB Campus Bengaluru, Karnataka, India.

in Association with

Organization The International Conference on Impact of Global Atmospheric Changes on Natural Resources (IGACNR 2018) was held at Department of Civil Engineering, Bangalore University, Jnanabharathi Campus, Bengaluru, India.

Conference Organization Chief Patrons Dr. K. R. Venugopal

Honorable Vice Chancellor, Bangalore University, Bengaluru.

Patrons Dr. B. K. Ravi

Registrar, Bangalore University, Bengaluru

Sri. Laxman

Chairman, KSPCB, Bengaluru.

Sri. Tushar Girinath

Chairman, BWSSB, Bengaluru

Co- Patrons Dr. A. Lokesh

Finance officer, Bangalore University, Bengaluru

Dr. H. N. Ramesh

Principal, UVCE, Bangalore University, Bengaluru

Convenor Dr. Usha. N. Murthy

Professor and Chairperson, Department of Civil Engineering, UVCE, Bangalore University, JB Campus, Bengaluru

Organizing Secretary Dr. Rekha. H. B.

Assistant Professor, Department of Civil Engineering, UVCE, Bangalore University, JB Campus, Bengaluru.

Members- Organizing Committee Dr. B. Santhaveerana Goud

Professor, UVCE, Bangalore University

Dr. M. S. Amarnath

Professor, UVCE Bangalore University

Dr. Harish. G. R

Professor, UVCE Bangalore University

Dr. A.S. Ravikumar,

Associate Professor, UVCE Bangalore University

Dr. Keshava Murthy. M


Dr. Manjesh. L


Mr. Bhavanishankar. S


Dr. Gangadhara. S


Dr. Govinda Raju. L


Dr. Nagaraja.P. S


Dr. Sadath Ali Khan Zai


Dr. Shivakumar.J. Nyamathi


Dr. A.V. Sriram


Dr. M. Inayathulla


Dr. Viswanath. B


Dr. Annapurna. B.P


Dr. Suresh.G.,


Dr. Krishna.A.,


Dr. Jayaramappa. N


Dr. Kiran.T


Dr. KVSB Raju

Assistant Professor, UVCE Bangalore University

Dr. Chethan. K


Dr. Mudduraju H C


Technical Committee Dr. B.S. Nagendra Prakash

Professor, Department of Civil Engineering, UVCE, JB Campus Bangalore University, Bengaluru

Dr. B.S. Jai Prakash,

Director, IEHMM, Office: VV Puram College of Science, K R Road, Bengaluru-04.

Sri. Mahesh

Senior Environmental Officer, KSPCB, Bengaluru.

Dr. Mayanayak

HOD, Department of Civil Engineering, BMSCE, Bengaluru.

Dr. K. C. Jayaram

Professor, Civil Engineering Department, BIT, Bengaluru and Chairman SEIAA, Karnataka.

Dr. Vijay U T,

Principal Scientific Officer, KSCST, IISc Campus, Bengaluru.

Dr. Doddashanaiah

Environmental Officer, KSPCB, Bengaluru.

Dr. Harinath

Professor, School of Civil Engineering, REVA University, Bengaluru.

Dr. Umadevi

Associate Professor, MSRIT, Bengaluru.

Dr. B M Krishna

Associate Professor, Department of Environmental Engineering, JSS Science and Technology, Mysore.

Dr. Ashok D. Hanjagi

Professor, Department of Geography and Geo informatics, Bangalore University, Bangalore-560056.

Dr. Nagaraj B C

Associate Professor, Department of Environmental Science, Bangalore University, Bengaluru.

Programme Committee International Advisory Committee Dr. Rampur Viswanath

Founder and President, ACHMM-India Chapter, Bangalore; International Ambassador – Alliance of Hazardous Materials Professionals, USA.

Dr. Kranehert

Professor, Stuttgart University, Germany.

Dr. Nicola, Professor

Vice-President and Director of Natural Resources Conservation, Department of Hydrology and Water Resources Management, KIEL University, Germany.

Yang-Long Wu

Secretary General, Chinese Taiwan Water Works Association, Taipei.

Dr. S Mahadev

President of Karins & Associates, Newark, Delaware, USA.

National Advisory Committee Dr. K Ranga

Former Principal, UVCE, Bengaluru.

Dr. G Ranganna

Professor (retd.), NITK, Surathkal, Mangalore.

Dr. Raman N S

Deputy Director, NEERI, Nagpur.

Dr. Lokesh K S

Registrar, JSS Science and Technology, Mysore.

Dr. J Karthikeyan

Professor, Tirupathi University, Andhra Pradesh.

Dr. N T Manjunath

Professor, UBDT, Davanagere.

Dr. B V Ravishankar

Vice Principal, BMSCE, Bengaluru.

Dr. T V Ramachandra

Professor, Centre for Ecological Science, IISc, Bengaluru

Dr. Chanakya

Professor, Centre for Ecological Science, IISc, Bengaluru.

Dr. Shivakumar Babu

Professor, Department of Civil Engineering, IISc, Bengaluru.

Dr. Nagesh Kumar

Professor, Civil Engineering Department, IISc, Bengaluru.

Dr. Shivanagendra

Associate Professor, IIT Madras, Chennai.

Dr. Nandini, Professor

Environmental Science Department, BUB, Bengaluru

Dr. A Ramesh

Chief Environmental Officer, KSPCB, Bengaluru.

Dr. Lakshmikanth

Environmental Officer, KSPCB, Bengaluru.

PREFACE This Book contains the proceedings of the International Conference on Impact of Global Atmospheric Changes on Natural Resources (IGACNR) held in Department of Civil Engineering, Bangalore University, Bengaluru, India, during November 19 th -20th 2018. Some of the best researchers will convey keynote addresses in the theme areas of the conference. This gives an opportunity to the delegates to interact with these specialists and to address some of the challenging problems in the area of civil engineering. The International Conference on the Impact of Global Atmospheric Changes on Natural Resources (IGACNR) pulled in more than 75 entries. Through thorough associate surveys, 54 High-quality papers were suggested by the program committee. The IGACNR- 2018 conference centers around the tools and modern techniques to reduce the impact on natural resources. IGACNR-2018 is committed to novel methods in the fields of sustainable water and waste water solutions, solid, Biomedical and hazardous waste, climate change, air pollution control, water/air modeling, innovative applications in structures. The conference features several keynote addresses in the area of Environmental Engineering. These regions have been perceived to be key advances ready to shape the cutting edge society in the following decade. On behalf of the organizing committee, I would like to acknowledge the support from Honorable Vice Chancellor and Registrar, Bangalore University, Bengaluru, Chairman, Karnataka State Pollution Control Board, Chairman, Bangalore Water Supply and Sewerage Board, President-Academy of Certified Hazardous Materials Managers (ACHMM), USA, Director- IEHMM, Bengaluru, Chairman-Indian Water Works Association and Indian Institution of public Health Engineering, Director, Council of Scientific and Industrial Research (CSIR) who helped Us to achieve our goals for the conference. I wish to express our appreciation to Scientific International Pvt Ltd for publishing the proceedings of IGACNR-2018. I would like to thank the authors for submitting their work, as well as the Technical Program Committee Members and Reviewers for their enthusiasm, time and valuable suggestions. The contribution from the Organizing Committee in setting up and maintaining the online submission system, assigning papers to the reviewers and preparing the camera ready version of the Proceedings is highly appreciated, I would like to profusely thank them for making the IGACNR 2018 a success.

November 2018

Dr. Usha N. Murthy Convenor IGACNR-2018 Professor and Chairperson Department. of Civil Engineering Bangalore University Bengaluru

CONTENTS SL NO

TITLE

Page No

1.

On–Site Investigation on Deviations of Pollutants Before, During & After Demolition of a Residential Building Harsha N, Umadevi B, S. M. Naik

1-6

2.

Inventorization of Air Quality Index (AQI) – A comparative study of Different locations Col. Rajshekar Hiremath, Ravi D.R, Indira B C, Prof Maya Naik

7-10

3.

Characterizing Visibility Meteorology and Air Quality for a Tropical Coastal City Savitha Ulavi, Shiva Nagendra S.M

11-14

4.

A Review on Status of Ambient Noise Levels in Major Cities across India G Savitha Swamy, G.P. Shivashankara

15-19

5.

Diurnal Variability Of PM2.5 & PM10 and Influence of Meteorological Parameters on PM in Urban Areas of Bangalore H.N. Sowmya, Dr. H.K. Ramaraju, G.P. Shivashankara

20-22

6.

Spatial Modelling Using Satellite Air Quality Data and its Association with Respiratory Cancers for Western Tamil Nadu, India Janani Selvaraj, Prashanthi Devi Marimuthu, Harathi Parasur Babu

23-26

7.

Air Quality Modeling Studies for Peenya Industrial Area Using ISCST3 Kiran D A, Usha.N.Murthy, Namrata V Reddy

27-30

8.

Reduction of Food Waste by Scientific Methods G Gayathri, Shashi Kiran C R, Beulah M

31-36

9.

Biogas Generation from Kitchen Waste Devendra G and Rekha H B

37-41

10.

Deriving Short Term Benefits through Proper Waste Management and Public Attitude towards Waste Derived Economy Kavya Siddeshwar , Mihigo Felix, Janani Selvaraj, Shivaraju.H.P, Prashanthi Devi.M

42-44

11.

Environmental Impacts of Solid Waste Pollution at Ariyamangalam Dump Yard of Tiruchirappalli District, Tamil Nadu Mihigo Felix

45-49

12.

Production of Bio-Diesel and Comparison of Waste Cooking Oil Produced Bio-Diesel with Low Cost Oils Somesh M U and Usha N Murthy

50-53

13.

Treatment of Coffee Processing Effluent by Electrochemical Coagulation Neshma C M, B M Krishna

54-57

14.

Nutrient Removal from Domestic Waste Water using Sequential Batch Reactor Nisha K S, Thanushree S, Pushpa Tuppad, Manoj Kumar B

58-60

CONTENTS 15.

Utilization of Bio-waste Resources using Bioreactor Landfill- an Analysis from the Perspective of Sustainability Ananya Ghosh, Jyoti Prakas, Sarkar, Bimal Das

61-64

16.

Chlorella Pyrenoidosa Mediated Phycoremediation of Landfill Leachate Abhilash T Nair, S M Shiva Nagendra

65-68

17.

Treatment of Rural Wastewater through a Vertical Subsurface Flow Constructed Wetland (VSSF CW) Shruthi R and Shivashankar G P

69-72

18.

Photochemical Treatment of Pharmaceutical Wastewater by Advanced Oxidation Process Devendra G and Rekha H B

73-76

19.

Assessment of Measurement Quality Through Law of Propagation of Uncertainty in Water Analysis M Bhamini

77-83

20.

Assessment of Groundwater Quality Status in the Residential Area Surrounding Peenya Industrial Area Waseem Raza, Aquib Nasir Razi, C. R. Ramakrishnaiah

84-87

21.

Ground Water Resources Assessment in Chickballapur District, Karnataka India. Tejaswini. C, Pallavi .R, Anusha Daroji, Nanjundi Prabhu, M.Inayathulla

88-93

22.

Hydrological Study of Bangalore Urban, Karnataka, India. Priyanka.V.Shastry, Vishnu Sai Mahesh, Yeshaswini Nandeesh, Nanjundi Prabhu, M.Inayathulla

94-101

23.

A GIS based Automatic Drainage Network Extraction for Hoskote Taluk using CARTOSAT-1 30M Digital Elevation Model Shwetha. A, Sampath Kumar. M.C, M. Rajyalakshmi

102-107

24.

Development of Intensity–Duration–Frequency Curves for Vrishabhavathi Sub-Watershed Jagadeesh C. B. and Nagaraj Sitaram

108-111

25.

GIS Based Groundwater Quality Mapping in Southwestern Part of Tumakuru District, Karnataka, India Nandeesha, Vishal.R.Khandagale, S.G.Swamy

112-117

26.

A GIS Based Groundwater Quality Assessment of Vrishabhavathi Watershed, Karnataka, India Srinidhi M S and Rekha H B

118-121

27.

Stormwater Quality Investigation at Sri Gali Anjaneya Temple Sub-Watershed Rachana H Savanur and Rekha H B

122-128

28.

Assessment of Seasonl Variations of Groundwater Quality for Shimsha Watershed Using RS And GIS Harshita K M, Usha.N.Murthy, Namrata V Reddy

129-134

CONTENTS 29.

A Review on Impact of Climate Change on Food Security Shashi Kiran C R and M C Sampathkumar

135-139

30.

Challenging Solutions to Resist Climate Changes Priya.V, Sampath Kumar.M.C, Balasubramanyan.N

140-145

31.

Climate Change - A Regional Scale Case Study on Meteorological Parameters L. Udaya Simha, Hemanth Kumar .G

146-150

32.

Water Resilency in the Era of Climate Change-How Communities Can Adapt Brabandan Borgais

33.

Evaluation of Red Earth Amended with GGBS and Bentonite Mixture as a Liner to Contain Chromium Ion Harijayakrishna A, Waseem Raza and Maya Naik

152-156

34.

Use of Fiber Reinforced Plastic Waste as an Additive in Bituminous Concrete Mixes for Flexible Pavement Constructions. Ranjith R, Manjesh L and Rekha H B

157-160

35.

Evaluation of Transport of Copper Ion Through Black-Cotton Soil Amended with Admixtures as a Liner Material Harijayakrishna A, Waseem Raza and Maya Naik

161-165

36.

Fiber Reinforced Plastic Waste in Concrete Pooja Tuppad, Rekha H B and N. Jayramappa

166-168

37.

Morphometry, Runoff Estimation and Hypsometry Analysis of Kabini Command area using RS and GIS Anjan Kumar and A S Ravikumar

169-175

38.

Quantitative Morphometric analysis and Estimation of Runoff using RS and GIS Techniques Ashwini G M and A S Ravikumar

176-180

39.

Trend Analysis of Temperature and Rainfall for Krishna River Basin Chauhan Prithviraj and A V Sriram

181-191

40.

Morphometry and Soil Loss Estimation of Malaprabha Watershed Hanamantray S Kakkalameli and Shivakumar J Nyamathi

192-196

41.

Sensitivity Analysis of Parameters Influencing Reference Evapotranspiration for Kabini Basin Pallavi S and A V Sriram

197-201

42.

Morphometric Analysis and Monthly Runoff Estimation for Yagachi Catchment Poornima H C and Shivakumar J Nyamathi

202-206

43.

Ecological Benefit of Construction of River Embankment and Vetiver Vegetation on Riparian of Venkatapura River Watershed Rohit Prabhakar Nayak and Shivakumar J Nyamathi

207-211

151

CONTENTS 44.

Drought Anlysis By using SPI and SDF Analysis of Drought and Wet Period in Krishna Basin Chauhan Prithviraj and A V Sriram

212-219

45.

Application of Fuzzy Logic Concepts for Reference Evapotranspiration Pallavi S and A V Sriram

220-223

46.

Seismic Analysis of Multi-Storey Building in P-Delta Effect N.Jayaramappa, B.P. Annapurna, Siddaling Gauli, Abhishek K

224-229

47.

Analytical Study of Ferrocement Panels as Loadbearing Wall Under Dynamic Loading Mr. Somashekar, N. Jayaramappa and B. P. Annapurna

230-234

48.

Experimental Study on Ferrocement Panels Under Static Loading Rashmi M, N. Jayaramappa and B. P. Annapurna

235-238

49.

Flexural Behaviour of Post Tensioned Beam Using Silica Fuma as Mineral Admixture N.Jayaramappa, Rajesha R N, Ankith M

239-241

50.

Performance of Steel and Polypropylene Fiber Square RC Slabs Subjected to Static and Impact Loading Metireddy Sai Kiran Reddy, Kiran T

242-245

51.

Static and Impact Behaviour of RCC Slabs With and Without Steel and Nylon Fibers Prashantha H M, Kiran T

246-250

52.

Compressive Strength of Concrete with and without Steel Fibers Under Different Curing Period Pushpalatha. N, Kiran. T

251-256

53.

Study on Strength Properties of Hybrid Fiber (Coir +Arecanut husk) Reinforced Concrete Manjunath Itagi, B.P. Annapurna

257-259

54.

Flexural Behaviour of Slab with Hybrid Fiber (Coir +Arecanut Husk) Reinforced Concrete Manjunath Itagi, B.P. Annapurna

260-263

Impact of Global Atmospheric Changes on Natural Resources (IGACNR)

On–Site Investigation on Deviations of Pollutants Before, During & After Demolition of a Residential Building Harsha N. Research Scholar, Department of Civil Engineering MSRIT, Bengaluru [email protected]

Umadevi B. Associate Professor, Department of Civil Engineering MSRIT, Bengaluru [email protected]

S. M. Naik Professor, Department of Civil Engineering MSRIT, Bengaluru [email protected]

number of epidemiological studies have shown excess mortality due to PM exposure from sources such as road traffic and industries. Furthermore, excessive inhalation of PM10 and PM2.5 has been linked to a variety of respiratory diseases, such as lung cancer, asthma and cardiovascular diseases, besides depression problems among Construction Workers. Numerous studies have reported increased risk of death due to ischemic heart disease among construction plasterers, masons and welders. Similar adverse health effects have also been observed among non-smoking workers at construction sites.

Abstract— Urban Infrastructure, such as buildings, roads and bridges, are replaced after the life cycle of individual assets. Building activities produce large quantities of pollutants that could be inhaled by onsite workers and people living in the neighbourhood, but studies assessing ambient exposure at the real world demolition sites are limited. This study investigates the interaction between pollutants & changes in meteorological data before, during & after demolition of a residential building. Daily mean concentrations of PM10 and Noise was found to exceed the Central Pollution Control Board target limit value of 100 µg/m3 and 75 dB (A) at the site but remained within the allowable exceedances before demolition and after demolition. In general, construction works were found to influence the concentrations of PM10 relatively more than PM2.5. Splitting of the data before demolition, during demolition and after demolition working hours (0900–1700 h; local time) showed higher concentrations of PM10 during demolition when compared with before and after demolition. Keywords — Construction Sites, Demolition Environmental Impacts, Meteorological Impacts, Particulate Matter, Workplace Air Quality.

There is a reasonable amount of literature on emissions of coarse (hereafter referred to PM2-5-10 fraction) and fine (PM2.5) particles from sources such as industrial works, road works, road vehicles and non-vehicular activities (Azarmi F. 2014) However, there are limited studies that have measured emissions and exposure to PM around operational building demolition sites, which is the focus of this paper. In order to fill the existing research gaps in the literature, this study investigates the interaction between pollutants & deviations from average meteorological data before, during and after demolition of a residential building. The objectives of this research were to:

I. INTRODUCTION Environmental safety is an important issue throughout the world. Construction activities have enormous direct and indirect effects on the environmental surroundings. Pollution sources resulting from demolition processes include harmful gases, noise, dust and solid wastes. The increased rate of building demolition could be linked to growing population of the urban areas and the need for improvements to meet new urban design guidelines and adopt building technologies. For example, the global urban population is expected to increase by about 60% in 2035 from the present levels. Demolition works can be contemplated as a part of construction activities. When concrete structures approach the end of their useful life, demolition and replacement are inevitable. All such building activities are known to release significant amounts of coarse particles into the local environment. However, the impact of Particulate Matter (PM) emitted in the coarse(PM2.5– 10; between 2.5 and 10 µm) and fine (PM2.5 ≤2.5 µm) particle size range from such activities is that it contains a wide variety of toxic organic substances and may adversely affect the respiratory health of nearby residents. Exposure to Particulate Matter (PM), including PM10 (≤10 µm) and PM2.5 (≤2.5 µm) are known to have adverse impacts on the human health. A

a.

Study a series of PM10 and PM2.5 measurements at three monitoring stations around the demolition of a residential building to assess their impact on the air quality in the surrounding areas.

b.

Identify and characterize the environmental impacts caused by PM emanating from demolition sites with different aerodynamic diameters (PM10, PM2.5) based on an exploratory study.

c.

Quantify the emission and exposure rates of particles and their dispersion in the downwind of demolished building. II. BACKGROUND

Environmental Provisions, Health and Safety To date there are no guidelines to promote safety and health for the people in demolition works. Public safety and health in demolition works can be found in Factories and Machineries Act (FMA) 1967 (Building Operations and Works

[1]

19TH – 20TH November 2018


of Engineering Construction) (Safety) Regulations 1986, Occupational Safety and Health (OSHA) 1994 and Guidelines for Public Safety and Health in Construction Site (GPSHCS) 1994. Safety, health and environmental aspects have to be referenced to other relevant requirements and regulations. Factories and Machineries Act (1986) is the earliest Act on safety regulations in demolition works, which concerned more about the general population related with the site like the representatives and subcontractors. A portion of the regulations from this Act had been incorporated inside GPSHCS 1994. Occupational Safety and Health 1994 is a self-regulation act which planned to promote a safety culture among professionals in almost all industries. It is an Act that uses to prosecute people who commit negligence where a Code of Practice or Guidelines can't do it even despite the fact that they are more detailed than the Act itself.

greater lung penetration and onward passage across the airblood obstruction.

Atmospheric Pollution and Particulate Matter

Standard references are set so as to separate between a polluted and non-polluted atmosphere. These are decided by air quality standards, which characterize the maximum concentration levels of an atmospheric pollutant, which considers that higher concentrations will influence the health and safety of the population (primary standard) and in addition result in damages to the flora, fauna, material and environment in general (secondary standard). These guidelines were set up based on logical examinations concerning the impacts produced by a particular pollutant. The WHO suggests the standards for PM2.5 air quality as 25 µg/m3 utilizing a 24-h averaging time, in light of surely understood short and longterm health effects. The objectives, which were developed for SO2, NO2, CO, O3 and TSP, are proposed to give background information, a uniform scale for evaluating air quality.

The lifetime of PM2.5 in the atmosphere is normally several days without precipitation, since fine particles have an insignificant sedimentation rate and are not evacuated quickly by dry deposition processes. They can be transported a large number of km and stay in the atmosphere for a number ofdays. Coarse dust particles can settle quickly from the atmosphere (within hours) and regularly travel only short distances. The chemical content of particulate matter is a fundamental component of information for evaluating its source and health effects. Information on chemical composition permits the identification of the potential destructive effect of PM. Air Quality Standards

Particulate Matter (PM) is made out of inert carbonaceous cores with numerous layers of different adsorbed molecules, including metals, organic pollutants, acid salts and biological elements, for example, endotoxins, allergens and pollen fragments. Particulate Matter is classified in the following types. The name given to particles of sizes upto about 50 µm is “Total Suspended Particulates” (TSP) (Ingrid P. S. Araujo 2014). The bigger particles in this class are too large to get past through noses or throats, thus they can't enter into human lungs. Sometimes they are from wind-blown dust and may cause soiling of structures and clothes. However, Total Suspended Particulates samples may likewise contain the small PM10 and PM2.5 particles that may go into humanlungs.

The WHO suggests the standards for PM2.5 air quality as 25 µg/m3 utilizing a 24-h averaging time, in light of surely understood short and long-term health effects. The objectives, which were developed for SO2, NO2, CO, O3 and TSP, are proposed to give background information, a uniform scale for evaluating air quality.

The degree to which airborne particles infiltrate the human respiratory system is resolved mainly by size, with conceivable health effects resulting from the existence of toxic substances. Visibility degradation is known to be a component of both the size and content of the airborne particles. A clear distinction is that particles smaller than 2.5 µm infiltrate into the alveoli and terminal bronchioles; bigger particles of up to 10 µm will drop fundamentally in the primary bronchi, and considerably bigger particles (up to 100 µm) will deposit in the nasopharynx.

The Clean Air Act was amended in 1990 and necessitates that the EPA supports the Standard or National Standard of Air Quality (NAAQS), for compounds thought about unsafe to human health and the environment. The Clean Air Act built up two types of European Union national air quality standards: the Primary Standard, which authorizes standards to characterize population health, including the health of sensitive populations, for example asthmatics, kids and the elderly; the Secondary Standard, which forces limits to protect public welfare, including assurance against decreased visibility, harm to animals, yields, vegetation and structures.

By a long shot the greatest number of particles falls into the ultrafine size range, comprising of PM with a diameter of 0.1 µm or less (PM0.1). These ultrafine particles (UFPs) influence the surface area of particulate pollution, yet don’t contribute to a great extent to the PM mass. These size fractions emerge basically from combustion emissions and also from particles produced by gas-to-particle transformation processes. They are characteristically insecure and develop into bigger particles through coagulation and condensation. These particles are influenced by sulphates, nitrates, organic carbon (OC) and in particular, elemental carbon (EC). Ultrafine particles (UFPs) present a specific health threat in that their small size permits

III. LITERATURE REVIEW Fisher B., et. al. 2012 studied the Ultra Fine Particle (UFP) fraction arising from building activities both due to length constraints and the lack of published information compared with larger size fractions. Preliminary evidence of UFP dust

[2]



released during the processing of concrete materials was presented and the importance of such emissions and associated exposure were discussed. The need for risk assessment and management strategies was also examined and some of the research gaps highlighted. Som C., et al. 2012 investigated the release of particles in the 5–560 nm range from three simulated building activities. The measured background sizedistributions showed modal peaks at about 13 and 49 nm with average background Particle Number Concentrations (PNC) 1.47 × 104 cm−3. The consequences of this investigation solidly illustrate the release of UFPs and raise a requirement for further detailed examinations and planning health and safety related exposure guidelines for laboratory workplaces and operational building destinations.

risk of mortality from lung cancer in demolition workers. The danger of silicosis mortality for many demolition workers was higher than 1/1,000 (unacceptable level of danger). Assessing the lifetime lung cancer mortality demonstrated a higher danger of mortality from lung cancer in building demolition workers. Nassib Jabboura, et. al. 2017 proposed a hypothesis for observation in view of the volatilization of materials at the concrete fracture interface. The outcomes from this investigation affirm that mechanical methods can produce ultrafine particles (UFP) from concrete, and that the particles are unpredictable. IV.

Mike Mulheron, et. al. 2014 measured Particle number and mass emissions from mixing, drilling and cutting. Emission factor and exposure during these simulated activities were estimated. Average PNC were 4–15 times higher over the background PNC during the activities. Average exposure doses varied up to about 38-times during the studied activities. Negligible fraction of PNCs for particles >300 nm was found during all activities. Mike Mulheron et. al. 2014 also investigated the release of particulate matter, including ultrafine particles, during the mixing of fresh concrete and the subsequent drilling and cutting of hardened concrete. Morawska L., et. al. 2014 measured size-resolved particles in the 5-560 nm range at five distances from a simulated concrete recycling source.

MATERIALS & METHODS

Research Methods The research strategy adopted in the present work was experimental. In addition, the measurement procedure followed a protocol of information gathering, created in order to standardize procedures and occasional fluctuation, measure the pollution concentrations of particulate matter and assess the impacts caused by the emission of these particles on the area. These findings were compared with the standards established by national and international bodies. The chemical and physical analyses of particles were carried out in a laboratory. The concentration of particulate matter was measured by a sampler. The concentration was calculated for PM2.5 8 h, PM10 8 h, SO2 8 h, NO2 8 h, CO 8 h and Noise Levels for 8 h. House demolition operations were carried out from 21st June 2017 to 1st July 2017. The choice of the right method of demolition work depends on many factors such as project condition, the availability of equipment and the sensitivity of neighborhoods. In the study site, demolition was performed using simple equipment and manual labour. Demolition operations did not contain any dust control systems such as water spray. Three to five demolition workers were employed in each day. Samples were collected during work hours (09:00 to 17:00) of work days. Meteorological parameters including air temperature and wind speed were observed in each studied site.

Ily Hanisah Mohd Fauzeya, et. al. 2015 reviewed Malaysia’s demolition work related safety and environmental provisions and ultimately proposed a methodology that can be used to assess safety risks and evaluate significant environmental aspects. Mike Mulheron, et. al 2015 investigated the release, occupational exposure and physicochemical properties of particulate matter, including UFPs, from over 20 different refurbishment activities occurring at an operational building site. Farhad Azarmi, et. al. 2016 assessed PM10, PM2.5 and PM1 concentrations from a building demolition. Physicochemical properties of particles using SEM and EDS were investigated. It was found that average exposure doses increased by up to 57-times during the demolition activities. Prasahant Kumar, et. al. 2016 assessed the impact of PM10 and PM2.5 arising from construction works in and around London. Measurements were carried out at 17 different monitoring stations around three construction sites between January 2002 and December 2013. The findings clearly indicate an impact of construction activities on the nearby downwind areas and a need for developing mitigation measures to limit their escape from the construction sites.

Description of the Demolition Site The demolition site studied is located in Jayanagar, It has an area of 1200 sq ft. The demolition site is located in aresidential urban area (up to 2 floors) with the presence of flora and fauna, including a park. Within an area of 100 m around the site, there is no presence of primary pollution sources, such as the presence of other construction sites, industries, traffic routes and airports. The equipments were installed in three points at the main entrance and two sides of the demolition site. PMCs were measured at the site in the downwind of demolition site, around the demolished building through the mobile monitoring through sequential measurements. Figure 1 showsthe sampling locations around the demolition site. Construction material of

Mohammad Normohammadi, et al. 2016 determined the amount of workers’ exposure to crystalline silica dust and assessed the relative risk of silicosis and the excess lifetime

[3]



building floors, stairs and supporting columns was reinforced concrete while the walls were made of brick.

Continuous air quality monitoring was carried out at the three points around the demolition site to measure the concentration of PM10, PM2.5, SO2, NO2 and CO. The measurements of PM concentrations analyzed in this study were during the periods of demolition, before and/or after the demolition works. Measurements were undertaken continuously and divided into working hours (referred to as working period) in weekdays between 09:00 and 17:00 h (local time) and non-working hours (referred to as nonworking period). Data were analyzed with reference to the CPCB Limit Values for annual and daily PM10 concentrations. V.

Fig. 1. Demolition Site

The data were collected for a total of 96 working hours between 09:00 and 17:00 h (local time) over a period of 12 days. The detailed summary of sampling durations is presented in Table 1. TABLE I. Day 21 – 06 – 2017 22 – 06 - 2017 23 – 06 – 2017 24 – 06 - 2017 25 – 06 – 2017 26 – 06 - 2017 27 – 06 – 2017 28 – 06 - 2017 29 – 06 – 2017 30 – 06 - 2017 31 – 06 - 2017 01 – 07 - 2017

SUMMARY OF SAMPLING DURATIONS Activity Before Demolition Before Demolition Before Demolition During Demolition During Demolition During Demolition During Demolition During Demolition After Demolition After Demolition After Demolition After Demolition

RESULTS & DISCUSSION

In view of the examination of the gathered information in three stages utilizing the proposed methodology, the requirement for changes in a portion of the methodological procedures embraced was seen keeping in mind the end goal to acquire palatable information as indicated by the reality of the demolition site. The favorable circumstances and drawbacks of the typology of the demolition site chosen for the investigation was an important topic discussed. Following the detailed review of project method statement it was identified that demolition work ought to be carried out in three unique stages such as pre-demolition, actual demolition and post demolition. As per test protocol IS 5182 (P – 23) 2006 RA 2012 the allowed limits for PM10 is 100 μg/m3 but the variation was 13% less than the allowed limit before and after demolition but during demolition it almost peaked to 32% more than the allowed limit. As per test protocol CPCB Guidelines May, 2011 the allowed limits for PM2.5 is 60 μg/m3 but the variation was 20% less than the allowed limit before and after demolition but during demolition it was 12% less than the allowed limit. As per test protocol IS 5182 (P – 2) 2001 RA 2012 the allowed limits for SO2 is 80 μg/m3 but the variation was 87% - 88% less than the allowed limit before and after demolition but during demolition it was 84% - 85% less than the allowed limit. As per test protocol IS 5182 (P – 6) 2006 RA 2012 the allowed limits for NO2 is 80 μg/m3 but the variation was 81% - 82% less than the allowed limit before and after demolition but during demolition it was 80% - 81% less than the allowed limit. As per test protocol for CO i.e. the Instrument method the allowed limits for is 2 mg/m3 but the variation was 72% - 73% less than the allowed limit before and after demolition but during demolition it was 65% - 66% less than the allowed limit. As per test protocol for Noise Levels the allowed limits is 75 dB but the variation was 6% - 7% less than the allowed limit before and after demolition but during demolition it almost peaked to 4% - 5% more than the allowed limit.

Duration 8 Hours 8 Hours 8 Hours 8 Hours 8 Hours 8 Hours 8 Hours 8 Hours 8 Hours 8 Hours 8 Hours 8 Hours

Instrumentation Envirotech Respirable Dust Sampler APM 550 was used to measure PM10, PM2.5, SO2 & NO2 along with the Ambient Meteorological Data. INDUS Model AGM 63X Flue Gas Analyser was used to measure CO and Sound Level Meter – EQ-805 was used to measure the Noise Levels. Field Measurements The methods of measurement of PM10 & PM2.5 were done using the Gravimetric Method. SO2 was measured using the Improved West & Gaeke Method. NO2 was measured using the Jacob & Hochheiser Modified Method and CO was measured using the Non Dispersive Infrared Spectroscopy Method.

Fraction of coarse particles (i.e. PM2.5-10) was found to be higher over the background level, compared with fine particles (i.e. PM2.5) that reduced by about similar percentage, against the background level after the demolition periods. This

[4]



observation unmistakably proposes a substantially higher increment of coarse particle emissions from building demolition.

to limit the exposure to onsite workers and people who live in the surrounding environment. What is more, this work shows the importance of monitoring the weather condition parameters, such as temperature, humidity and wind direction, in order to gain a better understanding of the behavior of the air pollution. The air quality depends on the quantity of the particulate matters emissions, but also the way in which the atmosphere reacts to their concentration or dispersion. However, due to the exploratory nature of this study, concentrating on finding the most appropriate methodology for determining PM concentrations around demolition site and the restricted amount of information on the measured concentration, it was unrealistic to relate weather condition parameters to PM concentration.

The average air temperature in the site was 270 C, respectively. The average wind speed in the site was 27 km/h and the relative humidity was around 64% respectively for all the days i.e. before, during and after demolition. According to meteorological data, the effects of air temperature and wind speed on exposure measurements were negligible. Measurements were done on days with no rain. Workers in studied sites worked more than 8 hours in a day during 12 working days. The building demolition workers did not use appropriate personal protective equipment in doing their tasks. This investigation found that workers had not used proper personal protective equipment such as respiratory protection devices.

VII. FUTURE WORKS

Wet cleaning, compressed air to remove Pollutants from clothes and personal protective equipment can be used to control Pollutants exposure in building demolition sites. A limitation of this study is that the numbers of sampling sites was relatively small. It is recommended that further research be undertaken in building demolition sites with a large sample size. More broadly, research is also needed to consider the impact of seasonal changes on occupational exposure to pollutants in demolition sites.

This study focuses on PM10 and PM2.5 generated from the demolition of a 2-storey brick-walled concrete building. The results showed effect of PM emissions on the exposure to people on and around such sites. The elevated Pollutants during the demolition represent a potential health risk due to exposure to a wide variety of toxic elemental species. The results are also important for the development of mitigation strategies prior to the demolition operations and accordingly choose special protective equipment to limit exposures during the demolition activities. The male subjects (demolisher) inhale more doses of particles than female (helpers) subjects, because of their higher body tidal volume and breathing frequency and that the rate of deposited particles could considerably increase during heavy exercises by workers for the same emission source. Further personal monitoring studies, focusing on individual workers with different level of physical activities at large-scale demolition sites, are recommended to the understanding of occupational exposure of on-site workers. In order to provide adequate protection to the workers and population living in neighborhood and given that demolition studies are yet limited, further studies involving monitoring of size-resolved particles from a wide variety of buildings under different urban morphology and meteorological settings are recommended.

VI. CONCLUSION Size-resolved mass distributions of particles were measured in the 2.5-10 µm size range through a combination of measurement strategies. The objectives of this study were to assess emission characteristics of PM emissions in various size ranges during the mechanical demolition of a building, in addition to understand their physicochemical characteristics and the occupational exposure of workers to PM10 and PM2.5 on and around the demolition site. The following conclusions were drawn: The coarse particles (PM10) contributed majority of the total PM. The largest PM10 and PM2.5 were detected in the downwind monitoring during the demolition of building’s ceiling and walls. The 24 h mean CPCB limit of value of 100 µg/m3 set by CPCB for PM10 not to be exceeded was breached during demolition at the site. These observations substantiate the previous literature findings that the demolition activities produce much larger PM10 emissions compared with PM2.5. The exposure to high PM can be minimized by staying indoors or being positioned upwind of demolition sites.

The methodology and results of this research can provide starting points for further studies aimed at measuring particulate matter emissions on demolition sites. Some recommendations for future research are:

The results presented in this study highlight the contributions of PM10 and PM2.5 from demolition works. The increase in the concentrations of PM10 and PM2.5 at the downwind monitoring stations suggest that there is a need to design more detailed and appropriate risk mitigation strategies

[5]

a.

To develop studies on demolition sites to support specific parameters of air quality around the building by varying distance from site

b.

To develop specific studies for each phase of Demolition

c.

To develop specific studies to correlate weather condition variables with PM concentration



d.

To develop and implement studies to evaluate technological and management solutions for the reduction of PM emissions from demolition sites.

[1]

Azarmi F., Kumar P., Mulheron M., 2014. “The Exposure to Coarse, Fine and Ultrafine Particle Emissions from Concrete Mixing, Drilling and Cutting Activities”. J. Hazard. Mater. 279, 268-279. Azarmi, F., Kumar P., Mulheron, M., Colaux, J., Jeynes, C., Adhami, S.,Watts, J., 2015b. “Physicochemical Characteristics and Occupational Exposure to Coarse, Fine and Ultrafine Particles during Building Refurbishment Activities”. J. Nanopar. Res. 17, 343. CPCB Guidelines May, 2011 – Guidelines for the measurement of ambient Air Pollutants, Volume 1 & Volume 2. F. Azarmi, P. Kumar 2016. “Ambient Exposure to Coarse and Fine Particle Emissions from Building Demolition” 137, 62-79. Farhad Azarmi, Prashant Kumar, Daniel Marsh and Gary Fuller, 2016. “Assessment of the Long-Term Impacts of PM10 and PM2.5 Particles from Construction Works on Surrounding Areas” Environ. Sci.: Processes Impacts, 18, 208. Fisher B., Kumar P., Mulheron, M., Harrison R.M., 2012a. “New Directions: Airborne Ultrafine Particle Dust from Building Activities: A Source in need of Quantification”. Atmos. Environ. 56, 262-264. Ily Hanisah Mohd Fauzeya, Fatemeh Nateghib, Farahbod Mohammadib, Faridah Ismaila, 2015. “Emergent Occupational Safety & Health and Environmental Issues of Demolition Work: Towards Public Environment” Social and Behavioural Sciences 168, 41–51. Ingrid P. S. Araujo, Dayana B. Costa and Rita J. B. de Moraes 2014 “Identification and Characterization of Particulate Matter Concentrations at Construction Jobsites” Sustainability, 6, 7666-7688; doi:10.3390/su6117666. IS 5182 (P – 2): 2001 RA 2012 – Methods for measurement of Air Pollution, Part 2 Sulphur Dioxide IS 5182 (P – 23): 2006 RA 2012 - Methods for measurement of Air Pollution – Part 23 Respirable Suspended Particulate Matter (PM 10), Cyclonic Flow Technique. IS 5182 (P – 6): 2006 RA 2012 - Methods for measurement of Air Pollution, Part 6 Oxides of Nitrogen. Kumar P., Mulheron, M., Som C., 2012b. “Release of Ultrafine Particles from Three Simulated Building Processes”. J. Nanopar. Res. 14, 771. Mohammad Normohammadi, Hossein Kakooei, Leila Omidi, Saeed Yari , Rasul Alimi 2016. “Risk Assessment of Exposure to Silica Dust in Building Demolition Sites” Safety and Health at Work, 7, 251-255. Nassib Jabbour, E Rohan Jayaratne, Graham R Johnson, Joel Alroe, Erik Uhde, Tunga Salthammer, Luke Cravigan, Ehsan Majd Faghihi, Prashant Kumar, Lidia Morawska 2017. “A Mechanism for the Production of Ultrafine Particles from Concrete Fracture”, Journal of Environmental Pollution, 222, 175-181. WHO, 2006. Air Quality Guidelines: Global Update 2005: Particulate Matter, Ozone, Nitrogen Dioxide, and Sulphur Dioxide. World Health Organization.

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[6]



Inventorization of Air Quality Index (AQI) – A comparative study of Different locations Col. Rajshekar Hiremath,

Ravi D.R

PhD Scholar, BMSCE, Basavangudi, Bangalore.

Prof. Maya Naik

Environmental Officer, KSPCB, Bangalore.

Indira B C

Prof & Head, Department of Civil Engineering, BMSCE, Basavangudi, Bangalore.

PhD Scholar, BMSCE, Basavangudi, Bangalore.

Abstract — Air pollution is one of the serious problems faced by the people globally, especially in urban areas of developing countries. Increase in the ambient air pollutant concentration has exposed the population to serious health hazards like respiratory diseases, cardio-vascular problems etc, resulting in excessive morbidity and mortality. Therefore there is a need for continuously monitoring the ambient air quality and to propose measures to mitigate the same. Bangalore has been selected as the study area. The fast growth of the city in the last two decades has crippled its infrastructure and polluted all spheres of its environment especially the ambient air. RSPM (PM2.5) levels (160-180 µg/m3) are almost 4-5 times more than the National Ambient Air Quality Standards (NAAQS) of 60 µg/m3, exposing people of Bangalore City to unhealthy levels of this pollutant. In this paper, data has been collected on selected air pollutants at four locations and analyzed for the period 2016 and 2017and is represented in the form of Air Quality Index (AQI). The higher index value indicates more pollution in relative terms. Based on this, air quality of the observed air samples were compared and inferred with range of AQI values and its level of health concern. AQI at Central Silk Board is found to be highest and can be attributed to high traffic flow and to the ongoing construction activities in that location. Kajisomanahalli has shown least AQI values. The present study shows that the values of RSPM have exceeded the threshold limit in the selected places. Keywords— Air Pollution, Urbanization, Health hazards, Air Quality Index, Air Quality Management System, RSPM, NAAQM. I. INTRODUCTION

Air pollution is one of the serious problems faced by the people globally, especially in urban areas of Developing Countries. It is mainly due to increased urbanization, industrial activity, vehicular growth, high levels of energy consumption, improper infrastructure facilities, absence of proper scientific air quality management system and improper enforcement strategy. The increase in the ambient air pollutant concentration has exposed the population to serious health hazards like increase in respiratory diseases, cardio-vesicular problems and etc, resulting in excessive morbidity and mortality among the people. Hence there exists a close relationship between the poor air and poor health. The phenomena involved in air pollution are complex. Once emitted into the atmosphere, primary

pollutants are transported by wind, turbulence and diffusion, which can undergo chemical reaction, change phase and finally removed from the atmosphere by dry and wet deposition [1]. An anthropogenic pollutant generated locally has affected the regional air quality. Therefore there is a need to monitor the ambient air quality continuously and to evolve a strategy to mitigate the same. Air quality monitoring of pollutants at critical areas represents concentrations and exposure of population to air pollutants. Air quality index (AQI) is the most sophisticated tool, which is used to measure the level of severity of pollution to public and policy makers. The Central Pollution Control Board (CPCB) has issued a Notification on Ambient Air Quality Standards (AAQS) on 18-09-2009. The AAQ standards prescribed by CPCB, for selected criteria pollutants [2] are given in Table-1 below. TABLE 1: AMBIENT AIR QUALITY INDIAN STANDARDS FOR SELECTED PARAMETERS (µG/ M3) Area

Concentration of different Air Pollutants in (µg/ m3) RSPM SO2 NO2

Industrial, Residential Sensitive Areas

60

50

40

Source: CPCB Notification dt 18 Nov 2009 for NAAQM Standards.[4]

II.

STUDY AREA

Bangalore has been selected as study area. The city is fast developing & is suffering from shortage of urban services and proper urban infrastructure management, which is required for efficient support of such growth. This also sets constraints on the expansion of the city and poses a need for immediate and consistent urban environmental management. The rapid growth of the city in the last two decades has crippled its infrastructure and polluted all the spheres of its environment, especially the ambient air. Bangalore urban road network and public transport system is far behind, which is required to foster economic development. The traffic problems are acute, and Government and local authorities are developing many strategic plans and proposing huge budget allocations for construction of highway/road network. Introduction of Metro (Ist & 2nd Stage) has

[7]

19TH - 20TH November 2018


also not improved the situation. The only means of motorized transport is road-based, insufficient road network has led to low speeds and long travel times (the average speed in the city area is as low as 10-13 km/hr), which is low, when compared to other growing Asian cities. The pollutant concentration in the ambient atmosphere is increasing due to preference for personalized mode of transport, increasing commuting hours and lack of efficient traffic management measures, have led to traffic congestion resulting in longer travel times, extra fuel consumption. The PM2.5 (called RSPM) levels (160180 µg/Nm3) are almost 4-5 times more than the Indian National Ambient Air Quality Standards (INAAQS) of 60 µg/Nm3, exposing people to unhealthy levels of pollutants, which has raised the concerns over growing pollution and health risk. The present study is carried out in four selected areas of Bangalore city. The details of study locations are shown in the Table-2 below. TABLE -2 : THE DETAILS OF STUDY LOCATIONS Sl.No 1 2 3

Location Peenya Industrial area Central Silk Board AMCO Batteries

4

Kajisomanahalli

Feature Industrial Area Heavy traffic area Commercial/ residential Area Rural (Control station)

III. MATERIALS AND METHODOLOGY

air quality. It includes the evaluation of different sets of emission control schedules to meet air quality goals. In this paper, an attempt has been made to collect data of air pollutants at different locations viz industrial area, high traffic area, Commercial and residential areas and it is represented in the form of air quality index. Air quality index is defined as a single number for reporting the status of air quality with respect to its effect on the human health. In an elaborated form, it combines many pollutants concentrations in some mathematical expression to arrive at a single number for air quality. Air quality index is calculated with the help of following equation concerning air quality rating with respect to each air quality rating parameters. AQI = [(RSPM/S RSPM + SO2/S SO2 + NO2/S NO2) * 100] / 3 Where, RSPM, SO2, and NO2 are observed values of air quality parameters, and SRSPM, SSO2 and SNO2 are standard values of that specific parameter notified by CPCB [5]. Based on the standard AQI values, air quality categories of the observed air samples are compared and inferred. Table-3 gives the range of AQI values and its level of health concern. TABLE: 3- RANGE OF AQI VALUES AND ITS LEVEL OF HEALTH CONCERN

Random sampling technique was adopted to collect air pollutant concentration at four selected locations viz Industrial area, Heavy traffic area, Commercial area and Residential area. Criteria parameters viz RSPM, SO2, and NO2 are considered for calculating Air Quality Index. High volume air sampler is used to collect the air samples at each sampling stations, for different parameters. Sampling was done following the procedure (as prescribed in NAAQM standard methods of sampling and analysis) for the period of 2016 and 2017. Atmospheric air was drawn at 8 hours time interval for a period of 24 hours at a flow rate of 0.8 m3 / min to 1.2 m3 / min through glass fibre filter. Then the amount of particulate matter per unit volume of air passed, was calculated on the basis of the difference between initial and final weight of the filter paper and the total volume of the air drawn during sampling. For gaseous sampling, the impinger was exposed for 24 hours at an impingement rate of 1lt/min, to get one sample in a day. NO2 was analyzed by employing the Jacob Hocheiser method on a spectrophotometer at a wave length of 540nm and SO2 was analyzed on a spectrophotometer at a wave length of 560 nm by employing West Gaeke method. IV. AIR QUALITY INDEX Air Quality Management System (AQMS) is a strategy to overcome the impact of air pollution and is most effective towards continual improvement of

Sl.No 1 2 3 4 5 6

AQI values 0 – 50 51 – 100 101 – 150 151 – 200 201 – 250 251 – 300

Levels of health concern Good Moderate Unhealthy for sensitive group Unhealthy Very unhealthy Hazardous Source: CPCB Manual 2015[5]

V. RESULTS AND DISCUSSIONS Based on the above formula, the Air quality index at different (selected) sampling stations for the year 2016 and 2017 is calculated and the same is presented in the Table 4 given below. TABLE: 4- AQI OF SELECTED STATIONS Sl. No 1 2 3 4

Station Name

2016

2017

Peenya Industrial Area Central Silk Board AMCO batteries Kajisomanahalli

92.54 106.30 93.79 64.38

85.28 105.23 79.71 62.48

The above table clearly indicates that the high traffic density area has high AQI indicating that, the level of health concern is “Unhealthy for sensitive group” and in the remaining areas it is “Moderate”. It is also observed that, even the residential area has moderate air pollution, which needs attention. It is observed that, compared to 2017, the year 2016 has higher AQI, indicating the contribution of meteorological

[8]



parameters on the overall air quality. It is observed that, high traffic area has more AQI, supporting the statement that vehicular pollution is significantly contributing to the overall deterioration of air quality in Bangalore, compared to industrial and commercial activity. There is not much difference between AQI of industrial & commercial area, as these areas are contributing equally for the air quality deterioration. The following graphs shows the annual variation of AQI at different location.

Monthly variationofAQI fortheyear2017 140.00 122.44

Concentration in µg/ m3

120.00

115.78

113.80

111.06 113.00 98.83

82.85

79.27

91.33 91.89 76.33 71.33 79.94

60.00

97.29 92.45

89.94

87.17

99.94 96.89

80.00

108.58

106.23

101.61 102.72

100.00 94.39

68.95

64.9 4 78 .61

70.78 65.50

62.72

61.33 53.56

Peenya ind area

81.30

88.72 75.30 79.61

78.38 6 3.58

Silk Board

85.50 80.36

73.71

72.17

AMCOBatteries 65.31 64.70

Kajisomanah alli

56.19 58.83 58.99 58.17

40.00 20.00 0.00 Jan-17 Fe b-17 Mar-17 Apr-17 Ma y-17 Jun-17 Jul-17 Aug-17 Se p-17 Oc t-17 Nov-17 Dec -17

Months

Fig. 3. Monthly variation of AQI for the year 2017. Yearlyvariationof AQIfor2016 and2017 100

Figures- 4 to 11, show the variations of pollutant concentrations with AQI for all the selected locations for the year 2016 and 2017 (monthly variations).

106.3

120 92.54

93.79 105.23

64.38

80 AQI

85.28

60

79.71 62.48

40

Pollutants concentration with AQIforpeenya industrial area Concentration in µg/m3

2017

0 Peenya Industrial AMCO Batteries Central Silk Board Kajisomanahalli Area Station name

Fig. 1. Annual variation of AQI at different locations.

91.1

108.0 103.0

120.0

99.4 93.3 117.0 85.5 113.0 90.0 99.0 102.0

98.0

100.0

76.6 1058.03.0 98.0

97.0

80.0 60.0

48.0 39.0 36.3 42.4 40.4 38.042.4

40.0

25.0 28.0

SO2 NO2

20.0

20.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.0

0.0 RSPM AQI

Months

Pollutants concentration with AQI for Central silk board 125.5 159.0 116.9 158.0 145.0 120.5

180.0 160.0 139.0 140.0 102.6 140.0 98.8 120.0 100.0 80.0 60.0 40.0 24.3 28.2 20.0 2.0 2.0 2.0 0.0

150.0 113.3

140.0 98.3 99.2 117.0 114.0

111.6 143.0 134.0 101.6 89.8

120.0 97.3 112.0 100.0

99.0

80.0

AQI

Concentration in µg/m 3

60.0 43.0 42.0 37.7 38.4 41.5 40.2 37.0

34.4

31.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0

40.5

40.0

SO2

20.0

2.0 2.0

NO2 RSPM AQI

0.0

Months

Fig. 5. Pollutants concentration with AQI for Central Silk Board. Pollut8a5n.2tsconcentration with AQI for Kajisomanahalli 140.0

124.0

75.2

74.8

120.0 100.0 85.059.4

67.7 64.1 97.0 94.0

100.0

96.8 61.5 61.0

70.0 50.0

64.0 64.0 54.0

40.0 13.0 2.0

12.7 2.0

15.0 18.0 2.0 2.0

23.6

28.9 21.5

90.0 80.0 70.0 60.0

48.8

60.0

20.0

88.0 57.8

54.6

76.0

80.0

66.1

40.0

AQI

Whereas Figure-3, which shows monthly variations of AQI for the year 2017, at different locations, indicates completely reverse of the 2016. The pollutant parameters during winter have shown an increasing trend, especially in high traffic junction, which needs reasoning. The graphs also reveals that, pollutant concentration has peaked during Febuary, August and October and is not comparable, when meteriological paramters are considered.

94.6 101.3 123.0

Fig. 4. Pollutants concentration with AQI for Peenya industrial Area.

Concentration in µg/m3

The following graphs depict the monthly variation of AQI for different locations. Figure-2 shows the monthly variation of AQI for the year 2016 at different locations. It clearly indicates that, the values are high during summer and low during rainy and winter seasons, justifying the impact of meteriological parameters like precipitation, wind speed, wind direction etc.

145.0 160.0 95.6 102.1 140.0 125.0 120.0 100.0 80.0 60.0 40.0 29.8 24.2 29.9

AQI

2016 20

30.0

SO2

21.2 21.0 22.0 19.0 21.1 20.0

NO2

2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 10.0 RSPM

0.0 AQI

0.0 Jan-16 Feb-16 Mar-16 Apr-16May-16Jun-16 Jul-16 Aug-16 Sep-16 Oct-16 Nov-16 Dec-16 Months

Fig. 6. Pollutants concentration with AQI for Kajisomanahalli.

Fig. 2. Monthly variation of AQI for the year 2016.

80.0 60.0 33.1 33.9 40.0 26.1 20.0 2.0 2.0 2.0

140.0

154.0 144.0 131.0 118.0 115.1 109.1

120.0 123.0 120.0 95.0

81.0 79.0 84.9 78.8 72.0 72.1 76.1 42.0 44.0 36.9 39.0 36.9 37.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0

0.0

100.0 102.0

95.5 93.8

87.2

80.0 60.0

31.0 31.0 35.1

AQI


Pollutants concentration with AQI for AMCO Batteries 180.0 160.0 133.0 124.0 140.0 120.0 100.0 97.0 97.8

40.0 20.0 0.0 AQI

SO2 NO2 RSPM

Months

Fig. 7. Pollutants concentration with AQI for AMCO Batteries.

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Pollutants concentration with AQI for peenya industrial area 115.00 108.00

120

120.00 115.00 92.00 91.99 81.00 90.16

100 99.94 96.89

91.33 91.89

80

37.00 38.00 36.00 32.00 33.00

40 20

94.74

72.17

78.38 79.61

102.00 100.00

80.00

88.72 79.94 78.61

60

91.42 106.12

80.36

85.50

AQI


140 122.00

60.00

SO2 NO2

31.41 31.00 32.34 32.99 34.12 31.68 33.00 40.00

2222 2222222

20.00

RSPM

0.00

AQI

2.00

0

Months

Fig. 8. Pollutants concentration with AQI for Peenya industrial. area Pollutants concentration with AQI for Central Silk Board 141.00 128.00

140.00

145.77 144.00 140.00

141.70

136.00 126.00

120.00 116.00 113.89 120.00 115.78 122.44 113.00 108.58 113.80 100.00 106.23 101.61 102.72 100.00 98.83 97.29 80.00 92.45 89.94 80.00 60.00 60.00 42.00 44.00 40.00 35.00 39.00 31.00 29.00 33.00 31.41 33.41 33.69 31.52 40.00 40.00 122.19

V. CONCLUSION AQI


160.00 143.00 152.00

20.00

20.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 0.00

SO2 NO2 RSPM AQI

0.00

Months

Fig: 9. Pollutants concentration with AQI for Central Silk Board. Pollutantsconcentration with AQIKajisomanahalli 73.71

70.78

80.00 65.50 70.00 75.00 60.00

62.72

61.33

80.00

53.56 70.00

40.00

27.00

27.00

30.00

24.00

60.00 50.00

60.00 56.68 53.62 31.41 32.45 28.00 28.84 29.00

58.00 30.00

70.00

69.76 72.00

66.00

50.00

80.00 65.31 64.70

56.19 58.83 58.99 58.17 79.97

55.47

40.00 33.53 30.26

28.00

AQI


90.00

SO2

30.00

NO2

20.00

20.00

RSPM

10.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 10.00

AQI

0.00

0.00 Months

Fig. 10. Pollutants concentration with AQI for Kajisomanahalli.


140.00 94.39

Pollutants concentration with AQI AMCO Batteries 120.00 87.17

136.00

120.00

71.33

68.95 64.94

100.00 80.00

103.00

96.00

60.00 43.00 41.00 39.00 75.00 40.00 20.00

79.26999665

76.33

81.30 75.30

63.58 92.32

84.00

100.00

82.85

60.00

96.28

72.68 68.00 34.00 34.00 32.69 31.00 31.98 3631..9069 33.64 34.58

80.00 AQI

111.06

160.00

81.27 87.00 38.00 40.00

20.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00

0.00

Particulate matter concentration i.e RSPM has exceeded the values in most of the stations, due to vehicular traffic and construction activities. The present study shows that the values of RSPM have exceeded the threshold limit. The AQI values indicates that the air quality is “Unhealthy for sensitive group” at high traffic junctions and nearing “Unhealthy for sensitive group” in industrial & commercial area, which needs high attention to policy makers to evolve strategy for mitigation. The results have also revealed that even though the AQI values for residential area is now “moderate”, it is showing an increasing trend in the recent past, due to construction activities which are undertaken in the respective locations.

SO2 NO2

RSPM 0.00 AQI

Air Quality Index (AQI) is an indicator of criticality of air pollution in the specific location. The AQI is basically an Air Stress Index, with no established standards, i.e., the index would not show a pronounced relation to the health of the people. It is not possible to characterize the air quality associated with the values of AQI and also to draw any definitive inferences about the category of air-quality. But it has the advantages of self-consistency, as it combines the synergistic effects of all the criteria pollutants. AQI is very much useful in defining the status of air in relative terms. Similarly by comparing AQI values, one could evaluate the air quality status of different locations in relative terms. For instance, if the value of AQI has increased at a given location, it would mean worsening of the air quality and vice versa. AQI can also be used to ascertain whether the air quality has worsened or improved over the months in different seasons. The present evaluation of AQI values indicate that, the air quality is “Unhealthy for sensitive group” at high traffic junctions and reaching towards “Unhealthy for sensitive group” in industrial & commercial area, which needs high attention to policy makers to evolve strategy for mitigation. The results have also revealed that even though the AQI values for Residential Area is now “Moderate”, it is showing an increasing trend in the recent past, due to construction activities, which are undertaken in the respective locations.

Months

VI. REFERENCES

Fig. 11. Pollutants concentration with AQI for AMCO Batteries.

The above graphs clearly indicate that, AQI at Central Silk Board is highest, when compared to the other locations. The same can be attributed to large number of vehicular movement, metro and flyover construction activities. Kajisomanahalli (control station) has shown least AQI values, when compared to the other stations. The result of air quality monitoring, indicates that the pollutant concentration was highly variable at different stations, depending on the source i.e. stationary and density of mobile pollution sources.

[1]

[2] [3] [4] [5]

[10]

Harinath S. and Usha N Murthy, Air Quality Index in Industrial Areas of Bangalore city – A case study, India, Journal of Industrial Pollution Control, 2010,26(2), 235-237. Annual Report, 2016-17, Karnataka State Pollution Control Board NAAQI CPCB Notification dt 18 Nov 2009 for NAAQM Standards. CPCB Manual – 2015.



CHARACTERIZING VISIBILITY METEOROLOGY AND AIR QUALITY FOR A TROPICAL COASTAL CITY Savitha Ulavi1, Shiva Nagendra S.M2 1

Ph.D Scholar, 2 Professor Department of Civil Engineering, IITM Chennai, India email: [email protected]

Abstract Visibility reduction due to air pollution is a major environmental concern in many metropolitan cities. Present study focuses on examining historical visibility data (2008 to 2017) in relation to meteorology and air emission characteristics of Chennai city. The mean visibility experienced by the city is 5.9 ± 0.93 km. The estimated haze index for city is 42 deciview indicating poor visual air quality. The Stepwise linear Regression performed showed that ambient temperature, PM2.5, RH and dew point temperature, solar radiation, O3 are significant predictors for visibility with R2 value of 0.64. The seasonal variations in visibility were not very significant presenting highest in pre monsoon (6.63±0.76)> summer (6.52±0.68)> winter (5.72±0.78)> post monsoon (5.37±1.07). This could be possibly due to elevated humidity levels almost throughout the year. Observation of diurnal visibility pattern showed visibility gradually rising in the night time with concomitant decrease in RH and increase in wind speed. This could be possibly also due to dilution dispersion of pollutants due to land sea breeze exchange at night. However increased RH and emissions at day time can lead to enhancement in scattering causing visibility reduction. Key words: Visibility, air pollution, meteorology with meteorology and air quality. I. Introduction Urban visibility reduction due to air pollution has gained increased concern worldwide recently. Visibility reduction on instances other than natural events like precipitation and fog are a clear indication of air pollution [1]. Increased incidences of low visibility events characterized by high aerosol load (PM & its precursors) have been reported in several cities across the globe [2, 3, 4, 5]. It has been reported that low visibility events (haze, smog) have a disruptive impact on city traffic, tourism, business, public health, in addition to aviation sector [6, 7]. Environmental impacts include decreased short wave solar radiation reaching earth, enhanced thermal stability of the lower atmosphere, increased incidences of fog over city(due to increased emissions at lower atmosphere acting as cloud condensation nuclei [2], and long term climate change impacts.

II. Study Area description and Data Handling Chennai is situated on coromandel coast of Bay of Bengal (13.0827° N, 80.2707° E). It’s a rapidly growing Indian metropolitan city with a population 8.6 Million [9]. Chennai experiences summer (MarchMay), winter (January & February). Monsoon period is classified as pre monsoon (South west Monsoon) June to September and post monsoon (north eastern monsoon) from October to December [10]. Chennai experiences tropical wet and dry climate as per Koppen climate classification.For the present study visibility and meteorological data for 10 years (from 2008-2017) were collected from nearby airport [11]. Extreme outilers (3IQR) were eliminated using Quartile method. Missing values were replaced by day average(found be 90% RH were excluded from study to ensure precipitation and fog effects are not contributing for urban visibilityreduction in the analysis (based on earlier studies [1, 4]. The air quality data (PM2.5 , SO2 , NO, NO2 and NOx ) were collected Velachery continuous monitoring station [12]. Velachery is the residential area in Chennai located 9.3km away from airport.

The basic mechanism causing visibility impairment is scattering and absorption (which intern depend on optical properties of aerosols). Thus visibility is strongly influenced by nature of particulate matter (PM) like size distribution, chemical composition, mixing state and hygroscopicity [8]. Atmospheric visibility is also influenced by meteorological conditions (like relative humidity, wind speed, wind direction, dew point and ambient air temperature) in addition to concentrations of particulate and gaseous emissions (at local & regional scale). Thus understanding the link between visibility, air quality, meteorology and geography is essential. Present study characterizes the visibility for a tropical coastal city and its relationship

III. Results and Discussion The results from descriptive statistical analysis for 2008-2017 clearly show that mean visibility experienced by the city is 5.9 ± 0.93 km. The average RH (%), ambient temperature (0C), Dew point temperature (0C) and wind speed (Km/h) for the entire study period is 70.29 ± 7.7, 29 ± 2.3, 22 ± 2.1 and 10.83±3.4 respectively. The box plot showing variation in visibility from 2008 to 2017 is presented in figure 1

[11]



Fig 2: Time series plot showing variations in visibility (VS), Relative Humidity (RH), Wind Speed (WS), Dew point temperature (DT) and Ambient Temperature (AT) for the year 2017

Figure 1: Box whisker plot for visibility from 2008-2017 for year 2011 the data is missing for 6 months (Jan to July)

Fig 3: Time series plot showing variations in visibility (VS), Relative Humidity (RH), Wind Speed (WS), Dew point temperature (DT) and Ambient Temperature (AT) for the year 2016

From the figure 1 it can be inferred that 50th percentile (or middle quartile) show no trend between 2008 -2010 and further a minor decrease in trend has been observed except for slight increase in 2015. Sample size can produce the difference in appearance of the box plot. The spread in the data is large for year 2011 since the sample size is small (N=128 against N=365 for other years). The star indicates the extreme low visibility days, three times the inter quartile range of that year (considering daily average).Over all it is observed that 45 to 56% of the time in a year visibility is in the range of 4-6 km and 36 to 48% in 6-8 Km and 3.8 to 9 % in 2-4 km range during the study period. The days < 2km visibility range are negligible (0.5%) A.

The seasonal analysis carried out (2008 to 2017) showed visibility is highest in pre monsoon season (6.63±0.76) i.e south west monsoon period followed by summer (6.52±0.68), winter (5.72±0.78) and post monsoon (5.37±1.07). For Chennai pre monsoon season is almost continuation of summer, as Chennai receive its rain fall from northeast monsoon. Thus slight decline in visibility during post monsoon season is observed. In the figure 4 below red line indicates one standard deviation and green line two standard deviation, black mean value (annualaverage of 2017). Low visibility are more predominant during post monsoon season.

Variations in Visibility and Meteorology

The time series plot comparing visibility and weather parameters clearly shows dependence of visibilityon meteorological parameters (Figure 2 and 3). It is observed that low visibility days occur when RH picks up in the environment. This could be due to the fact that hygroscopic aerosols absorb moisture at higher RH (>70) leading to increased cross section of particle size and further to enhanced scattering effect [13]. Thus visibility varies inversely with relative humidity. In addition wind speed, ambient temperature play vital role in dispersion of pollutants. During low visibility days it is observed that the difference between ambient temperature and dew point temperature decreases accompanied with lower wind speed/calm condition. This clearly indicates lower cloud base enhancing the stability lower atmosphere. Thus no longer unstable environmental conditions prevail and visibility drops reaching less than 3km during inversion conditions.

Fig 4: Seasonal visibility scatter plot for 2017

The influence of wind direction on visibility was investigated as wind reversal is prime feature of Indian climate. Chennai city (airport) experience wind blow from south during summer, south west during pre-monsoon and northeast during winter and post monsoon period. Thus it can be inferred that it receives wind blow from ocean picking up moisture from Bay of Bengal. [10] during north easterly winds. In addition to moisture it could also bring aerosols rich in Nacl which is deliquescent aerosol proved decrease visibility over region. Rest of the period during summer and pre monsoon period Chennai receives wind from south west direction (which is inland flow) and better visibility is observed during this time period.

[12]



B.

Diurnal variations in Visibility in Meteorological Parameters

the city.

The diurnal variations in visibility, RH, Wind speed ambient temperature, dew point temperature were analysed presented in figure 5. The variations in 24 hour cycle clear state visibility varies inversely with relative humidity, dew point temperature. The positive association is observed between ambient temperature, wind speed and visibility.

Figure 6: Diurnal variations in wind rose plot

C.

Visibility and air quality

The influence of air quality parameters on visibility was assessed by analysing the time series data for 2017 (shown in figure 7). Low visibility days have occurred predominantly during post monsoon season. The sharp decrease in visibility values (5/11/17, 10/11/17) is observed when there is an increase in PM2.5 levels. The increased of PM2.5 concentrations/ violation of

Figure 5: Diurnal variations in visibility, relative humidity, dew point temperature, ambient temperature and wind speed 2017.

The best visibility (8 km most often) appears during morning hours between (6 to 10 am Indian local time) and after 10 am the peak kept decreasing till 1 pm and further almost constant visibility was observed till 6 pm evening. Further decline in visibility is noticed reaching lowest visibility (80%), low windspeed (4 km initially approaching zero at 0 hours), higher dew point temperature (close to 20 0 C) has been

standard is accompanied by rise in NO, NOx and SO2 levels. Sulfate and nitrate are the important precursors for the formation of PM2.5. The earlier source apportionment studies have clearly stated the violation of PM2.5 and PM10 in many areas of the city [14, 15] and dominance of sulfate ions in particulate matter [16]. It needs a mention here that sulfate, nitrate, ammonium, sodium chloride (common in coastal environment) are the deliquescent aerosols which can are highly hygroscopic and lead to impairment of visibility.

observed indicating enhanced stability conditions (radiation inversion). Thus lowest visibility close to 2km prevail during these time periods. Further slowly visibility picks up after after 4 hours. This is possibly due to dissipation of relative humidity and aerosols due to land sea breeze exchange. Land sea breeze exchange is evident by observing diurnal wind rose plot shown in figure 6. It is clear from wind rose plot that wind speed range between 0-5 kms during 23 to 4 hours blowing from north or west. This suddenly picks up (>10 km) and starts blowing from north east direction. Thus land sea exchange leads gradual rise in visibility. Further after sunrise radiation inversion is broken down gradually leading to unstable atmospheric conditions (by 10 hrs where visibility is at peak) and RH drops to its lowest value ( rainy >winter.

Fig 5: Variation of COD removal efficiency under different Organic Loading Rate in rainy season

 Overall performance of the present pilot plant setup showed the VSSF CW reactor may be effective for reducing the strength of the rural wastewater in all seasons of the year.

Removal efficiency %

100 80 60

70 58

75

73

59

40

REFERENCES

20

[1]

0 20

25 30 35 40 Organic Loading Rate, g/m2/d

Removal Efficiency in %

Fig 6: Variation of COD removal efficiency at different Organic Loading Rate in winter season 100 80 60 40 20 0

COD BOD SUMMER

TKN RAINY

TP WINTER

TC

Fig 7: Seasonal variation of removal efficiency of COD, BOD, TKN, TP and TC

TKN showed significant variations during the operation period, as shown by the Table 4. This variations occur because the bacteria and the plants responsible for nitrogen removal are less efficient in low temperatures. Low effluent concentrations of ammonia, TKN indicates sufficient nitrification was achieved with sufficient denitrification. For PO43− and TP which is mainly removed by plant uptake and adsorption on the porous media showed no much variation as it is least dependent on microbial activities[11]. Better removal efficiency was achieved for coliform group of bacteria in all the seasons of the year. IV.

[2]

Technological Options for Solid and Liquid Waste Management in Rural Areas” Ministry of Drinking Water and Sanitation Swachh Bharat Mission (Gramin) Govt. of India , April 2015 B. T Shivendra., H. K Ramaraju. “Impact of onsite sanitation system on Groundwater in different Geological settings of Peri Urban areas” International Conference On Water Resources, Coastal And Ocean Engineering Aquatic Procedia 4 ( 2015 ) 1162 – 1172

G Ahmed, B Latifa , M Fabio , Martin Regelsberger “Constructed wetland as a low cost and sustainable solution for wastewater treatment adapted to rural settlements: the Chorfech wastewater treatment pilot plant” Water Science & Technology | 63.12 | 2011 [4] APHA-AWWA (2012)."Standard Methods for the examination of Water and Wastewater”. American Public Health Association, American Water Works Association 22nd Ed, [5] A P Pratik, D A Nishith “manmade wetland for wastewater treatment with special emphasis on design criteria” Sci. Revs. Chem. Commun.: 3(3), 2013, 150-160 [6] Constructed Wetlands Manual, United Nations Human Settlements Programme (UN-HABITAT), 2008 [7] Li Fengmin, Lu Lun, Xiang Zheng, Huu Hao Ngo, Shuang Liang, Wenshan Guo, Xiuwen Zhang “Enhanced Nitrogen removal in Constructed Wetlands: Effects of dissolved oxygen and stepfeeding” Bioresource Technology 169 (2014) 395–402 [8] I S Alexandros, A T Vassilios “Effects of loading, resting period, temperature, porous media, vegetation and aeration on performance of pilot-scale vertical flow Constructed Wetlands” Chemical Engineering Journal 181– 182 (2012) 416– 430 [9] H K Robert, D W Scott “Treatment Wetlands” Second Edition Taylor & Francis Group, Llc 2009 [10] Jan Vymazal “Review Horizontal sub-surface flow and hybrid constructed wetlands systems for wastewater treatment” Ecological Engineering 25 (2005) 478–490 [11] S A Christos, A T Vassilios “Effect of temperature, HRT, vegetation and porous media on removal Efficiency,% of pilot-scale horizontal subsurface flow Constructed Wetlands” Ecological Engineering 29 (2007) 173–191 [3]

CONCLUSIONS

 Characteristics of rural wastewater with average BOD and COD values of 235 mg/L and 300 mg/l respectively. A Positive Correlation between BOD and COD with R2=0.7495 showed biodegradable nature of rural wastewater.

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Photochemical Treatment of Pharmaceutical Wastewater by Advanced Oxidation Process DEVENDRA G

REKHA H B

PG Student, Department of Civil Engg UVCE, Bangalore University Bangalore, INDIA [email protected]

Assistant Professor, Department of Civil Engg UVCE, Bangalore University Bangalore, INDIA [email protected]

Abstract—In this study, the photochemical treatment using Ultraviolet (UV) is performed for amoxicillin (AMX) degradation, an antibiotic widely used in the world. Firstly, only UV treatment for the AMX containing water, but the results of the degradation is only 8.33% under 150 minutes. Secondly, the combination of UV and H2O2 (Hydrogen peroxide) gives the better degradation comparing to alone UV, the degradation achieved in 10ml of H2O2 is 66.67% under 150 minutes. Thirdly, AMX solution treated with a combination of UV and TiO2 (Titanium di-oxide), the degradation of AMX is achieved is 41.67% in 1.0grams of TiO2 under 150 minutes. The AMX solution treated with a combination of UV and Fenton in different ratios of 0.1(M) Molar concentrations under 150 minutes, the result shows AMX degradation is 35% in 1:8 ratio. Keywords—Photochemical Treatment using UV; Amoxicillin waste water; H2O2 ; TiO2; Fenton Process.

I.

INTRODUCTION

Pharmaceutical industry is classified as one of the most hightech and capital-intensive industries. It is considered as the “life line‟ industry because its products play a crucial role in remedifying the suffering of diseased persons. It is also significant contributor to the strength of any economy by creating jobs for millions and contributing to the export earnings. The distinctive feature of this industry is such that the goods produced by this sector neither can be substituted nor replaced. Pharmaceutical drugs being used for human as well as veterinary medicines are emerging as environmental pollutants. Different pharmaceuticals have been classified as Analgesics, Antibiotics, Antiepileptic, Antiseptics, Betablockers, Antihypertensive, Hormones, Contraceptives, Psychotherapeutics and Anti Virals (Chanti babu et al, 2015). The concentration of residues detected in the environment is quite low (μg/l–mg/l), the Eco toxicity of residues at mg/1 levels has been reported. Many research studies indicated that the antibiotic residues were resistant to conventional chemical and biological treatment methods. The accumulation of antibiotic residues in the environment might make the antibiotic ineffective in diseases treatment, causing a serious public health issue because of the development of antibioticresistant bacteria. Effective ways to eliminate the discharged antibiotic residues are required for environmentally sustainable development (Zhigang Yi et al, 2018).

II.

ANTIBIOTC

A. General The “antibiotic” term qua generic is used to specify any class of organic molecule that blocks or ravage microbes by specific interactions with bacterial marks, without considering any compound or class. Antibiotics are designed to act very effectively even at low doses and, in Case of intracorporal administration, to be completely excreted from the body after a short time of residence. They are non-biodegradable and can survive in aquatic environments for long periods. The entrance of these compounds into the environment owing to anthropogenic sources can result in a potential risk for organisms. Although antibiotics exist at residual levels, they can cause resistance in bacterial populations, making them inactive in the treatment of several diseases in the near future. In addition, they cause endocrine-disrupting effects when living organisms consume them. They interfere with the synthesis, secretion, transport, binding, action, and elimination of hormones in the human body (EPA, 2001). B.

Antibiotic and their Environmental Hazard The consumption of antibiotics leads to these compounds being introduced into the environment after incomplete metabolism, and they are not removable in conventional wastewater treatment systems. These compounds enter the environment through various means, such as wastewater from the pharmaceutical industry, hospitals, and from human and animal waste deposits. Following digestion and metabolism in the body, the residue of these compounds, together with their metabolites, is introduced into the environment through human urine and stool deposits. As emerging contaminants, antibiotics and related antibiotic resistance genes (ARGs) have received increasing attention because of their potential impact on human health and the ecosystem. The spread of antibiotic-resistant microorganisms in the environment is globally recognized as an important public health issue, and there are concerns on our future ability to treat infectious diseases. Compounds such as antibiotics influence the microbial population of a wastewater system. Inhibition of wastewater bacteria affects the degradation of organic compounds.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) C. Treatment Technologies for the Removal of Antibiotics There are various methods available for removing pharmaceutical compounds, including absorption by active carbon, reverse osmosis, air stripping, and biological methods. However, contaminants are not removed by such methods; rather, they are just transferred from one phase to another without degradation. For this purpose, especially before biological treatment, advance oxidation techniques are implemented to reduce the organic load and toxicity (Engin et al, 2008). Advance oxidation process (AOP) is based on a higher electrochemical oxidation potential and formation of hydroxyl radical. Hydroxyl radicals (OH.) in the medium react with all organics and products such as CO₂ and H₂O have been produced. The hydroxyl radical reacts more rapidly than ozone and hydrogen peroxide, so economic benefits in operational costs and in size of treatment system is achieved. In addition, OH radical is a powerful chemical oxidant (Loraine et al, 1992).

 Degradation of COD in Amoxicillin using different ratio of Hydrogen peroxide (H2O2) + Titanium di-oxide (TiO2).  Application of Response surface Methodology to the optimum method selected III. MATERIALS AND METHODOLOGY A. Materials Antibiotics used in the study amoxicillin, is in the form of crystal powder. Its chemical structure is presented in fig 2. Amoxicillin Trihydrate was supplied from Sigma-Aldrich and chemicals H2O2, TiO2 (98%), Ferrous sulphate,H2SO4 are of analytical grade used for the study. For the present study UV, setup was used which is shown in fig 3.

Fig 2: Chemical structure of Amoxicillin

Fig 1: Major types of Advanced Oxidation Process (Source: Loraine et al, 1992) Fig 3: UV Experimental Setup

D. Objectives The research work concerns with the study of parameters of optimization such as H2O2 dosage, TiO2 dosage, Fenton in presence of UV light for the degradation of antibiotic amoxicillin with the following objectives.  Degradation of COD in Amoxicillin using UValone  Degradation of COD in Amoxicillin using different dosages of Hydrogen peroxide.  Degradation of COD in Amoxicillin using different dosages of Titanium di-oxide.  Degradation of COD in Amoxicillin using different dosages of Fenton.

B. Methodology Model contaminant of the study, Amoxicillin solution was prepared by dissolving accurately weighed 500mg in 1litre of distilled water. 500ml of 500mg/L was used as a working volume for the study. 1) Ultraviolet: Amoxicillin solution of 500 mL was placed

inside the UV chamber through the inlet channel and allowed for UV exposure for 150 minutes duration. The samples were drawn at regular intervals of 30 minutes for a period of 150 minutes from the outlet channel to check the percentage of COD removal

[74]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) IV. RESULTS AND DISCUSSION

2) Ultraviolet and Hydrogen Peroxide

Amoxicillin solution added with different dosages of H2O2 like 1ml, 5ml, 10ml, 15ml and 20ml (The dosages are selected based on the preliminary investigation) and these solutions are added to the UV reactor. The samples are analyzed for characteristics like COD by drawing the samples at regular intervals of thirty minutes for a period of 150 minutes. Optimum Dosage of H2O2 was determined. 3) Ultraviolet and Titanium Dioxide

Different dosages of TiO2 of 0.5gms, 1.0gms, 1.5gms, 2.0gms and 2.5gms (The dosages was selected based on the preliminary investigation) are added to the UV reactor for a working volume of 500ml solution. The samples are analyzed for characteristics like COD by drawing the samples at regular intervals of thirty minutes for a period of 150 minutes. Optimum Dosage of TiO2 was determined 4) Ultraviolet and Titanium Di-Oxide and Hydrogen Peroxide.

A.

Degradation by Ultraviolet Studies The degradation efficiency was 8.33% with 30 minutes of residence time in table 3 reveals that, although the degradation increased with increasing the residence time, the results showed that uv radiation was not effective in destroying the amoxicillin when it is feed into the reactor. B. Combination of UV and H2O2 The results obtained showed that drug degradation increased when the residence time is increased. The degradation efficiency of drug improved by the addition of H2O2 from 1.0 mL to 10 mL and decreased with increased concentration of H2O2 from 15 mL to 20 mL. Maximum of 66.67% COD removal of Amoxicillin drug is achieved with an irradiation time of 150 min with 10 mL of H2O2. Hence, the study considered 10 mL of H2O2 with 150 minutes of UV as optimum conditions

From the above methods, optimum dosage of H2O2 and TiO2 concentrations for the maximum removal of COD in amoxicillin is arrived. From these optimum dosages of H2O2 and of TiO2 is added to the amoxicillin of concentration 500mg/l. This solution is loaded into UV reactor for an UV exposure upto 150minutes. For every 30min sample is drawn and it is analyzed for maximum COD removal. Maximum COD removal was determined. 5) Ultraviolet and Fenton

To determine the maximum yield of Fenton oxidation; different concentrations of Fe (II), H₂O₂ with 0.1Molar ratios are considered are 1:2, 1:4, 1:8, 1:10. The samples of different molar concentrations and ratios are drawn on every 30 minutes upto 150 minutes and then COD analysis were done for the samples to found optimal degradation of Amoxicillin. C. Determination of Degradation of Antibiotic Percentage removal of antibiotics was calculated in terms of COD removal;

CODₒ: Initial COD CODt: COD at regular time interval D. Experimental Protocol

Fig 5: Percentage Variation of COD at Different Dosages of H2O2 with UV.

C. Combination of UV and TiO2 The results obtained showed that drug degradation increased when the residence time is increased. The degradation efficiency of drug improved by the addition of TiO2 from 0.5gms to 1.0gms and decreased with increased concentration of TiO2 from 1.5 gms to 2.5 gms. Maximum of 41.66% COD removal of Amoxicillin drug is achieved with an irradiation time of 150 min with 1.0 grams of TiO 2. Hence, the study considered 1.0grams of TiO2 with 150 minutes of UV as optimum conditions

Fig 4: Experimental Protocol

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Amoxicillin with UV is achieved upto 41.66% in 150minutes 

The optimum dosage of Fenton was found to be 0.1M(1:8) ratio for the degradation of COD in Amoxicillin with UV is achieved upto 35.00% in 150minutes REFERENCES

[1]

[2] Fig 6: Percentage Variation of COD at Different Dosages of TiO 2 with UV.

D. Combination of UV and Fenton The results obtained showed that drug degradation increased when the residence time is increased. The degradation efficiency of drug improved by the addition of Fenton reagent for a ratio of (1:8) of 0.1M is 35% and decreased with increased concentration of Fenton reagent of (1:10) ratio of 0.1M.

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

[12] Fig 7: Percentage Variation of COD at Different Dosages of Fenton with UV. [13]

CONCLUSION  



Using UV the degradation of Amoxicillin was not effective and it is found to be 8.33%. The optimum dosage of hydrogen peroxide was found to be 10ml for the degradation of COD in Amoxicillin with UV is achieved upto 66.67% in 150minutes The optimum dosage of hydrogen peroxide was found to be 1.0grams for the degradation of COD in

[14] [15]

[16] [17]

APHA (1998), “Standard Methods for the Examination of Water and Wastewater”, 20th edition, American Public Health Association, Washington D. C. Babuponnusami A. and Muthukumar K (2014),”A Review on Fenton and Improvements to the Fenton Process for Wastewater Treatment”, Journal of Environmental Chemical Engineering Vol (2), pp: 557–572. Basker, M.J., Sutherland, R. (1977), "Activity of Amoxicillin, alone, and in combination with aminoglycoside antibiotics against streptococci associated with bacterial endocarditis," Journal of Antimicrobial Chemotherapy, vol. 3, pp: 273-282. Bokhimi, X. et al., (1999), “Copper Precursor Effect on Reducibility and Titania Phases Concentration of Sol + Gel Cu/TiO2 Catalyst”, Journal of Solid State Chemistry, Volume 144, and pp: 349-353. Bound, J.P., Voulvoulis, N. (2006), "Predicted and measured concentrations for selected pharmaceuticals in UK rivers: implications for risk assessment." Water Res., vol. 40, pp: 2885-2892. Bradley D (1999), "Double-dosed Amoxicillin formulation proves efficacious and safe," Pharmaceutical Science & Technology Today, vol. 2, no. 1, pp: 6. Chanti babu patneedi and Durga Prasad K (2015), “Impact of Pharmaceutical wastes on human life and Environment”, ISSN: 09741496, Vol 8(1), pp: 67-70. CPCB and MoEF (2007), “Advanced Methods for Treatment of Textile Industry Effluents”, Resource Recycling Series RERES/7, Central Pollution Control Board, Delhi – 32. De Laat J., Tace E. and Dore M (1994), “Degradation of chloroethanes in dilute aqueous solution by H2O2/UV”, Water Research, Vol (28), and pp: 2507-2519. Dombi A. and Ilisz I., (1999), Journal of Photochemistry and Photobiology, Vol-53. Eder D, A.H. Windle (2008), "Morphology control of CNT-TiO2 hybrid materials and rutile nanotubes". Journal of Materials Chemistry Vol (18), pp: 2036-2043. Engin Gürtekin, Nusret Şekerdağ 2008, "An Advanced Oxidation Process: Fenton Process," Journal of Engineering Sciences , pp. 229239. EPA, (2001), “Handbook on Advanced Non-Photochemical Oxidation Process”, US. EPA, Washington, DC. Fenton H.J.H, (1894),”Oxidation of Tartaric Acid in presence of Iron. Journal of the Chemical Society, Transactions” Vol (65), pp: 889-910. Loraine, G.A., W.H. Glaze 1992, "Destruction of Vapour Phase Halogenated Methane by Means of Ultraviolet Photolysis," Industrial Waste Conference Proceedings, Michigan, Vol 47. Muhammad Umar and Hamidi Abdul Aziz (2013), “Photocatalytic Degradation of Organic Pollutants in Water”. Zhigang Yi, JuanWang, Tao Jiang, Qiong Tang and Ying Cheng (2018), “Photocatalytic degradation of sulfamethazine in aqueous solution using ZnO with different morphologies”, Royal Society of open science.

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Assessment of Measurement Quality through Law of Propagation of Uncertainty in Water Analysis M.Bamini SHE Techno Laboratory Pvt. Ltd. Industrial Town, Rajajinagar Bangalore, India [email protected]

Abstract— The quality analysis in water depends on measurement quality which is a function of measurement traceability and uncertainty. Uncertainty expresses the range of possible values for a measurement or result. It is never possible to measure anything exactly. In order to draw valid conclusion the uncertainty must be indicated and dealt with properly. More effort is required to determine the uncertainty in a measurement than to perform the measurement itself. This article discusses the importance and usefulness of measurement quality through the magnitude of uncertainty reported with the result. The maximum permissible uncertainty is predicted by law of propagation of uncertainty from all the uncertainty components taken in to account in a comprehensive model of reality and compared for the fitness of the reported uncertainty to qualify the measuring system.

Keywords—Water Quality, Uncertainty, Law of propagation, Error, Measurement equation, Decision rule

I. INTRODUCTION Quality evaluation of analytical results is reflected in the integrated international standards being established as a determinant of competitiveness and comparability of analytical results. The measurement uncertainty is an important parameter in the quality of analytical results and it must be evaluated for any method of analysis following the “in-house-validation” procedure. It is generally recognized that an analytical result is not complete if does not include information about the uncertainty of outcome , and therefore more work to clarify the expression of uncertainty in measurement were performed. Moreover, experimental results depend on all components of uncertainty associated to their used method of analysis . One of the problems faced by analysts is whether the used methodology provides adequate results for the intended purpose. The concept of measurement uncertainty is integrated into the quality management system regarding method validation, internal quality control and participation in external quality assessment programs .The process must go through several stages for performing an assessment of uncertainty for a specific analytical method determination. The models for the identification and measurement of any potential sources of uncertainty and the calculation of expanded uncertainty measurement are the first to be considered. In order to decide whether the measurements are adequate for the proposed method, after achieving the estimation of measurement uncertainty, the following step is to appreciate whether the level of uncertainty is acceptable or not .

Measurement uncertainty (MU) provides information on the level of confidence that can be placed on the measurement result. The estimate of measurement uncertainty is a requirement for accreditation and should be communicated, on request, to the client to show the quality of the measurement. The accredited laboratories involved in the monitoring and testing of environmental control measures of pollution elements follows the ISO standard 17025, in their systems. The new revision of this standard emphasis on declaration of decision rule for the acceptance of results through measurement uncertainty including sampling. Since the environmental laboratories concerned only on the analytical uncertainty even though they are involved in sampling, there is a need to develop a model for the quantification total measurement uncertainty including sampling. For the betterment of representativeness of the result of the lot, effective sampling method and its uncertainties are to be considered. In this study, a comprehensive model for the estimation of total measurement uncertainty of a environmental pollution element is developed and checked for the fitness of intended use. Decision rule for the compliance of the pollution element is also discussed. This model will provide a protocol to estimate total measurement uncertainties for the various parameters of water quality under scope for accreditation. This work presents the calculation of expanded uncertainty measurement related to total iron determination by UV-Vis molecular spectrometry in surface water samples. The total iron in ground water is the pollution element selected for the model. For this model, bottom up approach –evaluation method ( law of propagation of uncertainty) is adopted. Model equation for quantification of total measurement uncertainty U is, U = x1 +x2 (1) x1 - Sampling uncertainty x2 – Analytical uncertainty. When a measurand, y, is calculated from other measurements through a functional relationship, uncertainties in the input variables will propagate through the calculation to an uncertainty in the output y. The manner in which such uncertainties are propagated through a functional relationship provides much of the mathematical challenge to fully understanding the GUM .

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) II. LAW OF PROPAGATION OF UNCERTAINTY (LPU) EXAMPLES

GUM is a description of the variation in the output caused by variations in the inputs which creates a mathematical input-output model (measurement equation). LPU can accommodate non orthogonality and bias using measurement models, covariance matrices, partial derivatives, and Taylor expansion and are therefore theoretically preferable when calculating measurement uncertainty. The advantage of LPU is that all relevant uncertainty components are taken into account, both when they can be estimated directly by statistical methods (type A) and when estimated by other means (type B), including by educated estimation based on experience. Usually two main types of functional relationships are used for the measurement model (measurement function): • addition/subtraction — combined uncertainty is obtained as a square root of the sum of squared absolute standard uncertainties (root sum of squares); • multiplication/division — combined uncertainty is obtained as a square root of the sum of squared relative standard uncertainties.

Theory of sampling extends the uncertainty evaluation and error avoidance to the sampling process as well. This review presents the central features of the methods related to measurement uncertainty and sampling error estimation. Also the uncertainties in digital signal processing and virtual measurements, and the alternative methods in evaluating those, are addressed. Byron G Kratochvi P , John K taylor etal, [4], has conducted a Survey on Sampling for Chemical Analysis .Sampling is one of the most important steps in chemical analysis, yet it is often poorly planned and executed. One reason is that key information on sampling is scattered and relatively inaccessible. This article summarizes the more important published articles obtained as the result of a literature search to obtain essential background information for the design of sampling plans and protocols for the National Environmental Specimen Bank. Each reference is briefly described so that its applicability to a specific sampling question can be judged. The compilation consists of 56 references on general aspects of sampling, 9 references on sampling agricultural and food products, 14 references on sampling atmospheres and gases, 18 references on sampling water and waste water, and 18 references on sampling miscellaneous materials In [5,6] Experiments for the calculation of measurement uncertainty in the determination of total phosphorus (TP) in surface water and wastewater have been accomplished by using a visible spectrophotometric method of analysis In order to identify sources of the associated uncertainties involved to the estimation of measurement uncertainty for total phosphorus determination, the flow diagram and causeeffect approach were established. Uncertainty associated with mineralization samples was assumed to be negligible.

III. REVIEW OF LITERATURE R. H. Norris and A. Georges, [1], has conducted the research on Design and Analysis for Assessment of Water Quality. Any variables that are measured to assess water quality (physical, chemical and biological) will have some degree of uncertainty associated with them. To make comparisons of water quality valid, the degree of uncertainty (precision) must be estimated and environmental variability must be accounted for in sampling. Ian Farrance and Robert Frenkel, [2] , has discussed about Uncertainty of Measurement. Helsinki, Markku Ohenoja, [3], has conducted the research on Measurement and sampling uncertainity, The uncertainty should always be taken into account when using a measurement result in decision making or process control. There are well defined methods of evaluating the measurement uncertainty both at instrument level and at system level. Metrology is a branch of science that focuses on the uncertainty estimation at instrument level and many guides regarding the topic have been published. Partly the same statistical methods are also applied to empirical system level uncertainty evaluation and some practical guides are well known and widely used for analytical measurements.

M. Rode and U. Suhr UFZ [7] has conducted research on Uncertainties in selected river water quality data. These sampling uncertainties are highly site specific. The estimation of uncertainty in sampling can only be achieved by taking at least a proportion of samples in duplicates. Compared to sampling uncertainties, measurement and analytical uncertainties are much lower. To reduce uncertainties in river water quality data especially with respect to matter flux calculations sampling strategies have been developed stressing high temporal variations and the importance of sampling at times of high discharge In this document, [8], an approach for collecting control samples during a monitoring program—it does not cover laboratory quality control procedures. The collection of quality control samples is essential in order to provide confidence in the results of a sampling program. IV. METHODOLOGY A. Estimation of Measurement Uncertainty of Total iron (Fe) content in water collected for routine monitoring including sampling A measurement almost invariably involves the process of taking a sample. This is because it is usually impossible to analyse the entire bulk of the material to be characterised (the sampling target). Sampling is the more important

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) contribution to uncertainty and requires equally careful management and control. Quality of sampling is quantifiable through the measurements that are made upon the resultant samples The two major categories involved in the grouping of uncertainty sources are : ‘sampling uncertainty’ and ‘analytical uncertainty’. Sampling uncertainty: The part of the total measurement uncertainty attributable to sampling. IUPAC (2005) • Analytical uncertainty: The part of the total measurement uncertainty attributable to chemical analysis. This Case study analyses the contribution of Measurement uncertainty on fitness of measurement through , Sampling uncertainty by Emprical approach . It relies on overall reproducibility estimates from either in-house or inter-organisational measurement trials) Analytical Uncertainty by Modeling approach . It is based on identifying, quantifying and combining all the sources of uncertainty of the measurement Steps in the evaluation of MU These steps are applicable to all MU evaluation approaches: 1. 2. 3. 4. 5. 6. 7.

Specify the measurand Specify the measurement procedure and measurement function Identify the sources of uncertainty Quantify the uncertainty components Calculate the combined standard uncertainty Review the uncertainty budget Calculate the expanded uncertainty

Modeling Advanced laboratories Extra work usually required Deep knowledge required Danger to underestimate uncertainty Promotes thinking, high value in teaching

Single-Lab validation Routine laboratories Lots of data needed Less extra work required Realistic uncertainty estimates Teaching value is lower than with modeling

Inter laboratory validation Minimal work or knowledge required SR value has to be known. Crude uncertainty estimates

PT approach Minimal work or knowledge required Crude uncertainty estimates Should be used only as first approximation.

Table I Estimation of uncertainty contributions in the empirical approach Effect class Process Random (precision) Systematic (bias) Analysis e.g. duplicate analyses e.g. certified reference materials Duplicate samples Reference sampling Sampling target, inter-organisational sampling trial Four classes of effects that contribute to the uncertainty of measurements, and methods for their estimation.

Statistical model for the empirical estimation of uncertainty x = X true +ε sampling +ε analysis In an investigation of a single sampling target, if the sources of variation are independent, the measurement variance σ2meas is given by, σ meas 2 =σ sampling 2 +σ analytical 2 (2) where σ sampling 2 is the between-sample variance on one target (largely due to analyte heterogeneity), and σ analytical 2 is the between-analysis variance on one sample. If statistical estimates of variance (s2) are used to approximate these parameters, we get s2meas = s2sampling + s2 analytical (3) The standard uncertainty (u) can be estimated using s meas , which is therefore given by u meas = s meas =√𝑠2𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 + 𝑠2𝑎𝑛𝑎𝑙𝑦𝑡𝑖𝑐𝑎𝑙 Empirical estimation of combined uncertainty including sampling uncertainty Four types of method are applicable to the estimation of uncertainty using the empirical approach Table II Four empirical methods for estimating combined uncertainty including sampling component estimated S L

Method description

Sampler

1

Duplicates

Single

2

Protocols

3

Most of the steps are the same for all approaches — Step 4 is different in the three approaches. Step 6 mainly refers to the modeling approach but, for all approaches, the obtained uncertainty can be compared with the target uncertainty and also with an uncertainty obtained in another laboratory. B. Estimation of Sampling uncertainty Sample target ;- KIADB Industrial area, Peenya industrial estate, Bangalore

4

Collaborati ve trial in sampling Sampling proficiency test

(persons)

Protocols

Sampling

Analytical

Precisi on

Bia s

Precis ion

Bia s

Single

Yes

No

Yes

No

Single

Multiple

between protocols

Yes

No

Multiple

Single

between protocols

Yes

No

Multiple

Multiple

between samplers

Yes

Yes

The duplicate method is the simplest and probably most cost-effective of the four methods described in Table II. It is based upon a single sampler duplicating a small proportion of the primary samples. Duplicate test portions are drawn from both of the test samples and analysed in duplicate (i.e. duplicate chemical analysis). This system of duplicated sampling and chemical analysis is known as a ‘balanced design’ (Figure 1). The duplicate method does not include any contribution from sampling bias, which must be either assumed to be negligible, or estimated separately using, for example,

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) multiple samplers, multiple protocols and/or inter organisational sampling trials as in the other three methods.

Standard deviation of Analysis Mean range of measurement D̅ i1 + D̅ i2 D̅ analysis = 0.0089 =

Sanalysis =

𝐷𝑎 ̅ 𝑛𝑎𝑙𝑦𝑠𝑖𝑠

= 0.0079

1.128

2

Standard deviation of measurement based on duplicate analysis 𝐷̅ Smeasurement = = 0.0027

Mean range of measurement D̅ = 0.0031

1.128

= √𝑠 𝑚𝑒𝑎𝑠² −

Standard deviation of sampling s sampling

Fig.1. Two stage nested design

Balanced experimental design for empirical estimation of uncertainty (i.e. two-stage nested design), using the ‘duplicate method’. Table III Some sources of uncertainty in sampling and sample preparation Sampling - Heterogeneity (or inhomogeneity) - Effects of specific sampling strategy (e.g. random, stratified random, proportional, etc.) - Effects of movement of bulk medium (particularly density selection) - Physical state of bulk (solid, liquid, gas) - Temperature and pressure effects - Effects of sampling process on composition (e.g. differential adsorption in sampling system) - Transportation and preservation of sample

Sample preparation - Homogenisation and/or subsampling effects

- Drying

- Milling - Dissolution - Extraction

- Contamination - Derivatisation (chemical effects) - Dilution errors - (Pre-)Concentration - Control of speciation effects

Sample A

0.09 5 0.09 5 0.09 5 0.09

Sample B

Xi12

Di1 = Xi11Xi12

X̅ i1

Xi21

Xi22

Di2 = Xi21Xi12

X̅ i2

Di =X̅i1X̅i1

0.12

0.025

0.107

0.09

0.105

0.015

0.097

0.102

0.1

0.005

0.097

0.1

0.11

0.015

0.102

0.09 5

0.1

0.005

0.097

0.09

0.1

0.01

0.095

0.096

0.09 5

0.005

0.092

0.09

0.105

0.015

0.097

0.095

0.09

0.005

0.0875

0.087

0.085

0

0.085

0.088

0.08 5

0.09

0.005

0.087

0.09

0.09 2

0.002

0.091

D̅ i1 =∑

Di1 𝑛

0.009 5

0.08 5 0.08 5

)² =

√2

0.0049 mg/L Since the analyses are based on a mean of duplicates the standard deviation of analysis is divided by square root of 2 in the formula above – standard error of the mean.

Fom the above analysis the estimated combined uncertainty for sampling is 0.0049 mg/L. The expanded uncertainty for sampling is 2 x 0.0049 = 0.0098 mg/L. Covariance of sampling is 𝑆 0.0098 CV sampling = 𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 x 100 = 0.09 x 100 = 10.8% 𝑀𝑒𝑎𝑛

Calculation demonstrating the use of range statistics for calculating standard deviation from duplicate samples and duplicate analysis (two split range statistics)

Xi11

𝑆𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠

(

D̅ i1 =∑

Di1 𝑛

0.008 3

̅ =∑ D

Di 𝑛

0.003 1

The estimated combined uncertainty of analysis = 0.0079 mg/L Expanded uncertainty for analysis = 0.0079 X 2 = 0.0158 𝑆𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠 0.0158 CV analysis= 𝑀𝑒𝑎𝑛 x 100 = 0.09x 100 = 17.5% Therefore , combined uncertainty for total measurement u meas = √𝑢 𝑠𝑎𝑚𝑝2 + 𝑢 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠2 . = √0.00492 + 0.00792 = 0.009 mg/L. Expanded uncertainty for total measurement at 95% confident limit is 2 X 0.009 = 0.018 mg/L. Covariance for total measurement : 𝑆𝑚𝑒𝑎𝑠 0.018 CV meas= x 100 = x 100 = 20.0 % 𝑀𝑒𝑎𝑛

0.09

Fitness of measurement : The fitness-for-purpose criterion used initially is that based on the percentage of total variance. In chemical analysis the uncertainty relative to the result could be as low as 0.1% or, for very difficult analysis, as high as 20%. (9) .Covariance of sampling = 10.8% Covariance of total measurement = 20% The estimated uncertainty for sampling is fit for intended use. Estimation of Analytical uncertainty Description : Estimation of Measurement Uncertainty of Total iron (Fe) content in water by 1,10 Phenonthroline method (APHA22nd -3500 Fe B) ( chemical analysis) Procedure: 1. Standard calibration curve 2. Prepared iron standards in the range 0 to 1.00 mg/l iron in 50ml volumetric flask. 3. The contents were transfered to labeled beakers. 1ml of hydroxylamine hydrochloride solution and 2ml of conc. Hydrochloric acid were added. It was boiled until the volume reduction to 20ml in the water bath.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) 4.

After cooling, 10ml of ammonium acetate buffer and 4ml of 1,10 phenonthroline solution were added for colour development. After 10 minutes, the red orange colour solution was made upto 50ml with distilled water. Obtained the absorbance readings of the standards at 510 nm and plotted the calibration curve. Sample preparation 50 ml of sample was taken into beaker. Followed the same procedure as stated above for standards. (Run the blank along with sample) Adjusted the temperature to be similar to those used for obtaining the standard curve obtained the reading for the sample.

5. 6. 7. 8.

9.

Calculation Mg Fe- / L = (Sample absorbance – Blank absorbance) x calibration factor x dilution factor / Volume of sample V. UNCERTAINTY COMPONENTS IDENTIFIED

(Working Fe standard solution) = u(V2) = √(0.016/10)2 + (0.016/100)2 = 0.0016ml 4) Uncertainty in Sample volume taken for analysis u(SPL) = 50ml ( 0.03 ml) Standard uncertainty u( spl ) = 0.03/√6 = 0.012ml 5) Uncertainty in Fe concentration, calculated from the absorbance = 0.036 , which gave 0.09 mg /L of Fe with standard uncertainty 0.014mg/L as obtained from the calibration curve. u(C) = 0.014/√3 = 0.008 mg/L 6) Uncertainty due to contamination ∆C , calculated from blank absorbance repeatability u(∆C) = 0.004 7) Uncertainty in spectrophotometer accuracy = 1% = 0.01, therefore u(INST)= 0.01/√3 = 0.0057 mg/L In order to obtain a value for k it is necessary to obtain an estimate of the effective degrees of freedom, veff, of the combined standard uncertainty uc(y). The GUM recommends that the Welch- Satterthwaite equation is used to calculate a value for veff based on the degrees of freedom, vi, of the individual standard uncertainties ui(y); therefore (4)

The degrees of freedom, νi, for contributions obtained from Type A evaluations are n -1, where n is the number of readings used to evaluate standard deviation Fig. 2. Cause and effect diagram

1) Uncertainty in repeatability u(R) = 0.09 ±0.008 mg / L. Derived statistically from 10 repeated measurements of iron analysis. 2) Uncertainty in CRM - u(CRM) (Stock Fe standard solution) 1001 ± 4 mg / L ( From certificate of Analysis) Uncertainty of standard Fe stock solution = 4/2 = 2 mg Fe /L 3) Uncertainty in Volume - u(V) Two step dilution ( 100x followed by 10x to obtain 1 mg Fe /L a) Inter mediate stock Fe standard solution u(V1) Diluted 1 ml (±0.0044ml)of standard stock solution to 100ml (±0.05ml)( with distilled water to obtain 10 mg Fe /L U(1ml pipette) = 0.0044/√6 = 0.0018 ml U(100 ml volumetric flask) = 0.05/√6 = 0.020 ml

(0.02/100)2 u(V1)

Table IV Uncertainty budget table

Symbol

sources of Uncertainty

u(R)

Repeatability

u(CRM )

Fe - CRM

u(V1) u(V2) u(SPL) u(C) u(∆C)

U( Intermediate stock Fe standard ) = √(0.0018/1.0)2 +

It is often possible to take the degrees of freedom, νi, of Type B uncertainty contributions as infinite, that is, their value is known with a very high degree of reliability. If this is the case, and there is only one contribution obtained from a Type A evaluation, then the process using the WelchSatterthwaite formula simplifies, as all the terms relating to the type B uncertainties become zero.

= 0.0018 ml

b) working Fe standard solution u(V2) Diluted 10ml (±0.04ml) of intermediate standard solution to 100ml (±0.05ml) with distilled water to obtain 1 mg Fe/L.. U(10ml pipette)=0.04/√6 =0.016 ml U(100ml volumetric flask) = 0.04/√6 = 0.016ml

u(INST )

Dilution factor Working standard Sample volume Concentratio n contaminatio n spectrophoto meter

X Value 0.09 mg/L 1001 mg/L 100 mg/L 10 mg/L 50ml

Distribution

Fact or

normal

1

normal

1

Triangular

√6

Standard uncertai nty u(X) 0.005mg /L

Relative standard deviatio n

2mg/L

0.002

0.0018m l 0.0016m l

0.00001 8

0.00045

Triangular

√6

Triangular

√6

0.012ml

0.00024 0.008

0.00016

0.09

Rectangular

√3

0.008mg /L

0

normal

1

0.004

0.004

Rectangular

√3

0.0057

0.00001 1

510

Combined uncertainty uc(y)

0.009

Expanded uncertainty U= 2 X uc(y) at 95 % confidence limit

0.0018

Estimated Analytical Uncertainty for the Fe concentration = 0.09 x 0.018 = 0.0016 mg/L

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) UWL – LWL M ・um Cp = (1.1 - 0.9) /( 4 x 0.05) = 1 Maximum permissible uncertainty / Target uncertainty (%) (MPU) or UT = 1/ Cp.............. (10) = 1/1 = 1 % 1% of 0.09 mg/L = 0.009mg/L Cp =

The combined uncertainty was calculated by law of propagation of uncertainty, from table IV is = SQRT of (0.000452 + 0.0022 + 0.0000182 + 0.000162 + 0.000242 + 0.0082+ 0.0042 + 0.0000112 ) = 0.009 mg/L.

Review of uncertainty Working budget Sample

standard 1% Dilution factor 0% Fe - CRM 13% repeatabili ty 3%

Other 83%

volume 2% Concentrat ion 54% contamina tion 27%

Fig.3. Review of Uncertainty budget

Remarks: The major contribution for the measurement uncertainty in the determination of total iron in water, is from the estimation of calibration chart using CRM and the contamination during analysis. Remedy : By following strict good laboratory practices in the laboratory and to use higher grade of CRM will reduce the magnitude of measurement uncertainty considerably.

Fitness of estimated uncertainty The quantified measurement uncertainty is an estimation of the measurement quality affected by the variability of the estimation process. If the estimated measurement uncertainty U is smaller than UT, it can be concluded that the measurement is fit for the intended use. However, if U is slightly above UT, it should be checked using “F” statistic,if U can be claimed to be statistically equivalent to UT. Estimated uncertainty = 0.0018 mg/L < Target uncertainty = 0.009mg/L Therefore the estimated uncertainty is fit for the intended use.

VI. TOTAL MEASUREMENT UNCERTAINTY Total measurement uncertainty = sampling uncertainty + analytical uncertainty The standard uncertainty (u) can be estimated using

Fig.4. Acceptance of measurement Result

s

meas , which is therefore given by u meas = s meas =√𝑠2𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 + 𝑠2𝑎𝑛𝑎𝑙𝑦𝑡𝑖𝑐𝑎𝑙 Standard /combined uncertainty of sampling = 0.0049 mg / L Standard / combined uncertainty of analytical uncertainty = 0.009 mg/L Therefore, standard uncertainty for total measurement = √0.00492 + 0.0092 = 0.01 mg/L Expanded uncertainty for total measurement at 95% confident limit = 0.01x2 = 0.02 mg/L The estimated measurement uncertainty for total iron in water = 0.09 x 0.02 = 0.0018mg/L. Measurement of Target uncertainty using Measurement capability index QC Sample for iron = 1 ± 10% ie 1±0.1 mg/L Control limits for the Shewart chart : UCL =1.1 mg/L LCL = 0.9 mg/L Standard measurement uncertainty um = 0.05mg/L and typically M = 4 (corresponding to a coverage factor, k = 2 and 95% confidence).

Decision rule : If R + (K X u) ≤ S : the estimated value of the measurand is accepted. If R +(K X u) > S : The estimated value of the measurand is rejected. Reported concentration of Fe in Water =( 0.090 ± 0.002) mg /L accepted value R = 0.090 mg/L Coverage factor used for 95% confident limit K = 2 Estimated uncertainty for Fe U = 0.002mg/L Upper limit of Tolerance specified S = 0.3 max as per IS 10500,2012. AU +K x u < TU 0.090 + (2X 0.002) = 0.094 Which is well within the specified upper tolerance (0.3 mg/L ). So the result conforms to IS 10500 specification. Reporting uncertainty Concentration of Total iron in the water sample is (0.09 ± 0.002)mg/L. and it conforms to IS 10500 Specification limit. “The statement of compliance with specification is based on a 95% coverage probability for the expanded uncertainty of the measurement results on which the decision of compliance is based”.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) VII. CONCLUSION Measurement data are used to make the decisions that impact all areas of technology. Whether measurements support research, design, production, or maintenance, ensuring the data supports the decision is crucial. Measurement data quality can be critical to the resulting consequences of measurement-based decisions. The relevance of the evaluation of measurement uncertainty for a competent presentation of the measurement result is evident from EN ISO/IEC 17025 as well as from legislation. An accredited laboratory must be able to report quantitative measurement results with uncertainty and guide clients on the interpretation of results considering measurement uncertainty For such a decision rule, the acceptance value R coincides with the tolerance limit, decisions to accept or reject inspected items are based on measured values; the true values cannot be known, lead to incorrect decisions called false acceptance and false rejection Risks and the consequences of incorrect decision making in conformity assessment can be minimized with the following three steps: 1. Set limits on maximum permissible measurement uncertainties (equivalently, minimum measurement capability) and on maximum permissible consequence costs at the specification stage of any task 2. Agree on acceptable locations of the uncertainty interval with respect to a specification limit 3. Optimize measurement uncertainty proactively, ahead of a series of measurements, by designing experiments so that the sum of costs of measurement and of incorrect decisions of conformity is at a minimum In this article, the total iron in ground water from the KIADB Industrial area has been analysed and estimated the total measurement uncertainty of the process by law of propagation of uncertainty method. (duplicate sample collection by a single sampler). The fitness and conformance to legislation are analysed for the estimated uncertainty of toal iron (0.09 ± 0.002)mg/L, at 95% confident limit. REFERENCES

[6]. SR EN ISO 6878, “Water quality - Determination of phosphorus Ammonium molybdate spectrometric method”, BSI, June 2005 [7]. Rode, M. and Suhr, U.: Uncertainties in selected river water quality data, Hydrol. Earth Syst. Sci., 11, 863-874, https://doi.org/10.5194/hess-11-863-2007, 2007. [8]. da Silva, Ricardo J.B. 2013. "Setting Target Measurement Uncertainty in Water Analysis." Water 5, no. 3: 1279-1302. [9]. AMC background paper, Analytical Methods Committee Background Paper No 1. June2004 © The Royal Society of Chemistry [10]. A.M. Joglekar 2003, Statistical methods for six sigma in R&D and manufacturing. Wiley, Hoboken ISBN: 0-471-20342-4 [11]. Standard Methods for the Examination of Water and Wastewater; APHA, AWWA AND WEF [12]. “R. Bettencourt da Silva, A. Williams (Eds), Eurachem/CITAC Guide:Setting and Using Target Uncertainty in Chemical Measurement, (1st ed. 2015). [13]. ISO/IEC 17025:2017, General requirements for the competence of testing and calibration laboratories [14]. Guide To The Expression Of Uncertainty In Measurement (GUM). BIPM, IEC, IFCC, ISO, IUPAC, IUPAP, OIML. International Organization of Standardization, Geneva [15]. Quantifying Uncertainty EURACHEM/CITAC Guide,

[16].

in

Analytical

Measurement.

M H Ramsey and S L R Ellison (eds.) Eurachem/EUROLAB/CITAC/Nordtest/AMC Guide: Measurement uncertainty arising from sampling: a guide to methods and approaches Eurachem (2007). ISBN 978 0 948926 26 6.

[17]. Quality of test results expressed through measurement uncertainty Marija Karajovic Zogovic1), Ivan Savovic2), Aleksandra Kokic Arsic3), Vesna Matovic4) [18]. Combined uncertainty factor for sampling and analysis Michael H. Ramsey1 • Stephen L. R. Ellison2 [19]. V J Leite1 and E C Oliveira2 1Pontifical Catholic University of Rio de Janeiro - PUC-Rio, Posgraduate Programme in Metrology, Marquês de São Vicente Street, 225 - Gávea, Rio de Janeiro - RJ, Brazil; 2Technology Management, Petrobras Transporte S.A., Rio de Janeiro – RJ, Brazil. [20]. Uncertainty from sampling, in the context of fitness for purpose Michael H. Ramsey Æ Michael Thompson

[1]. Norris R.H., Georges A. (1986) Design and Analysis for Assessment of Water Quality. In: De Deckker P., Williams W.D. (eds) Limnology in Australia. Monographiae Biologicae, vol 61. Springer, Dordrecht [2]. Ian Farrance, Tony Badrick, Robert Frenkel, Uncertainty in measurement: A review of the procedures for determining uncertainty in measurement and its use in deriving the biological variation of the estimated glomerular filtration rate, Practical Laboratory Medicine, Volume 12, 2018 [3]. Markku Ohenoja , Measurement and sampling uncertainty – a literature review, Research Report D2.1.13, Helsinki 2015 [4]. Byron Kratochvil and John K. Taylor, Sampling for chemical analysis, Analytical Chemistry 1981 53 (8), 924A-938A [5]. S. Ellison, M. Rosslein and A. Williams, “Quantifying Uncertainty in Analytical Measurement”, EURACHEM/CITAC Guide, 2nd edition, April 2000, pp. 1-120

[83]



Assessment of Groundwater Quality Status in the Residential Area Surrounding Peenya Industrial Area Waseem Raza1 of Civil Engineering, BMS College of Engineering, Visvesvaraya Technological University Bangalore, Karnataka, India [email protected] 1Department

Aquib Nasir Razi1 of Civil Engineering, BMS College of Engineering, Visvesvaraya Technological University Bangalore, Karnataka, India [email protected] 1Department

Abstract— The present study is aimed to assess the groundwater quality status in residential area surrounding Peenya Industrial area. The Peenya Industrial area is one of the largest and oldest Industrial Area in the South-East Asia. In this research work, studies are carried out to know the level of various parameters and also heavy metals concentration in the groundwater, the degree of pollution due to Industrial activities and water quality index of the study area. In this study, a total number of 25 groundwater samples were collected from different locations in residential area surrounding Peenya Industrial area and analyzed for various Physical and Chemical properties such as pH, Alkalinity, Chloride, Calcium, Magnesium, Total dissolved solids and Total hardness in the Laboratory using analytical methods. The concentration of heavy metals viz. Cadmium, Chromium, Copper, Iron, Lead, and Nickel was analyzed using AAS (Atomic Absorption Spectrophotometer). The present analysis reveals that groundwater of the study area needs some degree of treatment and should be protected from future contamination.

C. R. Ramakrishnaiah1* of Civil Engineering, BMS College of Engineering, Visvesvaraya Technological University Bangalore, Karnataka, India [email protected] 1Department

help us to gauge the level of treatment which could be required if in case the groundwater is of substandard quality. A. Study Area The city of Bangalore located at an altitude of 921 meters above mean sea level is situated at a latitude 12º 58’N and longitude 75º 35’E. Our study area is located around Peenya industrial area and the latitude longitude of the sampling location is mentioned in Table II. The boundary of Peenya industrial area is as shown in Fig.1

Keywords—Groundwater quality; heavy metal analysis; Peenya; contamination; industrial area.

I. INTRODUCTION Peenya Industrial Area is one of Asia’s oldest Industrial Area with a total of 2614 industries in its vicinity. It covers an area of 1.08 km2, with 4 phases, of which Phase I is the industrial and Phase II, III and IV are residential areas where majority of the workforces from the industrial area are settled. With over 35 years of operation and a variety of industries that reside in Peenya, it is one of the most polluted areas with respect to air and water environment in Bangalore city. These environmental problems pose a serious threat to the health of the surrounding residents. The various manufacturing industries that reside in Peenya include – automobile, pharmaceutical, electrical and electronic goods, paper mill, etc. to name a few. All of the aforementioned industries can fundamentally have an impact on the quality of groundwater. Incidences such as the Hinkley groundwater contamination – a cynical history, set a clear example of how things can go wrong in this modern day industrialized world. And so it becomes very important to continuously monitor the quality of water surrounding industrialareas. The present work will examine the quality of groundwater available in the residential area surrounding Peenya Industrial Area. By comparing our water quality tests results with the Indian and WHO standards, we will be in a better position to comprehend its suitability for drinking purposes. Based on the tests results, the present work will

Fig.1 Boundary of Peenya Industrial Area

II. METHODOLOGY The groundwater samples were collected from 25 bore and open wells in the Peenya residential area of Bangalore city. The samples were collected as per the standard methods recommended by APHA [5]. Before water sampling, all the containers were cleaned and rinsed thoroughly with distilled water and then samples were collected to be analyzed. The chemical analysis was done using standard methods. Sample collection and analysis: The groundwater samples were collected in the month of April 2017. The spatial representation of groundwater sampling points around Peenya industrial area is shown in Fig.2.Twenty-five groundwater samples were collected from the study area. The samples were then analyzed for different parameters such as pH, conductivity, TDS by using standard procedures recommended by APHA [5]. Alkalinity was determined titrimetrically using HCL, chlorides concentration using standard AgNO3 solution and sulfates bygravimetric method

[84]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) using barium chloride. All reagents used were of analytical grade. The heavy metal concentration of Cd, Cr, Cu, Fe, Ni, and Pb were determined using an Atomic Absorption Spectrometer.

(Table I), it is evident that 96% of the collected groundwater samples were of substandard quality; and more than 30% of the water samples were unsuitable for drinking purpose. A comparison of the WQI shown in Figure 3 shows the variation of WQI around Peenya industrial area and it is apparent that the water quality is worst at sample location no.9. TABLE I. CLASSIFICATION OF WATER QUALITY BASED ON WQI VALUE WQI value

Quality of water

Percentage of water samples

300

Water unsuitable for drinking

32%

Fig.2 Sampling Location

III. RESULTS AND DISCUSSION Indian standard code IS 10500:2012 was used to compare the results for acceptable limit and permissible limit with the determined values. Table II represents evaluation for different parameters in the groundwater samples of Peenya residential area of Bangalore city. It can be concluded from the analysis of groundwater of Peenya residential area of Bangalore city, that variables viz. TDS, Alkalinity, and sulfates are slightly higher and, hardness and chlorides are of lower concentration and within the permissible limit set by the IS code. The concentration of six heavy metals in groundwater viz. Cd, Cr, Cu, Fe, Ni, and Pb is listed in Table III. Heavy metal parameters – Ni and Cd were clearly higher than standards in many of the samples; 12 out of 25 samples had high concentration of Nickel and 13 out of 25 samples had high concentration of Cadmium. Pb concentration exceeded at 2 points and it was found to be as high as 6 times the acceptable limit. However Cr, Cu, Fe were found to be within permissible limits. A. Water Quality Index The various parameters studied are given weightage and emphasis on the significance and impact of that parameter on the water quality. The index was developed by Horton (1965) [6] and is widely accepted. The method used to calculate WQI was developed by National Sanitation Foundation Water Quality Index (NSFWQI). The mathematical expression used in this method is expressed below

Where, Qi = sub index for water quality parameter Wi = weight assigned to the ith parameter n = number of parameters Based on the water quality index (WQI) calculations, water quality is classified into various degree of quality

Fig. 3 Variation of Water Quality Index around Peenya industrial area

IV. CONCLUSION WQI of this study was established from various physiochemical parameters of water. The index can be summarized as that out of the 25 sample locations groundwater at 6 locations was found to be unfit for drinking purpose, however it can be used for everyday activities other than consumption. WQI at sample location 9 was extremely unfit and would require a high degree of treatment if it’s to be used for consumption. Among the heavy metal ions investigated, the concentrations of Nickel and Cadmium in the groundwater samples collected from Peenya residential area of Bangalore city were found to be much higher than the permissible limits. TDS were found to be within permissible limit though most of the samples had concentration more than the acceptable limit. Above cited results shows that the overall water quality in the study area is unfit for drinking purpose but can be used for domestic chores. However, alkalinity can be removed by filtration, while high concentrations of sulfates may cause diarrhea in humans but adults get accustomed to it within few days [4] and the groundwater can be fit for drinking after electrocoagulation method [2] for removal of heavy metals.

[85]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) TABLE II.

THE RESULTS OF PHYSICOCHEMICAL CHARACTERISTICS OF GROUNDWATER IN STUDY AREA

Sl. No.

Latitude

Longitude

pH

Conductivity (µs/cm)

TDS (mg/L)

Ca (mg/L)

Mg (mg/L)

Carbonate alkalinity (mg/L)

Bicarbonate alkalinity (mg/L)

Chlorides (mg/L)

Sulphates (mg/L)

1

13.0158

77.5148

8.3

1154

745

94.499

84.564

39.980

348.010

127.960

605.877

2

13.0129

77.5156

8.48

402

252

30.432

15.552

23.970

152.015

27.991

381.143

3

13.0111

77.5152

8.2

1109

728

70.474

61.236

47.984

368.008

129.960

349.038

4

13.0070

77.5001

7.95

1201

743

100.906

58.320

39.991

308.005

127.960

443.706

5

13.0036

77.4996

7.97

1353

877

131.338

76.788

55.991

384.004

197.939

517.794

6

13.0013

77.5007

8.63

429

308

36.839

21.384

23.957

144.022

31.990

471.695

7

13.0456

77.4897

8.06

1833

1461

179.388

84.564

47.989

268.005

311.903

575.418

8

13.0468

77.4863

7.75

3050

1950

219.430

138.024

31.994

340.003

527.836

488.159

9

13.0554

77.4868

7.33

3560

2330

294.709

188.568

7.998

416.001

739.771

564.717

10

13.0568

77.4799

8.14

2050

1030

99.304

92.340

31.986

376.007

271.916

465.932

11

13.0591

77.4801

8.08

2470

1602

155.363

135.108

47.988

356.006

389.879

465.109

12

13.0531

77.5329

7.6

889

632

48.050

64.152

7.996

172.002

117.963

419.010

13

13.0270

77.5359

6.98

804

506

73.677

20.412

-0.001

152.001

97.970

475.811

14

13.0261

77.5372

7.76

858

695

33.635

53.460

39.994

200.003

91.971

482.396

15

13.0265

77.5401

8.16

561

324

78.482

3.888

31.986

136.007

43.986

469.225

16

13.0178

77.5290

8

1191

815

60.864

54.432

55.990

344.005

105.967

333.397

17

13.0177

77.5294

8.04

1136

736

80.084

52.488

47.989

332.006

115.964

363.855

18

13.0293

77.5078

7.55

1580

1015

81.686

141.912

55.996

464.002

207.936

304.585

19

13.0318

77.5049

7.7

1322

880

115.321

56.376

31.995

364.002

147.954

232.143

20

13.0324

77.5019

7.46

1533

1021

116.923

92.340

15.997

332.002

255.921

87.259

21

13.0431

77.5169

7.47

1081

694

76.881

54.432

15.997

376.002

111.965

200.861

22

13.0507

77.5179

7.1

1106

660

83.287

34.992

-0.001

248.000

127.960

116.071

23

13.0398

77.5043

7.79

2310

1540

153.761

121.500

39.994

412.003

339.895

227.204

24

13.0407

77.4996

7.16

1512

895

134.541

63.180

-0.001

260.000

259.919

195.922

25

13.0194

77.5298

7.53

1123

1169

67.270

58.320

39.997

328.001

99.969

116.895

[86]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) TABLE III.

HEAVY METAL CONCENTRATION IN GROUNDWATER IN THE STUDY AREA

Sl. No.

Latitude

Longitude

Copper

Nickel

Chromium

Iron

Cadmium

Lead

1

13.0158

77.5148

0

0.06

0

0.22

0.01119

0.207

2

13.0129

77.5156

0

0.06

0

0.14

0.00142

1.012

3

13.0111

77.5152

0

0.06

0.09

0.3

0.004839

0.074

4

13.0070

77.5001

0

0

0.02

0.26

0.00612

0.344

5

13.0036

77.4996

0.01

0

0

0.4

0.005293

0.443

6

13.0013

77.5007

0

0.22

0

0.15

0.001198

0.582

7

13.0456

77.4897

0

0.23

0.01

0.24

0.0052

0.224

8

13.0468

77.4863

0.01

0.3

0

0.2

0.01063

1.099

9

13.0554

77.4868

0.01

0.17

0

0.26

0.02094

0.532

10

13.0568

77.4799

0

0

0

0.22

0.003896

0.876

11

13.0591

77.4801

0

0

0

0.24

0.004072

0.226

12

13.0531

77.5329

0.01

0

0.02

0.16

0.002508

0.432

13

13.0270

77.5359

0.01

0.07

0.02

0.12

0.001669

0.578

14

13.0261

77.5372

0

0

0.15

0

0.000912

0.585

15

13.0265

77.5401

0

0

0

0

0.000598

0.354

16

13.0178

77.5290

0.01

0.08

0

0.04

0.001562

4.06

17

13.0177

77.5294

0

0

0

0

0.000863

3.832

18

13.0293

77.5078

0

0.38

0

0.02

0.002477

0.666

19

13.0318

77.5049

0

0

0.07

0.2

0.002157

0.728

20

13.0324

77.5019

0.03

0.06

0

0.22

0.004071

0.897

21

13.0431

77.5169

0.01

0

0

0.02

0.002414

0.575

22

13.0507

77.5179

0.02

0

0.03

0.06

0.00314

17.14

23

13.0398

77.5043

0

0

0.02

0.1

0.005665

0.248

24

13.0407

77.4996

0

0.12

0.02

0.08

0.003847

0.899

25

13.0194

77.5298

0.08

0

0

0.1

0.001528

69.08

*All values are in units of mg/L REFERENCES [1]

[2] [3]

[4]

[5] [6]

Ali Vosoogh, Akbar Baghvand, Hatef Saghakhaneh, “Removal of heavy metals and hardness from groundwater via electrocoagulation method,” Article 5, Volume 3, Issue 2, Spring 2017, Page 213-224, https://jpoll.ut.ac.ir/article_60370_7938.html APHA (American Public Health Association) Standard method for examination of water and wastewater, NW, DC 20036, 1994 C. R. Ramakrishnaiah and N. Manasa, “Distribution and Migration of Heavy Metals in Peenya Industrial Area, Bangalore, Karnataka, India - A Case Study,” Journal of Geography, Environment and Earth Science International. 2016;6(2):1-13. EPA website. Sulfate in drinking water. U.S. Environmental Protection Agency; http://www.epa.gov/safewater/contaminants/unregulated/sulfate.ht ml Horton, R.K., “An index number system for rating water quality”, J. Water Pollu. Cont. Fed., 37(3). 300-305. 1965. Ramakrishnaiah CR, sadashivaiah C,Ranganna G, “ Assessment of water quality index for the groundwater in Tumkur Taluk, Karnataka State, India.,” E-Journals of Chemistry. 2009;6(2):523530.

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Ground Water Resources Assessment in Chickballapur District, Karnataka India. Tejaswini. C1, Pallavi .R2, Anusha Daroji3, Nanjundi Prabhu4, M.Inayathulla5. UG Student, UG Student, UG Student, Assistant Professor, Professor Department of Civil Engineering, NMIT, Bangalore, INDIA Email: [email protected], [email protected], [email protected]

Abstract Ground water contributes to about eight percent of the drinking water requirements in the rural areas, fifty percent of the urban water requirements and more than fifty percent of the irrigation requirements of the ratio. Central Ground Water Board has decided to bring out district level ground water information booklets high lighting the ground water scenario ,its resource potential, quality aspects, recharge – discharge relationship, Vulnerability area etc. , for all the districts of the country. In the present study, an attempt has been made to delineate the ground water potential zones in the Chickballapur district, Karnataka and GIS techniques. Information on geomorphology and land use in generated using the remote sensing data.. The study has been for targeting ground water in hard rock terrain by adopting remote sensing.

Key Words: Remote sensing, GIS Groundwater potential, Geomorphology, Land Use. I. INTRODUCTION

A.

PHYSIOGRAPHY OF THE STUDY AREA The study area is an undulating terrain with hills rising up to 1100m and a lowest elevation of 730m. Agricultureis the main occupation and ground water is exploited to a maximum extend due to limited surface water resources. Tanks are the main source for irrigation and other sources of water are open wells and bore wells. Drainage is mostly of dendritic pattern with more tanks in the N and N-E direction. The land adjoining the banks of the river course forms one of the most fertile lands, which is cultivated intensively for paddy and groundnut.

Ground water availability is the amount of water that is available for use from an aquifer. Ground water potential zones are governed by various geo environmental parameters such as drainage, texture, lineament and land use. In addition, geomorphology, geology, land use, slopes and rainfall play an important role. Hence, it is necessary to prepare these thematic maps to assess the ground water resource of an area. Chickballapur district is the eastern gateway to Karnataka. It formed by bifurcating old Kolar district in to Chickballapur and Kolar districts. It is land locked district and hard rock terrain of Karnataka in the maiden (plain) region and covers an area of 4208sqkm. The district lies almost in the central part of peninsular India, which has immense bearing on its geo climatic conditions. This district experiences tropical climate throughout the year. There are as many as 1243 tanks located in the district. The main Occupation of people is agriculture.

B.

LANDUSE PATTERN

11% of the total area of the district is covered by forest and 68% by cultivable land. 28% of the area is uncultivated. Area has shown in the district forms 40% of the total area of the district. C. STUDIES CARRIED OUT BY CGWB

II. STUDY AREA

Systematic and Reappraisal hydrological surveys were carried out in chickballapur district during different field season programs from 1984 to 2006 phase of exploratory drilling was carried out during 1988-1990 of maximum depth of 250mts and phase 2 of deep exploratory drilling was carried out between 2009-2012 with 500m rig capacity for the first time in hard rock terrain.

Chickballapur district lies between North latitude 130 130411 to 130 581 2911 and East longitude 770 211 5211 to 780 121 3111. It is bounded by Bangalore and Tumkur districts on the West, Ananthpur district of Andhra Pradesh on the north, chittoor district on the east and by Kolar district is divided onto 6 taluks, 26 hoblies, 151 grampanchayats and 1321 villages. The population as per the 2011 census is 12.54 lakhs and the density of population is 298per sq.km.

D. RAINFALL AND CLIMATE

[88]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Chickballapur district falls in the Eastern dry agro climatic zone. It experiences a Semi-acid climatic, characterized by typical monsoon tropical weather with hot summers and wild winters. The year is normally divided into four seasons. They are a) b) c) d)

December being as low as 100c. Potential evapotranspiration is around 1550mm annually ranging 170mm in April- May period to less than 100mm during Nov-Dec period. E.

Dry season during Jan-Feb Pre-monsoon season during Mar-May Southwest monsoon season during June-Sep Post or Northeast monsoon season during Oct-Dec.

GEOMORPHOLOGY AND SOILS

The topography of the district is undulating to plain. The central and eastern parts of the district forming the valley of PalerBasin are well cultivated. The northern part of the district forms a depression forming the valley of the North Pinakini River towards Gauribidanur. The general elevation varies from 249 to 911 m above mean sea level. The soils of Chickballapur district occur on different landforms such as hills, ridges, pediments, plains and valleys. The types of soils distributed range from red loamy soil to red sandy soil and lateritic soil. Of the total area, about 73% is suitable for agriculture and horticulture; about 3% for forestry, pasture and the remaining area is suitable for quarrying, mining and as habitat for wildlife.

There are 6 rain gauge Stations in the district, one in each of the 6 taluks. Data from these stations for the period from 1971 to 2000 is analyzed. Normal annual rainfall ranges from around 848mm at chintamani in west to around 651mm at Bagepalli in the east averaging 756mm in the district and for 2011, 676mm rainfall is recorded. The southwest monsoon contributes around 55% of the annual rainfall. The other monsoon (NE) yields around 30%. The balance of around 15% results from the premonsoon. September and October are the wettest months with over 100mm monthly rainfall. Thunderstorms are common during the month of May. The post monsoon season of ten gets copious rain due to passing depressions.

III. GROUND WATER SCENARIO A.

HYDROGEOLOGY:

Granites, gneisses, schist’s, laterites and alluvium underlie the district. Basic dykes intrude the above formations at places. Granites and gneisses occupy major portion of the district. Schist’s are mostly confined to the northwestern part of Gauribidanur taluks. Laterites occupy small portions in Chickballapur, and Sidlaghatta taluks. Alluvium is confined to river courses. Fractures or lineaments occupy well-defined structural valleys, majority of them trend NE-SW. Weathered zone, and fractures and fissures that exist in hard rocks control the occurrence and movement of ground water. In the district, ground water occurs in phreatic and semi-confined to confined conditions. It also occurs in alluvium under water table conditions. The weathered thickness varies from 6 to 18 m in the majority of the area, except in parts of Sidlaghatta and Chickballapur taluks where it ranges from 40 to 60 m. The depth of water level in piezometer generally ranges from 12 to 49 mg/l. Physiographic features and rainfall distribution essentially control the ground water levels. The hydrogeology map of the Chickballapur district is given in Fig – 2Mode of ground water extraction is through bore wells. Among the abstraction structures, bore wells are predominant. The yield of bore wells in hard rock varies generally from 15 to 200 m3/day. The depth of irrigation bore wells range in depth from 100to 300 mg/l and the yield of bore wells ranges from 0.5 to 20 m3/hour. Semi-confined to confined aquifer is formed due to fractures in hard formations. This

Fig.1 Drainage Map of Chickballapur District Karnataka.

There is one metrological observatory at KGF, which has long term records. The one at chickballapur is of recent origin. Normally April and May are hottest months with temperatures as high as 400c. They are generally lowest during

[89]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) aquifer system is developed by bore wells ranging in depth up to 300m. Its yield ranges up to 1200m3/day. and specific capacity ranges from 2 to 173 lpm/m and yield range was between 0.5 to 7.46 lps.

E. LONG TERM WATER LEVEL TREND (20022011) Out of 32 observations wells the long-term trend for premonsoon period is available for nine stations and all are showing rising trend in the range of 0.054 to 0.686 m/year. The post monsoon long term water level trend is available for 10 stations, out of which eight stations show rising trends in the range of 0.163 to 0.715 m/year and remaining two stations show falling trend in the range of 0.209 and 0.399 m/year. Out of 13 piezometer network stations, the long-term trends for premonsoon period is available for 10 stations. Five stations are showing declining trend in the range of 0.011 to 6.257 m/year and remaining 5 stations show rising trend in the range of 0.042 to 1.131 m/year. The long term trend for post monsoon period is also available for 10 piezometer network stations and five are showing declining trends in the range of 0.004 to 2.87 m/year. The reaming 5 stations are showing rising trends in the range of 0.36 to 2.63 m/year.

Fig.2 Hydrogeology of Chickballapur District , Karnataka.

F. AQUIFER

B. PREMONSOON WATER LEVEL

PARAMETERS/WELL

PARAMETERS OF UNCONFINED AQUIFER

Out of 32 NHS wells, the water level data is available for only 10 stations. In May 2011 premonsoon depth to water level varies from 1.80mts (Thondebhav, Gouribidnur taluk) to 11.35 mts (southern part of Chickballapur taluk). A generalized water level map of pre monsoon is given as Fig- 3. On a whole major part of the district comes under 2-10 m depth to water level. The water level recorded in Piezometer stations which represent semi confined aquifer, depth to water levels range between 8-26 m.

Specific capacity of dug wells ranges from 0.22 to 1.69 m3/min/m with unit area sp.capacity ranging from 0.357 to 47 l/m/m/m2.

C. POST MONSOON DEPTH OF WATER LEVEL Post monsoon Depth to water level in NHS dug wells ranges from 0.87 mts (Thondebhav, Gouribidnur taluk) to 13.35 mts (Irgampalli, Chintamaitaluk). In general major part of the district comes under 0-10 m range and small parts in Chintamani taluk show Depth to water level between 10-20 m. D. DECADAL WATER LEVEL FLUCTUATION [(MEAN OF 2001- 2010) COMPARED TO 2011] The mean water level from 2001 to 2010 when compared with water levels of 2011 the fluctuation was mostly in the range of 0-2 m in major parts of the area for premonsoon period (Fig-3) and it is in 0-4 m range for most part of the district for post monsoon period .

Fig.3 Depth to Water Level Pre Monsoon (May 2011)

G. AQUIFER PARAMETERS OF CONFINED AQUIFERS Central Ground Water Board, South western Region, Bangalore has carried out drilling in two phases. Under first phaseDuring1988-1990, 30 no E.W and 3 OW s were drilled. The depth range of these wells were in the range of 20 to 260m and yield range was between 0.5 to 7.46 lps.

[90]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) indicating that the water is alkaline in nature. In major part of the district, the specific conductance values are within 2000 us/cm at 25° C. Fluoride concentration of more than 1.5 mg/l. is reported from Bagepalli taluk. However, some of the exploratory bore wells also have recorded fluoride concentration of 2mg/l and above. Nitrate concentration of more than 45 ppm is reported from parts of Mulbagal, Bangarpet and Malur taluks. A ground water vulnerability map is presented as Fig –9; showing distribution of Fluoride, Nitrate in ground water and areas with fertilizer and pesticide contaminated ground waters in the district.

Fig.4 Ground water level fluctuation (May 2001-May2010)

Fig.6 Unit Area Annual Water Recharge Chickballapur District

A. STATUS OF GROUNDWATER DEVELOPMENT Wells are the major source of irrigation in the district. There are about 683 dug wells and 29016 bore wells in the district as per 4rd MI census. 345 dug wells and 930 bore wells have gone dry in the district due to lowering of water level. Talukwise breakup of the wells is given in table 2. Table 2:

Fig.5 Ground water level fluctuation (November 2001November 2010)

During second phase between 2008-2012 , 14 E.W and and 11 OWs were drilled with 500m capacity rig. The depth range of the wells ranging between 103.1 to 5001 m and discharges recorded were between 0.5 to 12.76 lps. The T values ranging between 4 to 50 m2/day. The location of exploration wells is given in Fig-6.The average annual unit draft of bore-wells for the district is 1.1 Ha.m. As per well census 2005-06 data the well density for the different taluks ranges from 4.47 (Bagepalli) to 7.96 wells/sq.km. (Shidlaghatta). The average well density for the district works out to be 6.65 wells/sq.km. IV.

Distribution of wells according to status as per MI Census 200506

SL N O 1

In general, the ground water is of acceptable quality for irrigation and domestic use. The pH value of ground water ranges from 7 to 8.67

[91]

Bagepally

Wells in Use Dug Shallo Well w BW s 380 4345

Wells dried up Dug B Well W s 193 225

248

5884

143

618

3

Chickballapu r Chintamani

17

5955

0

1

4

Gauribidanur

21

6226

0

26

5

Gudibanda

50

1302

9

48

6

Siddlaghatta

17

5304

0

12

Total

683

29016

345

930

2

GROUND WATER QUALITY

TALUK


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) improvement in the productivity of irrigation borewells.

V. GROUND WATER MANAGEMENT STRATEGY A. GROUND WATER DEVELOPMENT: Ground water is developed both for the domestic and irrigation purposes. Almost the entire domestic water requirement for 12.54 lakh population and the livestock is met by ground water. Well irrigation constitutes about 99% of total irrigation. 29016 tube wells/ borewells are in use in the district. As on 2005-06 there are altogether 5069 domestic borewells and 865 piped water supply in Chickballapur district which are wholly dependent on ground water. Even though Chickballapur district is having vast number of irrigation tanks (1243 tanks), their dependability for irrigation again depends upon rainfall conditions. Hence, ground water has a special significance for the all-round development of this water-starved district and plays a vital role in the development of this drought-prone area. As per the ground water resource estimation, all taluks, except Bagepalli come under the over-exploited category as shown in Fig-8. There is almost no resource for further development in these taluks.

Fig.9 Area Vulnerable to Ground Water Contamination- Chikaballapur District

A map showing artificial recharge plan for the district is presented as Fig-10. The whole district is feasible for artificial recharge practices. With the available resources as many as 16 subsurface dykes, 542 percolation tanks, 3213 check dams and as many as 145 point recharge structures can be constructed in the distric

Fig.8 Status of Groundwater utilization Chickballapur District

B.

WATER CONVERSATION AND ARTIFICIAL RECHARGE CGWB has carried out experimental artificial recharge studies under Central Sector Scheme in Gauribidanur taluk during 1994-95 to 1998-99. Under this, gravity recharge experiments in two wellfields at Belchikkanahalli and Hussainpura, Gauribidanur taluk, and roof-top rain harvesting structure and point recharge studies at five locations in Hosur (2 Nos.) Baktharahalli&Sonaganahalli in Gauribidanur taluk were experimented. The above studies have shown favorable results in building up storage in the area to the tune of 3 to 7 m. and resulted in an

Fig.10 Artificial Recharge Plan- Chikaballapur District

VI. RESULT The total scores obtained by integration have been classified into four categories to facilitate the delineation of very good,good, moderate and poor ground water potential zones. The maximum score is 790 and manimum is 175. Mean of the resultant map is 492 and the standard deviation is 114. Very good prospect zone is assigned a value of 606 and above, which is the addition of standard deviation and mean. Similarly good has given a value of 492,

[92]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) moderate as 378 to 492 and poor as less than 378. After assigning the value, the resultant is dissolved map is dissolved to get the final zonation map

13)

Department of Space,Government of India, Hyderabad.

14)

Sarkar, D.C., Deota, B, S.,Raju,P.L.N. and Jurgan, D.K 2001. A geographic system approach to evaluation of ground water potentially of Shamri micro watershed in the Shimla Taluk, Himachal Pradesh. J.ISRS, 29(3):152-164.

15)

Tiwari,A. andRai,B.1966.Hydrogeomorphological mapping for ground water prospecting using Landsat MSS images –A case study of part of Dhanbaddistrict.J.Indian Soc. Remote Sensing,24(4):282-285.

CONCLUSION Ground water potential maps aim at providing a clear picture regarding the ground water condition of an area. Probable ground water potential zones are delineated based upon multi criteria by evaluation by using geology, geomorphology , land use /land cover and slope themes, which are directly or in directly influencing the ground water potential and depending upon their importance to ground water augmentation . The map shows that the river courses, valley fills and moderate pediments associated with prominent lineaments and high frequency of lineaments intersection are classified into very good to good prospect zones. While the residual hills and shallow buried pediments, which are not intersected by lineaments, or demarcated under poor prospect zones. Good prospect zones are noticed adjacent to rivers. REFERENCES: 1)

Bangalore (Urban) District, District at a glance ., 2002, Government of Karnataka

2)

Central Ground Water Board, South Western Region, “ Water Year”

3)

Central Ground Water Board, South Western Region, 2001, “Experimental Artificial Recharge Studies in Gowribidanur and Mulbagal taluks, Kolar district., Karnataka”

4)

Department of Mines and Geology & Central Ground Water Board, South Western Region. 2005 “Report on Dynamic Ground Water Resources of Karnataka”

5)

Karanth, K.R., Ground Water Assessment, Development and Management

6)

Rainfall data - Source, Monitoring Cell,

7)

7. Reddy, M.C. Report on Systematic Hydrogeological Surveys in Bangalore South, Devanahalli and Bangalore North taluks, Bangalore (Urban) District, Karnataka.

8)

Central Ground Water Board, 1994 “Manual on Artificial Recharge of Ground Water “

9)

GSI 1994.Geological quadrangle map. Published by geological survey of India.

10)

Jothiprakash, v., marimuthu,G., muralidharan,R.and Senthilkumar,N.2003. Delineation of potential zones for artificial recharge using GIS,J. of indiansoc. Remote sensing,31910:37-47.

11)

Krishnamurthy, j., venkatesakumar , n., jayaraman, v. and manivel, M.1996. An approach to demarcate groundwater potential zones through remote sensing and GIS. International Journal of Remote Sensing,17(10):18671884.

12)

NRSA 1995. Integrated Mission for Sustainable Development Technical Guidelines. National Remote Sensing Agency,

Karnataka

State Drought

[93]



Hydrological Study of Bangalore Urban, Karnataka, India. Priyanka.V.Shastry1, Vishnu Sai Mahesh2, Yeshaswini Nandeesh3, Nanjundi Prabhu4, M.Inayathulla5. UG Student, UG Student, UG Student, Assistant Professor, Professor Department of Civil Engineering, NMIT, Bangalore, INDIA Email: [email protected], [email protected], [email protected]

Abstract This study is to show the hydrological study of Bangalore urban. The purpose of this study is to identify the consumption of water in Bangalore urban.Surface water is inadequate to meet the demand and the city has to depend on groundwater Bangalore is the sixth largest city of India and one of the fastest growing cities of Asia. It has acquired the name of ‘Silicon City”, due to its progressive trend in Information technology. Due to rapid urbanization, infiltration of rainwater into the subsoil has decreased drastically and recharging of groundwater has diminished. It is required to scientifically understand groundwater system in urban towns/cities using a comprehensive database at a proper spatial scale using hydrogeological models for assessing future resource availability for various scenarios. A framework for monitoring network in cities/towns to be formulated to understand the groundwater regime behavior at the relevant scale. Moreover, such networks should be continuous to capture the evolving groundwater conditions.

Key Words: Remote sensing, GIS Groundwater potential, Geomorphology, Land use

economic activities, trade, commerce and housing

I.INTRODUCTION

facilities. Especially, the enormous pressure on water particularly ground water in the district needs scientific planning and effective management of water resources.

Bangalore Urban is a district of the Indian state of Karnataka. It is surrounded by the Bangalore Rural district on the east and north, the Ramanagara district on the west and the Krishnagiri district of Tamil Nadu on the south. Bangalore Urban district came into being in 1986, with the partition of the erstwhile Bangalore into Bangalore Urban and Bangalore Rural districts. Bangalore Urban has five taluks: Yelahanka, Bangalore North, Bangalore East, Bangalore South and Anekal. The city of Bangalore is situated in the Bangalore Urban district. The district has 17 hoblies, 668 villages and 9 municipal corporations. Electronics City is situated in Anekal Taluk. The district had a population of 6,537,124 of which 88.11% is urban as of 2001. As of Census 2011, its population has increased to 9,588,910, with a sex-ratio of 908 females/males, the lowest in the state and its density is 4,378 people per square km. It is the central point for running the state administration and is now known as Bruhat Bengaluru Mahanagara Palike (BBMP). Bangalore is the sixth largest city of India and one of the fastest growing cities of Asia. It has acquired the name of ‘Silicon City”, due to its progressive trend in Information technology Now, after the IT boom, Bangalore city has suddenly overgrown its size and the district administration is facing a challenging task for providing necessary infrastructures to the related

II. STUDY AREA Bangalore has distinct wet and dry seasons. Due to its high elevation, Bangalore usually enjoys a more moderate climate throughout the year, although occasional heat waves can make summer somewhat uncomfortable. The coolest month is January with an average low temperature of 15.1 °C (59.2 °F) and the hottest month is April with an average high temperature of 35 °C (95 °F). The highest temperature ever recorded in Bangalore is 39.2 °C (103 °F) (recorded on 24 April 2016) as there was a strong El Nino in 2016. There were also unofficial records of 41 °C (106 °F) on that day. The lowest ever recorded is 7.8 °C (46 °F) in January 1884. Winter temperatures rarely drop below 14 °C (57 °F), and summer temperatures seldom exceed 36 °C

[94]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Fig: 1 Administrative set up Bangalore Urban

With a population estimated to be between 10,456,000 and 12,339,000, up from 8.5 million at the 2011 census, Bangalore is a megacity, and the fifth most populous city in India and the 18th most populous city in the world. Bangalore was the fastest- growing Indian metropolis after New Delhi between 1991 and 2001, with a growth rate of 38% during the decade. Residents of Bangalore are referred to as "Bangaloreans" in English and Bengaloorinavaru or Bengaloorigaru in Kannada. People from other states have migrated to Bangalore. According to the 2011 census of India, 78.9% of Bangalore's population is Hindu, a little less than the national average.[84] Muslims comprise 13.9% of the population, roughly the same as their national average. Christians and Jains account for 5.6% and 1.0% of the population, respectively, double that of their national averages. The city has a literacy rate of 89%. Roughly 10% of Bangalore's population lives in slums(a relatively low proportion) when compared to other cities in the developing world such as Mumbai (50%) and Nairobi (60%).

(97 °F). Bangalore receives rainfall from both the northeast and the southwest monsoons and the wettest months are September, October and August, in that order. The summer heat is moderated by fairly frequent thunderstorms, which occasionally cause power outages and local flooding. Most of the rainfall occurs during late afternoon/evening or night and rain before noon is infrequent. November 2015 (290.4 mm) was recorded as one of the wettest months in Bangalore with heavy rains causing severe flooding in some areas, and closure of a number of organizations for over a couple of days. The heaviest rainfall recorded in a 24-hour period is 179 millimeters (7 in) recorded on 1 October 1997. A.

Climatic Data

Table 1: Climate data for Bangalore

Climate data for Bangalore Month

Jan

Feb

Mar

Jul

Aug

Sep

Oct

Nov

Dec

32.8

35.9

(91)

(96.6)

37.3

33.3

33.3

33.3

32.4

31.7

31.1

(99.1)

(91.9)

(91.9)

(91.9)

(90.3)

(89.1)

(88)

Average high °C

27.9

30.7

33.1

34.0

33.3

29.6

28.3

27.8

28.6

28.2

27.2

26.5

29.6

(°F)

(82.2)

(87.3)

(91.6)

(93.2)

(91.9)

(85.3)

(82.9)

(82)

(83.5)

(82.8)

(81)

(79.7)

(85.3)

15.8

17.5

20.0

22.0

21.7

20.4

19.9

19.8

19.8

19.6

18.0

16.2

19.2

(60.4)

(63.5)

(68)

(71.6)

(71.1)

(68.7)

(67.8)

(67.6)

(67.6)

(67.3)

(64.4)

(61.2)

(66.6)

7.8 (46)

9.4

11.1

14.4

16.7

16.7

16.1

14.4

15.0

13.2

9.6

8.9

7.8

(48.9)

(52)

(57.9)

(62.1)

(62.1)

(61)

(57.9)

(59)

(55.8)

(49.3)

(48)

(46)

Average rainfall mm

112.9

147.0

168.3

48.9

1.9

5.4

18.5

41.5 (1.634)

(1.925

986.8

(0.728)

(6.626

15.7

(0.213)

(5.787

(inches)

(0.075)

(4.445 )

)

)

)

(0.618)

(38.85)

Average rainy days

0.2

0.4

1.1

3.1

7.2

9.9

8.3

3.8

1.4

58.1

211.7

2,360.9

Record high °C (°F)

Average low °C (°F)

Record low °C (°F)

Apr

May

Jun

107.4

Average relative humidity (%)

(4.228 ) 6.7

106.5 (4.193) 6.2

9.8

Year

30

B.

C. 2.3 Drainag e and Sewage

Population

Mean monthly sunshine

262.3

247.6

271.4

257.0

241.1

136.8

111.8

114.3

143.6

173.1

190.2

hours

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) No major rivers run through the city, although the Arkavathiand South Pennar cross paths at the Nandi Hills, 60 kilometres (37 miles) to the north. River Vrishabhavathi, a minor tributary of the Arkavathi, arises within the city at Basavanagudi and flows through the city. The rivers Arkavathi and Vrishabhavathi together carry much of Bangalore's sewage. A sewerage system,

denudational plateau, pediment/pediplain and valley fillsare mapped. A.

Denudational Hills:

Denudational hills are the remnants of the natural dynamic process of denudation and weathering. The geomorphic forms of denudational hills occur as exfoliation domes, linear ridges, mesas, low mounds and tors with partial scree or debris covered at the foot slopes. The geomorphic expression and shape of the denudational hills are controlled by lithology, and spacing of structural features like joints and fractures occurring in them. The denudational hills in basic intrusive occur as narrow linear ridges within the pediplain where dykes are seen. Denudational hills with an average height of 700 m above mean sea level occupy the southwestern part of the study area. They are exposed as a group of massive hills with resistant rock bodies and rounded summits and are formed due to differential erosion and weathering. Denudational hills are identified in the satellite imagery by their massive size and domes to elliptical shape. They appear as dark green in color in the satellite imagery. These hills are covered with big boulders and sparse vegetation in contrast to structural hills. This landform acts as a high runoff zone due to its deep slope. Denudational hills due to their relief acts as watershed boundary. The total coverage of this unit is 22.625 km2 and it occupies 1.054 % of the study area. The groundwater potential of this landform is very poor.

Fig: 2 Drainage Map of Bangalore Urban Area

constructed in 1922, covers 215 km2 (83 sq mi) of the city and connects with five sewage treatment centers located in the periphery of Bangalore.

B.

D. Rainfall

Denudational Plateaus:

Denudational Plateaus can be formed by a number of processes, including upwelling of volcanic magma, extrusion of lava, and erosion by water and glaciers. Magma rises from the mantle causing the ground to swell upward, in this way large, flat areas of rock are uplifted. Plateaus can also be built up by lava spreading outward from cracks and weak areas in the crust. Plateaus can also be formed by the erosional processes of glaciers on mountain ranges, leaving them sitting between the mountain ranges. Water can also erode mountains and other landforms down into plateaus. This landscape unit is dominant in the major part of the study area. The land of this unit is severely dissected by the streams of Akoli watershed giving size to a terrain consisting of flat-topped ridges and steep scarps. This unit has evaluation range of 450 to 500 m above msl and occupies 34.6% of the total area.

The rainfall of pre-monsoon season was recorded as excess in all the 4 taluks. During South-West monsoon, it was excess in 2 taluks, normal & deficit in one taluk each. However, during North- East monsoon one taluk had excess & the remaining 3 taluks received normal rainfall. The annual pattern shows that all the 4 taluks had excess rainfall. The district’s average rainfall was 1341 mm, which was 50 % more than normal rainfall of 896 mm.The rainfall of the district is accounted by the Pre monsoon (PRE), SW monsoon (SWM) and NE monsoon (NEM). Majority of the rainfall is contributed by SW Monsoon. In general, humid to semi arid climatic conditions prevail in the district. The average temperature is around 23.1˚C. The seasonal and annual normal rainfall of the four taluks of the district from the year 2016-2017 is considered for studying the rainfall pattern. III. GEOMORPHOLOGY Four important erosional and depositional geomorphicunits such as denudational hills,

[96]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) C. Pediment / Pediplain:

2000). Normally, they are covered with red, brown and black coarse gravel to sandy and clayey soils. In the study area, the valley fills are identified between the structural hills on the northern part of the study area. The drainage pattern over the valley fills is parallel to sub-parallel indicating that the drainage is by and large controlled by the lineaments. They exhibit dark reddish tone and medium texture in the satellite imagery, which indicates high moisture content due to intensive cultivation. This unit occupies in upper northern part of the study area and it covers 189.996 km2 areas and occupies 8.846 % of the study area. Groundwater prospects in valley fills are good to excellent because of the topographical location at the bottom of the hill and geological composition consisting of highly porous materials. Subsurface water potential is also good to excellent in the valley fills (Murthy and Rao, 1999).

The term 'pediment‟ is defined, as an eroded rock surface of considerable extent at the foot of a mountain slope or a face formed under arid to semi-arid climate erosion. The pediments have very thin cover of soil, but its thickness may increase away from the pediment junction. The pediment overlies all the lithological units with gentle to moderate slopes and is generally characterized by rugged appearance with number of small outcrops and supports scanty vegetation. Sheet erosion and gullying are very active in the zone of pediment exposing the underlying weathered mantle of bedrock at number of places. It usually meets the hill slope at an angular neck line, and may be covered by transported material. The low moisture content of this unit gives a bright signature in the satellite imagery, especially around the hills. Pediment follows steep slopes in the study area and is considered as the most suitable hydrogeomorphic class because it checks the velocity of surface runoff and thus provides more chance of water accumulation. A Pediplain is developed by a combination of process including stream erosion, weathering, sheet wash and lateral plantation. The pediplains are formed as a result of weathering under arid and semi-arid conditions, representing the end stage of cyclic erosion (King, 1950; Sparks, 1960). When the sediment developed over a large area as a result of continuous process of pedimentation, it is normally termed as a pediplain (Agarwal and Garg, 2000). The pediplains are characterized by the presence of relatively thicker weathered material. The extent and thickness of weathering depends on the slope, resistance of the underlying rock to weathering, presence of joints and fractures and precipitation and climatic conditions of the area. Depending upon the thickness of the weathered zone, the groundwater potential is moderate to good and eligible for construction of a well. This unit is scattered around the study area and its coverage is 327.225km2 and it occupies 15.238% of the study area. Overall groundwater prospect is good in this unit.

E.

Lineaments:

Lineaments(Figure 6) are the linear features of tectonic origin then are identified as long narrow and relatively straight tonal alignments visible in satellite images. A lineament may by a fault, fracture, master joint, a long and linear geological formation, the straight course of streams ,vegetation served may be the result of faulting and fracturing and hence it is inferred that they are the areas and zones of increased porosity and permeability in hard rock areas. These have more significance in the ground water studies. Remote sensing data provides useful information to identify structural features and lineaments. The satellite data of Resourcesat-2 LISS IV Imageries of false color composite have been visually interpreted to identify the lineaments of the basin area. The data have been checked by field studies and Survey of India topographical maps at the1:50,000 scale. The interpretation of satellite data indicates that the rocks appear to have been deformed repeatedly. F.

Drainage Density:

The drainage density is an inverse function of permeability. The less permeable a rock is, the less the infiltration of rain fall, which conversely tends to be concentrated in surface runoff. This gives rise to a well-developed and fine drainage system. Since the drainage density can indirectly indicate the suitability for groundwater recharge of an area because of its relation with surface runoff and permeability, it was considered as one of the indicators of groundwater potential.The drainage density map shows(figure 7) the flow of water throughout the study area. Drainage density is

D. Valley Flats: Valley flats are low linear areas occurring between hills. These units occupy the lowest reaches in topography with nearly level slope. The valley flat deposits are colluvium fluvialin origin derived from weathering and deposited by the action of streams at the floor of valleys. Depending upon the parent rock, the valley fills deposits vary in composition and texture (Agarwal and Garg,

[97]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) defined as total stream length per area.More the density high would be the runoff. Drainage Density it is group in to 3 classes:0-1.0 km; 1.0-2.0 km; 2.0-3.0 km. The suitability of groundwater potential zones is indirectly related to drainage density because of its relation with surface runoff and permeability.

received. Generally the depth of weathering varies, being more in the valley, and often extending up to 30 m in the dug wells. However the yield in the bore well is dependent upon factors like degree of weathering, presence of joints and fractures and its connectivity and the presence of intrusive bodies. Granites and Gneisses of peninsular gneissic group constitute major aquifers in the urban district of Bangalore. Laterites of Tertiary age occur as isolated patches capping crystalline rocks in Bangalore north taluk and ground water occur in phreatic condition. Alluvium of limited thickness and aerial extent of 20 to 25m thick occur along the river courses possessing substantial ground water potential.

G. Soil: Soil is an important factor for delineating the groundwater potential zones. The analysis of the soil type reveals that the study area (figure 8) is predominantly covered by Red loamy and sandy soils, Laterite soil.Red loamy and sandy soils generally occur on hilly to undulating land slope on granite and gneissic terrain. It is mainly seen in the eastern and southern parts of Bangalore north and south taluks. Laterite soils occur on undulating terrain forming plain to gently sloping topography of peninsular gneissic region. It is mainly covered in Anekaltaluk and western parts of Bangalore North and south taluks. The good prospective zones are along thevalley flat, pediplains, charnockite, ultramafics,major lineaments and weathered zones. The major portion of the study area falls under moderate prospective zones. The poor prospective zone falls under denudational hills and Granite Gneiss are the region where more withdrawal of water takes place for domestic and industrial purposes.Thus, the total amount of average annually exploitable ground water reserve is more for the moderate zone compared to the good zone, which is attributable to the larger area under the moderate zone. Therefore, these groundwater reserves can be considered as sustainable yields of the respective zones, which can be safely utilized to meet the water demands of different sectors in the study area. The occurrenceand movement of groundwater depend upon the formation of rocks present in study area. It also depends upon the topography, geomorphology & structure as well as hydrogeological properties of the water-bearing materials.

Fig:3 Hydrogeology of Bangalore Urban District

B.

Behavior of ground water level is essentially controlled by physiography, lithology and rainfall. Ground water level behavior is analysed based on monitoring of ground water level from the network hydrograph stations (NHS) established by CGWB. In Bangalore urban district, there are 22 NHS and 13 piezometers, which are monitored four times in a year during May, August, November & January. Apart from this, monthly urban monitoring is also carried out in Bangalore City.

IV. GROUND WATER SCENARIO A.

BEHAVIOUR OF GROUND WATER TABLE

Hydrogeology:

Ground water occurs in phreatic conditions or unconfined conditions in the weathered zone and under semi confined to confined conditions in fractured and jointed rock formations (Fig.3). The occurrence of Ground water movement and recharge to aquifers are controlled by various factors like fracture pattern, degree of weathering, geomorphological setup and amount of rainfall

Fig: 4 Piped and Ground water supply for Bangalore

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) properties from one location to another, even close by. Hence these maps should be treated as indicative of neighborhoodscale to ward- scale static water tables, and should not be interpreted as providing exact water table depths at point (example, household) scale. At the household scale the following factors can contribute to differences in observed groundwater depths from the interpolated maps: geologic variability, differing well depths and screening depths, and effects of pumping.

Fig: 5 Hydrograph of Bangalore

Fig: 7 Observed Groundwater Depths In Bangalore Urban District

V. WATER SORTAGE INBANGALORE URBAN

Fig: 6 Ground Water Scenario IN Bangalore Urban District

In the 1960s, the number of tanks and lakes was 280 in Bangalore, which dwindled to less than 80 by 1993. While the water needs of the city were met by these tanks and lakes, the number of lakes kept coming down due to development and encroachment and hence since 1970s, the scheme to pump water from the Cauvery river by raising the water up by 500 metres was introduced. Way back in 2001, the demand for water was 750 million litres per day, while the actual supply was only 570 million litres per day and the per capita usage was about 105 litres per day as against the national standard of 150 litres per day. These figures must have gone up by leaps and bounds in the last decade because of the sporadic development activities and increasing encroachment of land by land sharks. Of the 280-

C. GROUND WATER TABLE MAPS These maps are interpolated groundwater depths, in meters below ground surface. Between December 2015 and now (September 2017) monthly measurements of depth to the static water table were made at approximately 150 locations across Bangalore. These measurements were largely made at abandoned/unused borewells. Below, individual interpolated groundwater maps of the static water table can be viewed/downloaded. An animation is also provided. The locations of each well that was monitored are indicated by the circles.The measured depths were interpolated. Like all interpolations, there are uncertainties with interpolated data. This is especially true of Bangalore’s hard-rock aquifers, where there is large spatial variation in aquifer hydraulic

[99]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) 285 lakes that Bangalore once had, 7 cannot be traced, 7 are reduced to small pools of water, 18 have been unauthorisedly encroached by slums and private parties, 14 have dried up and are leased out by the Government and 28 lakes have been used by the BDA (Bangalore Development Authority to distribute sites and build extensions for residential areas. Even the remaining lakes are in fairly advanced state of deterioration.

A. METHODS

TO

IMPROVE

Parks and apartment complexes should be compelled to grow trees instead of maintaining ornamental gardens and lawns which only use more water and are of no help in eco- protection. 10. Reuse of wastewater should be introduced in all possible places for gardening, washing vehicles and bathrooms. 11. In public places, toilets with Indian commodes should only be built because the Western commodes use up more water, are not properly used and become unhygienic in such case. In most of the public toilets, the flush lever never works and people mess up the seat and the surrounding areas.

GROUNDWATER

LEVELS 1.

All building owners, especially the huge apartment complexes, malls, office buildings and multiplexes have to be compelled to harvest the rainwater and use solar lighting/heating systems.

2.

The existing greenery should be saved at any cost by proper planning instead of giving in to greedy short-sighted plans. Environmental scientists should be on the board of development committees and their suggestions should be seriously considered.

3.

Owners of houses who grow trees, harvest rainwater and help in conservation of energy and water should be given incentives by way of tax-rebates.

4.

A rule should be strictly enforced that every house should have at least a few metres of soil around the building to help water to percolate and improve the groundwater levels.

5.

Unwieldy increase of bore-wells should be checked immediately.

6.

Encroached lakes should be immediately recovered and the lakes developed with the cooperation of the local citizenry.

7.

Land

encroachers

should

be

12. All the politicians, planners and citizens should think not just of today but the future and realize that fresh water is a scarce commodity and all of us have the responsibility of saving it for our children. 13. It is disgusting to see leaky water supply pipes unattended for days on end. When people in the slums are not supplied even drinking water free of cost, it is atrocious to see people in higher classes filling their sumps and tanks from the public taps in front of their houses through long hose pipes. 14. The importance of urban greenery to check air and noise pollution, improve groundwater resource, bring down the heat in the atmosphere and maintain biodiversity is being highlighted time and again but the planners seem to totally ignore this point. Individuals do not need trees inside their compounds, outside their compounds on the roadsides, around malls and shopping complexes or parks. They get carried away by neat ornamental gardens, little realizing the damage that such gardens do to the ecology. Instead, a balanced mix of flowering shrubs and fruit yielding trees, medicinal plants and trees and ornamental climbers and creepers would do more good to the city. 15. Water scarcity is a serious problem and one that will lead to so many other problems like unhygienic living conditions, outbreak of epidemics, crime due to fights for water, etc.

severely

punished. 8.

BWSSB should keep a tap on leaky pipes, repair them immediately. They should make surprise visits to houses and fine people not attending to leaking taps. Each citizen should be conscious not to waste even a drop of fresh water.

CONCLUSION

9.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) In view of the Over exploited situation of ground water resources in the whole district following recommendations are made.

In recent years many countries have implemented water recycling projects successfully. In addition to this, dual water supply systems in the upcoming areas for potable and non-potable water separately, are to be considered as a viable option to minimize the pressure on fresh water supply.

Artificial recharge to ground water through structures like dug well recharge, watershed treatment, recharge trenches, percolation tanks, check dams, sub surface dykes, point recharge structures should be implemented based on site specific scientific investigation. The area feasible for artificial recharge has been demarcated and in total 861 check dams, 145 percolation tanks, 39 point recharge structures and 4 sub surface dykes. Such structures can be taken up in all the OE blocks of the district.

These recommendations can be implemented through public awareness and training through Central/ State administration, which will go in long way in realizing the civic responsibility towards very important element of life "water". REFERENCES: 1.

Census of India (2001) Karnataka, Provisional Population Totals, Rural-Urban Distribution of Population, Series 30, Bangalore.

2.

Bangalore Development Authority (2005) Master Plan – 2015: Land Use Zonal Regulations, Volume 4, Draft, Bangalore Development Authority, Bangalore.

3.

Bangalore Development Authority (1995) Comprehensive Development Plan (Revised), Bangalore, Bangalore Development Authority, Bangalore.

4.

BMRTL (2003) Report on Passenger Prediction Study for ELRTS – Bangalore, Bangalore.

There are more than 200 parks in Bangalore City, which has also large institutions, industries, public, and semi-public areas that can be utilized for rainwater harvesting. Additional water bodies in barren catchments of various campuses should be developed on a large scale to prevent wastage of run-off and to help augmenting ground water recharge. Multi approach method is ideal to have maximum benefits.

5.

Candler, J. (1996) Smart Cars, Smart Roads, Nation’s Business, pp. 31-34.

6.

Adaptation (2003) Trams, Buses and the London Underground, Architecture – Time Space and People, Vol. 3, No. 9, pp. 32-36.

7.

Ramanathan, R. (2000) Link between Population and Number of Vehicles: Evidence from Indian Cities, Cities, Vol. 17, No. 4, pp. 263-269.

Central ground Water Authority has circulated the Model Bill to enact in the state in 1996 and 2005. Accordingly, the state has enacted Karnataka Ground water Act, 2011 (Regulation and Control of Development and management) and Rule 2012 to regulate the over exploitation of ground water in the state. The state Government has established the Karnataka Groundwater Authority to implement the act and rules in the state.

8.

Armitage, G.H. (1979) ‘Central Area Transport Infrastructure’, pp. 102-111, in Roy Cresswell (Ed.) Urban Planning and Public Transport, The Construction Press Ltd., Lancaster.

9.

Gosling, D. (1979) ‘The Structure of Town and City Centers’, pp. 92-101, in Roy Cresswell (Ed.), Urban Planning and Public Transport, The Construction Press Ltd., Lancaster.

10.

Harrison, M. (1979) ‘Bus Services in Central Areas’, pp. 112- 126, in Roy Cresswell (Ed.), Urban Planning and Public Transport, The Construction Press Ltd., Lancaster.

11.

Gopalakrishna, B. (2000) Bangalore Metropolitan Transportation Corporation: Metro Bus Systems, IIE Workshop on Transportation Needs for the Millennium: Problems and Perspectives for Bangalore, Bangalore.

Lakes were created basically for hydrological reasons for checking floods, recharging, and maintaining the ground water table. They also act as sediment traps, prevent clogging up of natural valleys and reduce erosion by regulating run off. Lakes and Tanks belong to wetland ecosystem and have a larger biological and ecological role. Due to urbanization most of the tanks/lakes in the districts have been erased form the map. Hence, measures for rejuvenation of tanks and lakes in the district will definitely build up ground water resources.

Waste water recycling for secondary uses like gardening, industrial cooling, flushing and other secondary purposes through municipal supply, which will definitely help to keep a check on over exploitation of groundwater sources and thus building up the ground water resources in the district.

[101]



A GIS based Automatic Drainage Network Extraction for Hoskote Taluk using CARTOSAT-1 30M Digital Elevation Model Sampath Kumar. M.C. Department of Civil Engineering BMS College of Engineering, Bangalore, Karnataka, India [email protected]

Shwetha. A. Department of Civil Engineering Presidency University, Bangalore, Karnataka, India [email protected]

ABSTRACT ---Automated extraction of terrain topography from digital elevation model is an important application technique used for quantitative analysis of remote sensing observations of the earth system for a positional analysis in GIS domain, and for a complex analysis of multi-dimensional and polymorphic information. Cartosat-1 DEM is very useful for hydrological analysis and water resources management. In this paper, IRS CARTOSAT-1 DEM of 30m spatial resolution was processed by the D8 deterministic eight-node method to derive stream networks for Hoskote Taluk of Karnataka State, India. The flow direction and flow accumulation algorithms have been used in the study for estimation of drainage network. This paper utilizes a latest approach of drainage direction assignment which is often necessary to estimate drainage directions on surfaces i.e., for each spatial cell in which the direction of water flow. The extraction and delineation of stream network are carried out using various ArcGIS hydrology tools. The results showa drainage network st

nd

with the order lengths of 1 order: 413.89 km, 2 order: 145.28 km and 3rd order: 18.56 km. Natural drainage network provided by the topographic maps were compared with the automatic drainage network extracted from the DEM for the flow accumulation after sinks filled with the threshold value of >300. There is a good match between the drainage networks extracted from the DEM and topographic maps. The drainage network extracted from the DEM can be used to update the morphological changes of streams that occur temporally due to environmental processes and anthropogenic factors.

M. Rajyalakshmi Department of Civil Engineering BMS College of Bio-Technology, Bangalore, Karnataka, India [email protected]

calculation as it provides information on different terrain attributes that enhance the assessment and enable the simulation of complex hydrological processes. The availability of satellite based new topographic datasets have opened new venues for hydrologic and geomorphologic studies including analysis of surface morphology and channel network structure (Radhika, V.N. et al 2017). Drainage networks are useful for many types of research and hydrological modelling. In the past, topographical maps were the major sources of information for the derivation of the catchment characteristics in hydrological models, but presently DEM is playing an important role in the hydrologic and topographic characterization as it provides quick, economical and reliable information. A variety of methods have been developed to process raster DEMs automatically to extract drainage networks and measure their properties (O’Callaghan, J. et al 1084) II. OBJECTIVES OF THE STUDY 1. 2. 3.

Keywords--Stream network, Cartosat-1 DEM, Stream order, D8

To update the drainage network of Hoskote taluk using IRS Cartosat-1 Digital Elevation Model. To generate the automated drainage network from Carosat-1 DEM using ArcGIS Hydrology tool. To compare drainage networks extracted from DEM and Topographic maps

method, flow direction, flow accumulation.

A. Study area I. INTRODUCTION The use of digital elevation model (DEM) has significantly changed the way of studying Earth surface processes. Accurate delineation of drainage network is a prerequisite for many natural resource management issues (Patel A. et al 2016). Important hydro geomorphological parameters such as drainage density, drainage frequency and slope can be estimated, once surface water flow paths are determined. The water crisis in the study area could be reduced by better water resources planning and management using latest technologies. Digital Elevation Model (DEM) refers to a quantitative model of a part of the Earth’s surface in a digital form Burrough, P.A. et al (1988). In water resources management, the topography of the river basin plays an important role in the hydrological

Hoskote is a taluk in Bangalore Rural District of Karnataka. It forms the northern part of the district. The Hoskote taluk is located between 12°51' &13°15' N Latitudes and 77° 41' & 77° 58' E Longitudes, covering an area of 582 sq.km. The study area falls in Survey of India (1:50,000) toposheets Nos. 57 G/12, 57 G/16, 57 H/9 and 57 H/I 3. The highest elevation is seen near Nandagudi, which is about 940 m above MSL. The average annual rainfall of the study area is 776 mm. As the area is under semi-arid climatic condition, the temperature starts rising from January and reaches its peak value in May with a maximum temperature of around 37°C. The location of the study area is shown in the figure 1.

[102]



Fig.1 Base map of Hoskote Taluk in Karnataka State WGS 84

Datum

III. MATERIALS AND METHODS A. Workflow The base map is generated using Survey of India Topographical Map of scale 1:50,000. The drainage network from Cartosat DEM with 30 m data was extracted using the Hydrology module available in ArcGIS 10.3 Software. A flow chart illustrating various steps involved in the generation of the stream network is shown in Fig. 2. The watershed delineation process is based on the ‘eight-pour point’ algorithm (Jenson, S. K., 1985) which includes pit filling, calculation of flow direction and flow accumulation grids from DEM. Statistically, Cartosat-1 DEM is meeting the specification of vertical accuracy i.e. 8 m at 90% confidence. (Radhika et al., 2007; Van Zyl, 2001). From the flow accumulation grids, stream networks are extracted. In this study, the ‘threshold area’ method was adopted for the watershed delineation. A threshold of > 300 was used to create the stream networks. This instrumental setup allows for photogrammetric DEM generation with vertical accuracies of ±15–30 m. (Toutin, 2008). The data details are given in Table 1. Fig. 2. Flow chart of stream network extraction and

Table 1: Details of the data used

Data Type

Carosat-1 DEM

Entity ID

CartoDEM Version -2 R1

Acquisition Date

25-09-2017

Pixel Size

1 arc-second

Source

Bhuvan, ISRO

mapping

B. Sinks and peak filling The ArcGIS Hydrology tool was used to extract the stream network of the study area. The first step in the GIS Hydrology analysis was to remove the sinks from the DEM by implementing the fill-sink algorithm in the Hydrology tool. For

[103]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) each fill/cut operation the volume was calculated by the formula: vol = (cell area ∗ ∆Z) For each of the cut/fill operation, the area was calculated as the number of cells in the region multiplied by the cell size of theraster. According to the formula, in the region where material has been cut, the volume will be positive (larger value -smaller value> 0). When the material was added, the volume will be negative (smaller value - larger value < 0). C. Flow direction Flow direction is a measurement based on Digital Elevation Model (DEMs) which determines the paths of water, sediment and contaminant movement and shows the direction of the steepest down slope neighbor for each cell by colour coded direction. The direction of flow is determined bythe following equation. Maximum drop = = chnage in z−value

presented in (Jenson, S. K.,1985). Stream raster which represents a linear stream network and flow direction raster was used as input data for creating the stream link raster in the present study. E. Strahler ordering There are two methods for stream ordering proposed by Strahler (1957) and Shreve (1966). The Strahler method was used in the present study. With Strahler method, one can assign for all links without any tributaries an order of 1 and are referred to as the first order. The intersection of two first-order links creates a second-order link, the intersection of two second-order links create a third-order link, and so on.

F. Vectorising stream and drainage network This step converts a raster map that represents a linear stream network into vector format. Vectorization helps in analysis and quantification of map features.

𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒∗100

Where: G. Arc Hydrology Tool The denominator is the height difference between two locations and the denominator is the horizontal difference. Two adjacent cells have a horizontal distance of 1: two diagonal cells have a horizontal distance of √2. D. Flow accumulation The estimation of the flow accumulation is computed by tracing the waterway upstream from an outlet (or sink). This approach of deriving accumulated flow from a DEM is

ArcGIS Spatial Analyst provides a rich set of spatial analysis and modelling tools for both raster and vector datasets. The Arc Hydrology introduced by ESRI in the year 2002 is one of the geo-spatial analysis tool available in the Spatial Analyst extension. It offers variety of hydrological analysis viz., flow direction, flow accumulation, fill, stream link, stream order, stream to feature and watershed delineation, etc.

Fig. 3. Direction coding: the eight possible flow direction

[104]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) IV. RESULTS AND DISCUSSION The results show that the land surface modified by the removal or addition of surface material i.e., sinks filled area represents 18.56% of the total area. The first important step in deriving hydrologic characteristics of a surface is to determine the direction of flow from every cell in the raster Cartosat-1 DEM. This is done with the ArcGIS Flow Direction tool. The DEM shows the water flow direction for each cell. Eight possibilities identified in the present study are E, SE, S, SW, W, NW, N and NE which are shown in the Fig.3. The threshold value represents the minimum upstream area required to form a channel segment in which water starts to flow as channel runoff (Rieger, 1993). The stream network is normally displayed as inter-connected linear features and

these features begin where the threshold value is exceeded. In the present study, the drainage streams are delineated using stream threshold of 0.03 km2 from the input Cartosat -1 DEM. The order-wise streams generated for the study area is shown in Fig. 6. It is identified that the cumulative stream length is higher in first-order streams and decreases as the stream order increases. In almost all the cases, the basin length decreases as the order increases. This is due to the variation in relief over which the segments occur. The highest stream order i.e., 3rd order has a total length of 18.56 km covering 3.2% of the total study area. The 1st order has a length of 413.89 km and 2nd order has a length of 145.28 km covering 71.64% and 25.14%, of the study area respectively.

Fig.4 flow direction matrix with numerical value for each direction.

[105]



Fig.4 flow direction matrix with numerical value for eachdirection. Fig. 5. Flow accumulation of Hoskote Taluk result with thresholdvalue >300

Fig.5 Flow accumulation of Hoskote Taluk result with threshold value >300 .

Stream Length, Lu

Fig.6. Stream ordering of Hoskote Taluk with Strahler’s method

STREAM LENGTH OF THE STUDY AREA 500

413.89 145.28

18.56

Fig. 8 Stream ordering for Hoskote taluk from Topographic Maps

0 1st

2nd Stream Orders

3rd

Fig.7 Stream Length of the Study Area using Strahler`s method

[106]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) [5]

Radhika, V.N., Kartikeyan, B., Krishna, B.G., Chowdhury, S., Srivastava, P.K., 2007. Robust stereo image matching for spaceborne imagery. IEEE Trans. Geosci.Remote Sens. 45 (8), 2993–3000.

[6]

Rieger, W., 1993. “Hydrological terrain features derived from a pyramid raster structure”. In: Kovar, K., Natchtnebel, H.P. (Eds.), Application of Geographical Information Systems in Hydrology and Water Resources Management. IAHS Publication 211, Wallington, Oxford.

[7]

Toutin, T., 2008. ASTER DEMs for geomatic and geoscientific applications: a review. Int. J. Remote Sens. 29 (7), 1855–1875.

[8]

Van Zyl, J., 2001. The shuttle radar topography mission (SRTM): a breakthrough in remote sensing of topography. Acta Astronautica 48 (5–12), 559–565.

Stream Length, Lu

STREAM LENGTH OF THE STUDY AREA 600 400

403.01 147.15

200

20.56

0 1st

2nd Stream Orders

3rd

Fig. 9 Stream Length of the Study Area from Topographic Maps Stream Orders

Stream Length from Toposheet (km)

Stream Length from DEM (km)

Change in Stream Length (%)

1st 2nd 3rd Total

403.01 147.15 20.26 570.42

413.89 145.28 18.56 577.73

2.69 -1.27 -8.39 1.28

Table 2: Comparison of Stream orders from DEM & Topographic Maps

The Table 2 illustrates that decrease in stream length of 1.28% at the drainage network extracted from DEM compared to the drainage network extracted from topographic maps. The result shows that the stream length is increased by 2.69% for 1st Order and decreased by 1.27% for 2nd Order and 8.39% for 3rd Order, which corresponds to change in total stream length by 7.31 km. This study enables us to analyses the changes occurred in the morphology of surface drainage or streams changes from time to time due to environmental processes and anthropogenic factors.

V. CONCLUSION Morphology of surface drainage or streams changes from time to time due to environmental processes and anthropogenic factors. Hence, there is a need to update the drainage/stream network maps generated from the Topographical Maps regularly. Remote Sensing measurements are quick, economical and reliable and they provide alternative sources for generating updated stream network maps. The present study shows that the Cartosat-1 DEM product can effectively be used for steam network mapping using Deterministic Eight-node (D8) method of processing under GIS domain. REFERENCES [1]

Burrough, P.A., McDonnell, R.A., 1998. “Principles of Geographic Information Systems”. Oxford University Press, New York, p. 333.

[2]

Jenson, S. K., 1985. Automated derivation of hydrologic basin characteristics from digital elevation model data: Proceedings of Auto-Carta 7, Washington, D.C., pp. 301-310.

[3]

O’Callaghan, J., and D. Mark (1984). “The extraction of drainage networks from digital elevation data. Computer Vision, Graphics & Image Processing”, 28, 323–344.

[4]

Patel, A., Katiyar, S.K., Prasad, V., 2016. “Performances evaluation of different open source DEM using Differential Global Positioning System (DGPS)”. Egypt J. Remote Sens. Space Sci. 19, 7–16.

[107]



Development of Intensity–Duration–Frequency Curves for Vrishabhavathi Sub-Watershed Dr. Jagadeesh C. B.1 Dr. Nagaraj Sitaram2 1. Professor, Department of Civil Engineering, New Horizon College Engineering, Bangalore, Karnataka, India. 2. Professor, Civil Engineering, M.V.J. College of Engineering, Bangalore, Karnataka, India. Abstract - Intensity–Duration–Frequency (IDF) curves are normally used on the basis of the past significant rainfall data to quantify the highintensity short-duration rainfall. The present study endeavors to comprehend urban hydrology by deducing short-duration empirical reduction formula. The primary reason for adopting such an approach is the non-availability of short-duration rainfall data. Surface flooding is a major concern in the intensely growing urban areas of Bangalore caused by the disappearance of nearly 70 percent of the 262 water tanks that were functional in 1961. In the present study, the monthly rainfall data is gathered through some specific rain gauge centers, which are established by Karnataka State Natural Disaster Monitoring Centre (KSNDMC). Totally, the rainfall data of 16 stations from 1973 to 2014 were used for the whole watershed area. The short-duration rainfall was gauged by using the empirical reduction formula of India Meteorological Department (IMD). Gumbel’s Extreme Value is utilized for the analysis, prediction and probability distribution of the depth of rainfall for different periods. Initially, an empirical equation (Kothyari and Garde) and the probability distribution for maximum annual rainfall and is used for the derivation of the suggested IDF curves. Later, the equation is modified for the derivation of the IDF curves. The resultant IDF curves serve two purposes, viz., they are compatible with extreme rainfall and they render better outcomes in varying hydrologic conditions. The stations with values of peak rainfall were selected and the IDF curves were plotted for a return period of 2, 3, 4, 5, 10 and 25 years for short-duration rainfall of 1, 2, 6, 12 and 24 hours.

relationship [2]. The establishment of IDF curves and the assessment of extreme precipitation are both important in the ascertainment of hydrological risks [3] [4]. [5] used the maximum runoff discharges to evidence the relationship between rainfall duration and rainfall intensity. An IDF formula generalized for a particular location in the United States was presented by utilizing three elementary rainfall depths, namely, 101 R (1 h, 10-year rainfall depth), 10 24 R (24 h, 10year rainfall depth), and 100 1 R (1 h, 100-year rainfall depth) [6]-[8]. Kothyari and Garde formulated the association between the frequency, duration and intensity of rainfall with respect to the Indian conditions. The mean value of the annual rainfall (R) for 2 24 R (24 h, 2-year rainfall) was considered and the rainfall data of 78 Indian rain gauge stations was used by the researchers in developing the equation of the IDF curve. IDF signifies the statistical association between the intensity (i) of rainfall, duration (d) of rainfall, and the return period (T) of the rainfall [2]. High frequency rainfall extremes possessing the derived IDF curves and the long-term daily information were combined to extend the aforementioned approach, wherein an instrumental data set of less than 24 hours was considered. Statistical distribution methods for various return periods were used for a part of Saudi Arabia to develop the IDF curves. The criteria for developing a new drainage system must encompass adequate information about the recent hydrologic variations, depicted by the IDF relationship. High tidal conditions along with high intensity of rainfall cause flooding in coastal cities (like Mumbai) that are surrounded by creeks and sea. Hence, storm water drainage system can be effectively designed through adequate knowledge of rainfall intensity. A detailed study of rainfall in Mumbai was earlier conducted by Chawathe et al. [1], who used data from the Colaba and Santacruz and rain gauge stations for 24 years and 33 years, respectively. Zope et al. analyzed the spatio-temporal variation of rainfall in Mumbai city. The contemporary hydrological conditions have raised a concern that the IDF relationship must be updated. The present study aims to use lengthy observed rainfall data of Mumbai city for formulating the IDF curves. Initially, the empirical association developed by Kothyari and Garde [9] and the probability distribution technique for the annual

Key Words: Intensity, Rainfall, Return Period, Duration, IDF Curves, Frequency. 1. INTRODUCTION The changing patterns of rainfall cause several natural hazards among which flooding are perhaps the most severe. The elementary input in the designing of urban storm water drainage system is the evolution of the rainfall Intensity– Duration–Frequency (IDF) relationship [1]. Projects associated with water resources are planned, designed and operated by water resource managers through the utilization of the depth of rainfall derived from the IDF

[108]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) maximum rainfall was used to model the suggested IDF curves. Later, the concept of changing return period was incorporated to modify the same equation and deduce the IDF curves. The resultant outcomes were equated in accordance with the varying hydrological conditions, wherein the specific case of extreme rainfall in Mumbai (on26th July 2005) was considered.

Monthly normal rainfall contribution (%) in the upstream side of the Vrishabhavathi sub-watershed of Gali Anjaneya Temple

January February March

0%

1%

6% 2%

1%

April

5% 12% 9%

18%

May June July August

12%

2. STUDY AREA The sub-watershed of Gali Anjaneya temple (situated in Bangalore’s southern part) was selected as the study area. This sub-watershed witnesses a partial flow of the Vrishabhavathi valley. An area of 36 km2 (Figure 1) is totally covered by the study. The study area is geographically situated at a longitude of 70°32’6” E and latitude of 13°1’11” N. The subwatershed comprises of nine micro-watersheds draining into the Vrishabhavathi valley in Bangalore, Karnataka. The annual maximum rainfall distribution in the present study area receives most of the rainfall, with an average of about 174 mm, in the month of September. The maximum rainfall on a particular day is recorded as 123.5 mm that occurred in the month of July and the maximum monthly rainfall is recorded as 605.6 mm that occurred in the month of October. Hence, the rainiest month (October) received 21% of the total annual rainfall. The total normal rainfall of different months fixed by the rain gauge center is 881mm.

14%

September October November December

Figure - 2: Monthly Normal Rainfall Contribution 3. INTENSITY–DURATION–FREQUENCY CURVES Intensity–Duration–Frequency (IDF) curves can be used to predict a storm’s average intensity of rainfall and a storm’s duration for a specific return period. Generally, the duration of rainfall is plotted in hours on the x-axis and the intensity of rainfall is plotted in mm/hr. on the y-axis. The uninterrupted records of the rainfall data are required by the build-up of IDF curves.

1.

2.

3.

4. METHODOLOGY The IDF curves are prepared by adopting the belowmentioned procedure. Gathering of annual data of daily rainfall: The recent trends in the rainfall intensity are received through the data on daily rainfall from the year 2009 to the year 2014. Preparing the data of short-duration rainfall: The data on daily rainfall and the IMD formula are used to generate the series of short-duration rainfall (Table 2).

Distribution of Probability: The probability distribution for each of the chosen data series of duration is calculated by using the Gumbel’s Extreme Value distribution approach.

Figure - 1: The study area’s location map and prominent features including the nine subbasins

[109]


Table - 1: IMD

1/3rd Rule Being

Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Used for the Derivation of Short-Duration Rainfall from Daily Maximum Rainfall

Year/Duration in mm

1H

2H

6H

12H

24H

1969

45

31

17

9

5

1970

37

24

10

6

4

1971

41

26

14

7

4

1972

30

20

9

5

3

1973

41

27

9

6

4

1974

52

31

14

8

6

1975

60

47

16

8

4

1976

22

21

10

5

3

1977

42

22

8

5

3

1978 1979

36 60

18 59

9 22

5 11

2 6

1980

48

29

14

7

4

1981

42

29

11

5

3

1982

37

22

8

6

3

1983

37

30

12

6

3

1984

60

37

13

10

5

1986

65

37

16

9

4

1987

47

28

11

5

3

1988

149

105

63

36

19

1989

42

24

9

4

3

1990

41

36

13

7

3

1991

41

25

11

8

6

1992

31

28

13

7

3

1993

34

18

9

6

3

1994

23

19

7

4

2

1995 1996

44 57

31 37

12 14

6 7

3 4

1997

50

36

24

15

8

1998

72

47

19

10

5

1999

59

31

14

8

4

2000

62

39

14

7

4

2001

47

35

16

8

4

2003 Mean

53 49

43 33

18 14

9 8

4 4

Standard Deviation

22

16

10

6

3

where  is the mean annual daily max rainfall, while S is the std. dev. of annual daily max rainfall and K is the frequency factor that is provided below as:

According to Chow (1964), the belowmentioned equation of hydrologic frequency analysis can be used to express hydrological studies. The Gumbel’s distribution is used to determine the rainfall (PT) in a given return period (T), which is imparted by: PT = σ + K.S

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) TIME (T) Years K

Table - 2: Frequency Factors for Different Return Periods 2 5 10 50 0.164

0.719

1.305

The aforementioned equation was used to compute the frequency factors for the return periods of 2 years, 5 years, 10 years, 50 years and 100 years; and, the values were 0.164, 0.719, 1.305, 2.593, and 3.138, respectively. The

2.593

100 3.138

PT that corresponded to return periods of 2 years, 5 years, 10 years, 25 years, 50 years, and 100 years (for durations of 1 hour to 24 hours) was obtained by using the aforementioned values of frequency factors.

Table - 3: Gumbel’s method used to determine the intensity of rainfall at different durations of rainfall (in mm/hr) Return Period Duration 1H 2H 6H 12H 24H 4.

2 Year 45 31 13 7 4

5 Year 64 45 21 12 6

10 Year 77 54 27 15 8

IDF curves’ preparation: IDF curves are plotted for different return periods by using the information depicted in Table 3 (Figure 1). It was found that as the duration increases, the intensity value decreases. Kothyari and Garde Method being used to derive the IDF curves

The equation mentioned below is used to derive the intensity of rainfall (IT) for the return period T. ‘ItT’ is the intensity of rainfall (mm/hr); ‘T’ is the return period (years), ‘t’ is the rainfall’s duration (hr.), and ‘R224’ is the 24 hr. 2-year rainfall (mm). The value of constant ‘C’ is 7.1 for the present study; however, it differs across various Indian geographical regions.

100 Year 116 83 45 25 13

value of the correlation efficient was more than 0.999, the outcomes depicted a good match. It can be inferred that the empirical formula, which is derived for gauging the intensity of rainfall in the study area, can be used for shortduration rainfall. Water resource management projects can be planned and designed through the computation of peak discharge by using such empirical equations and IDF curves. [1]

[2] [3]

[4]

[5]

[6]

[7]

Figure - 3: IDF Curves for Various Return Periods for Bengaluru City

50 Year 105 74 39 22 12

[8]

5. CONCLUSION The intensity of rainfall for the return periods of 2 years, 5 years, 10 years, 50 years, and 100 years is optimally estimated by using the Gumbel’s Extreme Value Distribution. Since the

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REFERENCES M. M. Rashid Et Al, Modeling of Short Duration Rainfall Intensity Duration Frequency (Sdr-Idf) Equation For Sylhet City In Bangladesh. Arpn Journal Of Science And Technology, Issn 2225-7217, Vol.2, No.2, March 2012. Chowdhury et al., Short Duration Rainfall Estimation Of Sylhet: Imd And Uswb Method. Journal Of Indian Water Works Association. Pp. 285-292, 2007 Lamia Abdul Jaleel et al., Developing Rainfall IntensityDuration-Freqency Relationship For Basrah City, Kufa Journal Of Engineering (K.J.E) Issn 2207- 5528 Vol. 5, Issue 1, Dec., 2013,P.P.105-112. Munshi Md. Rasel et al., Modeling Rainfall Intensity Duration Frequency (R-IDF) Relationship For Seven Divisions Of Bangladesh, European Academic Research Vol. Iii, Issue 5/ August 2015. Jahnvi P. Bhatt Et Al, Generation Of Intensity Duration Frequency Curve Using Daily Rainfall Data For Different Return Period, Journal Of International Academic Research For Multidisciplinary Impact Factor 1.393, Issn: 2320-5083, Volume 2, Issue 2, March 2014. Zameer Ahmed et al., Rainfall Intensity Variation For Observed Data And Derived Data - A Case Study Of Imphal, Arpn Journal Of Engineering And Applied Sciences, Vol. 7, No. 11, November 2012 Daniel Dourte et al., Rainfall Intensity-Duration- Frequency Relationships For Andhra Pradesh, India: Changing Rainfall Patterns And Implications For Runoff And Groundwater Recharge, Journal Of Hydrologic Engineering · March 2013. Chow V.T. 1964. Handbook Of Applied Hydrology, Mcgraw- Hill, New York. 9-49, 9-62.



GIS Based Groundwater Quality Mapping in Southwestern Part of Tumakuru District, Karnataka, India Nandeesha1, Vishal.R.Khandagale2, S.G.Swamy3 1

2

Professor, Civil Engineering Dept., Siddaganga Institute of Technology, Tumkur. Research Scholar, Civil Engineering Dept., Siddaganga Institute of Technology, Tumkur. 3 Fellow and. Executive Secretary KSCST, IISc, Bangalore, Karnataka, Email: [email protected], [email protected] Three fourth of the earth’s surface is covered by water. Inspite of this apparent abundance of water, less than one percent is available for human use in the form of surface water as 97 percent is contained in oceans etc. and 2 percent is locked up in ice-caps and glaciers [1]. However as civilization and population increases man recognizes the importance of water from a quantity view point for agriculture, transportation, drinking and domestic usage with less significance given to its chemical and biological importance. Groundwater contamination is nearly always the result of human activity. In areas where population density is high and human use of the land is intensive, groundwater is especially vulnerable. Virtually any activity whereby chemicals or wastes may be released to the environment, either intentionally or accidentally, has the potential to pollute groundwater. When groundwater becomes contaminated by the dissolved elements and gases and by presence of suspended solids, bacteria, and viruses, it is difficult and expensive to clean up. Such water is no longer fit for a specific use, such as drinking, the water is said to be contaminated. If the water becomes heavily contaminated it is said to be polluted [2]. Groundwater quality is strongly influenced by various hydrochemical processes and increased agricultural activity in this region is likely to have an impact on the groundwater quality. Groundwater is largely contaminated by organic and inorganic pollutants in the rural area due the modern agriculture, by way of application of agrochemicals. Hence, it is necessary to determine the suitability of groundwater for the drinking and irrigation purposes. The present work aims at finding the groundwater quality of Turvekere taluk of Tumakuru district, Karnataka and hence determining its suitability for irrigation purposes. This block has semiarid climate and people are mostly dependent on groundwater for domestic and irrigation purposes.

Abstract - Groundwater is used for domestic, industrial and irrigation purpose in all over the world. Groundwater samples from 115 stations of the study area are collected and the study area is bounded by the latitude 13°- 13.34°N and longitude 76.5833° – 76.9167° E. The study area lies in the Southwestern part of Tumakuru district, Karnataka. The samples collected are distributed over Precambrian rocks such as granitic and gneissic terrains. The samples are analyzed for pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Calcium, Magnesium, Sodium, Potassium, Chloride, Nitrate, Bicarbonate, Carbonate and Sulfate. The results of all the samples are analyzed and compared with the Bureau of Indian standards (B.I.S). Overall, the groundwater in the study area is found to be slightly acidic to basic (pH ranging from 5.69 to 7.42) and soft to very hard (Total Hardness ranging from 72 mg/l and 810 mg/l) in nature. In 53 samples the concentration of Sulfate is showing above the maximum permissible limit. Hence, a Geographic Information System (GIS) based assessment of spatial-variation behavior of groundwater quality has been carried out in the region. A surface map was prepared by using the Arc GIS 10.3 software to assess the quality in terms of spatial variation and it showed that the high and low content regions of water quality varied spatially during the study period. All the other parameters are analyzed are in adherence to desirable limits of WHO and Indian Standards for drinking water. The spatial analysis of groundwater quality patterns of the study area shows seasonal fluctuations and these spatial patterns of physical and chemical constituents are useful in deciding water use strategies for various purposes. WQI indicates many of the samples are in the range of Good to poor category. The calculated values of Sodium Adsorption Ratio (SAR), Residual Sodium Carbonate (RSC) and Soluble Sodium Percentage (SSP) indicate many of the water samples are suitable for irrigation purposes. Magnesium Adsorption Ratio (MAR) and RSC values use for agricultural purposes. Groundwater chemistry is controlled by rock water interaction as expressed by Gibb’s diagram is also prepared. Key words- Spatial-variation, Arc GIS 10.3, Sodium adsorption ratio, RSC, MAR.

I.

II.

INTRODUCTION

STUDY AREA

The study area is located between latitude 13°13.34°N and longitude 76.5833° – 76.9167° E in

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Tumakuru district, Karnataka, India. The average annual rainfall in this region is 651.3mm. April and May are the hottest months and with onset of southwest monsoon in June temperature drops considerably. The study area consists of about 243 villages and 26 panchayats. The study area falls under semi-arid climatic zone. The schistone hills which covers larger area exhibits rolling topography, while the granitic hills and gneiss exhibit a rugged topography. The soil seen in study area is red loamy, red sandy, laterite soils, mixed and black soils. The area is characterized by undulating terrain interspersed by low ranges of rocky hills and the elevation rages from 789m to 858m and average of 794 m (2604 feet) above M.S.L. The main source of water for irrigation is by pumping of groundwater resources and some parts are irrigated by Hemavathi River.

on a rating scale from zero to hundred. Higher value of WQI indicates better quality of water and lower value shows poor water quality [4-8]. The concentration of various ions as obtained from chemical analysis of groundwater samples were converted to milli equivalent/litre (meq/L) and used to derive certain parameters. They are Sodium Adsorption Ratio (SAR), Soluble Sodium Percentage (SSP), Magnesium Adsorption Ratio (MAR), Residual Sodium Carbonate (RSC), Permeability Index (PI), Kelly’s ratio (KR) and Total Hardness (TH). These parameters help to evaluate the irrigational as well as domestic suitability of ground water in the study area. Moreover, these values were plotted on graphical diagrams like U.S.Salinity diagram, Wilcox diagrams to determine the suitability of groundwater for agricultural purposes. Arc GIS Software 10.3.is used for developing the iso contour maps to understand the geospatial distribution of various physio-chemical parameters.

Fig -1: Map flow of Turuvekere

III.

Fig.2. Sample Location Map

METHODOLOGY

IV.

Samples of groundwater are collected from bore wells & hand pumps ,during the pumping of water is taking place in a 1-liter plastic can and they were brought to the laboratory. About 115 samples were collected from various places of study area. Samples were drawn with a precleaned plastic polyethylene bottle. Prior to sampling, all the sampling containers were washed and rinsed thoroughly with then groundwater [3]. The pH was measured using digital meter immediately after sampling. TDS,EC, Carbonate, Bicarbonate, Total hardness, Calcium, Magnesium, Chloride, Nitrate, Iron, Sulphate, Sodium, Potassium and Fluorides were determined by using standard methods. Water quality index is one of the most effective tools to monitor the surface as well as groundwater pollution and can be used efficiently in the implementation of water quality upgrading programmes. The objective of an index is to turn multifaceted water quality data into simple information that is comprehensible and useable by the public. Water quality index provide information

RESULTS & DISCUSSION

The understanding of groundwater quality is important because it is the main factor which decides its suitability for domestic, agricultural and industrial purposes. The results of the analysis are compared with Indian standards (BIS) prescribed for drinking water in Tables 1. Table-I Summary statistics of different water quality parameters.

Parameter Calcium Chloride Magnesium Sodium Potassium Iron Bicarbonate Nitrate Fluoride Sulphate TDS

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Min (mg/l) 6.45 1.99 58.5 12.72 1 0.01 52 0.84 0.05 10 165

Max (mg/l) 77 609.81 174 59.9 15.9 0.54 832 50 1.3 250 1060

Mean (mg/l) 15.71 162.99 70.5 39.45 5.23 0.15 345.32 7.52 0.37 120.01 555.03

SD (mg/l) 8.76 137.54 29.6 11.03 3.12 0.082 102.5 6.36 0.21 47.51 187


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Total hardness pH EC (µs/cm)

72 5.69 199

810 8.45 2550

329.01 7.42 1067.20

130.9 0.39 366.6

2 3 4 5 6

The values of pH in the groundwater samples collected from the study area varied from 5.69 to 8.45, indicating a slightly acidic to slightly basic nature. The Electrical Conductivity (EC) of groundwater in the study area varies widely and ranges between 199 and 2550 μS/cm and 21 samples showed the conductivity value higher than permissible limit of 2000 μS/cm [12]. The TDS values varied between 165 and 1060 mg/l and 73 samples showed TDS value above the permissible limit of 2000 mg/l [12]. The total hardness (as CaCO3) values range between 72 and 810 mg/l and 104 samples were having hardness values above the permissible limit of 600 mg/l [12]. The concentration of Sodium and Potassium ranged from 12.72 to 59.9 and 1 to 15.9 mg/l, respectively. Among the 115 samples, 53 samples were having high Potassium content above the permissible limit and all sodium samples are within permissible limit. The concentration of Calcium and Magnesium was in the range of 6.45 to 77 and 58.5 to 174 mg/l, respectively. Among 115 samples, only 1 and 18 samples was having higher Calcium and Magnesium content in comparison to their BIS permissible limit of 200 and 100 mg/l, respectively. The Bicarbonate value ranging from 52 to 832 mg/l and 107 samples showed bicarbonate value higher than 600mg/l. In the area of investigation, the Chlorides are in the range of 1.99 to 609.81 mg/l and it was found that all the samples was having Chloride values within the permissible limit of 1000 mg/L (BIS 1998). The Sulfate content value varies from 10 to 250 mg/l, well within the permissible limit of 400 mg/l [12]. The Nitrate concentration in the region ranges from 0.84 to 50 mg/l. only one sample among all the samples is above the permissible limit of 45 mg/l [12]. Fluoride content values ranges from 0.05 to 1.3 and 2 samples showed the Fluoride value higher than permissible limit of 1.5mg/l. Iron value rages from 0.01 to 0.54mg/l and 5samples are above permissible limit of 0.3mg/l.

26-50 51-75 76-100 101-150 Above 150

Good Fair Poor Very poor Unfit for drinking

22.60 53 19 3.47 0.86

The distribution pattern of water types based on WQI indicates that ‘‘good water’’ & “Fair water” dominates the area. B. Trilinear Diagram

Fig.3. Piper/Trilinear Diagram of test values

Chemical data of representative samples from the study area presented by plotting them on a Piper-trilinear Diagram reveal the analogies, dissimilarities and different types of waters in the study area. The piper diagram is dominated by mixed CaMgCl type followed by Magnesium bicarbonate type facies and Calcium chloride type facies (Fig.3). The alkaline earth (Ca+Mg) Exceed alkalies (Na+K) and strong acids exceed weak acids. C. Iso – Contour Maps:

A. Water Quality Index (WQI) Analysis The WQI values of the study area of various samples are calculated separately. WQI has been calculated based on fourteen selected hydro chemical parameters given in Table below. Table-II Water quality classification based on WQI value

Sl. No. 1

WQI value 0-25

Status Excellent

% of samples 0.86

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[115]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) groundwater samples is less than 1 in about 114 samples out of 115 sample indicating good quality waters for irrigation. In the present study RSC value ranges from -8.68 to 9.92 with an average of -0.97 and 89.56% of water samples is safe for irrigation.

Fig. -13: Salinity classification of irrigation water samples using USSL (1954) diagram

The U.S. Salinity diagram (Fig.-13), the water samples fall in the C1-S1, C2-S1 and C3-S1 classes, and hence can be considered moderately suitable for irrigation.

Table-III Summary statistics of different indices of irrigation water

SAR

Min (me/l) 0.32

Max (me/l) 1.99

Mean (me/l) 0.98

SD (me/l) 0.262

SSP %

6.55

43.73

23.148

6.35

MAR

14.98

96.11

86.84

8.98

KR

0

1.44

0.30

0.173

PI

23.5

99.5

52.54

12.85

RSC

-8.68

9.91

-0.93

2.42

Parameter

Fig.-14: Wilcox’s diagram for irrigation water classification

Wilcox (Fig.14) is used for classification of irrigation waters. X-axis represented by electrical conductivity and Y-axis represented by soluble sodium percentage. Most of the samples are in “good to permissible” & “permissible to doubtful”, 11 samples are in “Doubtful to unstable” four samples are in “excellent to good category”.

In the present study the SAR values range from 0.32 to 1.99 with an average value of 0.98. Based on the SAR values all samples have low Sodium hazard. The SSP values range from 6.5 – 43.7 with an average value of 23.14. The results reveal that all investigated water samples have a MAR that is higher than 50% and are considered unsuitable for irrigation The Kelley’s ratio of the studied

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) respectively. According to RSC values, 89% of groundwater samples are safe for irrigation purposes. Based on the Wilcox classification, 9% of the water samples belong to doubtful to unsuitable category for irrigation use. Hence, it is suggested that suitable measures in terms of enhancement of drainage has to be made in areas were high Sodium content is observed for satisfactory crop growth. REFERENCE: [1] S. K. Dhameja, “Environmental Studies” S. K. Kataria & Sons, New Delhi. First edition, 2004, 403pp. [2] J. Delleur, “The Handbook of Groundwater Engineering”. CRC Press LLC, USA, 1999. [3] Brown E., Skougstad M.W. and Fishman M.J., Method for collection and analysis of water sample for dissolved minerals and gases, US Department of Interior, 1974, Book No. 5. [4] O. M. Omorogieva, O. I. Imasuen, M. I. Isikhueme, O. A. Ehinlaye, B. Anegbe 2 and M. O. Ikponmwen Hydrogeology and Water Quality Assessment (WQA) of Ikhueniro and Okhuahe Using Water Quality Index (WQI) Journal of Geography, Environment and Earth Science International 6(3): 1-10, 2016; Article no. JGEESI. 25615. [5] S. Singh, N. J. Raju, Ch. Ramakrishna, “Evaluation of Groundwater Quality and Its Suitability for Domestic and Irrigation Use in Parts of the Chandauli-Varanasi Region”, Uttar Pradesh, India”. Journal of Water Resource and Protection, 2015, 7, 572-587. [6] Z. T. Zewdu, “Ground Water Quality Determination of former Lake Haramaya, Haramaya District, Eastern Haranghe Zone, Oroma Regional State, Ethiopia.” J. Appl. Sci. Environ. Manage. Sept., 2012, Vol. 16 (3) 245–252. [7] A. Dhafer, N. Al-Jassim, T. Kenda and P. Hong, “Assessing the Groundwater Quality at a Saudi Arabian Agricultural Site and the Occurrence of Opportunistic Pathogens on Irrigated Food Produce.” Int. J. Environ. Res. Public Health 2015, 12, 12391-12411; doi: 10.3390/ijerph121012391. [8] G. R. Kalpana, D. P. Nagarajappa, K. M. Sham Sundar, B. Suresh, “Determination of Groundwater Quality Index in Vidyanagar, Davanagere City, Karnataka State, India” International Journal of Engineering and Innovative Technology (IJEIT), Volume 3, Issue 12, June 2014. [9]. BIS, Bureau of Indian Standards, Specifications for Drinking Water, IS: 10500, New Delhi, India, 2012. [10]. BIS (1998) Drinking Water Specifications (Revised 2003). IS:10500, Bureau of Indian Standards, New Delhi. [11]. Nagaraju, A., Muralidhar, P. and Sreedhar, Y. (2016) Hydrogeochemistry and Groundwater Quality Assessment of Rapur Area, Andhra Pradesh, South India. Journal of Geoscience and Environment Protection, 4, 88-99. [12] BIS (1998) Drinking Water Specifications (Revised 2003). IS:10500, Bureau of Indian Standards, New Delhi.

Fig.-15: Doneen’s diagram for classification of groundwater quality in the studied area

A Doneen chart divides the irrigation waters into three major types based on the PI and total ions in solution in meq/l. Class I shows that the waters have low PI values and are the best water types for irrigation. Class II indicates that the waters are acceptable for irrigation but they are lower in the quality compared to Class I. Class III denotes that the waters are unacceptable and may not be used for irrigation. According to the Doneen diagram, all investigated water samples fall in Class I (Chart-3) and can be categorized as the best water type for Irrigation.

V.

CONCLUSION

The study provides significant information on the groundwater quality in Turvekere taluk of Tumkur district. The groundwater in the study area is found to be slightly acidic to basic. The major ion chemistry data revealed that the ground water in the study area is slightly soft to very hard and fresh to brackish in nature. The sequence of the abundance of the major ions is in the following order of Mg >Na >Ca>K for cations and HCO>Cl>SO> F in anions. The alkaline earth (Ca+Mg) Exceed alkalies (Na+K) and strong acids exceed weak acids. . WQI indicates many of the samples are in the range of Good to poor category. Based on the classification of irrigation water according to SAR and Kelly’s ratio values, all the sample locations are suitable for irrigation purposes. Sodium Adsorption Ratio (SAR) values are categorized as ‘Excellent’ and the water samples fall in the C1-S1, C2-S1 and C3-S1 classes, and hence can be considered as suitable for irrigation based on the salinity classification. Irrigation water quality based on % Na indicates that 30% and 67% of the water samples belong to Excellent and good category

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A GIS Based Groundwater Quality Assessment of Vrishabhavathi Watershed, Karnataka, India SRINIDHI M. S.

REKHA H. B.

Former PG Student, Dept. of Civil Engineering University Visveswaraya College of Engineering Bangalore University, Bangalore, Karnataka, India E-mail: [email protected]

Asst. Professor, Dept. of Civil Engineering University Visveswaraya College of Engineering Bangalore University, Bangalore, Karnataka, India E-mail: [email protected]

Abstract — The Vrishabhavathi Watershed having an area of 380 Km2 is a tributary of the Arkhavathi River Basin which intern joins the Cauvery River at a later stage. The watershed lies in Bangalore urban and Ramanagara Districts, representing semi-aired tropical climate. The river receives large amount of wastewater released from industrial and domestic areas. The water is highly polluted and generates lot of foul smell in the flow stretch in urban region. In this study, groundwater samples were collected during September-October of the year 2017. The Physico-Chemical parameters such as pH, TA, EC, TDS, cations and anions were analyzed from the collected 35 groundwater samples. The analytical data was used in the calculation of the water quality index (WQI) for the study area. The analytical data were also used as input data for Arc GIS 10.1 to prepare the study area map and also spatial distribution map of water quality and also for calculating water quality index (WQI). The overall water quality of the study area was found to be 58.26 which fall in Moderately/Fairly polluted category. Keywords— Vrishabhavathi, WQI, Groundwater, GIS, Urban Catchment, Drinking Water, Bangalore Urban, Ramanagara

I.

INTRODUCTION

Groundwater is a pervasive and valuable resource. The quality of ground water deviates due to large number of individual hydrological, physical, chemical and biological factors. A threat is now posed by an ever-increasing number of soluble or dissolved chemicals from urban, industrial and from modern agricultural practices (Singha Soumya et al. 2014). River water pollution is the major global problem in an urban area. The quality of water is an important factor to be considered before it is used for domestic, industrial and irrigation purpose. Majority of industries in an urban area are water based and a considerable volume of contaminated water will be discharged in to the river course from them (Puthenveeduhari et al. 2014; Aravinda et al 2014). GIS has emerged as a powerful tool for storing, analyzing, and displaying spatial data and using these data for decision making in several areas including engineering and environmental fields (Burrough and McDonnell 1998; Yeung 2003; Singha Soumya et al 2014).GIS is used as an effective tool for developing solutions for water resources problems for assessing and mapping of ground water quality, understanding the natural environment and managing water resources on a required scale, assessing groundwater vulnerability to pollution. In the present study one such attempt have been

made to assess and map the groundwater quality of Vrishabhavathi river by creating spatial reference to point locations for which the quality of groundwater is known in an integrated environment using GIS and by determining Water Quality Index (WQI).Water Quality index provides a way to communicate information on overall quality status of water to the concerned user community and policy makers. It also summarizes large amounts of water quality data into simple terms (excellent, good, bad, etc) for reporting to managers and the public in a consistent manner (Hulya et al. 2009; Singha Soumya et al. 2014, Minakshi Bora et al 2017) II. STUDY AREA The Vrishabhavathi Watershed is the tributary of Arkhavathi River Basin. The Vrishabhavathi River flows mainly in Bangalore Urban and Ramanagara Districts. It covers an area of 380 Km2 (UTM WGS1984 43N) which lies between latitudes 12° 441 3711 to 13° 21 3111 N and longitudes 77°2311411 to 77° 341 5911 E. Vrishabhavathi river is perennial mainly due to the contribution of domestic and industrial effluents (Madhukar et al 2013; Aravinda et al 2014). Location Map of the Study area is shown in Fig.1.

Fig. 1: Study Area with Grids

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) III. MATERIALS & METHODOLOGY Thirty five (35) groundwater samples were collected during the monsoon season of year 2017 from different bore wells. Grab Sampling was done using the polythene water bottles of 2 liters capacity. In order to collect the groundwater samples from the watershed area, Systematic Grid Sampling methodology was used along with the formation (generation) of Grids on the watershed area map. About 31 numbers of grids were generated and out of 31 grids, it was made sure that at least 1 bore well sample is collected from each grid. ArcGIS 10.1 software was used to generate the grids, sampling location map and the spatial distribution maps. The collected groundwater samples were analyzed in the laboratory for pH, EC, TDS, TA, major cations and major anions. The pH was measured within a few hours by using digital pH meter and EC using conductivity meter. Ca and Mg were determined by titrimetric indirect calculation method using standard EDTA method and chloride was determined by silver nitrate titration (APHA, 2012) method. Carbonate and bicarbonate were calculated by indirect method (Swarna Latha et al. 2011). Sulphate was determined by photometric method using Visible UV-spectrophotometer (Elico SL 177). The Na and K were determined by Systronics flame photometer (APHA, 2012). A. Determination of WQI Thus WQI is useful and effective method which can be known as an indicator of water quality. In the study, WQI was calculated by using the Weighted Arithmetic Index Method as described by (Minakshi Bora et al 2017, Kosha et al 2017). Application of WQI is a useful method in assessing the suitability of water for various beneficial uses. Here attempt has been made to calculate the water quality index of the study area based on hydrochemical data (Horton 1965, Minakshi Bora et al 2017).

Vs = Standard permissible value of nth water quality parameter (Swarna Latha et al. 2011, Kalaivanan et al 2017). 3. Unit Weight (Wi) The unit weight (Wi) for the water quality index calculation is calculated using the expression given in Equation (3) below (Minakshi Bora et al 2017; Kalaivanan et al 2017) ................................ (3) Where, Sn = Standard permissible value of nth water quality parameter (Kalaivanan et al 2017) k = Constant of proportionality (Swarna Latha et al. 2011). 4. Water Quality Index (WQI) and Status The water quality parameters are selected based on its direct involvement in deteriorating water quality for human consumption. The standard for the drinking water, recommended by the BIS 2012 (Bureau of Indian Standards) was considered for the computation of quality rating (qi) and unit weights (Wi). The table I shows the Sampling locations of the study area (Minakshi Bora et al 2017) TABLE I. SAMPLING AREA WITH LATITUDE AND LONGITUDE

SL No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

1. WQI Calculation Calculation of WQI was carried out in this work by Horton’s method. The Equation (1) below was used for the calculation purpose of WQI of the groundwater samples using the hydrochemical parameters values.

................... (1) Where, qi = Quality rating of nth water quality parameter. Wi= Unit weight of nth water quality parameter (Swarna Latha et al. 2011, Minakshi Bora et al 2017, Kalaivanan et al 2017) 2. Quality Rating (qi) The quality rating (qi) is calculated using the expression given in Equation (2) below

................... (2) Where, Va = Estimated value of nth water quality parameter at a given sample location. Vi = Ideal value for nth parameter in pure water. (Vi for pH = 7 and 0 for all other parameters)

Area Bannikere Byramangala Karena Halli Chikkakuntana Halli Shyanumangala Timmegoudana Halli Gonipura Hosa Doddi Nagegoudana Palya Devaragolla Halli Kumbalagodu Inds. Area Kumbalagodu Inds. Area Tagache Kuppe Hejjala Subramanya Pura Turahalli Kengeri Upanagara Kengeri Bimana Kuppe Ramo Halli Banashankari Deepanjali Nagara Nayanda Halli Ullalu Basti Nagadevana Halli Sulikere Yallachikuppe Sheshadripuram Vijayanagara Ullalu Upanagara Sige Halli Malleshwaram Leggere Peenya Inds. Area Peenya Inds. Area

Latitude 12.757 12.772 12.784 12.794 12.798 12.827 12.833 12.823 12.869 12.876 12.871 12.876 12.863 12.858 12.897 12.897 12.921 12.912 12.897 12.906 12.942 12.953 12.946 12.951 12.937 12.939 12.949 12.993 12.968 12.967 12.974 13.003 13.012 13.027 13.023

Longitude 77.436 77.417 77.471 77.44 77.427 77.479 77.447 77.416 77.517 77.475 77.445 77.448 77.455 77.423 77.546 77.533 77.481 77.484 77.441 77.422 77.557 77.537 77.521 77.477 77.493 77.445 77.423 77.571 77.532 77.474 77.457 77.564 77.521 77.525 77.530

In brief, WQI is generally defined for a specific and intended use of water. In this study the WQI was considered

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) for human consumption and for drinking purpose. Based on the WQI values, the ground water quality is rated as Excellent, Good, Moderate, Poor and Very poor for drinking. The ranges of WQI, corresponding status and their possible use are given in table II (Minakshi Bora et al 2017; Kalaivanan et al 2017). TABLE II. WQI AND CORRESPONDING STATUS

SL WQI No. 1 0 - 25 2 25 – 50 3 51 – 75 4 76 – 100 5 101 - 150

STATUS

POSSIBLE USAGE

Excellent Good Moderate/Fair Poor Very Poor

Drinking, Irrigation and Industrial Domestic, Irrigation and Industrial Irrigation and Industrial Irrigation Restricted use for Irrigation

III. RESULTS & DISCUSSIONS The logarithm of the reciprocal of the hydrogen ion concentration (pH) in groundwaters varies from 6.98 to 7.99 indicating less alkaline nature. The pH 1) which indicates section is unstable and more likely erode river bank [6].However levee with revetment can take care of abrasive action of water by providing appropriate dimensions. Fig 4 shows variation in water level during 100 year flood peak at river reaches with and without embankment in the graphical representation.From figure 4,the variation of water levels at intial stations of reach negligibly low but for downstream water level raised marginally due to additional flow from tributary to downstream.However this drastic change is taken care by safe freeboard which is raised 1 to 1.5m above the HFL(High flood level)

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) section.The plate 3 shows tabulated embankment design parameters for different reaches.

Fig 4:Variation during 100 year flood peak at river reaches with and without embankment A. River Embankment design For this study stretch of the river; zoned embankment is designed.This type of protection work is suitable where the ample space for dressing the banks by filling or by excavation is possible. It should be noted that while dressing the slope by earth filling, the area inside the river banks should not be constricted beyond 10 to 15 % of the total original river width at that section. The descriptive design computation of protection work for sloping bank as per IS 14262-1995 is given below:  Velocity = 3.33m/s, 2.12m/s and 4.26m/s (average velocity flow under no embankment condition at reach upstream, tribuatary and downstream respectively)  Bank slope = 2 H:1 V= (θ =26.560)  Angle of internal friction of soil of bank material(Φ) = 350 (gravel mixed with sand)  Specific gravity of boulder stones (Sa) = 2.65  D50 stones being used for filling crates =175mm (for example as per specifications, the stones of size 125mm to 225mm are proposed(assumed). Therefore, D50 is assumed as 175mm(125+225)/2).[8] The other parameters are computed as per IS 14262-1995 recommendations.[3]. For each reach different bank slope is designed i.e for 2H:1V, 2.5H:1V, 3H:1V for upstream, tributary and downstream respectively. Provide toe wall of size 1m ×1m and depth upto hard strata(assumed that hard strata available reasonable depth).Provide top width of embankment 15m at turning of river alignment keeping other design criteria same as that of the corresponding reach.At unstable section(Fr>1) („Fr‟ is Froude‟s number ) provide extra 10cm or 0.1m thickness of stone pitching of crates then corresponding reach thickness.However it does not require extra dimension to downstream(D/S) because absence of super critical

Plate 3:Design parameter of the Embankment The design parameter are represented in schematically in figure 5 which represents embankment with launching apron for upstream reach 1272.37.

Fig 5:Embankment with 2H:1V for upstream reach at straight stretch of 1272.37 V. CONCLUSION One-dimensional mathematical model HEC-RAS was used to estimate the flood level in the river for discharge of 100 years return period. Based on the above studies following conclusion were made: 



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The study shown proper design and construction of river embankment/levee has greatly reduced flood risk and sediment erosion problem there by improving socio economic condition of residing societies of the country. [2] From plate 3,the area of inhundation is marginally reduced and maintained within the banks by construction of embankment with additional







  

dimensions at critical and supercritical section i.e at 4120,3695,2911,11480,10816,6843. It is to be visualized that construction of embankment increases velocity of flow for the same discharge(continuity equation) are taken by sloping surface embankment structure by safe transportation of storm water in case of flood without eroding bank material. It is proposed that the top level of the embankments of river main stream and tributaries may be finalized by using the maximum water levels obtained during model studies with embankments by adding sufficient free board of atleast 1-1.5m height above the HFL. It is preparably to use pitching stones of sufficient abrasive resistance to withstand strong erosive action of water. Vetiver grass performs better compared to stone pitching from the point of view of ecology.[3]



river reach is more of agriculture and less of sub urban land use). The time to time inspection and maintenance of embankment is required for efficient working of the structure upto it‟s design life.

ABBREVIATIONS RS –River distance(m) Min ch Elv – Minimum channel elevation(m) Fr –Froude‟s number(dimensionless) Q - Discharge of 100 year return period(m3/s) E G Elv-Energy grade elevation(m) W S Elv –Water surface elevation(m)

The of the top width of embankment can be kept around 3-3.5 m and at turning 15m based on type of land use behind the embankment(As the proposed

Plate 2:The hydraulic character of the reach before(right) and after(left) construction of embankment REFERENCES [1] J L. Florsheim,J.F Mount, A.Chinn(2008), “Bank Erosion as a Desirable Attribute of Rivers”, American Institute of Biological Sciences, Vol. 58 No. 6,June 2008,pp 519-522 [2] Central water commission(CWC),India(2012), “Handbook of anti erosion,flood protection and river training”,Delhi,july 2012, p24-45 [3] M. P. Islam,Md. Khairul Hassan Bhuiyan, M. Z. Hossain, “Vetiver Grass as a Potential Resource for Rural Development in Bangladesh”, Agricultural Engineering International: the CIGR Ejournal. Invited Overview No. 5. Vol. X. December, 2008. p1-7. [4] V S. Hegde, S R. Nayak(2015), “Evolution of Diverging Spits Across the Tropical River Mouths, Central West Coast of India” , Journal of coastal engineering, vol 8,issue 2,pp 2-5 [5] US army corps of engineer‟s(2010), “HEC-RAS 4.1 user manual”,(Jan 2010), p 60-89 [6] Chow Ven Te (1988), “Open channel hydraulics” ,2nd edition,1988,McGrawhill book company,p 13-20 [7] IS 14262-1995(2001), “Planning and design of revetment guidelines”,BIS,p 1-7 [8] N M. Naidu, N.V.N. Ravali, A. D. Vasudeo (2015), “Design of embankments and bank protection works for hilly rivers”, Journal of Civil Engineering and Environmental Technology, Volume 2, Number 9; April – June, 2015 pp 58-62

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Drought Anlysis By using SPI and SDF Analysis of Drought and Wet Period in Krishna Basin Chauhan Pruthviraj R1, A.V.Sriram2 PG Student, Assistant Professor Dept. Of civil engineering, UVCE, Bengaluru-560056, INDIA Email: [email protected], [email protected].

ABSTRACT Drought is one of the most damaging climate related hazards, it is generally considered as a prolonged absence of precipitation. His normal and recurring climate phenomenon has plagued civilization throughout history because of the negative impacts on economic, environmental and social sector. Drought characteristics are identified as a major factor in water resource planning and management. The purpose of this study is to detect the changes in drought frequency, severity and persistence of five rain gauge stations located in Krishna river basin. The frequency of drought events was calculated using the Standardized Precipitation Index (SPI). In this study the monthly precipitation data for five meteorological stations for period 1979-2013 is used. The main importance of the application of this index is its versatility, only rainfall data is required to deliver five major dimension of drought i.e. Duration, Intensity, Severity, Magnitude and Frequency. The drought can be calculated in different time steps. In the present study SPI is calculated for 1, 3, 6, 9 and 12 months and several drought events were detected in the covered period, these events contain several severe and extreme droughts. The frequency and severity of severe events increases due to many environmental and / or anthropogenic factors. Drought and exceptional wet periods are regional phenomena, which are considered to be an important environmental extravaganza, especially in the semi-arid regions of the world, such as South India. The development of severity-duration-frequency (SDF) relationships of droughts and wet periods over Krishna river basin is important in contemporary hydro climatic and agro climatic design and planning in the basin. The Standardized Precipitation Index (SPI) is used for a quantitative description of droughts and wet periods. Statistical tests and visual inspection indicate that the EV1 (Gumbell) distribution of wavelength sequentially sets all the marked durations of drought and wet periods. All the identified durations of droughts and wet periods, respectively. Additionally, SDF curves show that frequencies (i.e., increase frequency intervals) decrease in droughts and wet periods respectively. Moreover, the SDF curves show that decreasing frequencies (i.e. increasing recurrence intervals) correspond to increasing severities of droughts and wet periods, respectively. The results of the study indicate that there is a decreasing pattern of the severities of droughts and wet periods and that, for similar durations and return periods, the wet spells are, in general, more extreme than droughts in Krishna river basin. Keywords: Standardized Precipitation Index (SPI), (severity-duration-frequency (SDF) curve, Gumbel distributio Todisco, Mannocchi, L. Vergni in 2012 Severity– duration–frequency (SDF) curves are developed which are useful in the analysis of drought phenomena. Station-level information obtained from SDF curves can be interpolated to obtain severity maps for fixed return period, in order to jointly analyse the spatial variability of drought characteristics (e.g. severity, duration and frequency). The scope of the study consists of analysis of meteorological drought monitoring using the monthly precipitation data through SPI methodology. The purpose of the study is to defining and monitoring local severe and extreme drought. It was conceived to identify drought period and severity of drought at multiple time scale through SPI methodology. The development of severity-duration-frequency (SDF) relationships of droughts and wet periods over Krishna river basin is carried out and Iso-severity map for drought severity and wet period of the study area is drawn which help in hydroclimatic and agroclimatic design and planning and S-D-F curves

1. INTRODUCTION Drought is one of the most damaging environmental phenomena. In general, drought is a temporal reduction of environmental moisture status relative to the mean state. Because of the complexity of drought, it is often studied only by separate aspects of the phenomenon (e.g. meteorological drought, soil drought, etc.). Hence, approaches to drought identification are variable. The theory of drought identification, which permitted assessment of location, intensity, and duration of drought from study of environmental moisture status dynamics, was developed at the end of the 1960s. The verification and development of drought theory require monitoring of numerous environmental parameters. Remote sensing of the earth surface in arid areas is valuable for this purpose. S. Sangita Mishra and R. Nagarajan in 2010 the spatial and temporal characteristics of droughts were investigated by using SPI and F.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) can be helpful in providing useful guidelines for planning future drought mitigation strategies in the Krishna river basin. 2. STUDY AREA For Drought analysis the entire Krishna basin is selected. The average annual rainfall in the Krishna basin is 784 mm where predominant land use is Agriculture. The basin has a maximum length and width of about 701 km and 672 km and lies between 73°17’ to 81°9’ east longitudes and 13°10’ to 19°22’ north latitudes. Krishna Basin extends over an area of 258,948 Km2.which is nearly 8 % of total geographical area of the country. The basin lies in the states of Andhra Pradesh (76252 Km2 (29%)), Karnataka (113271 Km2 (44%)) and Maharashtra (69425 Km2 (27%)). Krishna basin receives rainfall from both the northeast and the southwest monsoons and the wettest months are June, July, august, and September. The climate of the area is classed as the seasonally dry tropical “savanna” climate with four seasons. The rainfall data was compiled from rainfall data of five Meteorological stations located in the Krishna Basin for time series 1979 to 2013 and used for the analysis.

Figure 2. Box and whisker plot for monthly rainfalls (mm) (1979 to 2013).

From fig. 2 for station 5 i.e. Kalgi the minimum value we got is 0.0mm, first quartile value is 1.2mm, second quartile or median is 22.4mm, third quartile value is 139.5mm and the maximum value we got from figure is 762.6mm and a outlier is of 346.9mm i.e. if we take the value more than 346.9mm in calculation it may lead to wrong answer. Choose such a method it will overcome this problem. 3. METHODOLOGY 3.1 Normal Ratio Method The normal ratio method is used to fill short breaks in records. The amounts at the index stations are weighted by the ratios of the normal rainfall values. 3.2 Computational Methodology for SPI The SPI is determined by normalizing the precipitation for a given station after it has been fitted to a probability density function as described by McKee and others (1993, 1995), Edwards and McKee (1997), and Guttmann (1998). A full description of the SPI computational procedure can be found in McKee and others (1993, 1995) and Edwards and McKee (1997). The basics, as taken from Edwards (1997), are described below.

Figure 1. Location map of the study area

Table 1. Location details of the study area

Statio n no. 1

Station name Talavade

2

Bommakal

3 4

Gumachinamar di Kalahalli

5

Kalgi

Latitu de 18°53 °9° 17°00 °59° 16°04 °48° 13°53 °40° 17°19 °43°

Longit ude 73°45 °00° 80°18 °45° 74°41 °15° 76°15 °00° 77°11 °15°

Eleva tion 776

3.2.1 SPI Algorithm Methodology

• The SPI calculation for any location is based on

54

the long-term precipitation record for a desired period. This long-term record is fitted to a probability distribution, which is then transformed into a normal distribution so that the mean SPI for the location and desired period is zero (Edwards and McKee, 1997).

672 802 444

• Positive SPI values indicate greater than median precipitation, and negative values indicate less than median precipitation.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) • Drought, according to the SPI, starts when the SPI value is equal or below -1.0 and ends when the value becomes positive. The SPI is an index based on the probability distribution of precipitation. This index depends on the distribution function, on the sample used to estimate the parameters of the distribution, and on the method of estimation. The twoparameter gamma distribution function is often

Fitting the distribution to the data requires α and β to be estimated. Edwards & McKee in 1997 suggest estimating these parameters using the approximation of for maximum likelihood as follows:

𝜶=

𝟏 [𝟏 𝟒𝑨

𝟒𝑨

+ √(𝟏

)]

𝟏

+

𝜷=

used but statistical goodness-of-fit tests are required before its adoption. The entire period of records is generally used to estimate the parameters of the gamma distribution function, e.g. by the method of maximum likelihood (Kite, 1988). n aprrdasc,ti2c0e,0th index (EdIw 0)eincoamgpivuetantiyoenaroif athned SPI calendar month j, for a k time scale requires:

∑ 𝒍𝒏𝒙

𝜶

with A = ln −

3

𝒏

Where n is the number of observations. Integrating the probability density function with respect to x yields the following expression G (x) for the cumulat iv𝒙e probabilit y: 𝟏 𝒙

G(x= ∫ 𝒈(𝒙)𝒅𝒙 =

∫ 𝒙𝜶−𝟏 𝒆−𝒙/𝜷

𝜷𝜶 √(𝜶) 𝟎

𝟎

1. Calculation of a cumulative precipitation series kij X, (i=1,…,n) for that calendar month j, where each term is the sum of the actual monthly precipitation with precipitation of the k-1 past consecutive months;

Substituting t = 𝒙x ⁄ β, (4) is reduced to:

2. Fitting of a gamma distribution function F(x) to the monthly series;

It is possible to have several zero values in a sample set. In order to account for zero value probability, since the gamma distribution is undefined for x = 0, the cumulative probability function for gamma distribution is modified as:

G(x) = 𝟏

∫ √(𝜶) 𝟎

3. Computing the non-exceedence probabilities corresponding to the cumulative precipitation values;

Finally, the cumulative probability distribution is transformed into the standard normal distribution to yield the SPI. Following the approximate conversion provided by [17], it results:

Z

𝟎

SPI ),𝒕=

√

- (𝒕 −

= 𝐥𝐧(

𝟏

).7

((𝑯(𝒙))𝟐

For 0 < H (x) < 0.5 and Z

=

𝒄𝟎+ 𝒄𝟏 𝒕+ 𝒄𝟐 𝒕𝟐 𝟏+𝒅𝟏+ 𝒅𝟐𝒕𝟐+ 𝒅𝒕𝟑𝟑

1

SPI ),𝒕=

= √

𝐥𝐧(

+ 𝟏

(𝒕 − ).8

(𝟏− (𝑯(𝒙))𝟐

For, 0.5 < H(x) < 1.0 where 𝑐0= 2.5155, 𝑐1 = 0.8028, 𝑐2 = 0.01032, 𝑑1 = 1.4327, 𝑑2= 0.18926 𝑎𝑛𝑑 𝑑3 = 0.001308. In this study, SPI program developed by the National Drought Mitigation Centre, University of Nebraska-Lincoln is used is used to compute time series of drought indices (SPI) for each station in the basin and for each month of the year at

Where α > 0 is a shape parameter, β > 0 is a scale parameter, and x > 0 is the amount of precipitation. Γ(α) is the gamma function, which is defined as: ∞

Γ(α) =∫

=

𝒄𝟎+ 𝒄𝟏 𝒕+ 𝒄𝟐 𝒕𝟐 𝟏+𝒅𝟏+ 𝒅𝟐𝒕𝟐+ 𝒅𝒕𝟑𝟑

severity is arbitrarily defined according to the SPI values as listed.(McKee et al., 1993). The SPI is computed by fitting an appropriate probability density function to the frequency distribution of precipitation summed over the time scale of interest (usually 3, 6, 12, and 24 months). This is performed separately for each time scale and for each location in space. Computation of the SPI involves fitting a gamma probability density function to a given time series of precipitation, whose probability density function is defined as: 𝜷

5

G(x) = q + (1- q) G(x) 6

4. Transforming those probabilities into the values of a standard normal variable, which actually are the SPI values. The SPI is a ‘z’ score and represents an event departure from the mean, expressed in standard deviation units. SPI is a normalized index in time and space. This feature allows comparisons of SPI values between different locations. Drought

g(x)= 𝟏 ∗ 𝒙𝜶−𝟏 ∗ 𝒆−𝒙/𝜷 𝜶

𝒕𝜶−𝟏 𝒆−𝒕 𝒅𝒕

𝒚𝜶−𝟏 𝒆−𝒚 dy 2 [214]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Where, x and σ are the mean and the standard deviation of the data, respectively.

different time scales (3, 6, 9 and 12 months). (in the present study the DrinC software/programing is used to calculate SPI values).

Step III: severity-duration-frequency (SDF) curves Using equation (9), drought and wet period severities estimates (∑Z) are computed, which correspond to return periods of 2, 5, 10, 20, 30, 50 and 100 years, respectively, for each identified drought duration. At this point, it should be mentioned that the low frequency events, i.e. events with return periods of 50 and 100 years have a much larger uncertainty than more frequent events. The reason for this implication is the small sample, which is what usually happens, and the extrapolation of the fitted theoretical frequency distribution to the most extreme events. Finely the graph is plotted duration in month at X-axis vs. (∑Z) cumulative drought severity at Y-axis for different return period.

3.3 Severity-Duration-Frequency Analyses A brief description of the steps, which are followed to develop the drought SDF relationships, is presented. The severity of drought is defined as the cumulative sum of successive negative values of the Z-index (SPI)(∑Z) and the duration of drought is defined as the corresponding number of successive months with continuous negative Zindex values. Moreover, frequency of drought is defined as the return period of a specific cumulative Z-index(SPI)(∑Z) value for successive months. Similarly, the severity (or intensity) of wet periods is defined as the cumulative sum of successive positive values of the Z-index (SPI)(∑Z), and the duration as well as the frequency of wet periods are also defined asabove.

4. RESULTS AND DISCUSSIONS 4.1 Drought Analysis

Step I: probability tables: Using the computed monthly Z-index time series, the drought episodes for each station are identified and tabulated based on the definition described above, i.e. the cumulative drought severity using successive (negative) values of Zindex (SPI) Values (∑Z) time series along with the corresponding duration in months. In this way more no. of events per year are also accounted in the SDF analysis. From this tabulated information for each station, several tables are produced, one for each duration in months. The final number of produced tables is equivalent to the number of identified classes’ durations. A same procedure is carried out for the cumulative positive Z-index (SPI) values (∑Z) in order to tabulate the wet periods with the corresponding duration for each station.

4.1.1 SPI values obtained for Kalgi Station for different time step SPI Values for 1- month time step:

Fig. 3. SPI values based on 1 month time step of Kalgi station.

SPI Values for 3- month time step:

Step II: fitting Of Gumbel distribution: For each drought duration and wet period duration the identified cumulative drought and wet period severities are plotted vs. the corresponding return period and a statistical distribution is fitted to the plotted data points. The extreme value law (Farrago & Katz, 1990; Demarée & Sneyers, 1986) is implemented to drought severities by fitting the EV1 distribution (Gumbel, 1958), which has the following cumulative distribution function (cdf), F(x):

F(x) = exp [-exp (-A*(x – U))]

9

Where “A” and “U” is the fitted parameters, which are computed for each duration from data and are equal to:


A = 𝟏.𝟐𝟖𝟑 10 𝝈 U = x + KT * σ 11

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) SPI Values for 6- month time step:



Table 2 and 3 gives the details of the various years of the occurrence of severely and extremely drought events respectively during the period 1979 to 2013, based on the 1,3,6,9 and 12 months of SPI values. Table 2 shows the various years during which the drought event was severe for different time scales. From the above table it can be seen that there were 13 events of drought from 19832013 considering 1 month time scale. Similarly considering 3 months’ time scale it is observed that there are a total of 21 events of extreme drought from 1980 to 2013. Similar explanation can be given for other time scale also. From table 2 it is seen that as per SPI_1 value the severe drought as occurred in the year 1983 during the month of August, 1985(May), 1988(November), 1991(October), 1997(July), 1999(November), 2000(August), 2005(August), 2005(November), 2007(October), 2012(June), 2012(September) and 2013(September). Whereas considering SPI_3 value severe drought occurred more frequently during in the year 2013(August and September). Considering SPI_6 value severe drought occurred more frequently during the year 2002(February, March, April, May, June and July), 2012(January, February, March, April, May, June, July and August) and 2013(August and September). Whereas considering SPI_9 value severe drought occurred more frequently during in the year 2001(November and December),2002(January, February, March, April, May, June, July and August), 2012( March, April, May, June and July ) and 2013(January, February, March, April, May, June, July and August), 2012( March, April, May, June, July and August). For SPI_12 value severe drought occurred more frequently during 2012((January, February, March, April, May, June, July, August, September, October, November and December. If theseresults



Fig. 7. SPI values based on 12 month time step of Kalgi station .

The above figures 3 to 7 illustrate the SPI Values of Kalgi station based on the 1,3,6,9 and 12 months’ time step respectively. The extreme drought event occurred during October 1980 as is evident from figure 3 and severe drought event during august 1983 as is evident from figure 3. The last extreme drought event occurred during November 1999 as observed from figure 5 and severe drought event during October 2012 as observed from figure 7. Table 2. Severely Drought Events According To Several Time Step for Kalgi Station.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) of SPI are compared with the rainfall record, there is a frequently small deficiency of the rainfall from average rainfall for a considered period 1979 to 2013 so the meteorological drought occurred frequently over a period when there is less rainfall occurred in Kalgi station area.

The table 4 shows the number of dry drought/wet episodes of different month duration time step for Kalgi station. For each episode or event for the different month time step need to do the separate calculation. For 3-month time step and dry drought episode the further calculation are shown below. There are 19 events are occurred for 3-month time step and dry drought episode. The probability table and the fitting of gumbel distribution to the data are as shown in table 5.36.

Table 3. Extremely Drought Events according To Several Time Step for Kalgi Station.

ExtremelyDryDroughteventsAccordingtoSeveralTimestepforKALGIStation(-2orless) SPI_1 YEAR 1980 1989 2002 2003 2004

MONTH 10 10 7 9 5

VALUE -2.34 -2.05 -2.44 -2.58 -2.58

SPI_3 YEAR 1989 1999 2006 2013

SPI_6 SPI_9 SPI_12 MONTH VALUE YEAR MONTH VALUE YEAR MONTH VALUE YEAR MONTH VALUE 10 -2.15 1989 10 -2.00 2011 11 -2.10 2011 11 -2.20 11 -2.80 1999 11 -3.03 2012 2 -2.00 12 -2.29 7 -2.04

Table 5. Probability Table of Cumulative Drought Severities (Negative ∑Z Values) Of Three-Month Duration for Kalgi Station.

Cumul ative Droug ht Severit y (∑Z) 7.9612 74 7.3326 7 6.7064 13 6.2949 36 6.1799 79 5.9587 55 5.9462 04 5.8169 59 5.2200 91

Exceed ence Probab ility

Retur n Perio d

K Facto r

∑Z estimate s of EV1(Gu mbel)

0.05

20

0.1

10

0.15 0.2

6.666 667 5

0.25

4

0.3

3.333 333 2.857 143 2.5

0.45

2.222 222

1.36057 1 1.07734 5 0.90681 5 0.78207 9 0.68212 3 0.59754 1 0.52326 3 0.45620 7 0.39431 9

10

5.1341 78

0.5

2

11

5.0738 82

0.55

1.818 182

12

4.5506 01

0.6

1.666 667

13

4.3812 97

0.65

1.538 462

14

4.2102 53

0.7

1.428 571

15

3.9486 67

0.75

1.333 333

16

3.4365 02

0.8

1.25

1.865 579 1.304 401 0.966 518 0.719 368 0.521 318 0.353 728 0.206 556 0.073 693 0.048 93 0.164 25 0.274 57 0.381 83 0.487 89 0.594 7 0.704 63 0.820 99

Ra nk

Table 3 shows the various years during which the drought event was extreme for different time scales. From the above table it can be seen that there were 5 events of drought from 1980-2004 considering 1 month time scale. Similarly considering 3 months’ time scale it is observed that there are a total of 4 events of extreme drought from 1989 to 2013. Similar explanation can be given for other time scale also. From table 5.26 it is seen that as per SPI_1 value the extreme drought as occurred in the year 1980 during the month of October, 1989(October), 2002(July), 2003(September) and 2004(May). Whereas considering SPI_3 value extreme drought occurred frequently during in the year 1989(October), 1999(November), 2006(December) and 2013(July). Considering SPI_6 value extreme drought occurred frequently during in the year 1989(October) and 1999(November). Whereas considering SPI_9 value extreme drought occurred frequently during in the year 2011(November) and 2012(February). For SPI_12 value extreme drought occurred frequently during 2011 in the month of November).

1 2 3 4 5 6 7 8 9

5.7.1 Severity-Duration-Frequency Relationships for Kalgi Table 4. Number of drought/wet episodes of different month time step duration for Kalgi station.

Duration (month)

Dry drought/wet episode 3 19/13 4 5/8 5 10/7 6 5/3 7-10 8/9 11-12 8/5 First of all need to find out the number of dry/wet period episodes or events for different month time step obtained from SPI methodology.

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0.35 0.4

0.33611 7 0.28044 1 0.22630 4 0.17277 7 0.11887

0.06338 8 0.00466 4



17

2.9374 73

0.85

1.176 471

18

2.3195 28

0.9

1.111 111

19

0.0340 5

0.95

1.052 632

0.5

2

0.2

5

0.1

10

0.05

20

0.04

25

0.02

50

0.01

100

0.949 19 1.100 2 1.305 35 0.164 25 0.719 368 1.304 401 1.865 579 2.043 592 2.591 966 3.136 291

0.06004 0.13625 -0.2398

0.33611 7

Fig. 9. Severity-Duration-Frequency curves of several return Periods (in years, Y) for Kalgi station: wet period severity (Positive ∑ Z values).

0.78207 9 1.07734 5 1.36057 1 1.45041 4 1.72717 9 2.00189 9

The figure 8 and 9 shows the SeverityDuration-Frequency curves of several return Periods (in years, Y) for Kalgi station: Drought Severity and Wet period severity (negative ∑ Z values and Positive ∑ Z values).The final SDF curves appeared to be as expected, since for decreasing frequencies (i.e. increasing recurrence intervals) there is a corresponding increase in severities of droughts and wet periods, respectively, which tend to become asymptotic to the x-axis. As long as drought duration increases, the corresponding severity also increases, as expected, although at lower rates. At longer durations, such as 12 months, where the drought phenomenon tends to be rare. For small time duration the cumulative drought severity and wet period is small and as we go for higher time step duration the cumulative drought severity and wet period is go on increasing as shown in figures 8 and 9.

The above table shows Rank, Cumulative Drought Severity (∑Z), Exceedence Probability, Return Period, K Factor and Drought Severity (∑Z) i.e. probability table and fitting of the Gumbel Distribution. Drought and wet period severities are computed, which correspond to return periods of 2, 5, 10, 20, 25 and 50 and 100 years, respectively, for each identified drought duration. At this point, it should be mentioned that the low frequency events, i.e. events with return periods of 50 and 100 years have a much larger uncertainty than more frequent events. The reason for this implication is the small sample, which is what usually happens, and the extrapolation of the fitted theoretical frequency distribution to the most extreme events. However, the sample used in this study is reasonably long (35 years) thus the uncertainty of extreme events.

5.8 Iso-Severity Map of Drought Severity and Wet Period for Krishna River Basin The corresponding isoseverity maps, such as Figures 10 and 11, show smooth and similar patterns for droughts and wet periods. For dry droughts severity Figures 10, of five-year return period and three-month duration there is a smooth pattern over the central part of Basin with mild to moderate severities. This pattern increases slightly towards south-southwest to the northeast. As long as drought duration increases, the corresponding severity also increases, as expected, although at lower rates. At longer durations, such as 12 months, where the drought phenomenon tends to be rare, the pattern of isoseverity maps also remains smooth. Isoseverity mapping of wet periods follows similar patterns. In particular, wet periods at five-year return period and three-month duration (Fig. 11), there is also a smooth pattern over the central part of Basin showing very wet to extremely wet severities increasing towards southeast, north, and northeast. At longer durations, there is a weak decreasing gradient in the severities

Fig. 8.Severity-Duration-Frequency curves of several return periods (in years, Y) for Kalgi station: drought severity (negative ∑ Z values).

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) of the wet periods from western to central and eastern Basin.

5. CONCLUSIONS 1) In the present Study Standardized Precipitation Index (SPI) is calculated to determine the severity of meteorological drought for five stations, for the period from 1979 to 2013. Effective information and early warning systems based on indicators such as the SPI are the foundation for overall effective drought adaptation plans mitigation strategies. 2) For the all stations considered for this study, severe drought and extremely severe drought events are observed, thereby it is understood that the study area is vulnerable to drought hit. Hence, with help of past incidences of drought using SPI, the future incidences of drought can be predicted, which will help the planners and decision makers to prepare better to draw mitigative measures in advance to reduce the vulnerability of drought in the study area.

Fig.10. Isoseverity map of Drought Severity for 3 month time step and 5 year return period for Krishna Basin.

3) Severity-duration-frequency (SDF) relationships of droughts and wet periods was developed which could be used for hydro climatic and agro climatic design and planning. 4) from Iso-severity map for drought severity and wet period of five-year return period and threemonth duration it is concluded that the For, Drought severity the severity is increasing from northwest to southwest, central part to the east and wet severities increasing towards southeast, north, and northeast. At longer durations, there is a weak decreasing gradient in the severities of the wet periods from western to central and eastern Basin. This will help the planners and decision makers to understand the situation of drought and wet period too.

Fig.11. Isoseverity map of Wet Period for 3 month time step and 5 year return period for Krishna Basin.

The analysis of drought indicates the need to further investigate droughts and wet periods over Basin, since there are differences between stations even in the same region, especially in droughts of long durations. Similarly, local extremes of wet periods, especially of long durations, should be further investigated. In general, isoseverity mapping provides an additional potential in the study of extreme events especially for design purposes. The resulted patterns of isoseverity mapping have shown consistency with the climatic division in Basin, which has allowed regional comparisons of droughts and wet periods. The majority of droughts in Basin are characterized by short duration and mild severity, whereas wet periods appear less frequently with high severity. Additionally, at all stations in the basin, high frequency cycles of famine are the frequencies of duration, dominance, and low frequencies for three to six months. These findings are reflected in SDF relationships and production tables, as well as isoseverity mapping.

6. REFERENCES 1.

2.

3.

4.

5.

6.

7.

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Ana A. Paulo & Luis S. Pereira (2006) Drought Concepts and Characterization, Water International, 31:1, 37-49, DOI: 10.1080/02508060608691913. Mishra, A. K. And Singh, V. P. ―A review of drought concepts,‖ Journal of Hydrology, vol. 391, no. 1-2, pp. 202–216, 2010. Bell, F. C. (1969) Generalized rainfall-durationfrequency relationships. /. Hydraul. Engng ASCE HY1, 311-327. Chen, C. J. (1983) Rainfall intensity-durationfrequency formulas. J. Hydraul. Engng ASCE 109(12), 1603-1621. Dattatraya R. Mahajan and Basavanand M. Dodamani (2016) Spatial and temporal drought analysis in the Krishna river basin of Maharashtra, India. Mahajan & Dodamani, Cogent Engineering (2016), 3: 1185926 http://dx.doi.org/10.1080/23311916.2016.1185926. De Silva, R. P., Dayawansa, N. D. K., Ratnasiri, M. D., (2007) A Comparison of Methods used in estimating missing rainfall data. The Journal of Agricultural Sciences, 3 (2): 101 – 108. Halwatura, D., Lechner, A. M. And Arnold, S., Drought severity–duration–frequency curves: Hydrol. Earth Syst. Sci., 19, 1069–1091, 2015.



Application of Fuzzy Logic Concepts for Reference Evapotranspiration Pallavi S1, A.V. Sriram2 1

PG student, WRE, Civil Engineering Dept., UVCE, Bangalore University, Bengaluru, India 2 Assoc. Professor, Department of Civil Engineering, UVCE, Bangalore University, Bengaluru, India Email: [email protected], [email protected]

AbstractThe major loss we face when it comes to irrigation water is Evapotranspiration and it has to be controlled in order to properly use. In this study Reference Evapotranspiration is calculated using Penman Monteith method FAO-56, and the FAO-56 Missing Data Methodology using only Maximum Temperature and Minimum Temperature values because we can easily measure the Temperature values having the thermometer, but when it comes to other parameters such as Solar Radiation, Relative Humidity the instruments used for measuring these are very costly and each of the meteorological station will definitely contain thermometer, so the method using only Temperatures are calculated. Fuzzy Logic model is developed for Antharasanthe station where the coefficients of regression equation are considered as constant and maximum temperature, minimum temperature and mean temperature are considered as the fuzzy membership functions, these membership function values are propagated in the FAO-56 Missing Data equations and Output ETo in Fuzzy nature is obtained which is defuzzied using Centroid method and the values are compared with FAO-Missing data method and Multiple linear regression model from which we infer that Fuzzy Linear Regression model can be effectively Used when compared to all other models. Here mean temperature fuzzy membership function is calculated using the fuzzy airthematic. I. INTRODUCTION Water is one of the most important limited natural resources. Declining water resources and water quality problems have resulted in dramatic increasing need for water conserving methodologies on field, watershed and regional scale and this makes efficient use of fresh water resources an obligation of each user. Efficient use of water resources in Agro-ecosystems of the world has become increasingly important because of rapid depletion of water resources, industrial development and population increase, drought conditions, and degradation of ground and surface water quality in many regions. India has also experienced a pronounced warming over the last few decades. Rising temperature is expected to strengthen the hydrological cycle because of the ability ofwarm air to hold and redistribute more moisture, which causes the change in atmospheric circulation (Allen and Ingram, 2002; Wentz et al.2007, Bates et al.2008; Durack et al., 2012; Ye et al., 2013). Despite responding to climate

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change in different ways, changes in precipitation, runoff, groundwater flow, evapotranspiration, and soil moisture indicate that the hydrological cycle has been intensified along with the rising temperature around the world during the 20th century (Allen and Ingram, 2002; Alan et al., 2003; Wentz et al.2007; Mitchell et al., 2012; Wang et al., 2012; Allan et al., 2013). Hydrological events are too complicated for precise descriptions, and therefore approximate or fuzzy reasoning must be introduced to obtain reasonable yet tractable models. Deterministic models ignore fuzzy human knowledge in stochastic and mathematical approaches. In the hydrology domain, it is most needed to depend on a model which can digest formulate human knowledge in a systematic manner. In this study Fuzzy Logic concept is applied to climatic variables which have ambiguity. Fuzzy sets were introduced by Zadeh (1965) as a mathematical way to represent vagueness in linguistic that can be considered a generalization of crisp values. In order to consider the fuzziness in regression analysis (FRL), Tanaka et al., (1992) first proposed a study of fuzzy linear regression model. They considered the parameter estimations of FLR models under two factors namely the degree of fitting and vagueness of the model. The estimation problems were then transformed into linear programming (LP) based on these two factors. In general, the fuzzy regression methods can be based on the fuzzy least squares. II. PROCEDURE A. FAO Penman-Monteith Equation: A consultation of experts and researchers was organized by FAO in May 1990, in collaboration with the International Commission for Irrigation and Drainage and with the World Meteorological Organization, to review the FAO methodologies on crop water requirements and to advise on the revision and update of procedures. The panel of experts recommended the adoption of the Penman-Monteith combination method as a new standard for reference evapotranspiration and advised on procedures for calculation of the various parameters. By defining the reference crop as a hypothetical crop with an assumed height of 0.12 m having a surface resistance of 70 s m-1 and an albedo of 0.23, closely resembling the evaporation of an


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) extension surface of green grass of uniform height, actively growing and adequately watered, the FAO Penman-Monteith method was developed. The method overcomes shortcomings of the previous FAO Penman method and provides values more consistent with actual crop water use data worldwide. The FAO Penman-Monteith equation is given by, …… (1)

The lower bound values are calculated using the equation ∝ (0) = µ ± 𝐾𝜎 where, µ is the mean, K= 1 is a constant and σ is the standard deviation. Where the upper value for ∝ (1) is a mean value for FAO-56 Missing Data method. Where we have three membership functions maximum temperature, minimum temperature and mean temperature. Mean temperature fuzzy membership function is calculated by doing fuzzy airthematic for minimum and maximum temperature membership values. The triangular membership functions are arrived at. III. DATA

Where, ETo = reference evapotranspiration [mm day-1], Rn = net radiation at the crop surface [MJ m-2 day-1], G = soil heat flux density [MJ m-2 day-1], T = mean daily air temperature at 2 m height [°C], u2 = wind speed at 2 m height [m s-1], es = saturation vapour pressure [kPa], ea = actual vapour pressure [kPa], es-ea = saturation vapour pressure deficit [kPa], ∆ = slope vapour pressure curve [kPa °C-1], γ = psychrometric constant [kPa °C-1]. The equation uses standard climatological records of solar radiation (sunshine), air temperature, humidity and wind speed. To ensure the integrity of computations, the weather measurements should be made at 2 m (or converted to that height) above an extensive surface of green grass, shading the ground and not short of water. B. Calculation of Average Monthly ETo The daily values of parameters for each month are averaged to a single value and average monthly value for that month is obtained and is done for all the months of the year (from 1979-2013) are plotted and the influence of parameters can be graphically observed. c. Calculation of ETo using missing data methodology. 1. Missing radiation data …………………… (2) Where Rs and Ra are solar and extraterrestrial, radiation, respectively (MJm-2day-1), Tmax is the maximum air temperature (ºC) Tmin is the minimum air temperature (ºC), KRs is the adjustment Coefficient. The adjustment coefficient KRs is empirical and differs for “interior” or “coastal” region: for interior location, where land mass dominates and air masses are not strongly influenced by a large water body, KRs 0.16; for coastal locations situated on or adjacent to the coast of a large land mass and where air masses are influenced by a nearby water body, KRs 0.19. 2. Missing relative humidity data

………………. (3) All other data are suitably assumed with respect to the latitude

Fig.1. Kaveri Basin. The data is collected from the website www.globalwearther.tamu.edu which was a gridded data for 12 stations which had max temperature, min temperature, relative humidity, wind speed and solar radiation. Temperature in ⸰C, relative humidity in %, wind speed in m/sec and solar radiation in MJ/m2. Table.1. of stations and their location. Station Station name Latitude in no decimal(N) 1 2 3 4 5 6 7 8 9 10 11 12

Mallapuram Kidanganad Antharasanthe Gudalur Gundlapet Hallare Kotagiri Madahalli Dasanooru Pungamapalli Talamalai Chamarajmagar

11.396 11.709 12.021 11.396 11.709 12.021 11.396 11.709 12.021 11.396 11.709 12.021

Longitude in decimal(E) 76.250 76.250 76.250 76.563 76.563 76.563 76.875 76.875 76.875 77.188 77.188 77.188

d. Fuzzy Logic Concept..

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Elevation in m 86 916 740 2099 1148 730 1620 914 718 282 1236 948

Impact of Global Atmospheric Changes on Natural Resources (IGACNR) In the above table the details of the study area, 12 meteorological stations are selected and the elevations, longitude and the latitude data’s that are collected from the website globalwearther.tamu.edu are presented above.

April May June July August September October November December

IV. RESULTS A. Results of Monthly Reference Evapotranspiration using FAO-56 Penman-Monteith (ETo) in mm/day for Antharasanthe station and FAO-56 missing data method. The ETo is calculated using the FAO -56 Penman- Monteith method with the full availability of the climatic data i.e., Maximum and Minimum Temperature, Relative humidity, Windspeed and Solar Radiation values which are applied to the equation 1. The ETo obtained is in mm/day for 35 years from 1979-2013. Here the climatic variables are averaged for each month and a single value is calculated as the average and ETo is calculated. The missing data values are calculated using missing data methodology.

January 6

ETo (mm/day)

5 4 3

1 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Years Full Data

2.4 2.2 3.4 4.2 4.2 3.9 3.1 2.3 2.8

17.8 16.8 15.9 15.7 16.0 16.6 16.9 16.0 15.3

1.4 1.6 2.1 2.1 1.7 1.4 1.7 2.3 2.7

Table 3. Fuzzy Membership Values for FAO-56 Missing Data Method for Antharasanthe station. Max. Temp. ⸰C αcut s Jan . Feb . Ma r. Ap r. Ma y Jun . Jul.

2

25.8 23.2 23.2 24.1 25.8 26.1 25.8 24.1 24.0

Au g. Sep . Oct . No v. De c.

0(mi n) 21.8 22.4 23.7 23.5 21.0 19.8 19.8 21.6 22.2 22.7 21.9 21.2

1 25. 6 26. 7 26. 9 25. 8 23. 2 23. 2 24. 1 25. 8 26. 1 25. 8 24. 1 24. 0

Min. Temp. ⸰C

0(ma x)

0(mi n)

29.4

11.5

31.1

13.1

30.0

16.4

28.2

16.4

25.5

15.3

26.6

13.9

28.3

13.6

30.0

14.3

30.1

15.1

28.8

15.2

26.4

13.7

26.8

12.6

1 14. 6 15. 6 17. 4 17. 8 16. 9 15. 9 15. 7 16. 0 16. 6 17. 0 16. 0 15. 3

Tmean in ⸰C

0(ma x)

0(mi n)

17.8

16.7

18.0

17.7

18.4

19.9

19.1

19.9

18.4

18.1

18.0

16.9

17.8

16.7

17.7

17.9

18.0

19.0

18.7

19.0

18.3

17.8

18.0

16.9

1 20. 1 21. 2 22. 1 21. 8 20. 0 19. 6 19. 9 20. 9 21. 4 21. 4 20. 1 19. 7

Missing Data

Table 4. Fuzzy Membership Function for Output ETo. Fig.2. Reference Evapotranspiration with Missing Data and Full Data for the Month of January. This is done for all other months using same methodology. B. Fuzzy Logic Model for FAO-56 Missing Data Method Station Antharasanthe. Table 2. Mean and Standard deviation values of Crisp values of Antharasanthe Station. Month January February March

Max. Temp. in ⸰C Std. Mean dev. 25.6 3.8 26.7 4.4 26.9 3.1

Min. Temp. in ⸰C Std. Mean dev. 14.6 3.2 15.6 2.4 17.4 1.0

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Output ETo mm/day 0(min)

1

0(max)

3.1

3.4

3.6

3.5

3.8

4.1

4.1

4.3

4.5

4.2

4.4

4.6

4.1

4.2

4.4

3.9

4.2

4.4

3.9

4.2

4.5

4.0

4.3

4.6

4.0

4.2

4.5

3.7

3.9

4.1

3.2

3.4

3.6

3.0

3.2

3.4


0(ma x) 23.6 24.6 24.2 23.7 22.0 22.3 23.0 23.9 23.8 24.9 22.4 22.4

Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Table 5. Comparison Between Multiple Linear Regression, Fuzzy Logic and FAO- 56 Missing Data model. Month

ETo in Multiple Linear Regression (mm/day) 3.49

ETo in Fuzzy Logic (mm/day)

Difference (FAO-56 MLR) (mm/day)

Difference (FAO-56FL) (mm/day)

Jan.

ETo FAO-56 MD method (mm/day) 3.37

3.46

-0.12

-0.09

Feb.

3.84

3.95

3.93

-0.11

-0.09

Mar.

4.27

4.35

4.33

-0.08

-0.06

Apr.

4.42

4.51

4.48

-0.09

-0.06

May

4.24

4.32

4.30

-0.08

-0.06

Jun.

4.15

4.20

4.23

-0.05

-0.08

Jul.

4.19

4.31

4.28

-0.12

-0.09

Aug.

4.32

4.45

4.40

-0.13

-0.08

Sep.

4.25

4.38

4.32

-0.13

-0.12

Oct.

3.93

4.12

4.00

-0.19

-0.07

Nov.

3.43

3.49

3.48

-0.06

-0.05

Dec.

3.20

3.31

3.27

-0.11

-0.07

V. 







[5]

Heshmaty,B., and Kandel, A., (1985) Fuzzy linear regression and its application to forecasting in uncertain environment. Fuzzy Sets Syst., 15:159–191. [6] Karimaldini, F., Teang Shui, L., Ahmed Mohamed, T., Abdollahi, M., and Khalili, N.(2012). Daily Evapotranspiration Modeling from Limited Weather Data by Using Neuro-Fuzzy Computing Technique. J. Irrig. Drain Eng., 138(1), 21–34. [7] Korner, R., Nather, W., 1998. Linear regression with random fuzzy variables: extended classical estimates, best linear estimates, least squares estimates. Information Sciences 109, 95–118. [8] Keskin, M.E., O. Terzi and D. Taylan, 2004. Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey. Hydrologic. Sci. J., 49(6): 1001-1010. [9] Panigrahi D., Mujumdar P., (2000). Reservoir Operation Modelling with Fuzzy Logic, Water Resources Research, 14: 89-109. [10] Pongracz, R., Bogardi, I. & Duckstein, L. (1999) Application of fuzzy rule-based modeling technique to regional drought. J. Hydrol. 224, 100–114. [11] Russell, S. O. & Campbell, P. F. (1996) Reservoir operating rules with fuzzy logic programming. J. Water Resour. Plan.Manage. 122(4), 262–269. [12] Shouyu, C. & Guangtao, F. (2003) A DRASTIC-based fuzzy pattern recognition methodology for groundwater vulnerability evaluation. Hydrol. Sci. J. 48(2), 211–220.

CONCLUSIONS

The Monthly Reference Evapotranspiration using FAO-56 Penman – Monteith method is calculated which has minimum ETo of 0.9 mm/day in the month of June and maximum of 5.50 mm/day in July. The Monthly Reference Evapotranspiration using FAO-56 Missing data Method is calculated. where the ETo has a minimum value of 3.10 mm/day in the month of June and Maximum of 4.70 mm/day in the July month. In Penman- Monteith method we can observe the monthly variations in the ETo values but in Missing data method the values will be more consistent this is due to assumed Solar Radiation values for the latitude 12.021 N. From this we can infer that solar radiation values are predominant than other climatic variables. Fuzzy Logic model gives less deviation from the FAO-56 Missing data method than the Multiple Linear Regression Model and hence preferred.

REFERENCES [1]

[2]

[3]

[4]

Allen RG, Pereira LS, Raes D, Smith M (1998). Crop evapotranspiration-Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper., 56: 1-13. Doorenbos, J., and Pruitt, W.O., 1977. Guidelines for predicting crop water requirements. FAO Irrigation and Drainage Paper No. 24. Rome: FAO. Droogers, P., and Allen, R.G., 2002. Estimating reference evapotranspiration under inaccurate data conditions. Irrig. Syst.16, 33-45. Dubois, D. & Prade, H. (1994). Possibility theory and data fusion in poorly informed environments. Control Engineering Practice 2(5), pp. 811-823. Dubrovin, T., Jolma, A. & Turunen, E. (2002) Fuzzy model for realtime reservoir operation. J. Water Resour. Plan. Manage. 128(1), 66– 73.

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Seismic Analysis of Multi-Storey Building in P-Delta Effect N.Jayaramappa1, B.P. Annapurna2, Siddaling Gauli3, Abhishek K4 1,2Assoc. *Student, 3,4 M.E

Student, Department of civil Engineering, UVCE, Bangalore University, Bangalore

1 [email protected], [email protected], [email protected], [email protected]

examination is no more a short dreary literature yet basic and direct which could be performed by modellers and experts. By and by a-days various programming ventures have the ability to examination and layout with P-Delta impacts. All need to is the assortment of results, for instance, centre point, moment and shirking, between P-Delta and Linear Static examination to perceive when the P-Delta examination will be performed, possible differences, performing strategy and arranging frameworks.

ABSTRACT: The economical execution of a concrete structure depends more on the overall layout of the structure with respect to the construction feasibility and cost (called construct ability) than on its theoretical analysis. This knowledge regarding economy is generally acquired only through experience and study of projects already carried out. On the other hand, the structural safety of the individual member depends primarily on the theoretical analysis and design. P-Delta is secondary order loading effect in structure directly related to stiffness as it reduces the stiffness of structural elements. The analysis procedures used to determine P-Delta effects vary from one software to another. Several methods of accommodating P-Delta effects in analysis have been developed. Some of these methods rely on a constrained problem or set of conditions, and will therefore have documented “limitations”; the critical issue is to understand the differences and be aware of the limitations and conditions. Analysis models are “modelling” of the real condition and only provide approximations simulation of the real world. Again, the P-Delta effect does not distinguish between directions and types of loading. It does not have idea about floors, floor levels, or the difference between a column and a beam. Proper care should be taken to work within the limitations of the analysis. By ETABS, leading structural engineering software, complete effects could be identified using appropriate command. In ETABS, a unique procedure has been adopted to incorporate the P-Delta effect into the analysis.

II. P-DELTA EFFECT Geometric stiffness matrix is an approach to include secondary effects in the static and dynamic analysis whereas, in Civil Structural Engineering it is commonly referred to as PDelta Analysis that is based on a more physical approach. For example, in building analysis the lateral movement of a story mass to a deformed position generates second-order overturning moments. This second-order behaviour has been termed the P-Delta effect since the additional overturning moments on the building are equal to the sum of story weights “P” times the lateral displacements “Delta”. Many techniques have been proposed for evaluating this second-order behaviour and takes place in two steps where linear static only consider one 1st order loading stage Figure 1. Many researchers, engineers tried to describe the phoneme in simple way. In short, P-Delta is secondary order loading effect in structure directly related to stiffness as it reduces the stiffness of structural elements. The analysis procedures used to determine P-Delta effects vary from one software to another. By ETABS, leading structural engineering software, complete effects could be identified using appropriate command. In ETABS, a unique procedure has been adopted to incorporate the P-Delta effect into the analysis. The extent of P-Delta impact is identified with the:  Greatness of pivotal weight  Difficulty/slimness of the assembly by way of entirety  Slenderness of separate components

Keywords: p-delta effect, seismic analysis.

I. INTRODUCTION In light of multifaceted nature, nonattendance of learning of PDelta examination fashioners, originators and models are slanted to perform Linear Static examination which may at last transform into an explanation behind catastrophic fall of the multi-storeyed building. P-Delta effect is an imperative issue which impacts the fundamental response severally, rejected for its unconventionality in examination time of the arrangement. Regardless of the way that the change of data and movement of development is extremely remarkable today, there are a not a lot of convenient trial considers on the PDelta effects of the structure. The most used fundamental examination for reinforced strong framework is immediate static examination, where P-Delta effect is avoided which is basic to fuse into examination and setup organize. Consequently, high rise structures may show potential frailty against parallel weights. Lacking of fitting idea pushes the RC structures, most constructed structure, to the uneconomical condition by creating shear divider and propping or feeble state arranged for succumb to catching. P-Delta examination may bring the second demand stacking impacts in the structure and plan the structure with its assets. This

Fig. 1 Diagram Depiction of P-Delta Upshot on a Edge.

Hence these building will collapse or need renovation. The destruction and renovation of Buildings result in a large amount of waste. Building waste often includes concrete, metals, glass, plastic s, wood, asphalt, bricks and more. This waste is often disposed of in either landfills or incinerators.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Not only does this pollute the land and the air, but the transportation required to remove such waste as a major impact on the environment as well. According to environmental protection agency, there were already over 170 million tons of debris generated in the construction and demolition of buildings in the US alone in 2003. 61% of which where produced by non residential buildings. Like this Buildings has directly or indirectly effect the Environment.

IV. ANALYSIS TECHNIQUES 1. Direct static investigation or equal static examination The immediate motionless examination for (G+4), (G+9), (G+14) and (G+19) story structures is completed deprived of seeing the P-Delta affect in ETABS v16.02 program. From the examination comes to fruition, expulsions, story shear, centre force and turning minute at the ignoble besides at several assistant people are procured. 2. Non-straight static or P-Delta examination: The non-coordinate static examination for (G+4), (G+9), (G+14) besides (G+19) story structures is completed seeing the P-Delta impact in ETABS v16.02 program. After the examination occurs, evacuations, story shear, centre point drive and curving moment at the dishonourable and at numerous assistant people are gained.

III. DESCRIPTION OF THEMODELS The studies are carried out for 10 and 20 storey and seismic zone III considered for the analysis. Following Table 1 shows the models considered in this dissertation work. The main objectives of this research are: The different articles considered for the present examination are as recorded beneath.  Analysis of G+9 and G+19 story R.C.C. structures with and without seeing P-Delta impacts.  On the road to measure the seismic limit of edifices by direct static examination strategy, utilizing ETABS form 16.02  Toward assess the seismic furthest reaches of assemblies by nonlinear static examination strategy, using ETABS variation 16.02  To assess the percentage difference in the estimations of pivotal powers, avoidances, story shears and bowing minutes with and without considering PDelta impact.

Mode l

G+9 G+1 9

Table 3.1: Models Considered for the Analysis Beam Column Lump Wall Score size size (m) thicknes thicknes of (m) s (m) s (m) concret e (N/mm2 )

0.3X0. 5 0.3X0. 6

0.50X0. 5 0.55X0. 6

V. LOAD ESTIMATION: Below loads are taken aimed at the examination of the structure. The lots remain occupied as per IS: 875 (Part 1) and (Part 2). 1. Dead load a) Self-weight b) Floor-finishing: 1.25 kN/m2 c) Wall stack 2. Live load a) Live load on intermediate floors Intensity of live load= 3 kN/m2 b) Live load on roof Intensity of live roof load= 1.5 kN/m2 3. Horizontal loads for P-Delta investigation: - By using IS 1893:2002 for zone 5, EQX: Earthquake stack in X-direction EQY: Earthquake stack in Y-direction Zone consider = 0.36 Soil: Type II Significance figure = 1

Grade of steel (N/mm2 )

0.15

0.3

M25

Fe500

0.15

0.3

M25

Fe500

VI. RESULTS ANDDISCUSSIONS The benefits of removals, story shear, pivotal constrain and bowing minutes for 10 and 20 story demonstrate that are gained by direct examination then P-Delta examination are sorted out cutting-edge bench no. 1, 2, 3 and 4 exclusively. The estimations of removals, story shear, pivotal constrain and twisting minutes from direct examination and P-Delta examination are taken a gander at and assortments as percent between the two sorts of examination are surveyed and grouped. The disparity of relocations, story shear, pivotal drive and bowing minute procured from direct static examination then P-delta examination remain planned in outlines by way of showed up in fig. 1, 2, 3 and 4 exclusively. Fig. 3.1: Plan Layout for 10 And 20 Storey Building Models

Case 1: Story Model No. of storeys

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Table 6.1: Displacement Values for 10 Storey Model Displacement (mm) Difference Linear static P(%)


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) investigation 28.8 27.4 25.5 22. 19.9 16.5 12.9 9.3 5.6 2.1 0

No. of Storeys

10 9 8 7 6 5 4 3 2 1 0

Investigation 41.4 3.8 37.4 34 29.9 25.1 19.6 13.8 8 2.8 0

The outrageous story shear gained from P-Delta examination is 970.1496 kN plus that since direct examination is 776.1845 kN besides the most extraordinary assortment among the two is seen to be 20%.

30.434 31.155 31.818 32.647 33.444 34.262 32.183 32.608 30 25 0

Table 6.3: Storey Axial Force Values for 10 Storey Model Displacement (mm) No. of Difference PLinear static storeys (%) investigation Investigation 10 8850.002 9174.002 3.531719309 9 17700.0041 18672.0041 5.205654384 8 26550.0061 28170.0061 5.750797477 7 35400.0081 37668.0081 6.02102451 6 44250.0102 47166.0102 6.182418203 5 53100.0122 56664.0122 6.289706397 4 61950.0142 66162.0142 6.366190708 3 70800.0162 75660.0162 6.423472058 2 79650.0183 85158.0183 6.467975782 1 88500.0203 94656.0203 6.503548301 0 88500.0203 94656.0203 6.503548301

12 10 8 6 4 2 0 0

10

20

30

40

12

50

10

LINEAR STATIC INVESTIGATION

No. of Storeys

Displacement in mm P-D INVESTIGATION

Graph 6.1: Difference of Displacement for the No. of Storey’s Acquired from Linear and P-Delta Analysis For 10 Storey Model

8 6 4 2

The most outrageous relocation of the 10 story model is 48.4mm then 21.8 mm gained from P-Delta examination in addition to straight examination independently then the best assortment amid the two is seen to be 34.26294821 %.

0 0

10 9 8 7 6 5 4 3 2 1 0

Displacement (mm) Linear Static PInvestigation Investigation 181.81338 227.2653 438.4185 350.7402 484.2129 605.25 732.9727 586.4029 661.4814 826.8003 713.6191 891.475 933.6297 746.9873 765.756 957.0652 967.4837 774.099 776.1845 970.1496 970.1496 776.1845

60000


80000

100000 120000

P-D INVESTIGATION

Graph 6.3: Difference of Axial Force for the no. of storey’s acquired from linear and P-Delta analysis for 10 storey.

The outrageous pivotal compel gained from P-Delta examination is 94656.0203 kN in addition to that after straight examination is 88500.0203 kN and the best assortment between the two is seen to be 6.503548301%.

Difference (%) 19.99949838 19.9987683 19.99786865 19.9966247 19.99502177 19.95074455 19.99105213 19.98915016 19.98841944 19.99331856 19.99331856

Table 6.4: Bending Moment Values for 10 Storey Model Displacement (mm) No. of PDifference (%) Linear static Storeys investigation Investigation 10 53100.0122 55044.0122 3.531719

12

No. of Storeys

40000

Displacement in mm

Table 6.2: Storey Shear Values for 10 Storey Model No. of Storeys

20000

9

106200.0244

112032.0244

5.205654

8

15300.0365

16020.0365

4.494372

7

212400.0487

226008.0487

6.021025

6

265500.0609

282996.0609

6.182418

5

318600.0731

339984.0731

6.289706

10

4

371700.0853

396972.0853

6.366191

8

3

424800.075

453960.0975

6.423477

6

2

477900.1096

510948.11

6.467976

4

1

531000.1218

567936.1218

6.503548

2

0

531000.1218

567936.1218

6.503548

0 0

200

400 600 800 Displacement in mm


1000

1200

P-D INVESTIGATION

Graph 6.2: Difference of Storey Shave for the No. of Storey’s Acquired from Linear and P-Delta Analysis for 10 Storey Model

[226]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) 12

The most outrageous removal of the 20 story perfect is 686.3 mm and 390 mm got after P-delta examination as well as straight examination independently and the best assortment among the dual is seen to be 63.03560009%.

No. of Storeys

10 8 6 4

Table 6.6: Storey shear values for 20 storey model. Displacement (mm) Linear static Pinvestigation Investigation 122.2906 361.837 291.5067 862.8673 443.3802 1312.4139 578.8486 1713.3983 698.845 2068.5956 804.322 2380.7807 896.2153 2652.7287 975.526 2887.2144 1043.3707 3087.0129 1100.6325 3254.8991 1148.049 3393.648 1186.5322 3506.0347 1216.984 3594.834 1240.3059 3662.821 1257.3968 3712.7706 1269.1549 3747.4578 1276.4776 3769.6576 1280.2664 3782.145 1281.4165 3787.695 1280.8582 3789.0825 1280.8582 3789.0825

2 No. Of storeys

0 0

200000

400000 600000 Displacement in mm


800000

20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0

P-D INVESTIGATION

Graph 6.4: Difference of Bending Moment for the No. Of Storey’s Acquired from Linear and P-Delta analysis for 10 Storey Model.

The outrageous bowing minute obtained from P-Delta examination is 567936.1218 kN-m as well as that since straight examination is 531000.1218 kNm and the best assortment amongst the two is seen to be 6.503548301% Case 2: Story Model:

No. of Storeys 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0

Difference (%) 43.1735393 44.1992987 45.5345359 47.1939903 49.1560557 51.3928199 53.8068779 56.253804 58.5707613 60.3929847 61.8348307 67.2644893 63.0356001 62.5376128 61.074868 58.2544257 53.4967125 45.6716418 32 7.75193798 0

20

10 5

0

5

20 19 18 17 16 15 14

0 800

Displacement in mm LINEAR STATIC INVESTIGATION

4000

P-D INVESTIGATION

The most outrageous story shear procured from P-Delta examination is 3789.0825 kN as well as that since straight examination is 1280.8582 kN besides the best assortment among the dual is seen to be 66.21208318%.

No. Of storeys

600

3000

Graph 6.6: Alteration of Storey Shave for the no. of storey’s adapted since rectilinear and P-Delta investigation for 20 storey model

10

400

2000


15

200

1000

Displacement in mm

20

No. of Storeys

15

0

25

0

66.202848 66.216509 66.216435 66.216343 66.216451 66.21604 66.215343 66.212208 66.201285 66.185357 66.170652 66.157431 66.146309 66.13796 66.133194 66.13291 66.138102 66.149727 66.168963 66.196086 66.196086

25

No. of Storeys

Table 6.5: Displacement values for 20 storey model Displacement (mm) Linear static Pinvestigation Investigation 390 686.3 381.9 684.4 371.4 681.9 358.5 678.9 343.4 675.4 326.3 671.3 307.6 665.9 287.5 657.2 266.1 642.3 243.9 615.8 220.9 578.8 173.4 529.7 173.4 469.1 149.4 398.8 125.3 321.9 101.4 242.9 77.8 167.3 54.6 100.5 32.3 47.5 11.9 12.9 0 0

Difference (%)

P-D INVESTIGATION

Graph 6.5: Difference of Displacement for the No. of Storey’s Developed from Linear And P-Delta Investigation for 20 Storey Model

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Table 6.7: Axial Force values for 20 storey model Displacement (mm) Linear static PDifference (%) investigation Investigation 1872.6466 2655.445 29.47899128 5662.2055 7293.6991 22.36853451 9451.7645 11931.532 20.78331182 13241.3234 16570.2072 20.08957257 17030.8823 21208.4613 19.69769962 20820.4412 25846.7153 19.44647141 24610.0002 30484.9694 19.27169131


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) 28399.5591 32189.118 35978.6769 39768.2358 43557.7948 47347.3537 51136.9126 54926.4715 58716.0305 62505.5894 66295.1483 70084.7072 73874.2661 73874.2661

35123.2234 39761.4775 44399.7316 49037.9856 53676.2397 58314.437 62952.7478 67591.0019 72229.2559 76867.51 81505.764 86144.0181 90782.2721 90782.2721

25

19.14307301 19.04446207 18.96645407 18.90320266 18.85088254 18.80680645 18.76937165 18.73700647 18.70879775 18.68399354 18.6620123 18.64239822 18.62478831 18.62478831

20

No. of Storeys

13 12 11 10 9 8 7 6 5 4 3 2 1 0

10 5 0 0

200000

400000 600000 800000 Displacement in mm


1000000

P-D INVESTIGATION

Graph 6.8: Difference of Bending Miniature for the no. of storey’s integrated from lined and P-Delta investigation for 20 storey model

The most extraordinary bowing minute procured after P-Delta examination is 794982.3083 kN-m then that after direct examination is 554056.9961 kN-m and the most outrageous assortment among the dual is seen to be 30.30574513%.

25

No. of Storeys

15

20 15 10

VII. CONCLUSIONS

5

1). After the outcomes the situation canister be contemplated that the P-delta affect should be measured in examination of multi-storied structures.

0 0

20000

40000

60000

80000

100000

Displacement in mm LINEAR STATIC INVESTIGATION

P-D INVESTIGATION

2). The Difference of hub drive, relocation, story shear and bowing minute is seen to be extraordinary for 20 story when appeared differently in relation to that of 10 story and along these lines can be assumed that the P-delta affect must be considered when the no. of stories is more than 10.

Graph 6.7: Modification of Axial Strength for the no. of storey’s reached from linear and P-Delta investigation for 20 storey perfect

The most outrageous pivotal compel found after P-Delta examination is 90782.2721 kN and that from straight examination is 73874.2661 kN and the best assortment among the dual is seen to be 29.47899128%.

3). The story shear gained from nondirect static examination considering p-delta effect are 66.21% more than that gotten from direct static examination. 4). The structures subjected to hub burdens are inclined to avoid more than expected. The outcomes have been demonstrated that an expansion in pivotal drive of sum 29.47% considering p-delta affect when differentiated and the results without considering p-delta affect.

Table 6.8: Bending Moment values for 20 storey model Displacement (mm) No. Of Difference (%) Linear static Pstoreys investigation Investigation 20 14044.8497 14044.8497 0 19 42466.5416 44095.468 3.694090286 18 70888.2335 76400.0629 7.214430448 17 99309.9254 110727.6174 10.3115124 16 127731.6173 146859.6018 13.02467409 15 156153.3093 184589.9741 15.40531383 14 184575.0012 223725.1793 17.4992275 13 212996.6931 264084.1503 19.34514326 12 241418.385 305498.3069 20.97554076 11 269840.0769 347811.5566 22.41773691 10 298261.7688 390880.2944 23.69485669 9 326683.4608 434573.4024 24.82663251 8 355105.1527 478772.2502 25.83004705 7 383526.8446 523370.6971 26.71984757 6 411948.5365 568275.0813 27.50895648 5 440370.2284 613404.2408 28.20880602 4 468791.9203 658689.4929 28.82960403 3 497213.6123 704074.6442 29.38055412 2 525635.3042 749515.9888 29.87003452 1 554056.9961 794982.3083 30.30574513 0 554056.9961 794982.3083 30.30574513

5). The removals gained from no direct static examination considering P-delta effect are 63.03% more than that gotten from direct static examination. 6). The bowing minute gained from nondirective static examination considering P-delta effect are 30.30574513 % more than that obtained from direct static examination. 7). The solidness and thinness of the segments and shafts incredibly impacts the P-Delta impact in multi-storied structures.

REFERENCES AND CODES [1]

[2]

[228]

T.J. Sullivan, T.H. Pham and G.M. Calvi, “P-Delta effects on tall RC frame-wall buildings”, The 14th World Conference on Earthquake Engineering Beijing, China October 12-17, 2008 Christoph ADAM, Luis F. Ibarra, Helmut Krawinkler, “Evaluation of P-Delta effects in non-deteriorating MDOF structures from equivalent SDOF systems”, 13th World Conference on Earthquake Engineering Vancouver, B.C., Canada, Page no. 3407, 2004


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) [3]

[4]

[5]

[6]

[7]

[8]

A.S. Moghadam and A. Aziminejad, “Interaction of torsion and P-Delta effects in tall buildings”,13th World Conference on Earthquake Engineering Vancouver, B.C., Canada, Page no. 799, 2004. Neeraj Kulkarni, S.M. Maheswerappa, Dr.J.K. Dattatraya, “Study of P-Delta Effect on Tall Steel Structure”, International Journal of Allied Practice, Research and Review, p.n. 26-36, 2015 Pham, T.H., “Seismic Design Considerations for Tall Buildings” MEEES Masters Dissertation supervised by Sullivan, T.J. and Calvi, G.M., held at ROSE School, University of Pavia, Italy. 85pages, 2008 Peng, B.H.H., Dhakal, R.P., Fenwick, R.C., Car, A.J., Bull, D.K. “Flexural, Axial Load and Elongation Response of Plastic Hinges in Reinforced Concrete Member,” Proceedings of 8 th Pacific Conference of Earthquake Engineering, Singapore. Paper No. 30, 2007. Priestley, M.J.N., Calvi, G.M., Kowalsky, M.J. “Displacement Based Seismic Design of Structures”, IUSS Press, Pavia, Italy, 2007 Shah B.A, Patel P.V, “Seismic evaluation of RCC framed structures using static pushover analysis”, International conference on recent trends in concrete technology and structures”, Kumara guru college of technology, Coimbatore, pp. 658-665, 2003.

[229]



Analytical Study of Ferrocement Panels as Loadbearing Wall Under Dynamic Loading Mr. Somashekar1, N. Jayaramappa2 and B. P. Annapurna3 1Post

Graduate Student, Dept. of Civil Eengineering, UVCE Bengaluru 56. Professor, Faculty of Civil Engineering, UVCE Bengaluru 56. 3Associate Professor, Faculty of Civil Engineering, UVCE Bengaluru 56. 2Associate

Abstract—folded plates are very efficient structures which have many advantages. There are various methods available for the analysis of folded plate structures. Some of the popular methods are the Simpson’s Method and the Whitney’s Method. These methods are accurate for the design purposes but these are very lengthy and cumbersome. Moreover large quantity of data can lead to errors and also for analyzing folded plates of different data will take too much time. Hence there should be a computer model for analyzing the folded plates. The present work is to carry out Linear dynamic analysis (Response Spectrum and Time History Analysis) on folded and plain ferrocement plates as infill elements for reinforced concrete frame. For this purpose RC frame is designed using ETABS Software, and these ferrocement panels are placed as wall component in the frame. The behavior of the structure is studied based on different parameters available in the software for linear dynamic Analysis. Such as Maximum storey displacement, Maximum storey drift, Maximum store shear and Maximum overturning moments. A basic comparison is made between the analytical results obtained from the dynamic Analysis. Keywords—ferrocement panels, dynamic analysis (response spectrum and time history analysis).

I.

INTRODUCTION

There are different ways of constructing folded structure in terms of their forms and the application of different materials they are made of. Based on the research and analysis of the formal potential of the folded constructions the systematization of folded structures was done in terms of shape and geometry. The term folded structure defines a folded form of construction, including structures made of plates and structures made of sticks which make a folded form by their mutual relationship in space. Some authors also call a folded structure the origami construction. In domestic and foreign literature there are various definitions that interpret the term folded (creased) structures: 

Folded structure is a construction whose load capacity and stiffness are a result of a folded shape of the construction,  Folded structures - folds belong to the spatial i.e. threedimensional structures, where the dimension of the elements is very small compared to the span of the construction. However all the civil engineering structures are part of the global atmosphere, the problems caused by these structures are directly related to the environment. So it is most important to construct earthquake structures resistant, as the earthquake is one of the major hazard.

II.

LITERATURE

1.

Albertus Sidharta Muljadinata et al. investigated experimentally “Redefining folded plate structure as a form resistant structure” Two different approaches toward folded plate structures has been presented. The first approach is based on its structural behavior, capacity and strength, and the second appreciation is based on the visibility of its folded form. Facts from the study examples in the paper are quite contrast. Building examples with true folded plate structures result in both, surface-active “form-resistant” structures and folded form structures. While the so-called “folded structures” confusing examples lead only to folded form structures, disregarding their structural features as folded plate structures. 2.

R Abasolo, C Bandivs, presented a case study on “utilization of ferrocement as flexural building member” This study focuses on the fabrication and the Maximum Moment Capacity of a Ferrocement beam. There were three batches with 3 specimens each. The beams were casted vertically by plastering. This study used a cement to sand ratio of 1:3 by volume, and a water to cement ratio of 0.5:1 by weight. It also used two layers of # 16 gage wire mesh kept constant on each batch. Tension bars of 8 mm dia. were used, the number of which increases by one on each batch. Nine specimens of 200mm x 200mm x 3000mm hollow box beam with a 25 mm thickness were casted. The testing of the beam was done after the 28th curing day period, and was conducted to failure in order to determine the Actual Moment Capacity of the design beam. The results show that Maximum Moment Capacity or Flexural Strength of the fabricated Ferrocement beams did not go below the calculated ultimate moment capacity for office occupancy of 5.3792 KN-m. This means that the beams are safe for use as floor joist beams in residential and commercial structures. 3.

Bahador Bagheri, Ehsan Salimi Firoozabad analytically investigated “Comparative Study of the Static and Dynamic Analysis of Multi-Storey Irregular Building” In present study, Multi-storey irregular buildings with 20 stories have been modeled using software packages ETABS and SAP 2000 v.15 for seismic zone V in India. This paper also deals with the effect of the variation of the building height on the structural response of the shear wall building. Dynamic responses of building under actual earthquakes, EL-CENTRO 1949 and CHI-CHI Taiwan 1999 have been investigated. This paper highlights the accuracy and exactness of Time History analysis in comparison with the

[230]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) most commonly adopted Response Spectrum Analysis and Equivalent Static Analysis.

2. 3.

III.

METHODOLOGY 4.

The general finite element package ETABS (version2015) has been used for modeling and analysis. It is a versatile and user-friendly program that offers a wide scope of features like static and dynamic analysis, nonlinear dynamic analysis and nonlinear static pushover analysis, etc. These features and many more, make ETABS the state-ofthe-art in structural analysis programs.

5. 6. 7. 8.

In this dissertation work an attempt has been made to develop two computerized models for the linear dynamic analysis of the ferrocement plates; namely a) Folded ferrocement plate and b) Plain ferrocement plate, based on Response spectrum and Time history analysis in ETABS.

Type 2: Linear dynamic response spectrum analysis with folded plate (zone II). Type 3: Linear dynamic response spectrum analysis with plain plate (zone V). Type 4: Linear dynamic response spectrum analysis with folded plate (zone V). Type 5: Linear dynamic time history analysis with plain plate (zone II). Type 6: Linear dynamic time history analysis with folded plate (zone II). Type 7: Linear dynamic time history analysis with plain plate (zone V). Type 8: Linear dynamic time history analysis with folded plate (zone V).

Assumptions for the analysis of folded plates using conventional methods 1. Material is homogenous, elastic and isotropic; Hook’s law is valid; thickness of the plate is small when compared to plate dimensions. 2. Joints are assumed to be rigid enough. 3. Problem will be treated as one dimension if plate is assumed to behave in beam action, but in two dimension if based on the theory of elasticity. IV.

MODAL DESCRIPTION

In this study, a 10 storey bare RC building with ferrocement infill system is considered. The ferrocement plates used are, a folded plate with cement mortar, and a plain plate with cement mortar systems are considered. These models are analyzed for linear dynamic condition (Response spectrum and Time history analysis). Medium type of soil is considered for linear static analysis in this study. Different zones considered for analysis are zone II, and zone V. 1.

Figure-1: Plan of the RC Building.

A brief summary of the RC frame is presented below           

 

Type of structure: SMRF. Grade of concrete: M 40. Grade of reinforcing steel: Fe 500. Number of storeys: 10 storeys. Building height: 30 m. Grid data: 4 X 4 Bay, 3.4m Spacing. Beams: 300×450 mm. Columns: 400×400 mm. Slab thickness: 150mm. Support condition: fixed at the ends. Modelling cases: 1. Linear dynamic analysis using plain ferrocement plate. 2. Linear dynamic analysis using folded ferrocement plate. Analysis type: 1. Linear dynamic response spectrum analysis. 2. Linear dynamic time history analysis. Model types: 1. Type 1: Linear dynamic response spectrum analysis with plain plate (zone II).

Figure-2: Elevation of the RC Building. 2.

[231]

A brief summary of the folded plate is presented below      

Type of structure: Folded Ferrocement Plate. Mortar mix type: Standard cement mortar. Grade of reinforcing steel: Fe 500. Plate height: 2.7 m. Plate length: 3.0 m. Plate width: 60 mm.


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) 

Figure-5: Three dimensional view of RC frame with ferrocement panels.

Plate thickness: 30 mm.

V. Figure-3: Cross-section of the folded ferrocement plate. 3.

A brief summary of the plain plate is presented below      

Type of structure: Plain Ferrocement Plate. Mortar mix type: Standard cement mortar. Grade of reinforcing steel: Fe 500. Plate height: 2.7 m. Plate length: 3.0 m. Plate thickness: 30 mm.

RESULTS AND DISCUSSION

1.

Response spectrum analysis Response spectrum method of analysis is the method of estimating peak responses (acceleration, velocity, displacement) to a particular component of ground motion when a family of SDOF systems is subjected to a prescribed ground motion. This method gives the structural designer a set of possible forces and deformations a real structure would experience under earthquake loads by utilizing the response spectra. 1)

Storey vs Displacement curves

Figure-4: Cross-section of the Plain ferrocement plate.

Graph-1: Comparison chart of storey vs displacement. From the above chart it is observed that maximum displacement for the plain plates are 2.1mm and 7.4mm for zone II and zone V respectively, but in case of folded plates the maximum displacement are 0.6mm and 2.1mm for zone II and zone V respectively. With these values it is able to make sure that the displacement value in zone V of the folded plate structure are matched with displacement value of zone II of the plain plate structure. Which directly indicate that use of folded ferrocement plate gives high strength to the structures over plain ferrocement plates. 2)

Storey vs storey drift curves

Graph-2: Comparison chart of storey vs storey drift. From the above chart it is observed that maximum storey

[232]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) drift for the plain plates are 0.000081 and 0.000284 for zone II and zone V respectively, but in case of folded plates the maximum displacement are 0.000023 and 0.000084 for zone II and zone V respectively. Similar to the case of displacement, here also folded plates are recommended over plain ferrocement plates in terms of strength of the structure. 3)

2.

Time history analysis 1) Storey vs Displacement curves


Graph-5: Comparison chart of storey vs displacement.

Graph-3: Comparison chart of storey vs storey shear. From the above chart it is observed that maximum storey shear for the plain plates are 638.86kN and 2246.43kN for zone II and zone V respectively, but in case of folded plates the maximum displacement are 331.33kN and 1192.72kN for zone II and zone V respectively. The above results shows the good response nature of the structure, here use of folded ferrocement plates resulted in 50% reduction of the storey shear for the same lateral loading conditions. 4)

From the above chart it is observed that maximum displacement for the plain plates are 2.1mm and 7.5mm for zone II and zone V respectively, but in case of folded plates the maximum displacement are 0.9mm and 3.2mm for zone II and zone V respectively. With these values it is able to make sure that the displacement value in zone V of the folded plate structure are matched with displacement value of zone II of the plain plate structure. Which directly indicate that use of folded ferrocement plate gives high strength to the structures over plain ferrocement plates. 2)

Storey vs overturning moment curves

Graph-4: Comparison chart of storey vs overturning moment.


Graph-6: Comparison chart of storey vs storey drift.

From the above chart it is observed that maximum overturning moment for the plain plates are 13187.64kN-m and 46371.43kN-m for zone II and zone V respectively, but in case of folded plates the maximum displacement are 6940.15kN-m and 24984.55kN-m for zone II and zone V respectively. Similar to the case of shears, here use of folded ferrocement plates resulted in approx. 50% reduction of the overturning moment for the same lateral loading conditions.

From the above chart it is observed that maximum storey drift for the plain plates are 0.00008 and 0.000287 for zone II and zone V respectively, but in case of folded plates the maximum displacement are 0.000037 and 0.000132 for zone II and zone V respectively. Similar to the case of displacement, here also folded plates are recommended over plain ferrocement plates in terms of strength of the structure.

[233]


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) VI.

3)

Storey vs storey shear curves

Graph-7: Comparison chart of storey vs storey shear. From the above chart it is observed that maximum storey shear for the plain plates are 1245.63kN and 4484.27kN for zone II and zone V respectively, but in case of folded plates the maximum displacement are 723.74kN and 2605.5kN for zone II and zone V respectively. The above results shows the good response nature of the structure, here use of folded ferrocement plates resulted in approx. 40% reduction of the storey shear for the same lateral loading conditions. 4)

Storey vs overturning moment curves

CONCLUSION

In the present investigation an attempt has been made to study the different parameters of ferrocement plates for dynamic behavior of the multi-storied building. The analysis is carried out using a typical symmetrical plan of the building. The results obtained from the dynamic analysis (Response spectrum & Time history analysis) of multistoried building for zone II and zone V are going to help in project work to investigate analytically the properties of the ferrocement panels. The following conclusions are drawn, 1. From response spectrum analysis it is observed that the displacements of the respective plates are increased by approx. 3.5 times for different zones, whereas comparing the plates keeping same seismic zones it is observed that approx. 3.5 times reduce in the displacements. However all the displacements values are with in permissible limit of 60mm (Total height of the structure/500) as per IS codes. 2. Similar to the case of displacements the maximum storey drift of the respective ferrocement plates are increased by approx. 3.5 times for different zones, and keeping same seismic zone condition it is observed that approx. 3.5 times drop in the drift values. But all the storey drift values obtained are with in permissible limit of 0.12 (0.004 times the storey height) as per IS 1893: 2002. 3. In stoery shears also between zonal comparisons there is a difference of 3.5 times the each other. Whereas, when the comparison is made between plates keeping same zonal condition folded plates shows approx. 50% drop in shear values to that of plain plates. 4. In the overturning moments, between zonal comparisons there is a difference of 3.5 times the each other. Whereas, when the comparison is made between plates keeping same zonal condition folded plates shows approx. 45% drop in shear values to that of plain plates. REFERENCES

Graph-8: Comparison chart of storey vs overturning moment. From the above chart it is observed that maximum overturning moment for the plain plates are 22540.14kN-m and 81144.47kN-m for zone II and zone V respectively, but in case of folded plates the maximum displacement are 11219.69kN-m and 40391.61kN-m for zone II and zone V respectively. Similar to the case of shears, here use of folded ferrocement plates resulted in approx. 50% reduction of the overturning moment for the same lateral loading conditions.

[ 1 ] “Redefining folded plate structure as a form-resistant structure” by Albertus sidharta muljadinata, Asian Research Publishing Network (ARPN), VOL. 11, NO. 7, April 2016. [ 2 ] Applications of Ferrocement in Strengthening of Unreinforced Masonry Columns by Abid A. Shah. [ 3 ] Utilization of Ferrocement as Flexural Building Member (Applied as a Hollow Box Joist)By R Abasolo, C Bandivs, Civil Engineering department College of Engineering Xavier University-Philippines. [ 4 ] Bahador Bagheri, Ehsan Salimi Firoozabad presented a paper on “Comparative Study of the Static and Dynamic Analysis of Multi-Storey Irregular Building” 2012. [ 5 ] Prakriti Chandrakar et al published a work on “A Review - Comparison between Response Spectrum Method and Time History Method for Dynamic Analysis of Multistoried Building” International Journal of Science and Research (IJSR) ISSN (Online): 23197064.

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Experimental Study on Ferrocement Panels Under Static Loading Jayaramappa N 2

Rashmi M 1, M.E. Student1, Department of Civil Engineering University Visvesvaraya College of Engineering, Bangalore University, Bengaluru 56

Associate Professor2, Department of Civil Engineering University Visvesvaraya College of Engineering, Bangalore University , Bengaluru 56

ABSTRACT— The paper describes the behaviour of plain and folded ferrocement panels under axial load and to evaluate the load caring capacity of both the plates. To study these parameter, experimental investigations are carried out , and also to study the flexural behaviour of ferrocement plane plate and folded plate. Plain ferrocement panels of length 3000 mm, depth 2700 mm and 30 mm thick is casted and tested after 28 days curing period. Similar dimensioned folded plate of same material is casted and tested after 28 day curing period is over. The cement mortar is the ratio of 1: 3 with a water cement ratio of 0.45 is adopted for both the panels, two layered chicken mesh is used and 8 mm HYSD bars are placed at 200mm C\C. Two point loads are applied gradually along the span of the panels corresponding deflection were measured for each increment of loading, the ultimate load carrying capacity of the ferrocement plane plate walls were compared with the folded ferrocement panel walls. The flexural load carrying capacity, load- defection relationship, crack patterns maximum deflection and mode of failure of specimens are studied, to compare the results load deflection graphs and bar charts are plotted and conclusions are drawn. Keywords— Ferrocement, folded panel, plain panel, mortar, wire mesh.

I INTRODUCTION Ferrocement is one of the construction materials which may be able to fill the need for Building light Structures. Ferrocement has proven itself as an excellent material for building construction as well as repair material. Ferrocement composite consist of cement-sand mortar and single or multi-layers of steel wire mesh to produce elements of small thickness having high durability, resilience and when properly shaped it have high strength and rigidity. These thin elements can be shaped to produce structural members such as folded plates. It is a very durable, cheap and versatile material. It has a very high tensile strength-toweight ratio and superior cracking behavior in comparison to conventional reinforced concrete Folded plates are used as roofing structures because they provide an economical and aesthetically pleasing design. A folded plate structure consists of a series of flat plates connected to one another along their edges usually used on long spans. Some architects prefer the aesthetics of folded plates to curved shell roofs. Folded plates provide good quality robust roofs

B P Annapurna3 Associate Professor3, Department of Civil Engineering University Visvesvaraya College of Engineering, Bangalore University , Bengaluru 56

II. LITERATURE SURVEY Al-Kubaisy et al (2000) presented a study of the flexural behavior of ferrocement tension zone cover. The results of tests on 12 simply supported slabs are presented. The parameters considered in this study were percentage of wire mesh reinforcement in the ferrocement cover layer, thickness of the ferrocement layer and the type of connection between the ferrocement layer and the reinforced concrete slab on the ultimate flexural load, first crack load, crack width and spacing, and the load–deflection relationship were examined. The results indicate that the use of ferrocement coverslightly increases the ultimate flexural load and increases in the first crack load Mohamad N. Mahmood Sura A. Majeed August 2009 The present paper describes the results of testing folded and flat ferrocement panels reinforced with different number of wire mesh layers. The main objective of these experimental tests is to study the effect of using different numbers of wire mesh layers on the flexural strength of folded and flat ferrocement panels and to compare the effect of varying the number of wire mesh layers on the ductility and the ultimate strength of these types of ferrocement structure. Seven ferrocement elements were constructed and tested each having (600x380mm) horizontal projection and 20mm thick, consisting of four flat panels and three folded panels Sirajul Muneer. M1, Dr.M.Neelamegam2 In this project the natural resources are replaced by industrial waste for sustainable construction and for economic reasons. The industrial waste are difficult to dispose and the disposal of these materials are costly. Hence, these industrial wastes were used in the construction material for construction purpose. This project reports on experimental investigation of wall panels and slab panels constructed by Ferro-cement techniques using industrial waste like copper slag, fly ash and gypsum. III. MATERIALS AND METHODOLOGY The materials used for preparation of Ferrocement Elements for each test, namely, cement, fine aggregate, water and wire mesh were tested in the laboratory as per relevant IS codes Cement The cement used in this study is Ordinary Portland cement of 43 grade conforming to IS:4031-1988.. It is fresh and free from lumps in order to provide adequate strength, density and uniform consistency.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) A. Fine Aggregate Normal weight fine aggregate which is clean, hard, sharp and free from organic impurities and deleterious substances is used. It is well graded and capable of passing a 2.36mm sieve.

IV. EXPERIMENTAL PROGRAM A. Geometry of the specimens: The tested ferrocement elements consist of plain panel and folded panel. The dimensions of the folded and plain panels are shown in fig. (1) and fig (2) .The panel dimensions for both the panels are equal to 3000 ×2700 mm, the thickness of all the elements is equal to 30mm. In handling the folded panel without wire mesh, it failed along the longitudinal folds after removing it from the mold so it has been excluded for the test results. The panels are constructed using the conventional ferrocement materials, which is composed of cement mortar and square wire meshes.

B. Water In ferrocement, water used for mixing cement mortar is fresh, clean and fit for construction purposes. The water of pH equal to or greater than 7 and is free from organic matter, silt, oil, chloride and acidic material. C. Mortar matrix The chemical composition of cement, nature of fine aggregates (sand), aggregate-cement ratio, and watercement ratio are the major parameters governing the properties of cement mortar mix. The mortar matrix is designed for its appropriate strength, maximum denseness and impermeability, with sufficient workability to minimize voids and to avoid map cracking. Cement mortar used in ferrocement acts as a good insulator and the reinforcing wire mesh can reduce surface spelling better than plain concrete. The cement mortar in the ratio of 1:3 with a water-cement ratio of 0.45 is adopted.

Sl. No

Table 1. Test result of ordinary Portland cement As per IS Properties Test Results 12269-1987

1

Normal Consistency (In %)

30

2

Specific Gravity

3.08

Setting Time (in Minutes) 3

22-35

Not less than 30 minutes

a) Initial Setting Time

95 Min

b) Final Setting time

490 Min

Not more than 600 minutes

Compressive Strength(MPa)

4

Not less than 16Mpa

(70.6*70.6*70.6mm Cubes) 3 days strength

22.5MPa

7 days strength

26.8MPa

14 days strength

31 MPa

5

Fineness of cement

4.25 percent

6

Soundness

5.00 mm

Not less than 22Mpa

Fig. 1 Cross-section of the Plain ferrocement

Fig.2 Cross-section of the folded ferrocement panel

B. Testing process The ferrocement folded plate wall and plain panel dimensions are 3000 x 2700 and the thickness of 30 mm preparation of the reinforcing steel mesh as per the design, i.e., with a spacing of 200mm c\c both in vertical and horizontal direction is to be done. Binding is carried out with the help of binding wire. Each sample is molded after fixing the two layered chicken wire mesh in central position through the thickness of the sample. Although it is not easy, particularly for the folded panels, special care and effort has been taken to maintain a uniform distribution of the wire meshes throughout the thickness of the panel Plastering of the plates are carried out in two stages, In the first stage thin layer of cement mortar paste is applied over the steel mesh, followed by two days of curing In the second stage, both the front and back surfaces are to be finished as per required specification in two consecutive days to get the structural specimen of the cross-sectional and Carry the test by applying the load simultaneously by two jacks at a load interval of 10kN, and record the deformation readings with respect to loads till first crack appears on the plate. Then proceed with the procedure till the plate get crushed load is applied in small increments and simultaneously the deflection at the center of the panel was recorded during the loading process up to failure. The deflection at mid span is measured by a dial gage having accuracy equal to 0.01mm. Cracking was carefully checked throughout the loading process and the corresponding cracking load is also noted

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Fig. 6 Cracking on plain and folded panels

Fig.3 Setup of the wire meshing of plain and folded panels

V. RESULTS AND DISCUSSION Using manually operated jacks two concentrated loads are gradually applied on top surface of the panels, corresponding deflections are measured, loads and corresponding deflections of plain panel are tabulated in table 2 , load verses deflection are shown in graph 1. loads and corresponding deflections of folded panel are tabulated in table 3, load verses deflection are shown in graph 2. Graph 3 shows the comparisons of both the panels. The results indicating the ultimate load, maximum deflection, number of cracks and flexural strength were presented below.

Table 2. Test result of Plain plate Sl No 1 1 2 3 4 5 6 7 8 9 10 11 12

Fig.4 Experimental setup of the plastering work of plain and folded panels.

Fig.5 Experimental setup of the tested plain and folded panels.

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Load in kN 0 10 20 30 40 50 60 70 80 90 100 110 120

Defection In mm 0 3 5 6.6 7.4 8.7 9 9.5 10 12.6 13 18 22

Table 3. Test result of folded plate Sl no l 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Load in KN 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 250 300 350 400 450 500


Deflection in mm 0 0.6 0.9 1.5 2.0 2.2 2.9 3.2 4 4.15 4.3 4.8 5.0 5.2 5.7 5.95 6.0 6.3 6.5 6.7 6.8 7.1 7.3 7.5 7.8 8.0 8.1

Impact of Global Atmospheric Changes on Natural Resources (IGACNR) 28 29 30 31 32 33

550 600 650 700 750 800

8.3 8.7 9.0 9.1 9.2 9.5

1000 800 600 400

Load in kN

plain

200

Graph 1 load deflection curve of plain panel

folded

0

140 120 100 80 60 40 20

VI. CONCLUSION

0 0

5

10

15

20

25

Deflection in mm

• Based on the above experimental results it can be concluded that the load caring capacity of folded panel is 6.67 times of plain panels. • Comparing the strength of folded panel is 5.36 times of plain panel . • Almost the plain panel fails at a self weight.

Graph 2 load deflection curve of folded panel

• The deflection of folded panel observes 2.3 times less to plain panel , it shows the folded panel is 2.3 times stiffer than plain panel. • From the graph it shows that the folded panel buckling is not started even after failure . • Finally the folded plate have more stiffness and less displacement compare to plain panel , so folded panels can be used as load bearing wall .

REFERENCES

Graph 3 Comparison of plain plate and folded plate

1. Jain, A.K., "Ferrocement Folded Plate Roofing Industrial Sheds", India Concrete Journal, Vol. 55, No. 6, June 1981, pp. 146-149 2. ASTM C 150, "Standard Specification for Portland Cement", American Society for Testing and materials, west Conshohocken, Pennsylvania 1989 3. Shaheen Y.B.I, Eltaly B and Kameel M, (2013), “Experimental and analytical investigation of ferrocement water pipe” , Journal of Civil Engineering and Construction Technology, Vol. 4(4), pp. 157-167, May, 2013. 4. Randhir J. Phalke and Darshan G. Gaidhankar, (2014), “ Flexural Behaviour of Ferrocement Slab Panels Using Welded Square Mesh by Incorporating Steel Fibres”, International Journal of Research in Engineering and Technology , Vol-03, Issue-05. 5. Prof Dr. Ing Martin Trautz, “The Application of Folded Plate Principles on Spatial Structures with Regular, Irregular and Free-Form Geometry”, 2009. 6. Dr.T.ChandraSekharRao, Dr.T.D.GunneswaraRao, Dr.N.V.RamanaRao, Ch.Rambabu, “An experimental study on ferrocement Box-beams under flexural loading”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 2, Issue 9, September 2012 7. Naveen G.M, Suresh G.S, “ Experimental study on light weight ferrocement beam under monotonic and repeated flexural loading”, International journal of civil and structural engineering ,volume 3, no 2, 2012, ISSN 0976 – 4399. .

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Flexural Behaviour of Post Tensioned Beam Using Silica Fuma as Mineral Admixture N.Jayaramappa1, Rajesha R N2, Ankith M3 1

Assoc. Professof, 2,3M.E Student, Department of Civil Engineering, UVCE, Bangalore University, Bangalore. 1 [email protected], [email protected], [email protected]

Abstract—An experimental study has been undertaken to investigate the Flexural behaviour of post tensioned prestress concrete beams. The main objective of this work is to study the deflection, initial load carrying capacity, ultimate load carrying capacity and flexural behavior of the beam. With increase in the innovation of concrete and prestressed members, the whole construction industry is heading towards a new era. The main study is to Investigate the role of Admixtures in the concrete under flexural loading for post tensioned beams. Deflection and Flexural Behaviour of post Tensioned Beam using Admixtures to concrete. Three Beams were casted for this study with M60 Concrete. In that Type-1 is the Conventional Concrete and Type-3 is replacement of 5 % of cement with Silica Fume. Both beams are post tensioned with required prestressing forces according to the Design. The beam dimensions are 400 mm width, 300mm depth and 2000mm length. Flexural Strength or Modulus of Rupture and deflection are measured using Flexural strength Testing machine. The Deflection, Cracking load, Ultimate load and Failure loads are recorded. From the results, Load v/s deflection graph are plotted and the results are compared. Conclusion is draw based on the experimental results obtained. Keywords: Silica Fume, Post Tensioned, Prestressed Forces, Cracking Load, Ultimate Load. I.

INTRODUCTION

Post-tensioning is a method of reinforcing (strengthening) concrete or other materials with highstrength steel strands or bars, typically referred to as tendons. Prestressed concrete has emerged very quickly as predominant material in field of construction industry. This method of reinforcing concrete enables a designer to take advantage of the considerable benefits provided by prestressed concrete while retaining the flexibility afforded by the cast-in-place method of building concrete structures. Admixtures are added to concrete with replacement of cement to increase the strength and efficiency. In the following research the three post tension beams are designed with different admixtures such 20% replacement of Silica fume and conventional concrete of M60. In the present research paper the use of Silica Fume in the post tension beam are studied in detail. Silica fume is a by-product of producing silicon metal or ferrosilicon alloys. One of the most beneficial uses for silica fume is in concrete. Because of its chemical and physical properties, it is a very reactive pozzolana. A silica fume to be added in the concrete is only about 5 – 15% to that of volume of cement.

II.

LITERATURE

There are number of investigation carried out on the flexural behavior of the post tensioned beam and their parameters such as deflection, ultimate load carrying capacity and initial cracking load are also studied. Andrea Dall’Asta., et.al. Carried out the research on the flexural strength of reinforced concrete beams prestressed by external tendons. From this, it has been confirmed that the flexural strength of the post tension beam depends on the deformation profile of the tendons. Gouda Ghanem., et.al. Presented paper on the flexural behaviour of strengthened RC beams using external post-tensioning technique under the effect of cyclic loads. Finally, the study stated that the ultimate loading capacity of the post tensioned beam is 50% more than non-strengthen beam. Hamid Saadatmanesh, et.al. Have conducted experimental research on behaviour of prestressed, composite steel-concrete beams. From this, it is concluded that adding prestressed bars to composite beams significantly increased the yield load and the ultimate load. III.

MATERIALS AND METHODOLOGY

Cement: Ordinary Portland cement of brand Birla super of grade 53 with specific gravity 3.2 and fineness 0f 2.83% conforming as per IS-12269:1987. Fine Aggregates: locally available river sand was used by conforming to zone II. The specific gravity was 2.65 and bulk density was 1736 kg/m3. Coarse Aggregate: locally available crushed coarse aggregate was used of 20mm and 12mm. the specific gravity was 2.63 and bulk density 1629 kg/m3. Silica fume: Dry powder available from Ultra Tech Aditya Birla was used. The specific gravity 2.26. Reinforcement steel: 12mm diameter of 4 bars were used in each beam with 200mm C/C of 10mm diameter bars of Fe 415 was used. Tendon: 15.4mm single tendon was adopted in the post tensioned beams. Water: Potable water with PH of 6.5 was used for mixing and curing purpose. IV.

DETAILS OF BEAM

The post tension beam is designed as per IS: 1343-1980. The live load considered for this study is 50kN/m2. The design details of all three post tension beam are as given below in the table 1.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Table-1: Details of the beam design. Mix designation

Dimension of beam(mm)

Conventional concrete 5% Silica fume +Cement

Main Steel (dia in mm)

Stirrups (dia in mm)

400x300

4 – 12#

400x300

4 – 12#

10# 2LVS @ 200mm C/C 10# 2LVS @ 200mm C/C

Length

Spacing

(mm)

(mm)

2000

200mm c/c

2000

200mm c/c

VII. RESULTS AND DISCUSSION Testing of the hardened concrete place an important role in controlling and confirming the quality of cement concrete works. The purpose of testing the hardened concrete is to know the development strength of the concrete. The tests are made by casting cubes and cylinders from the respective concrete mix before casting of beams. 1.

Mechanical strength

Properties

of concrete

Compressive

Figure-1: Detailing of Beam.

Conventional concrete beam C + Silica Fume

VI.

PRESTRESSING DETAILS Pre-stressing Force 358 kN

Losses 0.030 %

509.4 kN

0.021%

Figure-3: Compressive strength chart.

CASTING OF SPECIMEN

Three post tensioned beams are named sequentially as type-1 conventional concrete and type-3 5% silica fume +cement of size 400mm x300mm x2000mm were casted. The moulded were coated initially with oil so as to easy removal of the moulds. The moulds were removed after 24 hours of duration and curing of specimen was carried out for 28 days continuously using gunny bags.

2.

Split tensile strength

Splite Tensile Strength(N/mm2)

V.

10 9 8 7 6 5 4 3 2 1 0

M60 Conventional concrete M60 5% replacement of cement with silica fume

3 Days

7 Days

28 Days

3.185

6.01

8.64

3.755

6.81

9.3

Figure-4: Split tensile strength.

Figure-2: Experiment Setup.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) 3.

Flexural strength Table-3: Flexural Strength test Result.

Mix Designation

Initial cracking load (kN)

Ultimate load (kN)

Deflection (mm)

Flexural strength (N/mm2)

Conventional concrete

500

1000

0.6

55.55

GGBS + Cement Post Tensioned Beam

800

1500

0.8

83.33

Silica Fume + Cement Post Tensioned Beam

900

1800

1

100

4.

Load deflection behavior

The load deflection curve for the testes beams are illustrated in the graph below.

beam with cement and silica fume as cementatious material shows higher displacement ductility of 20% more than controlled specimens. Based on the above conclusions on Flexural behaviour of beam with different concrete matrices, it can be stated that post tension beam with 5% Silica Fume is the most efficient of all the specimens.

REFERENCES

Figure-5: Comparison of Load vs Deflection. One can state from the above figure 1.6 that the load taken by beam with silica fume is 1800 kN for a deflection of 1.0 mm is more than other two beams. The deflection of CCB is very high compared to the other two beams with very less load. VIII. CONCLUSION 1.

2.

3.

Among the mixes in the present investigation Conventional Concrete Beam and 5% Silica Fume + Cement Concrete Beam the compressive strength obtained at 28 days are 58.85 N/mm2 and 65.86 N/mm2 respectively. It is observed that 5% replacement of cement increases the Compressive Strength by 5% when compared to Conventional Concrete Beam. The result shows an increase in the compressive strength of the mixes with addition of admixture. The split tensile strength of the different mixes Conventional Concrete Beam and 5% Silica Fume + Cement Concrete Beam obtained at 28 days are 8.64 N/mm2 and 9.30 N/mm2 respectively. The ductility of beams is increasing with the replacement of cement by mineral admixtures. The

[ 1 ] Andrea Dall’Asta; Laura Ragni; Alessandra Zona; “Simplified Method for Failure Analysis of Concrete Beams Prestressed with External Tendons”. Journal of structural Engineering, Vol 133 Issue 1- Jan 2007. [ 2 ] Gounda Ghanem; Sayed Abd El-Bakey; Tarek Ali; Samah Yehia; “Behavior of RC Beams Retrofitted/Strengthened With External Post-Tension System”. International Journal of Civil, Mechanical and Energy Science (IJCMES), Vol 2 issue 2455-5304 Jan 2016. [ 3 ] M. Z Cohn; Z. Lounis; “Optimum Limit Design of Continous Prestressed Concrete Beam”. Journal of Structural Engineering, Vol 119 Issue 12 Dec 1993. [ 4 ] Wight. R.G; Green M F; Erki M A; “Prestrssed FRP Sheet For Post Strength Reinforced Concrete Beam”.Journal of Composities For Construction, Vol 5 Issue 1943-5614. [ 5 ] Hamid Saadatmanesh; Pedro Albrecht; Bilal M Ayyub; “Experimental Study of Prestressed Composite Beams”. Journal of Structural Engineering, Vol 115 Issue 9 Sept 1989. [ 6 ] Kenneth W; Shushkewich; “Moment – curvature Relationships for Partially Prestresses Concrete Beam” Journal of structural engineering, Vol 116 Issue 10 Oct 1990. [ 7 ] Mohsen EI Shashawi; Barrington Dev; “Fatigue of Partially Prestressed Concrete”, Journal of Structural Engineering, Vol 112 Issue 3 Mar 1986.

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Performance of Steel and Polypropylene Fiber Square RC Slabs Subjected to Static and Impact Loading Metireddy Sai Kiran Reddy1, Kiran T2 1

ME Structural Engineering Student, Department of Civil Engg, UVCE, Bangalore University, Bengaluru. 2 Associate Professor, Department of Civil Engg, UVCE, Bangalore University, Bengaluru.

Abstract : Worldwide, a great deal of research is currently being conducted concerning the use of fibers in concrete to improve the strength of Reinforced Concrete Members. Addition of Fibers to Concrete increases tensile strength and durability of concrete. Hence a research project has been undertaken to investigate the role of fibers in static and impact of slabs using M30 concrete. Strength properties such as 28 days compressive strength and flexural Strength are determined before performing the casting of slabs in order to confirm the design mix and characteristic compressive and flexural strength of concrete. Eight slabs are considered for this study in which 4 slabs are tested for static load and 4 slabs are tested under impact loading. Two types offibers are considered i.e. 1. Polypropylene fiber with a dosage of 900gm per volume of concrete and 2.Steel fiber with volume fraction 0.65% of concrete. The slab dimensions considered for this study is 600 mm X 600 mm with a thickness of 50 mm. static test is performed using a man operated hydraulic jack for the application and LVDT’s are placed at the bottom of slab to record deflections. Using the experimental data, load – deflection, first crack loads and punching shear strength of slabs are obtained. And drop weight impacttest is conducted to know the behavior of slabs, to obtain load deflection variation, no. of blows at first crack, no. of blows at final crack and energy absorption capacity slabs. Final conclusions are drawn on the basis of experimental output.

RCC Slabs when subjected to Static and Impact loading. An emphasis has been given to Strength and Deformation of Fiberreinforced Concrete Slabs. The load deflection response, first crack load, ultimate punching shear Strength, Ductility index and toughness index were evaluated in Static Studies. Load time histories, Displacement time histories and energy absorption capacity were evaluated in Impact Studies.Use of high concentration of chemical admixtures in concrete have adverse effects on environment. Annually large quantity of Industrial waste is generated. Use of fibers generated from such waste helps in reducing such waste and also helps in increasing strength of concrete. 1.1 CONSTRUCTION MATERIALS Ordinary portland cement of type 1 is used in the concrete. The maximum size of coarse aggregate is about 12mm. Fine aggregate had a finess modulus of 1.80.Eight bars of 8mm dia apiece are used as main reinforcement in both the directions as it was two way slab. M 30 concrete is considered for the Experimental Programme. The mix design had ratios of 1:1.5:3 of cement, sand and aggregates with a water cement ratio of 0.5. GGBS was used as mineral admixture. L20 Hypercrete was used as an Chemical admixture.

Key Words: Controlled specimens, polypropylene fiber, Steel fiber, Static test, Impact test. I.

1.2 FIBERS USED IN CONCRETE

INTRODUCTION

In the construction industry, concrete technology is heading towards an entirely new era by wayof using polymers and fibers in concrete. Increasing interest is being shown in the area of new materials in the past two decades. This is quite understandable because, it is slowly, but increasingly being recognized that economic progress in construction depends more on an intelligent use of the materials and constant improvement of available materials than on extreme refinements of structural analysis. It has been understood fromthe literature that many of engineering properties such on tensile strength, compressive strength, flexural strength, fracture toughness, energyabsorption capacity, impact resistance etc., of the conventional concrete could be improved by the addition of fibers and materials like latex on the static behavior over conventional concrete. Fiber reinforced concrete (FRC) is concrete containing fibrous material which increases its structural integrity. It contains short discrete fibers that are uniformly distributed and randomly oriented. Fibers include steel fibers, glass fibers, synthetic fibers and natural fibers. Within these different fibers that character of fiber reinforced concrete changes with varying concretes, fiber materials, geometries, distribution, orientation and densities.

Steel and Polypropylene fibers were used in this experimental programme. The properties of the fibers are shown in table below. Table 1 : Properties of Steel and Polypropylene fibers Fiber Name

Steel Fiber

Tensile Strength (MPa) Youngs Modulus (GPa) Ultimate Elongation % Specific Gravity Length, mm Diameter, mm

280-2800

Polypropylene fiber 560-770

203

3.5

0.5-3.5

25

7.8 36 0.45

0.9 24 0.01

The dosage of Polypropylene fiber was limited to 900g/m3 and steel fiber was of 0.65% volume fraction.

The Present Studyevaluates the performance of Fiber reinforced

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) 1.3 CASTING OF SLABS The steel reinforcements were cut on-site to the required length and mesh was prepared using binding wires as per requirements. Welded steel moulds of required dimension which were already present at the University were used. The mould was lubricated with oil before casting. The reinforcement mess was thenplaced in the formwork, taking care to provide the required clear cover on the bottom and sides. The Concrete was mixed using a small rotary mixer and shovelled into formwork, and vibrator was used to minimize air voids in slabs. Four sabs were casted at a time .The details of Slabs casted are given in table below. Table -2: Details of Slab specimens Sl. no

Designa tion

1

CS

2 3 4

CS + PF CS+SF CS + PF + SF

Slabs (600*600*50)mm

No of Specimens

RC Slabs with no fiber

2

RC Slabs with polypropylene fiber

2

RC Slabs with steel fiber RC Slabs with polypropylene and steel fiber

2 2

3 cubes of 150by 150 mm were casted for each type to study compressive strength of each specimen and 3 prisms were casted to study flexural strength of each specimen. 1.4 TESTING

1.5 CONCRETE PROPERTIES Compressive Strengths are obtained for 7 days and 28 days by crushing 3 150×150×150 mm cubes for each type of concrete mix.

All the Specimen were tested at Structural Engineering Labaratoryof UNIVERSITY VISVESWARAYYA COLLEGE OF ENGINEERING, BENGALURU. A static test setup is fabricated for the test programme, which should have relatively stiff supports and the hydraulic loading assembly is fixed to the supporting frame. Since the study is mainly aimed at evaluation of static response due to load, as also the cumulative effect of such loading on the concrete slab elements, a hydraulic jack type loading has been employed for static test. The impact testing machine used in the present investigation is of low velocity drop weight type instrumented impact testing machine. In impact test the impact load was applied by means of the free fall of a drop weight from 1m height. For free fall the drop weight was arranged, to fall between the guided cylinder and impact the specimen at midpoint. The drop weight had a steel circular load cell of diameter 5.5cm.

Chart -1: Compressive strength of cubes of different mix. From above graph, it is concluded that the combination of M30+PF+SF has highest compressive strength i.e, 37Mpa compared to others. The compressive strengthof M30+PF+SF is maximum and increases by 14.2% as compared to CS (M30). Flexural Strength is Obtained for 7 days and 28 days bytesting 3 100×100×500 mm prisms for each type of Concrete mix.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Table 3 : First crack Loads Sl No 01 02 03 04

Name of the slab M30 M30+PF M30+SF M30+SF+PF

First Crack Load kN Experimental Theoretical 7.022 15.35 8.962 15.61 12.280 16.02 14.923 16.42

E/T Ratio 0.45 0.57 0.76 0.90

Punching Shear Strength The term ultimate load is the maximum load which produces actual failure for the member consideration. The theoretical and experimental ultimate crack loads are tabulated as shown in Table 4.

Chart 2 : Flexural strength of cubes of different mix. From above graph, it is concluded that the combination of M30+PF+SF has highest flexural strength i.e, 9.24Mpa compared to others. The flexural strength of M30+PF+SF is maximum and increases by 49.51% as compared to CS (M30). II.

Table 4: Punching shear strength

EXPERIMENTAL RESULTS Static Results The behavior of fiber reinforced concrete test slab specimens under static loading for fixed end condition are observed from the load-deflection curve on the following parameters: deflection, first cracking loads and ultimate punching shear strength. The experimental cracking moments and cracking loads are compared with corresponding moments and loads calculated as per IS: 4562000

Sl No

Name of the slab

Punching shear strength kN

01 02 03 04

M30 M30+PF M30+SF M30+SF+PF

26.474 32.424 44.74 54.24

Experimental Theoretical 20.007 20.329 20.854 21.380

E/T Ratio 1.323 1.594 2.145 2.536

Impact Results Impact test results of 4 slabs with different combination of fibers and a conventional slab are given in tables 5 and 6. Tests results from tables 5 and 6 below presents the details of blows required to cause initial and ultimate crack at failure where ultimate crack at failure was noted when the reinforcement was visible from bottom of the slab. Table 5 : No of blows for First Crack Specimen number 1 2 3 4

Slab designation CS(M30) CS+PF CS+SF CS+PF+SF

No. of blows at first crack 3 3 5 4

Chart 3: Combined load-Deflection graphs of all slab test specimens It is observed from Figure 3, the load carrying capacity of slab with polypropylene and steel fiber (M30+PF+SF) is 54.24 kN whereas control specimen is 26.474 kN load followed by M30+PF and M30+SF respectively. First Crack Load The first crack load is also referred as yield load and can be used as a primary factor for design of Structures. The stage atwhich an appearance offirst crack within elastic limit occurs is called first crack load. The theoretical and experimental first crack loads are toabulated below. Chart 4: No. of Blows at First crack for test slab specimens

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Table 6: No. of blows at ultimate crack at failure slabs. Specimen number 1 2 3 4

Slab designation CS(M30) CS+PF CS+SF CS+PF+SF

No. of blows at ultimate crack 6 9 12 16

CONCLUSIONS 1) The 28 days compressive strength of cubes with polypropylene fibre increased by 3.24% and that of cubes with steel fibres is increased by 8 % and that of both Polypropylene and steel fibres is increased by 14.2% in comparison with the cubes with no fibres. Therefore it can be concluded that Compressive Strength of Concrete can be increased by addition of fibre. 2) The flexural strength of prisms is more in case ofspecimens with fibre addition. Addition of polypropylene and steel fibres increases flexural strength by 32% and 41% respectively whereas addition of both fibres increases flexural strength by 49.51%. 3) Load carrying capacity of Slabs is increased with the addition of fibres. Slabs with Steel fibre have more punching strength compared to that of slabs with polypropylene fibres. And combination of both polypropylene and steel fibre is more effective in preventing slab from punching.

Chart 4 : No. of Blows at Ultimate crack for test slab specimens 4) In comparing the fibre variation the load carrying capacity of slabs with SF and slabs with PF + SF are almost same therefore it can be concluded that SF fibres itself are efficient under impact.

Energy Absorption Capacity of Slabs Energy Absorption Capacity of Slabs is calculated by the formula;

5) The energy absorption capacity of slabs is increases with the addition of fibre and the slab with polypropylene and steel fibre is good in energy absorption.

E = N×(W×h) joules Where , E is the energy absorbed in joules W is weight of hammer in Newton, H is the height of drop in meter N is the no. of impact blows Here: h = 1 m and W = 10.2 kg

REFERENCES

1.

Milind V. Mohod “Performance of Steel Fiber Reinforced Concrete” International Journal of Engineering and Science” vol.1, Issue12 December2012, pp01-04. ISSN:2278-4721.

2. Table 7 : Energy absorption capacity of slabs at ultimate failure

Shende. A.M., Pande. A.M and M.Gulfam Pathan “Experimental Study on Steel Fiber Reinforced Concrete M-40 grade” International Refereed Journal of Engineering and Science (IRJES), ISSN 2319-183X, Volume 1, Issue 1, September 2012, PP 043-048.

3.

Trevor D Hrynyk and Frank J. Vecchio (2014) “ Behaviour of Steel Fibre reinforced concrete slabs under Impact Loads” ACI Structural journal Volume 111, No -5, pp-1213-1224.

FIBRE COMBINATION

4.

Parveen1, Ankit Sharma “Structural Behaviour of Fibrous Concrete Using Polypropylene Fibres” International Journal of Modern Engineering Research (IJMER),Vol.3, Issue.3, May-June. 2013 pp-1279-1282 ISSN: 2249-6645.

5.

V. Ramakrishna, S.P.Gollapude and R.C. Zellers. “Performance Characteristics and Fatigue strength of Polypropylene fibre Reinforced concrete”. ACI Special Publications.Vol.105, PP: 159178.

ENERGY ABSORPTION IN CONTROLLED SLAB

CS+PF

ENERGY ABSORPTION BY FIBRE REINFORCED SLABS 900.558

600.372 CS+SF

1200.744

CS+PF+SF

1609.920

It can be observed from the above table that the energy absorption capacity of slabs increases with addition of fibres compared to that of slabs without fibre. And the slabs with both polypropylene and steel fibre have very high energy absorption capacity compared to the CS slabs, CS + PF slabs and CS+SF

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Static and Impact Behaviour of RCC Slabs With and Without Steel and Nylon Fibers Mr. Prashantha H M1, Kiran T2 1

Post Graduate Student, 2Associate Professor, Dept. of Civil Engineering, UVCE Bengaluru 56..

Abstract— a research project has been undertaken to investigate the role of fibers in static and impact of slabs using M30 concrete. Strength properties such as 28 days compressive strength of cubes are determined before performing the casting of slabs in order to confirm the design mix and characteristic compressive strength of concrete. Eight slabs are considered for this study In that 4 slabs are tested for static load and 4 slabs are tested under impact loading. Two type’s fibers are considered i.e. Nylon fiber with a dosage of 0.2% weight of cement and Steel fiber with volume fraction 0.65% of concrete. Structural improvements provided by steel fibers and the resistance to plastic shrinkage improvements provided by nylon fibers. The slab dimensions considered for this study is 600 mm X 600 mm with thickness of 50 mm. static test is performed using a man operated hydraulic jack for the application and LVDT’s are placed at the bottom of slab to record deflections. Using the experimental data, load – deflection, ductility index and toughness index of slabs are obtained. And drop weight impact test is conducted to know the behavior of slabs, to obtain load deflection variation and time histories of slab (Load – Time, Deflection – Time) and energy absorption capacity of slabs. Final conclusions are drawn on the basis of experimentaloutput.

Keywords— Controlled specimens, Nylon fiber, Steel fiber, Static test, Impact test. I.

INTRODUCTION

Concrete is the most widely used construction material in India. It is well known that conventional concrete designed on the basis of compressive strength does not meet many functional requirements such as impermeability, resistance to frost, thermal cracking adequately. The India uses about 7.3 million cubic meters of ready-mixed concrete each year. Engineers are continually working on it, to improve its performance with the help of innovative supplementary or replacement materials. Extensive research work on FRC during the last two decades has established that combination of two or more types of fibres such as metallic and nonmetallic fibers increase overall performances of concrete. The Concrete is weak in tension and has a brittle character. The concept of using fibers to improve the characteristics of construction materials is very old. Use of continuous reinforcement in concrete (reinforced concrete) increases strength and ductility, but requires careful placement and labour skill. Alternatively, introduction of fibers in discrete form in plain or reinforced concrete may provide a better solution. The factors influencing the properties of FRC are types of fibre, material, geometrical properties and orientation properties of the cement-based composites. Addition of fibers to concrete makes it a homogeneous and isotropic material. When concrete cracks, the randomly oriented fibers start functioning, arrest crack formation and propagation, and thus improve strength and

ductility. The failure modes of FRC are either bond failure between fiber and matrix or material failure. This project shows the investigation on mechanical properties of Fiber Reinforced concrete. In this project I have selected two different fibers (nylon and steel fiber). Steel fiber is selected for the reasons to improve static and dynamic tensile strength, energy absorbing capacity and better fatigue strength. Nylon fiber is selected as a replacement material to increase the tensile property of concrete and also it lowest density and light weight material in the fiber. However all the civil engineering structure is part of the global atmosphere, the problems caused by the structures are directly related to the environment. .Replacement of chemical admixture we use by product of metallic and nonmetallic fibers to increase the strength. II.

LITERATURE

A O Baarimah and S M Syed Mohsin “Investigates the potential effect of steel fibre added into reinforced concrete slabs.” Four-point bending test is conducted on six slabs to investigate the structural behaviour of the slabs by considering two different parameters; (i) thickness of slab (ii) volume fraction of steel fibre. Both series of slabs are added with steel fibre with a volume fraction of Vf = 0%, Vf = 1% and Vf = 2% in order to study the effect and potential of fibre to compensate the loss in shear capacity. The experimental result suggests promising improvement of the load carrying capacity (up to 32%) and ductility (up to 87%) as well as delayed in crack propagation for the slabs with Vf = 2%. In addition, it is observed that addition of fibres compensates the reduction in the slab thickness as well as changes the failure mode of the slab from brittle to a more ductile manner. 1.

Jaya Saxena and anil saxena “Enhancement the Strength of Conventional Concrete by using Nylon Fibre.” This study focuses on the fabrication and the Maximum Moment Capacity of a Ferrocement beam. There were three batches with 3 specimens each. The beams were casted vertically by plastering. This study used a cement to sand ratio of 1:3 by volume, and a water to cement ratio of 0.5:1 by weight. It also used two layers of # 16 gage wire mesh kept constant on each batch. Tension bars of 8 mm dia. were used, the number of which increases by one on each batch. Nine specimens of 200mm x 200mm x 3000mm hollow box beam with a 25 mm thickness were casted. The testing of the beam was done after the 28th curing day period, and was conducted to failure in order to determine the Actual Moment Capacity of the design beam. The results show that Maximum Moment Capacity or Flexural Strength of the 2.

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) fabricated Ferrocement beams did not go below the calculated ultimate moment capacity for office occupancy of 5.3792 KN-m. This means that the beams are safe for use as floor joist beams in residential and commercial structures. Katrin Habel et al “experimental investigation on the performance characteristics of fibre reinforced concrete.” studied the response of ultra-high performance fibre reinforced concrete (UHPFRC) under static and impact loading. There are four-point bending response was determined on plates subjected quasi static loading. The static behaviour of UHPFRC was determined in bending on thirteen UHPFRC plate specimens, each plate had size of 600 mm x 145 mm x 50 mm. The specimens were tested under displacement controlled 1000 kN capacity Universal Testing Machine at a loading rate of 6 x 10-6 m/sec. They observed from the experiments that under quasi static loading, the UHPFRC considered in their study exhibited strain hardening to an elongation of 0.15% and a tensile strength of 11 MPa. Multiple crack was observed in the high moment region and final fracture occurred by fibre pull out in one localized bending crack at the centre of the specimen. 3.

III.

2386-1963 to determine Specific gravity, Bulk density and Fineness modulus. Table -2: physical properties of coarse aggregate Sl. No. Particulars Observations 1 Fineness Modulus 4.2 2 Specific Gravity 2.63 3. Fine aggregate Fine aggregate generally consists of natural sand or crushed stone with most particles passing through 9.5mm sieve. The code to be referred to understand the specification for the fine aggregate is IS: 383-1970. Table -3: physical properties of fineaggregate Sl. No. Particular Of Test Results 1 Fineness Modulus 2.3 2 Specific Gravity 2.70 4.

MATERIALS

Nylon fiber The fiber used in the experiment was purchased from A.H Industrial polymer. The fibers used were of length of 10 to 12mm. The dosage of the nylon fiber was limited to 2% of weight of cementitious material. The physical properties of Nylon fiber are tabulated below.

Concrete is the most commonly used construction material worldwide. Concrete is relatively strong in compression and weak in tension and tends to be brittle in behaviour. The weakness in tension can be overcome by providing steel bars and to some extent by the mixing of a sufficient volume of certain fibers. The fibers should not be used as primary reinforcement of concrete but only used as secondary reinforcemet. Cement In this present investigation Ordinary Portland Cement of 53 Grade with a brand name zuari cement used. Tests are conducted in accordance with the Indian standards confirming to IS-12269:1987. Physical properties are mentioned table in below.

Table -4: physical properties of Nylon fiber Fibre Type Nylon 6

1.

Sl. No. 1 2 3 4

2.

Table -1: physical properties of cement Test Requirements Results as per IS12269 : 1987 Specific 3.15 3.20 Gravity Fineness of Less than 10% 6.15% cement Standard Not Specified 29% Consistency Compressive Shall not be less 45.2 MPa Strength than 37.0 MPa (MPa) 7 Days

Coarse aggregate Coarse aggregates passing 12.5 mm sieve size and retained on 10 mm sieve were used. The sieve analysis of combined aggregates confirms to the specifications of IS 383: 1970 for well graded aggregates. The tests on the coarse aggregate were conducted in accordance with IS

Tensile Strength

770-840 MPa

Modulus of elasticity

4.2

Elongation Specific gravity

16-20% 1.14

Melting point

263°C

5.

Steel fiber Crimped end steel fiber supplied by Stewols India pvt.Ltd Nagpur, Maharashtra is used for the present investigation with volume fraction Vf 0.65% of concrete. The physical properties of the fiber are given below. Table -5: physical properties of steel fiber Property Density Ultimate strength Modulus of elasticity Poissions ratio length Diameter

Specifications 7860 kg/m3 1500 Mpa 2 *105Mpa 0.28 36mm 0.45mm

6.

Water Potable water as obtained from water supply at Civil Engg. Dept. Jnana bharathi, was used for the preparation of concrete mix. The pH of water is 6.8 and all the other contents of water are as per Indian Standards.

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7.

Ground Granulated Blast Furnace (GGBS). GGBS is a dry powder and supplied by M/s Ultra Tech Aditya Birla Group India Pvt. Ltd, Bangalore is used in experimental program. 8.

Chemical admixtures (super plasticizer La Hypercrete S-20 supplied by lacrete Duraken, Rajajinagar, Bangaluru. IV.

METHDOLOGY

An experimental testing program consisting of Concrete, Nylon Fibre Reinforced Concrete and Nylon & Steel Fibre Reinforced Concrete slabs specimens subject to hand operated hydraulic loading was performed. Four specimens with fixed support boundary conditions were tested to failure under cumulative hydraulic loading conditions. Static is available to evaluate the load-time behaviour, loaddeflection, punching shear strength, ductility index, toughness index and also to study the crack pattern under static loading. Experimental simulation of slabs under sequential highmass, low velocity repeated impact loading to study the behaviour is complex and tedious. The present investigation is to understand the behaviour of reinforced concrete slabs of CS, NFRC, SFRC and (NSFRC) under repeated low velocity impact loading. Drop weight impact test and projectile impact test are the feasible techniques available to evaluate the load-time behaviour under impact loading. In slabs the major impact loading will be in the vertical direction. The tests are conducted for different combination of fibers. Here the tests based on the combination of different fibers are to check the punching shearing capacity of slabs due to the inclusion of fibers. The slabs used are of size 600 mm x 600 mm x50mm and the fibers used for the present investigation are of three combinations (i) Nylon fibers and (ii) steel fibers (iii) nylon + Steel fibers. The results obtained from the testing of slabs are produced in the form of graphs and compared with the reference slab (Controlled Specimen) to check the role of fibers in carrying the load. V.

RESULTS AND DISCUSSION

1. Test specimens 1) Compression test on standard cubes The test is conducted as per IS: 516-1959 (Reaffirmed 2008). The experimental compressive strength values obtained on standard cube specimen of size 150 mm x 150 mm x 150 mm, cured for 7 days and 28 days for all four different concrete matrices are presented in the form of graphs and bar charts shown in figure

Graph-1: Compressive strength of different concrete matrices.

2) Flexural test on standard prisms The test is conducted as per IS: 516-1959 (Reaffirmed 2008). The experimental flexural test for concrete employs standard prism specimens of size 100 mm x 100 mm x 500 mm, cured for 7 days and 28 days for all eight different concrete matrices are presented in the form of graphs and bar charts shown in figure

Graph-2: Flexural strength of different concrete matrices. 2. Static test result This section presents the observations made during static test on test slab specimens in the form of load-midpoint deflection, ductility index, and crack patterns at different stages of loading. 1) Load deflection curve (p - δ curve)

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Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Graph-3: load vs. deflection graph of all the slabs combined.

4) Toughness index

From the above chart it is observed that load vs. deflection curve of all the slabs. The ultimate load carrying capacity of M30+SF+NF specimen was 51.73 kN The above results shows the M30+SF+NF specimen carrying ultimate load carrying capacity compared to controlled specimens.

2) First crack load Sl No

Table -5: physical properties of steel fiber Name of the First crack load E/T ratio slab Expt(E) Theo(T)

Tough index if all slabs

01

CS (M30)

7.022

15.35

0.45

02

M30+NF

8.462

15.57

0.54

The modified concrete test slab specimen with m30+nf+sf has a stiffer response in terms of structural behaviour and higher toughness as compared to other test slab specimens.

03

M30+SF

12.280

16.02

0.76

5) Punching shear strength

04

M30+SF+NF

13.62

16.522

0.82

The first crack loads obtained for different concrete matrices are presented in Table 7.1. From this table it can be observed that the slab specimens with fibre reinforced concrete show higher first crack load than the control slab specimens. Among the concrete slab specimens, the specimen M30+NF+SF recorded a first crack load of 13.62 kN, which is highest among all the slab specimens. This is mainly due to higher volume fraction of fibres present in this slab. 3) Ductility index

Table -6: physical properties of steel fiber Ultimate Punching shear Exp./IS:456 loads in Pu kN -2000 Experimental IS:456-2000 values M30 26.474 20.007 1.323 M30+NF 30.680 20.270 1.512 M30+SF 44.74 20.854 2.145 M30+SF+NF 51.730 21.501 2.404 Slab specimens

3. Impact test result This section presents the observations made during static test on test slab specimens in the form of load-no of blows, energy absorption, and load-time history of specimens. 1)

Load vs no of blows The peak load of each slab specimen and their % increase of Strength with respect to control specimen are listed in Table.

Sl No 1 2 3 4

Ductility factor for all slabs The modified concrete test slab specimen with M30+NF+SF has a stiffer response in terms of structural behaviour and higher ductility as compared to other test slab specimens. 2)

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Table-7 peak loads of all slab specimens Slab Peak Load % increase Specimen CS (M30) 7.2 0 M30+NF 9.2 37.24 M30+SF 15.2 111.11 M30+NF+SF 16.8 133.33

Energy absorption capacity of slabs


Impact of Global Atmospheric Changes on Natural Resources (IGACNR) Table-8: Energy absorption of slabs Slab Specimen energy energy % increase absorption absorption in energy in CS by FRC absorption 704.34 17.31 CS+NF 600.372 1200.744 100.06 CS+SF CS+NF+SF

1408.68

134.63

It can be observed from the above table that the energy absorption capacity of slabs increases with addition of fibres compared to that of slabs without fibre. And the slabs with both nylon and steel fibre have very high energy absorption capacity compared to the CS slabs, CS + NF slabs and CS+SF slabs. VI.

CONCLUSIONS

The energy absorption capacity of slabs is increases with the addition of fiber and the slab with Steel and nylon fiber is good in energy absorption. VII.

Further studies are needed in both analytical and experimental areas to supplement the present investigation on static and impact on slabs. The following studies are suggested: [ 1 ] Slabs can be tested with different edge condition [ 2 ] The behaviour of slabs under impact can be studied by varying the drop weight of slabs. [ 3 ] Finite element analysis can be done using software like Ansys. [ 4 ] Studies can be extended for ferrocement and geopolymer concrete slabs

In the present investigation an attempt is been made to study the role of fibres on the static and behaviour of slabs considering eight slabs with various combination of fibres. 1)

Mechanical properties

The 28 days compressive strength of cubes with nylon and steel fiber combination is increased by 15.5 % in comparison with the cubes with no fibres. Therefore it can be concluded that the cubes with both fibres are very good in taking compressive strength. The flexural strength of prisms is more in case of specimen with steel fibre and nylon fibre i.e. 8.94 % compared to the controlled specimens. Therefore the combination of steel fibre and nylon fibre itself is good in taking the flexural load. 2)

Static testing of slabs

In comparing the variation of fibres the slabs load carrying capacity of slabs is increasing with inclusion of fibre the slabs which reinforced with steel and nylon fibre are taking more load than other slabs. Therefore steel and nylon fibre in combined are more effect in preventing slab from punching. As the reinforcement ratio increases the ductility of slab in increasing and also the ductility is due to the addition of fibres. The slab with more reinforcement ratio and with SF + NF ductility index is 9.8% more than that of controlled specimens. Toughness index of slabs have increased with increase in thickness of slab and also due to the addition of fibres. The toughness index of slabs with SF + NF is 37.53% more compared Controlled specimens. Hence CS + SF + NF slabs are very tough in taking load. 3)

SCOPE FOR FUTURE WORK

VIII.

REFERENCES

[1] Abdul Ghaffar and Amit S. Chavhan and Dr. R.S. Tatwawadi “Steel fibre reinforced concrete” International Journal of Engineering Trends and Technology (IJETT)- Volume 9, No 15, ISSN:2231-5381 March 2014. [2] A O Baarimah and S M Syed Mohsin “Behaviour of reinforced concrete slabs with steel fibers” Materials Science and Engineering 271 (2017) 012099. [3] K.Manikandan A.Arun kumar², M.Deepak kumar³, V.Manikandan)“Experimental Investigation on Nylon Fiber Reinforced Concrete”. International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 03 | Mar -2017 e-ISSN: 2395 -0056 p-ISSN: 2395-0072. [4] Dr. S.K. Verma and nitin “Effect on Mechanical Properties of Concrete Using Nylon Fibers”. International Research Journal of Engineering and Technology (IRJET) Volume: 03 Issue: 07 | July -2016 eISSN: 2395 -0056 p- ISSN: 2395-0072. [5] Arul Raj and Bhaskar K “Experimental Investigation on Flexural Behaviour of Steel Fibre and Nylon Fibre ReinforcedConcrete Beam” International Conference on Recent Trends in Civil Engineering, Technology and Management (ICRTCETM-2017– ISSN: 2348-8352. [6] Avraham, N, Dancygier, and Davi Z. Yankelevsky, "Effect of Reinforced Properties on Resistance to Hard Projectile lmpact,"ACI structural Journal, V. 96, No.2, 1999, pp.259-267.

Impact testing of slabs

In comparing the fibre variation the load carrying capacity of slabs with SF is 15.26KN, NF is 9.67KN and slabs with SF + NF is 19.85KN respectively. Here load carrying capacity of SF and SF+NF are nearly same. Hence SF is good for load carrying capacity.

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Compressive Strength of Concrete with and without Steel Fibers Under Different Curing Period Pushpalatha. N#1, Kiran. T*2. #

Post Graduate Student in Earthquake Engineering, Department of Civil Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bengaluru. * Associate Professor, Department of Civil Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bengaluru. [email protected] [email protected]

Abstract: Curing is the process of controlling the rate and extent of moisture loss from concrete during cement hydration. Curing of concrete is an empirical process with an assumption to supplement water to the hydration process of cement and to control the moisture movement from the concrete structure. A research project has been undertaken to investigate the variation in compressive strength of concrete with and without curing. Additional materials include the use of steel fibers into the concrete mix. With the used of steel fibers, it can enhance the strength of the concrete. Crimped end fiber with size 0.45mm dia 36mm length and the percentage of steel fiber 0. 65% used in this research. A series of twenty four cube specimens were cast for each mix in that nine cubes were cured for 28 days curing and nine cubes for partially curing and nine cubes for not curing and results obtained from the experiment were compared and conclusion were drawn. Key words: Fully Curing, Partially Curing, Not Curing, Steel fibers, Compressive Strength.

I. INTRODUCTION. Concrete is the most widely used construction material today. It is versatile, has desirable engineering properties, can be moulded into any shapes more importantly is produced with cost effective materials. Ordinary cement concrete posses a very low tensile strength, limited ductility and little resistance to cracking. It has been found that different type of fibers added in specific percentage to concrete improves the mechanical properties, durability of the structure. Mostly steel fibers are seen to be performing well as compared to the random fibers. Curing is the process of controlling the rate and extent of moisture loss from concrete during cement hydration. When Portland cement is mixed with water, a chemical reaction called hydration takes place. The extent to which this reaction is completed influences the strength and durability of the concrete. Freshly mixed concrete normally contains more water than is required for hydration of the cement; however, excessive loss of water by evaporation can delay or prevent adequate hydration. The curing period may depend on the properties required of the concrete, the purpose for which it is to be used, and the ambient conditions, i.e., the temperature and relative humidity of the surrounding atmosphere. Uniform temperature should also be maintained throughout the concrete depth to avoid thermal shrinkage cracks. Also protective measures to control moisture loss from the concrete surface are essential to prevent plastic shrinkage cracks. II. Literature Review. Akinwumi. I.I1, Gbadamosi. Z.O2.et.al have the results of an experimental study on the effects of curing methods and curing ages on the compressive strength development of ordinary Portland cement concrete in a tropical environment. Fifteen (15)

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concrete cubes each were cured by immersion in potable water, immersion in lime water, covering with wet rug, covering with plastic sheets and airdrying. For each of these curing methods, the average compressive strength of concrete cubes was determined after 3, 7, 14, 28 and 90 days curing periods. The result obtained discourages the use of curing by air-drying method and also suggests limiting the use of the other curing methods to 28-days period. Generally, the highest compressive strength was obtained for concrete cured by immersion in lime water. T. James1, A. Malachi2, E.W. Gadzama3.et.al they have investigated the different curing methods are usually adopted to evaluate the compressive strength of concrete. This study reports the laboratory results of the effect of curing methods on the compressive strength as well as the density of concrete. A total of 72 cubes of mix ratio 1:2:4 were investigated after subjecting them to various curing conditions, with the aim of searching which of the curing method is best. The cubes were cured in the laboratory at an average temperature of 28◦C (82.4◦F). The results obtained showed that the average compressive strength values for 7, 14, 21 and 28 days, vary with curing methods. The results show that ponding had the highest compressive strength and density, followed by wet covering, sprinkling, then uncured for two days, with the totally uncured cubes having the least compressive strength and density as well as highest shrinkage limit. Ponding method of curing was recommended to be the best of all the curing methods. Milind.V1. Mohod2.et.al in this experimental investigation for M30 grade of concrete to study the compressive strength and flexural strength of steel fibers reinforced concrete containing fibers



varied by 0.25%,0.50%,0.75%,1%,1.5% and 2% by volume of cement. Slump tests were conducted to measure the workability mixes .Compressive strength and flexural strength on standard specimens were conducted for 3, 7 and 28 days. They found that as the fiber content increased, workability decreased. The compressive strength increased with increase in fiber content to a particular limit and then decreased thereafter. The flexural strength also showed similar trend. The optimum dosage of fiber content was 1% of compressive strength and 0.75% of flexural strength. Vikrant Vairagade.et.al. presented flexural strength of normal concrete to steel fibers reinforced concrete was evaluated and mechanical properties of steel reinforced concrete were analyzed in this experimental study cement, sand , CA, FA, water , Super plasticizer and steel fibers were used for compressive strength test of cube specimen dimensions 150*150*150mm were cast for M20 grade filled the applicability of previously published relation among compressive strength, with 0% and 0.5% fibers after 24 hours the specimens were to curing tank where in they allowed cure for 7 days and 28 days .Finally result of compressive strength for M20 grade of concrete on cube with 0% and 0.5% steel fibers for aspect ratio 50 and it is observed that for addition of 0.5% fibers shows slightly more compressive strength than normal concrete. III. MATERIALS AND METHODOLOGY. Cement: In this experiment Ordinary Portland cement of 43 Grade with brand name chettinad was used for concrete mixes. The physical properties of cement used are as given in table.1. Table.1: Physical Properties of Cement.

Sl No 1 2

3

4

Test

Results

IS specificatio ns and Test procedure

Specific Gravity Standard Consisten cy Initial Setting time in minutes Final Setting Time

3.11

IS:4031

30%

IS:4031& IS 269

120

>30, IS:4031& IS 269

450