AIR POLLUTION DISPERSION MODELING FOR ...

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Major open cast coal mines and many other small industries (beehive coke plant, bricks plant, etc.) are located in the region. As a result, the air environment of ...
AIR POLLUTION DISPERSION MODELING FOR MINING COMPLEX AND MODEL PERFORMANCE EVALUATION Jaiprakash1, Gurdeep singh2, A. K. Pal3 BY JAIPRAKASH

Department of Environmental Science & Engineering Indian School of Mines University Dhanbad - 826004

INTRODUCTION  Dhanbad, the capital coal of India is a rapidly growing urban center. The population inhabiting the Dhanbad area increased by several hundred thousand people, as has the process of industrialization and motorization. The combustion of fuel by industries, vehicles and mining industries are producing significant to condemnable air pollution load.  Dhanbad is also the heart of Jharia coal field (JCF). JCF contains the only remaining reserves of prime coking coal in India. The total coal reserves of Bharat Coking Coal Ltd (BCCL, a subsidiary of CIL) of JCF are estimates to be 17,077 million tones. Further, 2340 million tones of coal lie within jurisdiction of IISCO and TISCO.  Major open cast coal mines and many other small industries (beehive coke plant, bricks plant, etc.) are located in the region. As a result, the air environment of the region has been deteriorated over the years.

In order to evaluate air pollution dispersion phenomena , dispersion potential, in broad terms, is thus an indicator of potential for future growth keeping in view the resources such as air, water, land etc. Air environment is more important as most of the developmental activities tends to have an impact on the surrounding air quality. AERMOD model was applied for impact assessment from these sources. This will facilitates an in-depth investigation for evaluating dispersion potential of the air environment so as to devise appropriate air pollution control policy for the study area.

APPROACH AT THE INCPECTION OF THE STUDY Study Area (Dhanbad)

Emission inventory

PM 2.5 Data Collection

Mono-static SODAR Weather Monitor (WM-231)

PM10

Industries

Open Cast Mines

Vehicular Traffic Network

Ambient Air Monitoring

ISC – AERMOD Model Performance

SPM

N

23⁰ 54’

STUDY AREA (DHANBAD) Barwa Adda

Govindpur

23⁰ 51’

Katras

ISMU Dhanbad

Loyabad Bank More

23⁰ 48’

23⁰ 45’

Baghmara

23⁰ 42’ Jharia Baliapur

 Population Density – 958 Sq/km

23⁰ 39’

Sindri

Road network

 Total Population- 2,394,434 (As Per 2001 Census Report)

23⁰ 36’

 Area - 6078 hectares  23° 37’ 03” – 24° 51’ 40’’ N  86° 06’ 30” – 86° 32’ 04’’ E  Temperature - 14°C – 43°C

86⁰ 05’ 86⁰ 11’ 86⁰ 14’ 86⁰ 17’ 86⁰ 20’ 86⁰ 23’ 86⁰ 26’ 86⁰ 29’ 86⁰ 32’ 23⁰ 33’

 Humidity – high  Rainfall – 1300 mm

METEOROLOGICAL DATA COLLECTION A systematic meteorological study was conducted through Weather Monitor Station (WM-231) and Mono-static SODAR, set at the roof-top of the building of Department of Environmental Science and Engineering, Indian School of Mines University, Dhanbad. The meteorological database thus developed consists of:  Meteorological status (temperature, relative humidity, rainfall, wind speed and direction, stability class, mixing height, and ventilation coefficient etc.) on monthly basis during 2008-09 of the study area  Meteorological status on hourly/daily basis during air quality monitoring study (Winter, 2008-09)  Mono-static SODAR  Weather Monitor Station (WM-231)

EMISSION INVENTORY In order to determine the contribution of different air pollution sources, the data /information on emission inventory data were collected during winter (2008-09). Following three major sources were considered to prepare emission inventory of SPM and PM10 in the study area. 

Industrial point source

 vehicular emission source  open cast coal mines 35 major emission sources ( point, line and area sources) have considered in the study area.

N

23⁰ 54’

Prepared Emission Inventory of Study Area Barwa Adda

Govindpur

23⁰ 51’

Katras

Bank More

Baliapur

23⁰ 48’ 23⁰ 45’ 23⁰ 42’

Road network (L1-L6) Point sources (S1-S20)

23⁰ 39’ Sindri

Open cast mines (M1-M9)

86⁰ 05’ 86⁰ 11’

86⁰ 14’

86⁰ 17’

86⁰ 20’

23⁰ 36’ 86⁰ 23’

86⁰ 26’

86⁰ 29’

86⁰ 32’

23⁰ 33’

AMBIENT AIR MONITORING 23⁰ 54’

Ambient Air Monitoring locations of study area Govindpur (A1)

23⁰ 51’

Hirak Point (A15) Muraidih (A19)

Katras (A16) Tetulmari (A17) Steel Gate (A2)

Bus Stand (A6)

Block II (A20)

ISMU Main Gate (A3)

Bank More (A7)

Railway Station (A5)

Bastacolla (A8)

South Tisra (A13)

Jamadoba (A9) Patherdih (A10)

86⁰ 17’

86⁰ 20’

23⁰ 39’

23⁰ 36’

BIT Sindri (A14)

86⁰ 14’

23⁰ 42’

Chasnalla (A12)

Sudamdih (A11)

86⁰ 11’

23⁰ 45’

Court More (A4)

Sijua (A18)

86⁰ 05’

23⁰ 48’

86⁰ 23’

86⁰ 26’

86⁰ 29’

86⁰ 32’

23⁰ 33’

ISC-AERMOD MODEL ISC AERMOD used the six pathways to develop the run stream file. These pathways are the control pathway, source pathways, receptor pathway, meteorological pathway, terrain Grid pathway and output pathway. (User’s Guide ISC-AERMOD view) Control Pathway: - It is collective term used to specify the overall job control option including titles, dispersion option, terrain option, and pollutant options. It helps us for checking the data files. Source Pathway: - It enables the handling of multiple sources, including point, line and area source types. Several sources groups may be specified in a single run, with the source contribution combined for each group. Receptor Pathway: - The receptor pathway in the software allows for flexibility in the specification of receptor location. The user can specify multiple receptor networks in a single run. Cartesian Grid and polar Grid receptor networks can also be mixed in the same run. Meteorological Pathway: - In this pathway the model uses a file of surface boundary parameters and file of profile variables including wind speed, wind direction, and turbulence parameters. These meteorological inputs are generated by the meteorological pre processor AERMET. Terrain Pathway: - In the terrain grid pathway, the user may either use the terrain grid input file or leave option. If the option is used, the user is required to specify the location of the terrain grid file in UTM (Universal Transverse Meter) coordinates. Output pathway: - The output pathway of this model reveal following output data :-

Result & Discussion

METEOROLOGICAL STATUS  Prevailing wind direction was found to be north with average wind speed of 2.0 m/s during daytime, whereas during night time prevailing wind direction and average speed were north-west and 0.9 m/s respectively.  The maximum mixing height was about 1100 m during afternoon hours (12:00 to 14:00) and minimum is about 100 m during early morning and late evening hours. Diurnal variations in the atmospheric stability determined based on SODAR data indicate that stable conditions prevail during the night hours, whereas the atmosphere becomes unstable during noon hours.

Winter 2008-09 Average wind speed = 0.88 m/s

AMBIENT AIR QUALITY STATUS Ambient air quality monitoring for SPM and PM10 was done as per CPCB guide lines at 20 locations in the study area during winter (2008-09). 24 hour average maximum SPM concentrations were found to be 573.2, 534.8, 652.2, 426.7, 711.2, 655.2, 582.3 µg/m3 at Bastakolla, Sudamdih, South Tisra, Sijua, Katras Block II respectively. At Sindri (273.6 µg/m3) and ISMU main gate (263.4 µg/m3), comparatively lower concentration level were recorded.

The 24 hour average maximum concentrations were found to be 214.6, 337.4, 312.2, 327.1 342.9, 321.7, µg/m3 at Bank More Bastakolla, Sudamdih, South Tisra, Tetulmari, Sijua, respectively. Sindri (136.4 µg/m3 ) and ISMU main gate (144.8 µg/m3 ) registered lowest concentration levels.

Existing SPM concentration scenario due to open cast mining around Dhanbad

The 24 hour average maximum concentrations were found to be 214.6, 337.4, 312.2, 327.1 342.9, 321.7, µg/m3 at Bank More Bastakolla, Sudamdih, South Tisra, Tetulmari, Sijua, respectively. Sindri (136.4 µg/m3 ) and ISMU main gate (144.8 µg/m3 ) registered lowest concentration levels.

Concentration of PM10 (in µg/m3)

400 350 300 250 200 150 100 50 0 A1

A2

A3

A4

A5

A6

A7

A8

A9

A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20

Locations o f the study area

Integrated scenario of SPM concentration at Dhanbad

Existing PM10 concentration scenario due to open cast mining around Dhanbad

Integratedscenario scenario of SPM SPM concentration atconcentration Dhanbad Integrated of concentration Dhanbad Integrated scenario of PMat around Dhanbad 10

S. No

Name of locations

SPM (µg/m3) 24 h Average (Observed) 310.4 280.5

SPM (µg/m3) 24 h Average (Predicted) 145.2 216.2

PM10 (µg/m3) 24 h Average (Observed) 156.4 159.2

PM10 (µg/m3) 24 h Average (Predicted) 81.3 78.2

A1 A2

Govindpur Steel Gate

A3

ISMU Main 263.4 Gate Court More 281.6

182.1

144.8

73.2

182.1

153.4

60.2

311.2

252.6

206.6

124.3

A6

Railway Station Bus Stand

268.2

170.2

196.5

110.6

A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19

Bank More Bastakola Jamadoba Pathardih Sudamdih Chasnala South Tisra BIT Sindri Hirak Point Katras Tetulmari Sijua Muraidih

322.4 573.2 391.2 478.2 534.8 478.2 652.4 273.6 262.5 426.7 711.3 655.2 456.4

347.2 845.2 378.2 377.2 547.2 250.2 588.1 134.2 165.7 427.1 698.4 526.2 398.2

214.6 337.4 206.2 246.3 312.2 254.1 327.3 136.4 146.9 211.2 342.9 321.2 205.4

185.2 328.4 138.2 213.2 129.6 154.2 358.4 80.45 78.2 96.2 255.5 345.2 182.6

A4 A5

CRITICAL ZONE SPM /PM10CONCENTRATION  Isopleths of predicted SPM and PM10 concentrations were evaluated with respect to existing mines, beehive coke plants/small scale industries, and integrated activities which include both mines and industries along with vehicular movement through the major road networks in Dhanbad. As per the model outputs, the critical pollution zones are as given below: Existing mines Beehive/coke plants/small industries

Sijua, Kustore and Block-II open cast mines Govindpur and Barwaadda localities

Vehicular traffic Bank more and Railway Station Integrated activities including road traffic, Kusunda, and Kustore industries and open cast mines

Existing mines

Sijua, Kustore, and Bastakolla open cast mines

Beehive/coke plants/small industries

Govindpur and Barwaadda localities

Vehicular traffic

Bank more and Railway Station

Integrated activities including road Kustore, Kusunda, and Block II traffic, industries and open cast mines

MODEL PERFORMANCE EVALUATION  The performance of the AERMOD model has then been examined with the help of scatter plot diagram as shown in Figure SPM and PM10. A perusal of the figures of both SPM and PM10 revealed similar trend for both predicted and observed values. The correlation coefficient, 0.783 for SPM and 0.741 for PM10 also indicates moderate to high association. 450

Predicted PM10 Concentration (µg/m3 )

Predicted SPM Concentration (µg/m3 )

850

y = 1.226x - 148.2 R² = 0.783

750

650

550

450

350

250

400

y = 1.136x - 94.41 R² = 0.741

350

300

250

200

150

100

50

150 250

350

450

550

650

Observed SPM Concentration

750

(µg/m3

)

130

180

230

280

330

380

Observed PM10 Concentration (µg/m3 )

ISC-AERMOD MODEL ACCURACY  The model accuracy has been carried out computing several statistical errors, i.e., Model Bias (MB), Normalized Mean Square Error (NMSE), Correlation coefficient (r2), and Fractional Bias (FB).

Error

Ideal value

SPM

PM10

0

51.7

63.78

Least value 0.08026

0.1771

1

0.840611

0.82

±2

0.1312

0.334

CONCLUSION  Isopleths of predicted SPM and PM10 concentrations revealed maximum SPM concentrations at Bastakolla, Sudamdih, South Tisra, Sijua, Katras and Block II localities, whereas Sindri and ISMU main gate registered comparatively lower concentration levels.  Similarly, maximum predicted PM10 concentrations were observed at Bank More, Bastakolla,

Sudamdih, South Tisra, Tetulmari, and Sijua localities due to different mining activities, plying of higher number of vehicles, etc. Sindri and ISMU main gate, however, registered comparatively lower concentration levels for both SPM and PM10.  It has also been seen that open cast mines contributed 73 % of total SPM/PM10 emission load in the

study area, whereas small scale industries and vehicular traffic contributed about 20 % and 7 % of the total SPM/PM10 emission load respectively.  The predicted values from ISC-AERMOD model was compared with monitored values which

showed more or less good accuracy. The statistical error tests also justified the same. Thus, it may be concluded that AERMOD model is more suitable for the mining condition and area source  The study also provides some guidelines for understanding the complexity of air pollution problems

in Dhanbad region.  However, it is also felt to initiate an in-depth investigations for evaluating the assimilative capacity of

the air environment so as to devise appropriate air pollution control policy for the study area.

 24 hourly average maximum concentrations ( 426.7 -711.2 µg/m3 ) of SPM were found at Bastakolla, Sudamdih, South Tisra, Sijua, Katras Block II respectively, whereas Sindri (273.6 µg/m3) and ISMU main gate (263.4 µg/m3) registered comparatively lower concentration levels.  The 24 hourly average maximum concentrations (214.6-342.9 µg/m3) for PM10 at Bank More,

Bastakolla, Sudamdih, South Tisra, Tetulmari, Sijua, respectively, large number of vehicular movement, etc. Sindri (136.4 µg/m3) and ISMU main gate (144.8 µg/m3) registered lowest concentration levels.  Regression

modeling: The Spearman rank correlation coefficient between PM10 and meteorological parameters revealed inverse relationship. Multiple regression relationship between PM10 and meteorological parameters were developed for both industrial and residential sites. Statistical analysis through Normalized Mean Standard Error (NMSE) indicated good accuracy for both the multiple regression models whereas Model Bias (MB) showed over- predicting and underpredicting situations for residential and industrial sites respectively.  The predicted values from ISC-AERMOD model was compared with monitored values which

showed more or less good accuracy. The statistical error tests also justified the same. Thus, it may be concluded that AERMOD model is more suitable for the mining condition and area source.  This dissertation work provides some guidelines for understanding the complexity of air pollution

problems in Dhanbad region. However, it is also felt to initiate in-depth investigations for evaluating

assimilative capacity of the air environment so as to devise policy for the study area.

appropriate air pollution control

REFERENCES       

     

Bencala, K.E., Seinfeld, J.H., 1979. An air quality model performance assessment package. Atmospheric Environment 13, 1181-1185. EPA, 1998. User's Guide for the ISC-AERMOD Dispersion Models (EPA-450/4-92-008b). Environmental Protection Agency, North Carolina 27711. Fox, D.G., 1981. Judging air quality model performance - a summary of the AMS workshop on dispersion model performance. Bulletin of the American Meteorological Society 62, 599-609. Harrison, R.H., Perry, R., 1985. Hand Book of Air Pollution Analysis, 2nd Edition. Champman & Hall, London. Juda, K., 1986. Modeling of the air pollution in the Cracow area. Atmospheric Environment 20, 2449-2458. Khanna S.K., Justo C.E.G, 1991. Highway engineering, 7th Edition. Nemchand & Bros., Roorkee Publication NEERI, 1990. National Ambient Air Quality Monitoring Report (1973-90). National Environmental Engineering Research Institute,Nagpur, India. NEERI, 1995. Regional Environmental Impact Assessment Studies for Jamshedpur Region, report Vols. I and II. National Environmental Engineering Research Institute, Nagpur, India. NEERI, 1996. Carrying Capacity Based Developmental Planning of National Capital Region. National Environmental Engineering Research Institute, Nagpur, India. Singal, S.P., Lewthwaite, E.W.D, Wratt, D.S., 1965. Estimating atmospheric stability from mono static acoustic sounder records. Atmospheric Environment 19 (2), 221-228. Sivacoumar, R., Thanasekaran, K., 1999. Line source model for vehicular pollution prediction near roadways and model evaluation through statistical analysis. Environmental Pollution 104, 389-395. BCCL Dhanbad, 2007-08. Annual Report: Environment Division, Willmot, C., Wicks, D.E., 1980. An empirical modeling for the spatial interpolation of monthly precipitation within California. Physical Geography 1, 59-73.

SOFTWARE TOOLS 1. Wind rose software :(WRPLOT –Version5.9.0 & Enviroware version 3.2) 2. ISC-AERMOD VIEW (www.lakesenvironmental.com) 3. Statistical Software :- SPSS (version 10.0) 4. Statistical Software :- Origin lab (version 6.1) 5. MATLAB (Version-7)

Thank you