Project Report Template

Final Report

Co-benefits of Low Carbon Pathway on Air Quality, Human Health and Agricultural Productivity in India

Cobenefits of Low Carbon Pathway in India

© The Energy and Resources Institute 2018

Suggested format for citation T E R I [The Energy and Resources Institute]. 2018 Co-benefits of Low Carbon Pathway on Air Quality, Human Health and Agricultural Productivity in India, New Delhi: The Energy and Resources Institute. 121 pp. [Project Report No. 2015EE06]

Disclaimer The views/analysis expressed in this report/document do not necessarily reflect the views of Shakti Sustainable Energy Foundation. The Foundation also does not guarantee the accuracy of any data included in this publication nor does it accept any responsibility for the consequences of its use.

For private circulation only

For more information Project Monitoring Cell The Energy and Resources Institute Darbari Seth Block IHC Complex, Lodhi Road New Delhi – 110 003 India ii

Tel. 2468 2100 or 2468 2111 E-mail [email protected] Fax 2468 2144 or 2468 2145 Web www.teriin.org India +91 • Delhi (0)11


Shakti Sustainable Energy Foundation works to strengthen the energy security of the country by aiding the design and implementation of policies that encourage energy efficiency, renewable energy and sustainable transport solutions, with an emphasis on sub sectors with the most energy saving potential. Working together with policy makers, civil society, academia, industry and other partners, they take concerted action to help chart out a sustainable energy future for India (www.shaktifoundation.in).

iii

Project Team Project Advisors Dr. Prodipto Ghosh, Distinguished Fellow, TERI Dr. Atul Kumar, Associate Professor, TERI School of Advanced Studies Project Investigator Dr. Sumit Sharma, Associate Director and Fellow, TERI Co-Project Investigator Dr. Ritu Mathur, Director & Senior Fellow, TERI Prof. Suresh Jain, Head of Department, TERI School of Advanced Studies

Team Members Dr. Arindam Datta, Fellow, TERI Mr. Swapnil Shekhar , Research Associate TERI Mr. Jai Kishan Malik, Research Associate, TERI Ms. Anju Goel, Associate Fellow, TERI Dr. Chuba Jamir, Assistant Professor, TERI School of Advanced Studies Ms. Ilika Mohan, Research Associate, TERI Mr. Kabir Sharma, Research Associate, TERI Ms. Sugandha , Research Associate, TERI Ms. Seema Kundu, Associate Fellow, TERI

v


Table of Contents Table of Contents ................................................................................................................... vi List of Figures...................................................................................................................... viii List of Tables ........................................................................................................................... x ACKNOWLEDGEMENT ................................................................................................................. 11 EXECUTIVE SUMMARY ................................................................................................................. 12 CHAPTER 1 INTRODUCTION ....................................................................................................... 17 Structure of the report.......................................................................................................... 17 1.1 Background ..................................................................................................................... 17 1.2 Objectives ........................................................................................................................ 19 1.3 Scope ................................................................................................................................ 20 1.4 Overall methodology.................................................................................................... 21 1.5 Energy system modelling ............................................................................................. 22 1.6 Emissions modelling...................................................................................................... 22 1.7 Meteorological modelling ............................................................................................. 23 1.8 Air quality modelling .................................................................................................... 24 1.9 Impact modelling of air pollutant concentrations ..................................................... 25 1.9.1 Human health impact assessment ................................................................ 25 1.9.2 Crop loss assessment ...................................................................................... 26 CHAPTER 2 ENERGY SCENARIOS ................................................................................................ 27 2.1 Introduction..................................................................................................................... 27 2.2 Description of Energy Scenarios................................................................................... 27 2.2.1 Data & Assumptions Across Scenarios .............................................................. 27 2.2.2 Sector-wise Assumptions ..................................................................................... 29 2.3 Sectorial Analysis ........................................................................................................... 39 2.3.1 Agriculture Sector ................................................................................................. 41 2.3.2 Transport Sector .................................................................................................... 41 2.3.3 Residential Sector .................................................................................................. 43 2.3.4 Commercial Sector: ............................................................................................... 44 2.4 Key takeaways from the energy scenarios .................................................................. 45 CHAPTER 3 EMISSION INVENTORIZATION ............................................................................... 48 3.1 Emissions of different air pollutants ............................................................................ 48 3.1.1 Estimated PM10 emission from different sectors ......................................... 49 3.1.2 Estimated CO emission from different sectors ................................................. 50 3.1.3 Estimated NOx emissions from different sectors ....................................... 51 3.1.4 Estimated SO2 emissions from different sectors ............................................... 51 3.1.5 Estimated NMVOC emission from different sectors ................................. 52 CHAPTER 4 .................................................................................................................................... 54 AIR QUALITY SCENARIOS ........................................................................................................... 54 4.1 WRF and CMAQ modelling system............................................................................ 55 4.2 Air quality simulation ................................................................................................... 56 4.3 Air quality scenarios ...................................................................................................... 57 4.4 Results of air quality simulation .................................................................................. 57 4.4.1 PM2.5 ........................................................................................................................ 59 4.4.2 Ozone concentrations ........................................................................................... 62 CHAPTER 5 HUMAN HEALTH RISK ASSESSMENT .................................................................... 64 5.1 Introduction .................................................................................................................... 64 5.2 Methodology................................................................................................................... 66 5.3 Data collection ................................................................................................................ 66 5.3.1 Secondary data ...................................................................................................... 66 5.3.2 Primary data .......................................................................................................... 66 5.4 Data processing and analysis ...................................................................................... 68 5.4.1 Health risk function: Mortality ..................................................................... 68 5.5 Results and discussion ................................................................................................... 70 5.6 Survey Results ................................................................................................................. 70 5.6.1 Goa – Vasco ............................................................................................................ 70

vi


5.6.2 Goa – Panjim .......................................................................................................... 72 5.6.3 Chandigarh ............................................................................................................ 74 5.6.4 NCT of Delhi .......................................................................................................... 76 5.7 National level health impacts using WHO CRFs ....................................................... 78 5.8 City-specific health impacts using indigenous CRFs................................................ 79 CHAPTER 6 AGRICULTURAL IMPACTS ....................................................................................... 82 6.1 Background ..................................................................................................................... 82 6.2 Ozone concentrations and its impacts in India .......................................................... 83 6.2.1 Impacts of ozone on crop yield ..................................................................... 83 6.2.2 Impacts of ozone on crop yield in India ...................................................... 84 6.3 Ozone risk assessment studies ..................................................................................... 85 6.4 Methodology to assess the impact of ozone on crops ............................................... 85 6.4.1 Establishment of ozone-dose-response functions for crops grown in India 86 6.4.2 Ozone impact on crop yield data ........................................................................ 86 6.4.3 Establishing Indian dose-response function and comparison with the existing North American and European functions ........................................................... 87 6.5 Indian dose-response functions for crops and comparison with the existing North American and European functions.............................................................................. 88 6.6 Assessment of impact of ozone on different crops in India ................................... 101 6.7 Assessment of wheat losses in different energy-emissions scenarios analysed in this study .............................................................................................................................. 103 7. CONCLUSIONS ........................................................................................................................ 106 8. REFERENCES ........................................................................................................................... 111 APPENDIX A– MECHANISM OF OZONE ENTRY TO PLANTS .................................................. 119

vii


List of Figures Figure 1.1: Ratio of concentrations of pollutant in different Indian cities with the prescribed standards in 2015 ....................................................................................................................... 19 Figure 2.1(a-d): CO2 emissions across scenarios for total energy system, energy supply system, industry sector and transport sector (GT) ............................................................... 34 Figure 2.2: CO2 emission intensity of GDP across the scenarios ................................................. 35 Figure 2.3(a-d): Fuel wise power generation capacity across scenarios ..................................... 36 Figure 2.4(a-d): Fuel wise electric power generation across scenarios (TWh) .......................... 37 Figure 2.6: Total final energy required across scenarios (Mtoe).................................................. 39 Figure 2.7(a-d): Fuel-wise energy consumption in industrial processes across scenarios (Mtoe) .......................................................................................................................................... 40 Figure 2.8(a-d): Fuel wise energy consumption in agricultural sector across scenarios (Mtoe ...................................................................................................................................................... 42 Figure 2.9(a-d): Fuel-wise energy consumption of transport sector across scenarios .............. 43 Figure 2.10(a-d): Fuel-wise energy consumption in residential sector across scenarios ......... 44 Figure 2.11(a-d): Fuel-wise energy consumption in commercial sector across scenarios........ 45 Figure 3.1: Annual PM10 emissions from different sectors under different energy consumption scenarios ............................................................................................................. 49 Figure 3.2: Annual CO emissions from different sectors under different energy consumption scenarios ..................................................................................................................................... 50 Figure 3.3: Annual NOx emissions from different sectors under different energy consumption scenarios ............................................................................................................. 51 Figure 3.4: Annual SO2 emissions from different sectors under different energy consumption scenarios ..................................................................................................................................... 52 Figure 3.5: Annual NMVOC emissions from different sectors under different energy consumption scenarios ............................................................................................................. 53 Figure 4.1a: Framework of air quality simulation used in the study ......................................... 54 Figure 4.1b: Program flow of the WRF model run ...................................................................... 55 Figure 4.2: Basic flow of various processes in the air quality modelling simulation ............... 56 Figure 4.3: Annual averaged PM2.5 and ozone concentrations in India ..................................... 58 Figure 4.4: Comparison of annual average PM2.5 concentrations with modelled values in 2016 ...................................................................................................................................................... 59 Figure 4.5: Variation of (domain averaged) annual averaged PM2.5 concentrations in different scenarios in India ....................................................................................................................... 60 Figure 4.6: Annual average PM2.5 concentrations (g/m3) in different scenarios.................... 61 Figure 4.7: Ratio of (domain averaged) ozone concentrations in different scenarios with BAU in India ........................................................................................................................................ 62 Figure 5.1(a-c): Distribution of respondents in Goa-Vasco .......................................................... 71 Figure 5.2(a-c): Distribution of respondents in Goa-Panjim ........................................................ 73 Figure 5.3(a-c): Distribution of respondents in Chandigarh ........................................................ 75 Figure 5.4(a-c): Distribution of respondents in Delhi .................................................................. 77 Figure 5.5: Mortality due to PM2.5 under various policy scenarios at national level................ 78 Figure 6.1: Systematic literature review to establish ozone-dose-response functions for crops grown in India ........................................................................................................................... 86

viii


Figure 6.2: Dose-response relationship for wheat crops grown in India (a) M7 Exposure index; (b) AOT40 exposure index ........................................................................................... 98 Figure 6.3: Dose-response relationship for soybean crops grown in India (a) M7 Exposure index; (b) AOT40 exposure index. .......................................................................................... 98 Figure 6.4: Dose-response relationship for rice crops grown in India (a) M7 Exposure index; (b) AOT40 exposure index. ...................................................................................................... 98 Figure 6.5: Dose-response relationship for Maize crops grown in India (a) M7 Exposure index; (b) AOT40 exposure index ........................................................................................... 99 Figure 6.6: Dose response relationship for legumes crops grown in India (a) M7 Exposure index; (b) AOT40 exposure index ........................................................................................... 99 Figure 6.7: Dose-response relationship for mustard crops grown in India (a) M7 Exposure index; (b) AOT40 exposure index ........................................................................................... 99 Figure 6.8: Sensitivity of Indian crops (current study) (M7-M12 index) and (AOT40 index) .................................................................................................................................................... 100 Figure 6.9: Ozone induced yield losses on Rabi crops, wheat and mustard, for different states and union territories in India................................................................................................. 102 Figure 6.10: Ozone induced yield losses on Kharif crops, soybean, rice, and maize, for different states and union territories in India ..................................................................... 102 Figure 6.11: Comparison between the yield loss estimates using Indian dose-response and European dose-response functions ....................................................................................... 103 Figure 6.12: Percentage wheat yield loss estimation in 2016 (using Indian dose responses) 104 Figure 6.13: Loss of wheat crop (mt) estimated for different scenarios in India .................... 105 Figure A1: Mechanism of ozone entry to plants (Source: Singh, 2006) ..................................... 120

ix


List of Tables Table 1.1 Categorization of sectors and fuels in the study ........................................................... 23 Table 2.1: Share of electric vehicles across scenarios .................................................................... 31 Table 2.2: Industry-wise fuel consumption (Mtoe) ....................................................................... 40 Table 5.1: Mortality risk functions ................................................................................................... 69 Table 6.1: Criteria for selection of ozone impact on yield data................................................... 86 Table 6.2: Equations for the ozone exposure matrix .................................................................... 87 Table 6.3: Wheat: Summary of pooled Indian data yield loss at various ozone exposures .... 91 Table 6.4: Maize: Summary of pooled Indian data yield loss at various ozone exposures ..... 92 Table 6.5: Legumes: Summary of pooled Indian data yield loss at various ozone exposures 93 Table 6.6: Rice: Summary of pooled Indian data yield loss at various ozone exposures. ...... 94 Table 6.7: Mustard: Summary of pooled Indian data yield loss at various ozone exposures 95 Table 6.8: Comparison of Indian dose-response functions with North American doseresponse functions..................................................................................................................... 96 Table 6.9: Indian dose-response functions for AOT40 index compared with Mills et al. (2007) ...................................................................................................................................................... 97

x


Acknowledgement The Energy and Resources Institute (TERI) gratefully acknowledges the financial support provided by Shakti Sustainable Energy Foundation, New Delhi to undertake this study. We also thank different agencies in providing data which was essential to complete the project. We thank Dr. Atul Kumar (TERI School of Advanced Studies) who put in initial efforts in writing proposal for this project and provided his advices during the course of the project.

11


Executive Summary India has demonstrated its commitment to fast-track greenhouse gas (GHG) mitigation measures that align well with its development priorities. The National Action Plan on Climate Change (NAPCC) was launched in 2008, and a concerted effort was put in place to draw strategies that would help India in aligning its development with low-carbon actions. Moreover, the NAPCC also ensured that there was a broad spectrum of initiatives built-in towards such a goal. The intended nationally determined contribution (INDC) submitted by India to the United Nations Framework Convention on Climate Change (UNFCCC) in October 2015, is ambitious given that India has already been incorporating several low-carbon solutions and strategies across sectors. India has committed to reduce the GHG emissions intensity of its economy by 33–35% by 2030 as compared to the 2005 level. A consideration of co-benefits, such as improved access to clean energy, expected reductions in human health impacts, increase in agricultural yields and increase in employment prospects against a business-as-usual (BAU) pathway, can significantly strengthen the case for proposed climate actions by tilting the overall cost-to-benefit ratio favourably towards the latter. Furthermore, as elucidated in the NAPCC’s approach of simultaneous advancement of India’s development and climate objectives, cobenefits are viewed as more meaningful objectives to pursue rather than outright climate mitigation. Therefore, it is extremely relevant that while assessing the implications of a low carbon growth trajectory for India, the co-benefits to be derived from proposed policy measures designed to mitigate climate change are also evaluated and quantified. Additionally, consideration of co-benefits will support more informed prioritization among available policy options. In this context, TERI has conducted this study which is supported by the Shakti Sustainable Energy Foundation. The main objectives of the study were to carry out a co-benefits assessment of various energy policy scenarios. Four different alternative scenarios modelled within the energy (MARKAL) model, depicting varying levels of GHG mitigation options are considered to assess not only the CO2 emission reduction potential, but to also identify and examine the possible co-benefits of the alternative options. The co-benefits that are assessed and quantified include reductions in emissions of air pollutants, change in concentration of air pollutants, impacts on human health, and agricultural yields. The whole of India’s landmass is chosen as the study domain for the assessment. This study assesses the air quality improvement cobenefits of various energy development and mitigation pathways using integrated modelling techniques to convert energy use information under different scenarios to air pollutant emission and its corresponding impacts on human health and agriculture. The MARKAL model was used to depict the possible energy use under BAU and three different scenarios which focus on different levels of uptake of options, such as clean energy alternatives and energy efficient technologies. The BAU scenario represents climate policies in India that were already implemented before 2016, and an ambitious high GDP growth as envisaged necessary for sustainable development by the Indian Government. Other three scenarios are: i) INDC—takes into account various climate policies and targets formulated in India’s INDC submission; ii) ambition (AMBI)—higher GHG mitigation ambition than those formulated in 12


INDC submission by India while keeping development in India at the forefront; and iii) low growth (LG)—takes into account lower growth rate than the BAU. The Energy scenario modelling and analysis show that while the total primary energy consumption increases over the years in all scenarios, it declines across the Reference (BAU), INDC, and Ambition (the High Growth scenarios) as the stringency of mitigation actions increases. The capacity of coal based power plants successively declines across the three high growth scenarios, increasingly being replaced by renewables like solar and wind electricity. The key mitigation strategy in the demand sectors is efficiency improvement- either by shifting to better and more efficient and cleaner technologies (like electric pumpsets in agricultural sector, more efficient appliances in the residential sector, energy efficient buildings in the commercial sector, processes with lower SECs in industrial sector, etc.) or by fuel switching (electric vehicles in transport sector, shift from traditional biomass to LPG in cooking etc.). The industrial sector shows significant dependence on coal while the transport sector shows dependence on petroleum even in the Ambition Scenario. However, improvement in SEC in the former and engine efficiency and fuel switching (to EVs) in the latter lead to significant reduction in energy consumption in both INDC and Ambition Scenarios. The Low Growth Scenario on the other hand brings forth an interesting energy consumption pathway. The energy consumption in 2031 in this scenario is close to that of the Ambition Scenario which increases to slightly more than that in INDC Scenario by 2051. Energy use numbers from the MARKAL model under different scenarios were fed into the GAINS-ASIA emission model and estimates of emissions were derived for pollutants, such as PM10, NOx, SO2, CO, and non-methane volatile organic compounds (NMVOCs) for the year 2016, 2030, and 2050. The emission sources have been broadly classified into five major sectors, that is, a) Power, b) Transport, c) Domestic, d) Industry, and e) Others. The estimated emissions of air pollutants from the GAINS-ASIA were further fed into an air quality model (CMAQ) to assess the impacts on air quality, human health, and agricultural productivity in 2030 and 2050, under different scenarios. The estimated PM10 emission from different sectors indicates 49.9% and 8.5% increase during 2030 and 2050 respectively, compared to the emission in 2016 under the BAU scenario. However, the estimated PM10 emissions were projected to decrease during 2050 compared to 2030, due to penetration of LPG in residential sector, BS-VI vehicles in transport, and introduction of stringent standards for industries and power plants. On the other side, estimated annual NOx emission under the AMBI scenario suggests an increase of 30% to 60% during 2030 and 2050, respectively compared to 2016. Study indicates, in spite of reduction in total energy consumption, the AMBI scenario doesn’t suggest decrease in the emission of pollutants over the INDC scenario—this might be attributed to the shifts in the type and quality of fuels used in the energy sector. The study suggests that the emission of NMVOCs is expected to grow at a much faster rate compared to other pollutants due to absence of adequate standards for control. Emissions were fed into an air quality model to predict PM2.5 and ozone concentrations for the year 2016. The modelled values were compared with actual observation of pollutants for model 13


validation purpose. High pollutant concentrations were observed in the Indo-Gangetic plains mainly due to high population densities, vehicular movement, and presence of power plants. Higher concentrations are also observed in the western part of the country which is attributed to industries and boundary conditions showing contributions from outside the borders of India. While the INDC scenario shows a significant decrease in PM2.5 concentrations as compared to the Reference (BAU) scenario, the AMBI scenario does not show further decrease in PM2.5 concentrations indicating that CO2 emission reduction need not necessarily be synergistic with decreasing local air pollutants, and may need specific and concerted action to address the latter. Also, different air pollutants may vary differently as is noticed in the Ambition scenario which does not indicate a decrease in PM10 levels, but reflects a significant decrease in ozone concentration as compared to the INDC scenario. The Low Growth scenario shows lower PM 2.5 concentrations in 2030 than all the other scenarios, while it shows higher PM2.5 concentrations than the INDC scenario in 2050. The Low Growth scenario in 2050 assumes lower penetration of efficient technologies with low growth across sectors and hence shows lower PM 2.5 concentrations than the BAU (on account of lower growth), but higher concentrations than INDC (on account of lower penetration of efficient technologies). Ozone concentrations are expected to increase significantly in future in the BAU scenario due to projected increase in emissions of both of its precursors—NOx and VOCs. The INDC scenario shows some decrease in emissions of precursors and a decrease is also observed in the ozone concentrations. Ambition scenario shows the greatest decrease in ozone concentrations in India due to uptake of efficient technologies, which lead to reduction in emissions of ozone precursors. The predicted pollutant concentrations are fed into the impact models. A significant decline in the total respiratory and cardiovascular mortality is expected during 2030 and 2050, under different alternative scenarios. The AMBI and INDC scenario shows a decline of 9%–11% in mortalities which can be attributed to improvement in air quality. The loss of wheat is expected to increase from 27 Mt (in 2016) to 55 Mt and 153 Mt during 2030 and 2050, respectively under the BAU scenario. However, the wheat loss is expected to decrease by 10 Mt, 15 Mt, and 6 Mt during 2050 under the INDC, AMBI, and LG scenarios, with respect to the BAU scenario. The energy, emissions, concentrations of PM2.5 and ozone, and their impacts over human health and wheat productivity, in various scenarios are shown in Table E.1. Table E.1 Energy consumption, emissions, concentrations of PM2.5 and ozone, and their impacts over human health and wheat productivity, under various scenarios 2016 Base

2030 BAU

INDC

2050

AMBI

LG

BAU

INDC

AMBI

LG

Energy (PJ) Total consumption

28,626

54,720

52,147

49,895

50,996

110,231

95,388

85,612

90,383

CO2 (Mt)

2,053

4,734

4,519

4,232

4,001

10,373

9,209

8,080

8,317

PM10 (Kt)

13,619

20,425

18,734

18,759

19,088

16,536

13,790

14,887

16,307

SO2 (Kt)

8,417

10,144

10,047

10,154

8,883

14,408

13,411

13,157

11,605

Emissions

14


2016 NOx (Kt)

6,988

2030

2050

9,164

8,739

8,442

8,664

15,365

11,850

11,271

11,956

27.62

18.07

17.25

16.16

23.14

10.18

9.04

7.93

14.14

PM2.5 (g/m3)

51.7

69.5

66.5

66.4

66.3

69.6

60.3

62.1

64.8

Ozone (ppb)

51.9

53.5

53.1

52.8

53.3

57.4

55.7

55.0

55.9

0.79

1.05

1.02

1.02

1.02

1.22

1.09

1.11

1.16

26.9

55

54

53.1

54.7

152.3

141.7

137.2

143.9

Emission intensity (gCO2/Re) CO2 Air quality

Health Mortalities (million) Agriculture Wheat loss (million t)

The percentage change in energy, emissions, concentrations of PM 2.5 and ozone, and their impacts over human health and wheat productivity, in various scenarios is shown in Table E.2. Table E.2 Percentage change in energy, emissions, concentrations of PM2.5 and ozone, and their impacts over human health and wheat productivity, in various scenarios 2016 Base

2030 BAU*

INDC**

2050

AMBI**

LG**

BAU*

INDC**

AMBI**

LG**

Energy Total consumption Emissions

91%

-5%

-9%

-7%

285%

-13%

-22%

-18%

CO2

131%

-5%

-11%

-15%

405%

-11%

-22%

-20%

PM10

50%

-8%

-8%

-7%

21%

-17%

-10%

-1%

SO2

21%

-1%

0%

-12%

71%

-7%

-9%

-19%

NOx

31%

-5%

-8%

-5%

120%

-23%

-27%

-22%

-35%

-5%

-11%

28%

-63%

-11%

-22%

39%

PM2.5

34%

-4%

-4%

-5%

35%

-13%

-11%

-7%

Ozone

3%

-1%

-1%

0%

11%

-3%

-4%

-3%

33%

-3%

-3%

-3%

54%

-11%

-9%

-5%

104%

-2%

-3%

-1%

466%

-7%

-10%

-6%

Emission Intensity CO2 Air quality

Health Mortalities Agriculture Wheat loss

* change with respect to Base-2016

15


** change with respect to BAU in respective years (2030/2050) Study suggests that the decrease in the consumption of energy in different sectors does not necessarily lead to a proportionate decrease in the associated emission of air pollutants and their effects on human health and agriculture. However, there are significant co-benefits of low carbon energy policies on air quality, human health, and agricultural productivity. Evidently, there is a need for drafting integrated and synergistic strategies to control emission of both GHG and air pollutants. This will have reduced impacts of warming, and air pollution, at global, regional, and local scales.

16


Chapter 1 Introduction Structure of the report Present chapter of the report describes the background, objectives, and the scope of the work, besides describing the broad methodology used for the assessment. Chapter 2 focusses on the future energy scenarios assessed in the project. The chapter also shows the changes in sectorial energy use in the country under various possible scenarios. Chapter 3 shows the results of emission inventorization for different pollutants based on energy trajectories discussed in the previous chapter. Chapter 4 shows the results of air quality simulations using an air quality model to derive changes in air pollutant concentrations under different scenarios. Chapter 5 shows the varying impact of air quality on human health under different growth scenarios. Chapter 6 shows the impact of different energy scenarios on agricultural productivity in India. Chapter 7 is based on deriving conclusions from the different modelling exercises carried out in the project.

1.1 Background Given the juncture at which the country is currently poised, it is critical for policymakers, researchers, and key stakeholders in the fields of economic development and climate change to delineate undesirable growth trends at an early stage in order to avert any potential threats (economic or environmental) to the country as well as plan in advance for moving towards sustainable development. The Government of India has already undertaken several policy initiatives that are essential to steer the economy towards higher standards of living, poverty eradication as well as enhance access to cleaner forms of energy to larger sections of the population. Through the National Action Plan on Climate Change (NAPCC), the government recognized that India needs a directional shift in its growth pathway in order to achieve its developmental objectives while effectively addressing the threat of climate change. Post the NAPCC in 2009, India made a voluntary pledge to reduce, by 2020, the emissions intensity of its gross domestic product (GDP) by 20%–25% over the 2005 baseline. Since 2014–15 is the halfway mark for India to meet its emissions intensity pledge and it also coincides with a period in which India will be considering prospects for further action after the decision taken at COP-17, with the establishment of the Durban Platform for Enhanced Action, this is an opportune time to evaluate India’s low carbon development options and their associated costs and benefits. A consideration of co-benefits, such as improved access to clean energy, expected reductions in human health impacts, increase in agricultural yields and increase in employment prospects against a business-as-usual pathway, can significantly strengthen the case for proposed climate actions by tilting the overall cost-to-benefit ratio favourably towards the latter. Furthermore, as elucidated in NAPCC’s approach of simultaneous advancement of India’s development and climate objectives, co-benefits are viewed as more meaningful objectives to pursue rather than 17


outright climate mitigation. Therefore it is extremely relevant that while assessing the implications of a low carbon growth trajectory for India, the co-benefits to be derived from proposed policy measures designed to mitigate climate change are also evaluated and quantified. Additionally, consideration of co-benefits will support more informed prioritization among the available policy options. Given the need for informed decision making related to climate change issues, it is extremely important that all related policies and plans are formulated after considerable deliberation and on the basis of a rigorous analysis of various facets of the alternative options that may be adopted by the country. The Energy and Resources Institute (TERI) has been undertaking several scenario-based studies to assess India’s performance on its voluntary emissions intensity pledge, evaluate the implications of current policies and programmes that contribute to reducing greenhouse gas (GHG) emissions intensity, and explore new options that are still in the design phase or that merit increased consideration by policymakers on the basis of their development benefits. This study employs the MARKAL model to assess the future energy and carbon dioxide (CO2) emission trends associated with a) current policy portfolio, and b) additional policy options with potential to deliver climate and development co-benefits. A robust assessment of the cobenefits associated with these policy scenarios can help build a stronger case for the policy measures for the pursuit of low carbon growth in India. In this context, TERI undertook a co-benefits assessment of various energy policy scenarios. Four different alternative scenarios modelled within the MARKAL model, depicting varying levels of GHG mitigation options are considered to assess not only the CO2 emission reduction potential, but to also identify and examine the possible co-benefits of the alternative options. The co-benefits that are assessed and quantified include reductions in emissions of air pollutants, change in concentration of air pollutants, impacts on human health, and impact on agricultural yields. The whole of India’s landmass is chosen as the study domain for the assessment. The country has witnessed a steep trajectory of economic growth, especially in the last two decades. While on the one hand, it has led to improvements in socio-economic conditions and enhancements in human development indices, it has also resulted in increased degradation of environmental quality and increase in GHG emissions. Presently, about 80% of Indian cities violate the notified standards of air quality (Figure 1.1). Particulate pollution is consistently high in most parts of the country while gaseous pollutants like nitrogen oxides (NOx) are on the verge of violation. In fact, NOx forms ground level ozone and particulates through secondary mechanisms. It is essential to incorporate local environmental consideration and hence, shape the growth trajectories towards sustainable development pathways. This calls for simultaneously addressing both local and global environmental issues.

18


Figure 1.1: Ratio of concentrations of pollutant in different Indian cities with the prescribed standards in 2015 Source: NAMP database, CPCB, 2016

1.2 Objectives The major objectives of the project are to identify the economic, social, and environmental cobenefits of various mitigation policies and measures considered in the alternative scenarios, examine qualitatively and, where possible, quantitatively the associated co-benefits, and in particular, estimate the co-benefits of GHG mitigation strategies on clean energy access, increased employment potential, air quality, human health, and agricultural productivity. This will call for a) Preparation of a high-resolution emission inventory, based on the energy modelling results of the MARKAL model for the Reference (BAU) and Alternative scenarios that would be set up/ developed during the project; b) Conducting meteorological and air quality simulations to predict particulate matter (PM) and ozone concentrations across the study domain for current and future timescales; c) Identifying various co-benefits associated with low carbon scenarios and assessing the co-benefits qualitatively and quantitatively (where possible), and in particular estimating the impacts of PM and ozone concentrations on human health and agricultural productivity, respectively.

19


1.3 Scope Various modelling techniques and statistical/econometric analytical techniques were used to quantify the likely co-benefits derived in India by pursuing a low carbon growth trajectory. The project activities include the following: a) Downscaled national-level results on fuel use and technology deployment derived from the MARKAL model for the policy scenarios developed under the ongoing TERI study to the state level and finer resolutions for input into IIASA’s Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS)-ASIA model. b) Undertake primary and secondary research to determine India-specific input data for the various models to be used under this study. A few examples include emission factors, fuel quality standard, available tail pipe control technologies, and human health impact on mortality and morbidity. c) Examine the likely co-benefits associated with mitigation policies and measures considered in the MARKAL scenarios. d) Apply GAINS-Asia model to prepare emissions inventory for criteria pollutants, such as NOX, sulphur dioxide (SO2), and PM, for future years with spatial distribution at the grid level. e) Carry out meteorological simulations for the study domain using the Weather Research and Forecasting (WRF) model to generate meteorological fields which will act as an important input to the air quality simulation model to assess the transport and dispersion of pollutants. f) Use data related to emissions, meteorology, and terrain for the study domain, derived from emission inventory, and WRF modelling exercises, as inputs into the Community Multi-scale Air quality (CMAQ) model to generate pollutant concentrations at different spatial and temporal scales (air quality modelling). g) Document and assess the co-benefits of the various policies and measures that can be adopted for mitigation across the energy sector. h) Carry out impact modelling to estimate the impact on human health and agricultural crop yield using the results obtained from the air quality model. This will include usage of various health functions, such as increase in incidence for mortality/morbidity and Disability Adjusted Life Years (DALY), to assess the impacts of particulate matter pollution on human health. Additionally, the input the ozone concentrations at different spatial and temporal scales generated from air quality model to assess the impact on agriculture using crop yield statistical model. i)

20

Disseminate study findings via preparation of a study report and through stakeholder interaction.


1.4

Overall methodology

Various policies and measures that may be incorporated towards GHG mitigation can lead to significant social, economic or environmental co-benefits that may further strengthen the case for their early and enhanced adoption. Accordingly, while we examine the CO 2 reduction implications of various options across scenarios, in this study we seek to explore and assess the co-benefits that various interventions could bring over time. Air pollutant and GHG emissions are significantly linked to energy use in different sectors of the economy. These not only depend on the quantum of energy used but fuel/technology used is also equally important in determining the amount of emissions. Energy reduction strategies primarily reduce GHG emissions and also mitigate the air pollutant emissions as a co-benefit. The environmental damage to the environment from a unit of air pollutant emission depends on various factors such as, i) time of release (depends on life cycle phase), ii) height of release (depends on source technology), iii) meteorological conditions (site specific), and iv) distribution of receptors (such as population, site specific), etc. Therefore, integration of various types of models (Figure 1.2) is carried out in this exercise. This work has built upon TERI’s analytical research capability on energy, emissions, and air quality modelling. Model integration involved cascading of models using soft link. Outputs from one model were fed to other models as inputs. The common input parameters (if any) will be consistent across all models.

Figure 1.2: Overall methodology for the study

21


The following sub-sections present a brief description of each component of Integrated Modelling that were carried out to evaluate the various co-benefits of GHG mitigation scenario, such as those on air quality, health, and agriculture.

1.5 Energy system modelling This study builds on TERI’s ongoing development of the model and database for GHG mitigation analysis. TERI has been using the MARKAL model to examine alternative socioeconomic developmental pathways and the energy-related implications of different trajectories. MARKAL is a dynamic linear-programming (LP) model of a generalized energy system. The model uses linear programming methods to solve the technology mix that best meets the specified objectives. The end-use demands of energy services for each demand sector and for each time period are exogenously predicted using various techniques, such as econometric techniques, process analysis models, etc. The elements of MARKAL simulate the flow of energy in various forms (energy carriers) from the sources of supply (import, export, mining, and stockpiling) through transformation systems (resource, process, conversion, and demand technologies) to the demand devices that satisfy the end-use demands. Apart from indicating the minimized total system cost of the energy sector, the main outputs provided by the model include information regarding the level of uptake of total energy resources, their distribution across the consuming sectors, the choice of technological options at the resource supply, conversion and end use levels, CO2 emission level associated with resource supply, end use technological options adopted, etc. We started by using the existing MARKAL database and conducted model runs across a range of scenarios designed to reflect different levels of policy and technological progress and to subsequently examine the level of mitigation possible in each scenario. The Reference (BAU) and the Alternative scenarios are designed and set up based on a consultative process to decide on what the broad scenario storylines should reflect, and to represent updated data and inputs on the understanding related to these storylines. The national-level results on fuel use and technology deployment were further disaggregated to the state level and fed into the emissions model.

1.6 Emissions modelling The Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS)-ASIA model has been used for estimation of emissions based on energy and non-energy sources. GAINS-ASIA takes into account various sector-fuel combinations to estimate emissions using the basic approach: Emissions = Activity level × abated emission factor × % of capacity controlled Where in,

22


Abated emission factor = Unabated emission factor × (1 - % removal efficiency of the control strategy) The various fuels / energy sources which are considered in GAINS are: (i) coal, (ii) natural gas, (iii) petroleum products, (iv) biomass fuels, and others. The overall energy usage in India is categorized into six sectors and eight fuels (Table 1.1). Hence, the emissions are estimated based on the sector and fuel combinations. Table 1.1 Categorization of sectors and fuels in the study Fuel

/ Sector

Coal

Biomass

Heavy Fuel

Diesel

Gasoline

LPG

Natural gas

No fuel use

Domestic Industrial Combustion Transport Power Plant Other energy use Non-energy

For the emissions modelling, based on the energy inputs in the GAINS-Asia database, emissions are estimated for NOX, SO2, carbon monoxide (CO), and PM, and non-methane volatile organic compounds (NMVOCs). Based on the inputs (energy use pattern provided by MARKAL/ model) in the GAINS database, emissions inventory was prepared for criteria pollutants. Based on the output from MARKAL/ model and further analysis, emissions inventory of these pollutants was prepared for future years with spatial distribution at the 36 km x 36 km grid level. It is worthwhile to mention that for each model run, a grid level emissions inventory needs to be estimated separately. In this exercise, India-specific data on emissions factors, fuel quality standards, and deployment of tail pipe control technologies have been used.

1.7 Meteorological modelling Meteorology has an important role to play in dispersion and transport of air pollutants. TERI has been carrying out meteorological simulations for the specific timeframe using the Weather Research and Forecasting (WRF) model. The meteorological fields developed for the study domain act as an important input to the air quality simulation model to assess the transport and dispersion of pollutants. 23


The WRF model is a next-generation meso-scale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. It features multiple dynamical cores, a three-dimensional variational (3DVAR) data assimilation system, and a software architecture allowing for computational parallelism and system extensibility. WRF is suitable for a variety of applications across scales ranging from meters to thousands of kilometres. It also provides operational forecasting, a model that is flexible and efficient computationally, while offering the required advances in physics, numerics, and data assimilation contributed by the research community. The WRF model has been used to generate meteorological fields over the study domain for running an air quality model later.

1.8 Air quality modelling Air quality modelling was carried out using the Community Multi-scale Air quality model (CMAQ) which is currently primary air quality model of the US-Environment Protection Agency (US-EPA). It is a powerful third generation air quality modelling and assessment tool designed to support air quality modelling applications ranging from regulatory issues to science inquiries on atmospheric science processes. The CMAQ system can address fine particulate, tropospheric ozone, acid deposition, and other air pollutant issues in the context of ‘one’ atmosphere perspective, where complex interactions between atmospheric pollutant and regional and urban scales are confronted. This model requires input data related to emissions, meteorology, and terrain for the selected domains. These inputs will be obtained from results of emissions and metrological models from the previous steps. Thereafter, a number of processors are run for carrying out simulation of air quality using CMAQ. The CMAQ model includes processors, enumerated as follows: a) CCTM (CMAQ Chemical Transport Model): It simulates the relevant and major atmospheric chemistry, transport, and deposition processes involved throughout the modelling domains. It performs model simulations for multiple pollutants and multiple scales with the input data. b) MCIP (Meteorology-Chemistry Interface Processor): It links meteorological model WRF with the CCTM system to provide the complete set of meteorological data needed for air quality simulation. c) JPROC (Photolysis Rate Processor) : It produces the photolysis rates used in the CMAQ chemical transport simulation. d) ICON/ BCON (Initial and Boundary Condition Processor): They provide concentrations fields for individual chemical species for the beginning of a simulation and for the grids surrounding the modelling domain, respectively. The output of the air quality modelling exercise is in the form of pollutant concentrations at different spatial and temporal scales. We focussed on simulating PM2.5 and ozone concentrations to derive health and agricultural impacts, respectively, for the two pollutants.

24


1.9 Impact modelling of air pollutant concentrations Pollutant concentrations at different spatial and temporal scales obtained from the results of the air quality model were used for estimating health impacts to human beings and impacts on agricultural productivity. While particulate concentrations are known to have a higher impact on human health, ozone is linked to decrease in productivity of certain crops.

1.9.1 Human health impact assessment The study used air pollution concentration obtained from air quality modelling using CMAQ model, health and socio-economic datasets collected from various primary and secondary sources in the study regions. This study followed the methodology proposed by World Health Organization (WHO, 2009; Ostro, 2004; Gowers et al., 2014). However, human health risk assessment in terms of mortality and morbidity due to exposure to air pollution are also assessed by developing primary concentration response functions (CRF), also known as Relative Risk (RR) functions. These primary CRFs/RRs are developed using data collected through primary survey in the study regions (~3–5 cities/villages/metros) at household levels. For assessing the mortality risk due to air pollution, past mortality data has been collected from hospitals, Department of Health Research, Ministry of Health & Family Welfare, GoI and census data, etc. The data on air pollution was collected from the Central Pollution Control Board (CPCB) as well as the State Pollution Control Boards (SPCBs). The primary health surveys were conducted to collect selfreported health status due to exposure to air pollutants. This data helped in assessment of morbidity due to air pollution exposure. A questionnaire was used to elicit information about health aspects covering the past 12-month period of the subjects. For instance, prevalence of upper respiratory symptoms, lower respiratory symptoms, asthma in past three months and in the preceding one year; information about subject’s residential proximity to main road, type of cooking fuel use at home, the amount of time spent outside, and overall activity and behaviour. Other variables taken into account were age, gender, smoking, income, etc. Both data sets were used for developing CRFs/RR using various statistical tools, such as regression analysis. These CRFs/RR functions were used for estimation of mortality and morbidity in the study region due to air pollution exposure. Finally, the health burden of mortality and morbidity was estimated using DALY which includes Years Lost to Disease (YLD) and Years of Life Lost (YLL). For the same, apart from incidence data, baseline YLD and YLL data was required. This study helped in development of Indian CRFs/RR functions which were helpful in assessing more accurate impacts on human health due to exposure to air pollution compared to CRFs/RR developed by WHO. However, in this study, these functions were developed for limited study regions. At the same time, this study also assessed the impact on human health using the CRFs/RR developed by the WHO to compare the results. The expected outcomes have been outlined as follows: 

25

Development of primary mortality and morbidity CRFs/RR functions by using Indian data set for accurate measurements of impacts due to air pollution exposure.




Human health impacts due to air pollution exposure using both the approaches (WHO recommended as well as using Indian CRFs/RR functions)



Application of assessed estimates of co-benefits to overall national-level mitigation scenarios

1.9.2 Crop loss assessment The proposed work assessed the yield losses due to surface ozone (O3) on rice and wheat crops that are grown in India. Generally, two modelling methods have been used to estimate the ozone crop impact; a concentration-based model and a flux-based model. To apply the concentration- and flux-based risk assessment methods, a number of different datasets were required. For both the methods, hourly ozone concentration at crop height during the crop growing season was used. For the concentration-based method, hourly O3 concentration data was obtained from CMAQ model and characterized for each of the concentration-based indices investigated; for the flux-based method, the hourly O3 concentration data was used with associated meteorological data, the information was bought together in the DO3SE stomatal O3 flux model (Emberson et al., 2009). The O3 dose as well as flux was calculated for a specific period during the crop growing season, termed as the ‘accumulation period’. The accumulation periods of all O3 indices were defined according to crop phenology data. Crop distribution data defined the geographical area over which modelling was undertaken and national crop production statistics were used to determine yield, production, and economic losses resulting from O3 exposure. The O3 concentration data was provided on a 36 km x 36 km spatial resolution and combined with the district or national-level crop production data. The GIS tool was used for defining the geographical location of districts. State-wise crop phenology data (sowing and harvest dates) for India was collected from the national databases under the Ministry of Agriculture, Government of India. The data on district-level crop production and area of the crops for India was also collected. Crop data for the base year was collected to be consistent with the emissions data used in the ozone model. Both the concentration- and flux-based indices were used to characterize the impact of O3 on crops grown in India. In order to apply the dose-response functions, the O3 concentrations for each grid were characterized according to the O3 indices (AOT40, M7, and POD6) and then used with the appropriate functions to estimate crop yield loss. The crop production loss (CPL) in each grid was then calculated based on relative yield loss (RYL) and the actual crop production (CP).

26


Chapter 2 Energy Scenarios 2.1 Introduction This chapter discusses the energy scenarios developed for the study and analyses the results of these scenarios. TERI’s MARKAL (MARKet ALlocation) model was used for the purpose of developing and examining low carbon scenarios for India. Based on the discussions with Shakti, the Reference Scenario (BAU) and 3 Alternative Scenarios were laid out for this exercise; these have been elaborated in the following section.

2.2 Description of Energy Scenarios The four scenarios employed in this study were envisaged as follows: 

Reference (BAU): Scenario representing climate policies rolled out till 2016, and an ambitious high Gross Domestic Product (GDP) growth as envisaged by the Government of India



Intended Nationally Determined Contribution (INDC): Scenario including various climate policies and targets formulated in India’s INDC submission



Ambition (AMBI) : Scenario with a high mitigation ambition (beyond INDC and towards a ‘well below 2 DC world’) but keeping development and current socio-economic context at the forefront



Low growth (LG): Alternate Scenario looking at a ‘realistic’ GDP growth (lower rate of growth than the other 3 Scenarios) and the implications for mitigation

The Reference (BAU), INDC, and Ambition (AMBI) scenarios are based on high GDP growth (collectively referred to as High Growth scenarios in this report).

2.2.1 Data & Assumptions Across Scenarios The Government of India targets a doubling of per capita income every decade and has stated the same in its INDC document as an intention to maintain a high GDP growth rate. Therefore, in order to reflect this aspiration of the government, we also consider an aspirational growth rate of 8.3% up to 2030 and 6% thereafter, in line with the Government of India’s growth target as spelt out in the INDC document (Government of India [GoI], 2015). However, GDP growth has failed to reach such high levels in reality, and in order to reflect and examine a situation wherein the economy is not able to move along its aspirational development path, we also developed a Low Growth Scenario to reflect a trajectory with a more ’realistic‘ growth rate averaging around 6.5%. Apart from the nature and level of economic activity, the main driver of energy demands is the demographic profile of the country. The level of population, the extent of urbanization, and the lifestyles of different sections of people have implications on the patterns and levels of energy end-use demands. In this study, we consider India’s population to 27


increase to 1.7 billion by 2050 as per the Population Foundation of India (PFI) Scenario B (Population Foundation of India & Population Reference Bureau, 2007). The level of urbanization across all scenarios is assumed to increase from 30% in 2011 to 34% in 2030 and 38% in 2050. The end-use demands within each demand sector are estimated on the basis of partial end-use methods and/or econometric models, based on the basic drivers of population and GDP growth, etc. The choice of technologies for meeting these demands, governed by the existing policies and consumer behaviour, varies across scenarios to reflect a changing policy environment and thereby different choices within the available options. For instance, the Reference Scenario assumes a moderate penetration of LED-based lighting technologies while the INDC and Ambition Scenarios incorporate swifter and deeper penetration of the same. Specifically, the Reference Scenario (BAU) assumes the uptake of better/ more efficient technologies based on past trends, existing policies, and targets rolled out before 2016. As a result, the current renewable energy targets are only partially achieved; industrial efficiency improvement is seen mainly in the PAT designated consumers as delineated in the first PAT cycle; penetration of efficient appliances is slow and so is the phase out of traditional fuels and electrification of households; GRIHA1-rated buildings in the commercial sector are few, and their penetration is constrained by their higher costs and lack of appropriate policies; past trends continue in the share of railways, vehicular efficiency improvements, and share of electric pumps in the agriculture sector. The INDC Scenario is designed so as to chart out the strategies required to achieve the targets laid out in the NDCs of India. While the major targets quantified in the INDCs are emissions intensity reduction of GDP by 33%–35% of 2005 levels and developing a 40% non-fossil based capacity by 2030, achieving these requires a multi-dimensional development action plan. Accordingly, this scenario considers options for enhanced efficiencies of technologies across all sectors, sustainable and efficient urbanization patterns based on smart cities, fuel substitution in the transport and agriculture sector, from petroleum-based fuels to increasing share of decarbonized electricity, increased penetration of energy efficient buildings in the commercial sector, and a swifter phase out of traditional fuels. The Ambition Scenario (AMBI) takes up strategies for deeper decarbonization over and above the INDC Scenario. Consequently, the scenario assumes more rapid uptake of efficient technologies across all sectors, accelerated efficiency improvements for both appliances and vehicles, and aggressive efforts towards improvement of specific energy consumption (SEC) across the industrial sector. This scenario, therefore, assumes higher penetration of efficient and low carbon options, such as electric vehicles over petroleum-based vehicles, use of public modes of transportation over private vehicles, use of five star rated air conditioners, and enhanced renewable capacity. The higher levels of penetration are incorporated by relaxing constraints appropriately based on discussions with sectorial experts to identify the options where there may be possibilities to push towards deeper decarbonization across a particular end-use or technology. 1

28

Green Rating for Integrated Habitat Assessment


The Low Growth (LG) scenario traces a development strategy wherein the Indian economy grows at a relatively moderate growth rate of 6.5%. The scenario witnesses growth based on past trends which is largely focussed on selective technologies and end-uses. For instance, in case of industries, SEC improvement continues to be limited to the major energy-intensive industries. The uptake of renewable energy in this scenario is also gradual and urbanization and industrialization is slow and sporadic. Limited research and development leads to weaker improvement in efficiencies of the technologies. In some ways, therefore, this scenario replicates the trends of the Reference Scenario (BAU) under a Low Growth case. The detailed assumptions are listed out in the following section.

2.2.2 Sector-wise Assumptions This section lists the assumptions made in the aforementioned scenarios at a sectorial level.

Industry Sector Industry is the largest energy consumer and demand side carbon dioxide (CO2) emitter. Each of the industry sub-sectors assume that the scope for energy efficiency improvement across processes and technologies increases across the scenarios as they become successively more ambitious in terms of CO2 mitigation. Accordingly, this is reflected through the assumptions regarding possibility of improvement in their energy intensities. The SEC of BF-BOF processes in iron & steel industry is assumed to increase to the world average of 5.0 Gcal/tonne of crude steel by 2050 in the INDC scenario from its current value of around 6.52 Gcal/tonnes of crude steel. In the Ambition Scenario, the pace of efficiency improvement is faster such that an SEC of 5.0 can be reached around 2046. Also, while the share of scrap-based steel remains the same across Reference (BAU) and INDC Scenarios, its share is allowed to increase in the Ambition Scenario. The thermal SEC of cement is currently 726 kcal/kg clinker which is expected to reach 660 kcal/kg clinkers by 2050. On the other hand, the current electric SEC of cement industries in India is at 78 kWh/t of cement, but is assumed to reduce to 65 kWh/t cement by 2050 in both the INDC and Ambition Scenarios. The share of PSC (Portland Slag Cement) is assumed to increase to around 23% in the Ambition Scenario from 11% in the Reference Scenario (BAU). Further, the production of cement is allowed to shift from the 4-stage and 5-stage processes to the more efficient 6-stage processes (with co-generation) in the Ambition Scenario. The brick industry is assumed to improve its SEC from the current value of 1.4 MJ/kg of fired brick to 1.2 MJ/kg of fired brick in the INDC Scenario. In the Ambition Scenario, we also expect that the production shifts to non-biomass based production processes, such as BTK Zig-Zag technology. The fertilizer industry is assumed to improve its efficiency from the level of around 6.04 Gcal/tonne in 2013 to 5.35 Gcal/tonne in the INDC Scenario and less than 5 Gcal/tonne in the Ambition Scenario around 2051. The naphtha-based fertilizer plants are assumed to be increasingly replaced by natural gas-based plants. 29


For aluminium industry, the trajectory of efficiency improvement is assumed to be the same across the INDC and Ambition Scenarios. The current SEC of the industry is 14858 kWh/tonne of Aluminium but is assumed to improve by around 8% by 2031. The disaggregated and varied nature of MSME industries prevents a generalization of the sector. However, it is assumed that with focussed efforts towards clusters, efficiency improvements of about 15% and 30% can be achieved in the INDC and Ambition Scenarios, respectively.

Agriculture Sector Agriculture has two major energy consuming end uses, that is, land preparation and irrigation. The share of land preparation in fuel consumption is around 40% while irrigation consumes the rest of the energy in the sector. Tractors and tillers, both of which run on diesel, have been improving their efficiency over the years. However, their size and capacity vary significantly because of land holding size and usage based on renting of tractors. Accordingly, in this study we assume that efficiency improvements follow the same paths across all scenarios in case of land preparation. The share of diesel pumps in the Reference Scenario (BAU) is expected to decline to 22% by 2051. In the INDC and Ambition Scenarios, the decline is expected to be steeper; with the share of diesel pumps reaching 20% and 8%, respectively, by 2051. The Low Growth Scenario however assumes 30% share of diesel pumps. A modest penetration of solar pumps is also assumed in the Ambition Scenario increasing from 2 Lakh 30%

(1c) Occupation

2% 4%

Business/ Self-employed

18%

Regular Salaried-Pvt.

20%

Regular Salaried-Govt. Casual/Daily Wage 18%

Student House wife Unemployed

26%

10% 2%

Figure 5.1(a-c): Distribution of respondents in Goa-Vasco 71

Retired


5.6.2 Goa – Panjim A total of 700 households were surveyed comprising of 2,520 individuals and out of which ~64% respondents were males and rest were females. The locality of households were 83% in residential areas and 14% were in semi-commercial areas, and the rest 4% in industrial, commercial, and un-regularized colonies (Figure 5.2a). Approximately 54% of individuals were males and ~46% were females including all age groups, that is, 0–5 years, 5–10 years, 10–18 years, 18–25 years, 25–30 years, 30–40 years, 40–50 years, 50–60 years, and 60+ years. The average age of males and females including all age groups is statistically different (p-value < 0.05 at α = 0.05). The proportions of education, that is, primary, higher secondary, senior secondary, graduate, and post-graduate or greater was also statistically different (p-value < 0.05 at α = 0.05) for males and females of all age groups. The occupation of majority of individuals including males and females of all age groups were—~90% business/self-employed, regular salaried, in private companies and government organizations, students, and housewives (Figure 5.2c). Low percentage of housewives, that is, 20% shows high level of female employment. Approximately 76% of the interviewed households belonged to middle income groups whose monthly income lies in the range of INR 10,000 to 100,000 (Figure 5.2b). Nearly 11% of the households surveyed were in higher income group (greater than INR 100,000) and 13% in lower income group whose maximum monthly income was INR 10,000. The majority of people perceived that major sources of pollution was industries, roadside dust, vehicular exhaust, construction activities, and biomass burning. Approximately 85% of the respondents perceived that air quality is good/average/excellent/ while only ~10% perceived bad/worse air quality and rest were not aware. Nearly 15% of total individuals exercised, ~43% played different outdoor games and walking, and ~40% were not into any physical activities amongst all age groups. Respiratory diseases were found to be more than cardiovascular diseases in individuals of surveyed households. The major respiratory and cardiovascular diseases in individuals were common cold, sinus, allergic rhinitis, nasal irritation, wheezing cough, and chronic bronchitis, cardiac arrest, burning of eyes, nose and throat, asthma, chest pain, cancer in trachea, bronchus and lungs, cataract, high blood pressure, coronary heart disease, cough, tonsils, pneumonia, ischemic stroke, wheezing, skin allergies, cerebrovascular disease, and influenza.

72


1%

(2a) Locality of Households

1%

1%

Residential

14%

Commercial Industrial

83%

2%

(2b) Monthly Household Income 3%

3%

0-5,000

10%

6%

5,001-10,000 10,001-20,000 17% 26%

20,001-50,001 50,001-1 Lakh 1 L-1.5 Lakh 1.5L-2 Lakh >2 Lakh

33%

(2c) Occupation

2% 5%

Business/ Selfemployed Regular Salaried-Pvt.

15%

20% 17%

Regular Salaried-Govt. Casual/Daily Wage

10% 28% 3%

Figure 5.2(a-c): Distribution of respondents in Goa-Panjim

73

Student


5.6.3 Chandigarh A total of 1,428 households were surveyed that comprise of 4,998 individuals. Out of the total number of respondents, the percentage of males was ~79%, while the rest were females. The proportion of households in residential areas was ~94%, 3% in commercial areas, and the rest (3%) in semi-commercial, and un-regularized colonies (Figure. 5.3a). Further, considering the household characteristics, ~54% of the individuals were males and ~46% were females which included the following age groups: 0–5 years, 5–10 years, 10–18 years, 18–25 years, 25–30 years, 30–40 years, 40–50 years, 50-60 years, and 60+ years. The average age of males and females including all age groups is statistically different (p-value < 0.05 at α = 0.05). The proportion of education levels, that is, primary, higher secondary, senior secondary, graduate and postgraduate or greater were also statistically different (p-value < 0.05 at α = 0.05) for males and females of all age groups. Majority of the individuals (~95%), including both males and females, were engaged as business/self-employed, regular salaried in private companies and government organizations, students, and housewives (Figure. 5.3c). A low percentage of housewives, that is, 21% show that most of the females were employed. A major portion (~53%) of the interviewed households belonged to middle income groups whose monthly income lies in the range of INR 10,000 to INR 100,000 (Figure 5.3b). The higher (greater than INR 100,000) and lower (less than INR 10,000) income group households comprised of ~33% and 14% of the households, respectively. A majority of people perceived that the major sources of pollution were roadside dust, vehicular exhaust, cooking fuels, and cigarette smoking. While ~97% of the respondents perceived that air quality is good/average/excellent, only ~3% perceived bad/worse air quality and the rest were not aware. About 18% of total individuals including males and females exercised, ~ 80% played different outdoor games and walking, and ~18% were not into any physical activities amongst all age groups. Respiratory diseases were found to be more than cardiovascular diseases in individuals of surveyed households. The major respiratory and cardiovascular diseases in individuals were common cold, sinus, allergic rhinitis, nasal irritation, wheezing cough, and chronic bronchitis, cardiac arrest, burning of eyes, nose and throat, asthma, chest pain, cancer in trachea, bronchus and lungs, cataract, high blood pressure, coronary heart disease, cough, tonsils, pneumonia, ischemic stroke, wheezing, skin allergies, cerebrovascular disease, and influenza, pleurisy, angina, COPD, bronchitis, and emphysema.

74


(3a) Locality of households 1% 3% 2% Residential Commercial Semi-commercial Unregularized

94%

(3b) Monthly household income 4% 6% 11%

0-5,000

10% 12%

16%

19% 22%

5,001-10,000 10,001-20,000 20,001-50,001 50,001-1 Lakh 1 L-1.5 Lakh 1.5L-2 Lakh

Figure 5.3(a-c): Distribution of respondents in Chandigarh

75


5.6.4 NCT of Delhi A total of 2,711 households were surveyed that comprised of 13,826 individuals. Out of this, ~72% respondents were males and the rest were females. The locality of households comprised of ~83% in residential areas, and 11% were in semi-commercial areas, and the rest 6% in industrial, commercial, and unregularized colonies (Figure 5.4a). About 53% of individuals were males and ~47% were females, which include the following age groups: 0–5 years, 5–10 years, 10–18 years, 18–25 years, 25–30 years, 30–40 years, 40–50 years, 50–60 years, and 60+ years. The average age of males and females including all age groups is statistically different (pvalue < 0.05 at α = 0.05). The proportions of education levels, that is, primary, higher secondary, senior secondary, graduate, and postgraduate or greater was also statistically different (p-value < 0.05 at α = 0.05) for males and females of all age groups. The occupation of majority of individuals (~93%) including males and females of all age groups was business/self-employed, regular salaried in private companies/government organizations, students and housewives (Figure 5.4c). A low percentage of housewives, that is, 21% show that most females were employed. About 68% of the interviewed households belonged to middle income groups whose monthly income lies in the range of INR 10,000 to INR 100,000 (Figure 5.4b). The higher (greater than INR 100,000) and lower (less than INR 10,000) income group households comprised of ~25% and 7% of the households, respectively. The majority of people perceived that major sources of pollution was roadside dust, vehicular exhaust, construction activities, biomass burning, and industries. Nearly 69% of the respondents perceived that air quality is good/average/excellent while only ~31% perceived bad/worse air quality and rest were not aware. Nearly 15% of total individuals exercised, ~50% play different outdoor games and walking, and ~34% were not into any physical activities including all age groups. Respiratory diseases were found to be more than cardiovascular diseases in individuals of surveyed households. The major respiratory and cardiovascular diseases in individuals were common cold, sinus, allergic rhinitis, nasal irritation, wheezing cough, and chronic bronchitis, cardiac arrest, burning of eyes, nose and throat, asthma, chest pain, cancer in trachea, bronchus and lungs, cataract, high blood pressure, coronary heart disease, cough, tonsils, pneumonia, ischemic stroke, wheezing, skin allergies, cerebrovascular disease, influenza, respiratory tuberculosis, laryngitis, otitis media, emphysema, and COPD.

76


(4a) Locality of households 4% 2%

0%

Residential

11%

Commercial Industrial Semi-commercial 83%

Unregularized

(4b) Monthly household income 1% 6%

8%

0-5,000

6%

5,001-10,000 10,001-20,000

15% 11%

20,001-50,001 50,001-1 Lakh

22%

1 L-1.5 Lakh

31%

1.5L-2 Lakh >2 Lakh

(4c) Occupation 2% 5%

Business/ Self-employed 14%

Regular Salaried-Pvt. Regular Salaried-Govt.

21% 18%

Casual/Daily Wage Student

29%

9% 2%

Figure 5.4(a-c): Distribution of respondents in Delhi

77

House wife

Unemployed


5.7 National level health impacts using WHO CRFs The present study conducted a scenario-based modelling to compare the total estimated mortalities associated with respiratory and cardiovascular diseases included in the present study as per the ICD codes described in the previous section of the report. Figure 5.5 represents the variations in the estimated mortalities in the six scenarios, that is, INDC, AMB, and LG developed in comparison to the BAU scenario for both 2030 and 2050.

Figure 5.5: Mortality due to PM2.5 under various policy scenarios at national level The BAU 2016 depicted in the above figure provides the estimated total respiratory and cardiovascular mortality attributable to ambient air pollution in India in the year 2016, while the BAU 2030 and BAU 2050 projects the total mortality, if the present rate of increase in ambient air pollution levels is replicated in the coming years. It is relevant to point that a significant decline in the total respiratory and cardiovascular mortality is expected to be incurred in the alternative scenarios proposed in the current study as highlighted in Figure 5.5. The results of the analysis revealed that the total mortality is expected to increase by ~25% and ~36% from the BAU 2016 scenario in the BAU 2030 and BAU 2050 scenario respectively, if the current growth rate in the ambient air pollution is continued in future. However, a decline in the total mortality of the order ~1% to 3% is expected to be obtained in the scenarios proposed for the years 2030 and 2050. It was observed that maximum decline in the total mortality rates, that is, ~12.45% was observed in the INDC 2050 scenario, while a decline of ~10.29% was observed in the AMBI 2050 scenario as represented in Figure 5.5. The results of the analysis revealed that a similar reduction of the order ~3% in the total mortality values is observed when BAU 2030 is compared with all the three proposed scenarios, that is, INDC 2030, AMB 2030, and LG 2030. However, a significant decline in the total mortality is observed when a comparison is made 78


between BAU 2050 and the three proposed scenarios for the year 2050. A significant decline of more than 12% was observed from the BAU scenario in the INDC 2050 scenario which was followed by the AMB 2050 and LG 2050 scenario, respectively as represented in Figure 5.5. AMBI scenario shows slightly higher health impacts than INDC scenario, mainly due to higher PM2.5 concentrations due to the use of high ash indigenous coal. Thus, it is can be anticipated that the proposed scenarios have the potential to curb the rising levels of air pollutants in the country which will help in obtaining long term and population level health benefits.

5.8 City-specific health impacts using indigenous CRFs In order to estimate the impact of air pollution on the health status of the residents at city level, the relative risk functions were generated for the proposed scenarios to estimate the total respiratory and cardiovascular mortality in Chandigarh, Delhi, and Goa. The results of the analysis for estimating the total mortality in Chandigarh, Delhi, and Goa are presented in Figures 5.6(a), 5.6(b), and 5.6(c), respectively. The figures provide a graphical representation of the variations in the estimated mortality rates in the three cities, and provide a comparative view of the mortality values under the various scenarios.

79


Figure 5.6(a-c): The results of the analysis for estimating the total mortality in Chandigarh, Delhi, and Goa

80


The results of the analysis revealed that the values of estimated pre-mature deaths were highest for Delhi city, while lowest for Chandigarh city as depicted in Figure 5.6a-c. The relatively higher values of the estimated mortalities observed in Delhi can be attributed to the very high levels of PM2.5 concentrations in the ambient environment of the city. Apart from this, the population density of the city also plays a dominant role in determining the mortality levels in a city. Delhi being the second most populous urban agglomeration in India has a very high population density in comparison to Chandigarh and Goa which can be considered as one of the reasons for higher values of estimated premature deaths in the city. The estimated annual mortalities per lakh of population were found to be 45, 12, and 9 for Delhi, Chandigarh, and Goa, respectively. The overall results of the current study revealed that the health impacts due to exposure to ambient PM2.5 concentration are significantly high in India. Thus, it is imperative to take focussed actions to reduce the ever increasing air pollutant concentrations.

81


Chapter 6 Agricultural Impacts 6.1 Background Urbanization and developments in various fields have led to increase in background concentration of various air pollutants and affected the agriculture adversely (Naab et al., 2013). Amongst these pollutants the most damaging ones are particulate matter, sulphur dioxide, nitrous oxides, carbon monoxides, ozone, and lead. Of these six major air pollutants the most pervasive problem continues to be ozone, especially with respect to its impact on agricultural crops. Its effects on crops can be both national and regional while the effects of its precursors like nitrous oxide and sulphur dioxide are predominantly local (Fuhrer et al., 1997). It is also a major greenhouse gas, after carbon dioxide and methane, thus contributing to global warming (Royal Society, 2008). However, due to its short residence time, its concentration varies strongly in troposphere on global scale and subsequent radiative forces making its accurate quantification difficult (USEPA report). Ozone is a strong photochemical oxidant and reacts rapidly with organic material producing reactive oxygen species, such as hydrogen peroxide, hydroxyl, superoxide, peroxyl radicals, which cause oxidative destruction of lipids and protein. Exposures to ground level ozone have acute as well as chronic impacts. In South Asia, the burning of biomass and crop residues has led to increase in ozone precursors, such as volatile organic compounds, carbon monoxide , and nitrogen dioxide thereby leading to significant increase in ozone levels that frequently exceeds normal background (25–40 ppbv) (Wang et al., 2004). Singh and Agarwal (2011) reported that the background ozone concentration in the Indian region has been increasing at the rate of 0.5%–2% every year for the past several decades, with the current monthly average ozone concentration in the range of 35–55 ppb. The values over the Indo-Gangetic plains, which are the most important agricultural region in India, are higher than any other part of the country as it has suitable meteorological conditions which favour formation of ground level ozone like high temperature and long sunshine hours (Singh and Agarwal, 2011). In addition, ozone, being a secondary pollutant, migrates downwind of urban areas to the periurban and rural areas (active sites for agricultural production) in high concentration. Furthermore, ground level ozone concentrations in the Indian region are higher during MarchJune than the other months; during this period most of the winter crops are at flowering to harvest stage, the stage most crucial to crop yield (Royal Society, 2008). Given the high ozone concentration in the important agricultural regions of India during agriculture seasons, it is crucial to study the impact of ozone on crops that are grown in India. Therefore, a systematic literature review is carried out in the next chapter to understand the trends of ozone in India, its impacts, and the methodologies that are available for studying impacts of ozone on crops grown in India.

82


Experimental studies provide an in depth understanding of plant responses to ozone and help in establishing critical levels and thresholds. On the other hand, model studies upscale the experimental data into regional level risk assessment of crop responses to ozone exposure which help in developing air quality standards. Regional risk assessment models use metrics that consist of ozone exposure response functions of crops. The commonly used response functions developed in North America and Europe are a result of large-scale coordinated experiments conducted specifically for studying crop response to ozone. Studies have shown that Indian crops cultivars are likely to be more sensitive to ozone than the crops cultivars in Europe and the US (Emberson et al., 2009). In addition, the climatic and crop growing conditions in India are very different from that of North America and Europe. Data for establishing exposure response functions for Indian crops are limited. However, there are a number of fragmented studies that have been carried out by individual research on crop response to ozone in the region. To study the impact of ozone on agricultural crops in India, the study was divided into three steps. a. Establishment of ozone-dose-response functions for crops grown in India b. Comparative study between Indian dose-response functions and North-American/ European dose-response functions c. Ozone impact on crop assessment

6.2 Ozone concentrations and its impacts in India A review of literature has been provided in Appendix–A to explain the process of ozone entering into the plants and causing damages to the crop yields. Studies have been conducted to measure the ozone concentration in Indian regions, like in Varanasi, Delhi, and Pune. All estimated the ozone concentration to be more than 40 ppb (Mishra et al., 2013; Singh et al., 2010; Singh et al., 2012; Sarkar et al., 2010; Pandey et al., 2014; Sinha et al., 2015). They also tried to estimate monthly mean and seasonal variation of ozone over India. Different ozone monitoring stations in India namely, Thiruvananthapuram, Hyderabad, Varanasi, Mumbai, Bengaluru, Kolkata, Ahmedabad, Srinagar, showed that ozone concentration in Indian regions are maximum during May and June, and minimum during December. All these regions have experienced trend of increase in ozone. Such high concentrations in Indian regions will lead to negative impact on crop growth. The National Crop Loss Assessment Network (NCLAN), USA studies have reported that crop growth threshold for ozone as 35 ppb.

6.2.1 Impacts of ozone on crop yield Field experiments conducted by the NCLAN, USA and Open Top Chamber (OTC) Programme in Europe have shown loss in crop yield by 5%–10% (Debaje, 2014) and demonstrated that increased level of ozone concentrations in troposphere have deleterious impact on yield of crop plants, which pose a threat to food security. It is thus an issue of concern in a world where 83


expanding economies and urbanization have led to an increase in emissions of ozone precursors (Van Dingenen et al., 2009). Studies have shown that there is 2%–15% reduction in rates of yield due to present ozone exposure, and if global ozone precursor emissions continue to increase, we may further witness 10% decline in yields by 2030 (Avnery et al., 2013). Economic impact of ozone on crop yield is $2–4 billion for the USA and 4 billion Euros in Europe (Ashmore, 2005). Globally, high levels of ozone may reduce crop productivity in the year 2030 by 1.5%–10%, 0.9%–11%, and 2.1%–32% in wheat, soybean, and maize, respectively and lead to agricultural losses worth $17–25 billion annually, an increase of $6–17 billion in major crops, such as wheat, soybean, and maize (Avnery et al., 2011). It has been estimated that yearly rice grain yield losses may be equivalent to 3%–4% of the global rice production (Jing et al., 2015). Current ozone concentrations ranging between 31–50 ppb have reduced soybean yield by 7.7%, and future ozone concentrations (51–75 ppb) would further reduce soybean yield by 10%. Several experiments have shown that yield of soybean is under threat at current ozone concentrations (Zhang et al., 2014) with drastic loss of 53%. American soybean yield decreased by 20% when ambient ozone level increased from 56 ppb to 69 ppb.

6.2.2 Impacts of ozone on crop yield in India Numerous experimental (Ambhast and Agarwal, 2003) as well as modelling studies conducted in India (Avnery et al., 2011 and Ghude et al., 2014) have reported high yield losses in crops due to ozone. These findings are in line with global modelling studies that have shown that crop yield losses are highest in India followed by China (Debaje et al., 2014). Debaje et al. (2014) demonstrated wheat to be the most affected crop in India due to its high ozone sensitivity and relatively higher ozone concentrations during its growing season. Ghude et al. (2014) demonstrated that the overall wheat loss due to ozone is around 3.5 million tonnes followed by rice at 2.1 million tonnes. Ghude et al. (2008) and Deb Roy et al. (2009) have also shown that winter wheat relative yield loss is 23% over Indo-Gangetic plains in northern India, while modelling based study in Asia showed 5%–20% yield losses in major crops (wheat, rice, and legumes) (Singh et al. 2010). About 50% of the global population is dependent on rice, while ozone has great impact on its yield. There is 5%–10% reduction in the yield loss of Japanese rice under ambient ozone exposure. Ozone has negatively impacted the rice crops leading to 11%–15% yield loss in two Indian rice cultivars in Varanasi. The worst affected crops in terms of loss of production are wheat and rice. (Van Dingenen et al., 2009) as their M7 and AOT 40 values exceed the critical limit (Debaje et al., 2014). Therefore, agricultural crop production is at high risk, especially in developing parts of the world such as India, due to increased level in ozone concentrations. There will be an increase in loss of various Indian crops, that is, soybean, maize, rice, wheat, barley, potato, and beans in future in India (Pandey et al., 2014).

84


6.3 Ozone risk assessment studies There are several experimental studies and model studies conducted in India and globally. In India most of the studies have been conducted in Varanasi, while some have been conducted in Allahabad and Delhi. Studies have wheat, maize, soybean, gram, cotton, and rice as the experimental crops. In experimental studies, plants were treated differently, some of them are fumigated and others were studied under filtration chamber. Again, in some other experiments, 1-Ethylene diurea (EDU) was used. EDU is recognized as ozone injury suppressing chemical. Global studies have shown that relative yield loss, crop production loss, and economic crop loss due to ozone exposure of crops is highest in India (Debaje et al., 2014). Wheat and rice are two main staple crops of India. Debaje et al., 2014 study has demonstrated that wheat yield is mostly affected as it is extremely sensitive to ozone, due to prevalence of ozone in the growing season. Ghude et al., 2014 stated that overall wheat loss due to ozone is around 3.5 million tonnes followed by rice at 2.1 million tonnes. Presently, there are no air quality standards in India specifically for the protection of agricultural crops from ground level ozone (Ghude et al., 2014). There is a different crop response function for crops of different regions which usually depend on ambient conditions, such as temperature, humidity, soil type apart from genotype-related differences. But due to lack of experimental exposure response data for India, the exposure response functions which are used in Europe and North America are applied here without considering the sensitivity of crops of Asia (Van Dingenen, 2009). Experimental studies provide the best available data for plant responses, and help in establishing critical levels. Model-based studies use the experimental data and provide a broader picture of various effects seen in several experiments. It will help to develop air quality standards specifically for vegetation and set threshold values for different Indian cultivars. There are no Indian models to assess ozone impacts on various Indian cultivars. Hence, it is important to have Indian specific crop exposure curves/models. Given the high risk of ozone exposure to crops in India, it is imperative to have air quality standards for ozone precursors. At present, there are no such air quality standards in India that are developed specifically for the protection of agricultural crops from ground level ozone (Ghude et al., 2014). Formulation of effects based policies and development of air quality standards require robust knowledge on the extent and magnitude of the ozone risk to crops. Such risk assessments require exposure response functions to estimate the impact of ozone on crops. This study has focussed on establishing ozone exposure-yield response curves for crops grown in India using the available data from the literature and also compared it with the existing North American DR. The study also identifies existing gaps and challenges for establishing DR and proposes a methodological framework for establishing ozone exposure response functions.

6.4 Methodology to assess the impact of ozone on crops The key step to assess the impact of ozone on crops is to develop the dose-response curve which shows the relationship of decrease in crop yields with increasing ozone concentrations. 85


6.4.1 Establishment of ozone-dose-response functions for crops grown in India The ozone impact on crop yield data was collected from existing literature following strict criteria. The data was then used to establish ozone-dose-response relationships. The details of the data sources and data analyses for deriving the ozone dose-response functions are given below.

6.4.2 Ozone impact on crop yield data Ozone impact on crop yield data were collected from literature for different crops grown in India: wheat, soybean, rice, maize, gram (yellow and green) and mustard (Figure 6.1). A strict criteria was followed in order to add robustness to the data collection as listed in Table 6.1.

Figure 6.1: Systematic literature review to establish ozone-dose-response functions for crops grown in India

Table 6.1: Criteria for selection of ozone impact on yield data Parameter

Criteria

Rationale

Ozone exposure

Duration: Entire crop growing period or at least for a duration of 90 days before physiological maturation

Crop is most sensitive during US EPA.,2007 90 days period before the physiological maturation.

Ozone exposure data available for daylight

86

a. Maximum concentration

Reference

ozone Syrakov et al.,1997 is


Parameter

Criteria hours for at least 7h–12h

Rationale

Reference

during daylight hours b. Ozone entry into plants is through stomates; stomatal conductance is maximum during daylight hours

Plant growth conditions

Well watered , provided adequate nutrient supply and are free from pests and other environmental stresses

Crop yield impact is solely based on ozone exposure; all other crop growing conditions should be optimum

Heck et al.,1998

EDU treatment

Plant treated with at It has tested that it gives 100% Pandey et al.,2014 least 400 ppm of EDU protection from ozone

6.4.3 Establishing Indian dose-response function and comparison with the existing North American and European functions The ozone-dose-response functions for crops grown in India were established using the relative yield (in relation to control yield) data of crops under various ozone concentrations. For establishing these functions, ozone dose was quantified using two exposure matrices, a mean ozone exposure matrix (M7) and a cumulative ozone matrix (AOT40). M7 uses a Weibul equation to quantify ozone exposure while AOT40 uses a linear equation (Table 6.2). Table 6.2: Equations for the ozone exposure matrix Ozone exposure matrix M7

87

Equation

Reference

Lesser et al.,1990


Ozone exposure matrix

Equation

Reference

AOT40

y=mAOT40+c

Mills et al.,2007

where

y=relative yield, x=ozone concentration in ppb, z=reference ozone concentration (25 ppb), λ= shape parameter, ω=scale parameter Ozone dose-response functions were established for six different crops grown in India which were selected on the basis of the availability of ozone impact experimental data. These data were collected from the experiments that were conducted in India. However, for both wheat and rice, data from experiment carried out in the Indo-Gangetic Plains of Pakistan were also included. Most of the data for all the six crops were from the experiments conducted in one location in India (Varanasi).

6.5 Indian dose-response functions for crops and comparison with the existing North American and European functions Tables 6.3, 6.4, 6.5, 6.6, and 6.7 show the summary of pooled Indian data yield loss at various ozone exposure levels for wheat, maize, legumes, rice, and mustard crops, respectively. Table 6.9 shows the comparison of Indian dose-response functions with North American doseresponse functions. Table 6.10 presents the comparison of Indian dose-response functions for AOT40 index with those presented in Mills et al. (2007) The Indian dose-response functions for crops established in this study are different as compared to North American (Lesser et al., 1990 and Adams et al., 1989) and European (Mills et al., 2007) dose-response functions. Both the M7 and AOT40 functions indicate that Indian crops are more sensitive than their North American and European counterparts. This is in agreement with other studies in the literature (Emberson et al., 2009; Sinha et al., 2015). There were differences in the relative yields between the different experimental set ups (Tables 6.3–6.7). The data taken for establishing the dose-response functions for India was taken from different studies; filtration and fumigation and EDU treatment studies. In filtration study, chambers used were filtered air or charcoal filtered air (adsorbs around 92% of ambient ozone), which were considered as control, whereas in fumigation study no such filtered air chambers were present, here ambient concentration is considered as control. In both filtration and fumigation, to see the impact of ozone on crops, other chambers were filled with elevated ozone, which is 20–30 ppb higher than

88


that of ambient. In open top chambers (OTC), temperature rises 1–2 °C as they provide constant wind speed and it can lead to enhancement of plant growth, also humidity is higher than that of outside environment (Tomar et al., 2015). Due to this there could be differences in crop yield when grown in open field (Mills et al., 2007). Ambasht et al. (2002) stated that it is difficult to extrapolate results from chamber experiments to ambient air conditions as there would be considerable difference in microclimate between plants which are grown in chambers and plants grown outside because of differences in aerodynamic and boundary layer resistances, and thereby ozone intake. But according to Musselman et al. (2005) there is no significant difference in ozone uptake in chamber as compared to open plots; hence, they would not affect dose-response relationship. Some of the studies that were used in this current study to establish dose-response function (Rai et al., 2007; Rai et al., 2010; Singh et al., 2012; Tomar et al., 2015), also tried to see the difference in ozone concentration between open plots and chamber and found it to be very low, that is, 2%–10%, hence showed there would be no significant difference in crop yield between the two. Over the last few decades, extensive studies were performed using open top chambers to assess yield loss due to ozone but now EDU is considered as the best alternative to these chamber experiments in remote areas which have high ozone concentration and suffer frequent electric failure. EDU does not enter the cell but remains in apoplastic region where it acts as a scavenger to ozone (Singh and Agarwal, 2011) and thus, increases plant tolerance to elevated levels of ozone. It delays senescence, thereby, increasing vegetative growth and promoting carbohydrate allocation from leaves to roots but can be toxic to plants at higher ozone concentrations (Singh et al., 2010). Still its protective effect is not fully understood, but various studies have indicated that it could be due to increase in ascorbic acid concentration and anti-oxidative enzymes (Pandey et al., 2014). EDU experiments are conducted without the use of OTC, therefore there would be no chamber effect and are cheap and convenient. In India, most experiments were based on filtration and EDU studies and significant difference in yield loss could not be seen between the two. Crop sensitivity varies from one crop to another. Crop responses towards ozone are dependent on their genetic characteristics due to which plant cultivars within species are more susceptible to ozone than others (Musselman et al., 2005). Legumes and wheat were placed under highly sensitive crops (Mills et al., 2007) and rice, maize, and mustard as moderately sensitive crops (Mills et al., 2007). Photosynthetic unit of legumes, such as soybean are highly sensitive to ozone (Zhang et al., 2014). Genetic differences among cultivars make one cultivar more sensitive to ozone than other. Though not many studies have taken account of genetic variation among crops to study their sensitivity, there have been several studies for wheat and rice (Pandey et al., 2014). Maize and rice have high photosynthetic efficiency due to specialized anatomical and biochemical features which enable C4 photosynthesis. These C4 plants respond well to high temperature and sunlight, and have high water use efficiency and low stomatal conductivity and ozone uptake (Mcgrath et al., 2015). Legumes and wheat on the other harvest utilize C3 photosynthesis which makes them less responsive to high light and intense sunlight and hence lower yielding. Also in maize, an increase in weight of cob with increasing concentration of ozone occurs due to more translocation of photosynthates towards husk leaves during ozone stress in order to provide protection and to safeguard female reproductive organs. Hence, 89


maize has protective mechanism in the form of thick husk leaves that prevent direct exposure of its female reproductive structures to ozone and thus makes it moderately sensitive. There was less reduction in yield in QPM than NQPM because husk leaves in the former provided more protection than the latter (Singh et al., 2013). There are differences in environmental conditions as global warming in short term is likely to favour production of crops in temperate regions but will negatively impact tropical crop production which then could have consequences on international food prices and food security (Rai et al., 2010) and pollutant exposure characteristics. Furthermore, the air quality standards in Asia are less stringent as compared to North America and Europe. Since, in India there are large numbers of small and medium-size industries which use limited control technologies to control emission (Sarkar and Agrawal, 2010) it adds to the threat that ozone may pose threat to crops due to uncontrolled precursor gas emissions. Crop model studies for Indian crops, such as wheat, rice, cotton, maize, soybean, etc., have also shown decline in yield production when compared with North American crops and thus, have raised serious concerns for food security in South Asia (Sharma et al., 2016). However, given the fact that this study as well as previous studies has highlighted the higher sensitivity of Indian cultivars, and the fact that these global estimates have used the North American and European dose-response, there is high possibility that these values are underestimates of the actual losses. This emphasizes the need to develop local dose-response function based on local crops for a more robust risk assessment. There is thus an urgent need of building response functions for Indian cultivars, and also for a sitting need to set standards for ozone by government, for crops, as there are standards set for industrial, residential areas.

90


Table 6.3: Wheat: Summary of pooled Indian data yield loss at various ozone exposures Reference

Study site

Experimental type, (growth period) -field/pot-ozone monitoring method

Cultivar points)

Singh et al., 2009

Varanasi, India

EDU400 ppm(control), (Dec-Mar)-field -UV absorption

Tiwari et al., 2005

Varanasi, India

Singh et al., 2008

Varanasi, India

EDU 450(control) (Dec-April)-field -UV absorption EDU(400 ppm),(Dec-Mar)Field-Ozone analyser

Singh et al., 2010

Varanasi, India

EDU 400(control) (Dec-Apr)-fieldOzone analyser

Fumigation Studies Agrawal et al., 2005

Varanasi, India

Mishra et al., 2012

Varanasi, India

Ambasht et al.,2003

Varanasi, India

Filtration Studies Sarkar et al., 2010

Varanasi, India

(data

Ozone concentration in ppb

AOT40 ppm h

Relative Yield

HUW234(1), HUW468(1), HUW510(1), HUW343(1), Sonalika(1) Malviya 533(1), Malviya 234(1)

40.55 , 8h mean

0.5

74%–98%

43.1,8h mean

2.8

60%–99%

HUW234(1), HUW468(1), HUW510(1), PBW343(1), Sonalika(1) HUW 468(1)

40.5,8h mean

0.5

74%–98%

50.4,12h mean

9.4

72%

Fumigation(Dec-Mar)-field -wet chemistry Fumigation (Dec-Mar)field-UV absorption Fumigation (Nov-Apr)-field -UV-V spectrophotometer

Malviya 234(1), HP1209(1) HUW 37(1) K 9107 (1) Malviya 234(1)

70,4h mean

27

95%–92%

56.25,4h

14.6

61%–88%

70,4h mean

27

91%

Filtration –field-UV absorption Photometric Ozone analyser

Sonalika(3), HUW 510(3)

46.3, 50.4, 56.2 ,12h mean

4.8–14.5

61%–89% and 56–80%

EDU Studies

91


Reference

Study site

Rai et al., 2007

Varanasi, India

Tomar et al., 2015

New India

Delhi,

Experimental type, (growth period) -field/pot-ozone monitoring method Filtration (Dec-Mar) -field-UV absorption Filtration [(Dec-Apr (2008and 09)]-FieldOzone analyser

Cultivar points)

Filtration [(Dec-Apr (2009-10)] FieldOzone analyser

PBW343(3), HD 2936(3)

(data

HUV 234(2) PBW343(3), HD 2936(3)


AOT40 ppm h

Relative Yield

40.5,41.3 8h mean 30.9,31.8and 59.2, 7h mean

0.5–1.2

76%–73%

0.0–17.5

96%–84%

34.9,36.1 and 65.35, 7h mean

0.0–22.8

96%–81%

Table 6.4: Maize: Summary of pooled Indian data yield loss at various ozone exposures Reference

Filtration Mina, Bhatia and Kumar, 2012 Bhatia et al., 2014

Study site

Experimental type (growth period)- field/pot- ozone monitoring method

Cultivar (data point)

Ozone concentration ppb

in

AOT 40 ( ppm h)

Relative Yield

IARI, New Delhi

Filtration- fieldozone analyser

HQPM1 (2)

39 and 54, 7h

0–8.82

83%–66%

IARI, New Delhi

Filtration (June-Nov)- field- HQPM1 (2) ozone analyser (2008) Filtration (June-Nov)- field HQPM1 (3) ozone analyser (2009)

35 and 65,7h

0 and 15.75

75%–65%

29, 59 and 77, 7h

0, 11.97 and 23.31

82%–54%

70 and 89, 10h

27- 44.1

96%–94% 94%–89%

Fumigation Singh et al., 2014

92

Varanasi, India

FFumigation (Dec-April)- fieldozone analyser

HQPM (2) DHM117 (2)


Table 6.5: Legumes: Summary of pooled Indian data yield loss at various ozone exposures Reference

Study site


Cultivar (data points)


AOT 40 (ppm h)

Relative Yield

Filtration Singh et al., 2010

Varanasi, India

Filtration,(Jul-Oct)-fieldOzone analyser

Pusa 9712(2), Pusa 9814(2)

74.7 (2), 54.7 (2),8h

13.2-31.2

32%–60%

Varanasi, India

Fumigation(Jul-Oct)-fieldWet chemistry

PK 472(2), Bragg(2)

70(2), 100(2),4h

27-54

66%–90%

EDU Study Singh et al., 2010

Varanasi, India

EDU(400ppm),(Jul-Oct) -field-Ozone analyser

Pusa 9712(2), Pusa 9814(2)

54.7(2), 74.7 (2),8h

13.2-31.2

39%–73%

Rai et al., 2014

Varanasi, India

EDU(400),(Jul-Oct)-potOzone analyser

JS 335 (1)

37.7,12h

0.0

79%

Allahabad

EDU-500ppm (control)-(JulySep)-fieldPortable Gas Sampler

Malviya Jyoti (1)

33.25, 8h mean

0.0

69%

Singh and Agrawal, 2011

Varanasi

Azad1(1) BHU1 (1)

51.0

9.9

67%–71%

Singh et al., 2010

Varanasi

9.9

70%–87%

Varanasi

Barkha (1) Shekhar(1) TU-94-2 (1), 12h mean Malviya Janpriya (1)

51

Singh et al.,2010

EDU-400ppm (control)-(JulyOct)-field-UV absorption photometric ozone analyser EDU-400ppm(Control)(July-October)-fieldnon dispersive UV absorption ozone analyser EDU(400ppm)-March-Junepot-Ozone monitor

58.7,12h

16.8

67%

Fumigation Agrawal et 2005

Agrawal 2005

93

et

al.,

al.,


Table 6.6: Rice: Summary of pooled Indian data yield loss at various ozone exposures. Reference

Study site

Experimental type

Cultivar(data points)

Ozone concentration (ppb)

AOT40 (ppm Relative h) Yield

Filtration Studies Sarkar and

Varanasi

Agrawal, 2011 Rai et al., 2010

Varanasi

Filtration (June-Oct)- field- Malviya dhan 36 (3) ozone analyser Shivani (3)

49, 59 69,12h

and 10, 21 and 32

Filtration (June-Oct)- field- NDR 97 (2) ozone analyser Saurabh 950 (2)

34 and 35, 7h

EDU (Aug-Nov)-field -2B A-R (18) Tech monitor

46, 8h

60%–83% 55%–87%

0

85%–86% 89%–90%

EDU Studies Pandey et al., Lucknow 2015

94

4.32

70%–96%


Table 6.7: Mustard: Summary of pooled Indian data yield loss at various ozone exposures Reference

Study site

Filtration Studies Singh et al., 2012 IARI, Delhi


Cultivar (data points)


AOT40 (ppm h)

Relative Yield

Pusatarak (3)

30.1,25.9 and 57.6,

12.74

64%–86%

Filtration,[Oct-Mar (2010-11)] Field Filtration,(NovMar)Field-UV absorption Filtration,(NovMar)fieldUV absorption

Pusatarak (3)

30.5,33 and 60.5

16.96

65%–84%

Vardan(2) Aashirwaad(2)

44.6 and 45,12h

7.9

93%–81%

Kranti(2)

33.5 and 34.6,12h

0.0

84%-85%

Fumigation,(NovMar) -fieldUV absorption EDU,(Nov-Apr)field-UB tech ozone monitor

Sanjukta(1) and Vardan(1) Kranti(1), Peelasona(1)

49.4 and 59.4,8h

17.5

55%–60%

54.8,8h

16

49%–80%

New Filtration,[Oct-Mar (2009-10)] Field-

Singh et al., 2011 Varanasi, India

Singh et al., 2008 Varanasi, India

Fumigation Studies Tripathi al.,2012

et Varanasi, India

Pandey al.,2014

et Lucknow, India

95


Table 6.8: Comparison of Indian dose-response functions with North American dose-response functions Crops

Wheat

No. of

No. of

Function (y=α*EXP(-x/ω)^λ /α*EXP(-reference x/ω)^λ)

No. of

cultivars

data points

(y=relative yield, x=M7-M12 in ppb)

Locations

19

51

y=1*EXP(-x/67)^0.7)/1*EXP(-25/67^0.7)

5

(y=1*EXP(-x/137)^2.34)/1*EXP(-25/137)^2.34) (Lesser et al.,1990) Soybean

3

12

y=1*EXP(-x/40)^0.8)/1*EXP(-x/25/40)^0.8

1

(y=1*EXP(-x/107)^1.58)/1*EXP(-25/107)^1.58) (Lesser et al.,1990) Rice

34

44

y=1*EXP(-x/75)^0.8)/1*EXP(-25/75)^0.8

5

(y=1*EXP(-x/202)^2.47)/1*EXP(-25/202)^2.47) (Adams et al.,1989) Maize

2

11

y=1*EXP(-x/115)^0.5)/1*EXP(-x/115)^0.5)

2

(y=1*EXP(-x/124)^2.83)/1*EXP(25/124)^2.83)(Adams et al.,1989) Mustard

6

16

y=1*EXP(-x/65)^0.8)/1*EXP(-25/65)^0.8)

2

Grams

8

8

y=1*EXP(-x/60)^0.59)/1*EXP(25/60)^0.59

2

(black yellow)

96

and


Table 6.9: Indian dose-response functions for AOT40 index compared with Mills et al. (2007) Crops

No. of

No. of

R2

Cultivars data points Wheat

19 (9)

50 (52)

0.471(0.89)

Function (y=mx+c)

No. of

(y= relative yield, x=AOT40 in ppm h)

Locations

y=-0.007x+0.825

5

(y=-0.0161x+0.99)(Mills et.al.,2007) Soybean

3 (7)

10 (50)

0.782(0.61)

y=-0.015x+0.869

1

(y=-0.0116x+1.02) (Mills et.al.,2007) Rice

30(6)

35 (32)

0.278(0.20)

y=-0.008x+0.848

5

(y=-0.0039x+0.94)(Mills et.al.,2007) Maize

2 (1)

8 (19)

0.278(0.35)

y=-0.010x+0.943

2

(y=-0.0036x+1.02) (Mills et.al.,2007) Mustard

6

10

0.662

y=-0.020x+0.950

2

Gram

6

6

0.04

Y=-0.006x+0.793

2

(black and yellow)

97

The Indian dose-response functions for crops established in this study are different as compared to North American (Lesser et al., 1990 and Adams et al., 1989) and European (Mill et al., 2007) dose-response functions. Both the M7 and AOT40 functions indicate that Indian crops are more sensitive than their North American and European counterparts (Figures 6.3 to 6.9). This is in agreement with other studies in the literature (Emberson et al., 2009; Sinha et al., 2015) who also suggested that South Asian crop cultivars are likely to be more sensitive than that of the North American and European cultivars.

Figure 6.2: Dose-response relationship for wheat crops grown in India (a) M7 Exposure index; (b) AOT40 exposure index

Figure 6.3: Dose-response relationship for soybean crops grown in India (a) M7 Exposure index; (b) AOT40 exposure index.

Figure 6.4: Dose-response relationship for rice crops grown in India (a) M7 Exposure index; (b) AOT40 exposure index.


Figure 6.5: Dose-response relationship for Maize crops grown in India (a) M7 Exposure index; (b) AOT40 exposure index

Figure 6.6: Dose response relationship for legumes crops grown in India (a) M7 Exposure index; (b) AOT40 exposure index

Figure 6.7: Dose-response relationship for mustard crops grown in India (a) M7 Exposure index; (b) AOT40 exposure index In case of tropical countries, the solar radiation energy is spread over smaller area as sun rays arrive almost perpendicular to regions near equator whereas in temperate countries the sun lies at low angle in the sky. Also, as the distance from equator increases, the amount of heat falling per unit surface decreases. Hence, tropics are more intensely heated than any other part of the earth. High temperatures shorten the life cycle of most crops and adversely affect their reproductive efforts (Qaderi and Reid, 2009), although it varies from crop to crop variety. The data for establishing the dose-response functions for Indian crops were taken from different studies; filtration and fumigation, and EDU treatment studies. In the filtration and EDU studies, yield in filtered chambers (ozone concentrations are close to zero) and plants treated with EDU (nullify the effect of ozone) are taken as control, respectively. In fumigation studies, yield at ambient ozone concentration is taken as control. The results have shown variations in yield losses between different types of experiments. Loss in yield 99


experienced by crops in filtration and EDU studies are significant and similar, whereas fumigation study does not show significant loss in yield. It is because the relative yield loss is dependent on control yield at ambient ozone concentration (ozone concentration ranges from 25–40 ppm) in fumigation studies, while in the other two types of the studies, it is relative to control yield at filtered chambers (ozone concentrations close to zero) and EDU (ozone concentration ranges from 0–4 ppm). The dose-response functions established in this study show variations in the sensitivity of the crops to ambient ozone concentrations (Figure 6.8). This is to be noted that the two ozone indices also show differences in the order of sensitivity amongst different crop.

Figure 6.8: Sensitivity of Indian crops (current study) (M7-M12 index) and (AOT40 index)

The inter-crop variability in ozone sensitivity is likely due to the differences in sensitivity and efficiency of their photosynthetic units to ozone and difference in stomatal conductivity. For example, photosynthetic unit of legumes, such as soybean are highly sensitive to ozone (Zhang et al., 2014). Maize has high photosynthetic efficiency due to specialized anatomical and biochemical features, which enables C4 photosynthesis. These C4 plants are known to respond well to the high temperature and sunlight, and they also show high water use efficiency. In comparison to C3 plants, the C4 plants have low stomatal conductivity and ozone uptake (Mcgrath et al., 2015; Young & Long, 2000). Legumes, rice, and wheat utilize C3 photosynthesis which makes them less responsive towards intense sunlight and affects yield. Also in maize, an increase in weight of cob with increasing concentration of ozone was seen due to more translocation of photosynthates towards husk leaves during ozone stress in order to provide protection, and to safeguard female reproductive organs. Hence, maize has protective mechanism in the form of thick husk leaves which prevents direct exposure of its female reproductive structures to ozone, and thus makes it moderately sensitive. There was less reduction in yield in QPM than in NQPM because more protection was provided by husk leaves in the former than the latter (Singh et al., 2013). This explains why C4 species are more pollutant tolerant such that effect of same level of ozone on C4 maize is about half the effect on C3 wheat (Young and Long, 2000). Previous studies have placed legumes (includes soybean and grams) and wheat under highly sensitive crops while rice and maize were moderately sensitive crops (Mills et al., 2007; Heck et al., 1998). Furthermore, intra-crop variability was also observed in the study. This might be due to differences in their genetic characteristics, although not many studies

100


have taken an account of genetic variations among crops to study their sensitivity except for wheat and rice (Pandey et al., 2014; Biswas et al., 2011) Another reason could be stomatal conductance which is the main factor which determines exchange of various gases present in atmosphere including ozone. Stomatal conductance is maximum during daylight hours and hence the intake of ozone, which directly affects pigment formation, shoot productivity, etc., in crops (Gratz et al., 2015). Stomatal conductance rate varies from one cultivar to another. For example, wheat cultivars HUW-37 and K-9107 showed difference in their stomatal conductivity rate, and showed significant difference in yield. (Mishra et al., 2012) There are differences in data within and between experimental studies. The variations in data are as follows: a. Experiments were conducted in different environmental conditions, also not all experiments were conducted in the same year. The genetic composition of plants may change from one year to the next. It may lead to change in yield (positive or negative change). b. Number of hourly ozone exposure varies between the studies, and leads to variation in yield loss. Long-term cumulative effects would give a different result than shortterm responses (Pandey et al., 2014). Using data from all the existing experimental studies in India to plot a dose-response curve, though only limited data points are available for some crops, shows that the Indian crops are sensitive to ozone. The crop yield decreased by as much as 68% in legumes, and Indian dose-response function is below the North American dose-response curve suggesting the crop and their cultivars are more sensitive than North American ones.

6.6 Assessment of impact of ozone on different crops in India The ozone crop impact assessments were carried out using the AOT40 dose-response functions for all the crops where the response functions were established in the preceding section. Crop impacts due to ozone exposure were estimated as yield losses. The yield losses (YL) were estimated for the crops grown in India using two response functions: (i) Indian response function (which were established as part of this study and is described in the preceding section), (ii) European dose response function (Mills et al., 2007). The ozone impacts on crop yield were assessed on five crops; two Rabi crops (wheat and mustard) and three Kharif crops (soybean, rice, maize) that are grown in India. The results were then presented for each state in India as illustrated in Figure 6.10 and Figure 6.11. The yield losses were more pronounced in the Rabi (winter) crops as compared to that of the Kharif (monsoon) crops. This is due to the relatively higher concentrations of ozone during the Rabi season. The dose-response functions established using the Indian crop data in the current study also showed higher yield loss estimates (Figure 6.12) as compared to the functions from the European study (Mills et al., 2007). Earlier modelling studies have also reported that the Indian crop varieties are more sensitive to ozone exposure which is likely due to the cultivar specific sensitivity as well as the environmental conditions for favourable ozone impact on crops (Biswas et al., 2011; Emberson et al., 2009). 101


Figure 6.9: Ozone induced yield losses on Rabi crops, wheat and mustard, for different states and union territories in India

Figure 6.10: Ozone induced yield losses on Kharif crops, soybean, rice, and maize, for different states and union territories in India

102


Figure 6.11: Comparison between the yield loss estimates using Indian dose-response and European dose-response functions

6.7 Assessment of wheat losses in different energy-emissions scenarios analysed in this study Wheat being the most important crop in terms of production and impact of ozone has been chosen for estimation of relative losses due to the exposure to ozone concentrations in India under different energy-emission scenarios. The gridded ozone concentrations simulated in Chapter 4 are used along with information on meteorological variables, to derive grid-wise yield losses. The district-wise yield loss of wheat is shown in Figure 6.12. It is evident that the regions of the Indo-Gangetic plains are prone to high wheat yield losses.

103


Figure 6.12: Percentage wheat yield loss estimation in 2016 (using Indian dose responses)

For the purpose of making comparisons across different scenarios, the meteorology and wheat production rates have been kept constant in the future years. The production of wheat is projected to go up from 63.8 Mt in 2016 to 123 Mt in 2030 and 290 Mt in 2050, based on growth rate of agricultural sector in India. Figure 6.14 shows the estimates of wheat loss in different energy and air quality scenarios considered in previous chapter, that is, Reference (BAU), INDC, AMBI, and LG. The present loss of about 27 Mt in 2016 is expected to go up to 152 Mt in 2050. The loss in wheat is estimated to be about 30% currently which increases to 34% in 2050. In other words, India could have produced 30% more wheat than its present production. In alternative energy scenarios, small savings of 1.1 and 1.9 Mt can be made in 2030 in the INDC and AMBI scenario, respectively. In 2050, the savings in the two scenarios increase to 10 and 15 Mt, with respect to the BAU scenario. This suggests that there could be a reduced wheat loss of 2%–6% in INDC and 4%–10% in AMBI scenarios through intervention in energy sector during 2030–2050. LG scenario shows a saving of 8 Mt in 2050 (6% lower wheat loss than BAU).

104


Figure 6.13: Loss of wheat crop (mt) estimated for different scenarios in India It is observed that savings in crop yields due to intervention in the energy sector are significant but less than the health benefits. As per Sharma et al. (2016), a large contribution of ozone in India is also through the boundary conditions or from the regions outside India. Therefore, despite higher reduction in energy consumption, and emissions, the effect observed on ozone and crop losses is comparatively lower.

105


7. Conclusions This study assesses the co-benefits of various energy development and mitigation pathways (varying by growth patterns and variations in fuel and technology choices) in terms of air quality improvements and its impacts on human health and agriculture. The study uses integrated modelling techniques to convert energy use information in different scenarios into emissions, air quality, and their corresponding impacts on human health and agriculture. As a first step, to examine the possible energy and emissions pathways, the MARKAL model has been used. Apart from a Reference (BAU) Scenario, three alternative scenarios have been modelled, depicting varying levels of improvements in energy efficiency, and fuel and technology switching. The Reference (BAU) Scenario represents climate policies in India that were already implemented before 2016, and an ambitious high GDP growth as envisaged necessary for sustainable development by the Government of India. The other three scenarios are INDC (Intended Nationally Determined Contribution), Ambition, and Low Growth. The INDC Scenario takes into account various climate policies and targets formulated in India’s INDC submission while the Ambition Scenario envisages a higher greenhouse gas (GHG) mitigation ambition (beyond INDC & towards a ‘well below 2 DC world’) and keeping development in India at the forefront. The Low Growth Scenario looks at a lower rate of growth than the other 3 scenarios and the implications on mitigation. The energy scenario modelling and analysis shows that the final energy consumed across the Reference (BAU), INDC, and Ambition scenario (the High Growth scenarios), declines as the intensity of mitigation actions increases. The energy consumed in the Reference (BAU) scenario in 2031 is 1062 Mtoe which declines to 1034 Mtoe in the INDC Scenario (a decrease of 3%) and 977 Mtoe in the Ambition Scenario (decrease of 8%). In 2051, the gap increases further so that in the Reference Scenario, final energy consumed is 2315 Mtoe. Between Reference and INDC Scenarios, a saving of 242 Mtoe is visualized (10%), whereas another 229 Mtoe (20%) is saved between INDC and AMBI Scenarios. The total CO2 emissions in the Reference (BAU) Scenario are the highest followed by the INDC and Ambition Scenarios, because the stringency of mitigation policies successively increases from the Reference (BAU) Scenario to the INDC to the Ambition Scenarios. The Low Growth Scenario on the other hand witnesses a sluggish growth of gross development product (GDP), which is not only reflected in the demand of energy, but also in the uptake of energy-efficient alternatives. Consequently, the emissions in this scenario are initially lower due to depressed growth, but eventually increase beyond 2041 to be closer to the levels in the INDC scenario. In terms of emissions intensity, this scenario therefore reflects intensity worse than either the INDC or the Ambition Scenarios. It is interesting to note that total CO2 emissions for India under each of the four scenarios developed, do not show tendencies of peaking even in the Ambition Scenario. However, the slope of the graph declines in the later years, indicating a slower increase in emissions over the years. Following energy modelling, the energy consumption in different scenarios was converted to emissions reductions using standard emission factor-based techniques. Further, air quality simulations were carried out to assess change in PM2.5 and ozone concentrations in

106


India under different scenarios. Finally, the dose-response relationship approach was used to derive the impact of the pollutants on human health and agricultural productivity. Energy use numbers from the MARKAL model were fed into the GAINS ASIA emission model and estimates of emission loads are derived for pollutants, such as particulate matter (PM), nitrogen oxides (NOx), sulphur dioxide (SO2), carbon monoxide (CO), and nonmethane volatile organic compounds (NMVOCs). PM10 emissions indicate 49.9% and 8.5% increase during 2030 and 2050, respectively, compared to the loads in 2016 under the Reference (BAU) Scenario. The PM10 emissions are projected to decrease between 2030 and 2050, due to penetration of LPG in residential sector, penetration of BS-VI vehicles in transport, and introduction of stringent standards for industries and power plants. The PM 10 emissions were also estimated for different scenarios and INDC and Ambition Scenarios show a decrease of about 8% PM10 emissions. Despite reduction in overall energy consumption, the Ambition Scenario does not show improvement over the INDC scenario in terms of local air pollution-related parameters because of the shifts in the type and quality of fuels used in the energy sector. With the larger share of renewables in the mitigation low carbon scenarios, the model chooses more of the high ash low quality domestic coal, rather than imported coal (due to cost optimization). This compensates gains due to decrease in overall energy in the scenario. The Low Growth Scenario shows higher PM10 emissions than the INDC and Ambition Scenarios mainly due to lower penetration of energy-efficient technologies. The estimated annual NOx emission under the Ambition Scenario shows an increase by 31% during the period 2016–2030. The emissions more than doubled during 2016–2050. The study indicates that among different sectors, the transport sector contributes the major percentage of the estimated NOx emission. The emissions were 5%, 8%, and 5% lower in the case of INDC, Ambition, and Low Growth Scenarios, respectively, in 2030, and 23%, 27%, and 22% lower in 2050 with respect to the Reference (BAU) Scenario. The SO2 emissions also show similar trends. The increase in 2030 and 2050 was found to be 21% and 71% in Reference (BAU) scenario with respect to 2016 emissions of SO2. The reduction in INDC, Ambition, and Low Growth Scenarios in 2050 was found to be 7%, 9%, 19% with respect to the Ambition Scenario in case of SO2. NMVOC emissions are expected to grow at a much faster rate owing to absence of controls in the solvent sector. Reductions of 9%, 13%, and 29% are projected in INDC, Ambition, and Low Growth scenarios in 2050 with respect to the Reference (BAU) Scenario. Air quality simulations were carried out for the year 2016 for model validation purposes and were also carried out for different future scenarios at a spatially disaggregated level. High pollutant concentrations were observed in the Indo-Gangetic plains mainly due to high population densities, vehicular movement, and presence of power plants. Higher concentrations are also observed in the western part of the country which is attributed to boundary conditions showing contributions from outside the boundary of India. While the INDC Scenario shows a significant decrease in PM2.5 concentrations as compared to the Reference (BAU) Scenario, the AMBI Scenario does not show further decrease in PM2.5 concentrations, indicating that CO2 emissions reduction need not necessarily be synergistic with decreasing local air pollutants and may need specific and concerted action to address the later. Also, different air pollutants may vary differently as is noticed in the AMBI scenario which does not indicate a decrease in PM10 levels, but reflects a significant decrease 107


in ozone concentration as compared to the INDC Scenario. There is no further decrease in PM emissions projected in the Ambition scenario in comparison to the INDC Scenario, while the NOx emissions continue to decline. The Low Growth Scenario shows lower PM2.5 concentrations in 2030 than all the other scenarios, while it shows higher PM 2.5 concentrations than the INDC Scenario in 2050. The Low Growth Scenario in 2050 assumes lower penetration of efficient technologies with low growth across sectors and hence shows lower PM2.5 concentrations than the BAU (on account of lower growth), but higher concentrations than INDC (on account of lower penetration of efficient technologies). The BAU Scenario shows drastic increase in ozone concentrations, mainly due to increase in emissions of both of its precursors–NOx and VOCs. The INDC scenario shows some decrease in emissions of precursors and a decrease is also observed in the ozone concentrations. The Ambition Scenario shows the greatest decrease in ozone concentrations in India due to uptake of efficient technologies which lead to reduction in emissions of ozone precursors. The Low Growth Scenario due to its low growth character, also shows low ozone concentrations, and is comparable to the Ambition Scenario. The air pollutant concentrations are further used to assess the impacts on human health and agricultural productivity in 2030 and 2050 under different scenarios. A significant decline in the total respiratory and cardiovascular mortality is expected to be incurred in the alternative scenarios proposed in the current study. In the Reference (BAU) Scenario, the total mortality attributed to ambient air pollution in India is expected to increase by ~25% and ~36% during 2016 to 2030 and 2050, respectively. It was observed that a decline of 12% and 10% in mortality occurs in the INDC and Ambition Scenarios in 2050 with respect to the Reference (BAU) Scenario. The decline in mortalities was found to be smaller ~3% in the year 2030 in all the three proposed scenarios, that is, INDC 2030, Ambition 2030, and Low Growth 2030. In terms of agricultural productivity, the loss in wheat has been estimated under the different scenario, as it is the most important crop which is impacted due to ozone pollution in the country. The loss of wheat increases from 27 Mt in 2016 to 55 Mt in 2030 and 153 Mt in 2050 in the Reference (BAU) Scenario. The loss in wheat is found to decrease by 10, 15, and 6 mt in INDC, AMBI, and LG scenarios, in 2050. The overall results of energy, emissions, concentrations of PM2.5 and ozone, and their impacts over human health and wheat productivity, in various scenarios are shown in Table 7.1. The percentage change in energy, emissions, concentrations of PM2.5 and ozone, and their impacts on human health and wheat productivity, in various scenarios is shown in Table 7.2.

108


Table 7.1: Energy consumption, emissions, concentrations of PM2.5 and ozone, and their impacts over human health and wheat productivity, under various scenarios 2016

2030

Base

BAU

INDC

2050

AMBI

LG

BAU

INDC

AMBI

LG

Energy (PJ) Total consumption

28,626

54,720

52,147

49,895

50,996

110,231

95,388

85,612

90,383

Emissions CO2 (Mt) PM10 (Kt)

2,053 13,619

4,734 20,425

4,519 18,734

4,232 18,759

4,001 19,088

10,373 16,536

9,209 13,790

8,080 14,887

8,317 16,307

SO2 (Kt) NOx (Kt)

8,417 6,988

10,144 9,164

10,047 8,739

10,154 8,442

8,883 8,664

14,408 15,365

13,411 11,850

13,157 11,271

11,605 11,956

27.62

18.07

17.25

16.16

23.14

10.18

9.04

7.93

14.14

PM2.5 (g/m3) Ozone (ppb) Health

51.7 51.9

69.5 53.5

66.5 53.1

66.4 52.8

66.3 53.3

69.6 57.4

60.3 55.7

62.1 55.0

64.8 55.9

Mortalities (million)

0.79

1.05

1.02

1.02

1.02

1.22

1.09

1.11

1.16

26.9

55

54

53.1

54.7

152.3

141.7

137.2

143.9

Emission intensity (gCO2/Re) CO2 Air quality

Agriculture Wheat loss (million t)

Table 7.2: Percentage change in energy, emissions, concentrations of PM2.5 and ozone, and their impacts over human health and wheat productivity, in various scenarios 2016 Base Energy Total consumption Emissions

2030 BAU*

INDC**

2050

AMBI**

LG**

BAU*

INDC**

AMBI**

LG**

91%

-5%

-9%

-7%

285%

-13%

-22%

-18%

CO2 PM10

131% 50%

-5% -8%

-11% -8%

-15% -7%

405% 21%

-11% -17%

-22% -10%

-20% -1%

SO2

21%

-1%

0%

-12%

71%

-7%

-9%

-19%

NOx

31%

-5%

-8%

-5%

120%

-23%

-27%

-22%

CO2 Air quality PM2.5 Ozone Health

-35%

-5%

-11%

28%

-63%

-11%

-22%

39%

34% 3%

-4% -1%

-4% -1%

-5% 0%

35% 11%

-13% -3%

-11% -4%

-7% -3%

Mortalities

33%

-3%

-3%

-3%

54%

-11%

-9%

-5%

Agriculture Wheat loss

104%

-2%

-3%

-1%

466%

-7%

-10%

-6%

Emission Intensity

* change with respect to Base-2016 ** change with respect to BAU in respective years (2030/2050) 109


The study highlights the co-benefits of low carbon strategies in India in terms of improvements in air quality and decrease in associated impacts over human health and agriculture. It can be anticipated that the proposed low carbon scenarios also have the potential to curb the rising levels of air pollutants in the country, which will help in obtaining long-term health and agricultural benefits. However, it is also to be noted that the reduction in energy consumption is not directly proportional to reduction in air pollution and its impacts. This depends heavily on several factors including fuel and technological choices, source-controlled population and agricultural land distributions, meteorology, etc. In the present study, a percentage decrease in PM10 is found to be higher than ozone with reduction in energy consumption in the alternative scenarios. Correspondingly, decrease in health impacts was higher than decrease in crop loss, in the alternative scenarios. This suggests non-linear response of energy policies on different pollutants and their impacts on different receptors. The study suggests further research in drafting integrated and synergistic strategies for control of both GHG and air pollutants and deriving co-benefits out of them. Evidently, there are significant co-benefits of improved air quality due to adoption of low carbon pathways. Integrated policies need to be formulated to derive both climate and air quality benefits which can help the cause at global, regional, and local scales.

110


8. References Adams, R.M., Glyer, J.D., Johnson, S.L., McCarl, B.A., 1989. A reassessment of the economic effects of ozone on United States agriculture. Journal of the Air Pollution Control Association 39 (7): 960–968. Agrawal S.B., Singh A and Rathore D.,2005. Role of ethylenediurine in assessing impacts of ozone on VignaRadiata L. plants area in Suburban area of Allahabad. Chemosphere, 61: 218228. Aggarwal, P., Jain, S., 2015. Impact of air pollutants from surface transport sources on human health: A modelling and epidemiological approach. Environment International 83:146– 157. Akhtar, N., Yamaguchi, M., Inada, H., Hoshino, D., and Kondo, T.2010. Effects of ozone on growth,15 yield and leaf gas exchange rates of four Bangladeshi cultivars of rice (Oryza sativa L.), Environ. Pollut. 158: 2970–2973. Akhtar, N., Yamaguchi, M., Inada, H., Hoshino, D., Kondo, T., and Izuta, T.,2010. Effects of ozone on growth, yield and leaf gas exchange rates of two Bangladeshi cultivars of wheat (Triticumaestivum L.). Environ. Pollut., 158: 1763–1767. Ambasht R.S. and Ambasht N.K., 2002. Modern Trends in Applied Terrestrial Ecology, Springer. Ambhast.M.K. and Agarwal M, 2003. Effects of enhanced UV-B radiation and tropospheric ozone on physiological and biochemical characteristics of field grown wheat. BiologiaPlantarum 47 (4): 625-628. Anderson, J.O., Thundiyil, J.G., Stolbach, A., 2012. Clearing the air: a review of the effects of particulate matter air pollution on human health. Journal of Medicine Toxicology 8: 166–175. Ashmore, M. R. (2005). Assessing the future global impacts of ozone on vegetation. Plant, Cell and Environment 28(8): 949-964. Avnery S., Denise L. Mauzerall, Junfeng Liu, Larry W. Horowitz, 2011. Global crop yield reductions due to surface ozone exposure: 2. Year 2030 potential crop production losses and economic damage under two scenarios of O3 pollution. Atmospheric Environment 45: 22972309. Avnery S., D.L. Mauzerall, A.M. Fiore, 2013. Increasing global agricultural production by reducing ozone damages via methane emission controls and ozone-resistant cultivar selection. Glob. Change Biol., 19: 1285-1299, 10.1111/Gcb.12118. Bhatia A., Kumar V, KumarA, Tomar R.Singh S. Singh B., 2014, Effect of elevated ozone and carbon dioxide interaction on growth and yield of maize. Maydica 58(3): 291. Biswas, D.K. and Jiang, G.M., 2011. Differential drought-induced modulation of ozone tolerance in winter wheat species. Journal of Experimental Botany 62(12): 4153-4162. Büker, P., L.D. Emberson, M.R. Ashmore, H.M. Cambridge, C.M.J. Jacobs, W.J. Massman, J.Müller, N. Nikolov, K. Novak, E. Oksanen, M. Schaub, D. de la Torreh, 2007. Comparison

111


of different stomatal conductance algorithms for ozone flux modeling. Environment Pollution, 146, 726-735. Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components of the models-3 community multiscale air quality (CMAQ) modeling system. Appl. Mech. Rev. 55, 51e77. Byun, D.W., Ching, J.K.S. (eds), 1999. Science Algorithms of the EPA Models-3 Community Multi-scale Air Quality (CMAQ) Modeling System. NERL, Research Triangle Park. NC EPA/600/R-99/030. Choudhary and Agarwal., 2014. Role of gamma radiarion in changing phytotoxic effects of elevated levels of ozone in Trifolium Alexandrinum L. (Clover). Atmospheric Pollution Research, 5, 104-112. CPCB,2017,Air quality http://www.cpcb.gov.in/CAAQM/frmUserAvgReportCriteria.aspx).

database,

Debaje PK, 2014. Estimated crop yield losses due to surface ozone exposure and economic damages in India. Environ Sci. Pollut. Res. 21:7329–7338 Deb Roy, S., Beig, G., and Ghude, S. D.,2009. Exposure-plant response of ambient ozone over the tropical Indian region, Atmos. Chem. Phys. 9: 5253–5260. Dumont, J., F. Spicher, P. Montpied, P. Dizengremel, Y. Jolivet and D. Le Thiec. 2013. Effects of ozone on stomatal responses to environmental parameters (blue light, red light, CO2 and vapour pressure deficit) in three Populus deltoides, Populus nigra genotypes. Environ. Pollut. 173: 85-96 Emberson et al., 2009. A comparison of North America and Asian exposure response data for ozone effects on crop yields, Atmospheric Environment, 43, 1945-1953. Fuhrer J, Ska¨rby L, Ashmore M. 1997. Critical levels for ozone effects on vegetation in Europe. Environ Pollut 97: 91-106. Gent, J.F., Triche, E.W., Holford, T.R., Belanger, K., Bracken, M.B., Beckett, W.S., Leaderer, B.P., 2003. Association of low-level ozone and fine particles with respiratory symptoms in children with asthma. Journal of American Medical Association 290: 1859–1867. Ghude, S. D., Jain, S. L., Arya, B. C., Beig, G., Ahammed, Y. N., Kumar, A., and Tyagi, B., 2008. Ozone in ambient air at a tropical megacity, Delhi: characteristics, trends and cumulative ozone exposure indices. J. Atmos. Chem., 60: 237–252. Ghude,S.D. et al.,2014. Reductions in India’s crop yield due to ozone. Geophys.Res. Lett,41, 5685-5691 Government of India (GoI). 2015. India's Intended Nationally Determined Contribution: Working Towards Climate Change. New Delhi: Goverment of India. Gower, E., C. Estes, S. Blach, K. Razavi-Shearer, H. Razavi, 2014. Global epidemiology and genotype distribution of the hepatitis C virus infection. J Hepatol., 61(1 Suppl), S45-57 Gratz, L. E., D. A. Jaffe, and J. R. Hee, 2015. Causes of increasing ozone and decreasing carbon monoxide in springtime at the Mt. Bachelor Observatory from 2004 to 2013, Atmos. Environ., 109, 323–330, doi:10.1016/j.atmosenv.2014.05.076

112


Guttikunda, S., Goel, R., 2013. Health impacts of particulate pollution in a mega city – Delhi, India. Environmental Development 6; 8–20. Heck WW, Taylor OC, Tingey DT, eds. 1988. Assessment of Crop Loss from Air Pollutants, London: Elsevier Applied Science. Hoek, G., Krishnan, R.M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B., Kaufman, J.D., 2013. Long-term air pollution exposure and cardio-respiratory mortality: a review. Environmental Health 12: 23–35. Jain, S., Aggarwal, P., Sharma, P., Kumar, P., 2016. Vehicular exhaust emissions under current and alternative future policy measures for megacity Delhi, India. Journal of Transport & Health 3(3):404-412. Laden, F., Schwartz, J., Speizer, F.E., Dockery, D.W., 2006. Reduction in fine particulate air pollution and mortality: Extended follow-up of the Harvard Six Cities study. American Journal of Respiratory and Critical Care Medicine 173: 667–672. Leiva, G.M.A., Santibañez, D.A., Ibarra, E.S., Matus, C.P., Seguel, R., 2013. A five-year study of particulate matter (PM2.5) and cerebrovascular diseases. Environmental Pollution 181: 1–6. Lesser.et.al.,1990. Ozone effects on agricultural crops: Statistical methodologies and estimated dose-response relationships. Crop Sci. 30: 148-155. Maggs, R., Wahid, A., Shamsi, S. R. A., and Ashmore, M. R.,1995. Effects of ambient air pollution on wheat and rice yield in Pakistan. Air Soil Pollut. 85: 1311–1316. McConnell, R., Berhane, K., Gilliland, F., London, S.J., Vora, H., Avol, E., Gauderman, Margolis, W.J., Lurmann, F., Thomas, D.C., Peters, J.M., 1999. Air pollution and bronchitic symptoms in Southern California children with asthma. Environmental Health Perspective 107: 757–760. McGrath, J.M., Betzelberger, A.M., Wang, S., Shook, E., Zhu, X.G., Long, S.P. and Ainsworth, E.A., 2015. An analysis of ozone damage to historical maize and soybean yields in the United States. Proceedings of the National Academy of Sciences, 112(46): 14390-14395. Mills G, Buse A, Gimeno B, Bermejo V, Holland M, Emberson L, Pleijel H. 2007. A synthesis of AOT40-based response functions and critical levels of ozone for agricultural and horticultural crops. Atmospheric Environment, 41: 2630-2643. Mina U, Bhatia A. and Kumar U., 2012. Response of maize and its pest Chilopartellus to ozone. Maydica 57(3), 183-187 Mishra A.K., Rai R andAgarwal S.B., 2012. E Individual and interactive effects of elevated carbon dioxide and ozone on tropical wheat (Triticum aestivum L.) cultivars with special emphasis on ROS generation and activation of antioxidant defence system, Indian Journal of Biochemistry & Biophysics 50: 139-149. Mishra A.K., Rai R andAgarwal S.B., 2013. Effects of ozone on tropical wheat cultivars, Indian Journal of Biochemistry and Biophysics 50: 139-149. Musselman et al., 2005.. A critical review and analysis of flux based ozone indices, Atmospheric Environment, 40: 1869-1888.

113


Mustafić, H., Jabre, P., Caussin, C., Murad, M.H., Escolano, S., Tafflet, M., Périer, M.C., Marijon, E., Vernerey, D., Empana, J.P., Jouven, X., 2012. Main air pollutants and myocardial infarction: a systematic review and meta-analysis. Journal of American Medical Association 307: 713–721. Naab.Z.N .et al.,2013. Urbanisation and its impacts on agricultural lands in growing cities and developing countries: A case study of Tamale in Ghana. Modern Science 2(2): 256-287. Ostro B., 2004. Outdoor air pollution: Assessing the environmental burden of disease at national and local levels, Geneva, World Health Organization, 2004 (WHO Environmental Burden of Disease Series, No. 5). Pandey, A.K., Majumder, B., Keski-Saari, S., Kontunen-Soppela, S., Pandey, V., Oksanen, E., 2014. Differences in responses of two mustard genotypes to ethylenediurea (EDU) at high ambient ozone concentrations in India. Agri. Ecosyst. Environ. 196: 158-166. Pandey P, Ramegowda V, Senthil-Kumar M (2015) Shared and unique responses of plants to multiple individual stresses and stress combinations: physiological and molecular mechanisms. Front Plant Sci 6: 723 Peters, A., Liu, E., Verrier, R.L., Schwartz, J., Gold, D.R., Mittleman, M., Baliff, J., Oh, J.A., Allen, G., Monahan, K., Dockery, D.W., 2000. Air pollution and incidence of cardiac arrhythmia. Epidemiology 11: 11–17. Pleijel et al., 2000. An ozone flux relationship for wheat. Environment Pollution 109: 453-462. Pleijel, H., H. Danielsson, K. Ojanperä, L. De Temmerman, P. Högy, M. Badiani, P.E. Karlsson 2003. Relationships between ozone exposure and yield loss in European wheat and potato: A comparison of concentration based and flux-based exposure indices. Atmospheric Environment, 38, 2259 – 2269. Pleijel, H. ,2011. Reduced ozone by air filtration consistently improved grain yield in wheat. Environmental Pollution 159: 897-902. Pope, C.A. 3rd., Dockery, D.W., 2006. Health effects of fine particulate air pollution: lines that connect. Journal of Air and Waste Management Association 56: 709–742. Pope, C.A. 3rd., Burnett, R.T., Thurston, G.D., Thun, M.J., Calle, E.E., Krewski, D., Godleski, J.J., 2004. Cardiovascular mortality and long-term exposure to particulate air pollution: epidemiological evidence of general pathophysiological pathways of disease. Circulation 109: 71–77. Pope, C.A.3rd., Ezzati, M., Dockery, D.W., 2009. Fine-particulate air pollution and life expectancy in the United States. The New England Journal of Medicine 360: 376–386. Pope, C.A.3rd., Burnett, R.T., Krewski, D., Jerrett, M., Shi, Y., Calle, E.E., Thun, M.J., 2009. Cardiovascular mortality and exposure to airborne fine particulate matter and cigarette smoke: shape of the exposure-response relationship. Circulation 120; 941–948. Population Foundation of India & Population Reference Bureau. (2007). The Future Population of India A Long-range demographic view. Delhi: Ajanta Offset & Packagings Ltd. Purshotam Rao, SK Agarwal, 1984. Diurnal variations in leaf water potential, stomatal conductance and irradiance of winter crop under different moisture levels, BiologiaPlantarum, 26(1).

114


Qaderi, M. M., & Reid D. M., 2009. Crop Responses to Elevated Carbon dioxide and Temperature (chp1), In Singh S. N., (ed.), Climate Change and Crops, Environmental Science and Engineering, DOI 10.1007/978-3-540-88246-6 1, Springer-Verlag Berlin Heidelberg. Rabinovitch, N., Strand, M., Gelfand, E.W., 2006. Particulate levels are associated with early asthma worsening in children with persistent disease. American Journal of Respiratory and Critical Care Medicine 173: 1098–1105. Rai, R. and Agrawal, M., 2014. Assessment of competitive ability of two Indian wheat cultivars under ambient O 3 at different developmental stages. Environmental Science and Pollution Research 21(2): 1039-1053. Rai R., Agarwal M and Agarwal S.B., 2007. Assessment of yield losses in tropical wheat using open top chambers. Science Direct 41: 9543-9554. Rai, R., Agrawal, M., Agrawal, S.B., 2010. Threat to food security under current levels of ground level ozone: a case study for Indian cultivars of rice. Atmospheric Environment 44: 4272-4282. Rai R., Rajput M., Agarwal M and Agarwal SB., 2011. Gaseous Air Pollutants: A review on current and future trends of emissions and impact on Agriculture. Journal of Scientific Research 55: 77-102. Roy S.D., Beig G and Ghude., 2009. Exposure plant response of ambient ozone over tropical Indian region. Atmos. Phy. Chem., 9: 5253-5260. Royal Society, 2008. Ground-level O3 in the 21st Century: Future Trends, Impacts and Policy Implications, RS Policy Document 15/08. October 2008 RS1276. The Royal Society, London. Rückerl, R., Schneider, A., Breitner, S., Cyrys, J., Peters, A., 2011. Health effects of particulate air pollution: A review of epidemiological evidence. Inhalation Toxicology 23: 555–592. Sacks, J.D., Stanek, L.W., Luben, T.J., Johns, D.O., Buckley, B.J., Brown, J.S., Ross, M., 2011. Particulate matter-induced health effects: who is susceptible? Environmental Health Perspective 119: 446–454. Samoli, E., Analitis, A., Touloumi, G., Schwartz, J., Anderson, H.R., Sunyer, J., Bisanti, L., Zmirou, D., Vonk, J.M., Pekkanen, J., Goodman, P., Paldy, A., Schindler, C., Katsouyanni, K., 2005. Estimating the exposure-response relationships between particulate matter and mortality within the APHEA multicity project. Environmental Health Perspective 113: 88–95. Sarkar.A. and Agrawal S.B., 2010. Elevated ozone and two modern wheat cultivars: an assessment of dose dependent sensitivity with respect to growth, reproductive and yield parameters. Environmental and Experimental Botany 69 (3): 328-337. Sarkar. A. and Agrawal S.B., 2012. Evaluating the response of two high yielding Indian rice cultivars against ambient and elevated levels of ozone by using open top chambers. Environmental Management 95: S19-S24. Sarkar.A. and Agrawal.S.B., 2008. Identification of Ozone stress in Indian rice through foliar injury and differential protein profile. Environmental Monitoring and Assessment, 161, 205 – 215.

115


Schwartz, J., 2004. The effects of particulate air pollution on daily deaths: a multi-city case crossover analysis. Occupational and Environmental Medicine 61: 956–961. Shah, A.S.V., Langrish, J.P., Nair, H., McAllister, D.A., Hunter, A.L., Donaldson, K., Newby, D.E., Mills, N.L., 2013. Global association of air pollution and heart failure: a systematic review and meta-analysis. Lancet 382: 1039–1048. Singh E, Tiwari S, Agrawal M., 2009. Effects of elevated ozone on photosynthesis and stomatal conductance of two soybean varieties: a case study to assess impacts of one component of predicted global climate change. Plant Biology 11: 101-108. Singh et al., 2010. Responses of two cultivars of Trifloliumrepens L. to ethylenediurine in relation to ambient ozone. Journal of Environmental Sciences, 1096-1103. Singh S, Bhatia A, Tomer R, Kumar V, Singh B, Singh SD.,2013.Synergistic action of tropospheric ozone and carbon dioxide on yield and nutritional quality of Indian mustard (Brassica juncea (L.) Czern.). Environmental Monitoring Assessment 185(8). Singh, S. Agrawal, S. B., 2008. Use of ethylene diurea (EDU) in assessing the impact of ozone on growth and productivity of five cultivars of Indian wheat (Triticum aestivum L.). Environmental Monitoring & Assessment, 159 (1-4). 125-141. Singh, S. and Agrawal, S.B., 2010. Impact of tropospheric ozone on wheat (Triticumaestivum L.) in the eastern Gangetic plains of India as assessed by ethylenediurea (EDU) application during different developmental stages. Agriculture, Ecosystems & Environment 138: 214-221. Singh, S., Agrawal, S.B., 2011. Ambient ozone and two black gram cultivars: an assessment of amelioration by the use of ethylenediurea. Acta Physiol. Plant. 33, 2399-2411 Singh.P., Singh.S., Agrawal.S.B. and Agrawal.M., 2012. Assessment of the interactive effects of ambient O3 and NPK levels on two tropical mustard varieties (Brassica campestris L.) using open-top chambers. Environmental Monitoring and Assessment, 184(10), 5863 – 5874. Sinha B, Sangwan KS, Maurya Y, Kumar V, Sarkar C, Chandra BP, Sinha V, 2015. Assessment of crop yield losses in Punjab and Haryana using 2 years of continuous in situ ozone measurements. Atmos Chem Phys 15:9555–9576 Smith, K.R., Peel, J.L., 2010. Mind the gap. Environmental Health Perspective 118: 1643–1645. Sharma S., Chatani S., Mahtta R., Goel A., Kumar A., 2016. Sensitivity analysis of ground level ozone in India using WRF-CMAQ models. Atmospheric Environment 131: 29-40. Suresh K, Kirankumar M, Lakshmi kantha D, 2012. Variations in photosynthetic parameters and leaf water potential in oil palm grown under two different moisture regime. Indian Journal of Plant Physiology, 17(3). Tillett, T., 2010. A break in the continuum: analysing the gap in particle exposure research. Environmental Health Perspective 118: 543–555. Tiwari.S., Agrawal.M., and Manning.W.J.,2005. Assessing the impact of ambient ozone on growth and productivity of two cultivars of wheat in India using three rates of application of ethylenediurea(EDU). Environmental Pollution 138(1): 153-160.

116


Tomar SMS, Singh Sanjay K, Sivasamy M, Vinod . 2014. Wheat rusts in India: resistance breeding and gene deployment—A review. Indian Journal of Genetics and Plant Breeding 74(2): 129–156. Tomar R, Bhatia A., Kumar V.,Kumar A., Singh R., Singh B. , Singh S.D., 2015. Impact of Elevated Ozone on Growth, Yield and Nutritional Quality of Two Wheat Species in Northern India, Aerosol and Air Quality Research 15: 329–340. Tripathi. R., and Agrawal. S.B., 2012. Effects of ambient and elevated level of ozone on Brassica campestris L. with special reference to yield and oil quality parameters. Ecotoxicology and Environmental Safety. 85, 1 – 12. Tsai, S.S., Chang, C.C., Yang, C.Y., 2013. Fine particulate air pollution and hospital admissions for chronic obstructive pulmonary disease: a case-crossover study in Taipei. International Journal of Environmental Research and Public Health 10, 6015–6026. Turner, M.C., Krewski, D., Pope, C.A. 3rd., Chen, Y., Gapstur, S.M., Thun, M.J., 2011..Longterm ambient fine particulate matter air pollution and lung cancer in a large coh ort of neversmokers. American Journal of Respiratory and Critical Care Medicine 184 (12): 1374–1381. U.S. EPA. 2007. Review of the National Ambient Air Quality Standards for Ozone: Policy assessment of scientific and technical information. Staff paper. Office of Air Quality Planning and Standards U.S. EPA., 2009. Integrated Science Assessment for Particulate Matter (Final Report). U.S. Environmental Protection Agency, Washington, DC. U.S. EPA., 2010. Quantitative health risk assessment for particulate matter. North Carolina: U.S. Environmental Protection Agency, Washington, DC. Van Dingenen, R,,Dentener, F.J., Raes, F., Krol, M.C., Emberson, L., Cofala, J.,2009. The global impact of ozone on agricultural crop yields under current and future air quality legislation. Atmos. Environ. 43: 604-618. Wahid, A., Maggs, R., Shamsi, S. R. A., Bell, J. N. B., and Ashmore, M. R.,1995a. Air pollution and its impact on wheat yield in the Pakistan Punjab. Environ. Pollut. 88: 147–154. Wahid, A., Maggs, R., Shamsi, S. R. A., Bell, J. N. B., and Ashmore, M. R.,1995b. Effects of air pollution on rice yield in the Pakistan Punjab. Environmental Pollution 90: 323–329. Wahid, A.,2006. Influence of atmospheric pollutants on agriculture in developing countries: a case study with three new wheat varieties in Pakistan. Sci. Total Environ. 371, 304–313. Wang X and Mauzerall D.L., 2004,Characterisingdistributons of surface ozone and its impacts in China, Japan and South Korea. Atmospheric Environment 38: 4382-4402. WHO, 2005. Effects of air pollution on children’s health and development: a review of the evidence. Special programme on health and development. European Center for Environment and Health. Bonn, Germany: World Health Organization. WHO, 2009. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. WHO press, Geneva, Switzerland. ISBN 978 92 4 156387 1. Young, K.J. and Long, S.P., 2000. Crop ecosystem responses to climatic change: maize and sorghum. Climate change and global crop productivity, pp.107-131. 117


Zanobetti, A., Schwartz, J., 2009. The effect of fine and coarse particulate air pollution on mortality: a national analysis. Environmental Health Perspective 117: 898–903. Zeng G., Pyle, J.A., and Young P.J.,2008. Change in tropospheric ozone and its global budgets, Atmos. Phy. Chem. 8: 369-387. Zhang WW, Wang GG, Liu XB, Feng ZZ (2014) Effects of elevated O3 exposure onseed yield, N concentration and photosynthesis of nine soybean cultivars (Glycinemax(L.) Merr.) in Northeast China. Plant Science 226: 172–181.

118


Appendix A– Mechanism of Ozone Entry to Plants In plants, cuticle acts as barrier for direct diffusion of ozone into the plant cells. The primary avenue of ozone entry into the plants is through leaf internal air spaces, that is, sub-stomatal air pace and stomata (Rubin et al., 1996). Ozone in aqueous mesophyllic cell wall matrix gets absorbed, where it acts in response with water and solutes which are there inside the cell wall and on the plasma membrane, and form free radicles. The process of breaking down of ozone in water takes place very slowly at neutral pH and forms hydroxyl and peroxyl radicles and superoxide, which further leads to formation of hydrogen peroxide. These were thought to be linked with primary breakdown of ozone inside the apoplast. The rate of hydroxyl radical formation got enhanced when phenolic compounds are present, signifying that biologically relevant compounds augment ozone reactions. These imprudent oxygen species are responsible for ozone mediated injury. Scavenging system of plants, to a certain extent, remove ozone and reactive oxygen species (ROS), but the fraction of these still reach and break lipids of plasmalemma, vulnerable amino acids present in the plasma membrane proteins or apoplastic enzymes and various organic metabolite restricted in the cell wall, causing cell leakage and loss of solute. Ozonolysis products obtained contains carbon–carbon double bonds and altered plasma membrane protein function, these can serve as primary signals which leads to ozone responses. It is difficult to identify primary ozone reaction products as in the leaf apoplast compound biochemical network is there. Localized regions of reactive oxygen species (ROS) formation are linked with ozone response in sensitive plants. The process of ROS formation in the apoplast region of leaves has two stages, the first stage which is linked with direct effects of ozone and second stage linked with a plant-derived secondary oxidative burst. It is initially confined to a small area in the leaf apoplast and cell wall, afterward it gets bigger and moves into the cytoplasm and sub-cellular sections which leads to the formation of visible lesions. The entry of ozone in the leaf apoplast, leads to various negative impacts at the tissue level, which includes programmed and unregulated cell death and speeds up senescence of leaves. Inside the chloroplast ROS weakens the light and dark reactions of the photosynthesis and could change the thylakoid properties, which in turn affects the chlorophyll a fluorescence which block the photosynthetic reaction centres (Rai et al., 2011). Singh (2006) gives the pictorial view of ozone entry and its reactions with plants components.

119


Figure A1: Mechanism of ozone entry to plants (Source: Singh, 2006) Plants can protect themselves against various injuries through cuticles and by closing of stomata. They also protect tissues using antioxidants, such as ascorbate, glutathione peroxidase, and sulphur oxide diomutase by dissipating oxidizing power as they are highly reactive. Plants also have variety of mechanism for low level of stress, such as re-allocation of resources, change in root or shoot ratio, and production of new tissues. But these protection strategies such as stomatal closure lead to reduction in photosynthesis. When photosynthetic rate gets sufficiently reduced, plants respond by reallocating remaining carbohydrates. Various studies have shown that the root growth are more prone to ozone as compared to stem and leaf growth. Since the carbohydrates present inside the roots are fewer, therefore, the availability of energy is also less for root related functions to take place. In certain phenotypes, ozone exposure impedes with hormone level in plants which in turn leads to amassing of ethylene inside the leaves, and it hinders the functioning of abscisic acid, a plant hormone. Thus, in such phenotypes, ozone related crop yield losses are enhanced if plant is more often exposed to temperature or water stresses. While other phenotypes which could not control the stomatal opening under ozone stress, then they act as strong photo-oxidant which produces ROS, such as hydrogen peroxide, superoxide, etc., that changes the process of metabolism in the plants. Further, some of the phenotypes reduce the aperture of their stomata under ozone stress. Although, such mechanisms do reduce the intake of ozone by the plants but it also decreases the carbon dioxide intake which leads to reduction in the photosynthetic processes (Sinha et al., 2015).

120


Ozone alters the stomatal movements as it modifies opening and closing speed and response to variation in light intensity, vapor pressure deficit (VPD), soil moisture, CO2 concentration, and Ascorbic acid (ABA) concentration. (Dumont et al., 2012).

121


About TERI A unique developing country institution, TERI is deeply committed to every aspect of sustainable development. From providing environment-friendly solutions to rural energy problems to helping shape the development of the Indian oil and gas sector; from tackling global climate change issues across many continents to enhancing forest conservation efforts among local communities; from advancing solutions to growing urban transport and air pollution problems to promoting energy efficiency in the Indian industry, the emphasis has always been on finding innovative solutions to make the world a better place to live in. However, while TERI’s vision is global, its roots are firmly entrenched in Indian soil. All activities in TERI move from formulating localand national-level strategies to suggesting global solutions to critical energy and environment-related issues. TERI has grown to establish a presence in not only different corners and regions of India, but is perhaps the only developing country institution to have established a presence in North America and Europe and on the Asian continent in Japan, Malaysia, and the Gulf. TERI possesses rich and varied experience in the electricity/energy sector in India and abroad, and has been providing assistance on a range of activities to public, private, and international clients. It offers invaluable expertise in the fields of power, coal and hydrocarbons and has extensive experience on regulatory and tariff issues, policy and institutional issues. TERI has been at the forefront in providing expertise and professional services to national and international clients. TERI has been closely working with utilities, regulatory commissions, government, bilateral and multilateral organizations (The World Bank, ADB, JBIC, DFID, and USAID, among many others) in the past. This has been possible since TERI has multidisciplinary expertise comprising of economist, technical, social, environmental, and management.

122

Project Report Template

Project Report Template

Suggest Documents

IIED Project report template

Template: Project Completion Report

Project Report Template - TERI

Project Report Template - TERI

Project Report Template - LRRB

Project Report Template - LRRB

Project Report Template

Project Report Template - of Planning Commission

Project Report Template - of Planning Commission

Project Report Template - Defense Technical Information Center

Project Report Template - of Planning Commission

Submission template for Capstone Project Case Report

Report Template

Report template

Report Template

Report Template

Project coordinator CV template

JISC Project Plan Template

eChallenges Template - NeOn Project

Project Charter Template

NIJ Project Abstract template

JISC Project Plan Template

JISC Project Plan Template

JISC Project Plan Template