Supplementary Information

Year-long simulation of gaseous and particulate air pollutants in India

Sri Harsha Kota1,2,3, Hao Guo1, Lauri Myllyvirta4, Jianlin Hu2, Shovan Kumar Sahu3,

Rajyalakshmi Garaga3, Qi Ying2,5, Aifang Gao6,7, Sunil Dahiya8, Yuan Wang9, Hongliang

Zhang1,2,*

1Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, Louisiana 70803, USA2Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China3Department of Civil Engineering, Indian Institute of Technology Guwahati, 781039, India4Greenpeace East Asia, Beijing 100007, China5Zachry Department of Civil Engineering, Texas A&M University, College Station, Texas 77843, USA6School of Water Resources and Environment, Hebei GEO University, Shijiazhuang, Hebei Province 050031, China7Hebei Key Laboratory of Sustained Utilization and Development of Water Resources, Shijiazhuang, Hebei Province 050031, China8Greenpeace India, New Delhi 110049, India9Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, 91106 USA

*Corresponding author: Hongliang Zhang. Email: hlzhang@lsu.edu. Phone: +1-225-578-0140.

Table S1. Summary of studies predicting air pollutants in India.Study Model Species studied No of cities

Bran and Srivastava [2] WRF-CHEM PM2.5 9Gogoi, Babu [6] WRF-CHEM,

GOCARTBlack carbon 0*

Sarkar, Venkataraman [9] CAMx NO2, CO and O3 0*

Sharma, Chatani [11] CMAQ O3 6Govardhan, Nanjundiah [12] WRF-CHEM,

SPRINTARSBlack carbon 8

Surendran, Ghude [13] MOZART O3, CO and NO2 13Chatani, Amann [14] CMAQ O3 7Sahu, Sheel [15] MRI-CCM2 O3 1Moorthy, Beegum [16] GOCART,

CHIMEREBlack carbon 8

Kumar, Naja [17] WRF-CHEM CO, NOx and O3 7Sheel, Lal [18] MOZART NO2 4Roy, Beig [19] REMO-CTM O3, NOx and CO 4Beig, Gunthe [20] MOZART O3, NOx and CO 1This study CMAQ NO2, SO2, CO, O3

and PM2.5

9

* donates study using satellite data.

Table S2. Scaling factors of energy sector for different states to convert emissions in 2010 to 2015.

State PM VOCs NOx SO2

Andhra Pradesh 1.011 1.030 1.034 1.029Arunachal Pradesh 1.011 1.160 1.160 1.140Assam 1.099 1.040 1.298 1.045Bihar 1.694 1.730 1.734 1.734Chhattisgarh 1.643 1.800 1.813 1.799Capital Region 0.374 1.190 0.374 0.374Goa 1.425 0.740 0.740 0.740Gujarat 1.425 1.600 1.631 1.595Haryana 1.148 1.300 1.445 1.299Himachal Pradesh 1.000 1.000 1.000 1.000Jammu&kashmir 1.000 1.000 1.000 1.000Jharkhand 2.166 2.220 2.219 2.217Karnataka 1.407 1.430 1.460 1.439Kerala 1.000 1.000 1.000 1.000Madhya Pradesh 1.432 1.620 1.639 1.624Maharashtra 1.268 1.460 1.479 1.455Manipur 1.432 1.500 1.500 1.500Meghalaya 1.022 1.030 1.066 1.031Mizoram 1.022 1.100 1.100 1.100Nagaland 1.022 1.230 1.230 1.230Odisha 1.048 1.130 1.145 1.135Punjab 0.665 0.850 0.850 0.850Rajasthan 1.129 1.330 1.388 1.328Sikkim 1.129 0.950 0.950 0.950Tamil Nadu 1.858 1.960 2.002 1.959Telangana 3.324 3.360 3.370 3.356Tripura 1.228 1.010 1.010 1.010Uttar Pradesh 1.228 1.340 1.358 1.343Uttarakhand 1.341 2.020 2.024 2.024West Bengal 1.144 1.260 1.262 1.261

Table S3. Scaling factors of on-road and off-road sectors for different states to convert emissions in 2010 to 2015.

State PM VOCs NOx SO2

Andhra Pradesh 0.840 0.997 0.956 1.139Arunachal Pradesh 1.004 1.197 1.181 1.379Assam 0.857 1.017 0.969 1.159Bihar 0.828 0.982 0.932 1.118Chhattisgarh 0.981 1.167 1.136 1.339Capital Region 0.922 1.114 1.310 1.337Goa 0.661 0.777 0.674 0.864Gujarat 0.952 1.136 1.128 1.311Haryana 0.807 0.962 0.957 1.111Himachal Pradesh 0.875 1.042 1.025 1.199Jammu & Kashmir 0.871 1.036 1.003 1.187Jharkhand 0.915 1.090 1.073 1.255Karnataka 1.010 1.203 1.187 1.385Kerala 0.958 1.142 1.133 1.318Madhya Pradesh 0.898 1.067 1.027 1.220Maharashtra 0.825 0.981 0.953 1.125Manipur 1.327 1.577 1.508 1.802Meghalaya 0.763 0.908 0.886 1.042Mizoram 0.981 1.168 1.144 1.343Nagaland 0.824 0.985 1.003 1.144Odisha 0.840 0.997 0.953 1.138Punjab 0.798 0.951 0.948 1.099Rajasthan 0.974 1.159 1.137 1.333Sikkim 0.720 0.852 0.800 0.968Tamil Nadu 0.834 0.989 0.947 1.130Telangana 0.840 0.997 0.956 1.139Tripura 0.805 0.956 0.916 1.093Uttar Pradesh 0.820 0.974 0.933 1.112Uttarakhand 0.901 1.072 1.041 1.229West Bengal 0.814 0.967 0.932 1.106

Table S4. Scaling factors of nationwide emissions of agriculture, industry and residential sectors from 2010 to 2015.

PM VOCs NOx SO2

Agriculture 0.900 1.120 0.891 0.769Industry 1.470 1.418 1.407 1.363Residential 1.033 1.034 1.028 1.003

Table S5: Formulae used to estimate mean bias (MB), gross error (GE), mean fractional bias (MFB), mean fractional error (MFE), normalized mean bias (NMB) and normalized mean error (NME).

MB=∑i=1

N

(P i−Oi)

¿=∑i=1

N

|Pi−Oi|

MFB= 2N∑

i=1

N Pi−OiPi+Oi

MFE= 2N∑

i=1

N |P i−Oi|Pi+Oi

NMB=∑i=1

N

Pi−Oi

∑i=1

N

Oi

NME=∑i=1

N

|Pi−Oi|

∑i=1

N

Oi

Table S6. Seasonal change in OC to EC ratio in the nine selected cities in India.OC/EC ratio DEL LUC PAT KOL AHM MUM HYD BAN CHEPre-monsoon 3.74 3.81 3.44 2.87 2.62 2.27 3.17 3.39 2.67Monsoon 3.66 3.43 3.17 2.56 2.49 2.18 2.93 2.91 2.60Post-moon 3.97 3.87 3.45 2.93 2.99 2.60 3.42 3.08 2.74Winter 4.64 4.24 3.81 2.94 2.94 2.57 3.32 3.29 2.96Observation 5.4[21] 2.3[22] NA 1.75[22] 4.3[23] 2.0[24] 2.7[25] 5.4[26] 3.85[27]

Figure S1. Yearly and seasonal average emissions of PM2.5 (g/s), EC (g/s), OC (g/s), NOx (mole/s), VOCs (mole/s), SO2 (mole/s) and CO (mole/s) in India in 2015.

Figure S2. Daily variation of temperature predicted by WRF with observation data in 9 cities.

Figure S3. Daily variation of wind speed predicted by WRF with observation data in 9 cities.

Figure S4. Daily variation of wind direction predicted by WRF with observation data in 9 cities.

Figure S5. Daily variation of relative humidity predicted by WRF with observation data in 9 cities.

Figure S6. Predicted and observed monthly variations of 1-hour peak O3 in different cities in India. Note: Only data with observed concentrations are shown.

Figure S7. Predicted and observed variations of daily PM2.5 in each month in different cities in India. Note: Only data with observed concentrations are shown.

Figure S8. Diurnal variations in model performance of ozone, PM2.5, CO, SO2 and NO2. Data from all the cities, in which observations were available, were used for the analysis.

Figure S9: Comparison of averaged predicted PM2.5 components with observed values in Mumbai [1], Kolkata [3, 4], Hyderabad [5], Delhi [7], Chennai [8] and Bangalore [10]. Note: The observations are not during the same analysis period of this study.

Figure S10. Relative difference ((winter-yearly avg.) ×100/yearly avg.) of POA, EC, SO4, NO3, SO2 and NO2 between winter and yearly averaged concentrations.

Figure S11: Seasonal change in composition of PM2.5 components in Delhi and Beijing.

Figure S11 discusses the seasonal difference in PM2.5 and its components in the capital cities of India and China. Results indicate that except winter, PM2.5 concentrations in Delhi are more than in Beijing. The fraction of primary particulate matter (PPM), i.e. sum of POA, EC and other components, in total PM 2.5 is greater in Delhi compared to China in all seasons. On an average, PPM comprises of 75 and 37% of total PM2.5 in India and China, respectively. In contrary, fraction of secondary inorganic aerosols (SIA), i.e. sum of SO4, NO3 and NH4, in PM2.5 is higher in China compared to India. PPM and SIA dominate PM2.5 in Delhi and Beijing, respectively. China have a higher fraction of secondary organic aerosol (SOA) in winter and pre-monsoon, but SOA fraction is higher in India during monsoon.

References

1. Joseph, A.E., S. Unnikrishnan, and R. Kumar, Chemical characterization and mass closure of fine aerosol for different land use patterns in Mumbai city. Aerosol and Air Quality Resarch, 2012. 12(1): p. 61-72.

2. Bran, S.H. and R. Srivastava, Investigation of PM2.5 mass concentration over India using a regional climate model. Environmental Pollution, 2017. 224(Supplement C): p. 484-493.

3. Chatterjee, A., et al., Fine mode aerosol chemistry over a tropical urban atmosphere: characterization of ionic and carbonaceous species. Journal of Atmospheric Chemistry, 2012. 69(2): p. 83-100.

4. Das, R., et al., Trace element composition of PM 2.5 and PM 10 from Kolkata–a heavily polluted Indian metropolis. Atmospheric Pollution Research, 2015. 6(5): p. 742-750.

5. Guttikunda, S.K., et al., Receptor model-based source apportionment of particulate pollution in Hyderabad, India. Environmental monitoring and assessment, 2013. 185(7): p. 5585-5593.

6. Gogoi, M.M., et al., Radiative effects of absorbing aerosols over northeastern India: Observations and model simulations. Journal of Geophysical Research: Atmospheres, 2017. 122(2): p. 1132-1157.

7. Pant, P., et al., Characterization of ambient PM2. 5 at a pollution hotspot in New Delhi, India and inference of sources. Atmos. Environ, 2015. 109: p. 178-189.

8. CPCB, Air quality monitoring, emission inventory and source apportionment study for Indian cities. 2011, Ministry of Environment & Forests.

9. Sarkar, M., et al., Indian emissions of technology-linked NMVOCs with chemical speciation: An evaluation of the SAPRC99 mechanism with WRF-CAMx simulations. Atmospheric Environment, 2016. 134(Supplement C): p. 70-83.

10. TERI, Air quality assessment, emission inventory and source apportionment study for Bangalore city. 2010: New Delhi.

11. Sharma, S., et al., Sensitivity analysis of ground level ozone in India using WRF-CMAQ models. Atmospheric Environment, 2016. 131(Supplement C): p. 29-40.

12. Govardhan, G.R., et al., Inter-comparison and performance evaluation of chemistry transport models over Indian region. Atmospheric Environment, 2016. 125(Part B): p. 486-504.

13. Surendran, D.E., et al., Air quality simulation over South Asia using Hemispheric Transport of Air Pollution version-2 (HTAP-v2) emission inventory and Model for Ozone and Related chemical Tracers (MOZART-4). Atmospheric Environment, 2015. 122: p. 357-372.

14. Chatani, S., et al., Photochemical roles of rapid economic growth and potential abatement strategies on tropospheric ozone over South and East Asia in 2030. Atmos. Chem. Phys., 2014. 14(17): p. 9259-9277.

15. Sahu, L.K., et al., Seasonal and interannual variability of tropospheric ozone over an urban site in India: A study based on MOZAIC and CCM vertical profiles over Hyderabad. Journal of Geophysical Research: Atmospheres, 2014. 119(6): p. 3615-3641.

16. Moorthy, K.K., et al., Performance evaluation of chemistry transport models over India. Atmospheric Environment, 2013. 71(Supplement C): p. 210-225.

17. Kumar, R., et al., Simulations over South Asia using the Weather Research and Forecasting model with Chemistry (WRF-Chem): chemistry evaluation and initial results. Geoscientific Model Development, 2012. 5(3): p. 619-648.

18. Sheel, V., et al., Comparison of satellite observed tropospheric NO2 over India with model simulations. Atmospheric Environment, 2010. 44(27): p. 3314-3321.

19. Roy, S., G. Beig, and D. Jacob, Seasonal distribution of ozone and its precursors over the tropical Indian region using regional chemistry-transport model. Journal of Geophysical Research: Atmospheres, 2008. 113(D21): p. n/a-n/a.

20. Beig, G., S. Gunthe, and D.B. Jadhav, Simultaneous measurements of ozone and its precursors on a diurnal scale at a semi urban site in India. Journal of Atmospheric Chemistry, 2007. 57(3): p. 239-253.

21. Pipal, A.S., et al., Sources and characteristics of carbonaceous aerosols at Agra “World heritage site” and Delhi “capital city of India”. Environmental Science and Pollution Research, 2014. 21(14): p. 8678-8691.

22. Sharma, S.K., et al., Spatio-temporal variation in chemical characteristics of PM10 over Indo Gangetic Plain of India. Environmental Science and Pollution Research, 2016. 23(18): p. 18809-18822.

23. Rastogi, N. and M.M. Sarin, Quantitative chemical composition and characteristics of aerosols over western India: One-year record of temporal variability. Atmospheric Environment, 2009. 43(22–23): p. 3481-3488.

24. Venkataraman, C., et al., Aerosol size and chemical characteristics at Mumbai, India, during the INDOEX-IFP (1999). Atmospheric Environment, 2002. 36(12): p. 1979-1991.

25. Ali, K., et al., Carbonaceous aerosols over Pune and Hyderabad (India) and influence of meteorological factors. Journal of Atmospheric Chemistry, 2016. 73(1): p. 1-27.

26. Vreeland, H., et al., Chemical characterization and toxicity of particulate matter emissions from roadside trash combustion in urban India. Atmospheric Environment, 2016. 147: p. 22-30.

27. Pavuluri, C.M., et al., Characteristics, seasonality and sources of carbonaceous and ionic components in the tropical aerosols from Indian region. Atmospheric Chemistry and Physics, 2011. 11(15): p. 8215-8230.