Population exposure across central India to PM2.5 derived using remotely sensed products in a three-stage statistical model

Abstract Surface PM2.5 concentrations are required for exposure assessment studies. Remotely sensed Aerosol Optical Depth (AOD) has been used to derive PM2.5 where ground data is unavailable. However, two key challenges in estimating surface PM2.5 from AOD using statistical models are (i) Satellite data gaps, and (ii) spatio-temporal variability in AOD-PM2.5 relationships. In this study, we estimated spatially continuous (0.03° × 0.03°) daily surface PM2.5 concentrations using MAIAC AOD over Madhya Pradesh (MP), central India for 2018 and 2019, and validated our results against surface measurements. Daily MAIAC AOD gaps were filled using MERRA-2 AOD. Imputed AOD together with MERRA-2 meteorology and land use information were then used to develop a linear mixed effect (LME) model. Finally, a geographically weighted regression was developed using the LME output to capture spatial variability in AOD-PM2.5 relationship. Final Cross-Validation (CV) correlation coefficient, r2, between modelled and observed PM2.5 varied from 0.359 to 0.689 while the Root Mean Squared Error (RMSE) varied from 15.83 to 35.85 µg m−3, over the entire study region during the study period. Strong seasonality was observed with winter seasons (2018 and 2019) PM2.5 concentration (mean value 82.54 µg m−3) being the highest and monsoon seasons being the lowest (mean value of 32.10 µg m−3). Our results show that MP had a mean PM2.5 concentration of 58.19 µg m−3 and 56.32 µg m−3 for 2018 and 2019, respectively, which likely caused total premature deaths of 0.106 million (0.086, 0.128) at the 95% confidence interval including 0.056 million (0.045, 0.067) deaths due to Ischemic Heart Disease (IHD), 0.037 million (0.031, 0.045) due to strokes, 0.012 million (0.009, 0.014) due to Chronic Obstructive Pulmonary Disease (COPD), and 1.2 thousand (1.0, 1.5) due to lung cancer (LNC) during this period

Understanding the Influence of Meteorology and Emission Sources on PM 2.5 Mass Concentrations Across India: First Results From the COALESCE Network

The Carbonaceous Aerosol Emissions, Source Apportionment and Climate Impacts (COALESCE) is a multi-institutional Indian network project to better understand carbonaceous aerosol induced air quality and climate effects. This study presents time synchronized measurements of surface PM2.5 concentrations made during 2019 at 11 COALESCE sites across India. The network median PM2.5 concentration was 42 μg m−3 with the highest median value at Rohtak (99 μg m−3) and the lowest median value at Mysuru (26 μg m−3). The influence of six meteorological parameters on PM2.5 were evaluated. Causality analysis suggested that temperature, surface pressure, and relative humidity were the most important factors influencing fine PM mass, on an annual as well as seasonal scale. Further, a multivariable linear regression model showed that, on an annual basis, meteorology could explain 16%–41% of PM2.5 variability across the network. Concentration Weighted Trajectories (CWT) together with the results of causality analysis revealed common regional sources affecting PM2.5 concentrations at multiple regional sites. Further, CWT source locations for all sites across the network correlated with the SMoG-India emissions inventory at the 95th percentile confidence. Finally, CWT maps in conjunction with emissions inventory were used to obtain quantitative estimates of anthropogenic primary PM2.5 sectoral shares from a mass-meteorology-emissions reconciliation, for all 11 pan-India network sites. These estimates can help guide immediate source reduction and mitigation actions at the national level. © 2022. American Geophysical Union. All Rights Reserved