This bottom‐up modeling study, supported by new population census 2011 data, simulates ozone (O3) and fine particulate matter (PM2.5) exposure on local to regional scales. It quantifies, present‐day premature mortalities associated with the exposure to near‐surface PM2.5 and O3 concentrations in India using a regional chemistry model. We estimate that PM2.5 exposure leads to about 570,000 (CI95: 320,000–730,000) premature mortalities in 2011. On a national scale, our estimate of mortality by chronic obstructive pulmonary disease (COPD) due to O3 exposure is about 12,000 people. The Indo‐Gangetic region accounts for a large part (~42%) of the estimated mortalities. The associated lost life expectancy is calculated as 3.4 ± 1.1 years for all of India with highest values found for Delhi (6.3 ± 2.2 years). The economic cost of estimated premature mortalities associated with PM2.5 and O3 exposure is about 640 (350–800) billion USD in 2011, which is a factor of 10 higher than total expenditure on health by public and private expenditure.

This bottom-up modeling study, supported by emission inventories and crop production, simulates ozone on local to regional scales. It quantifies, for the first time, potential impact of ozone on district-wise cotton, soybeans, rice, and wheat crops in India for the first decade of the 21st century. Wheat is the most impacted crop with losses of 3.5 ± 0.8 million tons (Mt), followed by rice at 2.1 ± 0.8 Mt, with the losses concentrated in central and north India. On the national scale, this loss is about 9.2% of the cereals required every year (61.2 Mt) under the provision of the recently implemented National Food Security Bill (in 2013) by the Government of India. The nationally aggregated yield loss is sufficient to feed about 94 million people living below poverty line in India.

[1] In this work, we map and develop for the first time an independent satellite constrained NO x emission inventory for India for 2005 using an inverse technique and iterative procedure. We used OMI tropospheric NO 2 column retrievals over the Indian region, with tropospheric NO 2 columns simulated by the WRF-Chem model using the INTEX-B emission inventory. We determined the local relationship between modeled emissions and tropospheric columns and iteratively apply this relationship to OMI observations to derive an optimized NOx emission inventory on a 0.5°×0.5°grid. The optimized total NO x emissions for India amount to 1.9 TgN/y and agree within 25% with EDGARv4.1 and the INTEX-B estimate. Our top-down inventory captures many of the missing hotspots in the original inventory and suggests that the INTEX-B inventory overestimates emissions over the Western and Eastern Indo-Gangetic region and underestimates point sources. We further evaluate the effect of the top-down inventory on surface ozone, which clearly indicates significant changes in spatial distribution. Citation: Ghude, S.

[1] We used SCIAMACHY (10:00 LT) and OMI (13:30 LT) tropospheric NO 2 columns to study diurnal and seasonal patterns in NO 2 concentrations over India. Using characteristics of seasonal variability in tropospheric NO 2 columns, we present a simple methodology to identify the dominant NOx source category for specific regions in India. Regions where the dominant source category is classified as biomass burning are found generally to agree with the ATSR fire count distribution. Relating OMI NO 2 columns to surface NOx emission, we find that biomass burning emission account for an average flux of 1.55 Â 10 11 molecules cm À2 s À1 during the peak burning period. Furthermore, extrapolating this estimated flux to the total burned area for the year 2005, biomass burning is estimated to account for 72 Gg of N emissions. Additional analysis of fire events in Northeast India shows a marked increase in TES retrieved O 3 concentrations, suggesting significant photochemical ozone formation during the peak biomass burning period. Regions where the dominant source type was categorized as anthropogenic are in good agreement with the distribution of major industrial regions and urban centers in India. Tropospheric NO 2 columns over these anthropogenic source regions increased by 3.8% per year between 2003 and 2011, which is consistent with the growth in oil and coal consumption in India. The OMI-derived surface NO 2 mixing ratios are indirectly validated with the surface in situ measurements (correlation r = 0.85, n = 88) obtained from the air quality monitoring network in Delhi during August 2010 to January 2011. Most of the OMI-derived surface NO 2 values agree with surface-based measurements, supporting the direct utility of OMI observation for emission estimates. Finally, we use OMI NO 2 columns to estimate NOx emissions for selected large cites and major thermal power plants in India and compare these estimates with the INTEX-B and EDGAR emission inventory. We find that, for a few locations, OMI-derived emission show fair agreement; however, for many locations, NOx emissions differ from INTEX-B and EDGAR inventories.

Elevated PM 2.5 concentrations frequently cause severe air pollution events in Delhi. Till recently, the effect of crop residue burning on the air quality in Delhi has not been fully quantified and the approaches to control the impact of fire emissions have not been effective. In this study, for the first time, we quantified the statewise contribution of post-monsoon crop residue burning in the northwestern states of India to surface PM 2.5 concentrations in Delhi using several sensitivity experiments with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and FINNv1.5 fire emission inventory. Results were evaluated with ground-based observations in Delhi (21 stations), Punjab, and Haryana (14 stations). On average, ∼20% of PM 2.5 concentration in Delhi during the post-monsoon season (October−November) was found to be contributed by nonlocal fire emissions. However, on typical air pollution events, fire emissions contributed as high as 50−75% (80−120 μg/m 3 ) to PM 2.5 in Delhi, highlighting the importance of both external transport and local emissions to PM 2.5 pollution in Delhi.