Source Contributions to Fine Particulate Matter and Attributable Mortality in India and the Surrounding Region

Hourly emission amounts and concentration of water-soluble ions in primary particles from residential coal burning in rural northern China

Residential coal burning (RCB) stands as an important contributor to ambient pollutants in China. For the effective execution of air pollution control policies, it is essential to maintain precise emission inventories of RCB. The absence of hourly emission factors (EFs) combined with the inaccuracies in the spatial-temporal distribution of activity data, constrained the quality of residential coal combustion emission inventories, thereby impeding the estimation of air pollutant emissions. This study revised the hourly EFs for PM2.5 and water-soluble ions (WSIs) emitted from RCB in China. The hourly emission inventories for PM2.5 and WSIs derived from RCB illustrate the diurnal fluctuations in emission patterns. This study found that the emissions of PM2.5, NH4+, Cl-, and SO42- showed similar emission features with emission of 106.8 Gg, 1417.6, 356.8, and 5868.5 ton in erupt period. The results provide basic data for evaluating RCB emission reduction policies, simulating particles, and preventing air pollution in both sub-regions and time periods. The spatial emission and simulated concentration distribution of PM2.5 and WSIs indicated that emission hotspot shifted from North China Plain (NCP) to Northeast region in China. The emissions in China were well-controlled in '2 + 26' region (R28) priority region, with hotspots decreasing by 99.6% in BTH region. The RCB became the dominant contributor to ambient PM2.5 with a ratio in the range of 16.2-23.7% in non-priority region.

Unequal Health Risks and Attributable Mortality Burden of Source-Specific PM2.5 in China

Anthropogenic emissions, originating from human activities, stand as the primary contributors to PM2.5, which is recognized as a global health threat. The disease burden associated with PM2.5 has been extensively documented. However, the prevailing estimations have predominantly relied on PM2.5 exposure-response functions, neglecting the distinct risks posed by PM2.5 from various sources. China has experienced a significant reduction in the PM2.5 concentration due to stringent emission controls. With diverse sources and abundant mortality data, this situation provides a unique opportunity to estimate short-term source-specific attributable mortality. Our approach involves an integrated unequal health risk-oriented modeling in China, incorporating a source-oriented Community Multiscale Air Quality model, an adjustment and downscaling method for exposure measurement, a generalized linear model with random-effects meta-analysis, and premature mortality estimation. Adhering to the unequal health risk concept, we calculated the attributable mortality of multiple PM2.5 sources by determining the source risk-adjusted factor. In this study, we observed varying excess risks associated with multiple PM2.5 sources, with transportation-related PM2.5 exhibiting the most substantial association. An interquartile range increase (7.65 μg/m3) was linked to a 1.98% higher daily nonaccidental mortality. Residential use- and transportation-related PM2.5 emerged as the two principal sources of premature mortality. In 2018, a remarkable 53,381 avoiding deaths were estimated compared to 2013, and over 67% of these were attributed to reductions in coal-dependent sources. Notably, transportation-related PM2.5 emerged as the largest contributor to premature mortality in 2018. This study underscores the significance of a new source-oriented health risk assessment to support actions aimed at reducing air pollution. It strongly advocates for heightened attention to PM2.5 reductions in the transportation sector in China.

Continued Rise in Health Burden from Ambient PM2.5 in India under SSP Scenarios Until 2100 despite Decreasing Concentrations

Forecasting alterations in ambient air pollution and the consequent health implications is crucial for safeguarding public health, advancing environmental sustainability, informing economic decision making, and promoting appropriate policy and regulatory action. However, predicting such changes poses a substantial challenge, requiring accurate data, sophisticated modeling methodologies, and a meticulous evaluation of multiple drivers. In this study, we calculate premature deaths due to ambient fine particulate matter (PM2.5) exposure in India from the 2020s (2016-2020) to the 2100s (2095-2100) under four different socioeconomic and climate scenarios (SSPs) based on four CMIP6 models. PM2.5 concentrations decreased in all SSP scenarios except for SSP3-7.0, with the lowest concentration observed in SSP1-2.6. The results indicate an upward trend in the five-year average number of deaths across all scenarios, ranging from 1.01 million in the 2020s to 4.12-5.44 million in the 2100s. Further analysis revealed that the benefits of reducing PM2.5 concentrations under all scenarios are largely mitigated by population aging and growth. These findings underscore the importance of proactive measures and an integrated approach in India to improve atmospheric quality and reduce vulnerability to aging under changing climate conditions.

A district-level emission inventory of anthropogenic PM2.5 from the primary sources over the Indian Indo Gangetic Plain: Identification of the emission hotspots

A district-wise emission inventory was made for the states and union territories (UTs) of the Indian Indo-Gangetic Plain for the base year of 2018 to estimate the emissions of PM2.5 from various sectors. In addition to conventional sectors, emissions from road dust, fossil-fuelled irrigation pumps, and construction dust were also taken into account. Total primary anthropogenic PM2.5 emission was estimated to be 3157.3 Gg (or kilo-tones) for the year 2018 of which 32 % originated from the industrial sector, 27 % from domestic fuel consumption, 23 % from open burning, 14 % from road dust, 2 % from vehicular and 2 % from various unorganized sectors. The highest emissions were observed during the premonsoon (1013 Gg/year) followed by postmonsoon (802Gg/year), winter (788 Gg/year), and lowest during the monsoon (554Gg/year). Among the states and UTs, Uttar Pradesh contributes the most in total emissions (39 %), followed by Punjab (19 %), Bihar (17 %), West Bengal (13 %), Haryana (11 %), Delhi (0.9 %) and Chandigarh (0.1 %). Emission for per capita and for billion-rupee of state gross domestic product (GDP) were the highest for Punjab and Haryana. Results have identified the districts of Punjab (Firozpur, Ludhiana, Jalandhar), scattered pockets of Uttar Pradesh (Sonbhadra, Agra, Varanasi, Kanpur, Lucknow, Prayagraj) and lower Gangetic delta (Gaya, Muzaffarpur, Burdwan, both 24-parganas and Murshidabad) as potent hotspots of cumulative PM2.5 emissions. On the other hand, the districts of Punjab (Faridkot, Mansa, Muktsar, Fatehgarh) were found to be the hotspots for per capita emissions. High emissions were observed from the domestic sector, brick kilns, and micro and small-scale industries, and regulating norms should be more stringent for these sectors. Such a study will be a value add for the policymakers and health experts to assess emission hot spots, pollution simulation, and associated mortality analysis of the region.