Person:

Song, Shaojie

Emissions from power plants in China and India contain a myriad of fine particulate matter (PM2.5, PM≤2.5 micrometers in diameter) precursors, posing significant health risks among large, densely settled populations. Studies isolating the contributions of various source classes and geographic regions are limited in China and India, but such information could be helpful for policy makers attempting to identify efficient mitigation strategies. We quantified the impact of power generation emissions on annual mean PM2.5 concentrations using the state-of-the-art atmospheric chemistry model WRF-Chem (Weather Research Forecasting model coupled with Chemistry) in China and India. Evaluations using nationwide surface measurements show the model performs reasonably well. We calculated province-specific annual changes in mortality and life expectancy due to power generation emissions generated PM2.5 using the Integrated Exposure Response (IER) model, recently updated IER parameters from Global Burden of Disease (GBD) 2015, population data, and the World Health Organization (WHO) life tables for China and India. We estimate that 15 million (95% Confidence Interval (CI): 10 to 21 million) years of life lost can be avoided in China each year and 11 million (95% CI: 7 to 15 million) in India by eliminating power generation emissions. Priorities in upgrading existing power generating technologies should be given to Shandong, Henan, and Sichuan provinces in China, and Uttar Pradesh state in India due to their dominant contributions to the current health risks.

During recent years, aggressive air pollution mitigation measures in northern China have resulted in considerable changes in gas and aerosol chemical composition. But it is unclear whether aerosol water content and acidity respond to these changes. The two parameters have been shown to affect heterogeneous production of winter haze aerosols. Here, we performed thermodynamic equilibrium modeling using chemical and meteorological data observed in urban Beijing for four recent winter seasons and quantified the changes in the mass growth factor and pH of inorganic aerosols. We focused on high relative humidity (>60%) conditions when submicron particles have been shown to be in the liquid state. From 2014/2015 to 2018/2019, the modeled mass growth factor decreased by about 9%–17% due to changes in aerosol compositions (more nitrate and less sulfate and chloride), and the modeled pH increased by about 0.3–0.4 unit mainly due to rising ammonia. A buffer equation is derived from semivolatile ammonia partitioning, which helps understand the sensitivity of pH to meteorological and chemical variables. The findings provide implications for evaluating the potential chemical feedback in secondary aerosol production and the effectiveness of ammonia control as a measure to alleviate winter haze.

Fossil fuel and aerosol emissions have played important roles on climate over the Indian subcontinent over the last century. As the world transitions toward decarbonization in the next few decades, emissions pathways could have major impacts on India’s climate and people. Pathways for future emissions are highly uncertain, particularly at present as countries recover from COVID-19. This paper explores a multimodel ensemble of Earth system models leveraging potential global emissions pathways following COVID-19 and the consequences for India’s summertime (June–July–August–September) climate in the near- and long-term. We investigate specifically scenarios which envisage a fossil-based recovery, a strong renewable-based recovery and a moderate scenario in between the two. We find that near-term climate changes are dominated by natural climate variability, and thus likely independent of the emissions pathway. By 2050, pathway-induced spatial patterns in the seasonally-aggregated precipitation become clearer with a slight drying in the fossil-based scenario and wetting in the strong renewable scenario. Additionally, extreme temperature and precipitation events in India are expected to increase in magnitude and frequency regardless of the emissions scenario, though the spatial patterns of these changes as well as the extent of the change are pathway dependent. This study provides an important discussion on the impacts of emissions recover pathways following COVID-19 on India, a nation which is likely to be particularly susceptible to climate change over the coming decades.

Many cities in India experience severe deterioration of air quality in winter. Particulate matter is a key atmospheric pollutant that impacts millions of people. In particular, high levels of particulate matter reduce visibility, which has severely damaged the economy and endangered human lives. But the underlying chemical mechanisms and physical processes responsible for initiating haze and fog formation remain poorly understood. Here we present the measurement results of chemical composition of particulate matter in Delhi and Chennai. We find persistently high chloride in Delhi, and episodically high chloride in Chennai. These measurements, combined with thermodynamic modeling, suggest that in the presence of excess ammonia in Delhi, high local emission of hydrochloric acid partition into aerosol water. The highly water-absorbing and soluble chloride in the aqueous phase substantially enhances aerosol water uptake through co-condensation, which sustains particle growth leading to haze and fog formation. We therefore suggest that high local concentration of gas-phase hydrochloric acid, possibly emitted from plastic-contained waste burning and industry causes some 50% of the reduced visibility. Our work implies that identification and regulation of gaseous hydrochloric acid emissions could be critical to improve visibility and human health in India.

China has large but uncertain coal mine methane (CMM) emissions. Inverse modeling (top-down) analyses of atmospheric methane observations can help improve the emission estimates but require reliable emission patterns as prior information. To serve this urgent need, we developed a high-resolution (0.25° × 0.25°) methane emission inventory for China’s coal mining using a recent publicly available database of more than 10000 coal mines in China for 2011. This number of coal mines is 25 and 2.5 times, respectively, more than the number available in the EDGAR v4.2 and EDGAR v4.3.2 gridded global inventories, which have been extensively used in past inverse analyses. Our inventory shows large differences with the EDGAR v4.2 as well as its more recent version, EDGAR v4.3.2. Our results suggest that China’s CMM emissions have been decreasing since 2012 on the basis of coal mining activities and assuming time-invariant emission factors but that regional trends differ greatly. Use of our inventory as prior information in future inverse modeling analyses can help better quantify CMM emissions as well as more confidently guide the future mitigation of coal to gas in China.