An Integrated Modeling Approach for Identifying Major Sources and Health Impacts of PM2.5 in Asia

Regions across Asia, particularly in China and South Asia, experience the largest annual average concentrations of fine particulate matter (PM_2.5) in the world. Despite recent mitigation efforts, PM_2.5 and its precursors remain a health concern, with excess mortality attributable to PM_2.5 projected to increase throughout the region. Further development of reduction policies requires identification of major sources, which in turn depends on accurate predictions of PM_2.5, its precursors, and their sensitivities to regional emission changes. Two previous studies have identified dominant contributors to PM_2.5 mass and mortality in China and India based emissions from 2013 and 2015, respectively. These countries, however, have experienced unprecedented changes in energy consumption, efficiency, and fuel mix since 2013, which are not captured in past results. With recent advances in global and national-level emission inventories, there is now interest in identifying major sources of PM_2.5 and the associated disease burdens throughout Asia.

We present global simulations of PM_2.5, with a focus on China and India, to identify dominant sources and contributions to disease burden in Asia. Emission sensitivity simulations are conducted with the global GEOS-Chem chemical transport model, updated to included recent advancements in heterogeneous reactive nitrogen chemistry and physical loss processes. Anthropogenic emission inventories utilize the Community Emissions Database System (CEDS), which provides global inventories, disaggregated into subsectors and fuel types. CEDS leverages recently-released fuel consumption data and updated national emission inventories to extend and calibrate emissions past 2015. Simulated concentrations are further downscaled to 1 to 10 kilometer resolution using satellite-derived PM_2.5 surface estimates, calibrated to surface observations. Preliminary results suggest that residential, energy, and industrial emissions are dominant contributors with additional simulations providing information at the subsector level for coal and biomass use. Lastly, combining sectoral contributions with two concentration-response models provides updated estimates for the avoidable deaths associated with PM_2.5 sources throughout this densely-populated region.