A111-0001
Fine Particulate Matter and Global Health: An Integrated Modeling Approach to Quantify Sector and Fuel-Specific Sources of PM2.5 Mass and Mortality Across Global, National, and City Scales
Friday, 11 December 2020
Poster
Erin E. McDuffie1,2, Randall Martin3, Melanie Sarah Hammer1, Aaron van Donkelaar1,4, Joseph Spadaro5, Richard T. Burnett6, Patrick R O'Rourke7, Steven Smith8, Jamiu Adeniran9, Jintai Lin9 and Michael Brauer10,11, (1)Washington University in St. Louis, Energy, Environmental & Chemical Engineering, St. Louis, United States, (2)American Association for the Advancement of Science (AAAS) Science and Technology Policy Fellow, Washington, United States, (3)Washington University in St. Louis, Energy, Environmental & Chemical Engineering, St. Louis, MO, United States, (4)Dalhousie University, Dept Physics & Atmospheric Science, Halifax, NS, Canada, (5)Spadaro Environmental Research Consultants, Philadelphia, United States, (6)Health Canada, Population Studies Division, Ottawa, ON, Canada, (7)Pacific Northwest National Laboratory, Richland, WA, United States, (8)Joint Global Change Research Institute, College Park, MD, United States, (9)Peking University, Beijing, China, (10)University of British Columbia, School of Population and Public Health, Vancouver, BC, Canada, (11)University of Washington, Institute for Health Metrics and Evaluation, Seattle, United States
Abstract:
Long-term exposure to fine particulate matter (PM2.5) contributed to an estimated 4.1 million deaths in 2019. While these estimates are useful for motivating public policy, the development of effective mitigation strategies requires the identification of dominant PM2.5 sources. Strategy development is currently challenged by multiple factors, including the importance of secondary PM2.5 mass production from precursors emitted by a diverse group of sources. Further, contemporary emissions and exposure estimates are necessary to identify current PM2.5 sources, given rapid changes in population growth, energy use, and emission controls, particularly in highly polluted regions. In addition, the production and transport of PM2.5 mass does not adhere to political boundaries, highlighting the need to investigate PM2.5 sources across multiple spatial scales. Previous studies have used 3D atmospheric chemistry-transport and epidemiological models for PM2.5 mass source apportionment but have typically traded detailed sectoral and fuel-type information for global coverage.
Here we describe an integrated modeling approach to quantify the contribution of 16 emission sectors and 4 fuel types to the production of PM2.5 mass and the associated disease burden at multiple scales in 2017 and 2019. We couple novel sectoral and fuel-specific emissions from the Community Emissions Data System with the GEOS-Chem chemical transport model to quantify gridded global fractional source contributions. These are further downscaled to kilometer resolution using new satellite-derived exposure estimates and integrated with multiple epidemiological models to quantify source and fuel-specific disease burdens for 204 countries. Globally, the combustion of coal and solid biofuel contributes to over 1 million premature deaths in 2017. Regionally, PM2.5 exposure from coal and biofuel use in China and India are primarily from the residential (up to 23%), energy (up to 7%), and industry sectors (up to 8%). At the city-level, results reveal spatially heterogeneous contributions, with coal use in the energy sector as a dominant source of PM2.5 mass in cities such as Pretoria, South Africa (>25%). We summarize these and other results to illustrate the wealth of policy-relevant information provided by this integrated analysis.
