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Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty

van Donkelaar, A., M. S. Hammer, L. Bindle, M. Brauer, J. R. Brook, M. Garay, N. C. Hsu, O. V. Kalashnikova, R. Kahn, C. Lee, R. Levy, A. Lyapustin, A. M. Sayer, and R. Martin (2022), Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty, Environ. Sci. Technol., doi:10.1021/acs.est.1c05309.

Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and uncertainties during the period 1998−2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5−3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010−2019 by 1.6−2.6 μg/m3/year, with decreases beginning 2−3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellitederived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regionalscale representation, with residual uncertainty inversely proportional to the sample size.

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Research Program: 
Applied Sciences Program (ASP)
Atmospheric Composition Modeling and Analysis Program (ACMAP)
Terra- MISR