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Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors
- van Donkelaar, Aaron, Martin, Randall V., Li, Chi, Burnett, Richard T.
- Environmental science & technology 2019 v.53 no.5 pp. 2595-2611
- aerosols, ammonium, carbon, data collection, dust, epidemiological studies, health effects assessments, models, nitrates, organic matter, particulates, satellites, sulfates, North America
- An accurate fine-resolution surface of the chemical composition of fine particulate matter (PM₂.₅) would offer valuable information for epidemiological studies and health impact assessments. We develop geoscience-derived estimates of PM₂.₅ composition from a chemical transport model (GEOS-Chem) and satellite observations of aerosol optical depth, and statistically fuse these estimates with ground-based observations using a geographically weighted regression over North America to produce a spatially complete representation of sulfate, nitrate, ammonium, black carbon, organic matter, mineral dust, and sea-salt over 2000–2016. Significant long-term agreement is found with cross-validation sites over North America (R² = 0.57—0.96), with the strongest agreement for sulfate (R² = 0.96), nitrate (R² = 0.90), and ammonium (R² = 0.86). We find that North American decreases in population-weighted fine particulate matter (PM₂.₅) concentrations since 2000 have been most heavily influenced by regional changes in sulfate and organic matter. Regionally, the relative importance of several chemical components are found to change with PM₂.₅ concentration, such as higher PM₂.₅ concentrations having a larger proportion of nitrate and a smaller proportion of sulfate. This data set offers information for research into the health effects of PM₂.₅ chemical components.