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Ambient fine particulate matter (PM2.5) is the leading global environmental determinant of mortality. However, large gaps exist in ground-based PM2.5 monitoring. Satellite remote sensing of aerosol optical depth (AOD) offers information to fill these gaps worldwide, when augmented with a modeled PM2.5 to AOD relationship (η). This study aims to understand the spatial pattern and driving factors of η from both observations and modeling. A global observational estimate of η for the year 2019 is inferred from 6,118 ground-based PM2.5 measurement sites and satellite retrieved AOD from the MAIAC algorithm. A global chemical transport model, GEOS-Chem, in its high performance configuration (GCHP), is used to interpret the observed spatial pattern of annual mean η. Measurements and the GCHP simulation consistently identify a global population-weighted mean η of 92 – 100 μg/m3, with regional values ranging from 60.3 μg/m3 for North America to more than 130 μg/m3 in Africa. The highest η is found in arid regions where aerosols are less hygroscopic due to mineral dust, followed by regions strongly influenced by surface aerosol sources. Relatively low η is found over regions distant from strong aerosol sources. The spatial variation of η is strongly influenced by aerosol composition driven by its effects on aerosol hygroscopicity. Sensitivity tests with globally uniform parameters reveal that aerosol composition leads to the strongest η spatial variability, with a population-weighted normalized mean difference of 12.3 μg/m3, higher than that from aerosol vertical profile (8.4 μg/m3), reflecting the determinant composition effects on aerosol hygroscopicity and aerosol optical properties. [ABSTRACT FROM AUTHOR]

Trace metal distributions are of relevance to understand sources of fine particulate matter (PM 2.5), PM 2.5 -related health effects, and atmospheric chemistry. However, knowledge of trace metal distributions is lacking due to limited ground-based measurements and model simulations. This study develops a simulation of 12 trace metal concentrations (Si, Ca, Al, Fe, Ti, Mn, K, Mg, As, Cd, Ni and Pb) over continental North America for 2013 using the GEOS-Chem chemical transport model. Evaluation of modeled trace metal concentrations with observations indicates a spatial consistency within a factor of 2. The spatial distribution of trace metal concentrations reflects their primary emission sources. Crustal element (Si, Ca, Al, Fe, Ti, Mn, K) concentrations are enhanced over the central US from anthropogenic fugitive dust and over the southwestern U.S. due to natural mineral dust. Heavy metal (As, Cd, Ni and Pb) concentrations are high over the eastern U.S. from industry. K is abundant in the southeast from biomass burning. High concentrations of Mg are observed along the coast from sea spray. The spatial pattern of PM 2.5 mass is most strongly correlated with Pb, Ni, As and K due to their signature emission sources. Challenges remain in accurately simulating observed trace metal concentrations. Halving anthropogenic fugitive dust emissions in the 2011 National Air Toxic Assessment (NATA) inventory and doubling natural dust emissions in the default GEOS-Chem simulation was necessary to reduce biases in crustal element concentrations. A fivefold increase of anthropogenic emissions of As and Pb was necessary in the NATA inventory to reduce the national-scale bias versus observations by more than 80%, potentially reflecting missing sources. • A simulation of 12 trace metals over North America with a bias within twofold. • Pb, Ni, As and K are highly correlated with PM 2.5 spatially. • Long-range transport of dust dominates the crustal metal budget over North America. [ABSTRACT FROM AUTHOR]

Accurate data concerning historical fine particulate matter (PM 2.5 ) concentrations are needed to assess long-term changes in exposure and associated health risks. We estimated historical PM 2.5 concentrations over North America from 1981 to 2016 for the first time by combining chemical transport modeling, satellite remote sensing, and ground-based measurements. We constrained and evaluated our estimates with direct ground-based PM 2.5 measurements when available and otherwise with historical estimates of PM 2.5 from PM 10 measurements or total suspended particle (TSP) measurements. The estimated PM 2.5 concentrations were generally consistent with direct ground-based PM 2.5 measurements over their duration from 1988 onward ( R 2 = 0.6 to 0.85) and to a lesser extent with PM 2.5 inferred from PM 10 measurements from 1985 to 1998 ( R 2 = 0.5 to 0.6). The collocated comparison of the trends of population-weighted annual average PM 2.5 from our estimates and ground-based measurements was highly consistent (RMSD = 0.66 μg m -3 ). The population-weighted annual average PM 2.5 over North America decreased from 22 ± 6.4 μg m -3 in 1981, to 12 ± 3.2 μg m -3 in 1998, and to 7.9 ± 2.1 μg m -3 in 2016, with an overall trend of -0.33 μg m -3 yr -1 (95% CI: -0.35, -0.31).