| Summary: | Long-term exposure to ambient fine particulate matter (PM2.5) is a major health concern. This thesis presents three projects that take advantage of chemical transport modeling, ground-based monitoring and satellite remote sensing to advance the understanding of historical concentrations, source contributions, and chemical composition of PM2.5. Historical PM2.5 concentrations across North America from 1981 to 2016 were estimated by fusing satellite derived PM2.5 data and ground-based measurements with GEOS-Chem chemical transport model simulations. Comparison with ground-based PM2.5 measurements indicates consistency of the estimated PM2.5 concentrations with observations, especially in the later years with extensive PM2.5 monitoring. The estimated population-weighted annual average PM2.5 over North America decreased from 22 6.4 μg m-3 in 1981 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 -0.30), reflecting the significant reduction of anthropogenic emissions over the past decades. Sensitivity simulations were conducted using the GEOS-Chem chemical transport model to investigate the sectoral contribution to PM2.5 for Canada. We found that annually about 70% of population-weighted PM2.5 originates from Canadian sources and about 30% from the contiguous United States, with wildfires, transportation and residential combustion as the leading sectors in 2013. The relative contribution to population-weighted PM2.5 of different sectors varied regionally and seasonally. Sectoral trend analysis showed that the contribution from anthropogenic sources to population-weighted PM2.5 decreased from 7.1 g/m3 in 1990 to 3.4 g/m3 in 2013. Offline grid independent dust emissions driven by native high resolution meteorological fields were generated to harmonize dust emissions across simulations of different resolutions. The updated offline dust emissions can better resolve weak dust source regions, such as southern South America, southern Africa and the southwestern United States. We find that the performance of simulated aerosol optical depth (AOD) versus measurements from the Aerosol Robotic Network (AERONET) network and satellite remote sensing improves significantly when using the updated offline dust emissions with the total global annual dust emission strength of 2,000 Tg yr-1. The offline high resolution dust emissions are easily implemented in chemical transport models, with potential to promote model development and evaluation.
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