Source apportionment of fine particulate matter over the Eastern U.S. Part I: source sensitivity simulations using CMAQ with the Brute Force method

Exposure to elevated levels of fine particulate matter (PM2.5) is found to be associated with adverse effects on human health, climate change, and visibility. Identification of major sources contributing to PM2.5 is an important step in the formulation of effective reduction strategies. This study uses the U.S. EPA's Community Multiscale Air Quality (CMAQ) modeling system with the brute-force method (BFM) to conduct source apportionment of PM2.5 for 10 source categories over the eastern U.S. at a 12 km horizontal grid resolution for both January and July of 2002. Biomass burning is found to be the greatest contributor to domainwide PM2.5 with a monthly-mean domainwide contribution of similar to 14% (1.1 mu g m(-3)). The next two largest contributors in January are miscellaneous area sources and coal combustion with contributions of similar to 12% (0.9 mu g m(-3)) and similar to 11% (0.9 mu g m(-3)), respectively. In July, coal combustion, miscellaneous area sources, and industrial processes are the top three contributors (by similar to 31% (2.3 mu g m(-3)), similar to 9% (0.7 mu g m(-3)), and similar to 7% (0.5 mu g m(-3)), respectively). Site-specific source contributions indicate that industrial processes and biomass burning are the most important sources of PM2.5 at urban and rural sites, respectively, in January, while coal combustion dominates at both sites in July. While the BFM is theoretically simple and can capture indirect effects resulting from the interactions among precursor and secondary pollutants in the real atmosphere, it is computationally expensive and assumes that the source contributions to each emission category are additive. This assumption does not hold for secondary PM components because of the highly non-linear relationships between precursor emissions and all secondary PM components and, therefore, source apportionment provides no useful information whatsoever on the possible effect of emission reductions on secondary PM. (c) Author(s) 2011. This work is distributed under the Creative Commons Attribution 3.0 License.