Application of an improved gas-constrained source apportionment method using data fused fields: A case study in North Carolina, USA. (1st May 2022) - Huang, Ran; Li, Zongrun; Ivey, Cesunica E.; Zhai, Xinxin; Shi, Guoliang; Mulholland, James A.; Devlin, Robert; Russell, Armistead G.
Abstract: A number of studies have found differing associations of disease outcomes with PM2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO2, NOx, O3, and SO2 ), total PM2.5 mass, and speciated PM2.5 (including crustal elements) over North Carolina for 2002–2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants. Graphical abstract: Generate fields of total PM2.5 and its constituent species based on a data fusion method which combines observations and simulation results from CMAQ. Use this result and a receptor model to apportion PM2.5 and analyze spatial and temporal variations of sources to PM2.5 . The method is computationally effective and can be applied on various scales. Image 1 Highlights: ● Fusing observations and simulations to generate pollution fields ● A computationally effective method based on data fusion and receptor model to apportion PM2.5 ● Spatial and temporal variations of sources to PM2.5 in North Carolina