The characterization of fine particulate matter downwind of Houston: Using integrated factor analysis to identify anthropogenic and natural sources

Abstract

The interpretation of large air pollution datasets involves a great deal of complexity. To gain a better understanding of the complicated relationships and patterns within datasets, we perform factor analysis. Between December 2015 and December 2017, fine particulate matter (PM_2.5) samples were collected at a suburban site northeast of the Houston metropolitan area, TX. A total of 233 filter samples were analyzed for chemical composition. The average of all PM_2.5 samples consisted of 38.1% inorganic ions, 28.9% elements, 29.1% organic carbon, and 3.7% elemental carbon and other organic materials. Principal component analysis and positive matrix factorization were utilized to identify eight factors: regional aerosols, biomass burning, gasoline combustion, industry, crustal material, incineration, marine dust, and fireworks. The first three contributed more than 70% of the total PM_2.5 mass. The receptor models also captured the impact of fireworks and classified it as a source of PM_2.5 over Houston. To identify the origins of air masses transporting PM_2.5 to the site, we applied the NOAA hybrid single-particle Lagrangian integrated trajectory model and performed a cluster analysis of back trajectories and determined six cluster source regions: the Gulf of Mexico, the Southeast, two midwestern clusters, the Pacific Northwest, and the Southwest. The results of our analysis show that during the summer months, marine and crustal sources were often associated with an onshore flow from the Gulf of Mexico and that four clusters covering 38% of the West Liberty area were strongly influenced by trajectories originating from biomass burning. The results of this study represented a variety of sources that affect the PM_2.5 over the Houston metropolitan area. The quantified contributions of these sources could provide policymakers with useful information for developing more efficient control systems and making more effective decisions to cope with the harmful effects of ambient air pollution.