Spatial-temporal variations in regional ambient sulfur dioxide concentration and source-contribution analysis: A dispersion modeling approach

An understanding of the complexity of spatial-temporal variations in regional air quality and its respective source contributors is one of the priority research areas due to the adverse effects of air pollution on human health and the environment. In this paper, we integrate air dispersion modeling and Geographic Information System (GIS) based spatial analysis methods to characterize regional ambient air quality at a relatively fine geographical scale (1 km × 1 km) while ascertaining source contributors. The temporal variation analysis shows that sulfur dioxide (SO2) pollution in Dallas County, Texas did not consistently increase or decrease from 1996 to 2002. The lowest and highest mean levels of annual SO2 concentrations at all the receptors (n = 2000) were 0.39 μg m−3 and 2.32 μg m−3 in 2001 and 2002, respectively. Meanwhile, analysis results suggest that the annual SO2 concentrations in a small part of Dallas County slightly declined with the highest value of −1.00 μg m−3 over the 1996–2002 period, while most of the county experienced increased SO2 concentration levels from 0.00 to 0.25 μg m−3. In addition, the source apportionment analysis demonstrated that the variations in total annual SO2 concentrations in Dallas County from 1996 to 2002 were significantly different from those by source classification. That is, compared to industrial emission sources, on-road vehicle emission sources caused variations in annual SO2 concentrations with relatively larger extents (power of determinant = 0.42). However, extreme variations in concentrations were due to industrial emission sources (3.45% vs. 0.00%). Based on these observations, it can be concluded that the combination of air dispersion modeling and GIS-based spatial analysis shows promise to overcome the drawbacks of sparse intraurban air quality monitoring in characterizing the spatial-temporal micro-variations in regional ambient air quality and ascertaining roles of source contributors over long-term periods.