Spatial/temporal variations of elemental carbon, organic carbon, and trace elements in PM10 and the impact of land-use patterns on community air pollution in Paterson, NJ.

An urban community PM10 (particulate matter < or = 10 microm in aerodynamic diameter) air pollution study was conducted in Paterson, NJ, a mixed land-use community that is interspersed with industrial, commercial, mobile, and residential land-use types. This paper examines (1) the spatial/temporal variation of PM10, elemental carbon (EC), organic carbon (OC), and nine elements; and (2) the impact of land-use type on those variations. Air samples were collected from three community-oriented locations in Paterson that attempted to capture industrial, commercial, and mobile source-dominated emissions. Sampling was conducted for 24 hr every 6 days from November 2005 through December 2006. Samples were concurrently collected at the New Jersey Department of Environmental Protection-designated air toxics background site in Chester, NJ. PM10 mass, EC, OC, and nine elements (Ca, Cu, Fe, Pb, Mn, Ni, S, Ti, and Zn) that had more than 50% of samples above detection and known sources or are toxic were selected for spatial/temporal analysis in this study. The concentrations of PM10, EC, OC, and eight elements (except S) were significantly higher in Paterson than in Chester (P < 0.05). The concentrations of these elements measured in Paterson were also found to be higher during winter than the other three seasons (except S), and higher on weekdays than on weekends (except Pb). The concentrations of EC, Cu, Fe, and Zn at the commercial site in Paterson were significantly higher than the industrial and mobile sites; however, the other eight species were not significantly different within the city (P > 0.05). These results indicated that anthropogenic sources of air pollution were present in Paterson. The source apportionment confirmed the impact of vehicular and industrial emissions on the PM10 ambient air pollution in Paterson. The multiple linear regression analysis showed that categorical land-use type was a significant predictor for all air pollution levels, explaining up to 42% of the variability in concentration by land-use type only.