Development of land-use regression models for metals associated with airborne particulate matter in a North American city

Airborne particulate matter has been associated with cardiovascular and respiratory morbidity and mortality, and there is evidence that metals may contribute to these adverse health effects. However, there are few tools for assessing exposure to airborne metals. Land-use regression modeling has been widely used to estimate exposure to gaseous pollutants. This study developed seasonal land-use regression (LUR) models to characterize the spatial distribution of trace metals and other elements associated with airborne particulate matter in Calgary, Alberta. Two-week integrated measurements of particulate matter with <1.0 mm in aerodynamic diameter (PM1.0) were collected in the City of Calgary at 25 sites in August 2010 and 29 sites in January 2011. PM1.0 filters were analyzed using inductively-coupled plasma mass spectrometry. Industrial sources were obtained through the National Pollutant Release Inventory and their locations verified using Google Maps. Traffic volume data were obtained from the City of Calgary and zoning data were obtained from Desktop Mapping Technologies Incorporated. Seasonal wind direction was incorporated using wind rose shapes produced by Wind Rose PRO3, and predictor variables were generated using ArcMap-10.1. Summer and winter LUR models for 30 PM1.0 components were developed using SAS 9.2. We observed significant intra-urban gradients for metals associated with airborne particulate matter in Calgary, Alberta. LUR models explained a high proportion of the spatial variability in those PM1.0 components. Summer models performed slightly better than winter models. However, 24 of the 30 PM1.0 related elements had models that were either good (R2 > 0.70) or acceptable (R2 > 0.50) in both seasons. Industrial point-sources were the most influential predictor for the majority of PM1.0 components. Industrial and commercial zoning were also significant predictors, while traffic indicators and population density had a modest but significant contribution for most elements. Variables incorporating wind direction were also significant predictors. These findings contrast with LUR models for PM and gaseous pollutants in which traffic indicators are typically the most important predictors of ambient concentrations. These results suggest that airborne PM components vary spatially with the distribution of local industrial sources and that LUR modeling can be used to predict local concentrations of these airborne elements. These models will support future health studies examining the impact of PM components including metals.