A Land Use Regression Model for Predicting Ambient Concentrations of Nitrogen Dioxide in Hamilton, Ontario, Canada

Abstract

This paper reports on the development of a land use regression (LUR) model for predicting the intraurban variation of traffic-related air pollution in Hamilton, Ontario, Canada, an industrial city at the western end of Lake Ontario. Although land use regression has been increasingly used to characterize exposure gradients within cities, research to date has yet to test whether this method can produce reliable estimates in an industrialized location. Ambient concentrations of nitrogen dioxide (NO 2 ) were measured for a 2-week period in October 2002 at Ͼ100 locations across the city and subsequently at 30 of these locations in May 2004 to assess seasonal effects. Predictor variables were derived for land use types, transportation, demography, and physical geography using geographic information systems. The LUR model explained 76% of the variation in NO 2 . Traffic density, proximity to a highway, and industrial land use were all positively correlated with NO 2 concentrations, whereas open land use and distance from the lake were negatively correlated with NO 2 . Locations downwind of a major highway resulted in higher NO 2 levels. Cross-validation of the results confirmed model stability over different seasons. Our findings demonstrate that land use regression can effectively predict NO 2 variation at the intraurban scale in an industrial setting. Models predicting exposure within smaller areas may lead to improved detection of health effects in epidemiologic studies.

Intercity transferability of land use regression models for estimating ambient concentrations of nitrogen dioxide

“…The method is cost effective , captures greater spatial variability than air quality monitoring networks (Brauer et al, 2003;Sahsuvaroglu et al, 2006;Henderson et al, 2007) and performs well in comparison to other methods, such as dispersion modeling (Cyrys et al, 2005;Jerrett et al, 2005) and spatial interpolation . In addition, LUR offers practical advantages over these other approaches.…”

“…Regression methods are used to model pollutant concentrations measured at given sites on the basis of variables that characterize their surrounding land use, population density and traffic patterns, as described elsewhere Sahsuvaroglu et al, 2006;Henderson et al, 2007).…”

“…First used in Europe by Briggs et al (1997), LUR has been applied in a variety of European Lebret et al, 2000;Brauer et al, 2003) and North American settings Sahsuvaroglu et al, 2006;Smith et al, 2006;Henderson et al, 2007;Jerrett et al, 2007). The method is cost effective , captures greater spatial variability than air quality monitoring networks (Brauer et al, 2003;Sahsuvaroglu et al, 2006;Henderson et al, 2007) and performs well in comparison to other methods, such as dispersion modeling (Cyrys et al, 2005;Jerrett et al, 2005) and spatial interpolation .…”

“…Spatial pollutant interpolation relies exclusively on data from monitoring networks, which may be sparsely distributed and/or may fail to capture variability of pollution concentrations over short distances (Larssen et al, 1993;Briggs et al, 1997Briggs et al, , 2000Wu et al, 2005). In contrast, LUR typically utilizes existing land use data in combination with either readily available air quality measurements (e.g., from regulatory monitoring networks) (Ross et al, 2007) and/or measurements from dedicated sampling campaigns (Brauer et al, 2003;Gilbert et al, 2005;Sahsuvaroglu et al, 2006;Smith et al, 2006;Henderson et al, 2007).…”

“…Costs can be reduced by representing all traffic pollution with a single proxy pollutant like NO 2 (Hochadel et al, 2006;Ross et al, 2006;Sahsuvaroglu et al, 2006). This indicator is particularly useful because it can be measured using relatively inexpensive passive samplers.…”