Title: A land use regression model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Ontario, Canada.
Authors: Sahsuvaroglu, Talar; Arain, Altaf; Kanaroglou, Pavlos; Finkelstein, Norm; Newbold, Bruce; Jerrett, Michael; Beckerman, Bernardo; Brook, Jeffrey; Finkelstein, Murray; Gilbert, Nicolas L
Published In J Air Waste Manag Assoc, (2006 Aug)
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 (NO2) were measured for a 2-week period in October 2002 at > 100 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 NO2. Traffic density, proximity to a highway, and industrial land use were all positively correlated with NO2 concentrations, whereas open land use and distance from the lake were negatively correlated with NO2. Locations downwind of a major highway resulted in higher NO2 levels. Cross-validation of the results confirmed model stability over different seasons. Our findings demonstrate that land use regression can effectively predict NO2 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.
PubMed ID: 16933638
MeSH Terms: Air Pollutants, Occupational/analysis*; Environmental Monitoring; Forecasting; Models, Statistical; Nitrogen Dioxide/analysis*; Ontario; Regression Analysis; Reproducibility of Results