Modeling the intraurban variability of ambient traffic pollution in Toronto, Canada.
The objective of this paper is to model determinants of intraurban variation in ambient concentrations of nitrogen dioxide (NO2) in Toronto, Canada, with a land use regression (LUR) model. Although researchers have conducted similar studies in Europe, this work represents the first attempt in a North American setting to characterize variation in traffic pollution through the LUR method. NO2 samples were collected over 2 wk using duplicate two-sided Ogawa passive diffusion samplers at 95 locations across Toronto. Independent variables employed in subsequent regression models as predictors of NO2 were derived by the Arc 8 geographic information system (GIS). Some 85 indicators of land use, traffic, population density, and physical geography were tested. The final regression model yielded a coefficient of determination (R2) of .69. For the traffic variables, density of 24-h traffic counts and road measures display positive associations. For the land use variables, industrial land use and counts of dwellings within 2000 m of the monitoring location were positively associated with NO2. Locations up to 1500 m downwind of major expressways had elevated NO2 levels. The results suggest that a good predictive surface can be derived for North American cities with the LUR method. The predictive maps from the LUR appear to capture small-area variation in NO2 concentrations. These small-area variations in traffic pollution are probably important to the exposure experience of the population and may detect health effects that would have gone unnoticed with other exposure estimates.
Jerrett
M
M
Division of Environmental Health Sciences School of Public Health, University of California-Berkeley, Berkeley, California 94720-7360, USA. jerrett@berkeley.edu
Arain
M A
MA
Kanaroglou
P
P
Beckerman
B
B
Crouse
D
D
Gilbert
N L
NL
Brook
J R
JR
Finkelstein
N
N
Finkelstein
M M
MM
D000397:Air Pollution / Q000032:analysis*; D002947:Cities; D004784:Environmental Monitoring / Q000379:methods*; D005544:Forecasting; D008962:Models, Theoretical; D018986:Motor Vehicles; D009585:Nitrogen Dioxide / Q000032:analysis; D009864:Ontario; D012044:Regression Analysis; D015203:Reproducibility of Results
D009585:Nitrogen Dioxide