Assessing spatial variability of ambient nitrogen dioxide in Montréal, Canada, with a land-use regression model

被引:2
作者
Gilbert, Nicolas L. [1 ,6 ]
Goldberg, Mark S. [2 ,3 ]
Beckerman, Bernardo [4 ]
Brook, Jeffrey R. [5 ]
Jerrett, Michael [4 ]
机构
[1] Air Health Effects Division, Health Canada, Ottawa, Ont., Canada
[2] Department of Medicine, McGill University, Montreal, Que., Canada
[3] Division of Clinical Epidemiology, McGill University Health Centre, Montréal, Que., Canada
[4] Division of Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, United States
[5] Meteorological Service of Canada, Toronto, Ont., Canada
[6] Health Canada, 400 Cooper St., Ottawa, Ont. K1A OK9, Canada
基金
加拿大健康研究院;
关键词
Estimation - Mathematical models - Regression analysis;
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摘要
The purpose of this study was to derive a land-use regression model to estimate on a geographical basis ambient concentrations of nitrogen dioxide (NO2) in Montre´al, Quebec, Canada. These estimates of concentrations of NO2 will be subsequently used to assess exposure in epidemiologic studies on the health effects of traffic-related air pollution. In May 2003, NO2 was measured for 14 consecutive days at 67 sites across the city using Ogawa passive diffusion samplers. Concentrations ranged from 4.9 to 21.2 ppb (median 11.8 ppb). Linear regression analysis was used to assess the association between logarithmic concentrations of NO2 and land-use variables derived using the ESRI Arc 8 geographic information system. In univariate analyses, NO2 was negatively associated with the area of open space and positively associated with traffic count on nearest highway, the length of highways within any radius from 100 to 750 m, the length of major roads within 750 m, and population density within 2000 m. Industrial land-use and the length of minor roads showed no association with NO2. In multiple regression analyses, distance from the nearest highway, traffic count on the nearest highway, length of highways and major roads within 100 m, and population density showed significant associations with NO2; the best-fitting regression model had a R2 of 0.54. These analyses confirm the value of land-use regression modeling to assign exposures in large-scale epidemiologic studies. Copyright 2005 Air & Waste Management Association.
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页码:1059 / 1063
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