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

被引:163
|
作者
Gilbert, NL [1 ]
Goldberg, MS
Beckerman, B
Brook, JR
Jerrett, M
机构
[1] Hlth Canada, Air Hlth Effects Div, Ottawa, ON, Canada
[2] McGill Univ, Dept Med, Montreal, PQ H3A 2T5, Canada
[3] McGill Univ, Dept Epidemiol & Biostat, Montreal, PQ H3A 2T5, Canada
[4] McGill Univ, Hlth Ctr, Div Clin Epidemiol, Montreal, PQ, Canada
[5] Univ So Calif, Dept Prevent Med, Div Biosat, Los Angeles, CA USA
[6] Meteorol Serv Canada, Toronto, ON, Canada
[7] Univ So Calif, Dept Prevent Med, Div Biostat, Los Angeles, CA USA
来源
基金
加拿大健康研究院;
关键词
D O I
10.1080/10473289.2005.10464708
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
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 Montreal, Quebec, Canada. These estimates of concentrations of NO, 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 NO,. 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 R-2 of 0.54. These analyses confirm the value of land-use regression modeling to assign exposures in large-scale epidemiologic studies.
引用
收藏
页码:1059 / 1063
页数:5
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