Modeling the intraurban variability of ambient traffic pollution in Toronto, Canada
Modeling the intraurban variability of ambient traffic pollution in Toronto, Canada
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DOI：
10.1080/15287390600883018
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发表时间：
2007-02-01
影响因子：
2.6
通讯作者：
Finkelstein, M. M.
中科院分区：
文献类型：
--
作者：
Jerrett, M.;Arain, M. A.;Finkelstein, M. M.
本文的目的是在加拿大多伦多，城市内部的环境浓度的二氧化氮（NO2）变化的决定因素模型，与土地利用回归（LUR）模型。虽然研究人员在欧洲进行了类似的研究，但这项工作是在北美环境中首次尝试通过LUR方法来表征交通污染的变化。NO2样品收集超过2周使用重复的双面小川被动扩散采样器在95个地点横跨多伦多。在随后的回归模型作为NO2的预测因子的自变量来自弧8地理信息系统（GIS）。对土地利用、交通、人口密度和自然地理等85项指标进行了测试。最终回归模型得出的决定系数（R-2）为0.69。对于交通变量，密度的24小时交通计数和道路措施显示正相关。对于土地利用变量，工业用地和监测位置2000米范围内的住宅数量与NO2呈正相关。主要高速公路顺风1500米处的NO2水平升高。结果表明，一个很好的预测表面可以为北美城市的LUR方法。LUR的预测图似乎捕捉到了NO2浓度的小区域变化。交通污染的这些小区域变化可能对人口的接触经验很重要，并可能发现其他接触估计数可能未注意到的健康影响。
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 (R-2) 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.