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刻画加拿大多伦多环境中超细颗粒物的空间分布:一种土地利用回归模型。

Characterizing the spatial distribution of ambient ultrafine particles in Toronto, Canada: A land use regression model.

作者信息

Weichenthal Scott, Van Ryswyk Keith, Goldstein Alon, Shekarrizfard Maryam, Hatzopoulou Marianne

机构信息

Air Health Science Division, Health Canada, Ottawa, Canada.

Air Health Science Division, Health Canada, Ottawa, Canada.

出版信息

Environ Pollut. 2016 Jan;208(Pt A):241-248. doi: 10.1016/j.envpol.2015.04.011. Epub 2015 Apr 29.

Abstract

Exposure models are needed to evaluate the chronic health effects of ambient ultrafine particles (<0.1 μm) (UFPs). We developed a land use regression model for ambient UFPs in Toronto, Canada using mobile monitoring data collected during summer/winter 2010-2011. In total, 405 road segments were included in the analysis. The final model explained 67% of the spatial variation in mean UFPs and included terms for the logarithm of distances to highways, major roads, the central business district, Pearson airport, and bus routes as well as variables for the number of on-street trees, parks, open space, and the length of bus routes within a 100 m buffer. There was no systematic difference between measured and predicted values when the model was evaluated in an external dataset, although the R(2) value decreased (R(2) = 50%). This model will be used to evaluate the chronic health effects of UFPs using population-based cohorts in the Toronto area.

摘要

需要暴露模型来评估环境中超细颗粒物(<0.1微米)(UFPs)对健康的慢性影响。我们利用2010 - 2011年夏季/冬季收集的移动监测数据,为加拿大多伦多的环境UFPs开发了一个土地利用回归模型。分析共纳入405个路段。最终模型解释了平均UFPs空间变异的67%,包括到高速公路、主要道路、中央商务区、皮尔逊机场和公交路线距离的对数项,以及100米缓冲区内路边树木数量、公园、开放空间和公交路线长度的变量。在外部数据集中评估该模型时,测量值和预测值之间没有系统差异,尽管R(2)值有所下降(R(2)=50%)。该模型将用于利用多伦多地区基于人群的队列评估UFPs对健康的慢性影响。

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