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Assessing neighborhood air pollution exposure and its relationship with the urban form

机译：评估邻里空气污染暴露及其与城市形态的关系

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摘要

Previous studies on air pollution exposure have mostly focused on the urban, regional, national and global scales, but the outcomes cannot well support risk assessments in urban communities. To determine how the urban form and meteorology influence the air pollution distribution at a neighborhood scale (2 km*2 km), we performed a fine-scale investigation of a typical air pollutant (i.e., PM2.5) by mobile measurements in two communities in the inner and outer cities of Shanghai, China. The PM2.5 mass concentrations and potential impacting factors, such as PM2.5 background levels, road networks, traffic volumes, meteorological parameters, building heights and land use types, were collected at a 10 m spatial resolution, and their relationships were analyzed using both a generalized additive model (GAM) and land use regression (LUR). The modeling results showed that the GAM outperformed LUR in both study areas, with a higher adjusted R-2 and a lower RMSE. The PM2.5 was found to have drastic variations at the neighborhood scale, which was primarily driven by spatial patterns of the PM2.5 background levels and traffic volumes. The GAM-based PM2.5 concentration surface clearly disclosed the heterogeneous variation of the PM2.5 mass concentrations in each community, and demonstrated the prominent influence from the nearby highway, especially in the central urban area. This study provides the first empirical evidence of the exposure heterogeneity of the air pollution on a neighborhood scale, which can assist planners and policymakers in evaluating planning strategies with a full consideration of reducing local air pollution.
机译：先前有关空气污染暴露的研究主要集中在城市，区域，国家和全球范围内，但结果不能很好地支持城市社区的风险评估。为了确定城市形态和气象学如何影响邻里尺度（2 km * 2 km）的空气污染分布，我们通过移动测量在两个社区中对典型的空气污染物（即PM2.5）进行了精细规模的调查。在中国上海的内外城市。以10 m的空间分辨率收集PM2.5的质量浓度和潜在的影响因素，例如PM2.5的背景水平，道路网络，交通量，气象参数，建筑物高度和土地利用类型，并使用广义加性模型（GAM）和土地利用回归（LUR）。建模结果表明，GAM在两个研究领域均优于LUR，R-2调整后的值较高，RMSE较低。发现PM2.5在邻里尺度上有巨大变化，这主要是由PM2.5背景水平和交通量的空间格局驱动的。基于GAM的PM2.5浓度表面清楚地揭示了每个社区中PM2.5质量浓度的异质变化，并显示了附近高速公路的显着影响，特别是在市中心地区。这项研究提供了邻里尺度上空气污染暴露异质性的第一个经验证据，可以帮助规划者和政策制定者在充分考虑减少本地空气污染的情况下评估规划策略。

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  • 来源
    《Building and Environment》 |2019年第5期|15-24|共10页
  • 作者

    Gao Ya; Wang Zhanyong; Liu Chao; Peng Zhong-Ren;

  • 作者单位

    Shanghai Jiao Tong Univ, Ctr Intelligent Transportat Syst & Unmanned Aeria, State Key Lab Ocean Engn, Sch Naval Architecture Ocean & Civil Engn, Shanghai 200240, Peoples R China;

    Fujian Agr & Forestry Univ, Coll Transportat & Civil Engn, Fuzhou 350108, Fujian, Peoples R China;

    Univ Florida, Int Ctr Adaptat Planning & Design iAdapt, Sch Landscape Architecture & Planning, Coll Design Construct & Planning, POB 115706, Gainesville, FL 32611 USA;

    Shanghai Jiao Tong Univ, Ctr Intelligent Transportat Syst & Unmanned Aeria, State Key Lab Ocean Engn, Sch Naval Architecture Ocean & Civil Engn, Shanghai 200240, Peoples R China|Shanghai Jiao Tong Univ, China Inst Urban Governance, Shanghai 200240, Peoples R China|Univ Florida, Int Ctr Adaptat Planning & Design iAdapt, Sch Landscape Architecture & Planning, Coll Design Construct & Planning, POB 115706, Gainesville, FL 32611 USA;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    PM(2.5 )exposure; Community; Urban form; Generalized additive model;

    机译：PM（2.5）暴露;社区;城市形式;广义加性模型;

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