Predicting citywide distribution of air pollution using mobile monitoring and three-dimensional urban structure
Predicting citywide distribution of air pollution using mobile monitoring and three-dimensional urban structure
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DOI：
10.1016/j.scs.2021.103510
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发表时间：
2022-01-01
影响因子：
11.7
通讯作者：
Shakya, Kabindra. M.
中科院分区：
文献类型：
--
作者：
Cummings, Lucas E.;Stewart, Justin D.;Shakya, Kabindra. M.
了解城市结构模式和空气污染物之间的关系是可持续城市规划的关键。在本研究中，我们采用移动监测方法收集了2019年夏季宾夕法尼亚州费城的PM2.5和BC数据，并应用城市景观结构（STURLA）方法研究了城市结构与大气污染之间的关系。我们发现PM2.5和BC在不同的STURLA类别中存在差异，并且某些类别的污染浓度存在显著差异。我们还发现，STURLA成分在整个城市景观中的比例可以用来预测城市空气污染的空间分布。在频繁采样的STURLA类别中，gpl（草地、路面和低层建筑）的PM2.5平均浓度最高（16.60 +/- 4.29 μ g/m），而tgbwp（树木、草地、裸露的土壤、水、路面）的BC浓度最高（2.31 +/- 1.94 μ g/m）。此外，STURLA结合机器学习建模能够将PM2.5 （R-2= 0.68, RMSE 2.82 μ g/m(3)）和BC （R-2= 0.64, RMSE 0.75 μ g/m(3)）浓度与城市景观组成和整个城市的插值浓度相关联。这些结果证明了STURLA方法在模拟空气污染与城市结构模式之间关系方面的有效性。
Understanding relationships between urban structure patterns and air pollutants is key to sustainable urban planning. In this study, we employ a mobile monitoring method to collect PM2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that PM2.5 and BC vary by STURLA class, and some classes exhibit significant difference in pollution concentrations. We also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict the spatial distribution of urban air pollution. Among frequently sampled STURLA classes, gpl (grass, pavement, and low-rise buildings) hosted the highest PM2.5 concentrations on average (16.60 +/- 4.29 mu g/m(3)), while tgbwp (trees, grass, bare soil, water, pavement) hosted the highest BC concentrations (2.31 +/- 1.94 mu g/m(3)). Furthermore, STURLA combined with machine learning modeling was able to correlate PM2.5 (R-2= 0.68, RMSE 2.82 mu g/m(3)) and BC (R-2 = 0.64, RMSE 0.75 mu g/m(3)) concentrations with urban landscape composition and interpolate concentrations throughout the city. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and urban structure patterns.