Integrating meteorological factors for better understanding of the urban form-air quality relationship
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RESEARCH ARTICLE
Integrating meteorological factors for better understanding of the urban form-air quality relationship Ye Tian . Xiaobai A. Yao
. Lan Mu . Qinjin Fan . Yijun Liu
Received: 29 April 2020 / Accepted: 14 August 2020 Ó Springer Nature B.V. 2020
Abstract Context Understanding the urban form-air quality relationship is essential to mitigate intra-urban air pollution and to improve urban ecology. However, few studies considered urban form and meteorological factors integratively and analyzed their synthetic effects on air pollution. Objectives We investigate how to model the integrated effects on the spatiotemporal distribution of PM2.5 in the Atlanta metropolitan area to improve the understanding of the urban form-air quality relationship. Methods Two groups of models are developed: one uses urban form only and the other uses wind-direct urban form. Relative humidity, wind speed, and temperature are included as control variables. Both linear (Multiple Linear Regression) and nonlinear models (Random Forest and Artificial Neural Network) are constructed and tested with both tenfold Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10980-020-01094-6) contains supplementary material, which is available to authorized users. Y. Tian X. A. Yao (&) L. Mu Q. Fan Department of Geography, University of Georgia, Athens, GA 30602, USA e-mail: [email protected] Y. Liu Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
cross-validation and field PM2.5 data obtained from a portable device, AirBeam2. Results Random Forest overall outperforms other models suggesting that the urban form-air quality relationship is most likely to be nonlinear. Additionally, the group using wind-direct urban form outperforms the other group and the contribution of the same urban form metrics differs in different wind sections proving that meteorological factors and urban form have synthetic effects on PM2.5. Finally, the patch density, dominance, and aggregation of roads and vegetation, demonstrate higher attribute significance than other urban form metrics. Conclusions Urban planners, practitioners, and policymakers need to carefully consider not only the spatial configuration of roads and vegetation but also the local climate patterns to minimize intra-urban air pollution effectively. Keywords Wind-direct urban form PM2.5 Multiple linear regression Random forest Artificial neural network AirBeam2
Introduction Numerous studies have revealed a close relationship between long-term exposure to air pollutants and adverse health effects (Brunekreef and Holgate 2002;
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Landscape Ecol
Pope III and Dockery 2006; Lin et al. 2017). Among these pollutants, particulate matter smaller 2.5 lm in aerodynamic diameter (PM2.5) has been recognized as one of the principal pollutants that degrade the air quality and increase health burdens. Not only have epidemiologists and pathologists focused tremendous attention on the ai
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