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Exploring the Joint Impacts of Natural and Built Environments on PM 2.5 Concentrations and Their Spatial Heterogeneity in the Context of High-Density Chinese Cities

Shanyou Duan (), Qian Liu (), Dumei Jiang (), Yulin Jiang (), Yinzhi Lin () and Ziying Gong ()
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Shanyou Duan: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Qian Liu: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Dumei Jiang: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Yulin Jiang: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Yinzhi Lin: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Ziying Gong: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China

Sustainability, 2021, vol. 13, issue 21, 1-15

Abstract: Air pollution in China has attracted wide interest from the public and academic communities. PM 2.5 is the primary air pollutant across China. PM 2.5 mainly comes from human activities, and the natural environment and urban built environment affect its distribution and diffusion. In contrast to American and European cities, Chinese cities are much denser, and studies on the relationships between urban form and air quality in high-density Chinese cities are still limited. In this paper, we used the ordinary least square (OLS) and geographical weighted regression (GWR) models, selected an already high-density city, Shenzhen, as the study area, and explored the effects of the natural and built environments on air pollution. The results showed that temperature always had a positive influence on PM 2.5 and wind speed had a varied impact on PM 2.5 within the city. Based on the natural factors analysis, the paper found that an increase in the floor area ratio (FAR) and road density may have caused the increase in the PM 2.5 concentration in the central city. In terms of land use mix, land use policies should be adopted separately in the central city and suburban areas. Finally, in terms of spatial heterogeneity, the GWR models achieved much better performances than the global multivariate regression models, with lower AICc and RMSE values and higher adjusted R 2 values, ultimately explaining 60% of the variance across different city areas. The results indicated that policies and interventions should be more targeted to improve the air environment and reduce personal exposure according to the spatial geographical context.

Keywords: natural and built environment; PM 2.5 concentrations; spatial heterogeneity (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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