Optimizing Urban Green Spaces for Air Quality Improvement: A Multiscale Land Use/Land Cover Synergy Practical Framework in Wuhan, China

Shibo Bi, Ming Chen, Zheng Tian, Peiyi Jiang, Fei Dai () and Guowei Wang
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Shibo Bi: School of Design Art & Media, Nanjing University of Science and Technology, Nanjing 210094, China
Ming Chen: College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Zheng Tian: School of Design Art & Media, Nanjing University of Science and Technology, Nanjing 210094, China
Peiyi Jiang: School of Architecture & Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
Fei Dai: School of Architecture & Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
Guowei Wang: School of Design Art & Media, Nanjing University of Science and Technology, Nanjing 210094, China

Land, 2024, vol. 13, issue 7, 1-24

Abstract: Air pollution, particularly fine particulate matter (PM 2.5 ), poses a significant health risk, especially in high-density urban areas. Urban green space (UGS) can effectively mitigate this pollution. Despite their potential, strategies for effectively leveraging Land Use/Land Cover (LULC) optimization to combat PM 2.5 remain largely unexplored. Ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) were employed to investigate the spatial heterogeneity relationship between UGS conversion and PM 2.5 fluctuations across various scales and evolutionary stages, developing a multiscale practical framework for LULC synergy in combating air pollution. The areas of UGSs to/from other LULCs, PM 2.5 concentrations and corresponding variation zones exhibited significant spatial clustering. These UGS conversions explained more than 65% of the PM 2.5 changes in the study area, peaking at 76.4% explanatory power in the fourth stage. Compared to global spatial analysis (OLS: 0–0.48), local spatial regression analysis significantly improved the R 2 value (GWR: 0.32–0.75, MGWR: 0.48–0.90), but the fitting quality of local spatial regression analysis decreased with increasing scale, highlighting the importance of scale diagnosis. A 2 km scale was identified as optimal for assessing the spatial heterogeneity impact of UGS and other LULC conversions on PM 2.5 changes. Conversion areas from water bodies and bare land to UGSs maintain stable local spatial properties at this scale (bandwidths: 44–99). Our research provides new insights into LULC management and planning, offering a coordinated approach to mitigating urban air pollution. Additionally, a practical framework was established for addressing spatially continuous variables such as PM 2.5 , revealing effective approaches for addressing urban environmental issues.

Keywords: urban green space; land use and land cover; PM 2.5; spatiotemporal evolution; synergy optimization; spatial heterogeneity (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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