A new framework for analysis of the morphological spatial patterns of urban green space to reduce PM2.5 pollution: A case study in Wuhan, China

Abstract

Urban green space (UGS) can effectively reduce particulate pollution. However, the spatially heterogeneous nature of PM2.5 and the impact of UGS morphological spatial patterns (MSPs) on PM2.5 remain largely unknown, as most related studies have focused solely on global spatial performance. This study analyses the local relationships between MSPs and PM2.5 using geographically weighted regression (GWR). It provides a novel framework for systematic analysis by regarding landscape metrics (LMs) as indexes of MSPs (i.e., a MSP-LM framework). Compared with ordinary least squares (OLS) regression, GWR significantly improves the model's R2 (OLS: 0.002–0.233, GWR: 0.92–0.97) and yields a higher local R2 outside the second ring road. The local coefficients of perforation, core, and edge are significantly negative over 60% of the study area, while the coefficients of islet and branch are significantly positive over 66% of the area. In terms of the LMs of MSPs, improving the LMs of edges and cores can significantly reduce PM2.5. Increasing edge density has the best performance. Our study not only provides a basis for reducing PM2.5 but also contributes a common research method for exploring related environmental issues such as SO2 to promote sustainable urban development.