Spatiotemporal analysis of the impact of urban landscape forms on PM2.5 in China from 2001 to 2020

Abstract

Urban landscape forms can be effective in reducing increasing PM2.5 concentrations due to urbanization in China, making it crucially important to accurately quantify the spatiotemporal impact of urban landscape forms on PM2.5 variations. Three landscape indices and six control variables were selected to assess these impacts in 362 Chinese cities during different time scales from 2001 to 2020, using a spatiotemporal geographically weighted regression model, random forest models and partial dependence plots. The results show that there are spatiotemporal differences in the impacts of landscape indices on PM2.5. the proportion of urban green infrastructure (PLAND-UGI) and the fractal dimension of urban green infrastructure (FRACT-UGI) exacerbate PM2.5 concentrations in the northwest, the proportion of impervious surfaces (PLAND-Impervious) mitigates air pollution in northwest and southwest China, and shannon’s diversity index (SHDI) has seasonal differences in the northwest. PLAND-UGI is the landscape index with the largest contribution (30%) and interpretable range. The relationship between FRACT and PM2.5 was more complex than for other landscape indices. The results of this study contribute to a deeper understanding of the spatial and temporal differences in the impact of urban landscape patterns on PM2.5, contributing to clean urban development and sustainable development.

Keywords

• landscape index
• particulate matter
• spatiotemporal heterogeneity
• spatiotemporal geographically weighted regression model
• random forest