Spatiotemporal Analysis of Air Quality and Its Driving Factors in Beijing’s Main Urban Area

Abstract

Urban air pollution is a critical global environmental issue, necessitating an analysis of the spatiotemporal characteristics of air quality and its driving factors for sustainable urban development. However, the traditional urban air quality assessment system ignores the impact of internal urban spatial structures. Therefore, this paper proposes an assessment system that integrates natural, socio-economic, and urban layout factors by utilizing the air quality index (AQI) and 14 types of multi-source geographic data in the main urban area of Beijing from 2016 to 2020 and constructs geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR) models for spatiotemporal analysis. Our findings revealed an annual improvement in air quality, with a U-shaped seasonal pattern and significant spatial clustering (Global Moran’s I = 0.922). The MGWR model provided a superior fit over the GWR, capturing spatial variability more effectively. Variables such as NDVI, economic output (GDP), and humidity space adjustment capability (HSAC) showed significant positive spatial impacts on air quality, while population density (POP), temperature (TEMP), and road density (RD) exhibited negative effects. These results explain the changes in air quality in the main urban area of Beijing from a spatiotemporal perspective and provide planning input for urban air quality regulations.