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

Zhixiong Tan, Haili Wu, Qingyang Chen and Jiejun Huang ()
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Zhixiong Tan: School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Haili Wu: School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Qingyang Chen: School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Jiejun Huang: School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China

Sustainability, 2024, vol. 16, issue 14, 1-18

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.

Keywords: urban air quality; assessment system; multi-source geographic data; spatiotemporal analysis; multi-scale geographically weighted regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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