Modeling the Determinants of PM2.5 in China Considering the Localized Spatiotemporal Effects: A Multiscale Geographically Weighted Regression Method

• Published in: Atmosphere Vol. 13; no. 4; p. 627
• Main Authors: Yue, Han, Duan, Lian, Lu, Mingshen, Huang, Hongsheng, Zhang, Xinyin, Liu, Huilin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2022
• Subjects: Air pollution; Autocorrelation; Cities; Climate change; Estimates; GDP; Goodness of fit; Gross Domestic Product; Heterogeneity; Impact factors; Modelling; multiscale geographically weighted regression; Parameter estimation; Particulate matter; Patchiness; PM2.5; Pollutants; Regression models; Socioeconomic factors; spatial dependence; Spatial heterogeneity; Statistical analysis; Statistical methods; temporal dependence; Time series; Variables; China
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos13040627

Many studies have identified the influences of PM2.5. However, very little research has addressed the spatiotemporal dependence and heterogeneity in the relationships between impact factors and PM2.5. This study firstly utilizes spatial statistics and time series analysis to investigate the spatial and temporal dependence of PM2.5 at the city level in China using a three-year (2015–2017) dataset. Then, a new local regression model, multiscale geographically weighted regression (MGWR), is introduced, based on which we measure the influence of PM2.5. A spatiotemporal lag is constructed and included in MGWR to account for spatiotemporal dependence and spatial heterogeneity simultaneously. Results of MGWR are comprehensively compared with those of ordinary least square (OLS) and geographically weighted regression (GWR). Experimental results show that PM2.5 is autocorrelated in both space and time. Compared with existing approaches, MGWR with a spatiotemporal lag (MGWRL) achieves a higher goodness-of-fit and a more significant effect on eliminating residual spatial autocorrelation. Parameter estimates from MGWR demonstrate significant spatial heterogeneity, which traditional global models fail to detect. Results also indicate the use of MGWR for generating local spatiotemporal dependence evaluations which are conditioned on various covariates rather than being simple descriptions of a pattern. This study offers a more accurate method to model geographic events.