Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015-2018

Bin Guo; Xiaoxia Wang; Lin Pei; Yi Su; Dingming Zhang; Yan Wang

doi:10.1016/j.scitotenv.2020.141765

Identifying the spatiotemporal dynamic of PM_2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015-2018

Sci Total Environ. 2021 Jan 10:751:141765. doi: 10.1016/j.scitotenv.2020.141765. Epub 2020 Aug 18.

Authors

Bin Guo¹, Xiaoxia Wang², Lin Pei³, Yi Su², Dingming Zhang², Yan Wang²

Affiliations

¹ College of Geomatics, Xi'an University of Science and Technology, Xi'an, China. Electronic address: guobin12@xust.edu.cn.
² College of Geomatics, Xi'an University of Science and Technology, Xi'an, China.
³ School of Public Health, Xi'an JiaoTong University, Xi'an, China.

PMID: 32882558
DOI: 10.1016/j.scitotenv.2020.141765

Abstract

Fine particulate matter (PM_2.5) is closely related to the air quality and public health. Numerous models have been introduced to simulate the PM_2.5 concentrations at large scale based on remote sensing and auxiliary data. However, the data precision provided by these models are inadequate for epidemiology and pollutant exposure studies at medium or small scale. The present study aims to calibrate PM_2.5 concentrations at 1 km resolution scale across China during 2015-2018 based on monitoring station data and auxiliary data using a novel geographically and temporally weighted regression model (GTWR). The cross-validation (CV) method and the geographically weighted regression (GWR) model are conducted for validation and cross-comparison. Additionally, the spatial autocorrelation and slope analysis methods are implemented to detect the spatiotemporal dynamic of PM_2.5 concentrations. A sample-based CV of the GTWR model demonstrates an acceptable precision with a coefficient of determination equal to 0.67, a root-mean-square error of 10.32 μg/m³, and a mean prediction error of-6.56 μg/m³. This result proves that the GTWR model can simulate PM_2.5 concentrations at a higher spatial resolution and accuracy across China than some previous models. Besides, the heterogeneity and spatiotemporal dynamic of PM_2.5 concentrations are obvious, that is, the High-High (H-H) agglomeration areas with strong haze pollution were mainly concentrated in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Chengdu-Chongqing (CY), and Guanzhong Plain (GZP). In addition, the PM_2.5 concentrations are undergoing a decreasing trend in most of the study area, and the decrease in the BTH is dramatic. The results of the present study are helpful for calibrating and detecting the spatiotemporal dynamic of PM_2.5 concentrations and useful for the government to make decisions about decreasing haze pollution in urban agglomeration scale.

Keywords: Air pollutants; China; GTWR; GWR; Spatial autocorrelation; Spatiotemporal dynamic.