Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China

Lanfang Liu; Yan Liu; Feng Cheng; Yuanhe Yu; Jinliang Wang; Cheng Wang; Lanping Nong; Huan Deng

doi:10.1016/j.envpol.2024.124057

Remote sensing estimation of regional PM_2.5 based on GTWR model -A case study of southwest China

Environ Pollut. 2024 Apr 28:351:124057. doi: 10.1016/j.envpol.2024.124057. Online ahead of print.

Authors

Lanfang Liu¹, Yan Liu², Feng Cheng¹, Yuanhe Yu³, Jinliang Wang⁴, Cheng Wang⁵, Lanping Nong⁶, Huan Deng⁷

Affiliations

¹ Faculty of Geography, Yunnan Normal University, Kunming, Yunnan, 650500, China; Key Laboratory of Remote Sensing of Resources and Environment of Yunnan Province, Kunming, 650500, China; Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming, 650500, China.
² Faculty of Geography, Yunnan Normal University, Kunming, Yunnan, 650500, China; Weinan Railway Zili Middle School, Weinan, Shanxi, 714000, China; Key Laboratory of Remote Sensing of Resources and Environment of Yunnan Province, Kunming, 650500, China.
³ Faculty of Geography, Yunnan Normal University, Kunming, Yunnan, 650500, China; Key Laboratory of Remote Sensing of Resources and Environment of Yunnan Province, Kunming, 650500, China; School of Geography, Nanjing Normal University, Nanjing, 210023, China.
⁴ Faculty of Geography, Yunnan Normal University, Kunming, Yunnan, 650500, China; Key Laboratory of Remote Sensing of Resources and Environment of Yunnan Province, Kunming, 650500, China; Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming, 650500, China. Electronic address: jlwang@ynnu.edu.cn.
⁵ Faculty of Geography, Yunnan Normal University, Kunming, Yunnan, 650500, China; Key Laboratory of Remote Sensing of Resources and Environment of Yunnan Province, Kunming, 650500, China; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
⁶ Faculty of Geography, Yunnan Normal University, Kunming, Yunnan, 650500, China; Key Laboratory of Remote Sensing of Resources and Environment of Yunnan Province, Kunming, 650500, China.
⁷ College of Geography and Tourism, Zhaotong University, Zhaotong, Yunnan, 657000, China.

PMID: 38688385
DOI: 10.1016/j.envpol.2024.124057

Abstract

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM_2.5 (particulate matter with diameter ≤2.5 μm) being the main contributor. Establishing the spatial distribution of PM_2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM_2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM_2.5 concentrations. However, the R² values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R² values between the measured and remote sensed estimated PM_2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM_2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM_2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a "V" -shaped oscillation change.

Keywords: GTWR model; GWR model; OLS model; PM(2.5); Remote sensing estimation; Southwest China.