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

环境科学 归一化差异植被指数 中国 自然地理学 空间分布 地理 空气污染 人口 空气质量指数 普通最小二乘法 地理加权回归模型 气象学 气候变化 遥感 统计 人口学 地质学 生态学 数学 海洋学 考古 社会学 生物
作者
Lanfang Liu,Yan Liu,Feng Cheng,Yuanhe Yu,Jinliang Wang,Cheng Wang,Lanping Nong,Huan Deng
出处
期刊:Environmental Pollution [Elsevier BV]
卷期号:351: 124057-124057 被引量:9
标识
DOI:10.1016/j.envpol.2024.124057
摘要

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 PM2.5 (particulate matter with diameter ≤ 2.5 μm) being the main contributor. Establishing the spatial distribution of PM2.5 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), a digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM2.5 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 PM2.5 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 PM2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a "V" -shaped oscillation change.
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