点击次数：

影响因子：4.614

DOI码：10.3390/ijerph191811627

发表刊物：International Journal of Environmental Research and Public Health

关键字：PCA; GTWR; GWR; TWR; particulate matter; meteorological factors; NDVI

摘要：Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial–temporal heterogeneity of PM (PM2.5 and PM10) concentration in Heilongjiang Province during 2014–2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geographically weighted regression (PCA-GWR), PCA-based temporally weighted regression (PCA-TWR), and PCA-based geographically and temporally weighted regression (PCA-GTWR). Results showed that six principal components represented the temperature, wind speed, air pressure, atmospheric pollution, humidity, and vegetation cover factor, respectively, contributing 87% of original variables. All the local models (PCA-GWR, PCA-TWR, and PCA-GTWR) were superior to the global model (PCA-OLS), and PCAGTWR has the best performance. PM had greater temporal than spatial heterogeneity due to seasonal periodicity. Air pollutants (i.e., SO2, NO2, and CO) and pressure were promoted whereas temperature, wind speed, and vegetation cover inhibited the PM concentration. The downward trend of annual PM concentration is obvious, especially after 2017, and the hot spot gradually changed from southwestern to southeastern cities. This study laid the foundation for precise local government prevention and control by addressing both excessive effect factors (i.e., meteorological factors, air pollutants, vegetation cover) and spatial-temporal heterogeneity of PM.

合写作者：Qibang Wang,Jianhui Li,Huiran Jin

第一作者：Q1, Longhui Fu

论文类型：期刊论文

通讯作者：Qingbin Wei*,Zhen Zhen*

卷号：19

期号：18

页面范围：11627

ISSN号：1660-4601

是否译文：否

发表时间：2022-01-01

收录刊物：SCI