Influence and prediction of PM2.5 through multiple environmental variables in China

归一化差异植被指数 中国 环境科学 构造盆地 空间分布 主成分分析 自然地理学 共同空间格局 空气污染 风速 污染 气候学 分布(数学) 地理 气象学 气候变化 地质学 统计 生态学 遥感 数学 数学分析 生物 古生物学 海洋学 考古
作者
Haoyu Jin,Xiaohong Chen,Ruida Zhong,Moyang Liu
出处
期刊:Science of The Total Environment [Elsevier]
卷期号:849: 157910-157910 被引量:44
标识
DOI:10.1016/j.scitotenv.2022.157910
摘要

Fine particulate matter (PM2.5) is an important indicator to measure the degree of air pollution. With the pursuit of sustainable development of China's economy and society, air pollution has been paid more and more attention. The spatial distribution of PM2.5 is affected by multiple factors. In this study, we selected Normalized Difference Vegetation Index (NDVI), precipitation, temperature, wind speed and elevation data to analyze the impact of each variable on PM2.5 in different regions of China. The results show that the high-value areas of PM2.5 were mainly concentrated in the North China Plain, the middle and lower reaches of the Yangtze River Plain, the Sichuan Basin, and the Tarim Basin. PM2.5 showed an upward trend in North China, Northeast China and Northwest China, while in most of South China, especially the Sichuan Basin, PM2.5 showed a downward trend. Therefore, the northern region of China needs to take measures to curb the growth of PM2.5. In Northwest China, wind speed and temperature had a greater impact on PM2.5. In North China, wind speed had a greater impact on PM2.5. In southern China, temperature and NDVI had a greater impact on PM2.5. The deep learning model can better simulate the spatial distribution of PM2.5 based on the selected variables. The clustering effect of single variable is better than multivariate spatial information clustering based on principal component analysis (PCA). It is difficult to explain which variable has the greatest impact on PCA clustering. This study can provide an important reference for PM2.5 prevention and control in different regions of China.
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