Analysis on Spatial-temporal Characteristics and Driving Factors of PM2. 5 in Heilongjiang Province in the Past 20 years

被引：0

作者：

Qiao, Lu-Jing ^{[1
]}

Luan, Yi-Tong ^{[1
]}

Zeng, Yan-Li ^{[1
]}

Ju, Cun-Yong ^{[1
]}

Tao, Jin-Tao ^{[1
,2
]}

机构：

[1] Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, College of Forestry, Northeast Forestry University, Harbin

[2] Shandong Huayu Institute of Technology Admissions Office, Dezhou

来源：

Huanjing Kexue/Environmental Science | 2024年 / 45卷 / 12期

关键词：

driving factors; geographical detector; Heilongjiang Province; multi-scale geographically weighted regression model; PM[!sub]2.5[!/sub; spatial-temporal variation;

D O I：

10.13227/j.hjkx.202311170

中图分类号：

学科分类号：

摘要：

PM2.5 is an important indicator for measuring the degree of air pollution. Studying the space-time variation and the driving factors of spatial heterogeneity is important for controlling air pollution and improving regional air quality. Based on PM2.5 remote sensing data from 2000 to 2021，the Theil-Sen Median trend analysis，Mann-Kendall significant inspection，and spatial auto correlation were used to analyze the characteristics of space-time changes in PM2.5 concentration，and geographical detectors were combined with a multi-scale geographical weighted regression model to explore the key driver factor and its influence and direction of the impact and role of PM2.5 spatial differences. The results showed that ① The average PM2.5 value of Heilongjiang Province was between 22.01 and 41 µg·m−3 from 2000 to 2021. From 2008 to 2015，the average PM2.5 value was higher than the secondary concentration limit（35 µg·m−3）of the“Environmental Air Quality Standard.”The turning point of the PM2.5 concentration change that occurred in 2013 generally showed the trend of change and then a downward trend. Winter was the high incidence season for PM2.5 pollution. The PM2.5 concentration space was a distributed pattern in the south and north and the high-value zone was mainly based on Harbin，Daqing City，and the surrounding area. The low-value areas were distributed in the northern regions such as the Great Khingan Mountains Region and Heihe City. ② Factor detection results indicated that the average annual temperature was the most important driving factor that affected PM2.5 spatial differences. The remaining key driver factors were in turn：high-end，population density，average annual wind speed，land use，night lights，annual years precipitation，slope，annual relative humidity，and NDVI. Interactive detection showed that the interpretation of PM2.5 points after interaction was higher than a single factor after interaction，indicating that affecting PM2.5 spatial difference was the result of the common effect of each driver factor. The effect of natural factors was more obvious than that of social and economic factors. ③ The effect of different influence factors on PM2.5 had a significant spatial difference. The average annual temperature，average annual relative humidity，population density，and night lighting played a promotion effect on PM2.5 pollution and NDVI and land use played an inhibitory effect on PM2.5 pollution. PM2.5 was significantly different from the action role of various influencing factors and the average annual temperature，annual average wind speed，and NDVI impact scale were the smallest，with a variable bandwidth of 43；population density and land use impact scale were the largest，with a variable bandwidth of 140. © 2024 Science Press. All rights reserved.

引用

页码：6980 / 6992

页数：12

共 46 条

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