Geographic Detector-Based Spatiotemporal Variation and Influence Factors Analysis of PM2.5 in Shandong, China
Ming-Yang Yu 1  
,   Yue Xu 1  
,   Jing-Qi Li 1  
,   Xiao-Chen Lu 1  
,   Hua-Qiao Xing 1  
,   Ming-Liang Ma 1  
More details
Hide details
School of Surveying and Geo-infomatics, Shandong Jianzhu University, Jinan 250101, China
Hua-Qiao Xing   

School of Surveying and Geo-informatics,Shandong Jianzhu University, China
Submission date: 2020-01-04
Final revision date: 2020-03-31
Acceptance date: 2020-04-14
Online publication date: 2020-07-17
Publication date: 2020-10-05
Pol. J. Environ. Stud. 2021;30(1):463–475
The impact of PM2.5 pollution on the ecological environment and human health and safety has attracted worldwide attention. Previous analyses of large-scale PM2.5 spatiotemporal distribution have mainly been based on satellite observations. Due to the limitations of inversion methods, the real temporal and spatial variation of PM2.5 concentrations cannot be obtained from satellite data. Using PM2.5 data from 2014 to 2017 inversed from the daily PM2.5 data in Shandong Province and employing time series, spatial autocorrelation, and geographical detector methods, the temporal and spatial evolution of PM2.5 concentrations in Shandong Province and their driving factors are revealed. The results show that the annual variation trend in the PM2.5 concentration in Shandong Province is downward, with an average annual concentration of 57.6 μg/m3, while the monthly and winter months show significant high-level u-type changes, with small changes in spring and summer, and the number of days for air compliance in 2017 was 294 . Linyi and Dezhou have the largest annual decline in air quality, while coastal cities have a small decline in air quality. From 2014 to 2017, PM2.5 of Shandong Province showed obvious spatial agglomeration and disparity patterns. The correlation between PM2.5 and other elements differs significantly by month, and soot emission has the most significant effect on PM2.5 concentration. Geographic detection analysis indicates that the main driving factors for the change in PM2.5 concentration in Shandong Province are crop broadcast area and soot emissions.