[Analysis on Spatial-temporal Characteristics and Driving Factors of PM_2.5 in Heilongjiang Province in the Past 20...

PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 spatial differences. The results showed that： ① The average PM_2.5 value of Heilongjiang Province was between 22.01 and 41 μg·m^-3 from 2000 to 2021. From 2008 to 2015, the average PM_2.5 value was higher than the secondary concentration limit （35 μg·m^-3） of the "Environmental Air Quality Standard." The turning point of the PM_2.5 concentration change that occurred in 2013 generally showed the trend of change and then a downward trend. Winter was the high incidence season for PM_2.5 pollution. The PM_2.5 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 PM_2.5 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 PM_2.5 points after interaction was higher than a single factor after interaction, indicating that affecting PM_2.5 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 PM_2.5 had a significant spatial difference. The average annual temperature, average annual relative humidity, population density, and night lighting played a promotion effect on PM_2.5 pollution and NDVI and land use played an inhibitory effect on PM_2.5 pollution. PM_2.5 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.