Analysis of the spatial and temporal distribution characteristics and influencing factors of PM2.5 concentrations for the urban agglomeration on the northern slope of Tianshan Mountain is of positive significance for regional economic construction and environmental protection. The spatial and temporal distributions of PM2.5 concentrations in the Tianshan North Slope urban agglomeration from March to November 2015 to 2021 were obtained through the inversion of the MCD19A2 aerosol product combined with meteorological factors using a geographically weighted regression (GWR) model, followed by the analysis of change trends and influencing factors. The results were as follows:① the high PM2.5 concentrations in the study area were mainly distributed in the oasis city cluster between the northern foot of Tianshan Mountain and the Gurbantunggut Desert, showing the spatial distribution characteristics of being "low around and high in the middle" and "low in the west and high in the east." The annual average value of ρ(PM2.5) in the study area was 16.98 μg·m-3, with high values mainly concentrated in the urban part of Urumqi and decreasing towards Changji and Fukang. The monthly average ρ(PM2.5) distribution pattern was consistent with the annual average, but there were seasonal differences as follows:autumn (20.32 μg·m-3) > spring (18.25 μg·m-3) > summer (12.47 μg·m-3). The accumulation phenomenon was more pronounced in spring and winter. ② The study area's annual average PM2.5 concentration showed a decreasing trend from 2015 to 2021, and the average value from March to October also showed a decreasing trend, with only a slight increase in November. From the analysis of the spatial distribution of PM2.5 concentration trends, the decrease was concentrated in the urban parts of major cities, especially in the urban part of Urumqi and its surrounding areas, where the decrease was the largest and the change was the most drastic. ③ Temperature and air pressure were positively correlated with PM2.5 concentrations, whereas relative humidity, wind speed, atmospheric boundary layer height, and precipitation were negatively correlated with PM2.5 concentrations. The degree of influence of each factor was ranked from high to low as follows:atmospheric boundary layer height > relative humidity > air pressure > air temperature > wind speed > precipitation.
Keywords: analysis of influencing factors; change trend; geographically weighted regression (GWR) model; temporal and spatial distribution characteristics; urban agglomerations on the northern slope of Tianshan Mountains.