The relationship between the high-frequency time series of PM2.5 in the atmosphere and the air pollutants emitted by industrial firms is not yet fully understood. This study aimed to identify independent PM2.5 clustering regions in Shaanxi Province and to evaluate the spatio-temporal correlations of PM2.5 concentrations and pollutant emissions from industrial firms in these regions. To accomplish this, daily data on PM2.5 concentrations and air pollutants emitted by industrial firms were analyzed using the K-means spatial clustering method and cross-wavelet transformation. The results show that: 1) PM2.5 concentrations in Shaanxi Province can be divided into three independent clustering regions. 2) The lagged impact of industrial emissions on PM2.5 concentrations were about 1/4-1/2 period. 3) PM2.5 was mainly influenced by particulate matter (PM) emissions from industrial plants during the period of 16–32 days, while nitrogen oxides (NOx) significantly affected PM2.5 concentrations during the period of 32–64 days. 4) Emissions of PM, NOx, and sulfur dioxide (SO2) more significantly affect PM2.5 concentrations in northern and central Shaanxi, and pollutants emitted by firms in the thermal power generation, utility, and steel industries had more significant effects on PM2.5 concentrations than those emitted by the cement manufacturing and electric power industries. During the COVID-19 shutdown, the emissions of firms cannot significantly affect PM2.5 concentrations. These findings suggest that emission reduction initiatives should consider industrial, regional, and periodic differences to reduce PM2.5 pollution during winter.
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