Spatiotemporal Distribution of PM2.5 and Its\nCorrelation with Other Air Pollutants in Winter\nDuring 2016~2018 in Xi’an, China

Abstract

High concentration of PM2.5 has seriously affected people’s daily lives in recent years. It is necessary to analyze PM2.5 and the correlations with other pollutants in winter. Data presented in this paper were obtained from monitoring stations from 2016 to 2018. Attention was fixed on PM2.5 and its monthly and daily variations in winter. Furthermore, the correlations between PM2.5 and CO, SO2, NO2, O3 and PM10 were studied. The results showed concentrating PM2.5 was roughly consistent with the monthly and daily trends. It was January>December>February>November>March. The mass concentration ranges of PM2.5 before and after the adjustment of heating energy structures were 64.5-184.1 μg/m 3, and 86.4-140.1 μg/m3, respectively. The average concentrations of PM2.5 were 135.5 μg/m 3, and 109.1 μg/m3, decreased by 26.4 μg/m3. PM2.5/PM10 was changed from 64.6% to 62.6%, reduced by 2%. The linear correlation analysis revealed a strong correlation between PM2.5 and CO, SO2, NO2, and PM10, but a negative correlation between PM2.5 and O3. Two multiple linear regression models on the pollutants were established, respectively. This study helps understand the concentrating distribution of PM2.5 and other pollutants in winter. It will provide some useful references to control air pollution for some cities, which have a similar type of heating energy structure.