Spatial and temporal distribution characteristics of air pollutants in Xinjiang from 2016 to 2022
Objective To analyze the spatial and temporal distribution characteristics of inhalable particle(PM10),fine particle(PM2.5),NO2,SO2,CO and O3 in Xinjiang from 2016 to 2022,and to provide reference for air pollution prevention and environmental protection strategy formulation in Xinjiang.Methods The national air quality monitoring sites data in Xinjiang from 2016 to 2022 were collected,and the annual,monthly and regional distribution characteristics of the various pollutants were analyzed.Results Ambient air quality in Xinjiang has been improving over the past seven years.The average annual concentrations of PM10,PM2.5,SO2,NO2 and CO have shown a downward trend,with declines of 20.04％,24.57％,50.15％,22.56％and 33.68％,respectively.The average annual concentration of O3 was not significantly changed.The monthly average concentrations of PM10,PM2.5,SO2,NO2 and CO were in the form of"U",with higher concentrations in spring and winter than in summer.And the monthly average concentration of O3 showed an inverted"U"distribution,with higher concentrations in summer than in winter.Days with PM10 and PM2.5 reaching the standard showed an increased trend,whereas day with O3 reaching the standard showed a declined trend.The concentration distribution of various pollutants in Xinjiang showed significant north-south differences.The average annual concentrations of PM10,PM2.5,SO2,CO and O3 in southern Xinjiang were higher than those in eastern and northern Xinjiang,showing a pattern of decreasing from south to north.The average annual concentration of NO2 was the highest in Urumqi,northern Xinjiang.Conclusion Air quality of Xinjiang has improved year by year in 2016-2022,but the phenomenon of PM10,PM2.5 and O3 exceeding the standard still exists.In the future,Xinjiang should focus on the synchronous control of particulate matter and O3,as well as regional coordinated governance.
Air pollutantsTemporal and spatial distributionXinjiang
NETL
NSTL
万方数据
