Variation Characteristics of Main Air Particulate Matter in Major Cities of Xinjiang
Based on the data of six types of pollutants(PM2.5,PM10,SO2,NO2,CO and O3)from 40 environmental monitoring stations in 16 major cities of Xinjiang from 2015 to 2022,the spatial and temporal distribution characteristics of atmospheric pollutants were analyzed.The results showed that:(1)The main air pollutant in Xinjiang was particulate matter.(2)The concentration of PM10 gradually decreased from south to north,the highest concentration of PM10 was found in spring in Hotan city,with the air pollution level at the sixth level(546 μg·m-3).The distribution pattern of PM2.5 was consistent with that of PM10 in spring,summer,and autumn.The concentration values in cities on the northern slope of the middle Tianshan Mountains were high in winter,with the highest value of PM2.5 in Wujiaqu city at the air pollution level five(172 μg·m-3).The lowest concentrations of PM10 and PM2.5 in all seasons were found in Altay city,and the air quality was excellent.(3)The particulate matter concentration in Urumqi and Kashgar showed a declining trend.The lowest concentrations of both types of particulate matter were found in Altay,and the air quality was excellent.The concentrations of PM 10 and PM2.5 in Urumqi were high in the winter half year,and the highest concentrations of both PM10 and PM2.5 were found in January 2017,and the air pollution was at level five.The concentration of PM 10 in Kashgar was high from February to May,the concentration of PM2.5 was high from October to the following May,and both types of particulate matter reached their highest values in March 2016 with the air pollution level six.(4)The daily variations of the two types of particulate matter were similar,the concentrations were high around 23:00 in all seasons in Altay city.In Urumqi,the concentrations were high from 00:00 to 02:00 in spring,summer,and autumn,and at 22:00 in winter.In Kashgar,the concentrations were high around 01:00 in spring,summer,and autumn,and at 13:00 in winter.
国家科技期刊平台
NSTL
万方数据
