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阿拉善左旗空气质量状况与气象条件分析

Analysis of air Quality and meteorological conditions in Alxa Left Banner
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摘要 文章根据2015—2020年阿拉善左旗的环境空气质量监测数据和气象观测数据,利用AQI对大气中的SO_(2)、NO_(2)、PM_(2.5)、PM_(10)、CO、O_(3)等空气污染物的年际变化和季节变化特征进行了分析,并探讨了阿拉善左旗各污染物浓度与气象条件的关系。结果表明:2015—2020年阿拉善左旗的平均环境空气质量指数(AQI)为77.3,空气质量良好率达87%,污染程度较轻,且具有显著的季节变化特征,秋、冬季节空气质量优于春、夏季节。在这6年间AQI总体呈缓慢降低趋势,良好率有所上升,轻度污染—严重污染级别天数减少。阿拉善左旗首要污染物为PM_(10),超标率和污染物贡献率均最高,并且年内变化趋势与沙尘暴高发期时间同步。AQI与风速呈极显著的正相关;气温对污染物影响较大,气温变化越大越有利于NO_(2)、CO、SO_(2)浓度增加,风是影响颗粒物浓度的主要驱动力。 Based on the Air Quality Monitoring Data and the meteorological observation data in Alxa Left Banner between 2015—2020,this article studies the interannual and seasonal variation characteristics of air pollutants by using Air quality index(AQI).This article also investigates the relationship between pollutant concentrations and meteorological conditions in Alxa Left Banner.The study shows that between 2015—2020,the average AQI value in Alxa Left Banner is 77.3,the percent of days whose AQI less than 100 is 87%,air pollution is moderate and the air quality shows evident seasonal characteristics featuring better air quality in autumn and winter than in spring and summer.The AQI value exhibits an overall slight downward trend in the six years and the percent of days of AQI less than 100 has increased while the percent of days of AQI greater than 100 has decreased.In Alxa Left Banner,the major pollutant is PM10,accounting for the highest exceeding rate and pollutant contribution rate,which is synchronized with the pace of the dust storm.In addition,AQI value is significantly positively correlated with wind.Temperature has a relatively significant impact on pollutants.The greater the temperature change,the more beneficial to the increase of NO_(2),CO and SO_(2)concentration.Wind is the dominant factor for particulate matter concentrations.
作者 张玮麟 Zhang Weilin(Alxa Meteorological Bureau,Inner Mongolia Alxa 750306)
机构地区 阿拉善盟气象局
出处 《内蒙古气象》 2022年第4期20-26,共7页 Meteorology Journal of Inner Mongolia
关键词 空气质量 气象因子 相关分析 Air Quality Meteorological factors correlation analysis
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