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2016-2019年长江中游城市群空气质量时空变化特征及影响因素分析 被引量:26

Analysis on the Characteristics and Influencing Factors of Air Quality of Urban Agglomeration in the Middle Reaches of the Yangtze River in 2016 to 2019
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摘要 长江中游城市群是中国最早获批的国家级城市群,随着城镇化水平的提高、人口和工业的集中分布,空气污染问题越来越突出。论文选取2016-2019年各城市空气质量指数数据以及6项主要污染物(PM2.5、PM10、SO2、CO、NO2、O3)浓度数据,运用克里金插值和统计分析的方法,分析其时空变化特征,以及影响空气质量的因素。结果表明,(1)时间上,2016-2019年AQI值整体呈下降的趋势,在月份变化上呈“U”型形状,夏季指数值低,冬季偏高;在年时间尺度上,除ρ(NO2)和ρ(O3)外,其他污染物浓度均呈下降趋势;在月尺度上,ρ(PM2.5)、ρ(PM10)、ρ(CO)和ρ(NO2)都呈“U”型分布,6-8月浓度较低,11月至次年2月浓度较高。(2)空间上,除仙桃市、潜江市和天门市低于周围地区外,长江中游城市群2016-2019年AQI值整体上呈现“西北高、东南低”的特点;ρ(SO2)高值区主要分布于荆门市与江西省鹰潭、萍乡和新余,ρ(NO2)集中分布在武汉都市圈、环长株潭城市群、南昌市以及荆州市,ρ(PM2.5)、ρ(PM10)、ρ(CO)和ρ(O3)都呈西北高、东南低的特点。(3)PM2.5、PM10、NO2是影响AQI值的主要大气污染物,并且在春季和冬季相关性较强;在年尺度上以及各季节,ρ(PM2.5)和ρ(NO2)浓度与AQI值都呈显著正相关(α<0.01),对AQI值影响最大。(4)气象因子中平均气温和降水量对长江中游城市群空气质量指数影响最大(α<0.01),其次是日照时数(α<0.05),与AQI呈显著负相关。(5)社会经济因子中工业企业单位数(P=0.660)、能源消耗总量(P=0.456)、第二产业总值(P=0.652)、民用汽车拥有量(P=0.514)与AQI呈显著正相关(α<0.01),其中工业企业的决定系数(R2=0.542)最大,对空气质量的影响最大。 The urban agglomeration in the middle reaches of the Yangtze River is the first approved national level urban agglomeration in China.With the improvement of urbanization level and the centralized distribution of population and industry,the problem of air pollution has been becoming more and more prominent.We collected the data of air quality index(AQI)and the concentration of major pollutants(PM2.5,PM10,SO2,CO,NO2,O3),as well as the data of socioeconomic and meteorological from 2016 to 2019.Kriging interpolation and statistical analysis were applied to analyze the spatiotemporal variation characteristics of AQI and major pollutants,revealed the related meteorological and socioeconomic factors.The results showed that:(1)The air quality in the study area was characterized by annual optimization from 2016 to 2019.In details,the AQI expressed a“U”-shape variation within a year,air quality was better in summer than that in winter.So did the major pollutants(exceptingρ(NO2)andρ(O3)).(2)Spatially,except Xiantao City,Qianjiang City and Tianmen City which were lower than the surrounding areas,the AQI values of the urban agglomeration in the middle reaches of the Yangtze River from 2016 to 2019 were generally“high in the northwest and low in the southeast”;the high value areas ofρ(SO2)were mainly distributed in Jingmen City and Yingtan,Pingxiang and Xinyu of Jiangxi Province,andρ(NO2)was concentrated in Wuhan metropolitan area,surrounding Changsha-Zhuzhou-Xiangtan City Group,Nanchang City and Jingzhou City.(3)PM2.5,PM10 and NO2 were the main air pollutants affecting AQI,and the correlation was strong in spring and winter;the concentrations ofρ(PM2.5)andρ(NO2)had significant positive correlation with AQI value in annual scale and each season(α<0.01),and had the greatest impact on AQI value.(4)Among the meteorological factors,the average temperature and precipitation had the greatest impact on the air quality index of the urban agglomeration in the middle reaches of the Yangtze River(α<0.01),followed by sunshine hours(α<0.05),showing a significant negative correlation.And(5)among the socio-economic factors,the number of industrial enterprises(P=0.660),total energy consumption(P=0.456),the total value of the secondary industry(P=0.652),and the ownership of civil vehicles(P=0.514)were significantly positively correlated(α<0.01).Among them,the determination coefficient of industrial enterprises(R2=0.542)was the largest,which had the greatest impact on air quality.
作者 郭雯雯 陈永金 刘阁 宋开山 陶宝先 GUO Wenwen;CHEN Yongjin;LIU Ge;SONG Kaishan;TAO Baoxian(School of Environment and Planning,Liaocheng University,Liaocheng 252000,China;Key Laboratory of Wetland Ecology and Environment/Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102,China)
出处 《生态环境学报》 CSCD 北大核心 2020年第10期2034-2044,共11页 Ecology and Environmental Sciences
基金 中国科学院战略性先导科技专项(A类)(XDA19040500) 中国科学院青年创新促进会项目(2020234) 聊城大学社科平台项目(32102915)。
关键词 长江中游城市群 空气质量指数 时空分布 影响因素 urban agglomeration in the middle reaches of the Yangtze River air quality index spatial and temporal distribution influencing factors
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