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厦门市环境空气污染时空特征及其与气象因素相关分析
引用本文:肖建能,杜国明,施益强,温宥越,姚杰,高宇婷,林锦耀.厦门市环境空气污染时空特征及其与气象因素相关分析[J].环境科学学报,2016,36(9):3363-3371.
作者姓名:肖建能  杜国明  施益强  温宥越  姚杰  高宇婷  林锦耀
作者单位:中山大学地理科学与规划学院, 广东省城市化与地理环境空间模拟重点实验室, 广州 510275,中山大学地理科学与规划学院, 广东省城市化与地理环境空间模拟重点实验室, 广州 510275,集美大学理学院, 集美大学影像信息工程技术研究中心, 厦门 361021,中山大学地理科学与规划学院, 广东省城市化与地理环境空间模拟重点实验室, 广州 510275,集美大学理学院, 集美大学影像信息工程技术研究中心, 厦门 361021,集美大学理学院, 集美大学影像信息工程技术研究中心, 厦门 361021,中山大学地理科学与规划学院, 广东省城市化与地理环境空间模拟重点实验室, 广州 510275
基金项目:国家科技支撑计划项目(No.2015BAK11B02);广东省科技计划国际科技合作项目(No.2014A050503031);中山大学本科教学改革研究课题(No.37000-16300012)
摘    要:利用2014年3月—2015年2月厦门市18个监测站点实测数据,运用GIS技术、相关分析以及统计分析等方法,进行空气质量指数(AQI)及其污染因子的时空分析,结合厦门市土地利用分类专题图和主要重工业企业分布图进行厦门市环境空气质量状况污染源的分析.结果表明:厦门市首要污染物为PM10,其天数占全年的48%,PM2.5紧随其后占到36%;厦门市空气质量较好时间段主要集中在夏季,其中7月份是厦门市空气质量最好的月份,而厦门市秋冬两季的空气质量较差;AQI与温度相关系数达-0.813,具有极显著负相关性(p0.01),与气压相关系数达0.835,具有极显著正相关性(p0.01),而与风速和相对湿度气象因素相关性都不显著(p0.05);PM2.5、PM10、SO2、NO2、O3污染因子存在明显的空间分布差异,海沧区和集美区南部的空气污染比厦门其他地方明显更为严重;从土地利用图和主要重工业企业的分布图可以看出,污染最为严重的地区土地利用类型主要是建筑用地,而且这些地区还分布着许多钢铁厂和发电站.

关 键 词:空气质量指数(AQI)  PM2.5  污染因子  气象因素  相关分析  厦门市
收稿时间:2015/12/2 0:00:00
修稿时间:2016/2/25 0:00:00

Spatiotemporal distribution pattern of ambient air pollution and its correlation with meteorological factors in Xiamen City
XIAO Jianneng,DU Guoming,SHI Yiqiang,WEN Youyue,YAO Jie,GAO Yuting and LIN Jinyao.Spatiotemporal distribution pattern of ambient air pollution and its correlation with meteorological factors in Xiamen City[J].Acta Scientiae Circumstantiae,2016,36(9):3363-3371.
Authors:XIAO Jianneng  DU Guoming  SHI Yiqiang  WEN Youyue  YAO Jie  GAO Yuting and LIN Jinyao
Institution:Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275,Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275,Research Center of Image Information Engineering and Technology, School of Science, Jimei University, Xiamen 361021,Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275,Research Center of Image Information Engineering and Technology, School of Science, Jimei University, Xiamen 361021,Research Center of Image Information Engineering and Technology, School of Science, Jimei University, Xiamen 361021 and Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275
Abstract:In this paper, we analyze the distribution of air quality index (AQI) in Xiamen based on the data of 18 monitoring sites from March 2014 to February 2015 by using GIS, correlation analysis and statistical analysis. We also analyze the source of pollution based on land use classification map and the distribution of major heavy industry. The results show that PM10 and PM2.5 are the primary pollutants in Xiamen. For these two pollutants, the number of polluted days accounts for 48% and 36% of the year, respectively. The air quality in Xiamen is better in summer and the best in July. In comparison, the air quality in autumn and winter is poor. The correlation coefficient between AQI and temperature is -0.813, which shows a very significant negative correlation (p<0.01). The correlation coefficient between AQI and air pressure is 0.835, which shows a very significant positive correlation (p<0.01). However, the correlations between AQI and wind speed, relative humidity are not significant (p>0.05). The spatial distributions of these pollution factors, including PM2.5, PM10, SO2, NO2 and O3, are quite different in Xiamen. In addition, the air pollutions in Haicang and the south of Jimei are much more severe than the rest areas of Xiamen. According to the land use classification map and the distribution map of major heavy industry, we can see that the most seriously polluted area, Haicang and south of Jimei Districts, contain many steel plants and power stations.
Keywords:air quality index(AQI)  PM2  5  pollution factors  meteorological factor  correlation analysis  Xiamen City
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