引用本文:黄晓虎,韩秀秀,李帅东,杨浩,黄昌春,黄涛,等.城市主要大气污染物时空分布特征及其相关性[J].环境科学研究,2017,30(7):1001-1011.
HUANG Xiaohu,HAN Xiuxiu,LI Shuaidong,YANG Hao,HUANG Changchun,HUANG Tao,et al.Spatial and Temporal Variations and Relationships of Major Air Pollutants in Chinese Cities[J].Reserrch of Environmental Science,2017,30(7):1001-1011.]
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城市主要大气污染物时空分布特征及其相关性
黄晓虎1,2,3, 韩秀秀3, 李帅东3, 杨 浩3, 黄昌春1,2,3, 黄 涛1,2,3
1.江苏省地理信息资源开发与利用协同创新中心, 江苏 南京 210023 ;2.江苏省物质循环与污染控制重点实验室, 江苏 南京 210023 ;3.南京师范大学地理科学学院, 江苏 南京 210023
摘要:
为制订合理的大气污染物减排措施,利用中国环境监测总站公布的2015年1—12月299座城市实时发布的环境空气颗粒物(PM2.5和PM10)及气态污染物(CO、NO2和SO2)的质量浓度数据,对其进行了时空分布特征及其相关性研究. 结果表明:①2015年城市环境空气颗粒物污染严重,299座城市的ρ(PM2.5)、ρ(PM10)年均值分别主要集中在25~60和40~110 μg/m3,年均值达到GB 3095—2012《环境空气质量标准》二级标准的城市所占比例分别仅为24%和38%. ②城市大气污染物浓度具有明显的季节性特征,基本呈冬季>春秋季>夏季的趋势,其中冬季ρ(PM2.5)、ρ(PM10)、ρ(CO)、ρ(NO2)、ρ(SO2)分别为(73±27)(114±42)(1.49±0.61)(36±14)(42±33)μg/m3. ③高ρ(PM2.5)和ρ(PM10)主要集中在华北平原,年均值分别为(70±16)(117±22)μg/m3;高ρ(CO)主要出现在山西省,年均值为(1.76±0.48)mg/m3;高ρ(NO2)主要分布在京津冀、山东省和长江三角洲,年均值分别为(42±6)(39±9)(34±8)μg/m3;高ρ(SO2)主要分布在山西、山东两省,年均值分别为(54±10)(41±16)μg/m3. ④Pearson相关系数研究表明,我国城市环境空气颗粒物与气态污染物具有较强的复合性,并且具有秋冬季明显强于春夏季的季节性特征. 研究显示,我国城市大气污染具有较强的季节性、区域性与复合性,在降低环境空气颗粒物浓度的同时,对气态污染物的削减也不容忽视.
关键词:  环境空气颗粒物  气态污染物  时空分布  Pearson相关系数
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基金项目:国家重点基础研究发展计划(973)项目(2014CB953802);国家自然科学基金青年科学基金项目(41503075);江苏省高校自然科学基金面上项目(16KJD170001)
Spatial and Temporal Variations and Relationships of Major Air Pollutants in Chinese Cities
HUANG Xiaohu1,2,3, HAN Xiuxiu3, LI Shuaidong3, YANG Hao3, HUANG Changchun1,2,3, HUANG Tao1,2,3
1.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China ;2.Jiangsu Provincial Key Laboratory of Materials Cycling and Pollution Control, Nanjing 210023, China ;3.College of Geography Science, Nanjing Normal University, Nanjing 210023, China
Abstract:
Abstract: In order to effectively mitigate air pollution, the temporal and spatial distribution of atmospheric particulate matters (i.e., PM2.5 and PM10) and gaseous pollutants (i.e., CO, NO2 and SO2) concentrations and their correlations in 299 cities in China between January 1 and December 31,5, were investigated by using hourly data released by the National Environmental Monitoring Center. The urban atmospheric particulate matter pollution was serious in 2015. The annual mean concentrations of PM2.5 and PM10 in 299 cities were mainly distributed between 25-60 μg/m3 and 40-110 μg/m3, respectively, with only 24% and 38% of cities meeting the Grade II Chinese Ambient Air Quality Standards (35 and 70 μg/m3 for PM2.5 and PM10, respectively). There were obvious seasonal characteristics of gaseous pollutants, following the order:winter>spring and autumn>summer. The mean concentrations of PM2.5, PM10, CO, NO2 and SO2 in winter were (73±27), (114±42), (1.49±0.61), (36±14) and (42±33) μg/m3, respectively. Different gaseous pollutants had their own unique high pollution areas. The most polluted cities for PM2.5 and PM10 were mainly located in the North China Plain, where the annual mean concentrations were (70±16) and (117±22) μg/m3, respectively. The most polluted cities for CO were mainly located in Shanxi Province, where the annual mean concentrations were (1.76±0.48) mg/m3. The most polluted cities for NO2 were mainly located in the Beijing-Tianjin-Hebei region, Shandong Province and Yangtze River Delta, where the annual mean concentrations were (42±6) (39±9) and (34±8) μg/m3, respectively. The most polluted cities for SO2 were mainly located in Shanxi and Shandong provinces, where the annual mean concentrations were (54±10) and (41±16) μg/m3, respectively. The results of Pearson correlation analysis between urban atmospheric particulate matters and gaseous pollutants showed that there was strong complex between them, which was stronger in autumn and winter than spring and summer. Urban air pollution in China has strong seasonal, regional and complex characteristics. Therefore, gaseous pollutants should not be ignored while paying attention to urban particulate matter reduction.
Key words:  atmospheric particulate matters  gaseous pollutants  spatial and temporal distribution  Pearson correlation coefficients