Spatial and temporal distribution characteristics and influential factors of PM2.5 pollution in Beijing-Tianjin-Hebei

SU Mengqian; SHI Yusheng

doi:10.7523/j.ucas.2023.025

Journal of University of Chinese Academy of Sciences >

2024 , Vol. 41 >Issue 3: 334 - 344

DOI: https://doi.org/10.7523/j.ucas.2023.025

Research Articles

Spatial and temporal distribution characteristics and influential factors of PM_2.5 pollution in Beijing-Tianjin-Hebei

SU Mengqian ,
SHI Yusheng

Expand

1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2022-12-05

Revised date: 2023-03-27

Online published: 2023-03-27

Fold

Abstract

The fine particulate matter PM_2.5 could be harmful to human health and the atmospheric environment. Beijing-Tianjin-Hebei is one of the most serious regions in China in terms of atmospheric PM_2.5 pollution. Based on PM_2.5 concentrations data, natural factors data, and human activity factors data, this study used kriging interpolation and statistical analysis to explore the spatial and temporal distribution characteristics of atmospheric PM_2.5 pollution in 13 cities of Beijing-Tianjin-Hebei in 2017 and then used correlation analysis models and factor analysis models to explore its influential factors. The results show that in Beijing-Tianjin-Hebei, 1) PM_2.5 concentrations are low in the north and high in the south. The gradient of annual average concentrations between the southern and northern cities can reach up to 64μg/m³. 2) PM_2.5 concentrations are high in winter and low in summer, high in the morning and evening, and low in the afternoon. PM_2.5 concentration in winter is 1.3-2.8 times higher than in summer, and the daily differences in PM_2.5 concentrations in all seasons are between 11-29μg/m³. 3) Atmospheric PM_2.5 pollution is closely related to natural factors. Terrain and topography affect the processes of PM_2.5 aggregation, transport, and dispersion. Wind speed, sunshine hours, and relative humidity are the dominant meteorological factors affecting atmospheric PM_2.5 pollution, and PM_2.5 concentrations have the strongest correlation with meteorological factors in winter. 4) Atmospheric PM_2.5 pollution is closely related to human activities, which can be summarized into social economy factor, industrial pollutant discharge factor, and urban construction factor. The results of this study will help fill the gaps in air pollution prevention and control in Beijing-Tianjin-Hebei.

Key words： Beijing-Tianjin-Hebei; PM_2.5; spatial-temporal distribution; correlation analysis; factor analysis

Cite this article

SU Mengqian , SHI Yusheng . Spatial and temporal distribution characteristics and influential factors of PM_2.5 pollution in Beijing-Tianjin-Hebei[J]. Journal of University of Chinese Academy of Sciences, 2024 , 41(3) : 334 -344 . DOI: 10.7523/j.ucas.2023.025

References

[1] Shaddick G, Thomas M L, Mudu P, et al. Half the world’s population are exposed to increasing air pollution[J]. Npj Climate and Atmospheric Science, 2020, 3: 23. DOI: 10.1038/s41612-020-0124-2.
[2] Lu J, Li B, Li H, et al. Expansion of city scale, traffic modes, traffic congestion, and air pollution[J]. Cities, 2021, 108: 102974. DOI: 10.1016/j.cities.2020.102974.
[3] 刘乐乐, 赵小锋, 赵颜创, 等. 基于城市环境气候图的宁波大气环境分析与调控对策[J]. 生态学报, 2017, 37(2): 606-618. DOI: 10.5846/stxb201507091458.
[4] 刘海猛, 方创琳, 黄解军, 等. 京津冀城市群大气污染的时空特征与影响因素解析[J]. 地理学报, 2018, 73(1): 177-191. DOI: 10.11821/dlxb201801015.
[5] 杨旭. 京津冀地区空气污染特征与气象成因及其预报研究[D]. 兰州: 兰州大学, 2017.
[6] 中华人民共和国环境保护部. 2017年《中国生态环境状况公报》(摘录一)[J]. 环境保护, 2018, 46(11): 31-38.
[7] Wang Q, Wang J N, He M Z, et al. A county-level estimate of PM_2.5 related chronic mortality risk in China based on multi-model exposure data[J]. Environment International, 2018, 110: 105-112. DOI: 10.1016/j.envint.2017.10. 015.
[8] Shi Y S, Zhu Y, Gong S Y, et al. PM_2.5-related premature deaths and potential health benefits of controlled air quality in 34 provincial cities of China during 2001-2017[J]. Environmental Impact Assessment Review, 2022, 97: 106883. DOI: 10.1016/j.eiar.2022.106883.
[9] 李勇, 廖琴, 赵秀阁, 等. PM_2.5污染对我国健康负担和经济损失的影响[J]. 环境科学, 2021, 42(4): 1688-1695. DOI: 10.13227/j.hjkx.202008313.
[10] Yang D Y, Chen Y L, Miao C H, et al. Spatiotemporal variation of PM_2.5 concentrations and its relationship to urbanization in the Yangtze river delta region, China[J]. Atmospheric Pollution Research, 2020, 11(3): 491-498. DOI: 10.1016/j.apr.2019.11.021.
[11] Chen G J, Hu Y, Zhang R, et al. Evolution of south-north transport and urbanization effects on PM_2.5 distribution with increased pollution levels in Beijing[J]. Sustainable Cities and Society, 2021, 72: 103060. DOI: 10.1016/j.scs.2021.103060.
[12] 袁博, 肖苏林, 蒋大和. 我国城市群空气污染及其季节变化特点[J]. 环境科技, 2009, 22(S1): 102-106. DOI: 10.3969/j.issn.1674-4829.2009.z1.037.
[13] 王郁, 赵一航. 区域协同发展政策能否提高公共服务供给效率?：以京津冀地区为例的研究[J]. 中国人口·资源与环境, 2020, 30(8): 100-109. DOI: 10.12062/cpre.20200121.
[14] 陆大道. 京津冀城市群功能定位及协同发展[J]. 地理科学进展, 2015, 34(3): 265-270. DOI: 10.11820/dlkxjz.2015.03.001.
[15] 李慧, 王淑兰, 张文杰, 等. 京津冀及周边地区 “2+26” 城市空气质量特征及其影响因素[J]. 环境科学研究, 2021, 34(1): 172-184. DOI: 10.13198/j.issn.1001-6929.2020.12.26.
[16] 徐丹妮, 王瑾婷, 袁自冰, 等. 汾渭平原复杂地形影响下冬季PM_2.5污染分布特征、来源及成因分析[J]. 环境科学学报, 2021, 41(4): 1184-1198. DOI: 10.13671/j.hjkxxb.2020.0553.
[17] 金莲姬, 刘峥, 朱彬, 等. PM_2.5空中输送对北京地面污染影响的地形强迫机制模拟研究[J]. 环境科学学报, 2021, 41(8): 2976-2986. DOI: 10.13671/j.hjkxxb.2021.0284.
[18] 王黎明, 刘动, 陈枫林, 等. 雾霾模拟方法及其装置研究[J]. 高电压技术, 2014, 40(11): 3297-3304. DOI: 10.13336/j.1003-6520.hve.2014.11.001.
[19] 孟丽红, 李英华, 韩素芹, 等. 海陆风对天津市PM_2.5和O₃质量浓度的影响[J]. 环境科学研究, 2019, 32(3): 390-398. DOI: 10.13198/j.issn.1001-6929.2018.12.10.
[20] 王琪, 孙巍, 张新宇. 北京地区PM_2.5质量浓度分布及其与气象条件影响关系分析[J]. 计算机与应用化学, 2014, 31(10): 1193-1196. DOI: 10.11719/com.app.chem20141010.
[21] 马召辉, 梁云平, 张健, 等. 北京市典型排放源PM_2.5成分谱研究[J]. 环境科学学报, 2015, 35(12): 4043-4052. DOI: 10.13671/j.hjkxxb.2015.0584.
[22] 王振波, 梁龙武, 王旭静. 中国城市群地区PM_2.5时空演变格局及其影响因素[J]. 地理学报, 2019, 74(12): 2614-2630. DOI: 10.11821/dlxb201912014.
[23] 马丽, 康蕾, 金凤君. 京津冀工业发展与大气污染物排放时空耦合关系分析[J]. 环境影响评价, 2018, 40(5): 43-48. DOI: 10.14068/j.ceia.2018.05.009.
[24] 常亚敏, 闫蓬勃, 杨军. 北京地区控制PM_2.5污染的城市绿化树种选择建议[J]. 中国园林, 2015, 31(1): 69-73. DOI: 10.3969/j.issn.1000-6664.2015.01.014.
[25] 李俊晓, 李朝奎, 殷智慧. 基于ArcGIS的克里金插值方法及其应用[J]. 测绘通报, 2013(9): 87-90, 97. DOI: 10.1007/s12204-013-1367-4.
[26] 杜雪, 王景弟, 白彦锋, 等. 基于克里金插值法的湖南省慈利县森林碳储量专题图研究[J]. 西北林学院学报, 2022, 37(1): 198-204. DOI: 10.3969/j.issn.1001-7461.2022.01.29.
[27] 姜春雷. 克里格插值的加速和参数优化及其应用[D]. 北京: 中国科学院大学(中国科学院东北地理与农业生态研究所), 2016.
[28] 李海涛, 邵泽东. 空间插值分析算法综述[J]. 计算机系统应用, 2019, 28(7): 1-8. DOI: 10.15888/j.cnki.csa.006988.
[29] 刘竞妍, 张可, 王桂华. 综合评价中数据标准化方法比较研究[J]. 数字技术与应用, 2018, 36(6): 84-85. DOI: 10.19695/j.cnki.cn12-1369.2018.06.49.
[30] 周文华, 王如松, 张克锋. 人类活动对北京空气质量影响的综合生态评价[J]. 生态学报, 2005, 25(9): 2214-2220. DOI: 10.3321/j.issn:1000-0933.2005.09.012.
[31] 朱玉祥, 江剑民, 赵亮, 等. 不同计算形式的相关分析在气象中的应用综述[J]. 热带气象学报, 2021, 37(1): 1-13. DOI: 10.16032/j.issn.1004-4965.2021.001.
[32] 林海明, 张文霖. 主成分分析与因子分析的异同和SPSS软件：兼与刘玉玫、卢纹岱等同志商榷[J]. 统计研究, 2005, 22(3): 65-69. DOI: 10.19343/j.cnki.11-1302/c.2005.03.015.
[33] 任毅, 郭丰, 高聪聪. 京津冀城市群雾霾污染的时空特征与影响因素[J]. 首都经济贸易大学学报, 2019, 21(6): 80-91. DOI: 10.13504/j.cnki.issn1008-2700.2019. 06.008.
[34] 丁梦婷. 京津冀地区PM_2.5时空特征及环流分型研究[D]. 石家庄: 河北师范大学, 2022.
[35] 陈辉, 厉青, 李营, 等. 京津冀及周边地区PM_2.5时空变化特征遥感监测分析[J]. 环境科学, 2019, 40(1): 33-43. DOI: 10.13227/j.hjkx.201802104.
[36] 王冠岚, 薛建军, 张建忠. 2014年京津冀空气污染时空分布特征及主要成因分析[J]. 气象与环境科学, 2016, 39(1): 34-42. DOI: 10.16765/j.cnki.1673-7148.2016. 01.005.
[37] 王无为. 湖北省大气污染物时空分布特征及影响因素[D]. 武汉: 华中师范大学, 2018.
[38] 李青春, 李炬, 郑祚芳, 等. 冬季山谷风和海陆风对京津冀地区大气污染分布的影响[J]. 环境科学, 2019, 40(2): 513-524. DOI: 10.13227/j.hjkx.201803193.
[39] 张竞, 杜东, 白耀楠, 等. 基于DEM的京津冀地区地形起伏度分析[J]. 中国水土保持, 2018(9): 33-37. DOI: 10.14123/j.cnki.swcc.2018.0200.
[40] 吴兑, 廖碧婷, 吴蒙, 等. 环首都圈霾和雾的长期变化特征与典型个例的近地层输送条件[J]. 环境科学学报, 2014, 34(1): 1-11. DOI: 10.13671/j.hjkxxb.2014.01. 019.
[41] 孙航. 京津冀与长三角城市群大气污染时空特征及影响因素对比研究[D]. 兰州: 兰州财经大学, 2019.
[42] 朱倩茹, 刘永红, 徐伟嘉, 等. 广州PM_2.5污染特征及影响因素分析[J].中国环境监测, 2013, 29(2): 15-21. DOI:10.19316/j.issn.1002-6002.2013.02.006.
[43] 周静, 张岳军, 相栋, 等.太原市PM_2.5周期性特征及其成因分析[J].生态环境学报, 2018, 27(3): 527-532. DOI:10.16258/j.cnki.1674-5906.2018.03.017.
[44] Qi L, Zheng H T, Ding D, et al. Effects of meteorology changes on inter-annual variations of aerosol optical depth and surface PM_2.5 in China： implications for PM_2.5 remote sensing[J]. Remote Sensing, 2022, 14(12): 2762. DOI: 10.3390/rs14122762.
[45] 张宇静, 赵天良, 殷翀之, 等.徐州市大气PM_2.5与O₃作用关系的季节变化[J]. 中国环境科学, 2019, 39(6): 2267-2272. DOI:10.19674/j.cnki.issn1000-6923.2019. 0269.
[46] 王静, 邱粲, 刘焕彬, 等. 山东重点城市空气质量及其与气象要素的关系[J]. 生态环境学报, 2013, 22(4): 644-649. DOI:10.16258/j.cnki.1674-5906.2013.04.021.
[47] Liu Z D, Wang H, Shen X Y, et al. Contribution of meteorological conditions to the variation in winter PM_2.5 concentrations from 2013 to 2019 in Middle-Eastern China[J]. Atmosphere, 2019, 10(10): 563. DOI: 10.3390/atmos10100563.
[48] Charron A, Harrison R M. Fine (PM_2.5) and coarse (PM_2.5-10) particulate matter on a heavily trafficked London highway: sources and processes[J]. Environmental Science & Technology, 2005, 39(20): 7768-7776. DOI: 10.1021/es050462i.
[49] 王超. 镇江地区主要大气污染物时空分布及气象影响因子相关性研究[D]. 江苏镇江: 江苏大学, 2019.
[50] 刘燚. 京津冀地区空气质量状况及其与气象条件的关系[D]. 长沙: 湖南师范大学, 2010.
[51] 徐杰, 匡汉祎, 王国强, 等. PM_2.5与空气相对湿度间关系浅析[J]. 农业与技术, 2017, 37(9): 148-149, 157. DOI: 10.11974/nyyjs.20170532072.
[52] 徐祥德, 施晓晖, 张胜军, 等. 北京及周边城市群落气溶胶影响域及其相关气候效应[J]. 科学通报, 2005, 50(22): 2522-2530. DOI: 10.3321/j.issn:0023-074X.2005.22.014.
[53] Shen R R, Schäfer K, Schnelle-Kreis J, et al. Characteristics and sources of PM in seasonal perspective： a case study from one year continuously sampling in Beijing[J]. Atmospheric Pollution Research, 2016, 7(2): 235-248. DOI: 10.1016/j.apr.2015.09.008.
[54] 周媛, 石铁矛, 胡远满, 等. 基于城市气候环境特征的绿地景观格局优化研究[J]. 城市规划, 2014, 38(5): 83-89. DOI: 10.11819/cpr20140515a.
[55] 陈永林, 谢炳庚, 杨勇. 全国主要城市群空气质量空间分布及影响因素分析[J]. 干旱区资源与环境, 2015, 29(11): 99-103. DOI: 10.13448/j.cnki.jalre.2015.369.
[56] 张晓平, 林美含. 中国城市空气污染区域差异及社会经济影响因素分析：基于两种空气质量指数的比较研究[J]. 中国科学院大学学报, 2020, 37(1): 39-50. DOI: 10.7523/j.issn.2095-6134.2020.01.006.
[57] 任嘉敏, 马延吉. 吉林省工业增长与工业大气污染脱钩关系的时空演变[J]. 中国科学院大学学报, 2019, 36(1): 72-81. DOI: 10.7523/j.issn.2095-6134.2019. 01.011.
[58] 戴菲, 陈明, 朱晟伟, 等. 街区尺度不同绿化覆盖率对PM₁₀、PM_2.5的消减研究：以武汉主城区为例[J]. 中国园林, 2018, 34(3): 105-110. DOI:10.3969/j.issn.1000-6664.2018.03.018.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References