1. Beijing Key Laboratory of Regional Air Pollution Control, Faculty of Environmental Science and Engineering, Beijing University of Technology, Beijing 100124, China; 2. Environmental Development Center of the Ministry of Ecology and Environment, Beijing 100029, China
摘要 In order to study the spatiotemporal characteristics of PM2.5 and O3 concentrations and health risks in the Beijing-Tianjin-Hebei region and their cooperative control regions, this study constructed a Beijing-Tianjin-Hebei composite pollution correlation network by quantifying the Composite Pollution Health Index, taking the cities as the nodes and the similarity of the composite pollution among the cities as the weights; and based on the Girvan-Newman community detection method, the Beijing-Tianjin-Hebei region was divided into three cooperative control regions for PM2.5 and O3. The average annual concentration of PM2.5 in Beijing-Tianjin-Hebei decreases by 42.19% from 2017 to 2022, with a significant downward trend, while the average annual concentration of MDA8O3 decreases by 1.85%, with a fluctuating trend; the areas with severe PM2.5 are also the areas with deteriorating O3, and spatially show the characteristics of "high in the south and low in the north"; The eastern densely populated cities represented by Beijing and Tianjin have high health risks of PM2.5 and O3 exposure, with October-March as the health control period for PM2.5 and April-September as the collaborative health control period for PM2.5 and O3; Beijing-Tianjin-Hebei is divided into three cooperative control regions. The composite pollution characteristics of the three zones are more similar to each other.
Abstract：In order to study the spatiotemporal characteristics of PM2.5 and O3 concentrations and health risks in the Beijing-Tianjin-Hebei region and their cooperative control regions, this study constructed a Beijing-Tianjin-Hebei composite pollution correlation network by quantifying the Composite Pollution Health Index, taking the cities as the nodes and the similarity of the composite pollution among the cities as the weights; and based on the Girvan-Newman community detection method, the Beijing-Tianjin-Hebei region was divided into three cooperative control regions for PM2.5 and O3. The average annual concentration of PM2.5 in Beijing-Tianjin-Hebei decreases by 42.19% from 2017 to 2022, with a significant downward trend, while the average annual concentration of MDA8O3 decreases by 1.85%, with a fluctuating trend; the areas with severe PM2.5 are also the areas with deteriorating O3, and spatially show the characteristics of "high in the south and low in the north"; The eastern densely populated cities represented by Beijing and Tianjin have high health risks of PM2.5 and O3 exposure, with October-March as the health control period for PM2.5 and April-September as the collaborative health control period for PM2.5 and O3; Beijing-Tianjin-Hebei is divided into three cooperative control regions. The composite pollution characteristics of the three zones are more similar to each other.
黄子健, 段文娇, 亓浩雲, 侯晓松. 京津冀区域PM2.5和O3污染特征及协同控制分区[J]. 中国环境科学, 2024, 44(11): 5971-5979.
HUANG Zi-jian, DUAN Wen-jiao, QI Hao-yun, HOU Xiao-song. The spatiotemporal characteristics of PM2.5 and O3 in the Beijing-Tianjin-Hebei region and the health-based regional cooperative control strategy. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(11): 5971-5979.
[1] Lelieveld J, Evans J S, Fnais M, et al. The contribution of outdoor air pollution sources to premature mortality on a global scale [J]. Nature, 2015,525(7569):367-371. [2] Zhang X, Yan B, Zhou Y, et al. Short-term health impacts related to ozone in China before and after implementation of policy measures: A systematic review and meta-analysis [J]. Science of The Total Environment, 2022,847:157588. [3] Lu F, Xu D, Cheng Y, et al. Systematic review and meta-analysis of the adverse health effects of ambient PM2.5 and PM10 pollution in the Chinese population [J]. Environ. Res., 2015,136:196-204. [4] Yin P, Chen R, Wang L, et al. Ambient ozone pollution and daily mortality: A nationwide study in 272 Chinese cities [J]. Environ. Health Perspect, 2017,125(11):117006. [5] 刘 靖,单春艳,梁晓宇.唐山市基于GIS的PM2.5空间聚集性及分区管控 [J]. 中国环境科学, 2020,40(2):513-522. Liu J, Dan C Y, Liang X Y. Research on spatial aggregation of PM2.5and zoning control in Tangshan based on GIS [J]. China Environmental Science, 2020,40(2):513-522. [6] 张鸿宇,王 媛,卢亚灵,等.我国臭氧污染控制分区及其控制类型识别 [J]. 中国环境科学, 2021,41(9):4051-4059. Zhang H Y, Wang Y, Lu Y L, et al. Identification of ozone pollution control zones and types in China. [J]. China Environmental Science, 2021,41(9):4051-4059. [7] 杨文涛,黄慧坤,魏东升,等.大气污染联合治理分区视角下的中国PM2.5关联关系时空变异特征分析 [J]. 环境科学, 2020,41(5):2066-2074. Yang W T, Huang H K, Wei D S, er al. Spatio-temporal variation of PM2.5 Related relationships in China from the perspective of air pollution regional linkage control and prevention [J]. Environmental Science, 2020,41(5):2066-2074. [8] Qi H, Duan W, Cheng S, et al. Spatial clustering and spillover pathways analysis of O3, NO2, and CO in eastern China during 2017~2021 [J]. Science of The Total Environment, 2023,904:166814. [9] Zhang N N, Ma F, Qin C B, et al. Spatiotemporal trends in PM2.5 levels from 2013 to 2017 and regional demarcations for joint prevention and control of atmospheric pollution in China [J]. Chemosphere, 2018,210: 1176-1184. [10] Duan W J, Wang X Q, Cheng S Y, et al. Regional collaboration to simultaneously mitigate PM2.5 and O3 pollution in Beijing-Tianjin-Hebei and the surrounding area: Multi-model synthesis from multiple data sources [J]. Science of the Total Environment, 2022,820:16. [11] 李沈鑫,邹 滨,张凤英,等.PM2.5和O3污染协同防控区的遥感精细划定与分析 [J]. 环境科学, 2022,43(10):4293-4304. Li S X, Zou B, Zhang F Y, et al. Regionalization and analysis of PM2.5 and O3 synergetic prevention and control areas based on remote sensing data [J]. Environmental Science, 2022,43(10):4293-4304. [12] Zhang N, Guan Y, Jiang Y, et al. Regional demarcation of synergistic control for PM2.5and ozone pollution in China based on long-term and massive data mining [J]. Science of The Total Environment, 2022, 838(Pt 2):155975. [13] 王芳园.京津冀区域PM2.5和O3空气污染的健康效应及对污染物的减排响应研究 [D]. 北京:华北电力大学(北京), 2022. Wang F Y. Study on health effect of PM2.5 and O3 air pollution and its response to emission reduction of air pollutants in Beijing-Tianjin-Hebei Region [D]. Beijing: North China Electric Power University (Beijing), 2022. [14] Wang F Y, Qiu X H, Cao J Y, et al. Policy-driven changes in the health risk of PM2.5and O3exposure in China during 2013~2018 [J]. Science of the Total Environment, 2021,757. [15] Dedoussi I C, Eastham S D, Monier E, et al. Premature mortality related to United States cross-state air pollution [M]. Nature. 2020: 261-265. [16] Liu H, Liu S, Xue B, et al. Ground-level ozone pollution and its health impacts in China [J]. Atmospheric Environment, 2018,173:223-230. [17] Burnett R T, Pope C A, Ezzati M, et al. An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure [J]. Environmental Health Perspectives, 2014,122(4):397-403. [18] Lelieveld J, Barlas C, Giannadaki D, et al. Model calculated global, regional and megacity premature mortality due to air pollution [J]. Atmospheric Chemistry and Physics, 2013,13(14):7023-7037. [19] Organization W H. WHO global air quality guidelines: particulate matter (PM2.5and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide [M]. World Health Organization, 2021. [20] Stied D M, Burnett R T, Smith-dotron M, et al. A New Multipollutant, No-Threshold Air Quality Health Index Based on Short-Term Associations Observed in Daily Time-Series Analyses [J]. Journal of the Air & Waste Management Association, 2012,58(3):435-450. [21] 康梦蕾,倪 洋,曾 强,等.基于老年人群循环系统疾病寿命损失年的天津市空气质量健康指数的初步构建 [J]. 环境与健康杂志, 2020,37(8):684-687. Kang M L, Ni Y, Zeng Q, et al. Construction of air quality health index based on the years of life lost of cardiovascular disease in the elderly population in Tianjin [J]. Journal of Environment and Health, 2020, 37(8):684-687. [22] Du X, Chen R, Meng X, et al. The establishment of National Air Quality Health Index in China [J]. Environment International, 2020,138. [23] Newman M E J. Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality [J]. Physical Review E, 2001,64(1):016132. [24] Girvan M, Newman M E J. Community structure in social and biological networks [J]. Proceedings of the National Academy of Sciences of the United States of America, 2002,99(12):7821-7826. [25] Newman M E J. Analysis of weighted networks [J]. Physical Review E, 2004,70(5):DOI:10.1103/PhysRevE.70.056131. [26] Watts D J, Strogatzs S H. Collective dynamics of ‘small-world’ networks [J]. Nature, 1998.DOI:10.1038/30918. [27] Soffer S N, Vazquez A. Network clustering coefficient without degree-correlation biases [J]. Physical Review E, 2005,71(5Pt 2):057101. [28] 张南南.基于空气资源禀赋的PM2.5联防联控区域划定研究 [D]. 哈尔滨:哈尔滨工业大学, 2022. Zhang N N. Study on the regional demarcation for PM2.5 joint prevention and control based on air resource endowment [D]. Harbin: Harbin Institute of Technology, 2022. [29] Lu X, Hong J, Zhang L, et al. Severe surface ozone pollution in China: A global perspective [J]. Environmental Science & Technology Letters, 2018,5(8):487-494. [30] 罗悦函,赵天良,孟 凯,等.华北平原和山区城市PM2.5和O3变化关系比较分析 [J]. 中国环境科学, 2021,41(9):3981-3989. Luo Y H, Zhao T L, Meng K, et al. Comparative analysis of the relationship between PM2.5and O3in plain and mountainous cities in North China. [J]. China Environmental Science, 2021,41(9):3981-3989. [31] 安海岗,李佳培,张翠芝,等.京津冀及周边城市PM2.5污染空间关联网络及季节演化研究 [J]. 生态环境学报, 2020,29(7):1377-1386. An H G, Li J P, Zhang C Z, et al. Spatial correlation network and season evolution of PM2.5pollution in Beijing-Tianjin-Hebei and surrounding cities [J]. Ecology and Environmental Sciences, 2020, 29(7):1377-1386. [32] Chen M, Chen Y, Zhu H, et al. Analysis of pollutants transport in heavy air pollution processes using a new complex-network-based model [J]. Atmospheric Environment, 2023,292.DOI:10.1016/j. atmosenv.2022.119395. [33] Wang H B, Zhao L J. A joint prevention and control mechanism for air pollution in the Beijing-Tianjin-Hebei region in china based on long-term and massive data mining of pollutant concentration [J]. Atmospheric Environment, 2018,174:25-42.
