Spatiotemporal pattern analysis of PM2.5 and the driving factors in the middle Yellow River urban agglomerations

被引:32
作者
Mi, Yifeng [2 ]
Sun, Ken [1 ]
Li, Li [2 ]
Lei, Yalin [2 ]
Wu, Sanmang [2 ]
Tang, Wei [2 ]
Wang, Yizhen [2 ]
Yang, Jingjing [2 ]
机构
[1] North China Univ Water Resources & Elect Power, Coll Water Resources, Zhengzhou 450046, Peoples R China
[2] Minist Nat Resources Peoples Republ China, Key Lab Carrying Capac Assessment Resource & Envi, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
PM2.5; concentrations; Driving factors; Spatiotemporal heterogeneity; GTWR model; Middle yellow river urban agglomerations; FINE PARTICULATE MATTER; ENVIRONMENTAL KUZNETS CURVE; AIR-POLLUTION; ENERGY-CONSUMPTION; METEOROLOGICAL FACTORS; CHEMICAL-COMPOSITION; SPATIAL-DISTRIBUTION; REGIONAL DIFFERENCES; ECONOMIC-GROWTH; HAZE POLLUTION;
D O I
10.1016/j.jclepro.2021.126904
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
During the development of the Middle Yellow River Urban Agglomerations (Central Plains Urban Agglomeration (CPUA), Guanzhong Plains Urban Agglomeration (GPUA), Jinzhong Urban Agglomeration (JZUA)), "urban disease" was prominent, and the concentrations of PM2.5 in many cities in the region were ultra-high, resulting in serious air pollution. Taking into account the hysteresis of PM2.5 pollution, from the point of view of socioeconomic, this paper uses the geographically and temporally weighted regression (GTWR) model and the latest available data of PM2.5 concentration and socio-economic factors of the urban agglomeration in the Middle Yellow River from 2015 to 2018 to explore the spatiotemporal heterogeneity of PM2.5 concentrations and the driving factors. The results indicate that: (1) The spatiotemporal distribution profiles of PM2.5 concentrations in urban agglomerations in the middle Yellow River have certain regularities. It reveals a U-shaped pattern in each year, and the seasonal changes show the spring and winter are high and summer and autumn are low. (2) PM2.5 in the Middle Yellow River Urban Agglomerations has obvious spatial agglomeration characteristics. (3) There are obvious spatiotemporal heterogeneity in the change of the intensity and direction of each driving factor. Based on the above analysis, three suggestions were put forward: (1) Increase the proportion of new energy in heating. (2) Establish regional joint prevention and control and long-term incentive mechanisms. (3) Different urban agglomerations should formulate differentiated PM2.5 emission reduction strategies. (C) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数:11
相关论文
共 64 条
[1]   Energy innovation and renewable energy consumption in the correction of air pollution levels [J].
Alvarez-Herranz, Agustin ;
Balsalobre-Lorente, Daniel ;
Shahbaz, Muhammad ;
Maria Cantos, Jose .
ENERGY POLICY, 2017, 105 :386-397
[2]   SPECIFICATION TESTS ON THE STRUCTURE OF INTERACTION IN SPATIAL ECONOMETRIC-MODELS [J].
ANSELIN, L .
PAPERS OF THE REGIONAL SCIENCE ASSOCIATION, 1984, 54 :165-182
[3]   LOCAL INDICATORS OF SPATIAL ASSOCIATION - LISA [J].
ANSELIN, L .
GEOGRAPHICAL ANALYSIS, 1995, 27 (02) :93-115
[4]   Alzheimer disease starts in childhood in polluted Metropolitan Mexico City. A major health crisis in progress [J].
Calderon-Garciduenas, Lilian ;
Torres-Jardon, Ricardo ;
Kulesza, Randy J. ;
Mansour, Yusra ;
Oscar Gonzalez-Gonzalez, Luis ;
Gonzalez-Maciel, Angelica ;
Reynoso-Robles, Rafael ;
Mukherjee, Partha S. .
ENVIRONMENTAL RESEARCH, 2020, 183
[5]  
Chen Hui, 2019, Huanjing Kexue, V40, P39, DOI 10.13227/j.hjkx.201802104
[6]   Does the path of technological progress matter in mitigating China's PM2.5 concentrations? Evidence from three urban agglomerations in China [J].
Chen, Jing ;
Wang, Shaojian ;
Zhou, Chunshan ;
Li, Ming .
ENVIRONMENTAL POLLUTION, 2019, 254
[7]   Impacts of energy consumption structure, energy intensity, economic growth, urbanization on PM2.5 concentrations in countries globally [J].
Chen, Jing ;
Zhou, Chunshan ;
Wang, Shaojian ;
Li, Shijie .
APPLIED ENERGY, 2018, 230 :94-105
[8]   The spatiotemporal evolution of population exposure to PM2.5 within the Beijing-Tianjin-Hebei urban agglomeration, China [J].
Chen, Mingxing ;
Guo, Shasha ;
Hu, Maogui ;
Zhang, Xiaoping .
JOURNAL OF CLEANER PRODUCTION, 2020, 265
[9]  
[陈世强 Chen Shiqiang], 2020, [经济地理, Economic Geography], V40, P40
[10]   Spatial and Temporal Variations of PM2.5 and Its Relation to Meteorological Factors in the Urban Area of Nanjing, China [J].
Chen, Tao ;
He, Jun ;
Lu, Xiaowei ;
She, Jiangfeng ;
Guan, Zhongqing .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2016, 13 (09)