Spatial-temporal patterns of PM2.5 concentrations for 338 Chinese cities

被引:158
|
作者
Ye, Wei-Feng [1 ]
Ma, Zhong-Yu [1 ,2 ]
Ha, Xiu-Zhen [3 ]
机构
[1] Renmin Univ China, Sch Environm & Nat Resources, Beijing 100872, Peoples R China
[2] State Informat Ctr, Beijing 100045, Peoples R China
[3] Renmin Univ China, Sch Econ, Beijing 100872, Peoples R China
基金
国家重点研发计划;
关键词
PM(2.5 )concentrations; Spatial-temporal patterns; Spatial autocorrelation; China; PARTICULATE MATTER PM2.5; AIR-POLLUTION; CHEMICAL-COMPOSITION; SOURCE APPORTIONMENT; URBAN; PM10; HAZE; ASSOCIATION; EXPOSURE; EPISODES;
D O I
10.1016/j.scitotenv.2018.03.057
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Air pollution has become a major concern in cities worldwide. The present study explores the spatial-temporal patterns of PM2.5 (partides with an aerodynamic diameters <= 2.5 mu m) and the variation in the attainment rate (the number of cities attaining the national PM2.5 standard each day) at different time-scales based on PM(2.5 )concentrations. One-year of monitoring was conducted in 338 cities at or above the prefectural level in China. Spatial hot spots of PM2.5 were analyzed using exploratory spatial data analysis. Meteorological factors affecting PM2.5 distributions were analyzed. The results indicate the following: (1) Diurnal variations of PM2.5 exhibited a Wshaped trend, with the lowest value observed in the afternoon. The peak concentrations occurred after the ends of the morning and evening rush hours. (2) Out of 338 cities, 235 exceeded the national annual PM2.5 standards (535 mu g/m(3)), with slightly polluted (75-115 mu g/m(3)) cities occupying the greatest proportion. (3) The attainment rate showed an inverted U-shape, while there was a U-shaped pattern observed for daily and monthly mean PM2.5. (4) The spatial distribution of PM2.5 concentrations varied greatly, PM2.5 has significant spatial autocorrelation and clustering characteristics. Hot spots for pollution were mainly concentrated in the Beijing-Tianjin-Hebei area and neighboring regions, in part because of the large amount of smoke and dust emissions in this region. However, weather factors (temperature, humidity, and wind speed) also had an effect. In addition, southwest Xinjiang experienced heavy PM2.5 pollution that was mainly caused by the frequent occurrence of sandstorms, although no significant relationship was observed between PM2.5 and meteorological elements in this region. (C) 2018 Elsevier B.V. All tights reserved.
引用
收藏
页码:524 / 533
页数:10
相关论文
共 50 条
  • [1] Spatial Autocorrelation and Temporal Convergence of PM2.5 Concentrations in Chinese Cities
    Wang, Huan
    Chen, Zhenyu
    Zhang, Pan
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (21)
  • [2] Spatial and temporal variations of PM2.5 concentrations in Chinese cities during 2015-2019
    Shi, Guiqian
    Liu, Jiaxiu
    Zhong, Xiaoni
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH, 2022, 32 (12) : 2695 - 2707
  • [3] Analysis of Spatial-Temporal Characteristics of the PM2.5 Concentrations in Weifang City, China
    Li, Yixiao
    Dai, Zhaoxin
    Liu, Xianlin
    SUSTAINABILITY, 2018, 10 (09)
  • [4] Assessing Spatial Heterogeneity of Factor Interactions on PM2.5 Concentrations in Chinese Cities
    Jin, Yuhao
    Zhang, Han
    Shi, Hong
    Wang, Huilin
    Wei, Zhenfeng
    Han, Yuxing
    Cong, Peitong
    REMOTE SENSING, 2021, 13 (24)
  • [5] A Spatial-Temporal Model to Improve PM2.5 Inference
    Wang, Hui
    Dong, Yuhan
    Zhang, Kai
    2017 16TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS 2017), 2017, : 173 - 177
  • [6] Exploring the convergence patterns of PM2.5 in Chinese cities
    Wang, Yan
    Gong, Yuan
    Bai, Caiquan
    Yan, Hong
    Yi, Xing
    ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2023, 25 (01) : 708 - 733
  • [7] Exploring the convergence patterns of PM2.5 in Chinese cities
    Yan Wang
    Yuan Gong
    Caiquan Bai
    Hong Yan
    Xing Yi
    Environment, Development and Sustainability, 2023, 25 : 708 - 733
  • [8] Spatial-Temporal Evolution of PM2.5 Concentration and its Socioeconomic Influence Factors in Chinese Cities in 2014-2017
    Wang, Yazhu
    Duan, Xuejun
    Wang, Lei
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2019, 16 (06)
  • [9] Mining sequential patterns of PM2.5 pollution between 338 cities in China
    Zhang, Liankui
    Yang, Guangfei
    Li, Xianneng
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2020, 262 (262)
  • [10] Spatial-temporal evolution patterns and drivers of PM2.5 chemical fraction concentrations in China over the past 20 years
    He, Chao
    Li, Bin
    Gong, Xusheng
    Liu, Lijun
    Li, Haiyan
    Zhang, Lu
    Jin, Jiming
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (40) : 91839 - 91852