WANG Tao, WANG Ming-yue, HU Wei, ZHOU Yun-ping, ZHENG Yu-xin, LENG Shu-guang. Spatial distribution characteristics of PM2.5 in China in 2018—A study based on geographic information system[J]. Journal of Environmental and Occupational Medicine, 2020, 37(6): 553-557. DOI: 10.13213/j.cnki.jeom.2020.19845
Citation: WANG Tao, WANG Ming-yue, HU Wei, ZHOU Yun-ping, ZHENG Yu-xin, LENG Shu-guang. Spatial distribution characteristics of PM2.5 in China in 2018—A study based on geographic information system[J]. Journal of Environmental and Occupational Medicine, 2020, 37(6): 553-557. DOI: 10.13213/j.cnki.jeom.2020.19845

Spatial distribution characteristics of PM2.5 in China in 2018—A study based on geographic information system

  • Background  In recent years, more and more studies focus on the pollution status and spatial distribution characteristics of fine particulate matters with median aerodynamic diameter ≤ 2.5μm (PM2.5), but most studies are limited to a single city or regional scope, and a few national studies are limited to 31 provincial capitals, which are poorly representative of the whole country, and are not conducive to accurate implementation of air pollution intervention measures.
    Objective  The spatial distribution characteristics of PM2.5 in China in 2018 are analyzed to provide a scientific basis for the formulation of national air pollution control measures in the next stage.
    Methods  The real-time monitoring data of PM2.5 in 334 cities at prefecture level and province level in China in 2018 were collected. Firstly, global Moran's I was used to quantify countrylevel spatial distribution pattern of PM2.5. Secondly, local Moran's I was used to explore potential spatial aggregation regions, aggregation types, and exact locations of PM2.5 distribution. Finally, ordinary Kriging was used to interpolate the PM2.5 concentration on a national scale.
    Results  In 2018, the annual mean concentration of PM2.5 in selected 334 Chinese cities was (39.3±14.4)μg·m-3. The concentrations of PM2.5 in 196 cities (58.7%) were higher than the national limit (35 μg·m-3). The global spatial autocorrelation analysis results showed that the distribution of PM2.5 was spatially autocorrelated in China (Moran's I=0.58, P < 0.001). The local spatial autocorrelation analysis results showed 98 cities presented a high-high relationship with their neighbors (the annual mean concentration of PM2.5 was in the high values in a location, and the annual mean concentrations of PM2.5 were also high in its surrounded areas, thereafter), 15 cities had a low-high relationship, 2 cities had a high-low relationship, 99 cities had a low-low relationship. The PM2.5 concentration was high in Western Xinjiang, Beijing-Tianjin-Hebei and surrounding areas. Liuzhou City in Guangxi Zhuang Autonomous Region and Wuwei City in Gansu Province belonged to high-low regions, displaying a high PM2.5 concentration in the city and lower concentrations in surrounding cities. The results of Kriging interpolation showed that two highly polluted urban agglomerations were Western Xinjiang and the juncture of Hebei, Henan, Shandong, and Shanxi provinces.
    Conclusion  The annual average concentrations of PM2.5 in Chinese cities show spatial autocorrelation. Based on the characteristics of spatial distribution, we should strengthen the regional linkage management with Beijing-Tianjin-Hebei and surrounding areas at its core and jointly take solid actions in the critical battles of air pollution prevention and control.
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