王涛, 王明悦, 胡薇, 周云平, 郑玉新, 冷曙光. 中国2018年PM2.5的空间分布特征——基于地理信息系统的研究[J]. 环境与职业医学, 2020, 37(6): 553-557. DOI: 10.13213/j.cnki.jeom.2020.19845
引用本文: 王涛, 王明悦, 胡薇, 周云平, 郑玉新, 冷曙光. 中国2018年PM2.5的空间分布特征——基于地理信息系统的研究[J]. 环境与职业医学, 2020, 37(6): 553-557. DOI: 10.13213/j.cnki.jeom.2020.19845
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

中国2018年PM2.5的空间分布特征——基于地理信息系统的研究

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

  • 摘要: 背景

    近年来,越来越多的研究关注区域细颗粒物(PM2.5)的污染状况及其时空分布特征,但大部分研究局限于单一城市或区域范围,少数全国性研究也仅局限于31个省会城市,对全国代表性较差,且不利于空气污染干预措施的精准实施。

    目的

    分析中国2018年PM2.5的空间分布特征,为下一阶段全国大气污染防治措施的制定提供科学依据。

    方法

    收集2018年中国334个地级及以上城市PM2.5实时监测数据。基于城市年平均值,首先运用全局型Moran's I统计量分析PM2.5在中国整个区域的空间分布模式。其次运用局域型Moran's I统计量探明PM2.5的局部空间聚集性区域、聚集类型状态和确切分布位置。最后采用普通克里格法插值估计全国区域范围内的PM2.5浓度。

    结果

    2018年,中国334个地级及以上城市PM2.5年平均质量浓度(后称浓度)的均值为(39.3±14.4)μg·m-3。196个城市(58.7%)高于我国PM2.5年平均浓度限值(35 μg·m-3)。PM2.5存在全局空间自相关性(Moran's I=0.58,P < 0.001)。局域空间自相关探测到98个城市为高-高区域(代表该空间单元观测值高,其周围空间单元观测值也高,余同),15个城市为低-高区域,2个城市为高-低区域,99个城市为低-低区域。新疆西部、京津冀及周边地区PM2.5浓度高,呈现高值聚集区。广西壮族自治区的柳州市和甘肃省的武威市属于高-低区域,该城市的PM2.5浓度高,但其周围城市的PM2.5浓度较低。空间克里格插值结果显示有两个高污染城市聚集地,分别为新疆西部及以河北、河南、山东、山西交界处为中心的区域。

    结论

    中国城市PM2.5年平均浓度具有空间自相关性。基于空间分布特征,未来PM2.5污染治理仍应以京津冀及周边地区为核心,加强区域联动治理模式,共同打好大气污染防治的攻坚战。

     

    Abstract: 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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