地球信息科学学报

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (7): 1221-1230.doi: 10.12082/dqxxkx.2021.200367

• 地理空间分析综合应用 • 上一篇    下一篇

中国近地面PM2.5浓度与排放的时空分布及其关联分析

冯子钰1,2,3(), 施润和1,2,3,4,5,*()   

  1. 1.华东师范大学地理信息科学教育部重点实验室,上海 200241
    2.华东师范大学地理科学学院,上海 200241
    3.华东师范大学环境遥感与数据同化联合实验室,上海 200241
    4.华东师范大学资源与环境联合研究院,上海 200062
    5.华东师范大学崇明生态研究院,上海 202162
  • 收稿日期:2020-07-13 修回日期:2020-09-16 出版日期:2021-07-25 发布日期:2021-09-25
  • 通讯作者: * 施润和（1979— ）,男,上海人,博士,副教授,研究方向为环境遥感。E-mail: rhshi@geo.ecnu.edu.cn
  • 作者简介:冯子钰（1996— ）,女,四川绵阳人,硕士生,研究方向为环境遥感。E-mail: fzy_8978@163.com
  • 基金资助:
    国家重点研发计划项目(2016YFC1302602);教育部哲学社会科学研究重大课题攻关项目(19JZD023);上海市科委科技创新行动计划(19DZ1201505);中央高校基本科研业务费项目

Spatio-temporal Features and the Association of Ground-level PM2.5 Concentration and Its Emission in China

FENG Ziyu1,2,3(), SHI Runhe1,2,3,4,5,*()   

  1. 1. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
    2. School of Geographic Sciences, East China Normal University, Shanghai 200241, China
    3. Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University, Shanghai 200241, China
    4. Joint Research Institute of Resources and Environment, East China Normal University, Shanghai 200062, China
    5. Chongming Ecological Research Institute, East China Normal University, Shanghai 202162, China
  • Received:2020-07-13 Revised:2020-09-16 Online:2021-07-25 Published:2021-09-25
  • Contact: SHI Runhe
  • Supported by:
    National Key Research and Development Program of China(2016YFC1302602);Key Projects of Philosophy and Social Sciences Research, Ministry of Education(19JZD023);Shanghai Science and Technology Commission of Science and Technology Innovation Action Planning(19DZ1201505);The Fundamental Research Funds for the Central Universities

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摘要：

PM2.5是威胁人体健康的主要大气污染物之一。大量研究关注近地面PM2.5浓度的监测及其时空分布,但目前针对PM2.5排放及其与近地面浓度之间的关联研究较为缺乏。本文通过2000—2014年近地面PM2.5浓度格网数据和PM2.5排放格网数据,采用长时间序列分析法对PM2.5浓度和PM2.5排放从定性和定量两个角度进行时空变化趋势对比研究,并进一步结合标准差椭圆法和趋势分析法,分析了我国近地面PM2.5浓度和PM2.5排放的时空变化特征及其关联。结果表明,从总体时间序列趋势上,近地面PM2.5浓度和PM2.5排放之间在空间分布上基本呈现一致性,集中在胡焕庸线以东的人口密集区,但在时间上,PM2.5浓度和排放之间存在动态变化时间差。且PM2.5浓度的变化更为明显,2000—2007年高于35 μg/m3的国土面积占比增加了14.26%,2007—2014年减少了2.84%;从标准差椭圆分析来看,PM2.5浓度椭圆和排放椭圆在覆盖面积和方位角上与人口和经济分布吻合,但前者面积更大,长轴更接近于东西方向,二者存在约17°差异,而两类椭圆的中心位置随时间变化呈现出较一致的轨迹特征并呈现出滞后特点;此外,受大气扩散、点源排放等因素影响,PM2.5浓度变化趋势与排放变化趋势在胡焕庸线以东并不完全一致,部分区域排放呈降低趋势而浓度则反而呈升高趋势。因此,从全国层面来看,减排政策对浓度降低在时间上虽存在滞后,但边际效益显著,并已显露成效;而从局地来看,受地形、气象条件和大气化学过程等复杂影响,二者的变化在空间上仍会存在差异,有待进一步深入研究;从防控措施来看,在继续加强落实本地减排政策的同时,应考虑污染物的扩散迁移规律,加强联防联控,有效改善空气质量。

关键词: PM2.5浓度, PM2.5排放, 时间序列, 空间分布, 标准差椭圆, 趋势分析, 关联分析, 空气污染

Abstract:

PM2.5 is one of the major air pollutants that threaten human health. A large number of studies have focused on the monitoring of ground-level PM2.5 concentration and its spatio-temporal distribution, but there is currently a lack of research on the correlation between PM2.5 emissions and ground-level PM2.5 concentration. Based on the ground-level PM2.5 concentration grid data and PM2.5 emission grid data from 2000 to 2014 in China, a long-term sequence analysis method was used to analyze and compare the spatio-temporal changes of PM2.5 concentration and PM2.5 emissions from qualitative and quantitative perspectives in this study. Furthermore, combined with standard deviational ellipse analysis and trend analysis, the spatio-temporal variations of ground-level PM2.5 concentration and PM2.5 emissions and their correlation were analyzed. The results show that the spatial distributions of ground-level PM2.5 concentration and PM2.5 emissions were generally consistent, with dense populated areas concentrated in the east of the Hu Huanyong Line. However, there was still a situation of "low emission and high pollution" in parts of southern and central China. This was due to factors such as atmospheric transmission, topographical cumulative effect, and the conversion of PM2.5 concentration by precursors (SO2, CO, NO2, etc.). Temporally, there was a dynamic time difference between PM2.5 concentration and emissions, and the change of PM2.5 concentration was more obvious. The proportion of land area higher than 35 μg/m3 increased by 14.26% from 2000 to 2007, and decreased by 2.84% from 2007 to 2014. From the standard deviational ellipse analysis, the PM2.5 concentration ellipse and the emission ellipse were consistent with the distribution of population and economy in terms of the coverage area and azimuth, with the former having a larger area and a longer axis close to the east-west direction. There was a difference of about 17° between PM2.5 concentration ellipse and emission ellipse due to natural source pollution in the west and the diffusion of pollutants in the atmosphere. And the center positions of the two ellipses showed a clear trajectory and legacy characteristics over time. In addition, affected by factors such as meteorological parameters and point source emissions, the variations of PM2.5 concentration and the emission were not completely consistent in the east of the Hu Huanyong line. In some areas, the emission trend was decreasing while the concentration trend was increasing. Revealing the complex spatio-temporal correlation between PM2.5 concentration and the emissions in China can help formulate scientific prevention and control measures according to local conditions and effectively improve air quality.

Key words: PM2.5 concentration, PM2.5 emission, time series, space distribution, standard deviational ellipse, trend analysis, association analysis, air pollution