地理研究 ›› 2020, Vol. 39 ›› Issue (11): 2642-2652.doi: 10.11821/dlyj020190716

• 论文 • 上一篇    

基于“一街一站”的深圳市PM2.5时空特征及排名通报作用研究

梁景天1,2(), 吴健生1,3(), 赵宇豪1,3, 陈弼锴1, 王怡1   

  1. 1.北京大学深圳研究生院 城市规划与设计学院 城市人居环境科学与技术重点实验室,深圳 518055
    2.广州市规划和自然资源局 白云区分局,广州 510405
    3.北京大学 城市与环境学院 地表过程分析与模拟教育部重点实验室,北京 100871
  • 收稿日期:2019-08-20 修回日期:2020-07-15 出版日期:2020-11-20 发布日期:2021-01-19
  • 通讯作者: 吴健生
  • 作者简介:梁景天(1995-),广东肇庆人,硕士,主要从事土地利用规划研究。E-mail: liangjingtian@pku.edu.cn
  • 基金资助:
    深圳市科技计划(JCYJ20170412150910443);国家重点研发计划(2019YFB2102000);国家自然科学基金面上项目(41671180)

Spatiotemporal characteristics and effects of ranking proclamation of PM2.5 in Shenzhen based on sub-district monitoring network

LIANG Jingtian1,2(), WU Jiansheng1,3(), ZHAO Yuhao1,3, CHEN Bikai1, WANG Yi1   

  1. 1. Key Laboratory for Urban Habitat Environmental Science and Technology, School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, Guangdong, China
    2. Baiyun District Sub-bureau, Guangzhou Municipal Planning and Natural Resources Bureau, Guangzhou 510405, China
    3. Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
  • Received:2019-08-20 Revised:2020-07-15 Online:2020-11-20 Published:2021-01-19
  • Contact: WU Jiansheng

摘要:

深圳市于2018年6月建成了全国首个街道空气监测网络“一街一站”,并对全市74个街道进行月均PM2.5浓度排名,向公众通报空气质量排名倒数十位的街道,以落实基层治理大气污染的责任。本研究基于深圳市街道空气监测网络的数据,分析了深圳市PM2.5浓度的时空特征,使用双重差分模型检验空气质量排名通报是否能发挥环保部门所期待的效果,进一步促使PM2.5浓度排名靠后的街道改善其空气质量。结果表明:① 基于街道空气监测的深圳市年均PM2.5浓度为22.4 μg/m3,月均浓度最低值出现在7月,最高值出现在1月,与国家站监测结果之间无显著性差异。② 深圳市街道的PM2.5浓度具有较高的空间集聚性,高值主要聚集在西北部,低值主要聚集在中南部,而国家监测站难以准确表征深圳市PM2.5浓度空间分布特征。③ 考虑了个体和时间固定效应的双重差分模型分析结果表明,对深圳市月均PM2.5浓度较高、空气质量排名靠后的街道进行通报,其随后1~2个月的PM2.5浓度仍受该月污染的持续性影响,而通报行为无显著性影响;到随后第3个月,该月污染的持续性影响下降到较小的程度,此时通报行为对PM2.5浓度呈显著降低作用,且影响程度大于污染的持续性影响。

关键词: 污染排名, PM2.5, 双重差分模型, 时空特征, 深圳市

Abstract:

In June 2018, China′s first sub-district air monitoring network was put into use in Shenzhen. The monthly average PM2.5 concentrations of 74 sub-districts within the city are ranked, and the list of 10 sub-districts with the worst air quality is proclaimed to the public, in order to carry out the responsibility of air pollution control at the grassroots level. Based on the air quality monitoring data of sub-districts, we analyzed spatiotemporal characteristics of PM2.5 concentrations in Shenzhen in this study. By comparing the spatiotemporal characteristics of PM2.5 concentrations measured by national and sub-district air monitoring stations, we identified the advantages of sub-district air monitoring network. And then, a difference-in-difference (DID) model was used to explore whether ranking proclamation can effectively push the poorly-ranked sub-districts to improve their air quality, as the environmental protection department anticipated. The results show that: (1) according to the sub-district air monitoring, the annual average PM2.5 concentration in Shenzhen was 22.4 μg/m3, and the monthly average concentrations was the lowest in July and the highest in January, showing no significant difference compared with the results from the national air monitoring stations; (2) sub-district PM2.5 concentrations in Shenzhen presented a strong spatial clustering pattern, with a high value cluster in the northwest and a low value cluster in the central-south part, but the national air monitoring stations could hardly illustrate the spatial pattern of PM2.5 in Shenzhen accurately; (3) the DID model with individual and temporal fixed effects suggests that, PM2.5 concentrations of the poorly-ranked sub-districts in Shenzhen were still affected by continous monthly air pollution in the following 1-2 months after the proclamation, and the proclamation showed no significant effect. However, in the following third month, the continuity of monthly pollution decreased to a lower level, while the effect on decreasing PM2.5 concentration of proclamation became significant and was larger than the influence of persistent pollution. The study shows that ranking proclamation may be conducive to the implementation of grassroots governments' responsibility to control air pollution, which is due to the pressure and the impression restoration strategy after negative reports, but further research and confirmation are still required.

Key words: pollution ranking, PM2.5, difference-in-difference model, spatiotemporal characteristics, Shenzhen