地理学报 ›› 2021, Vol. 76 ›› Issue (11): 2814-2829.doi: 10.11821/dlxb202111015

• 土地利用与生态环境 • 上一篇    下一篇

中国城市空气质量的区域差异及归因分析

赵艳艳1(), 张晓平1(), 陈明星1,2, 高珊珊1, 李润奎1   

  1. 1.中国科学院大学资源与环境学院,北京 100049
    2.中国科学院地理科学与资源研究所 中国科学院区域可持续发展分析与模拟重点实验室,北京 100101
  • 收稿日期:2020-09-07 修回日期:2021-04-20 出版日期:2021-11-25 发布日期:2022-01-25
  • 通讯作者: 张晓平(1972-), 女, 河南南阳人, 博士, 副教授, 硕士生导师, 主要从事经济地理学相关领域的教学与科研。E-mail: zhangxp@ucas.ac.cn
  • 作者简介:赵艳艳(1995-), 女, 河北邢台人, 硕士生, 专业方向为产业与区域可持续发展。E-mail: zhaoyanyan18@mails.ucas.ac.cn
  • 基金资助:
    国家自然科学基金项目(41771133);国家自然科学基金项目(41822104);中国科学院战略先导科技专项(XDA19040403)

Regional variation of urban air quality in China and its dominant factors

ZHAO Yanyan1(), ZHANG Xiaoping1(), CHEN Mingxing1,2, GAO Shanshan1, LI Runkui1   

  1. 1. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    2. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2020-09-07 Revised:2021-04-20 Published:2021-11-25 Online:2022-01-25
  • Supported by:
    National Natural Science Foundation of China(41771133);National Natural Science Foundation of China(41822104);The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19040403)

摘要:

开展城市空气质量时空格局演变特征及其影响因素的研究,对于深入认识城市环境与社会经济系统的互馈机理、制定高效的环境治理措施、提升城市发展质量具有重要的理论与实践意义。本文以中国全面实施《大气污染防治行动计划》的2014年为起点,刻画了2014—2019年286个地级以上城市6种空气污染物浓度(CO、NO2、O3、PM10、PM2.5、SO2)的时空演变特征,并基于面板回归模型分析各污染物浓度之间的相互作用关系;进而利用随机森林模型对城市6种空气污染物浓度与13个自然和社会经济影响因子的关联强度进行探究,从中梳理出关键影响因子。结果显示:① 研究期内,O3污染加剧,其余污染物年均浓度逐年下降,其中SO2浓度降幅最大。虽然典型的重污染区范围有所减小,但京津冀、山东半岛、山西、河南等地区的城市空气污染物浓度仍相对较高。② 城市6种空气污染物浓度之间存在显著的相互影响关系,城市空气复合污染特征明显。③ 自然因素和社会经济因素对不同种类空气污染物浓度的影响差异较大,且与污染物浓度之间呈非线性响应关系。自然因素中,城市年均气温与空气污染物浓度的关联强度最大,其次是植被指数。社会经济影响因素中,土地城市化水平和二产比重是主导影响因子,其次是电力消耗总量和交通因子。偏依赖分析进一步刻画了不同污染物浓度对主导影响因子的响应突变阈值。鉴于人类对于自然环境和气象条件的控制能力有限,建议继续通过优化城市密度、控制人为排放源及严格的空气污染防控措施以进一步有效提高城市空气质量。

关键词: 城市空气质量, 时空演变, 随机森林模型, 影响因素

Abstract:

:Research on the spatiotemporal evolution of urban air pollution and its driving forces has great theoretical and practical significance because it helps to deeply understand the mutual feedback mechanism between urban environment and socio-economic system and improve the efficiency of environmental governance. This paper illustrated the regional evolution characteristics of six urban ambient air pollutants, namely, CO, NO2, O3_8h, PM10, PM2.5, and SO2, in 286 sample cities at the prefecture level in China from 2014 to 2019, starting from the year when the "Air Pollution Prevention and Control Action Plan" was fully implemented in China. The interactions between the concentrations of each pollutant were then analyzed on the basis of panel regression models. Furthermore, random forest model was employed to explore the correlations between concentrations of these six pollutants and thirteen natural and socio-economic impact factors so as to sort out crucial ones. The results are shown in three aspects. First, the average annual concentration of O3_8h increased while that of the other urban ambient air pollutants decreased year by year, among which SO2 concentration decreased the most. Although the typical heavy pollution areas had shrunk, cities in the Beijing-Tianjin-Hebei region, Shandong Peninsula region, Shanxi Province, and Henan Province still have witnessed relatively high concentrations of air pollutants. Second, there was a significant interaction between concentrations of these six pollutants, indicating that comprehensive measures for urban air pollution prevention are necessary. Third, the impact of natural factors and socio-economic factors on urban air quality varied greatly towards different air pollutants, together with a nonlinear response relationship with the pollutant concentrations. Within the selected five natural factors of temperature, precipitation, wind speed, humidity and NDVI, the urban annual average temperature had the strongest correlation with air pollutant concentrations, followed by NDVI. Among the eight selected socio-economic factors, the level of land urbanization and the proportion of secondary production were the two leading drivers of the urban air pollution, followed by the total power consumption and traffic factors. Besides, partial dependence model was used to further analyze the response threshold of different pollutant concentrations to the dominant influencing factors. In consideration of the limited ability of human to control the physical environment and meteorological conditions, it is recommended that urban air quality should be further effectively improved by means of the optimization of urban density, the control of man-made emission sources, and the implementation of strict air pollution prevention and control measures.

Key words: urban air quality, spatiotemporal evolution, random forest model, impact factor