地理学报 ›› 2019, Vol. 74 ›› Issue (12): 2614-2630.doi: 10.11821/dlxb201912014

• 资源环境与可持续发展 • 上一篇    下一篇

中国城市群地区PM2.5时空演变格局及其影响因素

王振波1,2(), 梁龙武1,2, 王旭静3   

  1. 1. 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
    2. 中国科学院大学资源与环境学院,北京 100049
    3. 山西师范大学地理科学学院,临汾 041004
  • 收稿日期:2019-03-07 修回日期:2019-11-23 出版日期:2019-12-25 发布日期:2019-12-25
  • 通讯作者: 王振波 E-mail:wangzb@igsnrr.ac.cn
  • 基金资助:
    国家重点基础研究发展计划(2017YFC0505702);国家自然科学基金项目(41771181);清华大学新型城镇化研究院开放基金课题(TUCSU-K-17015-01)

Spatio-temporal evolution patterns and influencing factors of PM2.5 in Chinese urban agglomerations

WANG Zhenbo1,2(), LIANG Longwu1,2, WANG Xujing3   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3. School of Geography Science, Shanxi Normal University, Linfen 321004, Shanxi, China
  • Received:2019-03-07 Revised:2019-11-23 Online:2019-12-25 Published:2019-12-25
  • Contact: WANG Zhenbo E-mail:wangzb@igsnrr.ac.cn
  • Supported by:
    National Key Research and Development Plan(2017YFC0505702);National Natural Science Foundation of China(41771181);Open Fund Project of New Urbanization Research Institute of Tsinghua University(TUCSU-K-17015-01)

摘要:

城市群作为中国新型城镇化主体形态,是支撑全国经济增长、促进区域协调发展、参与国际分工合作的重要平台,也是空气污染的核心区域。本文选取2000-2015年NASA大气遥感影像反演PM2.5数据,运用GIS空间分析和空间面板杜宾模型,揭示了中国城市群PM2.5的时空演变特征与主控因素。结果显示:① 2000-2015年中国城市群PM2.5浓度呈现波动增长趋势,2007年出现拐点,低浓度城市减少,高浓度城市增多。② 城市群PM2.5浓度以胡焕庸线为界呈现东高西低的格局,城市群间空间差异性显著且不断扩大,东部、东北地区浓度提升更快。③ 城市群PM2.5年均浓度空间集聚性显著,以胡焕庸线为界,热点区域集中东部,范围持续增加,冷点集中在西部,范围持续缩小。④ 城市群内各城市间PM2.5浓度存在空间溢出效应。不同城市群影响要素差异显著,工业化和能源消耗对PM2.5污染有正向影响;外商投资在东南沿海和边境城市群对PM2.5污染具有负向影响;人口密度对本地区PM2.5污染主要具有正向影响,对邻近地区则相反;城市化水平在国家级城市群对PM2.5污染有负向影响,在区域性和地方性城市群则相反;产业结构高级度对本地区PM2.5污染有负向影响,对邻近地区则相反;技术扶持度对PM2.5污染的影响显著,但存在滞后性和回弹效应。

关键词: 城市群, PM2.5, 时空演变格局, 影响因素, 空间面板杜宾模型

Abstract:

As the main form of China new urbanization, urban agglomerations are the important platform to support national economic growth, promote regional coordinated development and participate in international competition and cooperation, but they are also the core area of air pollution. This paper selects PM2.5 data from NASA atmospheric remote sensing image inversion from 2000 to 2015, and uses GIS spatial analysis and Spatial Durbin Model to reveal the temporal and spatial evolution pattern characteristics and main controlling factors of PM2.5 in China's urban agglomerations. The main conclusions are as follows: (1) From 2000 to 2015, the PM2.5 concentration of China urban agglomerations showed a volatility growth trend. In 2007, there was an inflection point. The number of low-concentration cities declined, and the number of high-concentration cities increased. (2) The concentration of PM2.5 in urban agglomerations was in the pattern of high in the east and and low in the west, with the "Hu Huanyong Line" as the boundary. The spatial difference between urban agglomerations is significant, and the difference is increasing. The concentration of PM2.5 is growing faster in urban agglomerations in the eastern and northeastern regions. (3) The urban agglomeration of PM2.5 has a significant spatial concentration. The hot spots are concentrated to the east of the "Hu Huanyong Line", and the number of cities continues to rise. The cold spots are concentrated to the west of the "Hu Huanyong Line", and the number of cities continues to decline. (4) There is a significant spatial spillover effect of PM2.5 pollution among cities within urban agglomerations. The main controlling factors of PM2.5 pollution in different urban agglomerations have significant differences. Industrialization and energy consumption have a significant positive impact on PM2.5 pollution. Foreign direct investment has a significant negative impact on PM2.5 pollution in the southeast coastal and border urban agglomerations. Population density has the significant positive impact on PM2.5 pollution in the region, and has the opposite result in the neighbouring areas. Urbanization level has a negative impact on PM2.5 pollution in national-level urban agglomerations, and has the opposite result in regional and local urban agglomerations. The high degree of industrial structure has a significant negative impact on PM2.5 pollution in the region, and has the opposite result in the neighboring regions. Technical support has a significant impact on PM2.5 pollution, but there are also lag effects and rebound effects.

Key words: urban agglomeration, PM2.5, spatial-temporal evolution, influencing factor, spatial Durbin Model