地理学报

地理学报 ›› 2015, Vol. 70 ›› Issue (11): 1720-1734.doi: 10.11821/dlxb201511003

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2014年中国城市PM2.5浓度的时空变化规律

王振波1,2(), 方创琳1,2(), 许光1, 潘月鹏3   

  1. 1. 中国科学院地理科学与资源研究所,北京 100101
    2. 中国科学院区域可持续发展与模拟重点实验室,北京 100101
    3. 中国科学院大气物理研究所,北京 100029
  • 收稿日期:2015-04-16 修回日期:2015-05-16 出版日期:2015-11-20 发布日期:2015-11-25
  • 作者简介:

    作者简介：王振波(1980-), 男, 山东禹城人, 博士, 助理研究员, 中国地理学会会员(S110007159A), 主要研究方向为城市化与生态环境效应。E-mail:wangzb@igsnrr.ac.cn

  • 基金资助:
    国家自然科学基金项目(41371177);国家自然科学基金重点项目(71433008);国家自然科学基金青年项目(41201168)

Spatial-temporal characteristics of the PM2.5 in China in 2014

Zhenbo WANG1,2(), Chuanglin FANG1,2(), Guang XU1, Yuepeng PAN3   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China
    3. Institute of Atmospheric Physics, CAS, Beijing 100029, China
  • Received:2015-04-16 Revised:2015-05-16 Online:2015-11-20 Published:2015-11-25
  • Supported by:
    Program of National Natural Science Foundation of China, No.41371177;Key Program of National Natural Science Foundation of China, No.71433008;Program of National Natural Youth Foundation of China, No.41201168

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摘要：

大气霾污染因其对人体健康、生态环境和气候变化的影响而成为全球关注的严重环境问题,PM2.5是中国霾污染频繁的主要原因。过去对国家尺度上PM2.5时空分布的认识主要基于卫星观测,因其反演方法的局限性,卫星资料难以真实反映近地面PM2.5浓度的时空变化规律。本文基于中国2014年190个城市中的945个监测站的PM2.5浓度观测数据,采用空间数据统计模型,揭示了中国PM2.5的时空格局。结果显示,2014年中国城市PM2.5平均浓度61 μg/m3,具有显著的冬秋高、春夏低的“U”型逐月变化规律和周期性U-脉冲型逐日变化规律;中国城市PM2.5浓度呈现显著的空间分异与集聚规律,以及两次南北进退的空间循环周期;胡焕庸线和长江是中国PM2.5浓度高值区和低值区的东西和南北分界线,胡焕庸线以东和长江以北的环渤海城市群、中原城市群、长三角城市群、长江中游城市群和哈长城市群等地区是2014年PM2.5的高污染城市聚集地,京津冀城市群是全年污染核心区;以珠三角为核心的东南沿海地区是稳定的空气质量优良区。

关键词: PM2.5, 时空特征, 监测数据, 中国

Abstract:

Haze pollution in China has become a severe environmental problem for people’s daily life as well as their health, among which PM2.5 makes significant contribution to poor air quality. Satellite observations played a leading role in the recognition in the spatio-temporal variation of PM2.5 nationally. However, based on the information and data obtained by satellites, the inversion method has limitations to truly reflect the spatio-temporal variation of PM2.5 concentrations near ground level. Based on the observed PM2.5 concentration data from 945 newly set-up air monitoring sites in 2014, our research reveals the spatio-temporal variations of PM2.5 concentrations in China by using spatial statistical model. The results show that (1) in 2014, the average PM2.5 concentration in China was 61 μg/m3. It had a periodical U-impulse type daily variation as well as a U-shaped monthly variation with a higher level in autumn and winter while a lower one in spring and summer. (2) Concentration of PM2.5 in urban China shows a significant spatial differentiation and clustering pattern with spatial-periodic occurrences in north and south China. (3) The Hu-line (Hu Population Line) and Yangtze River are respectively the east-west and north-south boundaries which separate the high-value zone and the low-value zone of PM2.5 concentrations in China. In 2014, the highly polluted cities by PM2.5 were mainly distributed in the urban agglomerations (Central Henan, Harbin-Changchun, the Bohai Rim Region, the Yangtze River Delta, and the Middle Yangtze River), east of the Hu-line and north of the Yangtze River. The Beijing-Tianjin-Hebei urban agglomeration was the most severely polluted region all the year round. The southeast coastal region centered on the Pearl River Delta had good air quality in a stable manner.

Key words: PM2.5, spatial-temporal characteristics, monitoring data, China