京津冀城市群PM2.5的空间分布及相关性分析

doi:10.19741/j.issn.1673-4831.2017.0818

生态与农村环境学报 ›› 2019, Vol. 35 ›› Issue (2): 174-179.doi: 10.19741/j.issn.1673-4831.2017.0818

京津冀城市群PM_2.5的空间分布及相关性分析

李雪梅^1,2, 许东明³

1. 天津城建大学经济与管理学院, 天津 300384;
2. 天津城镇化与新农村建设研究中心, 天津 300384;
3. 盐城碧桂园房地产开发有限公司, 江苏盐城 224000

收稿日期:2017-11-30 出版日期:2019-02-25 发布日期:2019-03-25
通讯作者: 李雪梅 E-mail:xuemei_321@tcu.edu.cn
作者简介:李雪梅(1976-),女,吉林蛟河人,副教授,博士,主要从事土地利用变化与碳排放研究。E-mail:xuemei_321@tcu.edu.cn
基金资助:
国家自然科学基金（71704128）；国家社会科学基金（16BGL141）；天津市艺术科学规划项目（D16007）；天津城镇化与新农村建设研究中心开放基金

Research on the Spatial Distribution and Correlation of PM2.5 in Beijing-Tianjin-Hebei Urban Agglomeration

LI Xue-mei^1,2, XU Dong-ming³

1. Tianjin Chengjian University, Tianjin 300384, China;
2. Urbanization and New Rural Construction Research Center of Tianjin, Tianjin 300384, China;
3. Yancheng Country Garden Real Estate Development Corporation, Nanjing 224000, China

Received:2017-11-30 Online:2019-02-25 Published:2019-03-25

摘要/Abstract

摘要：

以京津冀城市群2014-2016年1 090 d PM_2.5浓度日值数据为基础，基于ArcGIS 10.2软件，选择典型月份分析PM_2.5月优良天数比例、月重度及严重污染天数比例的时空分布特征及其空间相关性。结果表明，研究区城市之间各年PM_2.5浓度月优良天数比例与月重度、严重污染天数比例整体波动趋势基本一致，其中PM_2.5月优良天数比例高值集中在5-9月，PM_2.5月重度与严重污染天数比例高值集中在11-次年2月；从区域分布看，PM_2.5月重度与严重污染天数比例从石家庄、保定市向周边城市由高到低递减。选取典型月份对研究区PM_2.5进行空间相关分析，结果表明PM_2.5存在正空间相关性，即PM_2.5浓度的空间分布表现出空间聚集性。

关键词: PM_2.5, 时空分布, 空间相关性, 京津冀城市群

Abstract:

Based on daily averaged values of PM_2.5 mass concentration in Beijing-Tianjin-Hebei Urban Agglomeration from 2014 to 2016, this study employed ArcGIS 10.2 to analyze the temporal-spatial characteristics of pollution distribution. The spatial correlation of indices such as the percentage of superior, heavily and severely polluted days during typical PM_2.5 months was also performed. The results show that the profile of the monthly rate of non-polluted days was consistent with the trend of the overall air quality fluctuation during the observation period. The higher proportion of PM_2.5-superior period was concentrated from May to September. The heavily and severely polluted days spanned between November and February. From the regional distribution, the proportional spatial pattern of PM_2.5 heavy and severe pollution decreased from central cities as Shijiazhuang and Baoding to surrounding places. The typical months were selected to carry out spatial correlation of PM_2.5. Corresponding results indicate that PM_2.5 had a positive spatial correlation. The spatial distribution of PM_2.5 mass concentration showed spatial clustering profile.

Key words: PM_2.5, temporal and spatial distribution, spatial correlation, Beijing-Tianjin-Hebei Urban Agglomeration

中图分类号:

X823

李雪梅, 许东明. 京津冀城市群PM_2.5的空间分布及相关性分析[J]. 生态与农村环境学报, 2019, 35(2): 174-179.

LI Xue-mei, XU Dong-ming. Research on the Spatial Distribution and Correlation of PM2.5 in Beijing-Tianjin-Hebei Urban Agglomeration[J]. Journal of Ecology and Rural Environment, 2019, 35(2): 174-179.

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京津冀城市群PM_2.5的空间分布及相关性分析

Research on the Spatial Distribution and Correlation of PM2.5 in Beijing-Tianjin-Hebei Urban Agglomeration

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