2001-2020年湖北省PM2.5时空分布特征及气象驱动因子分析

doi:10.19741/j.issn.1673-4831.2022.1357

摘要/Abstract

摘要： 把握PM_2.5污染的时空演变规律是对其进行针对性治理的基础与前提。从PM_2.5地球表面浓度栅格数据提取湖北省2001-2020年各地级市PM_2.5年均浓度数据,利用空间自相关、核密度估计、地理探测器等方法研究PM_2.5时空分布及气象驱动因子特征。结果表明,湖北省各主要城市PM_2.5浓度二级标准达标率趋于增加。各城市PM_2.5年均浓度均值在2001-2013年由42.47 逐步上升至62.94 μg·m^-3,2013-2020年由62.94减少至32.85 μg·m^-3。核密度估计表明,2013年以前各城市PM_2.5年均浓度值随时间推移逐渐分散,2013年后逐渐集中于浓度较低的区间。以武汉市等中部城市为分界线,湖北省PM_2.5向东西2个方向均存在由高至低的浓度梯度,且西面浓度小于东面。2013年后浓度较高地区的扩散效应逐渐减小。PM_2.5空间分布存在显著的正相关聚集效应,潜江市、仙桃市、天门市基本表现出高-高聚集特征,恩施土家族苗族自治州、神农架林区均表现出低-低聚集特征,极少城市表现出高-低及低-高聚集特征。地理探测器分析表明,气象因子对PM_2.5浓度具有较显著影响。不同气象因子对PM_2.5浓度的平均解释程度排序为风速(0.798)＞温度(0.752)＞湿度(0.727)＞日照(0.694)＞降水(0.639)。不同年份主导驱动因子不同,2010年前温度为主导驱动因子,2010年后风速为主导驱动因子。

关键词: PM_2.5, 时空分布, 驱动因子, 空间自相关, 核密度估计, 地理探测器

Abstract: Grasping the spatial and temporal evolution of PM_2.5 pollution is the basis and prerequisite for its targeted management. The annual average PM_2.5 concentration data of each prefecture-level city in Hubei Province from 2001-2020 were extracted from the raster data of PM_2.5 earth surface concentration, and spatial autocorrelation and kernel density estimation methods and Geodetector were used to study the spatial and temporal distribution characteristics of PM_2.5 and the characteristics of meteorological driving factors. The main results of the study are as follows: The rate of compliance with the secondary standard for PM_2.5 concentration in major cities across Hubei Province appears to be increasing. The average annual PM_2.5 concentration in each city gradually increased from 42.47 to 62.94 from 2001 to 2013, and decreased from 62.94 to 32.85 from 2013 to 2020. The kernel density estimation shows that the annual average PM_2.5 concentrations in the cities were gradually dispersed before 2013, while the annual average PM_2.5 concentrations in the cities after 2013 were gradually concentrated in the lower concentration intervals. Taking Wuhan and other central cities as the dividing line, there is a gradient of high to low PM_2.5 concentrations in Hubei Province in both east and west directions, and the concentration in the west is lower than that in the east. The dispersion effect in areas of higher concentrations diminishes gradually after 2013. There was a significant positive aggregation effect in the spatial distribution of PM_2.5, Qianjiang, Xiantao and Tianmen basically showed high-high aggregation characteristics, while Enshi Tujia and Miao Autonomous Prefecture and Shennongjia Forest Area all showed low-low aggregation characteristics. Very few cities showed high-low or low-high aggregation characteristics. Geodetector shows that the meteorological factors had a significant effect on the PM_2.5 concentration. The average explanatory degree of various meteorological factors on PM_2.5 concentration is ranked as follows: wind speed (0.798) > temperature (0.752) > humidity (0.727) > sunshine (0.694) > precipitation (0.639). The dominant driving factors were different in different years, and temperature was the dominant driving factor before 2010, while wind speed was the dominant driving factor after 2010.

Key words: PM_2.5, temporal and spatial patterns, driving factors, spatial autocorrelation, kernel density estimation, Geodetector

中图分类号:

X513

周靖承, 姚衡, 曹艳晓, 朱熙, 陈宁. 2001-2020年湖北省PM_2.5时空分布特征及气象驱动因子分析[J]. 生态与农村环境学报, 2023, 39(12): 1559-1567.

ZHOU Jing-cheng, YAO Heng, CAO Yan-xiao, ZHU Xi, CHEN Ning. Analysis of Spatial and Temporal Distribution and Meteorological Driving Factors of PM_2.5 in Hubei Province from 2001 to 2020[J]. Journal of Ecology and Rural Environment, 2023, 39(12): 1559-1567.

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