中国274个地级市PM2.5-O3浓度空间分异的驱动因素解析

邹滨; 谢曼曼; 李莎; 刘宁; 李沈鑫; 熊鹰

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中国环境科学 ›› 2025, Vol. 45 ›› Issue (11) : 6032-6044.

大气污染与控制

中国274个地级市PM_2.5-O₃浓度空间分异的驱动因素解析

邹滨¹, 谢曼曼¹, 李莎^1,2, 刘宁³, 李沈鑫¹, 熊鹰⁴

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Exploring the drivers of PM_2.5-O₃ spatial differentiation in 274 Chinese prefecture-level cities

ZOU Bin¹, XIE Man-man¹, LI Sha^1,2, LIU Ning³, LI Shen-xin¹, XIONG Ying⁴

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摘要

结合城市统计年鉴与多源遥感数据(包括污染物浓度、夜间灯光、土地覆盖等),从气象、社会经济和城市形态多要素综合视角,解析中国274个地级市PM_2.5-O₃复合污染时空分布特征.结果表明:2005~2020年,中国城市PM_2.5浓度呈“先升后降”趋势,而O₃浓度则经历了升高、下降再升高的过程.两者的空间分布及协同关系均呈现显著的区域异质性,PM_2.5高值区主要集中在华北平原,O₃高值区分布于华北平原和东部沿海城市群,PM_2.5~O₃在南方及东北湿润地区多呈正相关,在北方半湿润半干旱地区多呈负相关.地理探测器分析显示,城市尺度PM_2.5和O₃浓度空间分异驱动因子排序均呈现出城市形态>气象>社会经济要素.其中PM_2.5浓度主要受生态空间占比(q=0.627)和农业空间占比(q=0.610)驱动,O₃浓度则受生态空间占比(q=0.512)和地形(q=0.466)影响显著.PM_2.5-O₃协同关系受降水(q=0.396)和温度(q=0.374)等单因子主导驱动,且在温度-地形(q=0.718)、降水-均匀度(q=0.708)等双因子的交互作用下呈现显著增强,表明了“城市形态约束”与“气象条件扰动”对城市PM_2.5-O₃复合污染显著的非线性协同效应.本研究可为PM_2.5-O₃复合污染精准协同防控提供科学依据.

Abstract

In this study, based on the urban statistical yearbook and multi-source remote sensing data—including pollutant concentrations, nighttime light intensity, and land cover—the spatiotemporal distribution characteristics of PM_2.5-O₃ compound pollution across 274 prefecture-level cities in China were examined from an integrated perspective of meteorological, socioeconomic, and urban form factors. The results showed that from 2005 to 2020, PM_2.5 concentrations in Chinese cities followed a 'rise-then-fall' trend, while O₃ exhibited a 'rise-fall- rise' trend. Both the spatial distribution and the synergistic relationship between PM_2.5 and O₃ displayed pronounced regional heterogeneity. High PM_2.5 concentrations were mainly concentrated in the North China Plain, whereas high O₃ concentrations occurred in both the North China Plain and the eastern coastal urban agglomerations. Positive correlations between PM_2.5 and O₃ concentrations were predominant in the humid regions of southern and northeastern China, while negative correlations were more common in the semi-humid and semi-arid regions of northern China. Geodetector analysis revealed that the spatial differentiation drivers of PM_2.5 and O₃ concentrations was primarily shaped by urban form factors, followed by meteorological and socioeconomic conditions. Specifically, PM_2.5 concentrations were strongly influenced by the proportion of ecological space (q=0.627) and agricultural land (q=0.610), whereas O₃ concentrations were significantly affected by the proportion of ecological space (q=0.512) and topography (q = 0.466). The synergistic relationship between PM_2.5 and O₃ was mainly controlled by the single factors such as precipitation (q=0.396) and temperature (q=0.374), with significant enhancement observed under dual-factor interactions, such as temperature-topography (q=0.718) and precipitation-Shannon's evenness index (q=0.708). These findings highlight the nonlinear synergistic effects arising from the coupling of 'urban form constraints' and 'meteorological disturbances' on PM_2.5-O₃ compound pollution, and provide a scientific basis for the precise and coordinated prevention and control of such pollution.

导出引用

邹滨, 谢曼曼, 李莎, 刘宁, 李沈鑫, 熊鹰. 中国274个地级市PM_2.5-O₃浓度空间分异的驱动因素解析[J]. 中国环境科学. 2025, 45(11): 6032-6044

ZOU Bin, XIE Man-man, LI Sha, LIU Ning, LI Shen-xin, XIONG Ying. Exploring the drivers of PM_2.5-O₃ spatial differentiation in 274 Chinese prefecture-level cities[J]. China Environmental Science. 2025, 45(11): 6032-6044

中图分类号： X51

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