中国近地面臭氧时空-属性模态分解及其影响因素解析  

Decomposing of Spatiotemporal Attribution Patterns of Near-Surface Ozone in China and Their Influencing Factors

作　　者：张湘雪 徐成东[2,3] 程昌秀 赖小莹 钟方宁 ZHANG Xiangxue;XU Chengdong;CHENG Changxiu;LAI Xiaoying;ZHONG Fangning(School of Geography,Geomatics and Planning,Jiangsu Normal University,Xuzhou 221116,China;State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China;College of Management and Economics,Tianjin University,Tianjin 300072,China)

机构地区：[1]江苏师范大学地理测绘与城乡规划学院,徐州221116 [2]中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101 [3]中国科学院大学,北京100049 [4]北京师范大学地理科学学部,北京100875 [5]天津大学管理与经济学部,天津300072

出　　处：《地球信息科学学报》2025年第1期226-238,共13页Journal of Geo-information Science

基　　金：国家自然科学基金项目(42130713);资源与环境信息系统国家重点实验室创新项目(O88RA205YA);林芝市科技计划项目(SYQ2024-12)。

摘　　要：【目的】近年来,由于工业化和城市化的快速发展,中国许多地区的近地面臭氧污染日益严重,对人们的生产生活产生了负面影响。鉴于此,本研究从时空-属性模态地视角分解气象条件和社会经济状况对中国近地面臭氧分布特征的影响。【方法】基于2014—2023年中国331个城市的空气污染、气象和社会经济数据,采用经验正交函数和地理探测器模型,多视角识别中国近地面臭氧污染典型的时空模态,量化各影响因素及其交互作用对臭氧污染不同时空模态的解释力。在此基础上,本研究创新性的结合地理演化树模型,进一步分析主要空间模态的动态演化过程。【结果】第一时空模态对总方差的贡献率为71.1%,揭示出高风险地区主要分布在华北和长三角地区,且呈现明显的季节变化,峰值主要集中在夏季,影响该模态的主导社会经济和气象因素分别是人口密度(Geo_q=0.20)和平均温度(Geo_q=0.86)。第二时空模态对总方差的贡献率为6.8%,揭示出高风险地区主要分布在包括珠三角在内的中国南方地区,且呈上升趋势,影响该模态的主导社会经济和气象因素分别是人口密度(Geo_q=0.26)和平均温度(Geo_q=0.37)。值得注意的是,各因素之间的交互作用较因素本身对臭氧污染的影响更大。【结论】中国臭氧污染的典型时空特征受多种因素的共同影响,且臭氧污染程度随着城市类型和城市发展阶段的不同而动态变化。[Objectives]In recent years,due to the rapid development of industrialization and urbanization,near-surface ozone pollution in many cities in China has become increasingly severe,disrupting people's daily lives and production activities.This study aims to deconstruct the influence of meteorological and socioeconomic conditions,as well as their interactions,on the distribution of near-surface ozone in China from the perspective of multi-spatiotemporal patterns.Moreover,the spatial-temporal variations of ozone exhibit multiple patterns,each affected by different dominant factors.However,most previous studies have explored the spatial-temporal characteristics of ozone and its influencing factors from a single perspective,with insufficient consideration given to the spatial-temporal heterogeneity of O3 pollution.The interactions among influencing factors were rarely quantified from various spatial-temporal perspectives.[Methods]Using ozone pollution,meteorological,and socioeconomic data from 331 cities in China from 2014 to 2023,this study used the empirical orthogonal function and GeoDetector model to identify the typical spatiotemporal patterns of near-surface ozone pollution from multiple perspectives.At the same time,it quantifies the determinant power of each influencing factor and their interactions on different spatial-temporal patterns of ozone pollution.The Geotree model was further used to analyze the dynamic evolution of the dominant spatial patterns.[Results]The results show that the first spatiotemporal pattern accounts for 71.1%of the total variance,revealing that high-risk areas are mainly located in North China and the Yangtze River Delta region,with obvious seasonal changes,peaking mainly in the summer.The dominant socioeconomic and meteorological factors affecting this pattern are population density(Geo_q=0.20)and average temperature(Geo_q=0.86),respectively.The second spatiotemporal pattern accounts for 6.8%of the total variance,indicating that high-risk areas are mainly distributed in South China,

关 键 词：臭氧 时空异质性 时空统计 时空模态 地理探测器 解释力 交互作用 

分 类 号：X515[环境科学与工程—环境工程]