Article(id=1314922988076630600, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922986466009203, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1754150400000, receivedDateStr=2025-08-03, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791431670445, onlineDateStr=2026-10-08, pubDate=1773936000000, pubDateStr=2026-03-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791431670445, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791431670445, creator=13701087609, updateTime=1791431670445, updator=13701087609, issue=Issue{id=1314922986466009203, tenantId=1146029695717560320, journalId=1234093305789726721, year='2026', volume='46', issue='3', pageStart='1185', pageEnd='1776', issueExtLink='null', onlineDate='null', pubDate='1773936000000', pubDateStr='2026-03-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1791431670061, creator='13701087609', updateTime=1791431955369, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314924183251935526, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922986466009203, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314924183251935527, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922986466009203, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1216, endPage=1228, ext={EN=ArticleExt(id=1314922988412174921, articleId=1314922988076630600, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Policy-driven spatiotemporal difference and dominant factors evolution of O₃ and PM_2.5 compound pollution in Chinese cities, columnId=1241049967662518582, journalTitle=China Environmental Science, columnName=Ozone Pollution Control, runingTitle=null, highlight=null, articleAbstract=

To evaluate the impact of air pollution prevention and control policies on the spatiotemporal evolution of Ozone (O₃) and fine particulate matter (PM_2.5), this study analyzed the spatiotemporal stratified heterogeneity of O₃ and PM_2.5 pollution across 331 Chinese cities from 2014 to 2024 using a Bayesian spatiotemporal hierarchical model. The GeoDetector model was further employed to quantify the determinant power and interactive effects of meteorological, socioeconomic, and other pollutant factors on summer O₃ and winter PM_2.5 pollution during the implementation of three key policies: the Air Pollution Prevention and Control Action Plan (2014~2017), the Blue Sky Defense Action Plan (2018~2020), and a circular on further Promoting the Nationwide Battle to Prevent and Control Pollution (2021~2024). The results indicate that O₃ concentration exhibited a trend of initial rapid growth followed by a slower rate, with hotspots mainly distributed in North China and the Yangtze River Delta (YRD). Conversely, PM_2.5 concentration continuously decreased, and high-value areas shrank and concentrated in North China, the YRD, and the Chengdu-Chongqing region. A positive correlation between O₃ and PM_2.5 was frequently observed in the southern and northeastern humid regions, whereas a negative correlation prevailed in the northwestern semi-arid areas. Although the dominant factors for O₃ and PM_2.5 evolved across policy stages, the determinant power of the interactions among different factors were consistently stronger than that of individual factors. Specifically, from 2014 to 2017, O₃ was mainly affected by maximum temperature (Geo_q=0.24), industrial output (Geo_q=0.22), and PM₁₀ (Geo_q = 0.60); PM_2.5 was primarily affected by maximum temperature (Geo_q = 0.41), population density (Geo_q= 0.40), and PM₁₀ (Geo_q = 0.88). From 2018 to 2020, the dominant factors for O₃ shifted to precipitation, population density, and PM₁₀, while those for PM_2.5 shifted to average temperature, population density, and PM₁₀. From 2021 to 2024, the dominant factors for both shifted toward average temperature, population density, and PM₁₀, albeit with significant differences in their determinant power. This research reveals the dynamic evolution of the spatiotemporal patterns of O₃ and PM_2.5 pollution driven by policies implementation, emphasizing the dominant role of multi-factor interactions in the composite pollution formation process. The findings provide a crucial scientific basis for formulating regionalized and phased synergistic control strategies for O₃ and PM_2.5.

, authors=Xiang-xue ZHANG¹, Cheng-dong XU^{2, 3, *}, Chang-xiu CHENG⁴, Shao-jie KONG⁵, Jie YU¹, authorsList=Xiang-xue ZHANG, Cheng-dong XU, Chang-xiu CHENG, Shao-jie KONG, Jie YU, authorCompany=null, correspAuthors=Cheng-dong XU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314922988819022410, articleId=1314922988076630600, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=政策驱动下中国城市O₃与PM_2.5复合污染的时空分异及主导因素演变, columnId=1241049969449292093, journalTitle=中国环境科学, columnName=臭氧污染与控制, runingTitle=null, highlight=null, articleAbstract=

为评估大气污染防治政策对O₃和PM_2.5时空演变的影响，基于贝叶斯时空层次模型分析了2014~2024年中国331个城市O₃和PM_2.5污染的时空分异特征，并利用地理探测器量化了《大气污染防治行动计划》、《打赢蓝天保卫战三年行动计划》和《关于深入打好污染防治攻坚战的意见》政策实施期间气象、社会经济及其他污染物因素对夏季O₃和冬季PM_2.5污染的主导作用．结果表明，O₃浓度呈现增速由快到慢的变化趋势，热点区主要分布在华北和长三角地区；PM_2.5浓度则持续下降，高值区收缩并集中在华北、长三角和成渝地区．O₃和PM_2.5在南方及东北湿润地区多呈正相关，在西北半干旱地区多呈负相关．尽管O₃和PM_2.5的主导因素随政策阶段演变，但不同因素间的交互作用对O₃和PM_2.5的解释力始终强于单一因素的作用．2014~2017年，O₃主要受最高温度（Geo_q = 0.24）、工业产值（Geo_q = 0.22）、PM₁₀（Geo_q = 0.60）影响；PM_2.5主要受最高温度（Geo_q = 0.41）、人口密度（Geo_q = 0.40）、PM₁₀（Geo_q = 0.88）影响．2018~2020年，O₃主导因素转变为降水、人口密度、PM₁₀；PM_2.5主导因素转变为平均温度、人口密度、PM₁₀.2021~2024年二者主导因素转变为平均温度、人口密度和PM₁₀，但解释力有明显差异．研究揭示了政策驱动下O₃与PM_2.5污染时空格局的动态演变，强调了多因素交互作用在复合污染形成过程中的主导作用，为制定分区、分阶段的O₃与PM_2.5协同控制策略提供了依据．

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张湘雪（1993-），女，河南周口人，讲师，博士，主要从事环境污染与人体健康、时空建模及应用研究．发表论文28篇．zxx@lreis.ac.cn.

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张湘雪（1993-），女，河南周口人，讲师，博士，主要从事环境污染与人体健康、时空建模及应用研究．发表论文28篇．zxx@lreis.ac.cn.

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