Article(id=1314917568406716779, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917567911788907, 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=1739808000000, receivedDateStr=2025-02-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430378294, onlineDateStr=2026-10-08, pubDate=1758297600000, pubDateStr=2025-09-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430378294, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430378294, creator=13701087609, updateTime=1791430378294, updator=13701087609, issue=Issue{id=1314917567911788907, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='9', pageStart='4737', pageEnd='5328', issueExtLink='null', onlineDate='null', pubDate='1758297600000', pubDateStr='2025-09-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791430378177, creator='13701087609', updateTime=1791431890195, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314923910001418489, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917567911788907, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314923910001418490, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917567911788907, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=4737, endPage=4748, ext={EN=ArticleExt(id=1314917568599654764, articleId=1314917568406716779, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Multi scale prediction of regional ozone concentration considering economic activity intensity, columnId=1241049967662518582, journalTitle=China Environmental Science, columnName=Ozone Pollution Control, runingTitle=null, highlight=null, articleAbstract=

In this study, nighttime light intensity was adopted as a proxy for economic activity, and a random forest model was constructed to integrate both anthropogenic and meteorological factors for O₃ concentration prediction. Panel data from the Beijing-Tianjin-Hebei region and surrounding areas (the "2+26" cities) during 2014~2022 were employed to empirically examine the differential contributions of economic activity intensity across various temporal scales, spatial regions, and pollutant types. The findings showed that:①economic activity intensity consistently exhibited significant predictive power across different temporal scales. On the annual scale, its importance reached 0.7053, surpassing all meteorological variables and becoming the dominant predictor. In contrast, on the daily scale, temperature and other meteorological factors were found to be more influential, with the contribution of economic activity reduced to 0.0869. ②Marked spatial heterogeneity was observed between high- and low- O₃ concentration areas. Temperature and economic activity had stronger impacts in high- O₃ regions, while low-concentration regions displayed irregular and spatially discontinuous patterns of predictor relevance. ③ Over long time horizons, economic activity was identified as a decisive factor in explaining both PM_2.5 and O₃ concentrations. However, on shorter time scales, although economic activity was the second most important factor for PM_2.5 prediction after temperature, its contribution to O₃ prediction remained relatively limited.

, authors=Ming-dong JIANG¹, Xin-yang WANG², Rong YAN³, Xin-xin YU⁴, Ze-peng WU^{5, *}, authorsList=Ming-dong JIANG, Xin-yang WANG, Rong YAN, Xin-xin YU, Ze-peng WU, authorCompany=null, correspAuthors=Ze-peng WU, 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=1314917568947781997, articleId=1314917568406716779, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=考虑经济活动强度的区域臭氧浓度多尺度预测, columnId=1241049969449292093, journalTitle=中国环境科学, columnName=臭氧污染与控制, runingTitle=null, highlight=null, articleAbstract=

以夜间灯光作为经济活动强度的代理变量，借助随机森林技术构建耦合人类活动和气象因素的O₃浓度预测模型，并基于京津冀及周边地区（“2+26”城市）2014~2022年数据展开实证研究，在此基础上进一步比较研究经济活动强度对不同时间尺度，不同地区和不同污染物浓度预测的贡献差异．结果发现：①在不同时间尺度，经济活动强度均对O₃浓度预测均有显著贡献，在年尺度，经济活动强度对O₃浓度预测的贡献度达到0.7053，超过各类气象因子成为首要贡献因素，但在日尺度，气温等气象因素的贡献度相对更高，经济活动强度贡献度仅为0.0869．②不同因素对O₃浓度高值区与低值区的预测贡献存在显著差异，气温和经济活动对高值区O₃浓度影响显著，而低值区城市表现出不同因子预测贡献的区域性和不连续性特点．③在长时间尺度，人类经济活动对城市PM_2.5和O₃浓度均有决定性贡献；在短时间尺度，经济活动对PM_2.5浓度的影响仅次于气温，但对O₃浓度的驱动贡献较小．

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姜明栋（1997-），男，江苏盐城人，讲师，博士，主要从事环境与可持续发展管理研究．发表论文40余篇．jmd@seu.edu.cn.

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姜明栋（1997-），男，江苏盐城人，讲师，博士，主要从事环境与可持续发展管理研究．发表论文40余篇．jmd@seu.edu.cn.

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