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sing 200soil As samples collected from suburban Changchun, this study combined the extreme gradient boosting tree (XGBoost) and the GeoShapley algorithm to explore the heterogeneity and nonlinear relationship between As and environmental factors, and to identify the local influencing factors and sources of As. The main conclusions are as follows: ① The XGBoost model had a fitted R² of 0.958, which was better than the geographically weighted regression and multiscale geographically weighted regression models, with fitted and predicted R² values of 0.958 and 0.729, respectively. ② The results of GeoShapley showed that the average contribution rate of manganese content ranked first and was the most significant characteristic variable affecting soil As, while the spatial location characteristic ranked second in importance, confirming the necessity of considering spatial coordinates. ③ The spatial distribution of GeoShapley values of influencing factors revealed that manganese content, alkali-hydrolyzable nitrogen, and sulfur content were the primary influencing factors for local As content at most sampling points, accounting for 99% of the samples. This indicated that parent material and agricultural production are the main sources of As. ④ The nonlinear relationship plots showed that there was a threshold effect between As content and various environmental variables, especially when the manganese content was higher than 2g/kg, the alkali-hydrolyzable nitrogen was higher than 175mg/kg, and the sulfur content was lower than 20mg/kg, As reached the peak, which should be paid special attention to in pollution prevention and control. This paper confirms the superiority of GeoShapley algorithm and the necessity of considering the spatial location, and provides data support for the governmental departments to formulate the policy of localized prevention and control of soil As pollution.

, authors=Xin-ying ZOU¹, Qing-wu YAN¹, Zi-hao WU^{1, *}, Yuan-li ZHU¹, Yi-yun CHEN², authorsList=Xin-ying ZOU, Qing-wu YAN, Zi-hao WU, Yuan-li ZHU, Yi-yun CHEN, authorCompany=null, correspAuthors=Zi-hao 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=1314922021222449177, articleId=1314922020404559895, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于GeoShapley算法的长春市城郊土壤砷含量局部主控因子探究, columnId=1234106394572550190, journalTitle=中国环境科学, columnName=土壤污染与控制, runingTitle=null, highlight=null, articleAbstract=

基于长春市城郊200个土壤砷样本，结合极限梯度提升树与GeoShapley算法，探究砷与环境因子间的异质性、非线性关系，并识别影响土壤砷含量的局部主控因子及来源．结果表明：①极限提升树模型优于地理加权回归和多尺度地理加权回归模型，其拟合和预测R²分别为0.958和0.729；②GeoShapley的结果显示，锰含量的平均贡献率居首位，是影响土壤砷最显著的特征变量，而空间位置的特征重要性排名第二，证实了考虑空间坐标的必要性；③结合影响因子的Geoshapley空间分布图集发现，锰含量、碱解氮和硫含量是99%样本点砷含量的首要影响特征，表明母质和农业生产是砷的主要来源；④非线性关系图显示砷含量与各环境变量之间均存在阈值效应，特别是当锰含量高于2g/kg、碱解氮高于175mg/kg、硫含量低于20mg/kg时，砷含量达到峰值，在污染防控时应予以特别关注．本文证实了Geoshapley算法的优越性和考虑空间位置因素的必要性．

, authors=邹心颖¹, 闫庆武¹, 吴子豪^{1, *}, 祝元丽¹, 陈奕云², authorsList=邹心颖, 闫庆武, 吴子豪, 祝元丽, 陈奕云, authorCompany=null, correspAuthors=吴子豪, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=rsYVuuEHuItwqlGbPx1mpw==, magXml=SLrIKIYIDN3Xo7duYOvnNg==, pdfUrl=null, pdf=q1gdaLHokQxu722pvEw/cA==, pdfFileSize=2773990, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=ZOz/ZGobaAt+pnUonRDOBg==, mapNumber=null, fund=null)}, authors=[Author(id=1314922023747420195, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314922020404559895, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=ZouXinying@cumt.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1314922024556920869, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314922020404559895, authorId=1314922023747420195, language=EN, stringName=Xin-ying ZOU, firstName=Xin-ying, middleName=null, lastName=ZOU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314922024959574054, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314922020404559895, authorId=1314922023747420195, language=CN, stringName=邹心颖, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.中国矿业大学公共管理学院，江苏 徐州 221116, bio={"content":"

邹心颖（2002-），女，江西宜春人，中国矿业大学硕士研究生，主要从事数字土壤制图和空间分析．发表论文1篇．ZouXinying@cumt.edu.cn.

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邹心颖（2002-），女，江西宜春人，中国矿业大学硕士研究生，主要从事数字土壤制图和空间分析．发表论文1篇．ZouXinying@cumt.edu.cn.

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