Article(id=1314923037108044752, 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=1791431682135, onlineDateStr=2026-10-08, pubDate=1773936000000, pubDateStr=2026-03-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791431682135, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791431682135, creator=13701087609, updateTime=1791431682135, 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=1460, endPage=1474, ext={EN=ArticleExt(id=1314923037800104913, articleId=1314923037108044752, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Explainable machine learning for uncovering PFAS adsorption mechanisms in soils, columnId=1234106389669409584, journalTitle=China Environmental Science, columnName=Soil Pollution Control, runingTitle=null, highlight=null, articleAbstract=

The soil-water partition coefficient (K_d) of per- and polyfluoroalkyl substances (PFAS) was predicted using machine learning (ML) models, and their partitioning behavior in soils was elucidated. A dataset comprising 1,227 samples of 47 PFAS was employed, with 16 PFAS physicochemical properties and soil parameters used as input features. It was demonstrated that the lgK_d values ranged from -1.40 to 3.95. When the number of CF₂ groups exceeded 5and the molecular weight (MW) was greater than 400g/mol, significant linear relationships were observed between lgKd and both the CF₂ number and MW, with R² values of 0.96 and 0.94, respectively. The influence of soil properties on PFAS adsorption was found to be dependent on the type of PFAS: non-zwitterionic PFAS were mainly affected by organic carbon content and silt content, whereas zwitterionic PFAS were primarily influenced by silt and sand content. After parameter optimization, the ML model exhibited good predictive performance, achieving an R² of 0.85, an RMSE of 0.35, and an MAE of 0.26. MW, organic carbon content, water solubility (lgS), and net charge density were identified as the most important features, with contribution rates of 31.6%, 29.8%, 19.7%, and 10.0%, respectively. Three-dimensional interaction analysis indicated that when soils with high organic carbon content, high cation exchange capacity (CEC), and low pH were combined with PFAS of large MW, lgK_d exceeded 0.40, reflecting strong adsorption. Conversely, under conditions where CEC was below 8.00 cmol/kg, organic matter content was less than 1.00%, and MW was below 380g/mol, lgK_d was lower than 0.40, indicating weaker adsorption and higher potential for environmental migration. The findings provide a reliable tool for predicting the adsorption behavior of PFAS.

, authors=Bing LIU^{1, *}, Ke-ke ZOU², Hui LIU¹, Kai-ge SHI¹, Yan-min CHEN¹, Xin LU¹, Shuai-jun HAN¹, Li GU³, authorsList=Bing LIU, Ke-ke ZOU, Hui LIU, Kai-ge SHI, Yan-min CHEN, Xin LU, Shuai-jun HAN, Li GU, authorCompany=null, correspAuthors=Bing LIU, 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=1314923038341170131, articleId=1314923037108044752, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于可解释性机器学习的PFAS土壤中分配机制, columnId=1234106394572550190, journalTitle=中国环境科学, columnName=土壤污染与控制, runingTitle=null, highlight=null, articleAbstract=

基于机器学习（ML）模型预测全氟和多氟烷基物质（PFAS）的土壤-水分配系数（K_d），并阐释其在土壤中的分配行为．数据集涵盖47种PFAS共1227个样本，采用16个PFAS物化性质及土壤理化参数作为特征．结果表明：lgK_d范围为-1.40~3.95；当CF₂官能团数>5且分子量（MW）>400g/mol时，lgK_d与CF₂数及MW呈显著线性关系（R²分别为0.96和0.94）；土壤特性对PFAS吸附的影响因类型而异：非两性离子PFAS主要受有机碳质量分数和粉砂质量分数影响，而两性离子PFAS则主要受粉砂与砂质量分数影响；经参数优化，ML模型预测性能良好，R²为0.85，RMSE为0.35，MAE为0.26；其中MW，有机碳质量分数，水溶性（lgS）和净电荷密度为关键特征，贡献率分别为31.6%，29.8%，19.7%和10.0%．三维相互作用分析表明，高有机碳含量，高阳离子交换能力（CEC）及低pH值土壤与大MW PFAS结合时，lgK_d>0.40，吸附性强；反之，当CEC<8.00cmol/kg，有机质含量<1.00%，MW<380g/mol时，lgK_d<0.40，吸附能力较弱，环境迁移潜力较高．研究结果为预测PFAS吸附行为提供了可靠工具．

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刘冰（1978-），男，河南许昌人，副教授，博士，主要研究方向为水体中含氮消毒副产物及前体物识别和控制城市污水处理原理与技术．发表论文30余篇．liubing_982002@163.com.

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刘冰（1978-），男，河南许昌人，副教授，博士，主要研究方向为水体中含氮消毒副产物及前体物识别和控制城市污水处理原理与技术．发表论文30余篇．liubing_982002@163.com.

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