Article(id=1240689596082738133, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1240689590315569990, 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=1722268800000, receivedDateStr=2024-07-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773733050604, onlineDateStr=2026-03-17, pubDate=1739980800000, pubDateStr=2025-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773733050604, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773733050604, creator=13701087609, updateTime=1773733050604, updator=13701087609, issue=Issue{id=1240689590315569990, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='2', pageStart='593', pageEnd='1184', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773733049228, creator=13701087609, updateTime=1773733150042, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240690013239825123, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1240689590315569990, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240690013239825124, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1240689590315569990, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=925, endPage=934, ext={EN=ArticleExt(id=1240689596351173605, articleId=1240689596082738133, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Occurrence and probabilistic health risk assessment of PFASs in electroplating contaminated soil, columnId=1234106389669409584, journalTitle=China Environmental Science, columnName=Soil Pollution Control, runingTitle=null, highlight=null, articleAbstract=

Electroplating is one of the most significant sources of perfluorinated and polyfluoroalkyl substances (PFASs) in the environment. In this study, the occurrence of PFASs in soil of a relocated electroplating plant was analyzed. The probabilistic health risk was evaluated using Monte Carlo simulation, and the soil screening levels of PFASs were suggested. The soil samples were detected with 8PFASs, and perfluorooctane sulfonate (PFOS) was detected with the highest detection frequency of 89% and concentration of 388ng/g among the targeted PFASs. The distribution of PFASs in soil was impacted by the locations of production units. The total concentration and concentrations of single PFAS such as PFOS, perfluorohexane sulfonate (PFHxS) were significantly higher in soil at chromium plating department (P<0.05). Intake through human oral exposure of soil PFASs accounted for 75.2%~77.6%. Carcinogenic risk of perfluorooctanoic acid (PFOA) was 6.22×10-11, showing no carcinogenic risk in soil. Non carcinogenic risk quotient of PFOS was larger than 1based on the deterministic values of the exposure factors, while other PFASs showed no non carcinogenic risk. Monte Carlo simulation showed that the risk quotient of PFOS ranged from 0.731 to 5.38, with 95% quantile being 3.85. The 95% quantile values of risk quotient of other PFASs were between 8.77×10-6 and 0.0137. The soil screening levels of targeted PFASs ranged from 78 to 38826ng/g, and PFOS was found to have the most strict level of 78ng/g.

, correspAuthors=Wen-yu ZHANG, 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, authorCompany=null, fund=null, authors=null, authorsList=Lin MA, Guan-song WANG, Lin JIANG, Tian-xiang XIA, Wen-yu ZHANG, Mao-sheng ZHONG, Jing LIANG, Ji-hong LI, Shuo YANG), CN=ArticleExt(id=1240689600600002793, articleId=1240689596082738133, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=电镀工业污染土壤中PFASs的污染特征及健康风险, columnId=1234106394572550190, journalTitle=中国环境科学, columnName=土壤污染与控制, runingTitle=null, highlight=null, articleAbstract=

电镀工业是环境中全氟和多氟烷基物质(PFASs)的重要来源之一,因此,本文选择华北某搬迁电镀企业检测土壤样品中PFASs,采用蒙特卡罗(Monte Carlo)模拟方法评估土壤PFASs对人群的概率健康风险,并进行筛选值的分析.结果表明,共有8种PFASs检出,检出率范围为0~89%,浓度范围为n.d.~388ng/g,全氟辛烷磺酸(PFOS)为浓度和检出率最高的PFASs.厂区土壤PFASs的空间分布与生产单元的分布具有一定相关性,镀铬车间土壤样品检出PFASs种类数量高于其他采样点,PFASs的总浓度显著高于其他样品(P<0.05),除PFOS浓度(388ng/g)高于其他采样点之外,全氟己烷磺酸(PFHxS)的浓度也相对较高(7.07ng/g).不同暴露途径摄入量分析结果表明,PFASs经口暴露量占总暴露量的75.2%~77.6%.全氟辛酸(PFOA)的致癌风险水平为6.22×10-11,致癌风险较低.土壤中PFOS的风险商大于1,对人体具有不可忽视的非致癌风险,其他检出的PFASs非致癌风险较低.对PFASs非致癌风险水平进行概率分析,PFOS风险商的范围为0.731~5.38,95%分位数为3.85,其他PFASs风险商的95%分位数的范围为8.77×10-6~0.0137.PFASs的土壤筛选值范围为78~38826ng/g,其中PFOS筛选值最低,为78ng/g.

, correspAuthors=张文毓, authorNote=null, correspAuthorsNote=
*责任作者,研究员,
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马琳(1989-),女,辽宁大连人,助理研究员,博士,主要从事新污染物环境行为及风险研究.发表文章13篇. .

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马琳(1989-),女,辽宁大连人,助理研究员,博士,主要从事新污染物环境行为及风险研究.发表文章13篇. .

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马琳(1989-),女,辽宁大连人,助理研究员,博士,主要从事新污染物环境行为及风险研究.发表文章13篇. .

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Critical Reviews in Toxicology201444(S1):1-81., articleTitle=A critical review of perfluorooctanoate and perfluorooctanesulfonate exposure and cancer risk in humans, refAbstract=null)], funds=[Fund(id=1240689612495048819, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, awardId=412177404, language=CN, fundingSource=国家自然科学基金资助项目(412177404), fundOrder=null, country=null), Fund(id=1240689612717346946, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, awardId=Y2022-006, language=CN, fundingSource=北京市生态环境保护科学研究院基金资助项目(Y2022-006), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1240689601019433239, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, xref=1., ext=[AuthorCompanyExt(id=1240689601052987675, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, companyId=1240689601019433239, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.Beijing Key Laboratory for Risk Modeling and Remediation of Contaminated Sites, National Engineering Research Centre for Urban Environmental Pollution Control, Beijing Municipal Research Institute of Eco-Environmental Protection, Beijing 100037, China), AuthorCompanyExt(id=1240689601061376284, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, companyId=1240689601019433239, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.北京市生态环境保护科学研究院,国家城市环境污染控制工程技术研究中心,污染场地风险模拟与修复北京市重点实验室,北京 100037)]), AuthorCompany(id=1240689601195594029, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, xref=2., ext=[AuthorCompanyExt(id=1240689601199788334, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, companyId=1240689601195594029, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China), AuthorCompanyExt(id=1240689601212371249, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, companyId=1240689601195594029, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.首都师范大学资源环境与旅游学院,北京 100048)])], figs=[ArticleFig(id=1240689609080886155, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=EN, label=Fig.1, caption=Composition ratio of PFASs in soil and the sketch map of sampling locations, figureFileSmall=8T6TdGw52OsVnYDhP3hsfg==, figureFileBig=aCzWK1Y787ZHzK0r1IZ77g==, tableContent=null), ArticleFig(id=1240689609177355158, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=CN, label=图1, caption=土壤样品中PFASs的组成与采样点分布, figureFileSmall=8T6TdGw52OsVnYDhP3hsfg==, figureFileBig=aCzWK1Y787ZHzK0r1IZ77g==, tableContent=null), ArticleFig(id=1240689609483539391, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=EN, label=Fig.2, caption=Cumulative probability curves of non-carcinogenic risk of soil PFASs, figureFileSmall=slMA62GPD3cMRVlQpEI2cw==, figureFileBig=08P/fJml0zkfG8OSdI0UWw==, tableContent=null), ArticleFig(id=1240689609580008396, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=CN, label=图2, caption=PFASs对人体非致癌风险的累计概率曲线, figureFileSmall=slMA62GPD3cMRVlQpEI2cw==, figureFileBig=08P/fJml0zkfG8OSdI0UWw==, tableContent=null), ArticleFig(id=1240689609680671707, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=EN, label=Fig. 3, caption=Sensitivity analysis of human health risk of PFOS, figureFileSmall=c7yUhOiElp94mykxgBFMgQ==, figureFileBig=ee6eTnpZNUN9zlFr2h5OBg==, tableContent=null), ArticleFig(id=1240689609798112235, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=CN, label=图3, caption=PFOS健康风险的敏感性分析, figureFileSmall=c7yUhOiElp94mykxgBFMgQ==, figureFileBig=ee6eTnpZNUN9zlFr2h5OBg==, tableContent=null), ArticleFig(id=1240689609919747062, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=EN, label=Table 1, caption=

Abbreviations, detection and quantification limits (LOD and LOQ), detection frequencies, concentrations, and cancer slope factoe (SF) and reference dose (RfD) of targeting PFASs

, figureFileSmall=null, figureFileBig=null, tableContent=
目标物简称LOD (ng/g)LOQ (ng/g)检出率(%)最高浓度(ng/g)平均浓度(ng/g)中位浓度(ng/g)SF[(kg·d)/mg]RfD[mg/(kg·d)]
全氟丁酸PFBA12.50----0.0011)
全氟戊酸PFPeA0.51.5112.612.612.61-0.00000382)
全氟己酸PFHxA0.510----0.00051)
全氟庚酸PFHpA0.250.50----0.0000053)
全氟辛酸PFOA0.10.25110.550.550.550.071)0.0000031)
全氟壬酸PFNA0.050.10----0.0000031)
全氟癸酸PFDA0.050.10----0.0000053)
全氟十一烷酸PFUnDA0.0250.05110.1320.1320.132-0.00031)
全氟十二烷酸PFDoDA0.0250.050----0.000051)
全氟丁烷磺酸PFBS0.010.02560.5060.4960.494-0.00031)
全氟己烷磺酸PFHxS0.010.02567.071.750.421-0.000021)
全氟辛烷磺酸PFOS0.010.028938856.06.530.0000021)
全氟癸烷磺酸PFDS0.010.02331.940.7080.157-0.0000122)
全氟烷基磺酰胺FOSA0.00250.005565.933.573.57--
全氟十四烷酸PFTeDA0.010.020----0.0011)
全氟十三烷酸PFTrDA0.010.020----0.0000122)
全氟庚烷磺酸PFHpS0.010.020-----
), ArticleFig(id=1240689610116879360, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=CN, label=表1, caption=

目标PFASs的质控参数、检出率、样品浓度以及致癌斜率因子(SF)、参考剂量(RfD)

, figureFileSmall=null, figureFileBig=null, tableContent=
目标物简称LOD (ng/g)LOQ (ng/g)检出率(%)最高浓度(ng/g)平均浓度(ng/g)中位浓度(ng/g)SF[(kg·d)/mg]RfD[mg/(kg·d)]
全氟丁酸PFBA12.50----0.0011)
全氟戊酸PFPeA0.51.5112.612.612.61-0.00000382)
全氟己酸PFHxA0.510----0.00051)
全氟庚酸PFHpA0.250.50----0.0000053)
全氟辛酸PFOA0.10.25110.550.550.550.071)0.0000031)
全氟壬酸PFNA0.050.10----0.0000031)
全氟癸酸PFDA0.050.10----0.0000053)
全氟十一烷酸PFUnDA0.0250.05110.1320.1320.132-0.00031)
全氟十二烷酸PFDoDA0.0250.050----0.000051)
全氟丁烷磺酸PFBS0.010.02560.5060.4960.494-0.00031)
全氟己烷磺酸PFHxS0.010.02567.071.750.421-0.000021)
全氟辛烷磺酸PFOS0.010.028938856.06.530.0000021)
全氟癸烷磺酸PFDS0.010.02331.940.7080.157-0.0000122)
全氟烷基磺酰胺FOSA0.00250.005565.933.573.57--
全氟十四烷酸PFTeDA0.010.020----0.0011)
全氟十三烷酸PFTrDA0.010.020----0.0000122)
全氟庚烷磺酸PFHpS0.010.020-----
), ArticleFig(id=1240689610288844814, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=EN, label=Table 2, caption=

Exposure parameters and values in deterministic and probabilistic methods

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露参数含义单位确定值分布类型参数取值
EFc[26]儿童暴露频率d/a350三角分布min=180,mode=345,max=365
EFa[26]成人暴露频率d/a350三角分布min=180,mode=345,max=365
EDc[27]儿童暴露期a6对数正态分布m=3.16,s=2.84
EDa[27]成人暴露期a24对数正态分布m=11.7,s=7
ATca[27]致癌效应平均时间d27740最小极值分布a=28089,b=404
ATc非致癌作用时间d2190--
BWc[27]儿童体重kg19.2对数正态分布m=16.68,s=1.48
BWa[27]成人体重kg61.8对数正态分布m=59.78,s=1.07
IRs-c[28]儿童土壤摄入量mg/d200三角分布min=66,mode=103,max=161
IRs-a[28]成人土壤摄入量mg/d100三角分布min=4,mode=30,max=52
PM10[29]可吸入颗粒物含量µg/m3119对数正态分布m=145.3,s=4.535
SAc[25,30]儿童皮肤暴露面积cm22848均匀分布min=1300,max=3100
SAa[25,30]成人皮肤暴露面积cm25374均匀分布min=3600,max=5760
IRa-c[27]儿童空气摄入量m3/d7.5均匀分布min=4.7,max=6.4
IRa-a[27]成人空气摄入量m3/d14.5均匀分布min=11.8,max=16.7
ABSd皮肤吸收效率-0.1--
AFc儿童皮肤黏附土壤系数mg/cm20.2--
AFa成人皮肤黏附土壤系数mg/cm20.07--
), ArticleFig(id=1240689611748462619, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=CN, label=表2, caption=

概率风险评估参数含义及取值

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暴露参数含义单位确定值分布类型参数取值
EFc[26]儿童暴露频率d/a350三角分布min=180,mode=345,max=365
EFa[26]成人暴露频率d/a350三角分布min=180,mode=345,max=365
EDc[27]儿童暴露期a6对数正态分布m=3.16,s=2.84
EDa[27]成人暴露期a24对数正态分布m=11.7,s=7
ATca[27]致癌效应平均时间d27740最小极值分布a=28089,b=404
ATc非致癌作用时间d2190--
BWc[27]儿童体重kg19.2对数正态分布m=16.68,s=1.48
BWa[27]成人体重kg61.8对数正态分布m=59.78,s=1.07
IRs-c[28]儿童土壤摄入量mg/d200三角分布min=66,mode=103,max=161
IRs-a[28]成人土壤摄入量mg/d100三角分布min=4,mode=30,max=52
PM10[29]可吸入颗粒物含量µg/m3119对数正态分布m=145.3,s=4.535
SAc[25,30]儿童皮肤暴露面积cm22848均匀分布min=1300,max=3100
SAa[25,30]成人皮肤暴露面积cm25374均匀分布min=3600,max=5760
IRa-c[27]儿童空气摄入量m3/d7.5均匀分布min=4.7,max=6.4
IRa-a[27]成人空气摄入量m3/d14.5均匀分布min=11.8,max=16.7
ABSd皮肤吸收效率-0.1--
AFc儿童皮肤黏附土壤系数mg/cm20.2--
AFa成人皮肤黏附土壤系数mg/cm20.07--
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Intake through different exposure routes and the result of Monte Carlo simulation

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途径全生命周期摄入量[mg/(kg·d)]儿童期摄入量[mg/(kg·d)]
PFOAPFOAPFOS
定值蒙特卡洛模拟结果定值蒙特卡洛模拟结果定值蒙特卡洛模拟结果
经口暴露7.032×10-1025%6.014×10-115.494×10-925%1.745×10-93.876×10-625%1.233×10-6
50%8.978×10-1150%2.333×10-950%1.650×10-6
75%1.313×10-1075%3.019×10-975%2.133×10-6
95%2.157×10-1095%4.010×10-995%2.821×10-6
皮肤接触暴露2.249×10-1025%3.868×10-111.565×10-925%6.852×10-101.104×10-625%4.834×10-7
50%5.448×10-1150%9.135×10-1050%6.445×10-7
75%7.623×10-1175%1.213×10-975%8.555×10-7
95%1.199×10-1095%1.596×10-995%1.177×10-6
呼吸土壤颗粒暴露6.586×10-1225%1.540×10-122.452×10-1125%1.309×10-111.730×10-825%9.235×10-9
50%2.165×10-1250%1.751×10-1150%1.235×10-8
75%3.079×10-1275%2.212×10-1175%1.560×10-8
95%5.078×10-1295%3.929×10-1195%1.942×10-8
), ArticleFig(id=1240689612075618360, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=CN, label=表3, caption=

PFOA和PFOS的不同途径摄入量及Monte Carlo模拟结果

, figureFileSmall=null, figureFileBig=null, tableContent=
途径全生命周期摄入量[mg/(kg·d)]儿童期摄入量[mg/(kg·d)]
PFOAPFOAPFOS
定值蒙特卡洛模拟结果定值蒙特卡洛模拟结果定值蒙特卡洛模拟结果
经口暴露7.032×10-1025%6.014×10-115.494×10-925%1.745×10-93.876×10-625%1.233×10-6
50%8.978×10-1150%2.333×10-950%1.650×10-6
75%1.313×10-1075%3.019×10-975%2.133×10-6
95%2.157×10-1095%4.010×10-995%2.821×10-6
皮肤接触暴露2.249×10-1025%3.868×10-111.565×10-925%6.852×10-101.104×10-625%4.834×10-7
50%5.448×10-1150%9.135×10-1050%6.445×10-7
75%7.623×10-1175%1.213×10-975%8.555×10-7
95%1.199×10-1095%1.596×10-995%1.177×10-6
呼吸土壤颗粒暴露6.586×10-1225%1.540×10-122.452×10-1125%1.309×10-111.730×10-825%9.235×10-9
50%2.165×10-1250%1.751×10-1150%1.235×10-8
75%3.079×10-1275%2.212×10-1175%1.560×10-8
95%5.078×10-1295%3.929×10-1195%1.942×10-8
), ArticleFig(id=1240689612180475974, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=EN, label=Table 4, caption=

Results of soil screening levels

, figureFileSmall=null, figureFileBig=null, tableContent=
PFASs本研究美国[21]加拿大[40]
PFBA3882678000114000
PFPeA148800
PFHxA1941332000800
PFHpA194800
PFOA116190700
PFNA11619080
PFDA194
PFUnDA1164819000
PFDoDA19413200
PFBS116481900061000
PFHxS77713002300
PFOS781302100
PFDS466
PFTeDA3882663000
PFTrDA466
), ArticleFig(id=1240689612310499416, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1240689596082738133, language=CN, label=表4, caption=

筛选值计算结果(ng/g)

, figureFileSmall=null, figureFileBig=null, tableContent=
PFASs本研究美国[21]加拿大[40]
PFBA3882678000114000
PFPeA148800
PFHxA1941332000800
PFHpA194800
PFOA116190700
PFNA11619080
PFDA194
PFUnDA1164819000
PFDoDA19413200
PFBS116481900061000
PFHxS77713002300
PFOS781302100
PFDS466
PFTeDA3882663000
PFTrDA466
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电镀工业污染土壤中PFASs的污染特征及健康风险
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马琳 1 , 王观送 1, 2 , 姜林 1 , 夏天翔 1 , 张文毓 1, * , 钟茂生 1 , 梁竞 1 , 李吉鸿 1 , 杨硕 1
中国环境科学 | 土壤污染与控制 2025,45(2): 925-934
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中国环境科学 | 土壤污染与控制 2025, 45(2): 925-934
电镀工业污染土壤中PFASs的污染特征及健康风险
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马琳1 , 王观送1, 2, 姜林1, 夏天翔1, 张文毓1, * , 钟茂生1, 梁竞1, 李吉鸿1, 杨硕1
作者信息
  • 1.北京市生态环境保护科学研究院,国家城市环境污染控制工程技术研究中心,污染场地风险模拟与修复北京市重点实验室,北京 100037
  • 2.首都师范大学资源环境与旅游学院,北京 100048
  • 马琳(1989-),女,辽宁大连人,助理研究员,博士,主要从事新污染物环境行为及风险研究.发表文章13篇. .

通讯作者:

*责任作者,研究员,
Occurrence and probabilistic health risk assessment of PFASs in electroplating contaminated soil
Lin MA1 , Guan-song WANG1, 2, Lin JIANG1, Tian-xiang XIA1, Wen-yu ZHANG1, * , Mao-sheng ZHONG1, Jing LIANG1, Ji-hong LI1, Shuo YANG1
Affiliations
  • 1.Beijing Key Laboratory for Risk Modeling and Remediation of Contaminated Sites, National Engineering Research Centre for Urban Environmental Pollution Control, Beijing Municipal Research Institute of Eco-Environmental Protection, Beijing 100037, China
  • 2.College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
出版时间: 2025-02-20
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电镀工业是环境中全氟和多氟烷基物质(PFASs)的重要来源之一,因此,本文选择华北某搬迁电镀企业检测土壤样品中PFASs,采用蒙特卡罗(Monte Carlo)模拟方法评估土壤PFASs对人群的概率健康风险,并进行筛选值的分析.结果表明,共有8种PFASs检出,检出率范围为0~89%,浓度范围为n.d.~388ng/g,全氟辛烷磺酸(PFOS)为浓度和检出率最高的PFASs.厂区土壤PFASs的空间分布与生产单元的分布具有一定相关性,镀铬车间土壤样品检出PFASs种类数量高于其他采样点,PFASs的总浓度显著高于其他样品(P<0.05),除PFOS浓度(388ng/g)高于其他采样点之外,全氟己烷磺酸(PFHxS)的浓度也相对较高(7.07ng/g).不同暴露途径摄入量分析结果表明,PFASs经口暴露量占总暴露量的75.2%~77.6%.全氟辛酸(PFOA)的致癌风险水平为6.22×10-11,致癌风险较低.土壤中PFOS的风险商大于1,对人体具有不可忽视的非致癌风险,其他检出的PFASs非致癌风险较低.对PFASs非致癌风险水平进行概率分析,PFOS风险商的范围为0.731~5.38,95%分位数为3.85,其他PFASs风险商的95%分位数的范围为8.77×10-6~0.0137.PFASs的土壤筛选值范围为78~38826ng/g,其中PFOS筛选值最低,为78ng/g.

全氟和多氟烷基物质  /  土壤  /  赋存  /  概率健康风险  /  筛选值

Electroplating is one of the most significant sources of perfluorinated and polyfluoroalkyl substances (PFASs) in the environment. In this study, the occurrence of PFASs in soil of a relocated electroplating plant was analyzed. The probabilistic health risk was evaluated using Monte Carlo simulation, and the soil screening levels of PFASs were suggested. The soil samples were detected with 8PFASs, and perfluorooctane sulfonate (PFOS) was detected with the highest detection frequency of 89% and concentration of 388ng/g among the targeted PFASs. The distribution of PFASs in soil was impacted by the locations of production units. The total concentration and concentrations of single PFAS such as PFOS, perfluorohexane sulfonate (PFHxS) were significantly higher in soil at chromium plating department (P<0.05). Intake through human oral exposure of soil PFASs accounted for 75.2%~77.6%. Carcinogenic risk of perfluorooctanoic acid (PFOA) was 6.22×10-11, showing no carcinogenic risk in soil. Non carcinogenic risk quotient of PFOS was larger than 1based on the deterministic values of the exposure factors, while other PFASs showed no non carcinogenic risk. Monte Carlo simulation showed that the risk quotient of PFOS ranged from 0.731 to 5.38, with 95% quantile being 3.85. The 95% quantile values of risk quotient of other PFASs were between 8.77×10-6 and 0.0137. The soil screening levels of targeted PFASs ranged from 78 to 38826ng/g, and PFOS was found to have the most strict level of 78ng/g.

PFASs  /  soil  /  occurrence  /  probabilistic health risk  /  screening level
马琳, 王观送, 姜林, 夏天翔, 张文毓, 钟茂生, 梁竞, 李吉鸿, 杨硕. 电镀工业污染土壤中PFASs的污染特征及健康风险. 中国环境科学, 2025 , 45 (2) : 925 -934 .
Lin MA, Guan-song WANG, Lin JIANG, Tian-xiang XIA, Wen-yu ZHANG, Mao-sheng ZHONG, Jing LIANG, Ji-hong LI, Shuo YANG. Occurrence and probabilistic health risk assessment of PFASs in electroplating contaminated soil[J]. China Environmental Science, 2025 , 45 (2) : 925 -934 .
全氟和多氟烷基化合物(PFASs)是一种受到广泛关注的持久性新污染物,主要包含全氟烷基磺酸及其盐、全氟烷基羧酸及其盐以及各种前体物等,是一类重要的工业和商品原料[1-2].PFASs热稳定性、化学稳定性高,疏水疏油,被广泛用于农药、表面活性剂、润滑剂、食品包装等[1].据估计,1951~2004年,全氟烷基羧酸类全行业直接(制造、使用、消费品)和间接(杂质和/或前体)来源的总排放量为3200~7300t[2]. PFASs可以通过污水厂出水和污泥施用、水成膜泡沫灭火剂的使用、氟化工业及相关产品的生产、垃圾填埋等多种途径进入环境[3-4].截至目前,已在饮用水[5]、地表水[6]、地下水[7]、沉积物[8]、土壤[6]等多种环境介质,以及人体体液[9]中检出PFASs.在欧洲23个国家的普查发现地下水中PFASs的检出率超过50%,全氟辛烷磺酸(PFOS)浓度高达133ng/L[1].在澳大利亚和瑞典的消防训练场土壤中,检出了浓度高达103~104ng/g的PFASs[10-11].
已有研究表明,PFASs可以通过食物链、饮用水、呼吸等方式进入人体[12].根据美国疾控中心有毒物质和疾病登记处、世卫组织国际癌症研究机构等组织的报告,人体暴露于全氟辛酸(PFOA)会导致患肾癌、生殖系统癌症概率增加,且有明确证据表明PFOS、全氟己烷磺酸(PFHxS)、PFOA会造成肝损伤、胆固醇水平紊乱等代谢性疾病[12].由于PFASs所具有的持久性和毒性,截至2022年底,《关于持久性有机污染物的斯德哥尔摩公约》已将PFOS、PFOA、PFHxS列入管控,采取不同的消除或限制措施[13].PFOS被列入OSPAR公约(东北大西洋环境保护委员会)的优先控制化合物名单中[14].我国也于2022年发布了《新污染物治理行动方案》,其中PFOS类、PFOA类及PFHxS类为重点管控的新污染物[15].
金属电镀行业采用含PFASs的抑雾剂来控制镀铬过程中六价铬的排放,是环境中PFASs的重要来源之一.因管控等原因,PFOS逐渐被其他PFASs代替(如6:2氯代多氟醚基磺酸盐),但长达几十年的使用依然会造成其残留于环境中[16].已有研究表明,电镀工业园区污水中的PFASs对周边水体造成了不可忽视的影响[17].而目前搬迁电镀企业遗留污染土壤中PFASs的污染特征及其健康风险尚未得到广泛关注.本文选择华北地区某已搬迁电镀厂,厂区在产时间为1985~2000年,停产后生产设施已移走,保留部分建筑,并保持封存状态.基于土壤中PFASs的赋存水平及组成特征分析,厘清了PFASs土壤污染与生产单元分布的关系;采用蒙特卡罗(Monte Carlo)模拟方法,选取本地特征模型参数进行概率风险评估,阐明了产生风险的PFAS种类及主要暴露途径;并提出了土壤筛选值.通过本研究,为我国电镀类工业企业PFASs的环境管理提供理论依据和数据支持,同时为我国新污染物的筛选值制定提供参考.
厂区总面积约8200m2,内共布设9个采样点;其中生产区面积约5000m2,在生产区共布设8个采样点,主要关注镀铬车间、镀锌车间、抛光车间、污水处理池等(如图1所示).于2020年10月进行土壤样品的采集,采样深度为0.2m,采样点位置见图1.土壤样品使用木勺采集后用锡纸包裹,然后装入聚丙烯(PP)密封袋,放入保温箱中保存,运回实验室后保存于4℃冰箱中.设置运输空白来实现对采样过程的质控,同时每个土壤样品采集2个平行样.
17种全氟化合物(见表1)、12种同位素内标(13C4-PFBA,13C5-PFPeA,13C2-PFHxA,13C4-PFHpA,13C4-PFOA,13C5-PFNA,13C2-PFDA,13C2-PFUnDA,13C2-PFDoA,13O2-PFHxS,13C4-PFOS,13C8-FOSA)均购于Wellington公司(加拿大).实验所用乙酸铵、甲醇均为色谱纯,购置于Thermo Fisher公司,高纯水取自Milli-Q超纯水机(Millpore,USA).
土壤样品分析采用超高效液相色谱串联质谱联用仪Aquity UPLC-Quatro Premier XE(Waters,美国)对目标物质进行定量分析,色谱柱为UPLC BEH C18液相色谱柱.样品前处理采用超声仪(新芝,中国)、固相萃取仪(Thermo,美国)进行,其他仪器、耗材包括采用WAX固相萃取柱(6mL,150mg,Waters,美国)、水浴恒温氮吹仪(峥嵘,中国)、玻璃纤维滤膜(GF/F,47mm,Whatman,英国)等.
将土样真空冷冻干燥48h,去除杂草、小石块后研磨过100目筛,土壤样品中的全氟化合物提取净化主要依据EPA 1633[18],以及Gan等[19]的研究进行.称取土壤加入内标物后,混匀避光恒温老化过夜,采用甲醇超声萃取,离心后收集上清液,重复3次.用超纯水将上清液稀释后过固相萃取柱净化富集,洗脱液使用柔和高纯氮气浓缩至0.5mL并过0.22µm PP材质的微孔滤膜,于进样小瓶中密封冷藏,待仪器分析.
仪器分析方法参考EPA 1633[18],以及Gan等[19]、Ma等[20]的研究.超高效液相色谱流动相为2mmol/L醋酸铵水溶液和甲醇.柱温为30℃,流速为0.3mL/min,进样量10µL.高效液相色谱的洗脱梯度为:从0~7min,甲醇由40%线性升高至100%,保持3.5min后,立刻恢复到初始进样比例,平衡3min.质谱条件为电喷雾离子化源(ESI),负离子电离模式,多反应检测方式(MRM).离子喷雾电压-4500V,温度450℃.
在整个采样、分析过程中需遵循质量保证/质量控制(QA/QC)程序,相对于其他微量有机化合物,PFASs的分析流程有其特殊性.为避免长链PFASs在玻璃容器上的吸附,实验整个流程中采用PP材质容器,此外仪器内部的PTFE管道部件均更换为不锈钢.样品分析采用内标定量法.同时设置基质空白样品,空白样品中均未检出PFASs.以目标PFASs色谱峰信噪比(S/N)为3/1的浓度作为仪器检出限(LOD),以色谱峰信噪比(S/N)为10/1的浓度作为方法定量限(LOQ),目标PFASs的LOD、LOQ、基本信息及分析结果见表1.
因本文关注的PFASs挥发性较低[21],其主要暴露途径为不慎摄入土壤颗粒、吸入土壤颗粒和皮肤接触土壤.假设土壤中PFASs的人体生物可给性为1,按照北京市地方标准《建设用地土壤污染状况调查与风险评估技术导则》(DB11/T 656-2019)[24]推荐方法,对不同暴露途径的污染物摄入量进行计算,如式(1)~(3):
式中:EDI为不同暴露途径的污染物摄入量,mg/(kg·d);C为土壤中污染物浓度,mg/kg.污染物总暴露量为各暴露途径暴露量之和,其余参数含义见表2,各参数儿童及成人暴露期取值以角标c和a区分.考虑污染物的致癌风险,将污染物平均到整个生命周期内平均每天的摄入量乘以经口、皮肤接触、呼吸土壤颗粒致癌斜率系数计算得出,见式(4):
式中:HRi为某种污染物不同摄入途径的致癌风险水平,无量纲;SF为各种暴露方式下的致癌斜率因子,(mg/(kg·d))-1,取值见表1.非致癌风险水平通过将污染物平均到整个暴露周期的每天摄入量除以每一种暴露方式的慢性参考剂量得出,见式(5):
式中:HIi为某种污染物不同摄入途径的非致癌风险水平,即风险商,无量纲;RfD为各暴露方式下的慢性参考剂量,mg/(kg·d),取值见表1;SAF为暴露于土壤的参考剂量分配系数,无量纲,取值为0.5[25].
根据相关导则[24-25],基于人体健康风险的土壤污染筛选值,是指当土壤中污染物含量等于或低于该值时,对人体健康的风险可以忽略;超过该值时,对人体健康可能存在风险,应当开展进一步的详细调查和风险评估,确定具体污染范围和风险水平,本研究可接受致癌风险水平取值10-6,可接受非致癌风险水平取值为1.将可接受风险水平代入式(4)和(5),PFASs的毒理学参数取值和数据来源见表2,同时结合式(1)~(3),可得污染物筛选值.
基于健康的风险评估过程中,推荐的模型参数确定性取值往往无法体现人群个体对毒性反应的差异性,这种暴露参数的变异性会使评估结果产生偏差,造成健康风险的高估或低估[27,31].将Monte-Carlo模拟方法应用于风险评估,可以量化不确定性,提供更加完整的污染物风险信息.具体步骤为首先将参数的概率分布引入评估模型,并通过随机抽样的方式进行多次迭代模拟,获取预测值来表征风险评估结果.
采用Crystal Ball 11.1.45(Oracle Co.,USA)进行Monte-Carlo模拟,首先确定风险评估模型的随机变量,根据暴露途径分析(式(1)~(3)),暴露频率、暴露期、作用时间、体重、土壤和空气摄入量、皮肤暴露面积为不确定参数,此外,可吸入颗粒物含量(PM10)用于计算吸入土壤颗粒途径的暴露量[25],其不确定性会导致吸入土壤颗粒途径风险的不确定性,因此本研究将PM10作为不确定参数.根据我国土壤污染风险相关研究及我国居民特征暴露参数分布相关研究,构建不确定参数的分布模型[27,30],如表2所示.根据Wang等[29]的研究可知,北京及周边地区空气PM10含量符合对数正态分布,以此代表本文所选择的华北地区的采样点空气PM10含量分布.
本研究设定的抽样次数为10000次,置信水平确定为95%.在蒙特卡罗模拟的分析过程中,可以同时进行敏感性分析,可确定对健康风险评估结果影响最大的因素并量化其贡献.
本研究采集已搬迁电镀厂地表土壤样品,并进行PFASs的分析,样品分析结果(表1图1)显示,17种目标PFASs中,有8种物质检出,其中,检出率最高的物质PFOS在8个生产区样品中均有检出,另PFBS、PFHxS和FOSA检出率超过50%,而PFOA、PFPeA、PFUnDA仅有一个样品检出.PFASs单体的浓度范围为ND~388ng/g,其中浓度最高的为PFOS,中位值浓度为6.53ng/g,平均值达到了56ng/g;其次为FOSA,中位值浓度为3.57ng/g;PFPeA仅在一个样品中检出,浓度为2.61ng/g,其他物质浓度较低,中位值均低于1ng/g.本研究土壤中PFOS浓度最高,可能是电镀厂在产期间(2000年前)PFOS作为铬雾抑制剂并未限制使用,造成了土壤中PFOS浓度显著高于其他PFASs.本研究中土壤PFOS最大浓度已经超过美国EPA的居住用地筛选值,未来若开发用于居住用地,将危害到居民健康[21].
厂区土壤PFASs的空间分布与生产单元的分布具有一定相关性.由图1可知,镀铬车间土壤样品检出PFASs种类多于其他采样点,共有6种物质检出,其中PFOA仅在镀铬车间样品中检出,其次为污水处理池处土壤样品,共检出5种物质,且PFPeA仅在此样品中检出.其他采样点基本仅检出1~3种物质,包括镀锌车间、酸料库等,说明镀铬工艺及镀铬废水是厂区土壤中PFASs的重要来源,而位于非生产区的S9检出了4种PFAS,可能是由于在产期间灭火装置的使用造成的.因电镀过程中约20%的PFOS通过空气排放[32],非生产区土壤PFASs的检出也可能来自大气沉降.土壤中PFASs的浓度也有类似规律,位于镀铬车间的土壤样品,PFASs的总浓度显著高于其他样品(P<0.05),除PFOS浓度(388ng/g)高于其他采样点之外,PFHxS的浓度也相对较高(7.07ng/g).
本研究中PFASs浓度,尤其是PFOS显著高于其他研究中非污染地区土壤.如在韩国西部沿海地区,土壤中PFOS浓度范围为n.d.~1.7ng/g[33];在挪威的天然林区,检出了浓度为0.0813~0.637ng/g的PFOS[34];上海的农业用地土壤中,PFOS检出的浓度范围为n.d.~0.7ng/g[35];而在尼泊尔Koshi流域土壤,PFOS检出的浓度仅为n.d.~0.1ng/g[36].从Washington等[37]的研究可知,全球范围土壤PFASs的背景浓度为ng/kg级别,本研究所选择的已搬迁电镀厂土壤PFASs浓度远超背景值,尤其是PFOS浓度的显著增加,说明本场地已受到PFASs的污染,特别是受到PFOS的污染.
与其他电镀工业园区土壤相关研究比较,本研究土壤中PFOS浓度水平相当,如浙江地区相关土壤PFOS浓度为17.1~309ng/g[16],上海地区为0.08~4.56ng/g[35],Li等[38]在电镀工业园区检出0.4~53.8ng/g的PFOS.其他用地类型PFASs污染场地相关研究表明,PFOS是污染土壤中检出率和浓度最高的PFASs之一,在不同用地类型土壤中的平均浓度范围为从ng/g~µg/g级别不等[4].其次PFOA浓度较高,在不同用地类型土壤中的平均浓度范围为2.9~24.4ng/g[30],主要在消防训练场、垃圾填埋场检出,如Mattias等在瑞典的污染土壤样品中发现PFOA的检出率为100%,最高浓度达到了287ng/g[10].与这些场地相比,本研究土壤样品中PFOA浓度相对偏低,这与电镀企业的工艺密切相关.
根据EPA相关文件[21]以及PFASs人体健康影响相关研究[39]可知,PFOA对人体具有潜在的致癌风险.经计算(表3),PFOA经口、经皮肤接触和吸入土壤颗粒3个途径的全生命周期摄入量分别为7.032×10-10,2.249×10-10和6.586×10-12mg/(kg·d),总摄入量达到9.347×10-10mg/(kg·d).其中经口暴露量占总摄入量的75.2%,经皮肤接触和呼吸土壤颗粒途径的摄入量分别占24.1%和0.7%,本研究中,PFOA的致癌效应主要是通过经口暴露途径产生的.对PFOA不同途径摄入量进行Monte-Carlo模拟,3种途径摄入量以及总摄入量的95%分位数分别为2.157×10-10,1.199×10-10,5.078×10-12和3.286×10-10mg/(kg·d).
多种PFAS会对人体产生非致癌风险,对于单一污染物的非致癌效应,考虑人群在儿童期暴露受到的危害.基于物质的最高检出浓度进行计算,摄入量最高的物质为PFOS,为4.997×10-6mg/(kg·d),比PFHxS、PFPeA、PFDS、FOSA的摄入量高2个数量级,比PFOA、PFUnDA、PFBS高3个数量级.各物质不同途径摄入量由大到小依次为经口、皮肤接触、呼吸土壤颗粒.其中,经口暴露量所占比例为77.6%,经皮肤接触摄入量占22.1%,而经呼吸土壤颗粒途径的摄入量占比为0.3%,即PFASs的非致癌效应也是主要通过经口暴露途径产生的.对儿童时期不同途径PFASs的摄入量进行Monte Carlo模拟,PFOA和PFOS的模拟结果如表3.
美国EPA给出了PFOA的致癌斜率因子为0.07(kg⋅d)/mg,基于其最大浓度(0.55ng/g)进行计算,致癌风险均水平为6.543×10-11,未超过10-6,说明选择的搬迁电镀厂土壤中的PFOA对人体的致癌风险较低.通过Monte Carlo模拟进行概率分析,PFOA经口、皮肤接触、吸入土壤颗粒暴露途径,以及所有途径产生的HR的95%分位值分别为1.541×10-11、8.50×10-12、3.61×10-13以及2.37×10-11.
根据相关标准[25],对于非挥发性有机污染物,暴露于土壤的参考剂量分配比例取值为0.5,基于导则推荐的参数定值(表2),利用最大浓度计算各检出的PFASs非致癌健康风险,HI由大到小顺序为PFOS、PFPeA、PFHxS、PFOA、PFDS、PFUnDA、PFBS,其中PFOS的HI达到了5.00,PFPeA的HI为0.018,其他污染物的风险商均小于0.01,对人体非致癌健康风险较小.因此本研究镀铬车间污染土壤中的PFOS对人体具有不可忽视的健康风险.与不同途径污染物摄入量的规律类似,各物质经口暴露途径的HI最高,高于其他两种途径1~2个数量级.其中,PFOS基于经口暴露途径产生的HI为3.88,其他两种摄入途径产生的HI小于1.从采样点位置来看,镀铬车间土壤样品PFOS的HI大于1,其他采样点均小于1.将各检出的PFASs的HI进行Monte Carlo模拟,结果如图2所示.PFOS的HI范围为0.731~5.38,95%分位数为3.85,HI大于1的概率为99.18%.其他6种PFASs的HI的95%分位数的范围为8.77×10-6~0.0137,均低于可接受的非致癌风险水平.
综上,此搬迁电镀厂土壤中的PFASs对人体主要产生非致癌风险,产生风险的PFASs种类为PFOS,原镀铬车间区域土壤中PFOS的非致癌风险水平超过1的概率为99.18%,其他PFASs对人体健康风险较低.
利用Monte Carlo模拟方法,对人体暴露参数进行了敏感性分析,可以反映各参数对风险结果的影响程度,结果如图3所示.儿童暴露期(EDc)对人群的致癌风险敏感性最强,解释了57.6%的不确定性,其次,成人暴露期(EDa)、儿童暴露频率(EFc)分别解释了15.1%和13%的不确定性,其他参数对致癌风险的敏感性低于5%.而对于非致癌风险,EFc的敏感性最强,解释了74.2%的不确定性,其次为儿童土壤摄入量(IRs-c),方差贡献为18.9%.
对目标PFASs中已有毒性参考剂量的物质的土壤筛选值进行计算,如表4所示.PFASs的筛选值范围为78~38826ng/g,其中,PFOS筛选值最低,为78ng/g,四碳的PFBA、PFBS,六碳的PFHxA,和长链的PFTeDA、PFUnDA由于RfD值较高,毒性较低,筛选值在105ng/g水平.其他PFASs的筛选值均在102~103ng/g水平.与EPA“区域筛选值(Regional Screening Levels) ”给出的居住用地土壤筛选值相比[21],其高于本研究1.6~2倍,此差异是由于选取的人体暴露参数取值不同,主要是体重取值不同所造成的(EPA的儿童体重为15kg,而我国取19.2kg[25]).由表4可知,加拿大居住用地土壤筛选值比本研究中大部分PFASs的筛选值更高,如PFOS、PFBA、PFHxS比本研究筛选值高1~2个数量级,PFPeA、PFHpA、PFOA、PFBS比本研究高4~6倍,这主要是由于加拿大标准中采用了相对更高的毒理学参考值导致的(以PFOS和PFOA为例,加拿大选择0.00006和0.000021mg/(kg·d)作为参考剂量[40],本研究选择USEPA给出的参考剂量0.000002和0.000003mg/(kg·d).
随着生物学研究的不断发展,PFASs的土壤筛选值也在不断更新.如美国EPA在2009年依据PFOS和PFOA相关毒理学数据(RfD分别为8×10-5和2×10-4mg/(kg·d)),提出了土壤筛选值分别为6×103和1.6×104ng/g[41].至2022年,EPA“区域筛选值”提供的RfD已更新为2×10-6和3×10-6mg/(kg·d),相应居住用地筛选值已更新为130和190ng/g[21].以上的土壤筛选值都是基于非致癌性来制定的,而从最新研究来看,PFOA和PFOS的致癌作用比已有认知更强[42-45],两种物质均被EPA归类为“可能致癌的物质”(Likely to Be Carcinogenic to Humans),EPA提出了草拟的PFOS和PFOA的致癌斜率因子,分别为39.5(kg·d)/mg[46]和29300(kg·d)/mg[47].美国州际环境技术和规则委员会(ITRC)建议,可以使用这两个值进行PFOS和PFOA的致癌风险评估[48],此时计算出的两种物质的筛选值分别为15.67和0.0211ng/g.目前,美国根据PFOS和PFOA新的致癌效应相关研究,提出了更加严格的饮用水标准建议,PFOA和PFOS可接受的最高污染水平均为4ng/L[49].
新的饮用水标准限制已经在全球引起了广泛的讨论.Arrieta-Cortes等指出,PFOS职业暴露和一般人群暴露研究中未观察到与癌症的相关性,认为基于人类数据,没有足够证据表明PFOS的致癌性[50].Chang等认为目前的流行病学证据不能够支持PFOS和PFOA与人类癌症之间的因果关系[51].因此,急需补充PFASs更充分和可靠的致癌毒理学研究,不断完善其在土壤中的筛选值制定.尽管PFASs的毒性研究仍然存在较大的不确定性,但从近几年不断发现的新证据,以及从水质标准和土壤筛选值的变化来看,未来土壤PFASs的筛选值可能将趋向更加保守.
3.1 搬迁电镀厂区内土壤中17种目标PFASs中共有8种检出,其中PFOS检出率为89%,PFBS、PFHxS和FOSA的检出率为56%,其余物质低于50%.PFASs单体浓度范围为ND~388ng/g,其中PFOS浓度最高,最高浓度为388ng/g,平均浓度为56ng/g.厂区土壤PFASs的空间分布与生产单元的分布具有一定相关性.
3.2 经口暴露是该场地土壤PFASs的主要摄入途径以及风险来源,占总摄入量和总风险的75.2%~77.6%.土壤中的PFASs对人体主要产生非致癌风险,产生风险的物质为PFOS.概率分析结果表明,PFOS的HI范围为0.731~5.38,95%分位数为3.85,其他6种PFASs的HI的95%分位数的范围为8.77×10-6~0.0137.儿童暴露频率对人群非致癌风险的敏感性最强.
3.3 对PFASs的筛选值进行了计算,其范围为78~38826ng/g,其中PFOS筛选值最为严格.
  • 国家自然科学基金资助项目(412177404)
  • 北京市生态环境保护科学研究院基金资助项目(Y2022-006)
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  • 接收时间:2024-07-30
  • 首发时间:2026-03-17
  • 出版时间:2025-02-20
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  • 收稿日期:2024-07-30
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国家自然科学基金资助项目(412177404)
北京市生态环境保护科学研究院基金资助项目(Y2022-006)
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    1.北京市生态环境保护科学研究院,国家城市环境污染控制工程技术研究中心,污染场地风险模拟与修复北京市重点实验室,北京 100037
    2.首都师范大学资源环境与旅游学院,北京 100048

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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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