Article(id=1320438137176830090, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438090615862080, articleNumber=null, orderNo=null, doi=10.11847/zgggws1147580, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1743436800000, receivedDateStr=2025-04-01, revisedDate=1754841600000, revisedDateStr=2025-08-11, acceptedDate=1756483200000, acceptedDateStr=2025-08-30, onlineDate=1792746584457, onlineDateStr=2026-10-23, pubDate=1775750400000, pubDateStr=2026-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792746584457, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792746584457, creator=13701087609, updateTime=1792746584457, updator=13701087609, issue=Issue{id=1320438090615862080, tenantId=1146029695717560320, journalId=1319373219989389464, year='2026', volume='42', issue='4', pageStart='385', pageEnd='512', issueExtLink='null', onlineDate='null', pubDate='1775750400000', pubDateStr='2026-04-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792746573356, creator='13701087609', updateTime=1792748076043, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320444393430040620, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438090615862080, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320444393430040621, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438090615862080, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=470, endPage=478, ext={EN=ArticleExt(id=1320438137382350987, articleId=1320438137176830090, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Current research status on distribution and health risks of heavy metals (metalloids) in atmospheric PM2.5 in tropical regions: a systematic review, columnId=1320427713148597230, journalTitle=Chinese Journal of Public Health, columnName=Review, runingTitle=null, highlight=null, articleAbstract=

This article systematically reviews the research progress in the concentration spectrum, spatio-temporal distribution, and health risks of 14 heavy metals (metalloids) in atmospheric PM2.5 in tropical regions around the world from 2014 to 2024. The results showed that the concentrations of aluminum (Al) and iron (Fe) in atmospheric PM2.5 among countries in tropical regions were both high and followed the trend of countries with mid-high income < countries with high income < countries with mid-low income. Other heavy metals (metalloids) also had certain regional composite pollution differences. Specifically, the pollution was dominated by barium (Ba), zinc (Zn), and manganese (Mn) in the countries with mid-low income, Zn, Mn, and copper (Cu) in the countries with mid-high income, and Zn, Mn, and lead (Pb) in the countries with high income. The hazard quotients (HQ) of non-carcinogenic risk of 14 heavy metals (metalloids) were low (HQ < 1). The carcinogenic risk (CR) of arsenic (As), chromium (Cr), and cobalt (Co) was high. Specifically, the CR of Cr and Co in the countries with mid-low income, Cr and As in the countries with mid-high income, and Cr in the countries with high income was high (CR > 1 × 10−6). This review can provide scientific references for tropical countries and regions in formulating differentiated environmental standards and health protection strategies.

, authors=Yanchun WU1, Yue YU1, Jiaxin LUO1, Jing′ai LI1, Changhua HE2, 3, authorsList=Yanchun WU, Yue YU, Jiaxin LUO, Jing′ai LI, Changhua HE, authorCompany=null, correspAuthors=Changhua HE, authorNote=null, correspAuthorsNote=
HE Changhua, E-mail:
, copyrightStatement=Copyright © 2026 Chinese Journal of Public Health. All rights reserved., 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=1320438139957653654, articleId=1320438137176830090, tenantId=1146029695717560320, journalId=1319373219989389464, language=CN, title=全球热带地区大气PM2.5中重(类)金属分布及健康风险研究进展, columnId=1320427713358312432, journalTitle=中国公共卫生, columnName=综 述, runingTitle=null, highlight=null, articleAbstract=

本文系统综述了2014—2024年全球热带地区关于大气PM2.5中14种重(类)金属的浓度谱系、时空分布及其健康风险研究进展。结果表明,热带地区国家间大气PM2.5中铝(Al)和铁(Fe)浓度普遍较高,地域性差异为中高收入<高收入<中低收入的国家;其他重(类)金属也存在一定的地域性复合污染差异,中低收入国家以钡(Ba)、锌(Zn)和锰(Mn)为主,中高收入国家以锌(Zn)、锰(Mn)和铜(Cu)为主,高收入国家以锌(Zn)、锰(Mn)和铅(Pb)为主。14种重(类)金属非致癌风险的危害系数(HQ)均较低(HQ<1);砷(As)、铬(Cr)和钴(Co)的致癌风险(CR)较为明显,其中中低收入国家以Cr和Co、中高收入国家以Cr和As、高收入国家以Cr的致癌风险较明显(CR>1×10−6)。本综述可为热带地区国家制定差异化环境标准与健康防护策略提供一定的科学参考。

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伍燕春(1997—),硕士在读,研究方向:环境与健康

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何昌华,E-mail:
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Archives of Environmental and Occupational Health, 2022, 77(2): 125-140., articleTitle=null, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1320438140196728983, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438137176830090, xref=1, ext=[AuthorCompanyExt(id=1320438140205117592, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438137176830090, companyId=1320438140196728983, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1School of Public Health, Hainan Medical University, Haikou 571199, China), AuthorCompanyExt(id=1320438140217700505, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438137176830090, companyId=1320438140196728983, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1海南医科大学公共卫生学院,海口 571199)]), AuthorCompany(id=1320438140284809370, 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journalId=1319373219989389464, articleId=1320438137176830090, language=CN, label=图4, caption=热带地区城市大气PM2.5中各重(类)金属浓度构成情况(2014—2024), figureFileSmall=SmFPbY8JJYreEjU17hqp+A==, figureFileBig=T8kZR8mk5yI+GULF10ALjw==, tableContent=null), ArticleFig(id=1320438144797880524, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438137176830090, language=EN, label=Fig.5, caption=Comparison of health risks of respiratory exposure to heavy metals (metalloids) in atmospheric PM2.5 in tropical regions from 2014 to 2024, figureFileSmall=njQaSebkkdgzANUrNvBcog==, figureFileBig=VG0SZ9GmLYFN5rqB1qbqSg==, tableContent=null), ArticleFig(id=1320438144869183693, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438137176830090, language=CN, label=图5, caption=热带地区大气PM2.5中重(类)金属经呼吸暴露的健康风险比较(2014—2024), figureFileSmall=njQaSebkkdgzANUrNvBcog==, figureFileBig=VG0SZ9GmLYFN5rqB1qbqSg==, tableContent=null), ArticleFig(id=1320438144961458382, 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Main sources of heavy metals (metalloids) in atmospheric PM2.5 in tropical regions from 2014 to 2024

, figureFileSmall=null, figureFileBig=null, tableContent=
重(类)金属污染来源构成及参考文献
Al土壤(地壳)[58–59]、道路粉尘(扬尘)[1,56,59]、建筑材料[61]、车辆尾气[56]、灰尘[9]、海洋喷雾[9]、有色冶金[28]、焚化炉排放[28]、土壤再悬浮[34]、
道路粉尘再悬浮[67]、建筑焊接活动[30]
As工业排放[72]、锅炉燃烧[1]、煤燃烧[28]
Cd车辆排放[57]、建筑钢材[61]、船舶/车辆排气[9]、钢铁厂[28]、燃煤源[67]、轮胎磨损(非废气交通排放)[67]
Co工业排放[19,72]、汽车废气[28]、车辆排放[19]、车辆制动排放(非废气交通排放)[67]
Cr金属表面磨损[61]、工业来源[56]、船舶排放[9]、道路粉尘(扬尘)[1,49]、汽车废气[28,67,69]、燃煤[54]、交通/扬尘[62]、车辆排放[19]、非废气排放[55]
Cu汽车排放[19]、土壤和道路粉尘[13]、钢铁工业[13]、煤燃烧[67]、冶金工业[59]、建筑材料及装饰[61]、船舶/车辆尾气[56,58]、船舶排放[9]、
灰尘[9]、工业排放[67]、非废气排放[55]、土壤源[30]
Fe煤燃烧[13]、车辆排放[34,57]、废金属回收[57]、土壤(地壳)[56,58–59, 61]、道路粉尘(扬尘)[1,59]、灰尘[9]、船舶排放[9]、钢铁厂[13,28]、交通/扬尘[62]、
土壤再悬浮[34]、非废气排放[55]、建筑焊接活动[30]、工业来源[49]
Mn交通排放[13]、煤燃烧[13]、车辆排放[57]、冶金工业[59]、土壤和机动车粉尘[61]、船舶/车辆尾气[58, 69]、工业来源[56]、船舶排放[9]、钢铁厂[28]、
交通/扬尘[62]、非废气排放[55]
Ni炼油[72]、交通排放[13]、地壳[61]、船舶/车辆尾气[56,58]、船舶排放[9]、船舶重油[1]、石油燃烧[28]、工业排放[19]、道路粉尘[49]
Pb汽车排放[56–57]、土壤和道路粉尘[13]、煤燃烧[67]、金属冶炼[59]、建筑油漆[61]、船舶/车辆尾气[56]、燃油燃烧[56]、车辆排气[9]、钢铁工业[13,28]、
工业排放[62]、化石燃料燃烧[34]、垃圾焚烧发电厂[55]、土壤源[30]
V燃煤电厂[72]、交通排放[13]、土壤和道路粉尘[13]、车辆排放[57]、船舶/车辆尾气[9,56,58]、工业来源[56]、燃油燃烧[56]、船舶重油[1]、石油燃烧[28]、
船舶排放[54]、汽车尾气排放[67]
Zn汽车排放[56–57]、废金属回收[57]、地壳[58,61]、工业来源[19]、灰尘[9]、海洋喷雾[9]、钢铁厂[28]、非废气排放[55]、垃圾焚烧发电厂[55]、土壤源[30]
Ba焚化炉排放[28]、汽车废气[28]、电路板制造[54]、车辆排放[19]、工业排放[19]、轮胎磨损及制动(非废气交通排放)[67]
Se燃煤电厂[72]、煤燃烧[28]、车辆排放[34]、化石燃料燃烧[34]
), ArticleFig(id=1320438145041150159, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438137176830090, language=CN, label=表1, caption=

2014—2024年热带地区大气PM2.5中重(类)金属主要来源构成

, figureFileSmall=null, figureFileBig=null, tableContent=
重(类)金属污染来源构成及参考文献
Al土壤(地壳)[58–59]、道路粉尘(扬尘)[1,56,59]、建筑材料[61]、车辆尾气[56]、灰尘[9]、海洋喷雾[9]、有色冶金[28]、焚化炉排放[28]、土壤再悬浮[34]、
道路粉尘再悬浮[67]、建筑焊接活动[30]
As工业排放[72]、锅炉燃烧[1]、煤燃烧[28]
Cd车辆排放[57]、建筑钢材[61]、船舶/车辆排气[9]、钢铁厂[28]、燃煤源[67]、轮胎磨损(非废气交通排放)[67]
Co工业排放[19,72]、汽车废气[28]、车辆排放[19]、车辆制动排放(非废气交通排放)[67]
Cr金属表面磨损[61]、工业来源[56]、船舶排放[9]、道路粉尘(扬尘)[1,49]、汽车废气[28,67,69]、燃煤[54]、交通/扬尘[62]、车辆排放[19]、非废气排放[55]
Cu汽车排放[19]、土壤和道路粉尘[13]、钢铁工业[13]、煤燃烧[67]、冶金工业[59]、建筑材料及装饰[61]、船舶/车辆尾气[56,58]、船舶排放[9]、
灰尘[9]、工业排放[67]、非废气排放[55]、土壤源[30]
Fe煤燃烧[13]、车辆排放[34,57]、废金属回收[57]、土壤(地壳)[56,58–59, 61]、道路粉尘(扬尘)[1,59]、灰尘[9]、船舶排放[9]、钢铁厂[13,28]、交通/扬尘[62]、
土壤再悬浮[34]、非废气排放[55]、建筑焊接活动[30]、工业来源[49]
Mn交通排放[13]、煤燃烧[13]、车辆排放[57]、冶金工业[59]、土壤和机动车粉尘[61]、船舶/车辆尾气[58, 69]、工业来源[56]、船舶排放[9]、钢铁厂[28]、
交通/扬尘[62]、非废气排放[55]
Ni炼油[72]、交通排放[13]、地壳[61]、船舶/车辆尾气[56,58]、船舶排放[9]、船舶重油[1]、石油燃烧[28]、工业排放[19]、道路粉尘[49]
Pb汽车排放[56–57]、土壤和道路粉尘[13]、煤燃烧[67]、金属冶炼[59]、建筑油漆[61]、船舶/车辆尾气[56]、燃油燃烧[56]、车辆排气[9]、钢铁工业[13,28]、
工业排放[62]、化石燃料燃烧[34]、垃圾焚烧发电厂[55]、土壤源[30]
V燃煤电厂[72]、交通排放[13]、土壤和道路粉尘[13]、车辆排放[57]、船舶/车辆尾气[9,56,58]、工业来源[56]、燃油燃烧[56]、船舶重油[1]、石油燃烧[28]、
船舶排放[54]、汽车尾气排放[67]
Zn汽车排放[56–57]、废金属回收[57]、地壳[58,61]、工业来源[19]、灰尘[9]、海洋喷雾[9]、钢铁厂[28]、非废气排放[55]、垃圾焚烧发电厂[55]、土壤源[30]
Ba焚化炉排放[28]、汽车废气[28]、电路板制造[54]、车辆排放[19]、工业排放[19]、轮胎磨损及制动(非废气交通排放)[67]
Se燃煤电厂[72]、煤燃烧[28]、车辆排放[34]、化石燃料燃烧[34]
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全球热带地区大气PM2.5中重(类)金属分布及健康风险研究进展
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伍燕春 1 , 余越 1 , 罗嘉欣 1 , 李井爱 1 , 何昌华 2, 3, *, *
中国公共卫生 | 综 述 2026,42(4): 470-478
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中国公共卫生 |综 述 2026 , 42 (4) : 470 -478
全球热带地区大气PM2.5中重(类)金属分布及健康风险研究进展
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伍燕春1, 余越1, 罗嘉欣1, 李井爱1, 何昌华2, 3, *, *
作者信息
  • 1海南医科大学公共卫生学院,海口 571199
  • 2海南省疾病预防控制中心公共卫生研究所,海口 571129
  • 3海南省预防医学科学院,海口 571129
通讯作者:
何昌华,E-mail:
作者简介:

伍燕春(1997—),硕士在读,研究方向:环境与健康

Current research status on distribution and health risks of heavy metals (metalloids) in atmospheric PM2.5 in tropical regions: a systematic review
Yanchun WU1, Yue YU1, Jiaxin LUO1, Jing′ai LI1, Changhua HE2, 3
Affiliations
  • 1School of Public Health, Hainan Medical University, Haikou 571199, China
  • 2Institute of Public Health, Hainan Provincial Center for Disease Control and Prevention, Haikou 571129, China
  • 3Hainan Academy of Preventive Medicine, Haikou 571129, China
  • Corresponding Author:
    HE Changhua, E-mail:
出版时间: 2026-04-10 doi: 10.11847/zgggws1147580
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本文系统综述了2014—2024年全球热带地区关于大气PM2.5中14种重(类)金属的浓度谱系、时空分布及其健康风险研究进展。结果表明,热带地区国家间大气PM2.5中铝(Al)和铁(Fe)浓度普遍较高,地域性差异为中高收入<高收入<中低收入的国家;其他重(类)金属也存在一定的地域性复合污染差异,中低收入国家以钡(Ba)、锌(Zn)和锰(Mn)为主,中高收入国家以锌(Zn)、锰(Mn)和铜(Cu)为主,高收入国家以锌(Zn)、锰(Mn)和铅(Pb)为主。14种重(类)金属非致癌风险的危害系数(HQ)均较低(HQ<1);砷(As)、铬(Cr)和钴(Co)的致癌风险(CR)较为明显,其中中低收入国家以Cr和Co、中高收入国家以Cr和As、高收入国家以Cr的致癌风险较明显(CR>1×10−6)。本综述可为热带地区国家制定差异化环境标准与健康防护策略提供一定的科学参考。

重(类)金属  /  大气细颗粒物(PM2.5)  /  健康风险  /  热带地区  /  致癌风险

This article systematically reviews the research progress in the concentration spectrum, spatio-temporal distribution, and health risks of 14 heavy metals (metalloids) in atmospheric PM2.5 in tropical regions around the world from 2014 to 2024. The results showed that the concentrations of aluminum (Al) and iron (Fe) in atmospheric PM2.5 among countries in tropical regions were both high and followed the trend of countries with mid-high income < countries with high income < countries with mid-low income. Other heavy metals (metalloids) also had certain regional composite pollution differences. Specifically, the pollution was dominated by barium (Ba), zinc (Zn), and manganese (Mn) in the countries with mid-low income, Zn, Mn, and copper (Cu) in the countries with mid-high income, and Zn, Mn, and lead (Pb) in the countries with high income. The hazard quotients (HQ) of non-carcinogenic risk of 14 heavy metals (metalloids) were low (HQ < 1). The carcinogenic risk (CR) of arsenic (As), chromium (Cr), and cobalt (Co) was high. Specifically, the CR of Cr and Co in the countries with mid-low income, Cr and As in the countries with mid-high income, and Cr in the countries with high income was high (CR > 1 × 10−6). This review can provide scientific references for tropical countries and regions in formulating differentiated environmental standards and health protection strategies.

heavy metals (metalloids)  /  PM2.5  /  health risk  /  tropical regions  /  carcinogenic risk
伍燕春, 余越, 罗嘉欣, 李井爱, 何昌华. 全球热带地区大气PM2.5中重(类)金属分布及健康风险研究进展. 中国公共卫生, 2026 , 42 (4) : 470 -478 . DOI: 10.11847/zgggws1147580
Yanchun WU, Yue YU, Jiaxin LUO, Jing′ai LI, Changhua HE. Current research status on distribution and health risks of heavy metals (metalloids) in atmospheric PM2.5 in tropical regions: a systematic review[J]. Chinese Journal of Public Health, 2026 , 42 (4) : 470 -478 . DOI: 10.11847/zgggws1147580
细颗粒物(fine particulate matter, PM2.5)空气动力学直径≤2.5 μm,因体积小、成分复杂和环境活性高的特点,易携带重(类)金属、细菌、毒素及致癌物等进入人体[1–2]。特别源于人为污染的重(类)金属元素更易聚集在大气PM2.5中[3–6],通过呼吸过程在人体内蓄积引发疾病[7]。此外,重(类)金属也会干扰生物地球化学循环,造成环境与健康风险[8–9]。已有研究表明,多数重(类)金属即使在极低浓度下也具有毒性,甚至产生致癌性[10–11]。2013年10月,国际癌症研究机构(IARC)已将空气污染的主要组成部分——细颗粒物归类为Ⅰ类致癌物[12–14],其中砷(As)、镉(Cd)、铬(Cr)和镍(Ni)是已知对肺、皮肤、膀胱的明确致癌物[15]。根据世界卫生组织(WHO)统计,空气污染每年可导致全球约700万人过早死亡[16]。相关报道指出,2017年全球约有330万人因空气颗粒物暴露而过早死亡,预计在2050年将增至约600万人[17]。现有研究显示,气候因素(气温、降水、日照等)变化会显著影响空气污染物的成分及浓度,从而间接或直接地影响空气污染物在人体内产生的健康危害强度[18–19]。热带地区承载了全球约40%的陆地面积与居住人口,预估到2050年居住人口将增至50%,未来将会承载大量的人口迁移与发展需求[20]。随着全球气候变化和工业化进程的加速,热带地区空气质量可能衍生的健康风险问题也将日益凸显。当前,许多研究主要集中在局地分析大气PM2.5中重(类)金属的来源、影响因素及健康风险上,在聚焦系统探析热带地区大气PM2.5中重(类)金属分布与健康风险整体现状的文献报道相对较少。本文通过综述近年来全球热带地区大气PM2.5中研究关注度较高的14种重(类)金属在浓度分布特征、污染源解析及健康风险的研究报道,旨在梳理和总结热带地区大气PM2.5中重(类)金属的种类、浓度水平、分布特征及健康风险。以期进一步了解热带气候、地理环境和人类活动等条件对大气PM2.5中重(类)金属分布特征、健康风险差异以及关键驱动机制的影响作用。为助推跨区域的大气环境与健康研究成果应用,促进并保障热带地区的生态环境安全,推动经济、社会和环境的可持续协调发展提供参考。
以中文数据库(中国知网、万方数据和维普中文期刊)和英文数据库(PubMed和Web of Science)为文献来源。中文检索词为“细颗粒物”“PM2.5”“健康”“金属”“元素”“来源”和“风险”;英文检索词为“fine particulate matter”“PM2.5”“health”“metal”“element”“source”和“risk”。通过检索词组合在5个数据库中全文检索2014年1月1日—2024年12月31日正式发表的期刊文献。累计获取6 650篇原始文献。聚焦全球热带地区开展的研究筛选文献,最终对77篇符合纳入标准的研究文献进行综述分析(图1)。
2014—2024年,热带地区大气PM2.5中重(类)金属的研究主要采用以下检测技术:电感耦合等离子体质谱法(inductively coupled plasma mass spectrometry, ICP-MS)[21–28]、电感耦合等离子体光谱法(inductive coupling plasma optical emission spectrometer, ICP-OES)[14,19,29–31]、能量色散X射线荧光光谱法(energy-dispersive X-ray fluorescence, ED-XRF)[11,32–36]、X射线荧光光谱法(X-ray fluorescence, XRF)[37]、全反射X射线荧光光谱法(total reflection X-ray fluorescence, TXRF)[38–39]、原子吸收分光光度法(atomic absorption spectrometry, AAS)等[12,40–42]。这些技术在分析流程的复杂性、检测限、元素特异性以及样品前处理要求等方面各具优势与局限。如ED-XRF技术具有非破坏性检测的特点[36];TXRF在分析过程中存在光谱线重叠和灵敏度较低的问题。但在测量低负载PM2.5中的K、Cu和Zn时显示出较高的可靠性;相对而言,ICP-OES在测定Ca、Cr、Ni和Pb方面则更为精确[43]。
基于文献筛选与结果分析,近年来,热带地区大气PM2.5中重(类)金属污染研究有明显的地域分布特征:亚洲地区(主要在东亚、南亚、东南亚和西亚)占主导地位,而非洲、大洋洲、北美洲和南美洲则呈现低关注度或研究相对较少。
本综述纳入的文献研究范围覆盖中低收入国家(科特迪瓦、贝宁、塞内加尔、尼日利亚、印度、越南、菲律宾)、中高收入国家(墨西哥、巴西、哥伦比亚、马来西亚、泰国、印度尼西亚、斐济、南非、中国)和高收入国家(美国波多黎各、新加坡、沙特阿拉伯)。监测点多数聚焦于城区、工业区、交通区、居民区和学校等大气PM2.5中重(类)金属污染敏感或典型暴露区域(图2)。在热带地区国家,Al和Fe是大气PM2.5中含量相对较高的金属元素[23–24,30,44–45]。其浓度特征表现为中高收入<高收入<中低收入的国家;其次是Zn、Mn、Cu、Pb和Ba等金属元素[31–32,46–47];其中Ba、Zn和Mn在中低收入国家,Zn、Mn和Cu在中高收入国家,Zn、Mn和Pb在高收入国家的大气PM2.5中浓度也相对较高(图3)。此外,Al在泰国、印度和尼日利亚,Fe在印度、中国和尼日利亚,Zn在斐济、中国和泰国,Mn在印度、泰国和新加坡,Cu在印度、中国和泰国的大气PM2.5中浓度也相对较高(图4)。值得关注的是,热带地区工业化城市或工业聚集区大气PM2.5中重(类)金属工业源贡献特征凸显[18,35,40,48]。如泰国罗勇工业区大气PM2.5中Zn、Fe、Pb和Mn浓度分别为374、279、28和21.9 ng/m3[49];印度比莱工业区大气PM2.5中Cr、Fe、Mn、Al、Co、Ni和Cu浓度分别高达5 350、2 530、2 450、1 920、650、1 610和1 690 ng/m3[42]。从空间上看,热带地区城市大气PM2.5中重(类)金属浓度通常呈现以下特征;农村低于中小型或轻工业城市<大或重工业城市[42],郊区<居民区<交通区<工业区[10,50–51],室外环境<室内场所[34,51];这些分布特征均不同程度与人类活动强度及污染空间分布相关。
近年来热带地区大气PM2.5中重(类)金属研究,采样周期多覆盖冬季、夏季,旱季、雨季,季风前期、季风期和季风后期等时段,而春季和秋季覆盖度相对较低。大气PM2.5中重(类)金属浓度一般为冬季高于秋季、春季和夏季[46,52–54],夏季低于冬季、秋季和春季[46,52–56],旱季高于雨季[2,31,49,57],季风后期高于季风前期和季风期[12]。季节浓度差异受大气层逆温、局地气象条件、地形和人为排放等多种因素影响[13,40]。就日含量而言,大气PM2.5中Cu、Mn、Zn、Pb和V白天浓度高于晚上[33,58],可能是晨间逆温层形成机制和交通高峰期尾气排放的双重作用所致。
对于大气PM2.5中重(类)金属来源的研究,近年来热带地区城市普遍采用的分析方法包括富集因子法(enrichment factor, EF)、主成分分析(principal component analysis, PCA)、反轨迹模型(hybrid single-particle lagrangian integrated trajectory, HYSPLIT)、化学质量平衡(chemical mass balance, CMB)以及正定矩阵分解模型(positive matrix factorization, PMF)等。各种分析方法均具有其特定的优势与局限性。EF法主要用于定性分析,能够有效区分重(类)金属的自然源与人为污染源[54,59–60]。PCA广泛应用于识别大气PM2.5中重(类)金属污染源的类型,然而其权重计算的缺陷限制了其定量解析能力[17,61–62]。HYSPLIT模型通过气象场模拟与粒子追踪技术,揭示气团迁移路径及污染物扩散机制,有助于识别大气污染的潜在来源[2,9,56,63–65]。CMB和PMF为定量的受体模型,其中CMB模型依赖于污染源化学剖面数据定量分析源贡献,但对排放数据的精度要求较高[66],而PMF模型源解析则无需识别污染排放源化学剖面[22,67–69]。同时,文献反映PMF模型也是当前应用到识别和量化大气PM2.5中重(类)金属来源的主流源解析工具。
当前,热带地区城市大气PM2.5中所含重(类)金属的源解析研究主要集中在重(类)金属与其他相关成分的协同源解析[32,62,70],以及跨越不同粒径谱系的成分研究。一方面,比较PM2.5与粉尘(PM10或PM1)中重(类)金属成分的源解析结果[3,15,71]。另一方面,鉴于源解析方法受限于输入源谱数据的完整性,不同的源解析方法可能导致源解析结果的差异性[57,68]。热带地区城市大气PM2.5中重(类)金属主要污染来源见表1。
近年来,热带地区大气PM2.5中重(类)金属健康风险评价研究,主要采用美国环境保护署推荐的人体健康风险评估模型。该模型综合考虑了大气PM2.5中重(类)金属暴露浓度、暴露时长和毒性强度系数,并结合参考剂量和人群人均寿命,从致癌和非致癌两个维度评估大气PM2.5中重(类)金属对人体健康的潜在影响。评估的重(类)金属包括国际癌症研究机构认定的Ⅰ类致癌物以及普遍认为可能对人类具有致癌性的有毒元素[16,49,55]。
经口腔摄入、呼吸道吸入和皮肤接触是重(类)金属进入人体并可能对健康造成危害的3种暴露途径。既往研究多聚焦于容易被忽视的呼吸道吸入途径[21],而对另外2种暴露途径的健康风险研究相对不足。近年来的研究显示,热带地区大气PM2.5中重(类)金属通过不同暴露途径所致的健康风险存在差异。如在印度丹巴德、沙特阿拉伯麦加和越南河内的研究均发现,PM2.5中重(类)金属经口腔摄入途径暴露风险相对较高[16,67,73];在印度赖布尔及中国珠三角地区和深圳市的研究表明,大气PM2.5中重(类)金属通过呼吸道途径暴露的健康风险更大[21,46,74]。
根据US EPA提出的健康风险评估模型,评估人体重(类)金属暴露的致癌风险(carcinogenic risk, CR),CR≤1×10−6表明致癌风险可忽略;1×10−6<CR≤1×10−4表明处于可接受的致癌风险范围内;CR>1×10−4表明致癌风险不可接受[35,52]。评估人体重(类)金属暴露的危害系数(hazard quotients, HQ)和危害指数(hazard index, HI),HQ或HI≤1表明非致癌风险在可接受范围内;HQ或HI>1表明存在不可接受的非致癌风险[52]。
近年热带地区主要关注As、Cd、Co、Cr(VI)、Ni和Pb金属元素对人类的致癌作用[16,49]。值得注意的是,在Cr(VI)的致癌风险评估中,多数研究直接利用大气PM2.5中Cr总浓度估算,而少数研究则根据US EPA推荐比例1/7Cr总浓度校正Cr(VI)的致癌风险[19,46,48,55]。在热带地区城市,As和Cr对成人[8,31–32,55,71–72]、Cd对成人和儿童的致癌风险相对明显(CR>1×10−6)[10, 71]。如泰国曼谷市、印度尼西亚泗水市、中国广州市和东莞市等地的研究显示,大气PM2.5中As和Cr对成人的致癌风险高于儿童3~5倍,且均超过1×10−6[8,48,55,71];泰国孔敬府的研究发现,大气PM2.5中Cd对儿童和成人的致癌风险分别高达0.23×101和4.4×10−1[10];泰国曼谷市的研究表明,大气PM2.5中Cd对成人的致癌风险为1.46×10−6[71]。在热带地区国家,As、Cr和Co的致癌风险较明显,中低收入国家以Cr和Co、中高收入国家以Cr和As、高收入国家以Cr的致癌风险较明显(CR>1×10−6)。
近年来热带地区主要探讨Al、Fe、Zn、Mn、Cu、Pb、Cd、As、Cr、Co、Ni、V、Ba以及Se金属元素的非致癌风险。在热带地区城市,14种重(类)金属通过呼吸道途径产生的HQ均<1[8,17,27,52],但Mn、Ni、Cr、As和Cd呈现相对较高的HQ[1,12,27,75];其中,Mn表现出较高的HQ,可能与其生物利用度相对较高有关[74]。在热带地区国家,尽管大气PM2.5中单一重(类)金属的HQ均<1,但需关注热带地区大气PM2.5中多种重(类)金属的HI。如泰国曼谷市、尼日利亚奥贡州和中国中山市等地大气PM2.5中多种重(类)金属的HI已超过1[26–27,71],表明存在不可接受的非致癌风险。
研究文献显示,致癌和非致癌的风险程度可能受以下因素的综合影响出现差异化特征。一方面,局地气象条件(风向、温度、湿度、降雨量等)[66,76]、人为活动(交通排放、工业排放)[65,77]、工业布局[3,10]、区域地理环境(如地形地貌、植被覆盖度)[8–9]、当地环境政策等影响因素可能导致热带地区不同城市间大气PM2.5中重(类)金属成分及含量暴露程度差异[56,59];另一方面,大气PM2.5中重(类)金属对人体健康的危害强度与呼吸频率、体重、暴露时间、致癌斜率因子、参考剂量和重(类)金属生物可利用度密切相关。如儿童的致癌和非致癌风险均要高于成人[3,16,39, 46,67],可能是因为儿童的呼吸频率和体重与成人不同所导致[39];成人的致癌和非致癌风险高于儿童,可能与成人在大气PM2.5中重(类)金属的暴露时间高于儿童有关[7,75]。Cr浓度(5.6 ng/m3)较低但对人体致癌风险(CR>6.3×10−5)较高,与其致癌斜率因子有关[54]。Fe和Zn浓度(248和553 ng/m3)较高,但对人体危害系数(HQ=1.7×10−4和HQ=8.85×10−4)较低,与其参考剂量有关[18];印度赖布尔市和加尔各答市的研究显示,大气PM2.5中重(类)金属对人体的健康风险可能因生物可利用浓度低于总浓度而被高估[74,78]。基于重(类)金属实际暴露水平[33],及其在人体内的生物可利用性浓度可更科学合理地评价大气PM2.5中重(类)金属健康风险[7,26,41,59,74]。
目前,关于我国热带地区城市大气PM2.5中重(类)金属的研究,多集中在浓度分布规律及化学形态分析方面,部分研究涉及了重(类)金属的来源、生物有效性评估及对人体的健康风险评价,但对大气PM2.5低浓度区域中的重(类)金属系统性研究尚显不足,亟须开展全面、系统的研究。未来可着重于以下方面研究:(1)深入分析热带气象条件(温度、降雨量等)与大气PM2.5中重(类)金属浓度之间的关系及其影响;(2)综合分析个体暴露浓度、重(类)金属生物可利用度和环境因素协同作用的影响,科学合理地研究大气PM2.5中重(类)金属的健康风险;(3)热带地区大气PM2.5中重(类)金属特定污染源的健康风险研究较为缺乏,深入研究特定污染源的健康风险可为热带城市优先防控大气PM2.5中重(类)金属污染源提供科学参考。本文虽对热带地区大气PM2.5中重(类)金属浓度分布特征、污染来源及健康风险予以广泛分析和探讨,但仍存在以下局限性:(1)时空可比性不足。现有文献中样本的采样时间多局限于短期或特定季节(如季风期等),部分国家缺乏长期或同步监测数据。本综述可能不完全代表全球热带地区情况;(2)研究区域异质性。现有研究多聚焦于单一城市或工业区,缺乏对不同功能区(如工业区、交通区、居民区)的系统性对比;(3)方法学差异。大气PM2.5样本采样方式、采样仪器、滤膜材质及仪器检出限的不统一,可能导致重(类)金属浓度数据存在方法误差。

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2026年第42卷第4期
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doi: 10.11847/zgggws1147580
  • 接收时间:2025-04-01
  • 首发时间:2026-10-23
  • 出版时间:2026-04-10
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  • 收稿日期:2025-04-01
  • 修回日期:2025-08-11
  • 录用日期:2025-08-30
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作者信息
    1海南医科大学公共卫生学院,海口 571199
    2海南省疾病预防控制中心公共卫生研究所,海口 571129
    3海南省预防医学科学院,海口 571129

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何昌华,E-mail:
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
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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