Article(id=1301137675512213714, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1301137643908128824, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2026.04.00057, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1776268800000, receivedDateStr=2026-04-16, revisedDate=1778688000000, revisedDateStr=2026-05-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1788144995811, onlineDateStr=2026-08-31, pubDate=1786550400000, pubDateStr=2026-08-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1788144995811, onlineIssueDateStr=2026-08-31, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1788144995811, creator=13701087609, updateTime=1788144995811, updator=13701087609, issue=Issue{id=1301137643908128824, tenantId=1146029695717560320, journalId=1146031591421210625, year='2026', volume='44', issue='15', pageStart='1', pageEnd='164', issueExtLink='null', onlineDate='null', pubDate='1786550400000', pubDateStr='2026-08-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1788144988276, creator='13701087609', updateTime=1788145858754, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1301141295053959998, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1301137643908128824, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1301141295053959999, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1301137643908128824, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=83, endPage=92, ext={EN=ArticleExt(id=1301137675696763091, articleId=1301137675512213714, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Progress, challenges and prospects of wastewater−based epidemiology in population health early warning in China, columnId=1150494642224591153, journalTitle=Science & Technology Review, columnName=Exclusive, runingTitle=null, highlight=null, articleAbstract=

Wastewater−based epidemiology (WBE) has become a key public health surveillance tool. In China, its use has expanded from illicit drugs to emerging contaminants, pathogen warning, and population health assessment. However, uncertainties and ethics remain challenges. This review synthesizes WBE progress in China concerning drug abuse, emerging contaminants risks, infectious diseases early warming, and population health. Proposes a future framework aligned with the 15th Five−Year Plan: network expansion with standardized sample banks, AI−driven data integration, and enhanced early warning and antimicrobial resistance surveillance. This framework aims to support future application of WBE in China.

, authors=Peng DU1, Ruyue ZHANG1, Ke MA1, Ziqi FANG1, Qiuda ZHENG2, Zhe WANG2, Lingrong ZHANG3, Jianfa GAO4, Xiqing LI5, authorsList=Peng DU, Ruyue ZHANG, Ke MA, Ziqi FANG, Qiuda ZHENG, Zhe WANG, Lingrong ZHANG, Jianfa GAO, Xiqing LI, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., 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=1301137676887945434, articleId=1301137675512213714, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=中国污水流行病学在群体健康预警中的应用进展、挑战与展望, columnId=1150494642375586098, journalTitle=科技导报, columnName=特色专题, runingTitle=null, highlight=null, articleAbstract=

污水流行病学已成为全球公共卫生监测预警的重要手段之一,中国自引入污水流行病学以来,其应用领域已从毒品滥用监测拓展至新污染物暴露评估、病原体预警及人群健康监测等。然而,中国污水流行病学在实际应用中仍面临诸多关键挑战,影响监测准确性与推广应用。系统梳理了中国污水流行病学在成瘾性物质监测、新污染物健康风险评价、传染病预警及生活方式评估等方面的研究进展。剖析了方法学不确定性与隐私伦理等关键制约因素,并基于“十五五”规划需求,提出涵盖监测网络下沉与标准化样本库建设、人工智能驱动的多源数据整合,以及传染病预警与耐药性监测响应提升的未来框架,旨在推动该技术的规范化应用与纵深发展。

, authors=杜鹏1, 张茹月1, 马可1, 方梓琪1, 郑秋达2, 王喆2, 张苓荣3, 高建发4, 李喜青5, authorsList=杜鹏, 张茹月, 马可, 方梓琪, 郑秋达, 王喆, 张苓荣, 高建发, 李喜青, authorCompany=null, correspAuthors=null, authorNote=

杜鹏,副教授,研究方向为污水流行病学,电子信箱:

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中国污水流行病学在群体健康预警中的应用进展、挑战与展望
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杜鹏 1 , 张茹月 1 , 马可 1 , 方梓琪 1 , 郑秋达 2 , 王喆 2 , 张苓荣 3 , 高建发 4 , 李喜青 5
科技导报 | 特色专题 2026,44(15): 83-92
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科技导报 |特色专题 2026 , 44 (15) : 83 -92
中国污水流行病学在群体健康预警中的应用进展、挑战与展望
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杜鹏1 , 张茹月1, 马可1, 方梓琪1, 郑秋达2, 王喆2, 张苓荣3, 高建发4, 李喜青5
作者信息
  • 1北京师范大学水科学研究院城市水文循环与海绵城市技术北京市重点实验室,北京 100875
  • 2昆士兰大学环境科学与健康研究所,澳大利亚布里斯班 4102
  • 3山东第一医科大学(山东省医学科学院)山东省职业卫生与职业医学研究院,济南 250000
  • 4深圳大学化学与环境工程学院,深圳 518060
  • 5北京大学城市与环境学院地表过程分析与模拟教育部重点实验室,北京 100871
作者简介:

杜鹏,副教授,研究方向为污水流行病学,电子信箱:

Progress, challenges and prospects of wastewater−based epidemiology in population health early warning in China
Peng DU1 , Ruyue ZHANG1, Ke MA1, Ziqi FANG1, Qiuda ZHENG2, Zhe WANG2, Lingrong ZHANG3, Jianfa GAO4, Xiqing LI5
Affiliations
  • 1Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China
  • 2Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Brisbane, Queensland 4102, Australia
  • 3Shandong Academy of Occupational Health and Occupational Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250000, China
  • 4College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
  • 5Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
出版时间: 2026-08-13 doi: 10.3981/j.issn.1000-7857.2026.04.00057
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污水流行病学已成为全球公共卫生监测预警的重要手段之一,中国自引入污水流行病学以来,其应用领域已从毒品滥用监测拓展至新污染物暴露评估、病原体预警及人群健康监测等。然而,中国污水流行病学在实际应用中仍面临诸多关键挑战,影响监测准确性与推广应用。系统梳理了中国污水流行病学在成瘾性物质监测、新污染物健康风险评价、传染病预警及生活方式评估等方面的研究进展。剖析了方法学不确定性与隐私伦理等关键制约因素,并基于“十五五”规划需求,提出涵盖监测网络下沉与标准化样本库建设、人工智能驱动的多源数据整合,以及传染病预警与耐药性监测响应提升的未来框架,旨在推动该技术的规范化应用与纵深发展。

污水流行病学  /  公共卫生  /  传染病  /  群体健康  /  生物标志物

Wastewater−based epidemiology (WBE) has become a key public health surveillance tool. In China, its use has expanded from illicit drugs to emerging contaminants, pathogen warning, and population health assessment. However, uncertainties and ethics remain challenges. This review synthesizes WBE progress in China concerning drug abuse, emerging contaminants risks, infectious diseases early warming, and population health. Proposes a future framework aligned with the 15th Five−Year Plan: network expansion with standardized sample banks, AI−driven data integration, and enhanced early warning and antimicrobial resistance surveillance. This framework aims to support future application of WBE in China.

wastewater−based epidemiology  /  public health  /  infectious disease  /  population health  /  biomarkers
杜鹏, 张茹月, 马可, 方梓琪, 郑秋达, 王喆, 张苓荣, 高建发, 李喜青. 中国污水流行病学在群体健康预警中的应用进展、挑战与展望. 科技导报, 2026 , 44 (15) : 83 -92 . DOI: 10.3981/j.issn.1000-7857.2026.04.00057
Peng DU, Ruyue ZHANG, Ke MA, Ziqi FANG, Qiuda ZHENG, Zhe WANG, Lingrong ZHANG, Jianfa GAO, Xiqing LI. Progress, challenges and prospects of wastewater−based epidemiology in population health early warning in China[J]. Science & Technology Review, 2026 , 44 (15) : 83 -92 . DOI: 10.3981/j.issn.1000-7857.2026.04.00057
污水流行病学(wastewater−based epidemiology,WBE)是通过检测生活污水中的化学与生物标志物,结合污水处理量、服务人口与校正因子等参数,反演估算目标区域内人群的化学物质消费水平与病原体负荷,交叉了环境科学、流行病学、公共卫生学、社会学、统计学、药代动力学等学科,从而实现区域人群化学物质消费监测、传染病疫情预警与群体健康状况评估[13]图1)。在全球范围,污水流行病学已成功应用于化学品消费监测,尤其在毒品滥用估算领域发挥了重要作用[4,5],为禁毒政策制定与完善提供了关键技术支撑[6]。新冠肺炎疫情暴发以来,基于污水流行病学的病毒载量动态监测[7]与人群感染规模估算[8]为疫情预警与防控提供了新思路,并被多国疾病控制与预防部门纳入常规疫情监测体系。
中国自2014年首次将污水流行病学应用于毒品滥用监测以来[9],其应用场景已逐步拓展至合法成瘾性物质滥用监测、群体污染物暴露健康风险评价、病原体监测与疫情预警、人群生活方式与健康状况评估等多个领域。然而,由于生物标志物在污水管网环境、样品采集与运输过程中的稳定性差异、人群排泄系数校准不足、公共利益与科研伦理权衡等问题,为污水流行病学的应用带来了不确定性,在一定程度上制约了污水流行病学应用场景的拓展与深化。“十五五”时期,国家在发展规划纲要中明确部署健全公共卫生体系,提出健全传染病防控与监管体系、提升传染病监测预警能力、强化对居民健康和体重管理的综合干预与管理等重点任务[10]。在上述战略目标指引下,污水流行病学在疫情预警与群体健康管理中的应用价值将得到进一步凸显与释放,同时也为其进一步拓展与深化应用带来了新机遇与挑战。
基于此,本文从应用进展、制约因素及未来展望的逻辑视角,系统梳理并分析中国污水流行病学的关键应用领域、核心瓶颈挑战与未来发展方向,旨在为推动中国污水流行病学的应用拓展、完善群体健康预警体系提供科学参考。
毒品滥用监测是污水流行病学在中国应用的初始领域。该技术经过多年发展,已逐步成为中国禁毒工作的重要技术支撑手段之一。自2014年中国首次开展基于污水流行病学的毒品滥用监测相关研究以来,国内多个研究团队围绕该领域开展大量研究工作。揭示了甲基苯丙胺(冰毒)和氯胺酮(K粉)是中国最为流行的毒品类型之一,相关生活污水监测数据显示,自2016年以来,中国主要城市这2类毒品的滥用水平呈持续下降趋势[11],这一结论与公安部的调查数据和毒品缴获统计结果高度吻合,进一步证实了中国毒品消费量的显著回落[12]。此外,海洛因、甲基苯丙胺、氯胺酮等毒品的滥用情况存在显著地域异质性[1315],其中,海洛因在西部地区为主要滥用毒品类型[16]。上述研究成果,不仅为毒品滥用与贩运管控工作提供了定量的数据支撑,也为各类禁毒干预措施的实施效果评估提供了可量化的科学指标[14]
自2018年起,中国依托污水流行病学技术,逐步构建起国家级与省市级的毒品监测实验室体系,重点构建了针对各类传统毒品及新型精神活性物质的监测网络。2020年,污水检测毒品滥用网络覆盖全国地级市,通过精准捕捉各类毒品的环境踪迹,进一步锁定毒品滥用及制贩可疑区域。公安部门据此开展针对性突击整治行动,成功捣毁数十个地下毒品制造窝点,有效遏制了毒品犯罪活动的蔓延[1]。目前,基于污水流行病学的毒品滥用监测正在向系统化方向演进。该技术通过精准把握毒品滥用动态,有力推动禁毒工作由被动应对向主动防控的转型。
除非法毒品滥用外,合法成瘾性物质的滥用风险亦不容忽视。根据《2023年中国毒情形势报告》与《2024年中国毒情形势报告》,中国毒品的滥用规模持续缩减,海洛因、冰毒、氯胺酮等主流毒品滥用人数呈稳步下降态势,但麻精药品滥用问题凸显、未列管成瘾性物质滥用形势严峻,部分省份未列管成瘾性物质的滥用人数已超过毒品滥用人数[12,17],亟需强化对合法成瘾性药物滥用的监测力度。污水监测结果显示,右美沙芬在全国范围内的污水样品中被广泛检出,且其使用量存在显著季节差异,在流感流行期使用量显著增加[18]。针对曲马多和芬太尼的监测显示,2016—2019年,芬太尼类物质的使用处于极低水平,而曲马多在全国范围内的使用较为广泛[19]。这与《2024年中国毒情形势报告》中“曲马多和右美沙芬滥用人数较多,芬太尼类物质未出现规模化滥用”的结论高度一致[12]。未来应进一步扩大合法成瘾性药物的监测覆盖范围,将污水监测数据与临床处方数据、药品销售数据进行整合分析,精准识别区域药物滥用风险,为推动合法成瘾性药物的临床规范化使用、优化用药管理策略提供科学支撑。
自21世纪初以来,新污染物已成为全球日益关注的环境问题,构成了威胁生态环境与人体健康的重要潜在风险因素[20]。Gao等[21]于2015年首次提出将污水流行病学应用于中国区域污染物暴露水平监测,此后该方法逐步被应用于开展新污染物的群体暴露表征与健康风险评价研究。目前多个团队已基于污水流行病学方法,针对诸如全氟化合物、邻苯二甲酸酯、农药等多种新污染物,开展了全国或区域范围的群体暴露估算与健康风险评价研究[2224],相关研究成果为高风险新污染物的筛查与优先管控提供了新的视角。为强化新污染物治理、保障生态环境安全与人群健康,中国先后于2022年和2024年发布《新污染物治理行动方案》和《新污染物生态环境监测标准体系表(2024年版)》,重点针对持久性有机污染物、内分泌干扰物、农药等新污染物实施系统管控。然而,当前污水流行病学在新污染物暴露水平估算与健康风险评价领域的应用仍相对有限(图2)。有待进一步拓展其应用范围,将其应用于内分泌干扰物等更多类型新污染物的群体健康风险评价[25],以期为新污染物环境监测标准的制定、修订和实施提供技术支持。
新冠肺炎疫情发生以来,污水流行病学凭借快速、非侵入性、可规模化应用的技术优势,成为全球72个国家开展疫情预警及持续监测的流行病学创新手段[26],已被全球多国广泛应用于传染病、病原体及微生物标志物的监测工作[27]。中国于2020年建立了城市污水监测体系(Chinese urban wastewater surveillance system,CWSS),区别于荷兰、美国等以市政污水处理厂为主的单一监测模式[2829],CWSS将监测范围延伸至入境航班及特定点位,形成了“口岸—城市—重点场所”的多层级监测网络[26],以打通不同行政归属下的监测数据,提升跨区域协同效率。截至2025年6月,CWSS监测点已增至1076个,覆盖全国169个城市、833座污水处理厂[26]。其中,入境航空器监测模块已在44个城市落地,主要针对埃博拉、中东呼吸综合征等12种高致病性境外病原体开展靶向筛查,在口岸端构筑起输入性风险的早期预警线。与此同时,CWSS在医院、养老院及学校等场所的布点试点,实现了对局部公共卫生风险的精准化管控,其监测层级较国际同类系统更为丰富。目前,CWSS已实现对新冠病毒、脊灰病毒、猴痘病毒、流感病毒、诺如病毒、百日咳杆菌、呼吸道合胞病毒等重点病原体的同步追踪,实现了污水流行病学监测全流程数字化管理[26,3031]。2026年,国家疾控局发布了《污水中新型冠状病毒监测技术规范》等系列技术标准,进一步完善了新冠病毒监测与疫情预警的技术体系,拓展了污水流行病学在常规公共卫生监测体系中的应用边界。然而,污水中其他病毒及微生物的识别仍处于起步阶段,多种关键病原体的时空监测仍面临诸多技术瓶颈,未来亟需构建非靶向监测体系与全流程技术标准,以支撑污水流行病学在公共卫生监测领域的系统化研究。
近年来,中国公共健康领域改革发展取得了系统性成就,全民健康素养与健康保障能力持续提升[32]。与此同时,工业化、城镇化、人口老龄化、疾病谱变化、生态环境及生活方式变化等,给健康维护与促进带来了一系列新的挑战[32]。《“健康中国2030”规划纲要》明确提出全民健康促进的总体目标,聚焦普及健康生活方式、实施慢性病综合防控、强化心理健康服务等关键领域,为健康中国建设提供了顶层设计与战略指引[32]。污水流行病学凭借经济高效、连续实时、覆盖广泛的技术优势,能够客观评估群体生活方式与健康状况水平[33],弥补传统流行病学调查的不足,成为健康政策制定与干预措施优化的有利补充手段。目前,国内多支研究团队已基于污水流行病学方法,围绕生活方式标志物、健康相关药物及内源性标志物等,开展了全国及区域尺度的健康评估研究。这3类标志物虽来源不同,但均能通过捕捉人群消费行为或机体代谢产物排入管网的“社会痕迹”,反演群体的真实健康状态。首先,针对吸烟、饮酒行为的污水流行病学监测,揭示了区域消费格局差异与人群行为模式特征[3435],为控烟履约、无烟环境建设及酒精滥用监测提供了客观数据支撑。除反映日常生活方式外,在健康相关药物监测方面,研究覆盖抗生素[3637]、乙肝药物[38]、降压药[39]、抗抑郁药[40]、降糖药[41]、痛风药[42]以及哮喘与过敏药[43]等品类,为疾病谱演变研究提供了全面、连续的量化数据[41,44]。进一步地,相较于外源性的物质摄入,随着临床生物标志物技术的发展,污水流行病学监测范围进一步拓展至内源性生物标志物,针对类固醇等应激生物标志物的研究实现了大学生群体心理压力水平的动态评估[45],为重点人群心理问题早期识别与精准干预提供了实时、可靠的数据支持。未来,应进一步扩大污水流行病学监测的生物标志物覆盖范围,拓展多类疾病患病率估算等研究方向[46],为精细化健康管理与公共卫生政策制定提供补充依据,助力健康中国战略的全面落地。
污水流行病学在中国已实现多领域广泛应用,主要涵盖成瘾性物质滥用监测、新污染物暴露表征及健康风险评价、病原体监测与疫情预警、人群生活方式与健康状况评估等核心方向。然而,该技术在发展过程中仍面临方法学优化、伦理风险管控等现实挑战,这些问题在一定程度上制约其规范化、规模化发展进程,须进一步开展系统性研究加以破解。
1) 方法学优化是提升污水流行病学监测准确性与可靠性的核心。虽然中国污水流行病学研究范围已从毒品、药物、污染物及生活方式相关生物标志物,拓展至病毒RNA及多种传染性病原体[46],但目前的监测体系在全链条不确定性量化方面仍面临严峻挑战。首先,在采样方面,目前国内监测多依赖瞬时采样或频率较低的等时间间隔混合采样,缺乏标准化的24小时复合采样方案(如≤15 min/次的采样频率、等流量体积混合采样等),难以捕获污水流量和浓度的剧烈波动,导致原始样本代表性欠佳[4647]。其次,生物标志物在真实污水管网体系中的稳定性受到物理、化学及微生物降解的复杂影响,现有研究多基于实验室受控环境,往往低估了长距离运输过程中的质量损失[4849]。进入检测环节,不同实验室间的预处理技术(如富集效率差异)与分析方法缺乏标准化校准,尤其是针对复杂基质中低丰度病原体或药物代谢物的检出限和回收率存在显著波动。在人口校正方面,服务区域内服务人口的动态波动(如流动人口、通勤压力)为计算基数引入了极大干扰,单一口径的人口普查数据或水质特征参数难以精准评估实时的服务人口波动[5051]。最后,在反演计算阶段,排泄系数的准确性至关重要。当前通用的参数多源于样本量有限的国外药代动力学研究,由于生理特征及饮食习惯差异,直接应用于中国人群会产生显著的估算偏差,且缺乏针对广域生物标志物在复杂人群中的排泄规律研究[47,52]。为提升监测结果的可靠性,未来可引入蒙特卡罗模拟或多模型集成方法,通过对各环节参数的概率分布建模,评估各环节带来的不确定性,构建覆盖上述全流程的不确定性量化评估框架[38,5354]
2) 科研伦理与公共利益的权衡是新时代污水流行病学研究领域需重点关注的议题。污水流行病学在公共卫生监测领域具有巨大应用潜力,尤其在新冠疫情后,其应用场景得到快速拓展,但其在实际应用中仍存在潜在伦理风险,需通过审慎评估与规范管理,在不侵害个人权益、不损害公众信任的前提下,充分发挥其技术价值。其中,隐私保护是污水流行病学面临的首要伦理挑战[55]。尤其是在小流域、低人口密度场景中,易引发社会歧视与群体污名化等伦理隐患[56]。因此,污水流行病学研究需要对敏感信息去标识化处理,并建立严格的隐私安全保障机制。此外,我国已在全国及部分区域开展污水监测研究[47],实践表明,相较于单一数据源分析,跨机构数据整合与共享可更全面揭示全国群体健康发展态势,提升研究深度与决策支撑效能[57]。在数据收集与共享过程中,需统筹公共利益与伦理风险防控,实现二者的科学权衡与动态平衡[58]。总之,污水流行病学的长远发展,需要在保障个人隐私与实现公共健康效益之间建立系统的伦理与规范框架,而不仅仅是技术层面的标准统一。
“十五五”期间,国家在公共卫生领域明确提出健全传染病防控与监管体系、提升传染病监测预警能力、强化对居民健康与体重管理的综合干预等战略目标,为污水流行病学技术的应用拓展提供了重要政策支撑与发展机遇。
相较于传统的流行病学调查、临床哨点监测及药品销售统计,污水流行病学在经济成本与公共卫生效益上具有独特优势。当前的临床和实验研究数据仍显不足,难以提供全面及时的群体暴露和疾病流行特征,传统方法的局限性突出[59]。传统的临床监测和大规模问卷调查往往面临人力成本高昂、样本代表性受限且数据反馈存在滞后性等挑战[6062]。此外,药品的获取便利性及家庭药物丢弃行为常导致销售统计值与实际生物代谢量之间存在显著差异,难以完全反映真实的人群摄入水平[61,63]。相比之下,污水流行病学技术通过对污水厂进水标志物进行回推计算,能够依托现有的污水处理基础设施,以较低的边际成本实现对全人群的、客观、全面且近乎实时的动态监测[1,64]。这种监测模式不仅能为国家评估公共卫生政策的实施效果提供准确的基线数据,还能在传染病监测预警中实现更高的投入产出比[59,65]。通过整合污水流行病学与多源数据,可为精准优化公共卫生资源配置提供明确的决策依据[66],从而更有效地服务于国家公共卫生监管体系的完善。
基于此,结合当前研究现状,本文构建了“基础设施—数据整合—预警响应”的递进框架,细化污水流行病学技术服务国家战略的落地路径(图3)。
1) 完善基础设施。推动监测网络下沉与标准化样本库建设。作为污水流行病学落地的物理基石,首要任务是扩大监测网络的时空覆盖面并实现样本的标准化管理。针对中国现有城市污水监测系统在农村地区覆盖率不足的现状,须逐步建立城乡一体化的监测体系。在浙江、广东等地的已具备成规模污水处理设施的农村地区[46],可结合被动采样技术采集农村生活污水[6768],并依托现有三级医疗卫生体系的低温物流网络完成样本流转。弥补农村人口健康状况监测的匮乏[67],更直接衔接《“健康中国2030”规划纲要》中“促进城乡公共服务均等化”的行动要求,为“健康乡村”建设提供基础数据支撑。此外,须参照相关标准,建立从容器准备、保护剂添加到标签设计与运输接收的全流程质控体系,构建国家级与省市级等多级污水样本库。通过完善样本数据档案与跨区域调度机制,解决偏远地区样本保存受限的问题,并确保样本的高效溯源与跨区域调度,为区域疾病谱演变及健康相关药物消费趋势研究提供可靠的底层物质库[41]
2) 强化数据整合。依托“人工智能+”深化多源数据解析。《中华人民共和国国民经济和社会发展第十五个五年规划纲要》(简称‌“十五五”规划纲要)明确提出“把握数字化、网络化、智能化发展大势”,为污水流行病学多源数据整合、智能化分析及监测能力提升提供了重要政策指引。一方面,借助人工智能(artificial intelligence,AI)与高分辨率质谱等非靶向分析技术,可将人体药代动力学数据与病原体基因库深度关联,构建新生物标志物的智能筛查算法模型[46]。以此提升对新型突发病原体、新型精神活性物质及特定靶向药物(如抗癌药物)的快速识别能力,满足当前监测范围持续扩大的现实需求。另一方面,可利用机器学习算法将污水流行病学数据与社会经济、临床诊断、处方统计及环境监测等多维度数据进行跨界整合。通过深挖上述复杂数据的内在关联,精准刻画特定区域人群的生活方式、健康状况及潜在风险因素,为慢性病高风险人群的早期识别与干预、政策优化提供客观依据[41],从而实质性地服务于《“健康中国2030”规划纲要》中“实施慢性病综合防控战略”的精准落地。
3) 提升预警响应。推动关口前移与重大公共卫生风险防控。“十五五”规划纲要明确提出推动卫生健康领域“从以治病为中心”向“以健康为中心”转变,这要求污水流行病学在传染病预警与抗生素风险管控中发挥前置防线作用。在传染病预警方面,随着微生物传感器技术的迭代,以电化学免疫传感器(现已成为新冠病毒的替代检测技术[69])为代表的便携式设备,有望实现传染性病原体的现场实时检测。将实时监测数据导入传染病动态预测模型,可将突发、新发传染病的预警窗口有效提前1~2周[7071]。该系统作为临床监测的重要前端补充,能够无缝接入《“健康中国2030”规划纲要》所部署的“国家多点触发传染病监测预警网络”,为公共卫生应急干预争取黄金时间。在耐药性常态化评估方面,针对东亚地区日益严峻的细菌耐药性负担[72],可通过动态监测生活污水中人用抗生素与抗性基因多样性、丰度的相关性,实现面向人群的精准风险评估[73]。重点聚焦致死率与住院负担极高的病原菌(如碳青霉烯耐药鲍曼不动杆菌、碳青霉烯耐药肺炎克雷伯菌、第三代头孢菌素/氟喹诺酮耐药大肠杆菌、甲氧西林耐药金黄色葡萄球菌、耐多药结核分枝杆菌等)及其对应抗生素的高频监测[72],从而量化抗菌药物临床应用分级管理等政策的实施成效[74],为国家多部门协同的耐药菌综合治理行动提供科学、客观的评估与预警工具。
本文系统梳理了污水流行病学在中国的应用现状与现阶段面临的核心挑战,并结合全球研究进展提出了未来发展框架,主要研究结论如下。
1) 污水流行病学在中国的传统应用主要为毒品及合法成瘾性物质滥用水平监测。随着新型精神活性物质滥用态势凸显与合法成瘾性药物滥用问题日益突出,亟需加强污水流行病学监测力度,精准捕捉毒品及成瘾性物质滥用的动态变化特征,为禁毒工作前瞻性转型提供科学支撑。
2) 污水流行病学在中国公共卫生领域的应用已拓展至新污染物的群体暴露与健康风险评价、病原体监测与疫情预警、人群生活方式与健康状况评估等多个维度,但当前监测物质覆盖仍存在局限。未来需进一步扩大监测物质种类,为精细化的公共卫生干预与管理政策制定提供数据支撑,充分释放污水流行病学在公共卫生监测中的应用潜力。
3) 中国污水流行病学的拓展应用仍面临技术优化与伦理风险的挑战,在一定程度上制约了技术的规范化应用与纵深发展。未来须进一步优化不确定性评估、伦理风险评估的机制。
为响应“十五五”规划纲要在公共卫生领域的战略部署,污水流行病学的发展需进一步完善基础设施,拓展农村及偏远地区监测范围,构建标准化样本存储与管理体系;强化数据整合,顺应数智化发展趋势,借助人工智能挖掘人体药代动力学与病原体基因库数据,建立新生物标志物筛查算法,并整合污水流行病学数据与临床、环境监测等多源数据,提高慢性病早期预防能力;提升预警响应,紧扣“十五五”规划纲要健康中国建设要求,搭建传染病预警系统、开展抗生素耐药性监测。多措并举优化污水流行病学应用方案与群体健康管理策略,可有效保障重大公共卫生应急需求,助力“健康中国”战略深入实施。
  • 国家自然科学基金面上项目(42171073)
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2026年第44卷第15期
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doi: 10.3981/j.issn.1000-7857.2026.04.00057
  • 接收时间:2026-04-16
  • 首发时间:2026-08-31
  • 出版时间:2026-08-13
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  • 收稿日期:2026-04-16
  • 修回日期:2026-05-14
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国家自然科学基金面上项目(42171073)
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    1北京师范大学水科学研究院城市水文循环与海绵城市技术北京市重点实验室,北京 100875
    2昆士兰大学环境科学与健康研究所,澳大利亚布里斯班 4102
    3山东第一医科大学(山东省医学科学院)山东省职业卫生与职业医学研究院,济南 250000
    4深圳大学化学与环境工程学院,深圳 518060
    5北京大学城市与环境学院地表过程分析与模拟教育部重点实验室,北京 100871
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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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