-
有机微污染物（organic micro-pollutants, OMPs）在水环境中多以痕量水平存在，可对水生生物产生直接或间接危害[1]。因具有毒性、持久性、生物可富集等特点，多氯联苯、多溴联苯醚、多环芳烃、有机氯农药等持久性有机污染物一直被重点关注[2-4]。新型污染物是一类新认定且对生态系统和人体健康具有潜在风险的化学污染物，主要包括药物与个人护理品、全氟化合物、有机磷阻燃剂、溴系阻燃剂等[5-7]。这些物质长期排放可能对水生环境及人体健康造成威胁，鱼作为水体中的一种典型脊椎生物，在监测和评估OMPs的种类、含量水平及生态风险方面具有重要地位。要对鱼体内种类繁多的OMPs进行全面定量分析，需要针对不同性质的目标化合物开发多种分析方法。另外，一些目标化合物标准品难以获取，鱼体复杂的基质问题较难解决，增加了靶向分析的难度。常规的靶向分析方法难以涵盖较宽范围的污染物，不能及时发现未知化合物。
近年来，基于高分辨质谱技术的非靶向筛查方法得以不断发展和应用，为实现样品的快速分析和未知污染物筛查提供可能。非靶向筛查技术能实现对样品中有机污染物的全面检测，无需获取相应的标准品和内标[7-9]。此外，该技术还可以实现对OMPs在环境介质中的降解转化产物的筛查研究[10-11]。目前，高分辨质谱技术已被证明可有效用于筛选环境污染物，可同时实现定量、可疑性及非靶向筛查。
-
现有的化合物种类繁多，目前注册CAS号的化学物质已超过1.6亿种，其中化学品监管清单包含394000种物质[12-13]，仅在欧洲范围内注册商用的物质可达140000种[14-15]。大约86000种生产的加工化学品按照《有毒物质控制法》在美国环境保护署注册[16]。一些特定的监管和执法机构对进入环境的可产生较高潜在风险的物质进行了阐述，如欧盟水框架指导文件中对优先污染物的描述，Norman对新型污染物进行了定义，斯德哥尔摩公约关于持久性有机污染物的规定[4,8,17-19]。但是，这些清单里只包含极小一部分在环境中可引起明显危害作用的物质[9,20]。现有的法规大多未对这些污染物进行全面管制，而且这些污染物在未来仍有不断增加的趋势[7,21-22]。
水体作为OMPs的主要受纳介质，会受到直接或间接的影响。OMPs的污染来源可概括为点源及非点源。点源指在某一点出现的高浓度物质，通过稀释或降解而随距离降低[23]。如工业及城市生活污水处理厂排放等。相比之下，非点源主要来自于多种扩散源，可理解为由不明确、没有特定排放点的活动而导致的污染，通常在广泛的地理范围内发生[24]。如农业径流、大气沉降等。OMPs的具体来源及传递路线如图1所示。大量OMPs通过点源或非点源的形式直接排入受纳水体，或经污水处理厂的不完全处理而进入水环境。这些物质可通过水解、光解、氧化和微生物代谢等方式进行生物和非生物转化，从而可能导致部分物质产生持久性及毒性强于母体物质的转化产物[7,22]。进入水体的OMPs可以吸附在沉积物、污泥上，并可通过生物富集作用储存于生物体内[25]。富集在沉积物及生物体内的OMPs也可能成为其他生物的富集来源[26]，并对水生生物表现出一定的急性及慢性毒性[21,27-31]。
鱼类的生存依赖于其周围水环境，进入河流、沉积物等水环境介质的多种类污染物可能通过生物蓄积或生物转化作用而进入鱼体，所以鱼类常被用作考察水环境污染的指示生物。另外，鱼作为人类的主要摄食水产品之一，鱼体内富集的OMPs可能通过食物链传递作用而对人类健康产生威胁[26,32-33]。因此，有必要对鱼体中OMPs的分布特征进行全面检测。
-
全球多个地区鱼体中的OMPs均有报道，本文对近五年的主要研究进行了综述（见表1）。Álvarez-Muñoz等[34]以欧洲不同国家河口采集的鱼体样本为研究对象，对鱼体内药物活性物质（pharmaceutically active compounds, PhACs）及内分泌干扰物（endocrine disrupting contaminants, EDCs）进行了分析，发现了10种PhACs与8种EDCs，其中，PhACs均低于方法定量限，而磷酸三（丁氧基乙基）酯含量最高，达到98.4 ng·g-1(dw)。值得注意的是，该研究是首次在海鱼中检测到PhACs[34]。Martins等[35]对大西洋西南部特有的一种极易富集污染物的濒危鱼类（巴西犁头鳐）进行了调查，结果表明一部分污染物在肝中的浓度最高，如双氯芬酸平均检出浓度为1474.25 ng·g-1（湿重）。多环芳烃、药物与个人护理品（pharmaceuticals and personal care products, PPCPs）是检出频率及检出浓度最高的污染物种类。这些检出浓度较高的物质可能导致慢性暴露，并且可能对人类健康产生影响[35]。Molbert等[3,36]的结果表明，母体污染物产生的代谢物浓度更高，并且这些代谢物可能呈现出更高的毒性水平。这些亲酯性物质的来源及生物积累性仍值得更深入的研究，并且可将代谢物研究作为全面评估水生生物生态风险的辅助工具[3,36]。这些结果也说明了对于一些未被关注或较少被关注的污染物及母体代谢物，常规定量分析较难实现准确监测的目的。
Lv等[37]对野生鱼体中6种EDCs的研究结果表明双酚A及对壬基酚是含量最高的两类物质，可达 μg·g−1级别。另外，鱼胆汁对EDCs的富集能力高于其他组织，且肝脏和胆汁对EDCs的代谢和排泄影响较大[37]。Emnet等与Arya等[38-39]分别对南极沿海及美国波托马克河鱼体中的PPCPs及激素类物质进行了研究，结果表明黄体酮、右美沙芬、雌酚酮及双酚A等物质浓度明显高于其他化合物。此外，大部分的PPCPs可在鱼类组织中表现出富集能力，且相对于其他类型的生物样本，对羟基苯甲酸甲酯更易于富集在鱼体内。Chen等[40-41]以中国华南地区的鱼塘与海水养殖场为研究对象，结果显示抗生素的平均浓度范围为0.05—200 ng·g−1，磺胺甲恶唑与甲氧苄啶是幼鱼体内的主要抗生素种类，并且甲氧苄啶在鱼肉中具有生物累积性[40-41]。
Dwiyitno等在采集的所有鱼组织样本中均发现了较高浓度的二异丙基萘（<10—660 ng·g−1）、直链烷基苯（90—3900 ng·g−1）和多环芳烃（10—70 ng·g−1），该研究也表明这些物质与亚洲近海海洋渔业资源的污染有关[42]。以上研究大多是针对鱼体内单一种类污染物的含量及风险水平开展工作，缺少对未被关注污染物风险水平的评估。因此，在对常规OMPs监测的基础上，有必要充分了解新型及未知污染物对水生生物及人体健康造成的影响，从而为制定相应的管控措施提供依据。
值得注意的是，表1所列针对鱼体中OMPs开展的定量研究多集中于特定种类的污染物，单次分析涵盖目标物较少，并且研究更多的集中于持久性有机污染物，如多环芳烃、多氯联苯、有机氯农药等，不能满足对鱼体中OMPs的全面监测的需求，同时也缺少新型污染物、优先控制污染物的监测数据。为了满足高通量、快速的筛查需求，高分辨质谱技术不断发展并逐渐被应用于监测研究中。
-
高分辨质谱（high resolution mass spectrometry, HRMS）以全扫描模式工作，其分辨率通常大于20000且质量误差较小[22,44]，质量范围广（一般在m/z 50—1700之间），可以提供更多的化合物信息（准确质量数及对应的同位素分布信息、碎片离子），并减少因共流出（即保留时间及质荷比非常接近）而导致的假阳性[45-46]。Krauss等关于常见的高分辨质谱仪器的灵敏度、分辨率、线性范围、扫描速度及质量误差进行了总结[47]。近年来，为了满足高选择性、质量准确性以及可提供充足的碎片信息的要求，一些新的联合使用方法及高分辨质谱不断出现，如Triple-TOF (AB Sciex)、Xevo G2 QTOF (Waters)、Q Exactive (Thermo)等[45]。本节主要对基于高分辨质谱技术的分析方法、数据采集模式及分析流程、筛查结果鉴定标准进行了综述。
-
基于现有的高分辨质谱仪，有文献报道的化合物分析方法主要包括靶向分析（target analysis）、非靶向筛查（non-target screening）、可疑性筛查（suspect screening）、拟靶向分析（pseudo-targeted analysis）[47-52]。HRMS在缺少标准物质时，可依据峰强度（峰面积）来实现半定量[22,53-54]。非靶向筛查在代谢组学方面应用较为广泛，无需相关的物质信息和参数，不受标准品这一条件的限制，只需对采集的样品按相应的前处理方式进行处理，以液相串联四极杆飞行时间质谱为例，利用液相色谱进行分离，利用全扫的方式对样品进行分析测定[55]，并将采集后的数据利用软件进行峰对齐等预处理，得到对应物质的保留时间、质荷比及强度，继而进行后续的数据分析。该方法具有较宽采集范围，高采集速率，高准确性和极高的分辨率，使其可以精确测量样品中任何可电离组分的质量[56]。可疑性筛查与非靶向筛查的关键区别是前者要获得一个可疑筛选化合物列表或特定的一组化合物（例如药物、农药等）以更好地了解特定的研究问题[51]，而后者则是采集所有的MS信号并将其当作是化合物进行评估，研究对象是未知的化合物，无需任何化合物的预筛选和优化过程[10,20,57]。
拟靶向分析是在非靶向筛查的基础上结合靶向分析的特点，来实现对待测样品中已知或未知的化合物进行同时监测的目的。其基本流程为：首先，使用色谱串联高分辨质谱对质控样品进行全扫描分析，并使用软件对采集数据进行解卷积分析以排除一些响应差且保留时间误差较大的峰；然后选择SIM定量离子；最后，在SIM模式下基于选定的定量离子获得对应的积分列表，从而得到每个组分的峰面积等信息[49,58-59]。该方法相对于非靶向筛查而言，线性范围更宽，灵敏度更高，数据的重复性等效果更好，但该方法较多用于代谢组学研究[60]。目前，利用非靶向与可疑性筛查开展的研究较多，其常见的工作流程如图2所示。
-
HRMS数据采集方式主要包括2种，分别为数据依赖型扫描（data-dependent acquisition/information-dependent acquisition, DDA/IDA）和数据非依赖型扫描（data-independent acquisition, DIA/MSE mode; Orbitrap MS叫做All-ion fragmentation, AIF）[22,61]。DDA是选取响应较好的一些母离子进行二级谱图的采集，这种工作模式下质谱仪可自动在一级全扫描（full scan MS）与二级扫描（MS/MS）之间进行转换，从而自动对目标离子进行碎裂并获取二级谱图。DIA是一种无偏见型离子碎裂方式，避免了对离子做预先的筛选（即在某一时刻所有检测到的离子均会被高能量打碎）[45,62]。此工作模式可在低碰撞能量与高碰撞能量之间进行切换，数据在MSE模式下采集，低能量通道保留组分的母离子信息及其同位素特征，从而帮助获得结构鉴定所需的分子式；高能量通道保留组分的碎片信息并结合保留时间有助于进行初步结构鉴定，再依据相应的软件进行数据库比对[10]。这种全扫描技术的数据采集模式在匹配母离子信息方面有一定的局限性，处理这些复杂数据仍然是一个挑战，需要选择可靠的数据处理方式及计算机软件来处理[22]。解卷积技术对于提高MS分析方法的选择性而言，是一种功能强大的数学工具[63]。不同的厂商一般会开发对应的软件，如Chromalynx (Waters)、TraceFinder (ThermoScientific)、Mass Profiler Professional (Agilent)、SCIEX OS (SCIEX)等[62]。在处理大批量样品时，这些软件的使用可以显著缩短分析时间，对污染物的高通量筛查工作而言是非常有必要的。
-
一般而言，不同厂商的仪器采集的数据为专有格式，不利于各平台及实验室采集的数据进行比较和交换分析。因此，一些开源的处理软件被广泛应用，其中MZmine与XCMS是应用较广泛的两种[64-65]。本文以MZmine 2为例，来介绍常规数据处理的具体过程，如图3所示。在利用MZmine 2等工具时需先将专有数据格式转化为开放数据格式，目前最常用的开放格式为.mzXML。为了达到数据格式转化的目的，开发人员建立了一套开放源码软件库（ProteoWizard:
http://proteowizard.sourceforge.net/download.html ），利用其msConvertGUI程序可实现对常见仪器供应商提供的数据格式的转化[66]。将格式转化后的原始文件导入到MZmine 2中，1）可以初步查看其二维或三维总离子流图，查看化合物总体分布情况并关注其基线高度（噪音水平）及最小峰的峰高，为后续峰提取步骤提供阈值参考。2）可根据需要按保留时间对色谱图进行剪切等操作。3）通过对噪音水平、峰宽、峰高以及质量误差（m/z tolerance）等参数进行设置，完成峰提取（色谱图构建），得到一组特征列表，可根据需要对所得特征列表进行解卷积操作。4）利用软件中的Isotopic peaks grouper功能对特征列表中独立的同位素峰进行鉴定。5）在对数据进行对齐归一化等处理之后（该步骤在代谢组学中尤其重要），可对处理后的数据集进行分子式预测及数据库比对，实现对特征峰的鉴定。具体的参数设置可参考Owis等发表内容[67-68]。
对于靶向分析而言，通常采用多反应检测模式依据具体的离子对以及保留时间来进行分析。但筛查工作一般是依据特定的数据库来进行，鉴定标准参考“3.4”节。对气相而言，由于其具有较完整的NIST EI-MS质谱库，可利用标准质谱图比对，保留指数校正来实现鉴定的过程。液相分析没有完整数据库，一些组织针对非靶向筛查开展了试验，并建立了相应的污染物数据库，如2013年NORMAN协会发起的关于多瑙河样本的非靶向筛查（
www.norman-network.net ）[69]，欧盟关于内分泌干扰物建立的优先污染物列表（https://ec.europa.eu/environment/chemicals/endocrine/strategy/being_en.htm ）等。另外，一些开源的数据库，如ChemSpider、MassBank等也在鉴定过程中起到重要作用[70-71]。为了进一步弥补化合物谱库信息的缺失，一些基于定量构效关系的计算机预测工具也逐渐被广泛运用。通过利用化合物本身的性质，如lg Kow（Kow正辛醇/水分配系数）等，来建立保留时间预测模型或进行MS/MS碎片预测，从而达到辅助定性的目的。本文将常见数据库、计算分子描述符和建立模型的软件、可进行碎片离子预测的信息整理，如表2所示。 -
作为主要研究对象，有机微污染物及其转化产物均属于小分子物质[73]。基于高分辨仪器的筛查结果确证可信度因研究介质与物质种类的变化而有所不同。为了简化对鉴定可信度的理解，Schymanski等针对小分子物质提出了一个可信度等级系统，如图4所示。其中，等级1为使用标准品进行结构确证的物质（经过一级数据MS，二级数据MS2，保留时间RT，标准品四层验证），可信度水平最高；等级2是通过免费谱库查找或诊断性数据进行结构匹配而得到的具有可能性结构的物质（经过一级数据MS，二级数据MS2，谱库二级数据MS2验证），没有标准品或文献未查到可确认的信息；等级3是仅含有一部分的结构信息，因此将其定义为候选物质（经过一级数据MS，二级数据MS2，部分结构及种类信息验证）；等级4是只具有明确的分子式，但缺乏明确的结构信息（经过光谱信息（如加合物、同位素及碎片信息）验证）；等级5则仅具有准确质量数，不具有明确的分子及结构信息（经过一级数据MS验证）。
依据中国农业农村部分别于2019年和2020年最新发布的《畜禽血液和尿液中150种兽药及其他化合物鉴别和确认液相色谱-高分辨串联质谱法》（
http://www.moa.gov.cn/nybgb/2019/201908/202001/t20200109_6334607.htm ）及《饲料中风险物质的筛查与确认导则液相色谱-高分辨质谱法（LC-HRMS）》（http://www.moa.gov.cn/govpublic/xmsyj/202007/t20200707_6347919.htm?keywords=+312 ），MS Scan模式可确定检出色谱峰的保留时间应与谱库中的保留时间偏差在± 2.5%之内，并且母离子的精确质量数和理论质量数偏差应≤ 5 ppm。满足这两个方面即可初步判断样品中含有该种目标物。这些目标物需至少有2个丰度较高的碎片离子，并且与谱库中对应的碎片离子的质量数偏差< 10 ppm。此外，以上采集的二级碎片离子应与对应标准品中的碎片离子相对丰度一致。通常情况下，还需要考察检出色谱峰的同位素丰度。在满足以上条件时，即可判定测试样品中含有该种待测物。 -
由于鱼类的生物富集作用，其可作为生物指示物来阐述环境污染物的水生行为，并可用来评价水生生物的暴露情况及健康情况[74-75]。但目前的定量研究可能忽略较少被关注的污染物。因此，越来越多利用高分辨质谱技术的筛查分析方法被开发应用（表3，表4）。
有机卤代污染物由于具有持久性、生物累积性以及毒性而被广泛关注，为了了解该类物质在鱼体中的污染情况，Pena-Abaurrea等于2011年首次利用GC×GC-TOF MS方法对地中海地区采集的金枪鱼体内的26种有机溴代污染物进行了研究，并初步鉴定到了一些新型衍生物[76]。在此之后，相继有研究利用GC×GC-TOF MS 以及GC-TOF MS对加拿大以及美国地区采集的鱼类进行了鉴定分析。其中，Byer等在加拿大东部鳗鱼体内共筛查检测到51种溴代化合物，这类物质大多被用于合成阻燃剂或作为中间体及最终产品而被应用[77]。Fernando等[54]利用非靶向筛查在美国五大湖地区鱼类中鉴定到了60多种卤化物，对其中的新型卤代有机污染物进行了全面分析，卤代甲氧基酚的检出率最高。以上两项结果均表明已鉴定卤代有机物大多为其代谢物或分解产物[54]。因此利用高分辨质谱技术对卤代物的代谢物及分解产物的研究也值得进一步关注。Liu等利用LC-Orbitrap MS对长江及汤逊湖地区鱼肉样品中的全氟和多氟烷酸类化合物（perfluoroalkyl and polyfluoroalkyl substances, PFASs）进行了非靶向筛查，除传统PFASs外，另外有超过10类330种其他类型氟化物，并且有4类是第一次被报道[78]。Du等的研究发现了19种新型或较少被注意到的有机污染物，包括乙酰苯胺等[79]。说明了非靶向筛查方法可同时用于对鱼类等生物介质中的多种类污染物及其代谢产物进行筛查鉴定，并为监测未知污染物提供可能。
Kaufmann等利用半靶向筛查的方法对购于超市的不同地区的养殖鱼类进行了兽药残留分析，共检测到了116种化合物[50]。Nacher-Mestre等[80-81]利用液相、气相系统串联不同高分辨质谱技术并结合了一种新型离子源（大气压化学电离源）对不同极性物质进行筛查分析，包括POPs（多环芳烃和农药）及抗生素。通过对超过133种农药、24种多环芳烃以及代表性的抗生素进行定性验证，证明选用的筛查方法具有一定的可靠性，可被用于水产养殖样品的筛查研究。另外，获取的准确质量质谱数据可用于回顾性分析，从而为检测到更多种类污染物提供了可能性[80-81]。另有研究专门对抗生素类及其代谢物进行了筛查分析，最终利用非靶向筛查方法在三文鱼中鉴定到4种磺胺类物质，鲈鱼中鉴定到5种大环内酯类物质[82-83]。以上所列鱼体样本大多采购于市场，缺少不同地区养殖鱼、野生鱼等类型样本的筛查数据，值得进一步关注。
由于高分辨质谱可单次分析多种污染物，有研究对澳大利亚及加拿大采集的笛鲷鱼、梭鱼及巴沙鱼体内的多种类污染物进行了筛查研究，这些物质包括紫外吸收剂、邻苯二甲酸酯类、全氟化合物、多氯联苯、多环芳烃及有机磷农药，涵盖了从极性到非极性的较宽范围的化合物。进一步验证了高通量筛查方法在研究环境介质及生物基质中的污染物暴露方面具有一定适用性，并且可用于未知或较少被关注污染物的筛查[84-86]。
-
河流及海洋等水环境是OMPs的主要受体，进入水环境的OMPs会对其中的鱼类造成不同程度的风险。而目前有关OMPs的标准以及风险评价方法多是基于常规定量建立的，关于各类型鱼体中OMPs的研究尚不够全面。本研究着重介绍了高分辨质谱从数据采集到最终鉴定的主要分析流程，鉴定过程需满足一定标准并借助一些开源数据库。此外，基于化合物自身性质利用计算机预测技术也可在一定程度上辅助定性。由于不受标准品限制、可进行回顾性分析等特点，高分辨质谱已被有效用于对生物样本中OMPs进行高通量筛查，可为发现未知或新型污染物提供可能。关于高分辨质谱筛查技术的展望如下：
（1）OMPs相关的谱库信息仍较缺乏，因此不可能对环境介质中污染物进行全面鉴定，不断完善开源数据库可以辅助了解OMPs在环境中的分布状态及行为变化。
（2）鱼体样本基质干扰较大，高分辨质谱获取的数据信息量丰富且复杂，优化前处理方法并开发便捷数据处理软件可大大减少基质干扰，从而增加数据分析鉴定的可靠性。
（3）应开展对不同类型鱼体样本中有机微污染物的筛查研究，以全面了解其富集状况及影响。
（4）目前关于环境中污染物的生态风险数据以及混合作用效应仍较短缺，利用高通量筛查方法对筛选出的污染物进行风险评价，并进而研究这些污染物的相互作用非常有必要，结合计算机工具预测其毒性也是值得关注的一个方向。
鱼体中有机微污染物的定量及筛查研究进展
Research progress of quantification and screening of organic micro-pollutants in fish
-
摘要: 化学品通过多种途径进入环境水体，可对水生生物及人体健康造成潜在风险。鱼类作为水环境污染物监测的指示生物，分析鱼体内有机微污染物的分布特征是评估水环境安全的重要方法。现有污染物种类繁多，且在生物体内存在降解转化的过程，基于靶向定量分析的监测方法难以系统、准确地评估鱼体中有机污染物的暴露特征。由于具有高分辨率、高质量精度、高通量和回顾性分析等特点，高分辨质谱技术逐渐被用于鱼体中有机污染物的筛查研究，为鱼体中高风险及未知污染物的定性、定量分析提供可能。本文综述了鱼体中有机污染物的定量分析现状，筛查分析方法以及其在鱼体中有机微污染物筛查研究中的应用。Abstract: Chemicals can enter environmental water bodies through various ways, which can lead to potential risks to aquatic organisms and human health. Fish are indicator organisms for monitoring water pollution. Analyzing the distribution of organic micro-pollutants (OMPs) in fish is an important method to assess water environmental safety. The monitoring method based on targeted quantitative analysis could not meet the requirements for a systematic and accurate assessment of exposure characteristics of OMPs in fish due to the presence of varieties of pollutants, and potential degradation and transformation metabolites in organisms. Therefore, high-resolution mass spectrometry (HRMS) has increasingly been used for the screening of OMPs in fish due to its high resolution, high mass accuracy, high throughput, and retrospective analysis. This provides the potential of qualitative and quantitative analysis of high-risk and unknown pollutants in fish. This article reviews the research status on quantitative analysis of OMPs in fish, screening analytical methods and their application in the screening research of OMPs in fish.
-
-
图 2 非靶向与可疑性筛查分析工作流程
Figure 2. The workflow of non-target and suspect screening analysis
表 1 有机微污染物在鱼体中的定量研究
Table 1. Quantitative studies of organic micro-pollutants in fish
研究地点
Location鱼体类型
Fish types分析仪器
Instrument前处理方法
Pretreatment methods检出限/(ng·g-1)
Detection limit目标物种类（数量）
Targets (number)浓度范围/(ng·g-1)
Concentration range参考文献
Reference法国马恩水文网 鲶鱼 GC-MS/MS ASE 0.01—13.6a PAHs (16)/PAEs (7)/PCBs (7)/OCPs (4)/PBDEs (6) — [3] 欧洲 金枪鱼/比目鱼 UPLC-MS/MS PLE+GPC
QuEChERS0.01—0.98 Pharmaceuticals (10)/EDCs (8) <MQL—98.4 (dw) [34] 巴西 吉他鱼 GC-MS Soxhlet extraction — Pesticides (6)/PPCPs (4)/PAHs (16)/
heavy metals (6)<LOD—2134.8 (ww) [35] 卢加诺湖/马焦雷湖 沙鱼/白鱼/鲈鱼 GC-PolarisQ Ion Trap Soxhlet extraction+GPC < 1 DDx (6)/PCBs (14)/PBDEs (8)/heavy metals (2) 114.5—1372 (lw) [36] 中国珠江三角 罗非鱼/鲢鱼/鲤鱼/草鱼/鲫鱼等 UPLC-MS/MS QuEChERS 0.19—2.0a Phenolic EDCs (6) 0.14—10520ws (ww)
0.10—10700 ds (ww)[37] 南极沿海 伯氏肩孔南极鱼 GC-MS ASE+GPC 0.5—17.5 PPCPs (17/steroid hormones (9) 1.6—26.9 (dw) [38] 美国波托马克河 白鲈鱼/带状剑鱼 GC-MS QuEChERS 1—10 Pharmaceuticals/steroids and xenoestrogens (29) — [39] 中国广州番禺/阳江市海陵岛 草鱼/罗非鱼/鲫鱼/鲮鱼/鲻鱼 UPLC-MS/MS Sonication+SAX/PSA+HLB 0.070—4.0 Antibiotics (21) 0.05—200 (ww) [40-41] 印度尼西亚雅加达湾 海鲇鱼等 GC-MS SPMs — DIPNs/LABs/Halogenated Compounds (7)/PAHs (16) <10—3900 (dw) [42] 日内瓦湖 白鳍鱼/欧洲鲈鱼/银鲫/湖拟鲤 GC-MS/MS
LC-MS/MS— 0—1721 Plastics/PCBs (12)/PAHs (16)/PBDEs (14)/OCPs (4)
NPs/BPA16.6—18039 [43] 注：加压液体萃取（pressurized liquid extraction, PLE）；凝胶渗透色谱（gel permeation chromatography, GPC）；加压溶剂萃取（accelerated solvent extraction, ASE）；净化半透膜（semipermeable membranes, SPMs）；强阴离子交换（strong anion exchange, SAX）；乙二胺-N-丙基硅胶（primary secondary amine, PSA）；a: 单位为ng.L-1; ws: wet season；ds: dry season；—: 文章未明确提出或无法查询数据；dw: dry weight；ww: wet weight；lw: lipid weight；QuEChERS: 快速（Quick），简单（Easy），低廉（Cheap），有效（Effective），稳定（Rugged），安全（Safe）；多氯联苯（polychlorinated biphenyls, PCBs）；多环芳烃（polycyclic aromatic hydrocarbons, PAHs）；多溴联苯醚（polybrominated diphenyl ethers, PBDEs）；有机氯农药（organochlorine pesticides, OCPs）；壬基酚（nonylphenols, NPs）；双酚A（bisphenol A, BPA）；DDx: 滴滴涕等系列农药；邻苯二甲酸酯（phthalate esters, PAEs）；LOD: limits of detection；MQLs: method quantification limits. 
下载: 导出CSV
表 2 数据库及计算机预测工具汇总
Table 2. Summary of databases and computer prediction tools
数据库
DatabaseRT预测模型
RT prediction model碎片离子预测
MS/MS prediction名称
Name描述
Description网址
Internet site分子描述符
Molecular descriptor模型软件
Model software名称
Name网址
Internet siteMassBank 约2000化合物/31000谱图 http://massbank.eu./MassBank PaDel EPI SuiteTMlog P predictions MetFrag https://msbi.ipb-halle.de/MetFrag/ Metlin 960000化合物 http://metlin.scripps.edu Dragon OPERA-RT CFM-ID http://cfmid.wishartlab.com mzCloud 18429化合物/7114615谱图 http://www.mzcloud.org/ E-dragon ACD/ChromGenius MAGMa+ https://sourceforge.net/projects/in-silico-fragmentation/ KEGG 40000化合物/120000谱图 http://www.genome.ad.jp/kegg/ Molecular descriptors software CAESAR MS-FINDER http://prime.psc.riken.jp/compms/msfinder/main.html GNPS 18163化合物/221083谱图 http://gnps.ucsd.edu QSARINS CSI FingerID https://www.csi-fingerid.uni-jena.de/ The CompTox Chemicals Dashboard 882000化合物 https://comptox.epa.gov/dashboard/ ACD/MS Fragmenter www.acdlabs.com PubChem 103274187化合物 https://pubchemdocs.ncbi.nlm.nih.gov/statistics MassFrontier www.thermoscientific.com ChemSpider 86000000化合物结构 http://www.chemspider.com/ MOLGEN-MS http://www.mathe2.uni-bayreuth.de/markus/ei-ms/user.html DSSTox database 875化合物 https://comptox.epa.gov/dashboard/downloads MASSIMO [72] Chemical and Products Database 49000化合物 https://www.epa.gov/chemical-research/chemical-and-products-database-cpdat ECHA - https://echa.europa.eu/ DrugBank 13585药物 https://www.drugbank.ca/ FOOTPRINT PPDB - https://uhra.herts.ac.uk/handle/2299/8377 HMDB 114222代谢物 www.hmdb.ca QSPR-THESAURUS database 44880 实验记录 http://www.qspr-thesaurus.eu/home/show.do Swedish Chemicals Agency 23000化合物 https://www.kemi.se/en/databases 注：RT: retention time; KEGG: The Kyoto Encyclopedia of Genes and Genomes; GNPS: Global Natural Product Social Molecular Networking; ECHA: European Chemicals Agency; HMDB: The public Human Metabolome Database; FOOTPRINT PPDB: FOOTPRINT pesticides Properties Database.
下载: 导出CSV
表 3 有机微污染物在鱼体中的筛查研究的仪器方法汇总
Table 3. Summary of instrument methods for screening of organic micro-pollutants in fish
分析仪器
Instrument扫描范围
Scan range (m/z)半峰宽FWHM 离子源模式
Ion mode碰撞能量
Collision energy色谱柱
Column软件
Software参考文献
referenceGC×GC TOF 50—750 — EI — HT-8 × BPX-50 column (30 m × 0.25 mm × 0.25 μm film thickness and 1.6 m ×0.1 mm ×
0.1 μm)ChromaToF software (Leco) [76] GC-TOF MS 45—800 >7000 EI/ECNI — Restek Rxi-XLB column (30 m × 0.25 mm × 0.25 μm film thickness) Masslynx 4.1 (Waters) [77] UPLC-Orbitrap — 25000 EI — SLB-IL60 (30 m × 0.25 mm × 0.2 μm film thickness and 2 m ×0.25 mm × 0.25 μm) ChromaTOF v1.90.60 (Leco) [54] GC×GC TOF 200—350 100000 ESI+ 20 Kinetex Core–Shell PFP (150 mm ×
2.1 mm × 2.6 μm)Qual Browser Xcalibur/MetWorks (Thermo Fisher Scientific) [50] UHPLC-QTOF MS 50—1000 10000 Z-spray-ESI+ LE: 4
HE: 15—40Acquity UHPLC BEH C18 (100 mm ×
2.1 mm × 1.7 μm)ChromaLynx XS (Waters) [80] GC-(APCI)-XEVO G2 QTOF — — APCI LE: 4
HE: 15—40fused silica DB-5MS capillary column (30 m × 0.25 mm × 0.25 μm film thickness) ChromaLynx XS (Waters) [81] UPLC-XEVO G2 QTOF 100—1000 > 10000 ESI− LE: −
HE: 15—35Acquity HSS T3 column (2.1 mm×
100 mm × 1.8 μm)(Waters) [87] LC-QTOF MS
GC-MS/MS100—950 30000 ESI+ ESI− 10 (-): reverse-phase XDB-C18 analytical column (4.6 mm× 50 mm × 1.8 μm); (+): XDB-C18 column (2.1 mm× 100 mm × 1.8 μm) MultiQuantTMsoftware (AB Sciex) [84] UHPLC-Orbitrap/
LC Q-Exactive100—900 70000 HESI+ 15/30/45 Hypersil Gold aQ C-18 column
(2.1 mm× 100 mm × 1.9 μm)Q-Exactive Tune 2.3 Build 1765/Chromeleon MS Link/Xcalibur (Thermo Fisher Scientific) [82-83,88] UHPLC-Orbitrap/
LC Q-Exactive80—1000 70000 HESI+
HESI−10/30/50 Supelco Ascentis Express C18 column
(2.1 mm × 75 mm × 2.7 μm)Xcalibur/ TraceFinder (Thermo Fisher Scientific) [89] UHPLC-QTOF MS 100—1700 6000—12400 ESI+
ESI−- Agilent ZORBAX Eclipse Plus C18 column
(2.1 mm× 100 mm × 1.8 μm)Profider/MPP (Agilent Technology) [79] HPLC-Orbitrap 150—1500 120000 ESI− 80/100 Xselect CSH C18 XP colum
(3 mm× 150 mm × 2.5 μm)(Thermo Fisher Scientific) [78] HPLC-TOF MS 50—1700 ESI+
ESI−0/10/20/40 Poroshell 120 Phenyl Hexyl column
(3.0 mm × 100 mm × 2.7 μm)Profider/MPP/PCDL (Agilent Technology) [85-86] LC-Q-Orbitrap 70—1050 70000 HESI+
HESI−- ACE UltraCore 2.5 SuperPhenylHexyl column (201 mm × 100 mm × 2.5 μm) Xcalibur/ TraceFinder/Compound Discover/mzCloud
(Thermo Fisher Scientific)[90] 注：LE: low-energy function; HE: high-energy function.
下载: 导出CSV
表 4 有机微污染物在鱼体中的筛查研究概况
Table 4. Overview of screening studies of organic micro-pollutants in fish
研究地点
Location鱼体类型
Fish types前处理方法
Pretreatment methods分析方法
Analytical methods鉴定目标物种类（数量）
Identified targets types (number)参考文献
Reference靶向分析
Target analysis可疑性筛查
Suspect screening非靶向筛查
Non-target screening地中海 金枪鱼 — √ √ Organobrominated compounds (26) [76] 加拿大玛格丽河 鳗鱼 GPC √ Halogenated compounds (51) [77] 美国-五大湖 湖鳟鱼 SPE √ Halogenated compounds (> 60) [54] 瑞士-超市购买 罗非鱼/三文鱼/鳕鱼/鲈鱼等养殖鱼类 ASE/GPC/PSA semi-targeted screening Veterinary drugs （226） [50] 西班牙-超市购买 三文鱼/鲈鱼/比目鱼等养殖鱼类 Sonication √ Antibiotics/pesticides/mycotoxins (> 70) [80] 西班牙-超市购买 三文鱼/鲈鱼/比目鱼等养殖鱼类 QuEChERS √ pesticides (> 133)/PAHs (24) [81] 加拿大-安大略湖 湖鳟鱼 Sonication √ √ PFASs (17) [87] 澳大利亚 笛鲷鱼 QuEChERS √ √ PPCPs/pesticides/PCBs/PAHs/OCPs
/PBDEs/musk (51)[84] 中国/美国-购买 罗非鱼/鳗鱼 PSA/Z-Sep/MgSO4
Oasis PRiME HLB√ √ Veterinary drugs （> 260） [88-89] 美国 — ASE √ √ Organic compounds (19) [79] 日本-欧洲-购买 三文鱼/鲈鱼 PSA/Z-Sep/MgSO4 √ Sulfonamides, macrolides and their metabolites [82-83] 中国长江/武汉汤逊湖 鲤鱼-白鲷 WAX √ PFASs (> 330) [78] 加拿大圣劳伦斯河/蒙特利尔 梭鱼/鲟鱼-巴沙鱼 Sonication √ √ PAEs (1)/BPs (10)/PFOS (1) [85-86] 西班牙-集市购买 金枪鱼-鲈鱼 Captiva EMR Lipid √ √ Pharmaceuticals/PAEs/PFOS/insects repellents (180, suspect screening: 17800) [90] 注：WAX: weak anion exchange cartridges; 全氟辛基磺酸（perfluorooctane sulfonate, PFOS）.
下载: 导出CSV
-
[1] YAN Z H, YANG H H, DONG H K, et al. Occurrence and ecological risk assessment of organic micropollutants in the lower reaches of the Yangtze River, China: A case study of water diversion [J]. Environmental Pollution, 2018, 239: 223-232. doi: 10.1016/j.envpol.2018.04.023 [2] GOTO A, TUE N M, SOMEYA M, et al. Occurrence of natural mixed halogenated dibenzo-p-dioxins: specific distribution and profiles in mussels from Seto Inland Sea, Japan [J]. Environmental Science & Technology, 2017, 51(20): 11771-11779. [3] MOLBERT N, ALLIOT F, SANTOS R, et al. Multiresidue methods for the determination of organic micropollutants and their metabolites in fish matrices [J]. Environmental Toxicology and Chemistry, 2019, 38(9): 1866-1878. doi: 10.1002/etc.4500 [4] GOTO A, TUE N M, ISOBE T, et al. Nontarget and target screening of organohalogen compounds in mussels and sediment from Hiroshima Bay, Japan: occurrence of novel bioaccumulative substances [J]. Environmental Science & Technology, 2020, 54(9): 5480-5488. [5] AGüERA A, BUENO M J M, FERNáNDEZ-ALBA A R. New trends in the analytical determination of emerging contaminants and their transformation products in environmental waters [J]. Environmental Science and Pollution Research, 2013, 20(6): 3496-3515. doi: 10.1007/s11356-013-1586-0 [6] BERLIOZ-BARBIER A, VAUCHEZ A, WIEST L, et al. Multi-residue analysis of emerging pollutants in sediment using QuEChERS-based extraction followed by LC-MS/MS analysis [J]. Analytical and Bioanalytical Chemistry, 2014, 406(4): 1259-1266. doi: 10.1007/s00216-013-7450-8 [7] BLETSOU A A, JEON J, HOLLENDER J, et al. Targeted and non-targeted liquid chromatography-mass spectrometric workflows for identification of transformation products of emerging pollutants in the aquatic environment [J]. TRAC Trends in Analytical Chemistry, 2015, 66: 32-44. doi: 10.1016/j.trac.2014.11.009 [8] MASIA A, BLASCO C, PICO Y. Last trends in pesticide residue determination by liquid chromatography–mass spectrometry [J]. Trends in Environmental Analytical Chemistry, 2014, 2: 11-24. doi: 10.1016/j.teac.2014.03.002 [9] GAGO-FERRERO P, SCHYMANSKI E L, HOLLENDER J, et al. Nontarget analysis of environmental samples based on liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) [J]. Comprehensive Analytical Chemistry, 2016, 71: 381-403. [10] ACENA J, STAMPACHIACCHIERE S, PEREZ S, et al. Advances in liquid chromatography-high-resolution mass spectrometry for quantitative and qualitative environmental analysis [J]. Analytical and Bioanalytical Chemistry, 2015, 407(21): 6289-6299. doi: 10.1007/s00216-015-8852-6 [11] SAMANIPOUR S, REID M J, BAEK K, et al. Combining a deconvolution and a universal library search algorithm for the nontarget analysis of data-independent acquisition mode liquid chromatography-high-resolution mass spectrometry results [J]. Environmental Science & Technology, 2018, 52(8): 4694-4701. [12] CAS. The gold standard for chemical substance information [EB/OL]. [2020-6-19]. Chemical Abstracts Service. American Chemical Society. Columbus, OH, USA, 2020,https://www.cas.org/content/chemical-substances. (Accessed: 19 June 2020, 14:00). [13] CAS. Regulated chemicals-CHEMLIST[EB/OL]. [2020-6-19]. Chemical Service. American Chemical Society. Columbus, OH, USA, 2020,https://www.cas.org/support/documentation/regulated-chemicals. (Accessed: 19 June 2020, 14:00). [14] UNEP. Global Chemical Outlook; towards sound management of chemicals[EB/OL]. [2020-6-19]. United Nations Environment Programme , 2013,https://sustainabledevelopment.un.org/index.php?page=view&type=400&nr=1966&menu=35. (Accessed: 19 June 2020, 14:00). [15] GUILLEN D, GINEBREDA A, FARRE M, et al. Prioritization of chemicals in the aquatic environment based on risk assessment: analytical, modeling and regulatory perspective [J]. Science of the Total Environment, 2012, 440: 236-252. doi: 10.1016/j.scitotenv.2012.06.064 [16] USEPA. United States Environmental Protection Agency. TSCA Chemical Substance Inventory [EB/OL]. [2020-6-19]. United States Envionmental Protection Agency , 2020,https://www.epa.gov/tsca-inventory/about-tsca-chemical-substance-inventory. (Accessed: 19 June 2020, 14:00). [17] DAUGHTON C G. Non-regulated water contaminants: emerging research [J]. Environmental Impact Assessment Review, 2004, 24(7-8): 711-732. doi: 10.1016/j.eiar.2004.06.003 [18] CRANE M, BABUT M. Environmental quality standards for water framework directive priority substances: challenges and opportunities [J]. Integrated Environmental Assessment & Management, 2007, 3(2): 290-296. [19] SOBUS J R, WAMBAUGH J F, ISAACS K K, et al. Integrating tools for non-targeted analysis research and chemical safety evaluations at the US EPA [J]. Journal of Exposure Science & Environmental Epidemiology, 2018, 28(5): 411-426. [20] GAGO-FERRERO P, KRETTEK A, FISCHER S, et al. Suspect screening and regulatory databases: a powerful combination to identify emerging micropollutants [J]. Environmental Science & Technology, 2018, 52(12): 6881-6894. [21] FARRé M L, PéREZ S, KANTIANI L, et al. Fate and toxicity of emerging pollutants, their metabolites and transformation products in the aquatic environment [J]. TRAC Trends in Analytical Chemistry, 2008, 27(11): 991-1007. doi: 10.1016/j.trac.2008.09.010 [22] CCANCCAPA-CARTAGENA A, PICO Y, ORTIZ X, et al. Suspect, non-target and target screening of emerging pollutants using data independent acquisition: assessment of a Mediterranean River basin, seven golden rules [J]. Science of the Total Environment, 2019, 687: 355-368. doi: 10.1016/j.scitotenv.2019.06.057 [23] RUFF M, MUELLER M S, LOOS M, et al. Quantitative target and systematic non-target analysis of polar organic micro-pollutants along the river Rhine using high-resolution mass-spectrometry-identification of unknown sources and compounds [J]. Water Research, 2015, 87: 145-54. [24] LAPWORTH D J, BARAN N, STUART M E, et al. Emerging organic contaminants in groundwater: a review of sources, fate and occurrence [J]. Environmental Pollution, 2012, 163: 287-303. doi: 10.1016/j.envpol.2011.12.034 [25] GAW S, THOMAS K V, HUTCHINSON T H. Sources, impacts and trends of pharmaceuticals in the marine and coastal environment [J]. Philosophical Transactions of the Royal Society B Biological Sciences, 2014, 369(1656): 3133-3146. [26] SANCHEZ-AVILA J, TAULER R, LACORTE S. Organic micropollutants in coastal waters from NW Mediterranean Sea: sources distribution and potential risk [J]. Environment International, 2012, 46: 50-62. doi: 10.1016/j.envint.2012.04.013 [27] DOM I, BIRE R, HORT V, et al. Extended targeted and non-targeted srategies for the analysis of marine toxins in mussels and oysters by (LC-HRMS) [J]. Toxins, 2018, 10(9): 10375. [28] MAIER D, BLAHA L, GIESY J P, et al. Biological plausibility as a tool to associate analytical data for micropollutants and effect potentials in wastewater, surface water, and sediments with effects in fishes [J]. Water Research, 2015, 72: 127-144. doi: 10.1016/j.watres.2014.08.050 [29] DAMALAS D E, BLETSOU A A, AGALOU A, et al. Assessment of the acute toxicity, uptake and biotransformation potential of benzotriazoles in zebrafish (Danio rerio) larvae combining HILIC- with RPLC-HRMS for high-throughput identification [J]. Environmental Science & Technology, 2018, 52(10): 6023-6031. [30] SHAO Y, CHEN Z, HOLLERT H, et al. Toxicity of 10 organic micropollutants and their mixture: Implications for aquatic risk assessment [J]. Science of the Total Environment, 2019, 666: 1273-1282. doi: 10.1016/j.scitotenv.2019.02.047 [31] ANGELES L F, MULLEN R A, HUANG I J, et al. Assessing pharmaceutical removal and reduction in toxicity provided by advanced wastewater treatment systems [J]. Environmental Science-Water Research & Technology, 2020, 6(1): 62-77. [32] CAPPIELLO A, FAMIGLINI G, PALMA P, et al. Determination of selected endocrine disrupting compounds in human fetal and newborn tissues by GC-MS [J]. Analytical and Bioanalytical Chemistry, 2014, 406(12): 2779-2788. doi: 10.1007/s00216-014-7692-0 [33] LIU M, JIANG Y, LIU R, et al. Structural features guided "fishing" strategy to identification of flavonoids from lotus plumule in a self-built data "pool" by ultra-high performance liquid chromatography coupled with hybrid quadrupole-orbitrap high resolution mass spectrometry [J]. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences, 2019, 1124: 122-134. doi: 10.1016/j.jchromb.2019.06.002 [34] ALVAREZ-MUNOZ D, RODRIGUEZ-MOZAZ S, MAULVAULT A L, et al. Occurrence of pharmaceuticals and endocrine disrupting compounds in macroalgaes, bivalves, and fish from coastal areas in Europe [J]. Environmental Research, 2015, 143: 56-64. doi: 10.1016/j.envres.2015.09.018 [35] MARTINS M F, COSTA P G, BIANCHINI A. Contaminant screening and tissue distribution in the critically endangered Brazilian guitarfish Pseudobatos horkelii [J]. Environmental pollution (Barking, Essex:1987), 2020, 265: 114923. doi: 10.1016/j.envpol.2020.114923 [36] GUZZELLA L M, NOVATI S, CASATTA N, et al. Spatial and temporal trends of target organic and inorganic micropollutants in Lake Maggiore and Lake Lugano (Italian-Swiss water bodies): contamination in sediments and biota [J]. Hydrobiologia, 2018, 824(1): 271-290. doi: 10.1007/s10750-017-3494-7 [37] LV Y Z, YAO L, WANG L, et al. Bioaccumulation, metabolism, and risk assessment of phenolic endocrine disrupting chemicals in specific tissues of wild fish [J]. Chemosphere, 2019, 226: 607-615. doi: 10.1016/j.chemosphere.2019.03.187 [38] EMNET P, GAW S, NORTHCOTT G, et al. Personal care products and steroid hormones in the Antarctic coastal environment associated with two Antarctic research stations, McMurdo Station and Scott Base [J]. Environmental Research, 2015, 136: 331-342. doi: 10.1016/j.envres.2014.10.019 [39] ARYA G, TADAYON S, SADIGHIAN J, et al. Pharmaceutical chemicals, steroids and xenoestrogens in water, sediments and fish from the tidal freshwater Potomac River (Virginia, USA) [J]. Journal Of Environmental Science And Health Part A, 2017, 52(7): 686-696. doi: 10.1080/10934529.2017.1312975 [40] CHEN H, LIU S, XU X R, et al. Antibiotics in typical marine aquaculture farms surrounding Hailing Island, South China: Occurrence, bioaccumulation and human dietary exposure [J]. Marine Pollution Bulletin, 2015, 90(1/2): 181-187. [41] CHEN H, LIU S, XU X R, et al. Tissue distribution, bioaccumulation characteristics and health risk of antibiotics in cultured fish from a typical aquaculture area [J]. Journal of Hazardous materials, 2018, 343: 140-148. doi: 10.1016/j.jhazmat.2017.09.017 [42] DWIYITNO, DSIKOWITZKY L, NORDHAUS I, et al. Accumulation patterns of lipophilic organic contaminants in surface sediments and in economic important mussel and fish species from Jakarta Bay, Indonesia [J]. Marine Pollution Bulletin, 2016, 110(2): 767-777. doi: 10.1016/j.marpolbul.2016.01.034 [43] FAURE F, DEMARS C, WIESER O, et al. Plastic pollution in Swiss surface waters: Nature and concentrations, interaction with pollutants [J]. Environmental Chemistry, 2015, 12(5): 582-591. doi: 10.1071/EN14218 [44] LIU Y N, D'AGOSTINO L A, QU G B, et al. High-resolution mass spectrometry (HRMS) methods for nontarget discovery and characterization of poly- and per-fluoroalkyl substances (PFASs) in environmental and human samples [J]. TRAC Trends in Analytical Chemistry, 2019, 121: 115420. doi: 10.1016/j.trac.2019.02.021 [45] HERNáNDEZ F, IBáñEZ M, BADE R, et al. Investigation of pharmaceuticals and illicit drugs in waters by liquid chromatography-high-resolution mass spectrometry [J]. TRAC Trends in Analytical Chemistry, 2014, 63: 140-157. doi: 10.1016/j.trac.2014.08.003 [46] HERNáNDEZ F, IBANEZ M, BOTERO-COY A M, et al. LC-QTOF MS screening of more than 1, 000 licit and illicit drugs and their metabolites in wastewater and surface waters from the area of Bogota, Colombia [J]. Analytical and Bioanalytical Chemistry, 2015, 407(21): 6405-16. doi: 10.1007/s00216-015-8796-x [47] KRAUSS M, SINGER H, HOLLENDER J. LC-high resolution MS in environmental analysis: from target screening to the identification of unknowns [J]. Analytical and Bioanalytical Chemistry, 2010, 397(3): 943-951. doi: 10.1007/s00216-010-3608-9 [48] BENNETT R V, GAMAGE C M, GALHENA A S, et al. Contrast-enhanced differential mobility-desorption electrospray ionization-mass spectrometry imaging of biological tissues [J]. Analytical Chemistry, 2014, 86(8): 3756-3763. doi: 10.1021/ac5007816 [49] LI Y, RUAN Q, LI Y L, et al. A novel approach to transforming a non-targeted metabolic profiling method to a pseudo-targeted method using the retention time locking gas chromatography/mass spectrometry-selected ions monitoring [J]. Journal of Chromatography A, 2012, 1255: 228-236. doi: 10.1016/j.chroma.2012.01.076 [50] KAUFMANN A, BUTCHER P, MADEN K, et al. Semi-targeted residue screening in complex matrices with liquid chromatography coupled to high resolution mass spectrometry: Current possibilities and limitations [J]. Analyst, 2011, 136(9): 1898-1909. doi: 10.1039/c0an00902d [51] SJERPS R M A, VUGHS D, VAN LEERDAM J A, et al. Data-driven prioritization of chemicals for various water types using suspect screening LC-HRMS [J]. Water Research, 2016, 93: 254-264. doi: 10.1016/j.watres.2016.02.034 [52] MOREIRA T D C S S. Targeted, semi-targeted and nontargeted screening for drugs in whole blood by UPLC TOF-MS with dataindependent acquisition (DIA) [D]. Porto: University of Porto, 2014. [53] BU Q W, WANG D H, LIU X, et al. A high throughout semi-quantification method for screening organic contaminants in river sediments [J]. Journal of Environmental Management, 2014, 143: 135-139. [54] FERNANDO S, RENAGULI A, MILLIGAN M S, et al. Comprehensive analysis of the great lakes top predator fish for novel halogenated organic contaminants by GCxGC-HR-ToF mass spectrometry [J]. Environmental Science & Technology, 2018, 52(5): 2909-2917. [55] PATTI G J, TAUTENHAHN R, SIUZDAK G. Meta-analysis of untargeted metabolomic data from multiple profiling experiments [J]. Nature Protocol, 2012, 7(3): 508-516. doi: 10.1038/nprot.2011.454 [56] IBáñEZ M, SANCHO J V, HERNáNDEZ F, et al. Rapid non-target screening of organic pollutants in water by ultraperformance liquid chromatography coupled to time-of-light mass spectrometry [J]. TRAC Trends in Analytical Chemistry, 2008, 27(5): 481-489. doi: 10.1016/j.trac.2008.03.007 [57] GAGO-FERRERO P, SCHYMANSKI E L, BLETSOU A A, et al. Extended suspect and non-target strategies to characterize emerging polar organic contaminants in raw wastewater with LC-HRMS/MS [J]. Environmental Science & Technology, 2015, 49(20): 12333-12341. [58] CHEN S L, KONG H W, LU X, et al. Pseudotargeted metabolomics method and its application in serum biomarker discovery for hepatocellular carcinoma based on ultra high-performance liquid chromatography/triple quadrupole mass spectrometry [J]. Analytical Chemistry, 2013, 85(17): 8326-8333. doi: 10.1021/ac4016787 [59] ZHENG F, ZHAO X, ZENG Z, et al. Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography–mass spectrometry[J]. Nature Ptotocol, 2020. [60] 唐才明, 新型卤代有机污染物及多环芳烃代谢物的准靶向定性分析[D]. 北京: 中国科学院大学, 2016. TANG C M, Quasi-targeted qualitative analysis of novel halogenated organic pollutants and hydroxylation-related metabolites of polycyclic aromatic hydrocarbons[D]. Beijing: University of Chinese Academy of Sciences 2016 (in Chinese).
[61] PEDERSEN A J, DALSGAARD P W, RODE A J, et al. Screening for illicit and medicinal drugs in whole blood using fully automated SPE and ultra‐high‐performance liquid chromatography with TOF‐MS with data‐independent acquisition [J]. Journal of Separation Science, 2013, 36(13): 2081-2089. doi: 10.1002/jssc.201200921 [62] BADE R, ROUSIS N I, BIJLSMA L, et al. Screening of pharmaceuticals and illicit drugs in wastewater and surface waters of Spain and Italy by high resolution mass spectrometry using UHPLC-QTOF MS and LC-LTQ-Orbitrap MS [J]. Analytical and Bioanalytical Chemistry, 2015, 407(30): 8979-88. doi: 10.1007/s00216-015-9063-x [63] XU G, STUPAK J, YANG L, et al. Deconvolution in mass spectrometry based proteomics [J]. Rapid Communications in Mass Spectrometry, 2018, 32(10): 763-774. doi: 10.1002/rcm.8103 [64] BENTON H P, WONG D M, TRAUGER S A, et al. XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization [J]. Analytical Chemistry, 2008, 80(16): 6382-6389. doi: 10.1021/ac800795f [65] PLUSKAL T, CASTILLO S, VILLAR-BRIONES A, et al. MZmine 2, Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data [J]. BMC Boinformatics, 2010, 11: 395. doi: 10.1186/1471-2105-11-395 [66] CHAMBERS M C, MACLEAN B, BURKE R, et al. A cross-platform toolkit for mass spectrometry and proteomics [J]. Nature Biotechnology, 2012, 30(10): 918-920. doi: 10.1038/nbt.2377 [67] BERTRAND S, AZZOLLINI A, SCHUMPP O, et al. Multi-well fungal co-culture for de novo metabolite-induction in time-series studies based on untargeted metabolomics [J]. Mol Biosyst, 2014, 10(9): 2289-2298. doi: 10.1039/C4MB00223G [68] OWIS A I, EL-HAWARY M S, EL AMIR D, et al. Molecular docking reveals the potential of Salvadora persica flavonoids to inhibit COVID-19 virus main protease [J]. RSC Advances, 2020, 10(33): 19570-19575. doi: 10.1039/D0RA03582C [69] ALYGIZAKIS N A, OSWALD P, THOMAIDIS N S, et al. NORMAN digital sample freezing platform: A European virtual platform to exchange liquid chromatography high resolution-mass spectrometry data and screen suspects in “digitally frozen” environmental samples [J]. TRAC Trends in Analytical Chemistry, 2019, 115: 129-137. doi: 10.1016/j.trac.2019.04.008 [70] SCHYMANSKI E L, SINGER H P, SLOBODNIK J, et al. Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis [J]. Analytical and Bioanalytical Chemistry, 2015, 407(21): 6237-6255. doi: 10.1007/s00216-015-8681-7 [71] ZEDDA M, ZWIENER C. Is nontarget screening of emerging contaminants by LC-HRMS succesful? A plea for compound libraries and computer tools [J]. Analytical and Bioanalytical Chemistry, 2012, 403(9): 2493-2502. doi: 10.1007/s00216-012-5893-y [72] GASTEIGER J, HANEBECK W, SCHULZ K P. Prediction of mass spectra from structural information [J]. Journal of Chemical Information & Computer Sciences, 1992, 32(4): 264-271. [73] SCHYMANSKI E L, JEON J, GULDE R, et al. Identifying small molecules via high resolution mass spectrometry: Communicating confidence [J]. Environmental Science & Technology, 2014, 48(4): 2097-2098. [74] VAN DER OOST R, BEYER J, VERMEULEN N P E. Fish bioaccumulation and biomarkers in environmental risk assessment: A review [J]. Environmental Toxicology and Pharmacology, 2003, 13(2): 57-149. doi: 10.1016/S1382-6689(02)00126-6 [75] HAMILTON P B, COWX I G, OLEKSIAK M F, et al. Population-level consequences for wild fish exposed to sublethal concentrations of chemicals - A critical review [J]. Fish and Fisheries, 2016, 17(3): 545-566. doi: 10.1111/faf.12125 [76] PENA-ABAURREA M, COVACI A, RAMOS L. Comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry for the identification of organobrominated compounds in bluefin tuna [J]. Journal of Chromatography A, 2011, 1218(39): 6995-7002. doi: 10.1016/j.chroma.2011.08.031 [77] BYER J D, PACEPAVICIUS G, LEBEUF M, et al. Qualitative analysis of halogenated organic contaminants in American eel by gas chromatography/time-of-flight mass spectrometry [J]. Chemosphere, 2014, 116: 98-103. doi: 10.1016/j.chemosphere.2014.02.032 [78] LIU Y N, QIAN M L, MA X X, et al. Nontarget mass spectrometry reveals new perfluoroalkyl substances in fish from the Yangtze River and Tangxun Lake, China [J]. Environmental Science & Technology, 2018, 52(10): 5830-5840. [79] DU B W, LOFTON J M, PETER K T, et al. Development of suspect and non-target screening methods for detection of organic contaminants in highway runoff and fish tissue with high-resolution time-of-flight mass spectrometry [J]. Environmental Science Processes & Impacts, 2017, 19(9): 1185-1196. [80] NACHER-MESTRE J, IBANEZ M, SERRANO R, et al. Qualitative screening of undesirable compounds from feeds to fish by liquid chromatography coupled to mass spectrometry [J]. Journal of Agricultural and Food Chemistry, 2013, 61(9): 2077-2087. doi: 10.1021/jf304478n [81] NACHER-MESTRE J, SERRANO R, PORTOLES T, et al. Screening of pesticides and polycyclic aromatic hydrocarbons in feeds and fish tissues by gas chromatography coupled to high-resolution mass spectrometry using atmospheric pressure chemical ionization [J]. Journal of Agricultural and Food Chemistry, 2014, 62(10): 2165-2174. doi: 10.1021/jf405366n [82] JIA W, SHI L, CHU X G. Untargeted screening of sulfonamides and their metabolites in salmon using liquid chromatography coupled to quadrupole Orbitrap mass spectrometry [J]. Food Chemistry, 2018, 239: 427-433. doi: 10.1016/j.foodchem.2017.06.143 [83] JIA W, SHI L, CHU X G, et al. A strategy for untargeted screening of macrolides and metabolites in bass by liquid chromatography coupled to quadrupole orbitrap mass spectrometry [J]. Food Chemistry, 2018, 262: 110-117. doi: 10.1016/j.foodchem.2018.04.090 [84] BADUEL C, MUELLER J F, TSAI H, et al. Development of sample extraction and clean-up strategies for target and non-target analysis of environmental contaminants in biological matrices [J]. Journal of Chromatography A, 2015, 1426: 33-47. doi: 10.1016/j.chroma.2015.11.040 [85] TIAN L, GOODYER C G, ZHENG J Y, et al. Thermal degradation of bisphenol A and bisphenol S in water and fish (cod and basa) fillets [J]. Food Chemistry, 2020, 328: 126999. doi: 10.1016/j.foodchem.2020.126999 [86] TIAN L, VERREAULT J, HOUDE M, et al. Suspect screening of plastic-related chemicals in northern pike (Esox lucius) from the St. Lawrence River, Canada [J]. Environmental Pollution, 2019, 255: 113223. doi: 10.1016/j.envpol.2019.113223 [87] CRIMMINS B S, XIA X, HOPKE P K, et al. A targeted/non-targeted screening method for perfluoroalkyl carboxylic acids and sulfonates in whole fish using quadrupole time-of-flight mass spectrometry and MSe [J]. Analytical and Bioanalytical Chemistry, 2014, 406(5): 1471-1480. doi: 10.1007/s00216-013-7519-4 [88] JIA W, CHU X G, CHANG J, et al. High-throughput untargeted screening of veterinary drug residues and metabolites in tilapia using high resolution orbitrap mass spectrometry [J]. Analytica Chimica Acta, 2017, 957: 29-39. doi: 10.1016/j.aca.2016.12.038 [89] TURNIPSEED S B, STOREY J M, LOHNE J J, et al. Wide-scope screening method for multiclass veterinary drug residues in fish, shrimp, and eel using liquid chromatography-quadrupole high-resolution mass spectrometry [J]. Journal of Agricultural and Food Chemistry, 2017, 65(34): 7252-7267. doi: 10.1021/acs.jafc.6b04717 [90] MUSATADI M, GONZáLEZ-GAYA B, IRAZOLA M, et al. Focused ultrasound-based extraction for target analysis and suspect screening of organic xenobiotics in fish muscle [J]. Science of the Total Environment, 2020, 740: 139894. doi: 10.1016/j.scitotenv.2020.139894 -
点击查看大图
图( 4) 表( 4)
计量
- 文章访问数: 4201
- HTML全文浏览数: 4201
- PDF下载数: 66
- 施引文献: 0
