Assessing Transformation Products of Chemicals by Non-Target and Suspect Screening : Strategies and...

Assessing TransformationProducts of Chemicals by Non-Target and Suspect Screening −

Strategies and WorkflowsVolume 1

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Drewes and Letzel; Assessing Transformation Products of Chemicals by Non-Target and Suspect Screening Strategies and ...ACS Symposium Series; American Chemical Society: Washington, DC, 2016.

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ACS SYMPOSIUM SERIES 1241

Assessing TransformationProducts of Chemicals by Non-Target and Suspect Screening −

Strategies and WorkflowsVolume 1

Jo rg E. Drewes, EditorTechnical University of Munich

Garching, Germany

Thomas Letzel, EditorTechnical University of Munich

Garching, Germany

Sponsored by theACS Division of Environmental Chemistry, Inc.

American Chemical Society, Washington, DC

Distributed in print by Oxford University Press

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Library of Congress Cataloging-in-Publication Data

Names: Drewes, Jorg E., editor. | Letzel, Thomas, 1970- editor. | AmericanChemical Society. Division of Environmental Chemistry.

Title: Assessing transformation products of chemicals by non-target andsuspect screening : strategies and workflows / Jorg E. Drewes, editor,Technical University of Munich, Garching, Germany, Thomas Letzel, editor,Technical University of Munich, Garching, Germany ; sponsored by the ACSDivision of Environmental Chemistry.

Description: Washington, DC : American Chemical Society, [2016]- | Series:ACS symposium series ; 1241 | Includes bibliographical references andindex.

Identifiers: LCCN 2016053208 (print) | LCCN 2016053607 (ebook) | ISBN9780841231931 (v. 1) | ISBN 9780841231924 (ebook)

Subjects: LCSH: Pollution. | Speciation (Chemistry) |Pollutants--Biodegradation. | Ecological risk assessment. | Environmentalchemistry.

Classification: LCC TD196.C45 A87 2016 (print) | LCC TD196.C45 (ebook) | DDC628.1/68--dc23

LC record available at https://lccn.loc.gov/2016053208

The paper used in this publication meets the minimum requirements of American NationalStandard for Information Sciences—Permanence of Paper for Printed Library Materials,ANSI Z39.48n1984.

Copyright © 2016 American Chemical Society

Distributed in print by Oxford University Press

All Rights Reserved. Reprographic copying beyond that permitted by Sections 107 or 108of the U.S. Copyright Act is allowed for internal use only, provided that a per-chapter fee of$40.25 plus $0.75 per page is paid to the Copyright Clearance Center, Inc., 222 RosewoodDrive, Danvers, MA 01923, USA. Republication or reproduction for sale of pages in thisbook is permitted only under license from ACS. Direct these and other permission requeststo ACS Copyright Office, Publications Division, 1155 16th Street, N.W., Washington, DC20036.

The citation of trade names and/or names of manufacturers in this publication is not to beconstrued as an endorsement or as approval by ACS of the commercial products or servicesreferenced herein; nor should the mere reference herein to any drawing, specification,chemical process, or other data be regarded as a license or as a conveyance of any rightor permission to the holder, reader, or any other person or corporation, to manufacture,reproduce, use, or sell any patented invention or copyrighted work that may in any way berelated thereto. Registered names, trademarks, etc., used in this publication, even withoutspecific indication thereof, are not to be considered unprotected by law.

PRINTED IN THE UNITED STATES OF AMERICA

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ForewordThe ACS Symposium Series was first published in 1974 to provide a

mechanism for publishing symposia quickly in book form. The purpose ofthe series is to publish timely, comprehensive books developed from the ACSsponsored symposia based on current scientific research. Occasionally, books aredeveloped from symposia sponsored by other organizations when the topic is ofkeen interest to the chemistry audience.

Before agreeing to publish a book, the proposed table of contents is reviewedfor appropriate and comprehensive coverage and for interest to the audience. Somepapers may be excluded to better focus the book; others may be added to providecomprehensiveness. When appropriate, overview or introductory chapters areadded. Drafts of chapters are peer-reviewed prior to final acceptance or rejection,and manuscripts are prepared in camera-ready format.

As a rule, only original research papers and original review papers areincluded in the volumes. Verbatim reproductions of previous published papersare not accepted.

ACS Books Department

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PrefaceThe identification and quantification of the widespread occurrence of trace organic

chemicals at minute concentrations in the aqueous environment impacted by humanactivities is a result of rapid advances in environmental analytical chemistry. The bodyof knowledge regarding the characterization, fate and transport of these chemicalsof emerging concern (CECs) in the natural water environment and engineered watertreatment processes, as well as their toxicity, has grown substantially over the last twodecades. Recently, the focus in the environmental chemistry community has shiftedfrom these CEC parent compounds to the fate, transport, and toxicity of transformationproducts, which are generated through abiotic and biotic mechanisms in naturalsystems and during engineered advanced water treatment processes.

This book evolved from a symposium presented at the 250th ACS NationalMeeting & Exposition in Boston, MA in August 2015. The symposium was entitled“Assessing Transformation Products by Non-Target and Suspected Target Screening:The New Frontier in Environmental Chemistry and Engineering,” and the topicsfocused on featuring studies and recent advancements towards the development ofmore harmonized strategies and workflows using non-target and suspects screeningmethods, including suitable bioassay approaches to assess the overall relevance oftransformation products. Scientific research on the topic of transformation productsis rapidly growing, and we are glad that participants in the symposium and someadditional authors took time out of their busy schedules to prepare contributions forthis book project.

A total of 21 chapters are included in this book, with contributions frommost of thespeakers from the symposium and additional research institutions, as well as LC-MSvendors. For convenience, this book is divided into two volumes. Volume I coversthe relevance of transformation products and international strategies to manage CECs,new methods for a comprehensive assessment of transformation products, and the fateand transport of transformation products in natural systems. Volume II addresses thefate and transport of transformation products in engineered systems, assessing theirtoxicity, commercial strategies in non-target and suspects screening, and concludes withdevelopments towards harmonized strategies and workflows. This book is ideal forenvironmental scientists and engineers, particularly chemists, environmental engineers,public health officials, regulators, other chemistry-related professionals, and students.

We are very thankful to the chapter authors for their contributions, the manyreviewers assisting in the peer-review process, and Arlene Furman, ElizabethHernandez, and Bob Hauserman at the editorial office of ACS Books for their support.Special thanks go to Chloe Tuck and Brennan Tapp for their efficient handling ofthe manuscripts. Without the dedication and patience of these individuals, this bookwouldn’t have happened.

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Jörg E. DrewesChair of Urban Water Systems EngineeringTechnical University of MunichAm Coulombwall 8 , Garching 85748Germany

Thomas LetzelChair of Urban Water Systems EngineeringTechnical University of MunichAm Coulombwall 3, Garching 85748Germany

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Chapter 1

Chemicals of Emerging Concern andTheir Transformation Products in the

Aqueous Environment

Jörg E. Drewes* and Thomas Letzel

Chair of Urban Water Systems Engineering,Technical University of Munich, 85748 Garching, Germany

*E-mail: [email protected].

The interest in understanding the environmental relevanceof transformation products (TPs) which are generated fromchemicals of emerging concern (CECs) via abiotic and bioticprocesses has increased significantly in the recent past.Studies published so far have elucidated numerous aspectsof TPs from CECs including the development of appropriateanalytical methods for their identification and quantification,their formation pathways during various processes includingbiodegradation, chemical oxidation and photolysis, strategiesto predict transformation pathways, and assessments regardingtheir toxicological relevance. In order to assess the relevance ofTPs in the aquatic environment, appropriate and standardizedanalytical approaches and assessment protocols are needed toaddress the selection, identification and quantification of TPs,their role in natural water systems and engineered treatmentprocesses, and their toxicological relevance.

Introduction

The presence of trace organic chemicals in the aqueous environment has beenreported for several decades, but for the last 20 years attention has shifted fromlegacy contaminants including polychlorinated biphenyls, polycyclic aromatichydrocarbons, solvents and pesticides to chemicals that are released into theenvironment via discharges of municipal wastewater effluents, urban stormwater,and agricultural runoff (1, 2). These “chemicals of emerging concern (CECs)”

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are comprised of pharmaceutical residues and their metabolites, householdchemicals, personal care products, endocrine disrupting compounds, andemerging disinfection by-products and pesticides. A vast number of studies onCECs has been published both on the fate and transport in the natural environmentand engineered systems and their toxicological relevance to aquatic and humanhealth. These studies focused primarily on the parent compounds (PC). Both, inthe natural water environment and during engineered water treatment processes,CECs are not completely mineralized but may undergo transformation by bothabiotic and biotic processes resulting in intermediates which are usually morepolar. Transformation products (TPs) are mainly formed through hydrolysis,oxidation, hydroxylation, conjugation, cleavage, dealkylation, methylation,and demethylation (3). While most TPs are less persistent in the aquaticenvironment (i.e., half lifes of less than two months), more polar and thereby lessbioaccumulative, and less toxic than the parent compounds (4), there are a numberof prominent exceptions. Indeed, some TPs can be more persistent in engineeredor natural systems and some might exhibit higher sublethal, behavioral ordevelopmental effects in aquatic organisms or potential adverse effects to humanhealth as compared to the parent compounds (5, 6). Thus, this topic deservesfurther research and, where action is warranted, appropriate mitigation strategies.

In the early 1970s, TPs were first documented for halogenated and laternitrogeneous disinfection by-products, generated during the disinfection of waterand wastewater, although the specific parent compounds weren’t always known(7, 8). In the 1980s and 1990s, research on the formation of TPs was expanded todegradation pathways of pesticides (9). Since then, interest also grew to evaluatetransformation products from parent compounds of CECs (10, 11), which isalso illustrated by the increasing number of studies published recently in thepeer-reviewed literature on this subject. Figure 1 illustrates the steady increaseregarding the number of published items and citations per year for the last tenyears on the topic of ‘transformation products in the aqueous environment’ basedon a Web of Science™ query (www.webofknowledge.com).

The studies published during this period have elucidated numerous aspectsof TPs from CECs including the development of appropriate analytical methodsfor their identification and quantification, their formation pathways during variousprocesses including biodegradation, chemical oxidation and photolysis, strategiesto predict transformation pathways, and assessments regarding their toxiciologicalrelevance.

Considering the number of chemicals in commerce and estimates of atotal of 80,000 to 100,000 individual chemicals in municipal wastewater [12],the identification of transformation products is a daunting task given the vastnumber of possible structures, the complexity of matrices, and their (often) lowconcentrations. Considering international legislation regulating chemicals today,there is very little recognition given to parent compounds as well as transformationproducts of CECs. Thus, to focus efforts directed to assess the relevance of TPsin the aquatic environment, appropriate and standardized analytical approachesand assessment protocols are needed to address the selection, identification andquantification of TPs, their role in natural water systems and engineered treatmentprocesses, and their toxicological relevance.

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Figure 1. Number of published items (total 294) and citations (sum of times cited3,182) according to Web of Science™ on the topic of “transformation products in

the aqueous environment” for the time period 2005-2015.

Analytical ChallengesThe analytical approaches, which are currently employed for the

quantification and identification of PCs and TPs require effective moleculeseparation and accurate triple quadrupole or high resolution mass spectrometers(HRMS). Advances in the development of these instruments have enabledreliable selective target analysis as well as screening for expected and unknowncompounds. The initial method of choice for the analysis of known TPs inaqueous samples has been target analysis (Figure 2). However, this approachrequires prior knowledge of the target chemical and for their quantificationthe availability of reference standards. Frequently, these reference standardsfor specific TPs are not readily available commercially and synthesis is costprohibitive for many laboratories.

With the advent of reversed phase liquid chromatography coupled withhigh resolution mass spectrometry (RPLC-HRMS), in particular time-of-flight(ToF) and Orbitrap MS instruments, very powerful tools are now available todetect PCs and their TPs at very low concentrations in various environmentalmatrices. Since these instruments are capable of screening and detecting avery high number of compounds as long as they ionize under the experimentalconditions, databases can be built that record retention time (RT), fragmentation,exact masses, and isotopic pattern. Examples of these databases are MassBank,StoffIdent, ChemSpider, Chemicalize, or DAIOS (3, 13). These databases can beused in combination with computational (in silico) prediction tools (e.g., MetFrag,

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http://pubs.acs.org/action/showImage?doi=10.1021/bk-2016-1241.ch001&iName=master.img-000.jpg&w=315&h=188

Eawag-PPS, CATABOL, PathPred, Meteor) for the tentative identification ofthe molecule instead of reference standards in an analytical strategy called‘suspect screening’ (14). Confirmation of structure applying MS/MS analysismight strengthen the analytical strategy and increases the confidence levels ofidentification (15).

Figure 2. Analytical strategies for the identification and quantification ofparent compounds and transformation products. Adapted with permission from

Reference (13). Copyright 2016 Elsevier.

The third option is described as ‘non-target screening’ and implies thetentative identification of novel TPs without any previous knowledge. Fornon-target screening, high-resolution MS is required in order to have highmass accuracy for confirmation of proposed molecular formula and reliableinterpretation of the MS/MS spectra (14). An example where non-targetworkflows have been successfully applied is the identification of TPs ofthree benzotriazoles (16). Given the generation of massive quantities of data,post-acquisition data-processing tools are necessary (e.g., MZmine; EnviMass).However, the procedures applied in these software tools and workflows can differwidely and more specific and harmonized workflows are needed for suspect andnon-target analysis.

Subsequent chapters of this book will report on new methods for acomprehensive assessment of TPs and illustrate approaches to harmonizeworkflows for suspect and non-target screening. These discussions includecommercial strategies offered by HR-MS vendors for non-target and suspectedscreening of water samples.

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Toxicological Relevance of TPs

In order to prioritize the most relevant TPs, the identification should becoupled with an assessment of their toxicological relevance since conservationof structure in TPs might also imply conservation or even creation of bioactivityacross multiple biological endpoints (6). Such an assessment can occur byemploying either effect-directed analysis (EDA) or by using bioanalytical toolsfor specified endpoints. Bioanalytical tools are defined as in vitro cell-based andin vivo bioassays indicative of modes of toxic action that are relevant for humanand/or ecosystem health (17). EDA is a common approach to identify non-targetchemicals based on their toxicological effects for select endpoints and is beingused in regulatory settings to toxicity identification and evaluation (18). Howtoxicity of TPs occurring in natural and engineered systems can be assessed isreported in this book in two chapters.

Role of TPs in Water Treatment Processes

TPs occurring in the aquatic environment can be classified into two maincategories including products formed during abiotic and biotic processes in naturaland engineered water systems (14). Abiotic TPs are generated by processesinvolving hydrolysis, photolysis, and photocatalytic degradation pathways innatural water systems as well as engineered water treatment processes (e.g.,chlorination, ozonation, advanced oxidation processes). Biotic pathways canresult in TPs involving microbial activities in streams, groundwater aquifers butalso engineered biological treatment processes (e.g., activated sludge treatment,biofiltration, wetlands). However, numerous different TPs may be formed withinonly one type of treatment, applied under sometimes even very similar operatingconditions (19).

While processes employed in water and wastewater treatment ususally reducethe total concentration of parent compounds as well as TPs (20, 21), mass balancesfor select chemicals revealed that the efficiency of treatment processes based on theremoval of the PC is actually negligible if the fate of all TPs is being considered.For example, Schulz et al. proposed degradation pathways of five major TPs ofthe triiodinated X-ray contrast medium iopromide under oxic redox conditions(22). When investigating conventional activated sludge processes, iopromide wasrapidly transformed but the accumulated molar concentration of iopromide and itsTPs after secondary treatment remained the same.

The fate and transport of PCs and TPs in both natural and engineered systemsis reported in several chapters of this book suggesting strategies but also pointingto limitations for the development of a more comprehensive assessment of CECsin the aquatic environment.

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Chapter 2

An Assessment of International ManagementStrategies for CECs in Water

Stefan Bieber,1 Tanja Rauch-Williams,2 and Jörg E. Drewes*,1

1Chair of Urban Water Systems Engineering, Technical University ofMunich, Am Coulombwall 3, 85748 Garching, Germany

2Carollo Engineers, Broomfield, Colorado 80021, United States*E-mail: [email protected].

This study investigated management strategies among differentcountries worldwide (USA, Australia, Switzerland, EU,and Germany) for mitigating the risk associated with traceorganic chemicals of emerging concern (CECs) in the aqueousenvironment. Although national strategies are adapted tospecific geographic conditions and consider local occurrencepattern of CECs, two basic principles for reducing the releaseof chemicals could be identified among different countries.Risk-based strategies rely on regulating maximum allowableconcentrations, which intend to limit the release of specificallyknown hazardous compounds to the aqueous environment.Strategies based on the precautionary principle aim to minimizethe occurrence of undesired trace organic compounds in waterbodies in general regardless of an identified risk. Both principleswere implemented by suitable measures, which allowedminimizing or reducing the concentration of compounds inwater bodies. Such measures can target single chemicals orgroups of chemicals. Although strategies and implementedmeasures for the management of CECs were multifold, allstrategies relied on comprehensive monitoring programs usingtarget analysis for CECs. However, non-target and suspectedtarget screening analyses are playing an increasing role inidentifying relevant chemicals for inclusion in futuremonitoringprograms.


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IntroductionNearly all human activities result in the release of chemicals into the

environment. The spectrum of these compounds is very broad, reaching fromcarbon dioxide formed in cellular respiration to synthetic chemicals, produced inindustrial processes. With currently more than 110 million registered chemicalcompounds and several thousand in daily use, it is no surprise that many of thesecompounds enter the environment by accident or after their intended use (1). Theoccurrence of organic chemicals in the aqueous environment is of high concern,since many of these constituents can potentially adversely affect human andenvironmental health including pesticides, biocides, pharmaceuticals, hormones,or household chemicals (2). Although such compounds are commonly detected inthe nanogram per liter (ng/L) to microgram per liter (µg/L) concentration ranges,the compound specific mode of action may remain active and pose a potential riskto environmental or human health. Adverse effects on aquatic health by chronicexposure by trace organic compounds have been documented previously andalso mixtures of several compounds can result in adverse health effects (3–6).Through use of impaired water sources for drinking water supply, trace organiccompounds might also pose a risk to human health (7). In addition to compounds,being discharged to the aquatic environment, transformation processes formed inthe environment and engineered water treatment processes, can contribute newcompounds with unknown characteristics (8).

General concerns and proven adverse effects of trace organic compoundsregarding environmental health have resulted in management strategies andlegislative action in many countries worldwide. Although, these countriesshare the same concern, the proposed national management strategies are ratherheterogeneous due to differences in environmental, geographic and economicconditions and different opinions regarding relevant health endpoints targeted toprotect species from adverse effects by trace organic compounds. Nevertheless,the core principles of these different approaches can be elucidated providing anopportunity to identify promising and effective strategies in managing the riskfrom trace organic chemicals in the aqueous environment.

Principles of Management StrategiesThe occurrence of trace organic compounds associated with potentially

adverse health effects in waterbodies are an international challenge (9). Theoccurrence patterns of these compounds, however, can be highly variable acrossdifferent countries and depend on population density, land-use, wastewaterdilution in streams, compound usage and others factors. These factors providethe framework in which national and regional management strategies have to beadopted. Most commonly, management strategies aim to protect environmentalhealth and/or human health against adverse effects from trace organic compounds.These strategies are directed to manage CECs in the entire water cycle and caneither be immission- or emission-driven. Immission-driven strategies intendto preserve or restore a desired environmental state of a receiving water body

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whereas emission-driven strategies intend to minimize discharges from point andnon-point sources. The specific targets for both strategies can be based on specificevidence for adverse effects that allows deriving a safe risk level or are motivatedby considerations following the precautionary principle to achieve a generalreduction of specific chemicals. The following sections investigate what drivershave resulted in the development of different management strategies for CECsin different countries worldwide including the USA, Australia, Switzerland, theEuropean Union (EU), and Germany.

Australia has suffered from extreme weather phenomena in the last twodecades resulting in extended periods of severe water scarcity and extremeflooding. As a result, water reuse schemes have been considered and implementedto reduce pressure on drinking water supply during times of limited supplies. Inorder to manage risks associated with potable water reuse practices, a strategyfollowing the World Health Organizations guidelines for drinking water wasimplemented in Australia (10–12). This strategy is based on a hazard analysisand critical control point concept (HACCP). All processes involved in waterreuse and drinking water supply have to be evaluated and potential risks andcritical points of the entire supply system (from source, over treatment to finaluse) identified. Such points or process steps are defined as critical control pointsand specific procedures for risk minimization are implemented. The preventionof hazardous events and risk exposures associated with them are ensured by theimplementation of multiple barriers (11). Organic and inorganic chemicals areregarded as potential hazards for human health and the Australian guidelines forwater recycling provide health-based guideline values for certain trace organiccompounds (11). These values are non-enforceable and should only provideorientation values regarding safe levels for compound concentrations. Stategovernments, which have the competence to implement enforceable qualitystandards, can choose different compounds and compound concentrations asthreshold, than those provided in the guidelines.

Health-based guideline values as well as enforceable MAC values areintended to provide a measurable amount of safety of exposure for selectedcompounds. Such values can be derived by toxicity testing of sensitive species orpredictive models like quantitative structure–activity relationships (QSAR) (13,14). Safety factors can be applied on effect concentrations in order to provide anappropriate and measurable degree of safety (15). Guideline and MAC values canbe used for the protection of both, human and/or environmental health. Valuestargeting the protection of human health can be applied for surface, ground- ordrinking water, while those for environmental health are mainly applied in surfacewaters or related waterbodies.

The concept of enforceableMAC values among others is applied in the UnitedStates (U.S.) and the EU. Trace organic compounds are assessed regarding theirtoxicological relevance and threshold level concentrations are determined. TheEU Water Framework Directive (WFD) provides MAC values for the protectionof environmental health in surface ground and coastal waters (16). In the U.S.,emerging contaminants in drinking water are identified and prioritized within theframework of the Unregulated Contaminant Monitoring Rule (UCMR) and theContaminant Candidate List (CCL) (17, 18).

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The EU has implemented a comprehensive regulative framework, aimingto prevent the emission of trace organic compounds to the aquatic environmentcovering the entire life cycle of chemicals from authorization, over production anddistribution to disposal (16, 19, 20). The instrument of MAC values is applied forchemicals identified as hazardous compounds. The classification as a hazardouscompound results in a mandatory reduction of compound emissions, but mightalso trigger complete phase out (16). The EU WFD requires individual memberstates to define river basin districts, to evaluate the chemical and ecological statusof water bodies, and to specify a program of measures (PoMs) to reach a certaincondition within these river districts. The main goal of the WFD is to restorea natural state in all waterbodies, showing no or only “low levels of distortionresulting from human activity” by specific target dates (16). The requirements forgood chemical and good biological status are defined and environmental qualitystandards (EQS) are set for specific contaminants in either aqueous or tissuesamples.

The currently most ambitious and thoroughly planned strategy for themanagement of trace organic compounds is implemented in Switzerland. Traceorganic compounds are already part of the national water quality regulation (21).The Swiss strategy aims to minimize the emission of trace organic compoundsinto the aqueous environment in general (22). All compounds of anthropogenicorigin, which can be detected in environmental waterbodies, are regarded aspotential threat to environmental health and concentrations should be minimized.Toxicologically derived MAC values are not commonly applied in Switzerland,but threshold values can be assigned for different compound groups. Such valuesare applied for pesticides in groundwater, where single compounds shall notexceed 0.1 µg/L and the sum of all pesticides should be below 0.5 µg/L. (23) . TheSwiss national strategy includes upgrading 100 of the approximately total numberof 700 wastewater treatment plants with advanced water treatment processes(i.e., either ozonation or activated carbon filtration) over a period of 40 years,targeting to treat approximately 50% of the entire wastewater flow generated inSwitzerland after full implementation of the program (24). The efficiency ofmeasures for compound emission reduction in wastewater treatment plants isassessed by monitoring a subset of twelve indicator compounds. Criteria for theselection of these performance indicators were ubiquitous occurrence in Swisssurface and wastewater, insufficient biodegradability and removal efficiency byozonation or activated carbon (25). Effects of emission reduction measures onreceiving streams will be monitored, too.

Underlying these at first glance quite diverse strategies to manage traceorganic compounds in the aquatic environment are two main principles.Risk-based strategies rely on MAC values for hazardous compounds derived fromtheir toxicological relevance, which provide safety against adverse effects forhuman or environmental health, if concentrations in environmental compartmentsdo not exceed proposed values for specific chemicals. Strategies based on theprecautionary principle aim to prevent the emission of compounds into theenvironment regardless of a proven health-relevance level. Risk-based strategiesare well suitable for the control of known, hazardous trace organic compoundsand are, among others, implemented in the United States, Australia, and the EU

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(10, 11, 16, 26, 27). Procedures for the derivation of such quality standardsand determined concentrations are usually summarized in specific nationalregulations.

To verify conformity with legal requirement, measured environmentalconcentrations (MECs) are compared to maximal allowable concentrations. Incase of exceedance of allowed concentrations in water bodies, measures for thereduction of environmental compound concentration have to be implemented.Although the risk-based approach is readily applicable for previously detectedand identified trace organic compounds, it cannot be used to control previouslynot detected and unknown compounds. A toxicological assessment of allcompounds, detectable or possibly contained in environmental water samples isnot practicable, due to high costs and time consumption. Possible adverse effectscaused by mixture toxicity of several compounds, including transformationproducts which co-occur in waterbodies have to be assessed separately (28).

Strategies based on the precautionary principle do not rely solely oncompound specific toxicity assessments to preserve a desired water quality.Based on the concept that chemical compounds of anthropogenic origin do notbelong into the environment and all compounds possibly may pose a risk to theenvironment, the emission of compounds into the environment in general shouldbe prevented. A complete elimination of compound emissions is in most casesnot realistic, thus strategies that can result in a significant emission reductionare favored. While for some strategies a toxicological assessment of individualchemical compounds is not intended, groups of compounds or compounds withknown mode of action can be assigned threshold values. These threshold valuesare typically generic values and not based on toxicological data. Such criteriahave been defined to minimize discharge of pesticides to drinking water sourcesin the European Union and in Switzerland (23, 29). If stricter values for a certaincompound are defined in other regulations, these have to be applied. Compared torisk-based strategies, precautionary principle-based strategies are more difficultto implement. This is because not all pathways compounds can take to enterthe environment are known or can be effectively managed. While point sourceemissions from wastewater treatment plants can be reduced through implementingadvanced wastewater treatment technologies, non-point sources are more difficultto locate and restrict. A reduction of compound emissions does not only result ina reduced risk posed by known compounds, but also by unknown or undetectedcompounds. Additionally, the risk from transformation products being formedcan be reduced, too. Switzerland has implemented a management strategy, whichis entirely grounded on the precautionary principle (22). The strategy to manageCECs in the European Union combines elements from both described principles(16). Maximal allowable concentrations, derived from toxicity assessments aredetermined for known hazardous compounds (risk-based). For these compoundsemissions have to be reduced or the chemicals are phased out entirely. As asecond part of the strategy, a reduction of compound emissions in general isaimed by defining water quality status of river districts that restore a natural stateto benefit aquatic life but also humans (precautionary principle).

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Mitigation MeasuresImplementing strategies to reduce CEC discharge to the aquatic environment

have to be accompanied by measures and tools allowing to control or reducetrace organic compound emissions or environmental concentrations effectively.The impact of certain measures can reach from the reduction of single compoundemissions to a removal of a broad spectrum of compounds. The character of animplemented measure depends upon the realized core principle and the healthendpoint, which should be protected against adverse effects, caused by certaincompounds. Measures for the protection of drinking water can target compoundemissions into source waters or be implemented in drinking water utilities. Forthe protection of the aquatic environment, measures have to target the emissionof compounds.

Compound Specific Measures

Source control measures might be motivated by both risk-based strategies andthe precautionary principle, controlling the emission of single compounds. Theregulation of compounds or restrictions of compound usage are an option to reduceor phase out emissions of hazardous compounds into the environment. Developingregulations for chemicals can be impeded by stakeholder opposition includingmanufacturers or users of compounds. For compounds like pharmaceuticals,regulation restricting their use is problematic due to ethical reasons. Thesubstitution of hazardous compounds by compounds with comparable mode ofaction but lower hazardous potential is another option to reduce or phase outemissions of a specific compound. Suitable substitutes might not always beavailable and potential alternative compounds have to be toxicologically assessedbefore substitution to ensure a true reduction of overall health risks. Measuresinvolving participation of consumers, like voluntary waiver of compound usageor incentive systems, require a high degree of public information and outreach.While regulation can result in an immediate and complete phase out of compoundemissions into the environment, the impact of voluntary emission reductionmeasures cannot be predicted and depends on several factors.

Technological Measures

Technological measures can be applied for emission reduction of a broadcompound spectrum. Such measures are often favored over source controlprograms due to easier implementation, but are limited in reducing the emissionof compounds from non-point sources. Advanced wastewater treatment ascompound emission reduction measure is therefore proposed and implemented inselect river districts across Europe and Switzerland (16, 24). In these countries,the implementation of advanced wastewater treatment prior to discharge inreceiving streams is a consequence of following the precautionary principle ormeeting specific EQS values for the protection of the environment. Powdered orgranular activated carbon and ozonation are currently evaluated and implementedfor the emission reduction of trace organic compounds due to their applicability,

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removal/reduction efficiency and robustness (24, 30, 31). Advanced oxidationprocesses (e.g., UV/H2O2 or Fenton/H2O2) might be suitable for the removal oftrace organic compounds, but applicability of these techniques at full-scale stillhas to be demonstrated (32). Technologies used in wastewater treatment plantsfor the emission reduction can also be applied in drinking water utilities, but servethere mainly to protect human health.

The efficiency of technological measures for the reduction of compoundemissions can be assessed by monitoring of performance-based indicatorcompounds through the reduction/removal process (25, 33). Ideally, indicatorcompounds are present in untreated samples and can be detected and monitoredthrough the whole treatment process. Adsorptive removal techniques likepowdered or granular activated carbon are most suitable for the removal ofmedium to non-polar compounds. Some polar compounds can partly be removedby activated carbon techniques (34). Ozonation and advanced oxidation processes(AOP) can result in the formation of transformation products which tend to bemore polar than the parent compounds (35). Transformation products, resultingfrom oxidation or biological treatment techniques, display an uncertainty sinceidentity and toxicological potential are often unknown. The possible formationof oxidation by-products, which might be toxicologically relevant, also has tobe considered before implementation (30). As a consequence, the spectrum ofdetectable compounds can change after implementation of a specific removalprocess.

Water Quality Monitoring as a Cornerstone of AllManagement Strategies

While the core principles of national strategies can be diverse, comprehensivemonitoring of CECs in environmental water bodies and drinking water isimplemented in all countries investigated in this study. For strategies followingboth a risk-based and precautionary principle approach, target analysis,suspected-target screening and non-target screening methods are employed,although to a different extent (Figure 1). Monitoring CECs can serve the functionof triggering the implementation of measures to control compound emissionsin the future and for compliance to monitor the success of already implementedmeasures. The analysis of CECs in water samples is commonly conducted usinggas (GC) or liquid chromatography (LC), coupled to mass spectrometric detection(36). Detection and quantification of known compounds is achieved by targetscreening using (isotope labelled) reference substances (37). For the identificationand monitoring of unknown compounds non-target and suspected-target screeningare employed (37). Non-target screening aims to identify detected masses derivedfrom instrumental analytics. This approach can be ambiguous, since one masscan account for numerous different compounds with (nearly) identical masses,but different formulas or chemical structures. Thus, while no quantitative datacan be derived, non-target screening approaches can serve as a comparativefingerprint analysis or assist in the identification of unknown trace organic

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chemicals. Suspected-target screening utilizes additional information for theanalyzed sample in order to identify compounds. However, a final verification ofcompound identity can solely be made using target screening involving referencesubstances.

In risk-based strategies, target-screening is used to compare measuredenvironmental compound concentrations with MAC values. For precautionaryprinciple based strategies, compliance of environmental compound concentrationswith generic limiting values is proven by target-screening, too. In order todetect the occurrence of new potentially hazardous emerging contaminants inwater bodies, monitoring cannot solely be based on target-screening. Non-targetand suspected target screening approaches are utilized when certain masses arefrequently detected in water samples. Information like the origin of a sampleor applied water treatment processes, but also specific databases can assist inclassifying chemicals into suspect compound groups or classes. Where advanced(oxidative) treatment technologies were applied for treatment of wastewater orsurface water, water samples possibly contain transformation products. Thisinformation can be used to generate lists of common oxidative transformationproducts, which are likely to be present in a sample. Based on such lists, massspectrometric data can be searched for matching masses, which might indicatethe presence of a compound. For the final verification of a compound’s identity,reference substances have to be obtained. Such monitoring strategies are alreadypart of some national regulations and provide the basis for the monitoring ofemerging contaminants, as the Watch List under the European Water FrameworkDirective or the Unregulated Contaminant Monitoring Rule (UCMR) andContaminant Candidate Lists (CCL) under the United States Safe Drinking WaterAct (17, 36–39).

Suspected and non-target screening play an important role in monitoringstrategies already, but will likely become even more important in the future. Theraising awareness about the presence of trace organic compounds and the useof advanced water treatment processes for their mitigation in wastewater, waterreuse, and drinking water applications require more comprehensive monitoringstrategies to assess the safety and quality of different water types. The changingpolarity spectrum of detectable trace organic compounds including transformationproducts of chemical and biological treatment processes towards higher polarity,more appropriate requirements for instrumentation conditions and alternativechromatographic techniques have to be evaluated. Additional informationsources, which can provide support for the identification process of detectedcompounds, will gain increased interest. With several thousand detectable massesin a water sample, all available information should be utilized to reduce thenumber of compounds present in a sample to those of environmental relevance.Although occurrence patterns of trace organic compounds can differ by region,these contemporary monitoring strategies can be applied irrespective of location.Therefore, screening strategies and identification processes should be harmonizedon a global basis. This will also improve comparability of results betweendifferent institutions and save resources in developing appropriate analyticalapproaches at an individual national level.

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Figure 1. Different screening strategies for trace organic chemicals in watersamples following different management principles.

Acknowledgments

The authors would like to thank Dr. Sonia Dagnino, Dr. Eric Dickenson,Dr. Giorgia Greco, Prof. Thomas Letzel, and Prof. Shane Snyder for theircontributions. This study was part of research project #4494 of theWater ResearchFoundation, which is greatly acknowledged for the financial support received.

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35. Boxall, B. A.; Sinclair, C. J.; Fenner, K.; Kolpin, D. W.; Maund, S. J. WhenSynthetic Chemicals Degrade in the Environment. Environ. Sci. Technol.2004, 38, 368–375.

36. Richardson, S. D.; Kimura, S. Y. Water Analysis: Emerging Contaminantsand Current Issues. Anal Chem. 2016, 88, 546–582.

37. Letzel, T.; Bayer, A.; Schulz, W.; et al. LC – MS screening techniques forwastewater analysis and analytical data handling strategies: Sartans and theirtransformation products as an example. Chemosphere. 2015, 137, 198–206.

38. Snyder, S. A. Emerging Chemical Contaminants: Looking for GreaterHarmony. J. Am. Water Works Assoc. 2014, 106, 38–52.

39. European Commission. COMMISSION IMPLEMENTING DECISION(EU) 2015/495 of 20March 2015 Establishing aWatch List of Substances forUnion-Wide Monitoring in the Field of Water Policy pursuant to Directive2008/105/EC of the European Parliament and of the Council. Off. J. Eur.Union 2015, L78 (40).

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Chapter 3

HRMS Approaches for EvaluatingTransformations of Pharmaceuticals in

the Aquatic Environment

Michael Hannemann,1 Bozo Zonja,1 Damià Barceló,1,2and Sandra Pérez*,1

1Water and Soil Quality Research Group, Department of EnvironmentalChemistry (IDAEA), Spanish National Research Council (CSIC),

c/ Jordi Girona, 18-26, 08034 Barcelona, Spain2Catalan Institute of Water Research, c/Emili Grahit, 101, Edifici H2O,

Parc Científic i Tecnològic de la Universitat de Girona,E-17003 Girona, Spain

*E-mail: [email protected]. Phone: ++34-93 400 6100 ext 5310.Fax: ++34-93 204 5904.

Pharmaceuticals and their related transformation products(TPs) are distributed into the aquatic environment. UPLC &HPLC combined with HRMS techniques are able to detectthese emerging contaminants in environmental samples.However, due to the lack of commercially available standardsfor detection and quantification of TPs, suspect screeningis rapidly gaining popularity in the scientific community ofHRMS users. This chapter reports on the application of suspectscreening to detect pharmaceutical compounds and their TPsin environmental samples and also on its new application forthe evaluation of the fate of selected pharmaceuticals in theaquatic environment. The authors include two examples usingsuspect screening published in the literature: the evaluationof the biodegradation of lamotrigine and the photolysis ofICMs. Qualitative methods for the investigation of the fateof drugs are described for manual workflows as well asfor more sophisticated approaches using suspect screening;further it provides advantage of recent software developmentsin automated data analysis. Hence, suspect screening is


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straightforward, and if the transformation pathways of theparent compounds are unknown, the combination of lab-scaleexperiments and new approaches based on HRMS like theanalysis of MS/MS fragmentations, mass defect, and isotopicpattern are useful methods that help researcher achieve theirobjectives. While detection and quantification are gainingadequate results, it is important to include newly identified TPsin environmental fate studies of pharmaceuticals and, if theyare associated with a high environmental risk, to consider themfor inclusion in future water quality guidelines.

Introduction

Pharmaceuticals are a group of chemical substances that have medicinalproperties. In 2015 the industry generated approximately one trillion US-$revenue worldwide, with the biggest markets being located in North America andEurope (1). The occurrence of pharmaceutical compounds in the environmentis ubiquitous (2–5). In the aquatic environment they can be transported anddistributed in rivers and streams and are subject to degradation by both abioticand biotic processes. These processes form transformation products (TPs)which can be more mobile and polar than the parent compound and may exertadverse effects on aquatic organisms (6–8). In case of bioactive compoundstheir pharmacological effect can be retained when the pharmacophore of themolecule remains intact after its transformation. The most frequent detectionof transformation products of emerging contaminants (ECs) occurs in surfacewater and wastewater samples and they are formed mainly by biodegradation andphotolysis processes (9–11). In order to assess the biodegradability of a compoundat lab-scale there are several standardized test like e.g. the OECD guidelines (12).However, they rely on applying small doses of wastewater or activated sludgebiomass to degrade a compound, which may result in a considerable increasein the degradation rate. On the other hand, many degradation studies reportedin the literature use mixed liquor from aeration tanks of wastewater treatmentplants (WWTPs). As an alternative, batch reactors can be filled with activatedsludge and diluted with either ultrapure water, groundwater of wastewatereffluent (13). This gives the additional benefit of performing the degradationin more related conditions as it happens in the WWTP, but the dilution wouldreduce the effect of sorption (13). So, the typical concentration of the activatedsludge is about 4 - 5 gas/L of total suspended solids (TSS) and can be lowereddown to 0.5 - 1 gas/L TSS without compromising the reaction kinetics. As thebiological transformation is evaluated, appropriate control reactors (withoutbiological activity) have to be run in parallel in order to determine the relevanceof abiotic processes such as chemical hydrolysis or light-induced degradation.These control reactors are typically filled with activated sludge, which haseither been autoclaved or supplemented with inhibitors like formaldehyde orazide. In some cases, WWTP effluent can also be used. For reliable resultsit is also important to control and maintain the pH, temperature and reactor

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volume constant over the course of the degradation period. Biodegradation is themain degradation pathway of pharmaceuticals in WWTPs. However, when theyare discharged to surface waters photodegradation is considered a key processgoverning the whereabouts of organic micro-pollutants (14). In order to evaluatethe photodegradability of a compound experiments with spiked surface waterwith exposure either simulated solar radiation or natural sunlight are performed(15). The samples are irradiated with appropriate dark-control experiments toaccount for possible hydrolytic reactions. The performance of these processes forthe degradation of pharmaceuticals can be evaluated using liquid chromatographyhigh resolution mass spectrometry (LC-HRMS). Two HRMS platforms, namelytime-of-flight instruments (ToF) and Orbitrap are now the most powerful toolsfor the determination of pharmaceuticals and their TPs because the large majorityof them are frequently detected at very low concentrations in complex matricesand there is a lack of available commercially standards. Three main analyticalapproaches are now applied for environmental analysis using HRMS: targetanalysis, suspect screening and non-target analysis. This book chapter deals withsuspect screening analysis applied to environmental analysis and evaluation oftransformation processes of pharmaceuticals.

Suspect Screening Approach

Currently the method of choice for the analysis of pharmaceuticals inenvironmental samples is a target analysis. This term refers to the analysisof a predefined set of known substances in an environmental sample whichcovers at best 1-2% of all the contaminants present therein (9, 16). It relies onthe availability of reference standards for quantification, which substantiallyincreases the cost, and poses particular problems when a wanted target compoundis not commercially available or is difficult to obtain by chemical synthesis. Bycontrast, if the given chromatographic method is likely to separate all compounds,HRMS instruments are capable of screening and detecting a virtually unlimitednumber of compounds, provided that they ionize under the given experimentalconditions and their m/z values are within the mass range of the full-scan MS dataset (4, 17). One of such techniques is the so-called suspect screening, for whichdatabases (instead of reference standards) are used to tentatively identify andconfirm the presence of known analytes. A suspect list has to be created includingknown parental chemical structures and their TPs, their elemental compositionsand their exact monoisotopic masses. Then, all m/z values are searched in theenvironmental samples and confirmed on the basis of mass accuracy, retentiontime (RT), isotopic pattern determination, and structure confirmation usingMS/MS experiments, Figure 1. Additional benefit is that spectral libraries canbe shared by many laboratories or can be accessed via online repositories likeMassBank. However, suspect screening is not making a difference betweenecotoxicological effects. It is further necessary to evaluate ecotoxicologicaleffects of TPs due to the cocktail mixture of synthetic contaminants present insidethe aquatic environmental compartments (18). New approaches like effecteddirected analysis (EDA), the use of multivariate statistics, and environmental

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risk assessments are leading to more focused studies based on environmentaldetection (16). Needless to say that any TPs originating from the degradation ofpharmaceuticals are a priori also detectable without having the reference standardat the laboratory (19).

Applying Suspect Screening for Environmental Samples

Suspect Screening for Detection of Pharmaceuticals and Their TransformationProducts

Ultra/high performance LC (UPLC or HPLC) separates well polar compoundswith functional groups, such as acids, phenols and amines; further UPLCcombined with HRMS is especially useful for thermolabile, polar compounds.Table 1 shows state to the art UPLC/HRMS suspect screening methods foranthropogenic organic micropollutants such as pesticides, pharmaceuticals,drugs of abuse, sweeteners, surfactants, flame retardants, benzothiazoles andbenzotriazoles, phthalates, disinfectants, and nowadays due to resistant bacteria,additionally antibiotics; further it summarizes the software which was usedfor detection purposes. Thereby, suspect screening achieves adequate resultsfor tentatively identified analytes, as all of the examples show positive results.Furthermore, to meet the challenges posed when analyzing a mixture of manyknown and unknown compounds at low concentrations in complex matrices, arange of different HRMS instruments have been developed in recent years (19).Eichhorn et al. 2012 (20) compared the two most prominent (QExactive vs.QTOF), and while it is still not clear which is the best option for environmentalsamples, both are important resources for suspect and non-target screening ofenvironmental water samples. This is mainly due to their selectivity, sensitivity,and the capacity of MS/MS fragmentation. Manual data mining is outsourced andthe parameters, like metabolomics, pathways, or chemical reactions have to be setin the beginning of the experiment. However, software is normally reducing theeffort and skill needed, but in the case of novel computer aided approaches it isgetting more complex. HRMS is possible to detect several thousand compoundsin water samples and manual data mining faces the challenge of noise elimination.Analytical methods vary from experiment and research group capability, butdetection has to be confirmed by degradation experiments or, at least by literaturedata about ECs.

As described in the introduction, TPs should be considered during monitoringof environmental samples. Overall, toxicological data on effects of target TPsof PPCP’s on ecosystems is rare; in particular, there are no systematic studieson their environmental impact and therefore research on this topic should beencouraged (2). Thereby, suspect screening is a promising approach due to itsno longer requirement of analytical standards, and therefore opens possibilitiesof detecting new ECs; while exact mass filtering gives the possibility to quantifymatching measured RT to predicted RT, fragment patterns with MS/MS toMS/MSdatabases, of suspect compounds.

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Table 1. Examples of Suspect Screening Methods Applied to Different Environmental Water Samples; SPE = Solid Phase Extraction,API = Active Pharmaceutical Ingredients; Usual Software Like Xcalibur (Thermo Fischer Scientific) Is Not Additionally Mentioned.

Table modified and adapted from Reference (41), Copyright 2015, Trends Anal. Chem.

Matrix Analyticaltechnique

Suspect screening list andsource of the compoundlist

Data mining(name of thesoftware)

Tentatively identifiedanalytes

Confirmedanalytes

Reference

Detection and evaluation of the fate

Wastewater SPE-UHPLC-ESI-QTOF MS

± 500 pesticides,pharmaceuticals, drugs ofabuse and transformationproducts

MassLynx v4.1 -ChromaLynx

4 pesticides, 3pharmaceuticals, 1 drugof abuse, 1 metabolite ofdrug of abuse

n.a. (42)

WWTP effluent 4 x SPE-UH-PLC–ESI-QExactive MS

internal TP-list, 12-13pharmaceuticals

Mass Frontier(FISh), SIEVE

1 pharmaceutical, 3 humanmetabolites, and a syntheticimpurity

1 pharmaceutical,three humanmetabolites, and asynthetic impurity

(32)

WWTP effluent SPE-UHPLC-ESI-TOF MS

147 pharmaceuticals and54 metabolites

MassLynx v4.1 -ChromaLynx XS

25 pharmaceuticals 4 pharmaceuticals (43)

WWTP effluent SPE-HPLC-ESI-LTQ-Orbitrap MS

1706 LC-MS and ESIamendable compoundsproduced or used in localindustry + 325 chemicalsreported to occur insurface water

MZmine v2.9,R-nontarget,MetFrag

13 compounds 1 UV filter,4 chemicalsynthesisintermediates, 1pharmaceutical

(44)

Continued on next page.

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Table 1. (Continued). Examples of Suspect Screening Methods Applied to Different Environmental Water Samples; SPE =Solid Phase Extraction, API = Active Pharmaceutical Ingredients; Usual Software Like Xcalibur (Thermo Fischer Scientific) Is

Not Additionally Mentioned





Confirmedanalytes

Reference

WWTP effluent SPE-HPLC-ESI-LTQ-Orbitrap MS

394 compounds from 15classes of homologousseries (e.g. linear alkylbenzyl sulfonates,sulfophenyl alkylcarboxylic acids, etc.)

RMassBank,R-nontarget,enviMass,MetFusion

69 compounds related to 11classes of homologous series

n.a. (45)

WWTP effluent,surface water

UHPLC-ESI-QTOF MS

metabolites of 6pharmaceuticals

LecoChro-maTOF,PeakView- IDAExplorer

6 metabolites 2 metabolites (46)

WWTP effluent 4x SPE-HPLC-ESI-QExactive MS

867 API on suspectscreening list, 119 knowntarget compounds (IMSdata)

ThermoScientificFormulatorenviMass v1.2enviPat R

77 APIs 26 new APIs ontarget screenininglist

(47)

WWTP effluent,surface water

LC–ESI–QTOFMS

5 pharmaceuticals MassLynx v4.1MetaboLynx XS

22 transformation products 14 transformationproducts

(48)

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Confirmedanalytes

Reference

WWTP effluent& influent

SPE-HPLC-ESI-LTQ-Orbitrap MS

17 parent/TP pairs withbatch experiment

R’s enviPick, R’senviMass, R’sOrgMassSpec,R’s Nontarget,R’s MassBankMOLGEN-MS/MS,ProteoWizard3.0 MetFrag &MetFusion

13 pairs of TPs (mixbetween suspect andnon-target screening, butnew interesting approach)

4 transformationproducts > 0.5,thereby 1 is 100%confirmation(non-target)

(16)

WWTP effluent SPE-LC-ESI-LTQ Orbitrap

146 pollutants,pharmaceuticals (33)& metabolites (113)

ExactFinder 2.5 69 compounds - (49)

WWTP effluent& surface water

SPE-UHPLC-QTOF MS& SPE-LC-LTQ-OrbitrapMS

107 pharmaceuticals andillicit drugs

TraceFinderChromaLynx

28 compounds 18 compounds (50)

Surface water SPE-HPLC-ESI-LTQ-Orbitrap MS

1794 predicted or knowntransformation productsof 52 pesticides andpharmaceuticals

MassFrontier,UM-PPS,

19 pesticides andpharmaceuticals

12 transformationproducts

(22)


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Table 1. (Continued). Examples of Suspect Screening Methods Applied to Different Environmental Water Samples; SPE =Solid Phase Extraction, API = Active Pharmaceutical Ingredients; Usual Software Like Xcalibur (Thermo Fischer Scientific) Is

Not Additionally Mentioned





Confirmedanalytes

Reference

Surface water LVI-HPLC-ESI-QTOFMS

1200 pharmaceuticals andpersonal care products

PeakView -IDA ExplorerAnalyst TF 1.5MultiQuant

5 pharmaceuticals n.a. (51)

Surface water SPE-HPLC-ESI-Q-Orbitrap MS

140 pesticides andtransformation productshaving log Kow < 5 (waterrelevant substances) andat least one heteroatom(ESI amendable)

MassFrontier6.0, MetFrag

19 pesticides and 11transformation products (nottaking into account predictedMS/HRMS fragments)

13 pesticides and5 transformationproducts

(52)

Surface water SPE-UHPLC-ESI-QTOF MS

1212 pharmaceuticals,546 pesticides, 378polyphenols and 233mycotoxins

Analyst, PeakView 1.0MultiQuant 2.0MarkerViewFormula Finder

31 pharmaceuticals, 8pesticides, 1 polyphenol, 2mycotoxin

n.a. (53)

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Suspect Screening Applied To Evaluate the Fate of Pharmaceuticals

For the study of transformation processes, the typical scheme consists ofthe generation of TPs in laboratory settings, followed by their identificationby applying an array of analytical techniques, the detection, and ultimatelyquantification (if possible), of the TPs in the environment (15, 21). Twoapproaches can be distinguished: a “TPs profiling approach” and a more“sophisticated method approach” using HRMS. The overall goal is identical butdue to advances in data analysis software, analytical workflows have changedleading to an improvement of the whole procedure (16, 19, 22–24). TPs profilingis mostly done manually; not only is it time-consuming but also prone to missingTPs of minor intensity in the complex full-scan MS data set. In the sophisticatedapproach, using suspect screening, largely automated data analysis allows for amore exhaustive screening in much reduced time. The first part of the suspectscreening is to create a list. In order to generate a suspect screening list for agiven pharmaceutical and its TPs, required for evaluating the transformation ofthe parent compound in the aquatic environment, different options have beenproposed in the literature: mining for known TPs already reported in the literature,identification of novel TPs in controlled lab-scale experiments, and in silicoprediction of TPs based on common degradation pathways of compounds likexenobiotics. Different setups have been used for simulating the transformationprocesses that pharmaceuticals can undergo in the aquatic environment, includingthe commonly used sunlight simulator and simple custom-made batch-reactorsfor biodegradation studies (see introduction). In addition, innovative software canbe of help for the prediction of transformation pathways such as the University ofMinnesota Pathway Prediction System (UM-PPS) and the Meteor EnvironmentalPathway Prediction System (Lhasa Limited, UK) (25). Following the detection ofthe TPs in samples from the degradation studies or the in silico prediction of themetabolites, a suspect list can be created and then used to search for TPs in realsamples from the aquatic environment. Although this search can be performedmanually by successively extracting from the total ion chromatogram (TIC) theion masses of the suspect analytes with narrow mass windows, an automaticsearch largely facilitates this process. To this end, several software packagesare available in the public domain such as MZmine (26) or XCMS (27) whilecommercial solutions such as MetWorks or SIEVE (Thermo Fisher Scientific) arealso designed for rapid feature detection. Using an alternative protocol HRMSdata analysis, MS/MS data can be screened for fragment ions that might beshared by the parent compound and its TPs (MassFrontier, HighChem, ThermoScientifc). Furthermore, working with HRMS allows to take advantage ofmass-defect filters (MDF) which can be an interesting approach for the detectionin HRMS data of TPs differing in their structure from the parent compound byonly minor modifications. The mass defect is defined as the difference betweenthe exact mass of the molecule and its mass number expressed in atomic massunits and thus can be either positive or negative. Simple transformation reactionssuch as hydroxylation or demethylation result in only very small changes of themass defect, and thus applying a MDF help detect such TPs. Processing software

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of different platforms have implemented MDF algorithms allowing for automatedmass defect filtering.

Once the list of TPs has been created, the next step is their structuralelucidation. However, since it is essential to focus the efforts on the identificationof the most relevant TPs, a prioritization step has been proposed before initiatingthe tedious compound identification process. By this, the complete list of TPs,observed for example in a laboratory experiment, can be reduced to a few keydegradates. In general, the identification comprises of several steps. The firststep determines the change in elemental composition between the pharmaceuticaland the TP taking into account their differences in exact masses. The secondstep consists of comparing their MS/MS spectra, which aims to differentiatestructural moieties having been altered during the degradation process from thoseremaining unchanged in the TP. As with the feature search and the MDF, insteadof time-consuming manual data analysis the comparison of MS/MS spectra canalso be accelerated by software-assisted algorithms. For example, Mass Frontier(HighChem) and MetFrag (28) are software used for the prediction of MS/MSfragmentation pathways. Finally, in order to ensure the complete coverage ofall TPs formed in controlled laboratory experiments, a mass balance betweenuntreated and treated sample for the target pharmaceutical and its TPs has to bequantitative (Figure 1).

Examples of Suspect Screening for the Evaluation of the Fate of Pharmaceuticalsin the Environment

Suspect screening, when used, is mostly applied for the evaluation of thefate of a target pharmaceutical in the aquatic environment using the combinationof laboratory degradation experiments and suspect screening with HRMStechniques. In this case two types of compounds were chosen for a detailedexplanation: lamotrigine (LMG) and X-ray contrast media (ICM).

ICMs are one of the most frequently detected compounds in environmentalsamples. These, highly polar compounds are used in high amounts, up to 200g for one examination, as imaging agent for organs or blood vessels duringmedical diagnostic tests (15). Due to their metabolic stability in the human body,they are collected as unmodified parent compounds in WWTP where their canundergo microbial degradation or adsorption only to a certain degree. Thus theyeventually break through the facility and are discharged with the treated effluentinto surface waters. Accordingly, their concentrations in sewage-impacted riversare considerable (15, 29, 30).

On the other hand, LMG is an anticonvulsant for the treatment of epilepsy, andbipolar disorder and is commonly used in therapy together with carbamazepine.In the U.S. neuro-active pharmaceuticals are estimated to be used by about 8%of the population (31). Unlike iopromide, LMG is highly metabolized in thehuman body (32–34). Ferrer et al. (34) could demonstrate that LMG and its TPsare almost completely bypassing the treatment process in the WWTP and occurin measurable concentrations in various environmental compartments includingwastewater effluents, surface water, groundwater and drinking water samples.

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Figure 1. Workflow of the application of suspect screening for detection ofpharmaceuticals and their TPs as well as for the evaluation of the fate of

pharmaceuticals in the aquatic environment.

Biodegradation Studies of Lamotrigine

In 2010 Ferrer & Thurman (34) reported the first detection of LMG andits two major metabolites in surface water, groundwater, drinking water, andwastewater. Concentrations of LMG ranged from 488 ng/L in wastewater to 108ng/L in surface water, with two out of seven drinking water samples having beentested positive. The concentrations of N2-glucuronide LMG were 209 ng/L inwastewater and 195 ng/L in surface waters. In humans LMG undergoes hepaticmetabolism to form this prominent N2-glucuronide, along with N2-methyl-LMG.Both were reported to display similar activity as the parent compound (34). Theauthors used a QToF-MS to acquire full-scan MS data, ran a peak detectionalgorithm, and automatically assigned the most likely elemental compositionsof the detected peaks based on their accurate mass, isotope spacing and relationisotope intensities. They then filtered the resulting list by compounds containingtwo chlorine atoms, which displayed their characteristic isotope pattern. Thiseventually provided strong evidence for the presence of LMG and its two humanmetabolites in the water samples. The authors emphasized that the occurrence of

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http://pubs.acs.org/action/showImage?doi=10.1021/bk-2016-1241.ch003&iName=master.img-000.png&w=323&h=267

drug glucuronides in WWTP effluents should be more thoroughly investigated.In follow-up article from 2014 the same authors reported on the degradationpathways of LMG under advanced treatment by UV, hydroxyl radicals, and ozone(33). The result of this study showed that LMG responded only slowly to directphotolysis or oxidation by ozone. In wastewater treatment advanced oxidationprocesses using hydroxyl radicals (HO·) are effective but this degradation processshowed low degradation rates. The main transformation pathway was hydroxylgroup addition to the benzene ring during the reaction with (HO·), while ozoneopened the triazine ring structure and direct photolysis dechlorinated the benzenestructure. With the molecular structure containing a triazine ring and a benzenering a fast degradation is not favored (33). The work of Zonja et al. 2016 (32,35) has shown the complexity of the fate of LMG in the environment. Theauthors analyzed influent and effluent samples from WWTPs for a total of twelveLMG-related compounds including human metabolites, impurities, and TPsgained from literature search, further batch experiments, and detection in watersamples. Thereby, the batch reactors were spiked with the parent compoundLMG and additionally with labeled 13C3-LMG in order to gather evidence thatsupported the MS-derived identities of the TPs postulated in the degradationof unlabeled LMG. The parent compound, three metabolites and one syntheticimpurity (known as OXO-LMG) were quantified in wastewater effluents, whileglucuronide oxidation, deconjugation, and biotic transformation were confirmedand are the proposed transformation pathways. The appearance of OXO-LMGwas unexpected and additional batch experiments provided evidence that theN2-glucuronide LMG was the actual source, while LMG itself was not furtherdegraded. The relevance of LMG N2-glucuronide TPs in the transformationpathway of LMG inWWTPs was confirmed after conducting mass balance studiesfor corresponding raw and treated sewage. Both sewages were compared to eachother and mass balance calculation was possible. Only when taking all TPs andmetabolites into account the mass balance could have been closed (Figure 2).

In 2013 Writer and colleagues (31) described the natural attenuation of 14neuro-active pharmaceuticals and their associated metabolites, including LMGandCBZ. The authors used a newly developed lagrangian samplingmethod, whichfollowed a stretch of the river as it flows downstream. Thereby, LMG and itsmetabolites were confirmed to be persistent. The primary mechanism of theirremoval was interaction with bed sediments and stream biofilms. LMG is ingeneral more persistent than its metabolites and there is a clear need for moreinvestigations on the environmental fate of LMG.

While the majority of research on attenuation processes of neuro-activedrugs has used controlled laboratory studies, the approach comparing differencesbetween natural and laboratory conditions is a valuable approach. Schollée etal. 2015 (16) analyzed influent and effluent wastewater of a WWTP and thosefrom a lab-scale batch experiment followed by multivariate statistics of HRMSand MS/MS data. The authors compared peaks detected in the influents andeffluents with those found in the batch experiments, WWTP influent was spikedwith parent compounds and human metabolites, and WWTP effluent with knownTPs whose commercial standards were available. This proof-of-concept studywas used to see whether it would be possible to link parent compounds with

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potential TPs formed in the secondary treatment of a WWTP with the non-targetapplied to spectral symmetry comparison. Later, in a real sample with non-targetanalysis, they detected a surfactant homologue series with their associated TPs.A reduction of the number of quantified compounds between influent and effluentindicated degradation during the treatment; with principal component analysis(PCA) the separation of influent and effluent compounds was achieved. Thirteenbasic modification reactions were used; further filtering of RT reduced the overallnumber of potential links of parent compounds and TPs in influent and effluent.In order to elucidate the structures behind the pairs, MS/MS information wascollected. To further prioritize elucidation efforts on pairs, the spectral similaritybetween supposed parents and TPs was calculated, as it is postulated that similarstructures would yield similar spectra. MS/MS similarity was visualized withhead-to tail plots. It was possible that the intensity of the TPs peak was higherthan the intensity of the parent compound in the influent samples. Linking theparent and the TP fragmentation ions opened the possibility to search for newTPs in the related sample from WWTP to assign possible structures for the TP.Admittedly, when a parent compound structure is unknown, the identification ofthe TP structure cannot be accomplished. The procedure is not yet as successfulas anticipated but useful for wastewater comparison and considered a new methodin its early stages.

Figure 2. Workflow sketch approach for LMG, Bozo et al. 2016 (32) and ICMcompounds, Pérez et al. 2006 & 2009, (37), (38); full line is TP profilingapproach, dotted line is more sophisticated approach and dashed line is bothpossibilities combined or at the same time; mass balance is taken into account,

but some approaches bypass directly to target analysis.

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X-ray Contrast Media Photolysis Study

In surface waters one of the main processes to transform organic pollutantsis photolysis (7, 21, 33, 36). For its evaluation, similar approaches to the onesapplied to wastewater samples have been used. The typical approach consistsof conducting experiments in lab-scale batch-reactors, analysis of the aqueoussamples by full-scan MS in conjunction with targeted or untargeted MS/MSdata acquisition, and manual inspection of the MS data. This workflow wassuccessfully applied by Pérez et al. 2009 (37, 38) and is sketched in Figure 2 forTP profiling and further more sophisticated approaches. In two publications theauthors described TP profiling for the evaluation of the bio/ photodegradability ofiopromide, in waste and surface waters (15, 38). The objective of the first studywas to identify the TPs of iopromide when the parent compound was degradedin mixed liquor from a WWTP in a batch-reactor experiment. The generatedbiodegradation products were structurally characterized by ESI-ion trap-MS(low resolution instrument) in combination with H/D-exchange experiments.The authors detected three TPs, could elucidate their structures based on theMS/MS data, and proposed the metabolic pathway as oxidation of the primaryalcohol on the side chains forming carboxylates. Three years later they applieda similar approach to identify photoproducts of iopromide. The acquisition of ahigh-resolution QToF-MS (10,000 resolving power) allowed the characterizationof photoproducts by accurate mass measurements in combination with H/Dexchange experiments. The experimental approach consisted of spiking surfacewater samples with iopromide followed by exposure to artificial sunlight. Fourprincipal photoreactions were detected for the photolysis of iopromide: gradual,and eventually complete, deiodination of the aromatic ring, substitution of thehalogen by a hydroxyl group, N-dealkylation of the amide in the hydroxylatedside chain, and oxidation of a methylene group in the hydroxylated side chainto the corresponding ketone (38). The knowledge on the photodegradationpathways of the iodine-bearing X-ray contrast agent iopromide was advancedby investigating the formation of photoproducts originating from a series of fivestructurally related X-ray contrast media. In order to avoid the tedious processof manual data mining, in this instance the photo-TPs were searched while usingpeak-picking software (SIEVE, Thermo Scientific) allowing to detect differences,i.e. putative photoproducts, in the TICs of treated and control samples. Oncechromatographic peak alignment was performed with described software (moresophisticated approach), this yielded a list of 108 photoproducts, which was usedto build a compound database (15). With the goal of assessing the environmentalrelevance of these photoproducts obtained under controlled laboratory conditions,real surface water samples were probed for their presence based on accurateMS/MS data and retention time matching. For confirmation purposes masserrors of up to 5 ppm were accepted. This led to the prioritization of elevenphotoTPs based on their high detection frequency in real samples. Their structureelucidation was eventually accomplished by comparison of the characteristicfragmentation patterns with those of the respective parent compounds. Finally, inorder to quantify and elucidate the structures of the priority photoTPs in surfacewater samples, semi-preparative LC of the irradiated laboratory samples obtained

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reference standards of the transformation products. The median concentrationof the parent compounds ranged from 29 ng/L to 6 μg/L (iomeprol) whilethe photoTPs were found at median concentration of 30 ng/L, with maximumconcentrations of 0.4 μg/L for one of the photoTPs of iomeprol (15).

Conclusion and Future Advances

This chapter has reviewed HRMS strategies to detect and evaluate the fateof pharmaceuticals in the aquatic environment using suspect screening methods.Each approach has its advantages and disadvantages and should be consideredwhile choosing the method for following experiment. Thereby the sophisticatedapproach is the more promising one. The disadvantage of TP profiling is thenarrow screening window of compounds, and the time effort in preparation.However, the big advantage of sophisticated approaches is the overall viewon environmental samples. Screening for thousands of compounds within oneexperiment is stunning and even retrospective analysis is furthermore possible.Indeed, pre-knowledge has to be given, or gained in upstream experiments, butthe raise and future advantage of databases is improving the approach, excludingthe pre-experimental part. Additionally, the more sophisticated method gives abetter overall view combined with PCA approaches and as the interest in ECsrises in the community, related bioactive compounds like TPs, impurities, orglucuronides, are investigated and their occurrence in the environment can beconfirmed. Nevertheless, TPs profiling provided and still provides adequateresults of specific compounds, which are of greater interest for the scientificcommunity. Therefore, it is of high significance to realize the importance ofincluding these newly found TPs, however detected in environmental monitoringstudies and, if they are associated with a high environmental risk, to considerthem for inclusion in future water quality guidelines (39, 40). It seems to bestill a long way to declare new bioactive substances to compounds of higherconcern, especially if humankind does not discharge/ produce them, while theseare transformed from parent compounds into the environment, as the EU-watchlist approach shows. Some well known drug metabolites often exceed the parentcompound concentration (17). Consequently, it should be noted that if metabolicroutes of the parent compound are known, suspect screening is straightforward.If the metabolism, however, has not been revealed, new approaches like massbalance calculations, analysis of MS/MS fragmentations, and isotopic patternanalysis are useful methods that, supported by software solutions, open newwindows of investigation. Most of these new approaches are associated withhuge efforts and therefore not used by default. Furthermore, the collaborationbetween research institutions should be fostered to achieve common standardsat the level of identification confidence and the generation of extensive MS/MSspectra libraries. HRMS helps provide an overview of anthropogenic pollutantsand their TPs in the aquatic environment; a recent tendency is the combination ofsuspect screening with target analysis or non-target analysis. Computer-assisteddata analysis has proved highly valuable and lab-scale batch experiments openthe possibility to identify metabolites first under controlled settings and later their

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quantification in environmental samples. A breadth of new methods and softwareis creating sophisticated approaches for detection of ECs in water samples.Investigations based on non-target approaches combined with software, and thesearch in comprehensive HRMS spectra databases is a promising way in futureenvironmental studies. Non-target analysis is complex and still in its early stages.However, computer aided suspect screening is a sophisticated approach with lotsof potential, even if in years the non-target approaches should be the future leader,suspect screening is leading the non-target approach to its future achievementswithout any doubt.

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47. Singer, H. P.; Wössner, A. E.; McArdell, C. S.; Fenner, K. Rapid Screeningfor Exposure to “Non-Target” Pharmaceuticals from Wastewater Effluentsby Combining HRMS-Based Suspect Screening and Exposure Modeling.Environ. Sci. Technol. 2016 DOI:10.1021/acs.est.5b03332.

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49. Llorca, M.; Lucas, D.; Ferrando-Climent, L.; Badia-Fabregat, M.;Cruz-Morató, C.; Barceló, D.; Rodríguez-Mozaz, S. Suspect Screeningof Emerging Pollutants and Their Major Transformation Products inWastewaters Treated with Fungi by Liquid Chromatography Coupled toa High Resolution Mass Spectrometry. J. Chromatogr. A 2016, 1439,124–136 DOI:10.1016/j.chroma.2015.10.077.

50. Bade, R.; Rousis, N. I.; Bijlsma, L.; Gracia-Lor, E.; Castiglioni, S.;Sancho, J. V.; Hernandez, F. Screening of Pharmaceuticals and Illicit Drugs inWastewater and Surface Waters of Spain and Italy by High Resolution MassSpectrometry Using UHPLC-QTOF MS and LC-LTQ-Orbitrap MS. Anal.Bioanal. Chem. 2015, 407, 8979–8988 DOI:10.1007/s00216-015-9063-x.

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52. Moschet, C.; Piazzoli, A.; Singer, H.; Hollender, J. Alleviatingthe Reference Standard Dilemma Using a Systematic Exact MassSuspect Screening Approach with Liquid Chromatography-HighResolution Mass Spectrometry. Anal. Chem. 2013, 85, 10312–10320DOI:10.1021/ac4021598.

53. Masiá, A.; Campo, J.; Blasco, C.; Picó, Y. Ultra-High Performance LiquidChromatography – Quadrupole Time-of-Flight Mass Spectrometry toIdentify Contaminants in Water: An Insight on Environmental Forensics. J.Chromatogr. A 2014, 1345, 86–97 DOI:10.1016/j.chroma.2014.04.017.

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Chapter 4

Statistical Approaches for LC-HRMS DataTo Characterize, Prioritize, and IdentifyTransformation Products from Water

Treatment Processes

Jennifer E. Schollée,*,1,2 Emma L. Schymanski,1and Juliane Hollender1,2

1Eawag, Swiss Federal Institute of Aquatic Science and Technology,8600 Dübendorf, Switzerland

2Institute of Biogeochemistry and Pollutant Dynamics,ETH Zürich, 8092 Zürich, Switzerland*E-mail: [email protected].

Studying the formation of unknown transformation products(TPs) from water treatment processes can be a dauntingtask due to the high volume of information generated withmodern analytics such as non-targeted liquid chromatrographyhigh-resolution mass spectrometry. To disentangle and selectthose unknown compounds, including TPs, a variety ofstatistical methods can be applied. Significance testing and foldchanges can provide an overview of those non-target features inpost-treatment samples that are both statistically significant andlarge in magnitude. Time trend analysis can select non-targetfeatures that follow expected intentisty trends. Finally,multivariate analysis such as principal component analysis,hierarchical clustering, and partial least squares can cope withco-varying features to help characterize and group unknownnon-targets. With proper sampling and pre-processing, thesetools can help to prioritize and identify potential TPs thatmay be relevant in the environment. In this review, differentapproaches are presented using examples from the literatureand our own research.


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Introduction

The search for transformation products (TPs) in the aquatic environmentoften focuses on known compounds, especially TPs of contaminants of emergingconcern (CECs), such as pharmaceuticals, pesticides, artificial sweeteners,X-ray contrast media, flame retardants, etc. While target and suspect screeningapproaches are very successful at detecting known compounds, non-targetscreening methods are being increasingly applied to detect unexpected TPs (1–3).Since non-target refers to anything not included in target and suspect screeninglists (4), this can constitute a large number of peaks, especially when usinghigh-resolution mass spectrometry (HRMS). With the inclusion of steps such assolid-phase extraction (SPE) and chromatography for enrichment and separation,large numbers of compounds can be measured simultaneously (5–7). Thereforein many cases measurement or detection of these TPs is no longer the bottleneck;the focus has turned to analyzing these large datasets and prioritizing which ofthese non-target peaks may be TPs and therefore interesting for identification.

This chapter will explore the various statistical approaches that have beenemployed so far to find TPs that are generated from water treatment processes.The statistical tools presented here cover a variety of possible methodologies,from univariate to multivariate analyses (MVA), from unsupervised to supervisedmethods, and inclusion of additional data for prioritization. Many of these toolsare implemented in open source or in vendor software.

Treatment processes comprise different matrices and applications and can beapplied for many reasons, e.g., wastewater treatment, drinking water treatment,and river bank filtration. During these treatments, various physical, chemical, andbiological processes such as sorption, advanced oxidation, and biodegradationremove compounds from the water matrix. Occassionally multiple steps areapplied in series, e.g., in wastewater treatment where filtration may act as aprimary treatment, followed by a secondary treatment such as conventionalactivated sludge (Figure 1). Within one treatment (step), multiple processesmay also be occurring, e.g., in powder activated carbon where both sorptionand biodegration can lead to the removal of compounds. When studying thesetreatment processes, researchers sample the pre-treated water (also referred to asthe influent) and the post-treated water (also known as the effluent) of either theentire treatment train or of individual steps. In this way a comparison can be madeabout the changes that have occurred within the treatment step(s) in question.

Figure 1. Example of a wastewater treatment train with multiple types oftreatment processes applied in combination.

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From these natural and engineered systems it is expected that TPs behave inmany cases in predictable ways that are therefore amenable to confirmatory dataanalysis such as hypothesis testing. In the simplest form, it is expected that a parentcompound is removed and in its place a TP is formed by a specific process such asoxidation. Of course the real situation is much more complicated and one alwaysneeds to keep this in mind when applying statistics to finding TPs. For example,there is not necessarily a one-to-one relationship between parent and TP. Oneparent compound can form multiple TPs (8) but also the same TP can come frommultiple parent compounds (9). Especially when dealing with mixtures it can be adaunting task to unravel these relationships. Additionally, newly formed TPs canthemselves become “parent” compounds, leading to more TPs (10). Dependingon the rate of the reaction mechanism sometimes these first generation TPs are noteven detected or observed (8). Reactions are also not only one-directional. A TPmay transform back to the parent compound under the right conditions, completelyreversing the trend that is expected (11). These situations can prove challenging forstatistical hypothesis tests. It can therefore be worthwhile to consider exploratorydata analysis. This type of analysis may be more useful initially to visualizetrends in the data, after which more specific hypotheses can be constructed. Thesetypes of techniques can include simple univariate visualizations, such as box plotsor scatter plots, or multidimentional methods, e.g., principal component analysis(PCA).

The application of statistical tools to finding non-target TPs that result fromthese processes is still in the early stages. In contrast, within the metabolomicscommunity, extensive time and effort has already been dedicated to investigatingand evaluating various different statistical approaches to select unknownmetabolites. Metabolomics is the study of small molecules which are the resultof specific cellular processes and the metabolites that are studied are thereforebiological molecules such as amino acids, sugars, vitamins, etc. which likelyperform a cellular function. Metabolomics studies often compare control andtreated samples to identify differences in the presence of a stressor such asdisease. Hence there is some comparibility between studies searching for TPs andthose that seek to identify metabolomics changes. Therefore a short introductionto the tools and workflows common in metabolomics will be given, with an eyeon how this may be applied to studies searching for TPs. This chapter focuseson studies which used liquid chromatography coupled to high-resolution massspectrometry (LC-HRMS) since CECs and their TPs, especially those relevant inwater samples, are best detected with this method.

Considerations Prior to Statistical Analysis

Prior to any statistical analysis, sampling, measurement, and pre-processingare crucial steps to obtaining useful data. Challenges in sampling includeaccounting for and removing variability, if possible. For example, inputs towastewater treatment plants (WWTPs) vary with time of day, day of the week,and location (12). In contrast, effluents are more averaged values due to mixingand the retention time of the wastewater plant. Therefore it is important to selecta proper sampling strategy which takes into account this variability (13).

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Analytics start with selecting an appropriate method for measurementto adequately detect the compounds of interest. In the case of LC-HRMSmeasurement, this includes proper chromatographic separation, correct ionizationmode, sufficient MS resolution and accuracy, and a limit of detection (LOD)low enough to detect compounds in the expected concentration range. Since nomethod will cover all compounds, during data analysis the researcher should beaware of the types of chemicals which can be detected with the analytical methodselected. Additionally, sampling and measurement may introduce their ownbiases in the data, which can affect the data analysis.

When applying univariate metrics to compare signal intensity between peaksin different samples, one needs to be aware of potential matrix effects comingfrom different sample matrices. For example the influent and effluent of a WWTPor even different surface waters can have very unique matrices. These differentmatrices can lead to ion suppression, which in turn makes it appear that thereare differences in concentration when there in fact are not or vice versa. Thesematrix effects can partially be reduced during sample preparation; for example ithas been shown that diluting influent and effluent wastewater samples reduces thematrix effects, but this can lead to the loss of compounds (14). To account forthe possible matrix effects, quality control samples can be included in the studydesign to calculate matrix factors for each sample type. Additionally, the behaviorof internal standards in different sample types can be used to correct for matrixeffects, but this only applies to target compounds. Matrix effects are not consistentacross a sample (Figure 2) and extrapolation of this phenomenon to unknown peaksis difficult (14–17).

After measurement comes pre-processing. The conversion of rawmeasurement data to a dataset that can then be analyzed is a crucial and non-trivialpoint. As demonstrated by many, for example Nürenburg et al. (14), a properpre-processing workflow is necessary to obtain reliable data. This includesgenerally the following four major areas:

(a) Peak picking, where distinct chromatographic and mass peaks aredetected and separated;

(b) Feature building, where detected peaks are then combined acrosssamples;

(c) Feature grouping, where peaks belonging to the same compounds suchas isotopes and adducts are grouped together;

(d) Blank and blind subtraction, where peaks belonging to matrix,contamination, or background are removed from the sample data.

Peak picking is the processes of identifying unique chromatographic featuresmeasured by the HRMS. Good peak picking starts with a proper method sincepeaks that are not chromatographically separated will remain challenging for analgorithm to detect. Also broad peaks or very low intensity peaks may not bedetected during peak picking, depending on the algorithm and settings applied.Additionally, without sufficient mass accuracy, different isotopic peaks may not

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be resolved. The user must select the analytical method which has the highestpeak capacity for the relevant compounds but this remains a challenge whenthe compounds of interest are unknown before analysis, such as in non-targetscreening (18).

Figure 2. Ratio of areas of each internal standard for matrix factor correction.While a slight downward trend could be observed, the correlation was not strongenough to justify a blanket matrix factor correction of all non-target peaks,demonstrated by the low R2-value of the linear regression. The outlier at 4.78

minutes is the compound Ritalin.

Feature building consists of comparing peaks detected in each sample inorder to construct a data matrix which includes all peaks detected in all samples.While in theory each compound should have a defined mass to charge ratio (m/z)and retention time (RT) associated with it, in reality shifts may occur duringmeasurement. Drift can occur on an instrument, both in RT and m/z, whichwill need to be either corrected for with an algorithm or accounted for with atolerance window. Many strategies have been suggested for tackling these driftsbut this still remains a source of error in constructing data matrices. Additionally,after constructing the data matrix, there is the issue of missing values. In mostcases a missing value is not unimportant; a non-detect may be just as valuableas a detection. But the researcher must determine how best to represent this inthe dataset, depending on the study question. Many different strategies existincluding replacing the missing values with zero, half the LOD, mean, or morecomplicated imputations. The technique selected can have large implications onthe downstream data analysis (19, 20).

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Feature grouping involves grouping together detected peaks that result fromthe same compound (also termed componentization). For example in LC-HRMS,isotopes, adducts, and/or in-source fragments are detected in addition to themonoisotopic peak of a compound. Grouping these peaks together into a feature isan often overlooked but nonetheless important aspect of pre-processing. In manystatistical methods the presence of co-varying variables may lead to bias or skewin the data analysis. Since these peaks are resulting from the same compound,they should inherently be co-varying. Therefore their removal is important forthe construction of a data matrix. As these peaks provide information aboutthe molecular formula, which is in turn important for structure elucidation, theinformation must be retained separately. Similar challenges face feature groupingas for feature builiding, e.g., mass accuracy of the measurement may lead to theneed for a m/z tolerance window. Additionally, depending on the resolution of themeasurement instrument, isotope peaks may not be completely resolved, leadingalso to a shift of the measured m/z.

Finally, selecting a proper blank and/or blind sample is critical (21) but canbe a challenge. The more representative a blank/blind is of the true background,the more noise can be eliminated from the data analysis. For example, someresearchers have used the pre-treatment sample as a background and subtracted itfrom the post-treatment sample, resulting in a new chromatograph which showsonly those peaks higher after the treatment, and therefore likely to be TPs (22,23). But this background sample can be difficult if not impossible to obtain.Prioritization based on mass defect to select compounds which contain halogens(and are therefore likely to be anthropogenic) was also applied successfully toseparate out e.g., relevant non-targets from sediment matrix (24). In the case ofexperiments which involve spiking, an unspiked sample can easily serve as thereference for blank subtraction (14, 25), but this is not an option when workingwith unspiked samples. Two methods for blank correction have also beensuggested—blank exclusion, where any feature detected in a blank is removed,and blank subtraction, where the intensity of a feature in a blank is subtractedfrom the intensity of the same feature in the sample. Blank exclusion is the moreconservative approach, although it has been shown that there was not a significantdifference between the two (14). A safety factor can also be included, wherefeatures that are, for example, ten times more intense in the sample than in theblank are still retained.

Metabolomics Workflows

Within the metabolomics community, a number of tools have establishedthemselves as the standard for non-target screening, generally knownin metabolomics as untargeted screening. For example, XCMS Online(xcmsonline.scripps.edu) (26, 27) is a powerful online platform which includesmany features for data visualization and statistical analysis. The latest versionof the software includes pre-processing of the data as described above, usingthe XCMS approach (28, 29), as well as many univariate and multivariate data

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visualization techniques. In the classical study design, a two-group comparisonis made (e.g., control vs. treated). Relevant features can then be selected basedon the significance of the difference in peak intensity with the selected statisticaltest as well as on their fold change. This can be then further visualized in a cloudplot. In a recent update to XCMS Online, tests are implemented not only for twogroup comparisons, but also for multigroup comparisons.

MVA is also prevalent in metabolomics research since this provides anopportunity to search for interesting spectral features that differ between sampletypes while being robust against intra-class variability. The two main tools usedfor this are PCA and partial least squares projection to latent structures (PLS)(30). XCMS Online provides PCA to explore the relationship between sampletypes and which features may be characteristic of a specific group. Anothercommon software for MVA in metabolomics is SIMCA (Umetrics Inc.) (31). Thissoftware includes two additional multivariate techniques, PLS and orthogonalprojection to latent structures (OPLS). PLS has the advantage over PCA that it isa supervised method; a response vector can be included to provide informationabout the classes to which the samples belong. For example, samples can beordered into their respective sample types or a more complicated response vectorcan be used, such as time or concentration. The method then seeks to find thevariables which best explain the difference between the indicated response vector.This method can be especially useful if there are large variations within sampletypes which PCA would then seek to explain (e.g., variations which may arisefrom changing inputs or fluctuating treatment efficiencies). But the user should becautious, since PLS and OPLS assume that there is a difference between sampletypes and can on occasion over-fit the data (32). Generally it is advised to usean unsupervised method first, to investigate the inherent structure of the data, forexample to visualize the intra-class vs. inter-class variability and identify possibleoutliers. After this, supervised methods can be applied to answer more specificresearch questions.

Compared with environmental analyses, statistical analysis of metabolomicsdoes still have some advantages. The matrix effects are generally less betweencontrol and treated groups in a metabolomics study than in, for instance, theinfluent and effluent of a wastewater treatment process. Additionally, the chemicalspace of interest in metabolomics is significantly smaller, e.g., few structurescontain halogens. Extensive time and effort has been put into developingand sharing spectral libraries of metabolites (33). This has made it possiblefor metabolomics to start to move away from non-target studies and towardquantitative analysis (34). In environmental analysis this trend is actuallyreversed. For many years analyses focused on the set of known environmentalpollutants, using those on a regulatory list. But it has become clear that theseknown pollutants do not explain the effects that are observed in biologicalcommunities exposed to these environmental samples. Therefore, with theadvent of new analytical techniques, especially highly-resolved and accurateMS, environmental analyses has moved towards non-target analyses (3). Andwhile there has been progress in developing spectral libraries of environmentallyrelevant compounds (35–37), the number of chemicals necessary to include isconsiderably larger and therefore the task much more challenging.

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Temporal and Spatial Trend Analysis

The simplest system in which to investigate TPs formed during watertreatment is a batch experiment. In this case, a sample from the treatment plant(such as activated sludge) is spiked with the parent compound(s) of interestand the treatment process under investigation is applied for a certain length oftime. Samples are collected after treatment either once or multiple times andthen compared to the initial t=0 sample to observe the behavior of the parentcompound and formation of any TPs.

In order to study biotransformation in a WWTP, Helbling et al. spiked12 micropollutants into batch reactors with activated sludge and observed theremoval of those compounds over a number of days (10). To find unknown TPs,two samples (t=0 and t>0) from a single biotransformation experiment werecompared and then a series of logical filters were applied. For example, sinceTPs are generally smaller and more polar than their parent compounds (38), theyshould have a lower m/z and elute at an earlier RT. Additionally, intensities in thet>0 sample were required to be 1.5 higher than in the t=0 sample to account forTP formation. With this method 26 TPs were identified from the spiked parentcompounds. This workflow was further developed in Gulde et al. (8) There, inaddition to the logical filters, a series of points were used to establish a trend ofincreasing concentrations using Sieve (Thermo Scientific) software, which leadto the identification of 101 TPs from 19 parent compounds. A similar method wasused in Li et al. to find 11 TPs from 9 parent compounds from water/sedimentbatch experiments (39).

These types of experiments have several advantages. As mentioned, usingspiked samples means unspiked samples can serve as a relevant blank. Also, usinga set of spiked parent compounds makes subsequent structure elucidation morestraightforward, since the unknown TP is related in some way to the known parentcompound. Finally, these experiments can also provide additional informationsuch as reaction rates and may therefore also help prioritize TPs which are morelikely to be persistent. But unfortunately testing all parent compounds in thismanner is unfeasible; therefore researchers have moved toward looking for TPsin real-world samples.

For investigation of non-target peaks within the Rhine River, a novel methodwas applied in Ruff et al. (2) Samples were collected at six points along the stretchof the river from Switzerland to the Netherlands. A list was generated of the 150most intense features measured at all stations. Then at each sampling location,site-specific unknown compounds were found by selecting those features detectedat all downstream sampling locations but not at upstream sampling locations.This systematic comparison of upstream and downstream samples resulted in 57unknown substances for further investigation. While this study was not directlyfocused on TPs, since WWTP effluents are the main anthropogenic input into theriver system (40, 41), it is likely that a number of relevant TPs were present.

Gago Ferrero et al. incorporated temporal information into the identificationevidence for supposed TPs found during non-target analysis of their samples,rather than into the selection of potential TPs (42). They postulated thatpharmaceuticals and their TPs would follow similar weekly and/or diurnal trends.

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While this approach worked well for selected examples, e.g., clarithromycin,N-desmethyl clarithromycin, and hydroxyclarithromycin (Figure 3 in GagoFerrero et al.), the trends were less obvious for other cases such as venlafaxineand TPs as well as nicotine and TPs (Figure S5, Gago Ferrero et al.). In the endthis information was useful to provide some additional evidence but not as clearas may have been anticipated.

Univariate Statistics

Depending on the question at hand, simple methods can be used to selectpeaks of interest effectively. Zonja et al. used frequency of detection in real-worldsamples to select TPs which were environmentally relevant (43). They conductedbatch experiments of six X-ray contrast media, then prioritized unknown massesby comparing to a list of predicted TP masses. These masses were then selectedby their presence in real-world samples such that identification focused only onthose TPs which have been detected in surface waters. This efficient method ledto the detection of 11 TPs. While the workflow was not truly non-target because ofthe incorporation of a suspect screening step with predicted TPs, it demonstrates amethod for directly translating batch experiment results to environmental samples.

In a study on 10 Swiss wastewaters, non-target substances of interest wereselected based on the presence in all samples, prioritized by highest averageintensities (1). On the one hand, this captured omnipresent substances, which arethus potentially highly relevant for the environment, such as an exposure-relevantTP from the industrial solvent benzothiazole. But on the other hand also matrixsubstances and natural mater that are present everywhere were prioritized. Aninteresting result of this was that certain groups of substances that degradeddifferently in various WWTPs were missed. The glycol ether sulfate (GES)surfactant series found in Gago Ferrero et al. (42) was also found in Swisswastewater influents (Figure 3a) and retrospectively in some effluents fromSchymanski et al. (shown here in Figure 3b), but was well removed in othereffluents (Figure 3c, where several members of the series are close to or belowdetection limits). As a result of the intensity prioritization, this series was missedin the original investigation, despite very high signals in some samples.

One of the first examples of applying statistical methods to prioritize non-target peaks was reported by Müller et al. (44) Their strategy focused on usingmathematical operators to analyze how features changed in different sample types.In this case, they selected features that were detected in both a landfill leachatesample and a downstream groundwater sample, while exluding those in the blank.In contrast to time-trend analyses, no specific trend was needed here, merely thepresence of the feature in the relevant sample type; hence only presence/absenceinformation was necessary for the features. For this reason one data restrictionwas the detection of a feature in multiple samples, to reduce false positives. Fromthis method, they selected three non-target features which appeared in the drinkingwater after ozonation and identified the source of the contamination. This type ofapproach could be easily adapted for different study questions, provided the correctsamples are collected.

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Figure 3. The glycol ether sulfate (GES) surfactant series in (a) Swiss wastewaterinfluent and (b) wastewater effluent where the series is poorly removed and (c)Swiss wastewater effluent where the series is well removed (note the intensitydifferences). Inset: series members and the structure. Data from Schymanski et

al. (1) and Gago Ferrero et al. (42)

Other studies have applied more metabolomics-like workflows to identifypotential TPs. Negriera et al. used a combination of Student’s t-test and foldchange to select those features which were significantly different between thecontrol and spiked samples in a chlorination experiment (45). They detected 19TPs from the parent pharmaceutical. In another example of batch experiments,Singh et al. selected TPs which were formed during advanced oxidation basedon those features with a 2-fold change intensity between the pre-treatment andpost-treatment samples (25). Means of the treatment types were comparedwith one-way ANOVA, Tukey honestly significant difference (HSD), andBenjamini-Hochberg false discovery rate (FDR). Using this method, 75%reduction in unknown peaks could be achieved. As has already been discussed,the presence of relevant blanks in both these studies was likely crucial for thesuccessful implementation of the workflow. Since spiked wastewater was usedfor these batch experiments, an unspiked wastewater sample could be used forblank subtraction, particularly useful in a complex matrix such as wastewater.

Fold change and univariate statistics have also been evaluated and applied inour own work. Samples were collected from the influent and effluent of a WWTPequipped with anaerobic and aerobic conventional activated sludge treatment;further details in Schollée et al. (15) In a validation test, a number of parentcompounds and TPs were spiked at environmental concentrations into the influentand the effluent, respectively, and then compared with fold change and p-value

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based on a combination of Welch’s t-test and Mann-Whitney test implementedwith the package ‘muma (46). in the R statistical software (47). These resultsare shown in a volcano plot, which is a common type of scatter plot to visualizechanges that both large in magnitude and statistically significant (Figure 4). It canbe seen that all compounds were found to be significant (p-value<0.05) exceptethofumesate-2-keto, where the variability in the effluent samples was very largeand therefore the difference between influent and effluent was not significant. Butmany compounds failed to show a difference of more than one order of magnitudebetween the influent and effluent. In total 17 out of the 42 compounds investigateddid not meet this threshold. There may be several reasons why some compoundshave small fold changes, for example only partial removal or reformation in thetreatment or matrix effects. Using this criteria 29% of parent compounds and48% of TPs would not have been selected for further analysis, which limits theapplicability of this approach.

Figure 4. Volcano Plot where log2(fold change) vs. -log10(p-value) is plotted.The horizontal line represents p-value 0.05; anything above the line is thereforesignificant. The two vertical lines represent one order of magnitude fold change.Shown are the parent compounds (spiked in the influent) and transformation

products (spiked in the effluent) which were measured in a wastewater treatmentplant.

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A technique from community ecology was also tested, the indicator speciesanalysis. This test was developed to identify indicator species which reflectenvironmental conditions (48). The hypothesis was that instead of indicatorspecies, the detected features could be used to characterize sample types. This testconsiders both how often an indicator is found within a group and the abundance(in this case feature intensity) within a group. The results indicate to which group(if any) the indicator belongs and the confidence level. Of the 42 compoundsconsidered, this analysis worked for 8 parent compounds and 14 TPs, correctlyselecting them as indicators for the influent and effluent, respectively. But 50% ofthe compounds tested were not selected as an indicator for either class, likewiselimiting the applicability of this approach to non-target data analysis.

Multivariate Analysis

Rather than using univariate methods, MVA can be an alternative whenanalyzing complex data sets. These techniques include exploratory techniques,clustering methods, and regression analysis. But there are some issues whichthe researcher needs to be aware of prior to applying MVA; for example, thenumber of observations (i.e., samples or independent variables) is much morethan the number of measurements (i.e., features or dependent variables). Thisdata structure makes traditional linear regression not possible (30). It also leadsto the problem that models can easily be overfitted, which can occur whentoo many dependent variables are used to explain the underlying relationshipamong the independent variables (31). One way to control for overfitting is tosplit the data into a training set and a test set but, depending on the number ofsamples available, splitting the data may not always be possible. Another way tocontrol for over-fitting is through cross-validation and a number of suitable crossvalidation procedures have been suggested (31). Unfortunately, cross-validationis not yet often applied.

Preprocessing steps for MVAmay also be more complicated. Questions aboutdata normalization and data scaling need to be considered. Normalization can beapplied to remove systematic variance in the data. Scaling should be consideredbecause otherwise the MVA will only focus on the intense features in a data set.Since spectral features can vary by four or five orders of magnitude, standardizinggives all variables equal variance and therefore equal weight in the MVA. Manydifferent possibilities exist for data scaling (30). It should be noted that scaling canalso increase the influence of noise in the MVA, therefore an appropriate blanksubtraction is worthwhile. Finally, since multivariate methods may be sensitiveto many co-varying variables, it is beneficial to implement a strict pre-processingmethod to limit the number of variables considered.

The linear projection models PCA and PLS have been most often applied todate. These methods transform the given set of variables into new variables, inPCA called principal components and in PLS called latent variables. The outputfrom a PCA or PLS can best be understood through the interpretation of twoplots—the scores plot and the loading plot. The scores plot is the projection of the

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observations onto the selected transformed variables. In this way, it is possible tosee how samples group together. The loading plot in contrast is the projection ofthe variables onto the selected transformed variables. From this plot it is possibleto see how each variable relates to each other and to the transformed variables.Additionally, the scores and loading plots can be interpreted together, sincevariables that lie in one region of the loading plot are related to those observationsthat lie in the same region of the scores plot.

Numerous other methods exist for MVA but most have not yet becomecommon in environmental analysis. Different clustering techniques such ashierarchical clustering analysis, nearest-neighbor clustering, or k-means analysiscan be used to define sample classes based on spectral data. More computationallyintense methods such as support-vector machines (SVMs) and artificial neuralnetworks (ANNs) could be incorporated into workflows and offer the advantageof being non-linear, as opposed to the linear projections of PCA and PLS.

Examples Applying MVA

Masía et al. published one of the first examples of using PCA to understandrelationships between sample types and to characterize unknown features (16).Here it was clearly shown that the scores plot from the PCA could be usedto differentiate between samples from surface water, wastewater influent, andwastewater effluent. A subsequent principal component variable grouping(PCVG) analysis could then order the features into groups with similar trends.MS/MS information was available for unknown features but case no finalstructure could be assigned to the selected non-targets because of similarity infragmentation patterns in the candidates.

Recent work has also attempted to use PCA loadings to select features whichmay be representative “markers” for a particular sample type (49). This idea wasalso applied in our workflow, where the loading information from a PCA wasused to characterize peaks as either potential parent compounds or potential TPs(15). This idea was verified first with a set of 42 validation compounds. Fromthe scores plot (Figure 5a) there was a clear separation between the influent andeffluent sample types. It was also clear that the parent compounds were correlatedwith the influent while the TPs were correlated with the effluent samples (Figure5b). Only three parent compounds were incorrectly assigned for reasons furtherdiscussed in Schollée et al. (15) and none were without classification, which was amarked improvement over the results with the univariate analysis (Figure 4). TheTP that was associated with the influent was correctly classified, since it is a humanmetabolite that enters the WWTP. This method was therefore further applied toclassify non-target peaks. Again a clear separation could be identified between theinfluent and effluent samples (Figure 5c). This could then be used to classify thenontarget features, 9758 with the influent, and 3011 with the effluent (Figure 5d).These non-target features were then further prioritized for investigation.

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Figure 5. (a) Scores plot (which displays the observations, here the samples)of the principal component analysis (PCA) using target screening of spikedwastewater influent and effluent samples. (b) Loading plot (which displays thevariables, here the validation compounds) of the PCA. Reproduced/Adaptedwith permission from reference (15). Copyright 2015 ACS. (c) Scores plot

(which displays the observations, here the samples) of the PCA using non-targetscreening of unspiked wastewater influent and effluent samples. (d) Loading plot

(which displays the variables, here the non-target features) of the PCA.

Another strategy considered for this dataset was hierarchical cluster analysis,where samples and/or features are clustered together based on their similarity, ascalculated by a distance matrix. In this study, both samples and features wereclustered together and visualized in a heat map (Figure 6). It was clear that twodistinct clusters could be found for the samples (seen in the top dendogram),which corresponded to the influent and effluent sample types. Additionally,the color scheme and clustering of the features revealed that in general TPshad lower concentrations in the influent than in the effluent and vice versa forthe parent compounds. It was also clear that some target compounds did notbehave like the other compounds. Carbamazepine, venlafaxine, and N- and

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O-desmethylvenlafaxine were the most dissimilar, which can be seen both in theconcentration pattern and the fact that these compounds were grouped in theirown cluster (seen in the left dendogram). These were also the compounds whichwere, for the same reasons, classified differently in the PCA analysis above. Forexample, carbamazepine was persistent in the wastewater treatment investigated,showing little different between the pre-treatment and post-treament samples.

Figure 6. Heat map of target screening results of spiked wastewater samples,showing the hierarchical clustering of samples (along the horizontal) and targetcompounds (along the vertical). Grey-scale depicts the measured intensities ofeach target compound, with white being highest intensities and black lowest

intensities.

But while the hierarchical clustering was overall capable of producing theexpected sampling types and clustering the features, it can be seen how thismethod of analysis quickly becomes cumbersome when the data set is expanded.First, computational time becomes increasingly impractical. To combat thisproblem, a top-down clustering method such as k-means can be applied asopposed to the bottom-up approach of hierarchical clustering, but k-meansrequires the user to select the number of desired clusters a priori. Second,

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interpretation of the clusters becomes unwieldy, both in hierarchiecal clusteringand k-means. Already with only 42 target compounds not all of the clusters canbe explained sensibly; for example why ethofumate-2-keto, a TP which clearlyfollows a pattern more characteristic of an effluent feature, is actually groupedwith parent compound features. When this is expanded to a data set of hundredsor thousands of non-target features the computational and interpretation timemake this no longer an attractive method, unless one is only interested in theclustering of the samples without needing to extract specific feature clusters orapplies a subsequent method such as PCVG.

Hierarchical clustering and PCA are both unsupervised methods where theresearchers’ knowledge of the sampling design is ignored. Supervised methodssuch as PLS can therefore be beneficial by including metadata information whichmay assist in interpretation. In one of the few examples of PLS applied in anenvironmental investigation, Hug et al. used this method to select relevantmicropollutants detected in the effluent of a WWTP that could be responsible formutagenic activity (50). Samples were collected weekly for six weeks. Spectraldata was collected using an SPE-LC-HRMS/MS system and mutagenicity wasmeasured with the Ames Fluctuation Test. For the PLS analysis, the data matrixconsisted of spectral data measured in each sample and the response vectorwas the mutagenicity measured in each sample. Therefore the PLS focused onexplaining the differences between the samples with low toxicity and those withhigh toxicity using the spectral data. This method of course did not only focuson TPs but any relevant micropollutants which may pass through the WWTP.The complexity of the model was reduced by iteratively removing variableswhich had a low influence, finally selected 205 features as co-varying with thebiological activity. The validity of the PLS was also checked with multiple setsof randomly selected variables. From these 205 features, eight could be identifiedor tentatively identified with structure elucidation and one which was predicted tobe mutagenic. This demonstrates even with an effective statistical analysis, manychallenges remain for a successful identification of unknown compounds.

In a further example of how PLS may be used to study drinking watertreatment systems, Sanches et al. applied thismethod to understandmicropollutantremoval during nanofiltration (51). Since there exists a mixture of micropollutantsin natural waters, their behavior may affect one another. Additionally, theparameters which govern micropollutant removal are various and intertwined;therefore the authors advocated for the application of MVA to investigate theseinfluences. This study used physio-chemical properties of known compoundsas well as operating conditions as the input matrix and defined the responsevector as the rejection/adsorption for each micropollutant. They could definethree response variables which adequately described the observed rejection andadsorption. While this study also did not specifically focus on TPs, if applied toother treatment processes it could perhaps be used to define parent compoundswhich are well-eliminated and therefore likely to form TPs.

One final consideration that has been applied in a few studies is theincorporation of expert knowledge in conjunction with statistical analysis. Forexample, since TPs are formed through some reaction mechanism in the treatmentprocess, knowledge about these reaction mechanisms can be incorporated. For

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example, Singh et al. looked at the formation of TPs from iopromide andiopamidol in an advanced oxidation process (25). After selection of the potentialTPs, structure elucidation commenced with formula assignment on the unknownmasses. Based on the expected reaction mechanisms in advanced oxidation,TP formulas were restricted to those with the same number or lower of carbon,nitrogen, and iodine, since only the addition of oxygen and/or hydrogen wasexpected. This provided another 75% reduction in unknown features selected foridentification.

This strategy to include knowledge about the reaction mechanisms has alsobeen applied elsewhere (15, 52). In our work, unknown masses were selectedafter a peak classification with PCA and sorted into potential parent compoundsand potential TPs, as described above (Figure 5d). These compounds were thenlinked together by their exact mass using biotransformation reactions expectedin activated sludge. From the 12769 potential parents and TPs, 12005 potentiallinks were selected. A RT restriction was applied on those reactions which wereexpected to produce more polar TPs, which reduced the number of links to 8501.Further univariate methods (Student’s t test) were applied to determine which pairswere most interesting for structure elucidation and finally, an unknown surfactantseries with the associated TPs were found in the wastewater samples.This methodallowed for the selection of many non-target features with statistical analysis,which were then further prioritized with expert knowledge.

In the end, while statistical tests can assist in the selection, characterization,and prioritization of non-target features, identification and/or structure elucidationof these features requires additional time and expert knowledge. Extra structuralinformation can be obtained by supplementary analytics, such as tandem massspectrometry (MS/MS) (8, 53) or nuclear magnetic reasonance (NMR) (54, 55).Prediction tools such as RT prediction can be used to compare calculated RT toexperimentally measured values (56–58). Additionally, potential environmentalrelevance of TPs can be estimated with exposure- or effect-driven approaches (38).These tools can support the selection of likely candidates for structure elucidationand TPs which are environmentally relevant.

Conclusions/Outlook

The examples discussed here demonstrate that statistical methods providemany tools for selecting and prioritizing unknown TPs. Univariate measures helpselect TPs in simple study designs, for example those that compare pre-treatmentand post-treatment samples with similar matrices. More complex study designsmay require more complicated methods such as MVA and a combination oftools often offers the most thorough evaluation of the data. For successfulimplementation of any statistical method, the correct information needs to becollected. Therefore sampling and measurement needs to be designed with thedata analysis already in mind, in ensure that the appropriate type and number ofsamples are collected. If this is done, then these methods can be used to helpunravel the relevance of TPs in environmental systems with regards to occurenceand toxicity.

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Abbreviations

ANN – artificial neutral networkCEC – contaminant of emerging concernDA – discriminant analysisFDR – false discovery rateGC – gas chromatographyGES – glycol ether sulfateHRMS – high-resolution mass spectrometryHSD – honestly significant differenceLC – liquid chromatographyLOD – limit of detectionMS/MS – tandem mass spectrometryMVA – multivariate analysism/z – mass to charge ratioNMR – nuclear magnetic reasonanceOPLS – orthogonal projection to latent structuresPCA – principal component analysisPCVG – principal component variable groupingPLS – partial least squares projection to latent structuresRT – retention timeSPE – solid phase extractionSVM – support vector machineTP – transformation productWWTP – wastewater treatment plant

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Chapter 5

Lab-Based Approaches To Support theScreening and Identification of Transformation

Products by LC-HRMS

Bettina Seiwert, Cindy Weidauer, Kristin Hirte,and Thorsten Reemtsma*

Department of Analytical Chemistry, Helmholtz Centre for EnvironmentalResearch - UFZ, Permoserstrasse 15, 04318 Leipzig, Germany


The screening for transformation products (TPs) ofcontaminants in complex environmental samples is a difficulttask. Lab-based systems that simulate environmentaltransformations are helpful for both the detection and theidentification of TPs formed or occurring in the environment.Oxidation, reduction, hydrolysis, photolysis and conjugationmay be performed in lab experiments and the productmixtures analyzed by LC-HRMS with all-ion fragmentationor data-dependent MS/MS experiments. The full massspectrometric data are stored. It is not necessarily recommendedto elucidate all TPs of the lab-based approaches, but to focus onthose peaks that occur also in the environmental sample. Thisapproach allows detection and tentative identification of TPs inthe environmental sample and to ascribe it to the right parentcompound. As elevated concentrations may be used in the labexperiment more meaningful fragment spectra are produced,which facilitates the structure elucidation.

Introduction

The detection of transformation products (TPs) of contaminants inenvironmental samples, their assignment to parent compounds and theiridentification is cumbersome. Targeted approaches by multimethod that focuson TPs as for pesticide TPs are rare due to the limited number of available


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standards of these TPs (1). For pharmaceuticals that often enter the environmentby wastewater, standards of the main TPs are often commercially available, butfor pesticides and industrial chemicals this it is not necessarily the case. Thussuspect screening and non-target screening are the only possible techniques todetect their TPs. However as stated by Zedda et al. only a limited number ofcompounds have been identified or tentatively identified so far in environmentalsamples by non-target screening (2).

Non-target screening approaches are often used to compare differenttreatments, environmental samples of different regions or time points bymultivariate statistics. This approach is powerful and works well to show thatthere are differences and it may lead to potential targets of interest (3). Generally,however, even the selection of peaks of interest by non-target approaches istime consuming and it depends on the ionization efficiency of the compound ofinterest and the matrix effects of the respective sample whether a transformationproduct is detected with a sufficient intensity and thus selected or overlooked.Adduct formation sometimes prevents the elucidation of molecular formulas. Theidentification of TPs, however, in such a real world sample remains cumbersome.Without sufficient a priori information (such as the possible parent compound of aTP or the reaction conditions that causes its transformation) structure elucidationoften turns out to be impossible.

High resolution mass spectrometry provides information on the exactmolecular mass and, supported by the isotopic pattern, information on thepresence of certain elements. The preferred ionization mode and the likelihoodof adduct formation together with the fragmentation pattern supply informationon the absence or presence of functional groups. But if there are not enoughcharacteristic fragment ions such efforts do not lead to the final proposal of amolecular structure.

Additionally, structural isomers may not be distinguishable. For examplecarbamazepine epoxide and hydroxy-carbamazepine (both carbamazepine + O(m/z 253.0977)) lead to the same fragment ions (m/z 236.0712 (C15H10NO2);m/z 210.0919 (C14H12NO)) as these are stable ions. Even more worrying the TPtrans-dihydroxy-dihydro-carbamazepine experiences an in-source loss of waterthat leads to a primary ion of the same mass as carbamazepine plus O rather thanto a molecular ion. Only a careful search for adducts with sodium or potassiumwill prevent misinterpretation.

The relative intensities of fragment ions are not necessarily comparablebetween mass spectrometers of different vendors. This may limit the benefit ofmass spectral libraries like MassBank (4). If the sample amount is limited andthe analytes or their corresponding TPs become quite polar so that large volumeinjection is not possible, then the chance to detect characteristic fragment ionsdecreases.

Mass spectral databases that link parent compounds with known TPs wouldhelp. Such information will already guide the user to further TPs and dependingon whether or not the set of TPs is detected the tentative identification will bestrengthened or put into question. One attempt for such a database in the contextof water contaminants is the DAIOS database, which provides links from parent

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compounds to their expected TPs. However this database stores retention timesand selected fragment ions, but no full mass spectra (5, 6).

As far as microbial transformation is concerned one possibility is touse software-based approaches (EAWAG-BBD Pathway Prediction System(http://eawag-bbd.ethz.ch/predict/index.html)), which predict possible TPsbased on known transformation processes for structural moieties of the parentcompound. Another option are mass spectrometry vendor based-approaches likeUNIFI from Waters to generate possible molecular formulas and simply screenfor their exact masses. But the suspect screening for software predicted TPsusually results in a large number of false positive findings, especially when thecontrol sample is not adequate.

The problem of detecting and identifying TPs is especially challenging whensubstances are investigated that contain only the atoms C, H, N, O. In complexsamples like influents or effluents of wastewater treatment plants (WWTPs),mixtures of chemicals and their TPs are present and linking a TP to a certainparent compound is cumbersome due to the possibility of structural isomers. Oneexample is tramadol ([M+H]+, C16H26NO2, m/z 264.1958, MassBank Record:AU111702), that can occur in the effluent of a WWTP, but desvenlafaxine(MassBank Record: EA105303) with the same molecular formula can be presentas well. According to the mass spectral library MassBank, a water loss leads tothe same fragment ion (m/z 246.1856) for both compounds and there are onlyminor further fragment ions expected for tramadol and desvenlafaxin (4).

In such situations information on the retention time of TPs can be decisive. Adirect correlation between and a molecule’s polarity (expressed as log P or log D)and its retention time is not necessarily found. Moreover, the error in predictinglog P is usually too large to allow a useful retention time prediction. This iseven worse on the basis of log D prediction, as the prediction of the requiredpKa-values increases the error further (7). One example where this approach failedare structural isomers, as 2- and 3-hydroxy-carbamazepine. They have the samepredicted log D value (log D 2.4, pH 7.4, ChemAxon) but elute at very differentretention times, whereas the log D value of Oxcarbazepine (log D 1.87, pH 7.4)differs, but this compound nearly coelutes with 3-hydroxy-carbamazepine afterseparation on a reversed phase column (8, 9).

The final confirmation depends on the availability and the agreement ofthe mass spectrometric data and the retention time with reference compounds.Another option is to verify the structural proposal by NMR spectroscopy likeshown by Kaiser et al., but this often requires a preconcentration and clean-up asNMR needs elevated analyte concentrations and a high purity (10).

The Principle of Using Lab-Based Approaches

Because of these difficulties in detecting and identifying TPs directly fromLC-HRMS data of environmental samples, it can be very useful to combine itwith a simulation of transformation reactions in the laboratory. Data exploitationthen changes from a non-target screening to a suspect screening.

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The approach is illustrated in Figure 1. The lab experiment leads to a newclass of suspects, which we called here smart suspects. Their structure is notconfirmed, but by having the same retention time and the same fragment ions asthose in the environmental sample the initial information content is higher than for“normal” suspects; the origin (parent) is confirmed and the conditions of formationare known. For “normal” suspects only proposed structures and– depending onits origin (software-based or based on literature data) expected fragment ions areavailable.

Figure 1. Scheme of the identification approach by lab experiments.

The parent compound is subjected to one or more different transformationreactions and the reaction solution subsequently analyzed by LC-HRMS understandard conditions using all-ion fragmentation or data-dependent fragmentation.One option is to elucidate all formed TPs and to store the information in a librariesof tentatively identified TPs (4). Another option is to perform the tentativeidentification only for those peaks , that match with peaks in the environmentalsample, with respect to retention time, exact mass, associated adducts and isotopicpattern. By that the identification of possible peaks of interest in a real-worldsample is already shifted from a non-target or statistical approach to a suspectedapproach. This last approach is much less time consuming. Information on otherTPs is retained in the data set and can be utilized in future screenings for TPsof the respective parent compound. Structure elucidation is then best performedusing the fragment ion data of the lab sample, which are usually less noisy, and bysearching for possible isomers and comparing their fragmentation and retentiontimes.

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The lab experiment can be performed at elevated concentration, so that moreintense product ion spectra are obtained. Furthermore it is possible to developand validate enrichment or clean-up methods for the TPs in the sample of interest.This also allows to produce sufficient material of a TP for further elucidationby NMR and for quantification after certain clean up steps. Thus small peaksin real samples without enough information on fragment ions can be elucidated.Additionally by having a mixture of possible TPs of the compound of interest onecan optimize the LC-MS method, including the ionization mode and the additionof organic or inorganic modifiers. The influence of different chromatographicconditions on the retention time (pH, organic solvent, modifier, or column) mayhelp in further structure elucidation. If the sample amount is limited lab-basedexperiments help to generate TPs for further experiments. In one particularexample the chromatographic separation at two different pH-values (pH 3 andpH 5) and with different solvent (methanol or acteonitril) helped to elucidate thestructure of one of the TPs (11).

Fragment ion spectra do not only provide information on the structure of theparticular TPs. Rather, characteristic fragment ions found in the spectra may beused for a screening in the whole data set for yet undetected and structurally relatedTPs. With this approach one additional TP was detected from CBZ (11). Thisillustrates that laboratory experiments are very useful to support TP detection andidentification, even though they may not always provide us with the full set of TPsformed in the environment.

Environmentally Relevant Processes

Environmentally relevant processes are reduction, oxidation and hydrolysis,which may happen abiotically or by biotic catalysis, as well as direct and indirectphotolysis. Additionally conjugation processes may occur in higher organisms,that may further mask possible TPs. Laboratory methods to simulate thesetransformations should be fast and performed in a reproducible manner, so thatthey are largely independent from the laboratory in which they are performed.Figure 2 provides an overview on the environmental transformation processesand suitable lab methods to simulate them.

In the environment the above-mentioned processes may not necessarilyhappen individually. Rather one initial transformation may lead to more reactivespecies that are then prone to further transformations. One advantage of lab-basedmethods in general is that it is easy to perform an additional experiment, toprolong the reaction times or to select harsher reaction conditions to generateadditional knowledge on the stability of TPs or to form subsequent TPs.

Hydrolysis

Hydrolysis is one key reaction in the environment as water is a ubiquitousreactant. It often leads to smaller and more polar TPs, which preferably enter theaqueous phase and, if stable, remain in the water cycle. Otherwise this process

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frequently serves as an initial step to enable consecutive reactions. There aredifferent functional groups that are prone to hydrolysis like carboxylic acid estersand amides, their cyclic analogs lactons and lactams, thioesters, carbamates,ureas and sulfonylureas, thio- and dithiocarbamates, halogenated aliphatichydrocarbons, epoxides, phosphates, phosphonates as well as thiophosphates,imides and sulfonamides. Hydrolysis itself is mainly affected by pH andtemperature, but often only the kinetics are studied and less attention is given tothe pH-dependence of the hydrolytic degradation pathways and the hydrolysisproducts. If only one hydrolysable group is present the hydrolysis product iseasily predicted. If more than one such group is present in a parent compoundor if there is the possibility of follow-up reactions, e.g. to form cyclic structuresthat are more stable - these pathways may lead to structurally diverse hydrolysisproducts that are not predictable by software-based approaches. As was shownby Hirte et al. for amoxicillin, a simple experimental setup with three differentbuffer systems (pH 3, pH 7 and pH 11) led to a detailed understanding whatmay happen (12). Amoxicillin (AMX) is a widespread β-lactam-antibiotic and,together with some of its TPs (AMX penicilloic acid, AMX penilloic acid,AMX 2’,5’-diketopiperazine, and 3-(4-hydroxyphenyl)pyrazinol), a knownenvironmental contaminant.

Figure 2. Overview of the possible processes in the environment and lab-basedmethods to simulate them.

A comparison of lab samples with samples from WWTPs confirmed thatAMX penicilloic acid was the major TP in the WWTP influent compared tothe other three TPs in the effluent. Additional matches were not detected in the

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effluent and related surface water samples. But the lab-based experiment resultedin additional information on the stability of these detected TPs in the environment.The first three TPs of the above mentioned were not stable but transformed into23 yet unknown hydrolysis products within three to four weeks. Consequently thelarge number and slow formation kinetics which will lead to comparatively lowenvironmental concentrations of these later TPs of AMX may explain why theywere not detected in the environment so far, even after applying an adapted SPEmethod to screen for them. This example illustrates the importance of severalaspects – the information about possible peaks (with retention times, exact massesand expected fragment ions) that are connected to a compound and additionallyabout the variability of TPs and their stability that may be investigated by usingthe lab-based approach. This provides information on whether TPs formed in thelab experiment may be expected to occur in the environment.

Oxidation

The most important degradation pathways of xenobiotics in the environment,such as biodegradation or photolysis involve redox reactions. Oxidation reactionsmay also proceed abiotically, catalyzed by metal or metaloxides, for examplewith Fe(II) or Mn(III/IV) oxide present in suspended particles in surface water orin soils. Electrochemistry (EC) off-line or on-line coupled to mass spectrometryis a well-accepted method to study phase I and phase II metabolism of drugs(13). It has been shown in many publications that EC is a powerful tool asit is able to simulate a large number of transformation reactions, such as thehydroxylation of activated aromatics, benzylic hydroxylation, N-dealkylationof amines, dealkylation of ethers and thioethers, S-and P-oxidation, oxidationof alcohols to aldehydes and dehydration (14). Transformation by EC has beencompared to liver microsom for several pharmaceuticals and other substances(15). One advantage of EC transformation is that it proceeds without the additionof any reagents. Therefore, the reaction solutions can be analyzed directly withoutany need for clean-up, contrary to when liver homogenate was used to simulatebiotransformation by higher organisms. Such clean-ups may lead to the loss ofTPs, which then remain un-recognized. Often, however, the diversity of TPsformed by EC is larger than that found with microsomes. These surplus TPs canbe useful, too, as they may help elucidate other transformation processes takingplace in WWTPs or upon ozonation (16).

EC is equally helpful for the determination and identification of TPs generatedby microbial processes. Correspondingly, EC has been used to support screeningfor TPs of the recalcitrant pharmaceutical carbamazepine (CBZ) formed by thewhite-rot fungusPleurotus ostreatus (16). ECwith LC-HRMS facilitates detectionand identification of TPs since the TP mixture is not superimposed by biogenicmetabolites and elevated substrate concentrations can be used. Ten TPs formed inthe microbial process were detected by comparing the data (m/z versus retentiontime) of the fungally treated CBZ with the EC treated CBZ solution (16).

In such cases it is not recommended to elucidate every peak in theelectrochemically generated mixture. Rather only those signals have to be studiedclosely that occur also in the environmental sample under investigation. However

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the information on additional TPs generated by EC supports the structureelucidation.

Furthermore, transformation pathways are best elucidated by applying EC tointermediate TPs and follow their transformation (17). While in biological studies,this requires a complete set of new exposure experiments, which may last for daysto weeks, EC oxidation to examine a certain pathway is performed in minutes. Inthe presented example this was proven for the oxidation of acridine to acridone(11).

Instead of EC, other oxidizing agents such as the fenton reaction, KMnO4,ferrate (Fe(VI)) or MnO2 may be used to study the oxidation of anthropogenicsubstances in the lab (18–21). However, the advantage of the electrochemicalcell is its variability in terms of the applied oxidation potential and that it is apure reagentless method, which may be coupled online with mass spectrometry.As shown by van Leeuwen et al. the observation of short-lived intermediatesis possible that may help to elucidate follow-up reactions by understanding thetransformation pathways (22). Furthermore by using different redox potentialsthe product spectra and more important the relative intensities of the TPs areinfluenced without requiring longer reaction times. Another parameter that maybe varied is the working electrode. It is known that a boron-doped-diamondelectrode (BDD) and the glassy carbon electrode are leading to different TPs, dueto the possibility of the BDD electrode to generate hydroxyl radicals.

Reduction

Reductive transformation processes can be of importance in sediments as wellas in the subsurface.

To simulate reductive transformation either catalytic hydrogenation (Pt/H2)or reduction with an electrochemical cell may be used. However, apart fromdehalogenation of iodinated analytes and reductive metabolism of nitro aromaticxenobiotics there are no examples in literature, where the electrochemical cell hasbeen used to simulate reductive transformation (23, 24). This may be due to thefact that the reductive potentials (down to -3 V for a BDD electrode and down to-2 V for a glassy carbon electrode, both measured against Pd/H2) are insufficientto dehalogenate chlorinated compounds.

Recently the BDD electrode was used to transform brominated flameretardants. Tetrabromobisphenol A (TBBPA) showed a single, a twofold andthreefold debromination. However the direct comparison with anaerobic bacteriathat yield bisphenol A as a final product failed, because two TPs were missing(see Figure 3) (25). Moreover, a cleavage product (dihydroxydibromo benzene)formed by electrochemical conversion was not formed by the strictly anaerobicbacteria.

The catalytic hydrogenation (Pt/H2) is a clean system allowing theidentification of TPs formed under strongly reducing conditions and thus showsextreme conditions. A surface reaction takes place, where the H–H-bond of thesorbed H2 molecules is weakened and a syn-addition to C–C double bonds takesplace. By this approach König et al. formed a total of nine reduction products ofCBZ, where an increasing degree of hydrogenation correlated with an increase

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in the retention time (26). The presence of two independent reduction pathwayswas proven by separate experiments with 10,11-dihydro-CBZ ((2H)-CBZ) as aprecursor. (2H)-CBZ could not be further reduced, indicating that the stepwisereduction of CBZ to the fully saturated alicyclic (16H)-CBZ proceeds alonganother pathway. However, in natural samples from bank filtration sites noreduction product except 10,11-dihydro-CBZ ((2H)-CBZ) could be detected sofar (26).

Figure 3. Comparison of the mass chromatograms of the reductivedehalogenation of Tetrabromobisphenol A by strictly anaerobic bacteria (bottom)

and the products derived by an reduction in a electrochemical cell (top).

Combined Reduction/Oxidation

As reductive and oxidative zones may be found in close proximity to eachother in the subsurface, it can be of interest to study the combination of reductiveand oxidative transformation also in the lab. For example, in our lab a solutionof CBZ was reduced to form (2H)-CBZ and subsequently oxidized by theelectrochemical cell at a redox potential of 1.5 V (measured against Pd/H2). Anoxidative transformation of (2H)-CBZ along a pathway quite different from thatknown for CBZ was observed. All TPs of (2H)-CBZ had lost the carbamoylmoiety. As a first step hydroxylation of one of the two aromatic rings occurredprobably in para-position to the nitrogen, which was proven by the presence of

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the corresponding quinone imine. This quinone imine is suspected to be quitereactive (Michael acceptor), which probably prevents their observation in theenvironment. In this case the lab experiment did not lead to an identificationof TPs found in the environment but provided an explanation why no furtherreaction products of (2H)-CBZ have yet been found in the environment. It alsoexplains why a removal of dihydro-CBZ is observed in WWTPs but so far no TPswere detected that correlated with this removal.

Photolysis

For compounds that are not biodegradable, photolysis is one reaction that maybe important to initiate degradation in surface waters. Photolysis reactions aredivided into direct and indirect photolysis. In the latter process singulet oxygen(1O2), hydroxyl radicals (.OH), peroxy radicals (.OOR) or photo-excited organicmatter can be involved. For the investigation of possible photolysis products it isextremely important to use a light source that adequately simulates the sunlightspectrum in terms of intensities and wavelengths. Otherwise quite different TPsmay be formed (27).

Thus a xenon-lamp is the best choice. Commercially available are the suntest-XPS and theQSun test chamber as a test-box that additionally keep the temperaturestable during photolysis and enable the easy implementation of filters to simulatethe wavelength distribution of natural sunlight. As proven by Sevilla-Moran etal. even the kinetic parameters obtained with this setup are in good comparisonto degradation by sunlight thus the same is valid for the product distribution (28).With slight modification, even direct coupling to an LC-HRMS is possible (29).

Furthermore, the pH during photolysis may influence the product distributionand thus the comparability with environmental samples. As shown for lamotrigine(pKa 5.7) the photolysis of a solution at pH 3 and pH 7 results in different productdistribution (30). Dechlorination without hydroxylation (TP220, see Figure 4)occurs only if the neutral species is present.

Additives can influence the product distribution and help to elucidate thereaction mechanism of the photolysis: acetone acts as a triplet photosensitizer,non-polar solvents like acetonitrile favour mechanisms involving radicalintermediates, isopropanol or bicarbonate are radical scavengers, nitrate enhancesthe involvement of OH radicals, degasing by nitrogen avoids the involvementof oxygen species (either as 1O2 or 3O2), while addition of sodium azide canverify the involvement of 1O2 as it generates 1O2 (31–33). The knowledgeon the formation pathway may support the elaboration of structure proposals.One such example is shown in Figure 4. Three lamotrigine phototransformationproducts are selected – two are formed by a radical driven mechanism and oneis considerably increased by the addition of the radical scavenger isopropanol.Thus the dechlorination leading to TP220 and the isomerisation leading to TP256take place via a radical intermediate, whereas it was proven that no radicalhydroxylation leads to TP259. Together with the fragment information the threestructures were proposed.

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Figure 4. Selected phototransformation products of lamotrigine and the influenceof the solvent on their formation that helps to elucidate their structure.

The stability of primary TPs against further redox reactions and, thus, theirstability in the environment can be tested by using the initial reaction mixture fora subsequent oxidation or reduction experiment. In Figure 5 this is shown forthe three photolysis products of lamotrigine (structurs shown in Figure 4) at ECpotentials of +2 V (oxidation) and -2 V (reduction). In this case TP220 was provento be sensitive to oxidation.

Figure 5. Stability test of TPs of the photolysis of lamotrigine, by oxidation andreduction in the electrochemical cell.

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Furthermore the investigation of derivatives or TPs together with the parentcompound helps to elucidate structures. An illustrative example is shown in Figure6, where the photolysis of carbamazepinewas investigated. Two peaks (tR=4.9minand tR=5.2 min) are observed in the extracted ion chromatogram for m/z 251.081.The sum formula leads to three different possible structures. One of them is BQM,an ozonation product of CBZ. Its structure was already proven by NMR (34).

By comparing the retention time, peak shape and fragment ions with aozonated sample of CBZ the identity of the peak with tR =5.2 min in thephotolysis sample of CBZ was proven to be BQM (structure b in Figure 6).Possible structures for the second TP with m/z 251 (tR 4.97 min) are structure aand c shown in Figure 6.

An additional photolysis experiment with Oxcarbazepine (Ox-CBZ) in whichthe same TP at tR = 4.9 min was formed rules out structure c). Therefore this peakat 4.9 min in Figure 6 should correspond to structure a).

Figure 6. Extracted ion chromatogram of m/z 251.081 for the photolysis of CBZ,Ox-CBZ and ozonolysis of CBZ, the possible structures of TP251(a,b,c) and the

observed fragment spectra are included.

Biodegradation Batch Experiments

Simulation of biodegradation under well-defined conditions in a batchexperiment at elevated concentrations of the analyte of interest is an additionalapproach that is often applied (3, 6, 35). In principle any microbial community(fungi or bacteria) including mixed cultures (e.g. sewage sludge) may be used. Tocheck for the biodegradability there are three OECD tests available: The closedbottle test (CBT, OECD 301 D), which simulates surface water, the manometricrespirometry test (MRT, OECD 301 F) and the Zahn-Wellens test (ZWT, OECD302 B) that simulates biodegradation in wastewater treatment (3). These tests arewidely used and also applied to investigate the formation of TPs, e.g. for different

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pharmaceuticals or benzotriazoles (6, 36, 37). Each experiment consisted ofactive bioreactors and several controls. However, such experiment require daysto weeks. Spiked and non-spiked samples are run in parallel and analyzed byLC-HRMS to determine peaks of interest.

Using time-resolved measurements, the increasing concentration of theformed TPs helps to elucidate peaks of interest and possible pathways and to closethe mass balance like shown for lamotrigine (38). Furthermore a combinationwith sand-filled columns is possible to simulate in the lab what happens in bankfiltration sites (39, 40). As presented above for photolysis, the experiments maybe performed with different commercially available TPs to prove degradationpathways. Brezina et al. showed that the degradability of 2-OH-carbamazepineand 3-OH-carbamazepine on sand filters differed strongly (39) An anaerobicbatch degradation test led to interesting TPs of carbamazepine by reduction,which had not been reported before (26). The molecular formula (C16H19N2)and the fragment ions suggest that here the carbamoyl moiety of (2H)-CBZ, theprimary reduction product, was further reduced to an amine and then methylated.This example illustrates that such an experiment even covers follow up reactions.

However, all these experiments, when analyzed by LC-HRMS, lead torelatively complex data sets. As not only the concentration of the selected parentcompound and its TPs but also of secondary metabolites produced by the bacterialcommunity may change over time, the selection of signals of interest in suchdata sets is intricate. This is especially the case if the parent compound does notexhibit a unique isotopic pattern or mass defect. Consequently, exploitation ofsuch data sets takes considerably more time than required for the less complexand cleaner chemical transformations mentioned above. Moreover, as for theother approaches the outcome has to be proven in the real environment.

In-Vitro and in-Vivo Methods to Simulate Phase II Metabolism

Phase-II metabolites are of interest, when humans, animals or plantsare involved. Conventional methods to study the metabolism and preparemixtures of potentially formed TPs are in-vivo methods using animals likerats. Animal testing, however, has to be reduced and replaced. Liver-basedin-vitro technologies are often used in such instances. However, in human livermicrosomes enzymes like N-acetyltransferase (NAT), Glutathione-S-transferase(GST) and cytosolic cofactors are missing. This limits the metabolic reactionsand reduces the ‘metabolic competence’ (41). Consequently the number of TPsis limited, which is undesirable when libraries of possible TPs are the aim of theinvestigation.

As an alternative the zebrafish embryo (ZFE) with its recently elucidatedextensive metabolism may be used for metabolism studies (42). The ZFE is small,develops fast and it is not considered an animal test until 96 hours post fertilization(TG OECD 236). The ZFE is highly metabolically active already in early lifestages, i.e. within the first four days of development. A broad variety of phaseI and phase II TPs have been shown to be formed, which corresponds to geneexpression studies (43). The detected TPs are potentially formed by humans aswell and may, therefore, also be found in wastewater. In case of clofibric acid it

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was shown that compared to other animals additional TPs were formed by the ZFE(42). Their cyclic core structure suggests formation from di-hydroxy clofibric acidvia decarboxylation. This TP, however, was not found in environmental samplesso far.

In the case of metoprolol the comparison of the TPs of ZFE and theinfluent and effluent of a WWTP led to the identification of an additional TP–ketometoprolol. This TP is not known from humans or other animals, buthas been detected before as a transformation product produced in WWTP. Asketometropol is not available as a reference compound the ZFE offers a way to“produce” it in the lab and, also, to verify its formation from metoprolol as parentcompound.

Another important potential of the ZFE is its possibility to perform phase IIreactions as outlined by Brox et al. (42). An illustrative example to show thetransferability of phase II metabolism from one species to another is shown inFigure 7. An acetylated carbamazepine was detected in cucumber by comparisonwith the ZFE extract.

Figure 7. TP252 of CBZ detected in ZFE and a cucumber extract by comparison,the signal intensity of the fragment spectra allowed the structure elucidation

only for the ZFE extract.

As far as metabolism in plants is concerned, plant cell experiments may beused instead of whole plants, as shown by Macherius et al. (44). Such plant cellscan show the same TP pattern as real plants but the establishment of plant specificcell cultures can be cumbersome.

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Conclusion

The selected examples show that lab experiments combined with LC-HRMSanalyses using all ion fragmentation can be a very useful combination to detect andto identify transformation products in environment samples. The lab experimentsare generally easy to perform. Examples for hydrolysis, oxidation, reduction,photolysis and conjugation, as well as combined processes were provided forillustration.

Thre are two options how to process and to utilize the LC-HRMS data of thelab experiments: either all possible TPs are processed, their structure elucidatedas far as possible and the data with structure proposals stored in a lab-based oropen-source mass spectral database for (later) use. Or the whole data set is storedbut only those signals are actually processed that are also found in the sample ofinterest. The latter approach saves time but keeps all data for a later comparisonwith other samples. Therefore it is essential that standard conditions for the LC-HRMS analysis of the lab experiments and the environmental samples have beenestablished in a lab.

Then, the lab experiments are a powerful tool and source of LC-HRMS datato detect and to tentatively identify transformation products in environmentalsamples, even when reference standards are not (commercially) available.Furthermore such lab experiments can clarify whether TPs are really expected tooccur in the environment or whether follow up reactions likely take place.

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33. Duran-Alvarez, J. C.; Prado, B.; Gonzalez, D.; Sanchez, Y.; Jimenez-Cisneros, B. Environmental fate of naproxen, carbamazepine and triclosanin wastewater, surface water and wastewater irrigated soil - Results oflaboratory scale experiments. Sci. Total Environ. 2015, 538, 350–62.

34. McDowell, D. C.; Huber, M. M.; Wagner, M.; Von Gunten, U.; Ternes, T.A. Ozonation of carbamazepine in drinking water: Identification and kineticstudy of major oxidation products. Environ. Sci. Technol. 2005, 39,8014–8022.

35. Huntscha, S.; Hofstetter, T. B.; Schymanski, E. L.; Spahr, S.; Hollender, J.Biotransformation of benzotriazoles, insights from transformation productidentification and compound-specific isotope analysis. Environ. Sci.Technol. 2014, 48, 4435–43.

36. Quintana, J. B.; Weiss, S.; Reemtsma, T. Pathways and metabolites ofmicrobial degradation of selected acidic pharmaceutical and their occurrencein municipal wastewater treated by a membrane bioreactor. Water Res.2005, 39, 2654–64.

37. Huntscha, S.; Hofstetter, T. B.; Schymanski, E. L.; Spahr, S.; Hollender, J.Biotransformation of benzotriazoles: insights from transformation productidentification and compound-specific isotope analysis. Environ. Sci.Technol. 2014, 48, 4435–43.

38. Zonja, B.; Perez, S.; Barcelo, D. Human Metabolite Lamotrigine-N(2)-glucuronide Is the Principal Source of Lamotrigine-Derived Compounds inWastewater Treatment Plants and Surface Water. Environ. Sci. Technol.2016, 50, 154–64.

39. Brezina, E.; Prasse, C.; Wagner, M.; Ternes, T. A. Why Small DifferencesMatter: Elucidation of the Mechanisms Underlying the Transformationof 2OH- and 3OH-Carbamazepine in Contact with Sand Filter Material.Environ. Sci. Technol. 2015, 49, 10449–56.

40. Baumgarten, B.; Jahrig, J.; Reemtsma, T.; Jekel, M. Long term laboratorycolumn experiments to simulate bank filtration: factors controlling removalof sulfamethoxazole. Water Res 2011, 45, 211–20.

41. Fasinu, P.; J. Bouic, P.; Rosenkranz, B. Liver-Based In Vitro Technologiesfor Drug Biotransformation Studies - A Review. Curr. Drug. Metab. 2012,13, 215–224.

42. Brox, S.; Seiwert;, B.; Haase;, N.; Küster;, E.; Reemtsma, T. Metabolismof clofibric acid in zebrafish embryos (Danio rerio) as determined byliquid chromatography–high resolution–mass spectrometry. Comp BiochemPhysiol C Toxicol Pharmacol 2016, 185–186, 20–28.

43. Zebrafish Model Organism Database (ZFIN); University of Oregon, Eugene,OR 97403-5274; http://zfin.org/, (accessed 03.06.2016).

44. Macherius, A.; Seiwert, B.; Schroder, P.; Huber, C.; Lorenz, W.;Reemtsma, T. Identification of Plant Metabolites of EnvironmentalContaminants by UPLC-QToF-MS: The in Vitro Metabolism of Triclosan inHorseradish. J. Agric. Food Chem. 2014, 62, 1001–1009.

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Chapter 6

New (Practical) Strategies in Target, Suspects,and Non-Target LC-MS(/MS) Screening:Bisoprolol and Transformation Products

as an Example

Thomas Letzel,1 Sylvia Grosse,1 Wolfgang Schulz,2 Thomas Lucke,2Angela Kolb,3 Manfred Sengl,*,3 and Marion Letzel3

1Chair of Urban Water Systems Engineering, Technical University ofMunich, Am Coulombwall 3, D-85748 Garching, Germany

2Zweckverband Landeswasserversorgung, Laboratory for OperationControl and Research, Am Spitzigen Berg 1, 89129 Langenau, Germany

3Bavarian Environment Agency, Bürgermeister-Ulrich-Str. 160,86179 Augsburg, Germany


Various chemicals of emerging concern (CEC), likepharmaceuticals, their human metabolites and furthertransformation products (TPs) enter wastewater treatmentplants on a daily basis. A mixture of known, expected aswell as unknown molecules are discharged into the aquaticenvironment since only partial elimination takes place for manyof these chemicals during physical, biological and chemicaltreatment processes. In this study, an array of LC-MS methodsfrom three collaborating laboratories was applied to detect andidentify the beta blocker bisoprolol and its TPs in differentwater samples. Starting with theoretical predictions of TPs,an efficient workflow using the combination of suspects andnon-target strategies has been developed for the identificationof these TPs in a lab-scale wastewater treatment plant and soilcolumns. A screening workflow including an inter laboratoryapproach was used for the identification of transformationproducts in the effluent samples. Subsequently, newly identifiedcompounds were successfully analyzed in effluents of real


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wastewater treatment plants and river waters. The new TPswere included in target analysis and continuously quantified insampling campaigns of routine monitoring.

Introduction

Various chemicals of emerging concern (CEC), including pharmaceuticals,personal care products, household chemicals, as well as transformation products(TPs) from natural (e.g., wetlands, managed aquifer recharge) and engineeredwater treatment processes (e.g., activated sludge systems; biofiltration; ozonation;UV/AOP) have been found in the aquatic environment (1–8). CEC often enterwastewater due to their extensive industrial and domestic usage. The total removalof these substances in not achieved during conventional biological wastewatertreatment and, hence, a part of the compounds as well as their TPs are presentin receiving surface waters (3, 9–11) and groundwater (12–15).

As a result of demographic change and an increasing number of new drugsentering the market, the overall consumption of pharmaceuticals is increasing(16). In ageing societies it can be expected that an increasing number ofantihypertonic drugs is being used. This is also true for beta blockers. Bisoprololand Metoprolol for example are belonging to the group of beta blockers, a classof drugs used primarily in cardiovascular diseases. Between 2002 and 2009,for instance, the consumption of bisoprolol increased by more than 140% inGermany, reaching an average of 8 tons per year (16). Concentrations up to 1.4µg L-1 were reported to occur in wastewater effluents (17). Bisoprolol appears toremove incompletely during wastewater treatment with mean removal efficienciesof 25-40% (18–20). It was rather quickly removed in water-sediment systems,with a 50% decrease in 9-28 days (21). In contrast to many other pharmaceuticals,no data was published for bisoprolol TPs in the aquatic environment so far. Inthe past, biotransformation of bisoprolol was studied and reported in degradationexperiments with the pure substance using eighteen filamentous funghi and sixactinomycetes species (22). Therein they described the O-dealkylated metabolite(M4) for eight Cunninghamella strains and for Gliocladium deliquescens. Amongall strains tested, only Gliocladiurn deliquescens performed an oxidation of M4 tothe corresponding acid (M1), which is also known as the main human metabolite(23). The metabolites named M2 (oxidation of the terminal methyl group) andM3 (ether cleavage followed by oxidation) detected in humans or animals werenot observed with microorganisms and funghi (22). All metabolites were formedby a metabolic attack on the 6-methyl-2,5-dioxaheptyl side-chain whereas theisopropylaminopropoxy side-chain remained stable.

TPs have known as well as unknown chemical structures, physico-chemicalproperties, and effects on aquatic organisms. In some cases, they can be evenmore persistent (24) and toxic (25, 26) than their parent compounds. Until now,the knowledge of TPs occurring in the aquatic environment and their potentialeffects to organisms has been unsatisfactory (27, 28). A pre-identificationapproach via suspects screening and hidden target screening using lists or

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databases filled with expected as well as theoretically predicted substances canassist in identifying potentially relevant compounds. These can be “home-made”databases, public compound libraries (like STOFF-IDENT (29) and DAIOS (30))or comprehensive chemical databases like ChemSpider (31) and Chemicalize(32). In these databases, the chemical formula for the calculation of monoisotopicmasses and the isotopic pattern is usually available for compound suggestionsalong with other physico-chemical properties.

Transformation products sometimes are not included in monitoring programs.For this reason, qualitative and quantitative results from environmental samplesrarely can be found. In such cases screening techniques like LC-MS/MS becomeinteresting tools, e.g. for the detection and the identification of hidden targets (e.g.sartans and TPs) (33) and classifying them by using the knowledge level (34, 35).

The aim of this study was to prove the applicability of an efficient strategy forthe suspects and hidden target LC-MS screening of water samples by collaboratinglaboratories originally using their different workflows. A concept is describedfor the identification of TPs from the theoretical prediction of the transformationpathways to the final (target) analysis of water from different resources usingsynthesized reference materials. Bisoprolol serves as an example for studyingwidely used pharmaceuticals.

Materials and MethodsChemicals and Materials

Formic acid (purity >98%), ammonium acetate (purity >98%), andhyper-grade methanol for HPLC-MS analysis were purchased from Merck(Darmstadt, Germany) or Sigma-Aldrich (Seelze, Germany), sodium azide wasfrom Merck (Darmstadt, Germany). HPLC water was prepared from deionizedwater using a Millipore Milli-Q system (Billerica, MA, USA) or bought fromFluka (Buchs, Switzerland). Acetonitrile HiPerSolv Chromanorm was purchasedfrom BDH (Poole, UK). Bisoprolol fumarate (CAS 104344-23-2) was purchasedfrom Chemos (Regenstauf, Germany). The TPs bisoprolol M1 and bisoprololM3 were synthesized by aromaLAB AG (Planegg, Germany) (general remark: inthis study the names of bisoprolol TPs are used according to Schwartz et al. (22),other bisoprolol TPs are named as BIS_molecular mass, e.g. BIS_295; see alsoTable 1).

Lab-Scale Wastewater Treatment Plants

The biodegradation of bisoprolol (LogD value 0 at pH 7.8, i.e. the pH valuein the LWTP) was investigated in continuously operating lab-scale wastewatertreatment plants (LWTP); for detailed information, see Letzel et al. (36).Bisoprolol was continuously dosed in the LWTPs for 52 days at concentrationsof 10 µg L-1 and 40 µg L-1, respectively. A control plant (without dosing targetchemicals) was operated in parallel. Influent and effluent samples were taken

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weekly for the quantification of the parent compound. The effluent sample of day50 was collected in order to analyze TPs formed during the biological wastewatertreatment process using LC-MS system 1 (see below 2.5). All samples weretransported and stored at 4°C until analysis. Centrifugation was done in eachlaboratory before analysis.

In order to validate the TPs detected with LC-MS system 1 a second LWTP-experiment was performed with a bisoprolol dose of 1 mg L-1 taking into accountthe lower sensitivity of LC-MS system 2. The effluent samples of days 28 and 31were analyzed with LC-MS system 2 (see below 2.5).

Soil Columns

Soil column experiments were designed and performed according to DIN19528. Glass columns (length 30 cm, inner diameter 5.9 cm) were filled withsediments and water from well-characterized aquifers. The soil materials weretaken from sites with differing redox conditions (aerobic and anaerobic) to checkthe influence of oxygen on the elimination of CEC. Aerobic material was usedwith oxygen rich groundwater (O2 = 9.7 mg L-1) and anaerobic material wastreated with anaerobic groundwater (O2 < 0.5 mg L-1) taken from a samplingsite nearby. Spiked groundwater (bisoprolol concentrations 50 µg L-1) wasrecirculated (deviating in that point from DIN 19528 which foresees flow-throughexperiments) for 50 days at a flow rate of 0.2 mL min-1 in order to simulate alonger duration of bank filtration. Two identical columns were used for eachexperiment. Two further columns filled with quartz sand represent a control.To one of these reference columns 1 g L-1 sodium azide was added to suppressmicrobial activity.

Wastewater and Surface Water Samples

Grab samples for bisoprolol analysis from four full-scale wastewatertreatment plants (WWTP) effluents (WWTP-1: 1,300,000 population equivalents(PE); WWTP-2: 75,000 PE; WWTP-3: 250,000 PE; WWTP-4: 125,000 PE)were taken from April 2014 to December 2014. Grab samples were taken fromthe rivers Ebrach (a small river of 23 km length, east of Munich, highly influencedby WWTP effluent discharges), Fränkische Rezat (with a remarkable influenceof WWTP effluent), Würm, Amper, Main, Isar, Loisach and Danube (south ofRegensburg) - all in Bavaria, Southern Germany - between January and December2014. All samples were stored at 4°C until analysis. Centrifugation was done ineach laboratory before analysis.

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Analytical LC-MS Systems

a. RPLC-ESI-QqTOF-MS Analysis (LC-MS System 1)

Non-target screening (mainly ‘Hidden Target’ and also ‘Unknown Target’see Figure in the Appendix) and suspects screening with LC-MS system1 were carried out for TPs formed during lab-scale wastewater treatmentby reversed-phase liquid chromatography (RPLC) coupled to a quadrupoletime-of-flight mass spectrometer (QqTOF-MS: TripleTOF 5600, Sciex, FosterCity, CA) via electrospray ionization (ESI) in positive and negative ionizationmode. For details of the analytical methods see Letzel et al. (33). UnknownTargets were characterized by retention time, empirical formula and MS/MS data.Later was compared with similarity search techniques. This data is not shown inthe presented publication due to clearness reasons.

b. RPLC-HILIC-ESI-TOF-MS Analysis (LC-MS System 2)

The screening results of LC-MS system 1 (non-target and suspects data) werecompared with an analytical LC-MS system 2 containing two Agilent HPLCsystems series 1260 Infinity (Waldbronn, Germany). This system was coupledwith a time of flight-mass spectrometer equipped using a Jet Stream ESI interface(Agilent Technologies, Santa Clara, CA, USA). The samples were analyzed inextended resolution mode with a mass range (50 -1700 m/z) in full scan mode.Further information regarding the chromatographic details is given in Greco et al.(37) and Rajab et al. (38).

c. RPLC-ESI-QqQ-MS Analysis (LC-MS System 3)

Suspects screening and target analysis with LC-MS system 3 were performedwith reversed phase liquid chromatography coupled to a triple-quadrupole-likesystem QTrap 4000 (Sciex, Foster City, CA, USA). External calibration was usedfor the quantification of bisoprolol and the TPs M1 as well as M3.

d. Combined Use of LC-MS Systems

Initially, the effluents of the LWTPs were screened applying two similaranalytical systems, i.e. reversed phase liquid chromatography (RPLC) coupledwith time-of-flight mass spectrometry (ToF-MS). Both analytical systems usedC18-modified silica as stationary phase in RPLC connected with an accuratehigh resolution mass spectrometer. LC-MS system 1 had the option to performfragmentation by tandem mass spectrometry (i.e. QqToF-MS) leading tostructural molecule information (39) and in addition LC-MS system 2 had theoption to retard and separate molecules from an extended polarity range (i.e.using a combination of hydrophilic interaction liquid chromatography (HILIC)

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with RPLC) (37, 38). Each detected molecule was defined by its accurate massand normalized retention times (see (29)). Typically, molecules were furtherconsidered if they showed a comparable chromatographic behavior within bothsystems and the same empirical formula. The mass spectrometer scanned amass range of 50-1000 Da (LC-MS system 1) and 50-1700 (LC-MS system 2),respectively, thus the monitoring strategy can be called ‘non-target screening’.

With LC-MS system 3 (the triple-quadrupole-like system) analyticalmeasurements were performed in MRM (multiple reaction monitoring) moderesulting in ‘suspects screening’ and ‘target analysis’ (Table 1). This techniqueincludes a sensitive single molecule detection strategy. Currently this setup is themost sensitive detection method for molecules using mass spectrometry. One hasto keep in mind that system 1 and 3 have the same ‘collision induced dissociation(CID) cell’ from the same vendor (i.e. a very well comparable fragmentationpattern). Thus the fragmentation values and properties can easily be transferredfrom system 1 to system 3 and there be used for sensitive MRM measurements.

Theoretical Predictions of Transformation Products

The Biodegradation Database Pathway Prediction System of the University ofMinnesota (UM-PPS, now exclusively provided from EAWAG as EAWAG-BBDPathway Prediction System) (40) has been previously used for prediction of TPsin several studies (33, 41–45). In this study, predictions were limited to four levelsof transformation. Thus a list of 62 predicted TPs was generated by the programfor bisoprolol (UM-PPS-list).

The SMILES string (i.e. the output from UM-PPS) of each predicted TP wasused for further calculations using EPI SuiteTM v4.10 (46). This tool processesmolecular formula and logP values to assess ‘primary degradation’ (Biowin4) and‘ready biodegradability’ (modeledwith Biowin3 and 5). Themonoisotopicmassesof predicted TPs were calculated with MolE-Molecular Mass Calculator v2.02(47).

Consequently, the predicted transformation products fromUM-PPSwere usedas a hidden target / suspects list to detect possible TPs from LC-MS analysis ofLWTP and soil column samples.

Results and DiscussionTransformation Products of Bisoprolol Degradation in LWTP

First of all, the biodegradation of bisoprolol was investigated in continuouslyoperating lab-scale wastewater treatment plants (LWTP). The eliminationefficiency for bisoprolol in LWTP showed an average value of 30% (data notshown, see final report of the project RISK-IDENT) (48) which is consistentwith data from literature (18–20). Transformation products present in LWTPeffluents were analyzed and identified using various screening techniques incomplementary laboratories (or equipment) as recently described in detail (33).

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a. Transformation Products Known by Literature

Besides the bisoprolol biotransformation products M1, M2, M3, and M4reported by Schwartz et al. (22) no additional TPs were described in literature sofar.

b. Transformation Products Known by Databases

A search for bisoprolol TPs in environmental samples without the knowledgeof chemical structures in 2012/13 was not successful using general chemicaldatabases like ChemSpider (31) or Chemicalize (32). Even in specializedcompound databases for water-relevant substances like STOFF-IDENT (29) orDAIOS (30), where searchable hints for TPs are included, bisoprolol TPs werenot found at that time. In the meantime - using the results of this study - theauthors included the relevant bisoprolol TPs into the databases STOFF-IDENT(29) and DAIOS (30) for a broader dissemination. By the way, since 2012 thedatabase STOFF-IDENT (29) is regularly updated by the authors with organictrace compounds and newly identified TPs found in the aqueous environment(also from external information sources) and since 2009 DAIOS (30) is regularlybe updated by the authors with newly identified compounds found in the aqueousenvironment and technical and metabolic degradation products.

c. Expected TPs by Predictions via UM-PPS

The number of predicted TPs from the UM-PPS (in 2013) was reduced in thisstudy to the most expected occurring TPs, allowing “very likely”, “likely” and“neutral” transformations, similar to Howard and Muir (49). In total 62 TPs couldbe predicted for bisoprolol. The already known metabolites M1, M3 and M4 werealso predicted by UM-PPS whereas M2, however, was not predicted.

Since 2013 the UM-PPS changed into the EAWAG-PPS (40) and this toolis currently be merged into a new tool called EnviPath (50). Latter tool has notbeen tested in this study, but will be the tool for predictions of microbiologicaldegradation products in the future.

No further prediction tools were applied at that time; however new predictiontools will come up in the next years which may be typically be applied in strategieslike in this chapter.

d. Analytical Strategy for the Screening of TPs in LWTP Effluent

The effluent samples were independently be analyzed in parallel with theLC-MS systems 1 and 2. Thus full scan data files acquired with accurate highresolution mass spectrometers could be obtained. The data was processed with thevendor software Sciex PeakView and Agilent MassHunter Software, respectively,extracting features for accurate mass (thus observing later empirical formula for

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detected compounds). In this study the resulting (ion) masses were compared viaextracted ion chromatograms (EIC) with the molecule (ion) masses known fromliterature and predicted with UM-PPS, i.e. ‘hidden target screening’. The EIC ofeach TP was defined by a compound mass accuracy within 10 ppm mass tolerance(taking into account the performance of the LC-MS systems used). EICs wereevaluated for peak shape and peak intensity (S/N > 3/1). TPs that are exclusivelypresent in the effluent water samples (or significantly higher in the effluentcompared to the influent) were used for further investigation. For compoundsdetected by both laboratories the structures were validated by accurate MS/MSfragmentation in LC-MS system 1 if applicable.

Overall four TPs of bisoprolol could be found in LWTP effluents with system1 (see Table 1) and their structures could be confirmed by MS/MS spectra (seeFigure 1). LC-MS system 2 could confirm these compounds by accurate mass.

Several observations could be obtained for the dominating ‘M’-labeled TPcompounds. M1 and M3 could exclusively be found in the effluent whereasbisoprolol M2 was not detected. The analysis of the original bisoprolol materialused for the experiments showed the presence of TP M4 probably stemmingfrom the synthesis. As the concentrations in the spiking solution and the LWTPeffluents were comparable, TP M4 was not confirmed as a real eliminationproduct. Bisoprolol TP BIS_295 was only found by LC-MS system 1 includingMS/MS spectrum. This was leading to an allocation to category 4a identificationwith uncertain results (34, 35). Thus no reference material was synthesized atthat time.

Other features for ‘unknown targets’ could be observed (sometimes incl. alsoMS/MS data) from system 1. These results are not presented in this chapter.

Transformation Products of Bisoprolol Degradation in Soil Columns

Bisoprolol showed a different elimination behavior in real aquifer columnswhere elimination efficiencies after 42 days were 96.5% under aerobic and 84.2%under anaerobic conditions. In matrix samples and sterile controls bisoprolol wasnot degraded (Figure 2).

The use of real aeorobic and anaerobic aquifer materials and waters ensuredthat a redox-specific microbiological biocenosis was actively reacting on the testsubstance and longer adaptation periods were not necessary. Other studies usingquartz sand or aerobic sediments as soil samples had to accept an adaptation periodof up to 2 years (51, 52).

Leachates from one of each column run under aerobic and anaerobicconditions as well as from the sterile control were checked for the formation ofTPs after 14 and 42 days. Three out of the four TPs detected in LWTPs (Table1) were identified and confirmed by MS/MS spectra. The formation of TPs M1and M3 was higher under anaerobic conditions in comparison to aerobic columns(Figure 3).

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Table 1. Monitored Bisoprolol TPs in the LWTP Effluent Including StudyName (Like Cited Reference), Category (34, 35), Structure, Detection

Comment (a) for LWTP and (b) for Soil Columns) and Source of Information

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Figure 1. MS/MS spectra of three dominating signals regarding to BisoprololTPs including the expected fragment ions.

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Figure 2. Elimination of bisoprolol in soil columns experiments for 42 days.

Figure 3. Formation of TPs of bisoprolol in soil columns under aerobic andanaerobic conditions.

As TPs M1 and M3 were so far only known from degradation studies withfunghi (22) both substances were synthesized and could be unambiguouslyidentified by both mass spectrometric systems.

Since the signal of TP M4 was already present in the spiking solution incomparable concentrations - most likely as a by-product of bisoprolol - this TPwas excluded from further analysis.

In the soil column experiments masses of further TPs predicted with UM-PPS (like BIS_240 (C12H16O5), BIS_239 (C12H17NO4), BIS_209 (C10H11NO4),and BIS_111 (C6H7O2)) were found using LC-MS system 1. However, these fourcompounds were not used further on because they were either detected with lowintensity (thus no MS/MS spectra were available) or the isotopic signal patternaccuracy was not below 10 ppm.

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Transformation Products of Bisoprolol Degradation Monitored in RealWaters

Quantitative analysis was performed for the synthesized bisoprolol TPs M1and M3 in 8 effluent samples of 4 different WTPs and 16 river samples from 8rivers in Southern Germany. The limit of quantification (LOQ) for both TPs was0.025 µg L-1.

M1 and M3 were found in most WTP effluents in concentrations of 0.060 –0.200 µg L-1 and 0.027 – 0.300 µg L-1, respectively. M3 could be detected onlyonce in rivers characterized by high waste water effluent impact (Ebrach, 0.050µg L-1 and Fränkische Rezat, 0.028 µg L-1) whereas M1 was not found above theLOQ. These results indicate that the TPs M1 and M3 should only be included inriver water monitoring programs if LOQs below 0.025 µg L-1 can routinely beachieved.

Generalization of the Workflow Strategy and Further Linkages

These findings of TPs in samples from laboratory experiments are animportant tool within a general strategic workflow for real samples. Figure 1in Letzel et al. (33) presents an overview of conventional analytical strategiessimilar to Helbling et al. (44), Letzel (53), Krauss et al. (54) and Hernandez etal. (55) dealing with LC-MS(/MS) techniques for known TPs (target analysis),expected TPs (suspects screening), hidden TPs (non-target screening; via ‘KnownUnknowns’) as well as unknown TPs (non-target screening; via ‘UnknownUnknowns’). In comparison to other workflows (also reviewed in Bletsou et al.(56)) the presented workflow is characterized by its comprehensiveness and thelinkages between laboratories and screening instruments. The complementarystrategies mainly differ in mass spectrometric detection and additional parameterslike the availability of reference materials and/or databases. Thus the combinedarrangement can be generalized as an overall scheme from prediction throughdetection to quantification of newly identified targets. Results for TPs from theseprocedures with different levels of knowledge can be sorted by allocating them toa classification system (33–35).

Further tools can be linked in (if needed), like relevant databases (withoccurrence data in real environment as the EMPODATdatabase (57), like exposure(58), like toxicological databases (e.g. human toxicology or ecotoxicology) (59)or like other properties as the usage and tonnage (in STOFF-IDENT).

ConclusionsThe workflow started with degradation experiments using LWTP and

soil columns to identify the significance of potential transformation products.The workflow strategy for analyzing degradation vs. transformation productscombines knowledge in the scientific community with a following analyticalmeasurement. Thus literature searches were performed as well as transformationprediction systems like the former UM-PPS software. In a next stepcomplementary (laboratory) LC-MS (/MS) systems were applied for the

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non-target screening strategy. The combination of both (knowledge and analysis)leads to the identification via the ‘hidden target screening’ strategy recentlydescribed in Letzel et al. (33). Finally synthesized reference materials evaluate theoutcome and bring the compound into the target screening of various monitoringcampaigns.

This strategy led to new compound identifications of category 1 for BisoprololTPs in LWTP and soil columns. The TPsM1 andM3 could bemonitored in severaltreatment plant effluents in ng L-1 scale. It is recommended to study the fate andtransport of the TPs M1 andM3 in real subsurface environments such as riverbankfiltration systems. The newly identified TPs can be found today in open-sourcedatabases like STOFF-IDENT and DAIOS in order to enable analytical chemiststo quickly identify these substances by suspects / hidden target screening methods.

The flow of the overall scheme from prediction through discovery toquantification of newly identified targets merges effectively the screeningactivities of cooperating laboratories. Furthermore, this strategy can beextended by the linkage of risk management tools like human toxicological andecotoxicological databases.

Acknowledgments

The authors want to acknowledge A. Bayer, S. Bertsch, F. Rehberger, W.Schüssler and M. Fioretti for their skillful technical assistance. This work wasfinanced by the German Federal Ministry of Education and Research within theRiSKWa program, funding code 02WRS1273.

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Chapter 7

Widening the Analytical Perspective: PolarityExtended Separation for Monitoring of TraceOrganic Compounds in Surface Water Matrices

Stefan Bieber,1 Steffen Ruppe,2 Sylvia Grosse,1 Jörg E. Drewes,1and Thomas Letzel*,1

1Chair of Urban Water Systems Engineering,Technical University of Munich, Am Coulombwall 3,

85748 Garching, Germany2Environmental Protection and Energy Agency of the Canton Basel-Town,

Hochbergerstrasse 158, 4019 Basel, Switzerland*E-mail: [email protected].

Today monitoring trace organic compounds in water bodiesis part of many strategies aiming to protect environmentalhealth or drinking water quality. The occurrence of hazardouscompounds in water bodies can be assessed using differentanalytical screening strategies. Reversed phase chromatography(RPLC) coupled to mass spectrometric detection is a commonlyused technique. RPLC is well suited for the separation anddetection of medium to non-polar compounds, but can hardlybe used for the detection of polar compounds. To cover theentire range from non-polar to very polar compounds in waterbodies, a serial coupling of RPLC and hydrophilic interactionliquid chromatography (HILIC) or a supercritical fluidchromatography (SFC) system, both coupled to a time-of-flightmass spectrometer (TOF-MS) pose a new separation anddetection technique. Both novel techniques were appliedfor target and suspect target screening. The polarity rangeof the two techniques was comparable and covered the fullrange from non-polar (log DpH 7 = +7.67) to very polar (logDpH 7 = -7.86) properties. In addition to the extension ofaccessible polarity space for separations, the application of


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RPLC-HILIC/TOF-MS and SFC/TOF-MS in parallel improvesthe level of confidence in compound verification. For theverification of suspect compounds in water samples, thecomparison of RPLC-HILIC/TOF-MS and SFC/TOF-MS datacould substitute tandem-mass spectrometric data.

Introduction

The occurrence of chemical compounds in the environment has been welldocumented for many decades. In the 1960s, public concerns emerged thatthe presence of pesticides in the environment may cause adverse effects onenvironmental health (1). As a consequence, the environmental protectionmovement gained strong interest and legislative measures followed in manycountries worldwide. For the protection of the environment and the selectionregarding suitable protective management strategies, appropriate and sensitiveenvironmental monitoring is required. While initially environmental monitoringwas limited to a few compound groups mainly in use in industrial applicationsand agriculture, it became obvious quickly that many chemical compounds ofdaily use have the potential to enter the environment. Increasing knowledgeabout origin, source contributions and fate of compounds in the environment ledto a strong increase of monitoring efforts, mainly relying on gas phase and liquidphase chromatography coupled to sensitive detectors, like mass spectrometry, todetect organic compounds (2). The huge diversity and broad polarity spectrumof detectable compounds in environmental water samples make it necessaryto develop new analytical techniques. The emergence of polar transformationproducts from parent compounds generated during chemical or biological watertreatment processes, emphasized the need to include new objectives in waterquality monitoring (3). In response to these needs, stationary phases in reversedphase liquid chromatography (RPLC) were modified with polar groups. Althoughsuch ‘non endcapped’, ‘polar endcapped’ or ‘polar embedded’ stationary phaseshave enhanced the RPLC separation power for polar compounds (logarithmicoctanol-water distribution coefficient at pH 7, log DpH 7 >-2.5), ‘endcapped’RPLC remains more suitable for non-polar to medium polar compounds. Whereashydrophilic interaction liquid chromatography (HILIC) would be more suitablefor the separation of (very) polar compounds (4). One option to combine thepolarity ranges of separations is presented by a serial coupling of RPLC and HILICrepresenting two orthogonal separation techniques. This offers the separationof both, (very) polar and non-polar compounds in a single run (5). Besides LCtechniques, supercritical fluid chromatography (SFC) can be used for polarityextended separations. The mobile phase of SFC separations is mainly comprisedof carbon dioxide, which is considered a green solvent reducing the generation ofless environmentally friendly solvent waste (6). Although SFC separations aregenerally regarded to be comparable to normal phase, the polarity range of SFCseparations is significantly broader than common normal phase separations (7, 8).The serial coupling of RPLC-HILIC and SFC have already been used in screeningfor trace organic compounds and can also be applied for non-target screening

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(9). A combined approach using both techniques coupled with time-of-flight(TOF) MS detectors provides the benefit of increasing certainty in compoundidentification without necessarily utilizing tandem-mass spectrometry.

Target screening is a key element of environmental water quality monitoring.This approach is ideal for monitoring fully characterized, known compoundsof environmental relevance. The concentration of target compounds inenvironmental water samples can be determined by using (isotope-labeled)internal reference substances (10). In addition to these known compounds,unknown expected and/or unexpected compounds can be present in samplesfrom aquatic environments. There is concern that some of the undetectedcompounds might also pose a risk to the aquatic environment or human health.As a consequence, additional and more sophisticated monitoring efforts areneeded to gain information about the chemical universe contained in thesewater samples. Suspect and non-target screening strategies can be used todetect and identify such compounds at different levels of certainty (10, 11).Suspect screening utilizes different sources of information about the sample toidentify possible compounds. As a basis for suspect screening serves a list ofcompounds, which might be detectable in environmental samples. In contrastto this compound-focused approach of suspect screening, non-target screeningis based on mass spectrometric results (accurate mass and/or fragmentationpatterns), which can be used to calculate the elemental composition of a compoundand reveal chemical structure information. Furthermore, database matchingcan be used for the verification of compound identity, which corresponds tohidden-target screening (10). A final confirmation of the compound identity ispossible if a reference substance is available, raising non-target screening up totarget screening level (10–12).

Material and Methods

Acetonitrile (ACN) and methanol, HiPerSolv Chromasolv LC-MS grade,were obtained from VWR (Darmstadt, Germany). Carbon dioxide (CO2, purity99.995%) was obtained from Westfalen AG (Muenster, Germany). Ammoniumacetate was acquired from Sigma-Aldrich (Seelze, Germany). Isotope-labeledstandards were obtained from Toronto Research Chemicals (Toronto, Canada),Neochema (Bodenheim, Germany), Dr. Ehrenstorfer (Augsburg, Germany),Sigma-Aldrich (Buchs, Switzerland), CDN Isotopes (Augsburg, Germany), EQLaboratories (Augsburg, Germany) and ReseaChem (Burgdorf, Switzerland). Theisotope-labeled standards were dissolved in different solvents and then transferredinto four ethanol-based mix-solutions with concentrations ranging from 2.5 to20 µg/mL. Standard compounds for standard suspect screening were purchasedfrom Dr. Ehrenstorfer (Augsburg, Germany), Fluka (Buchs, Switzerland), Merck(Darmstadt, Germany), Parchem (New Rochelle, New York, U.S.A.), SantaCruz Biotechnology, Inc. (Dallas, Texas, U.S.A.) and Sigma-Aldrich (Seelze,Germany). Non-polar standard compounds were dissolved in acetonitrile,medium polar to (very) polar standard compounds in acetonitrile/water (50/50,

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v/v), resulting in stock solutions of 1 mM. Aliquots of the stock solutionswere merged to working standard mixtures, containing 10 µM of each standardcompound in acetonitrile/water (50/50, v/v).

The Rhine river water was obtained from the international monitoringstation located in Weil am Rhein, Germany (river kilometer 171). The 24-htime-proportional composite sample was sampled and refrigerated (at 4°C) byautomated samplers. The samples were extracted according to Kern et al. (13, 14)Briefly, the water samples were collected in glass bottles. 0.5 L of the sample wasfiltered through a glass fiber filter. The filtered samples were buffered using 0.5mL 1M ammonium acetate and pH was adjusted to 6.5. A mix of isotope-labeledstandards was then added to the samples. The samples were run through a packedsolid phase cartridge. The cartridges were filled with 350 mg of a mixture ofStrata-X AW, Strata-X CW (both Phenomenex, Aschaffenburg, Germany), ENV+(Isolute, Biotage, Uppsala, Sweden) and 200 mg of Oasis HLB Material (Waters,Eschborn, Germany). After adsorption of compounds, the cartridges were set todry for 30 minutes using a stream of nitrogen. The adsorbed substances werefirst eluted with 9 mL of ethyl acetate/methanol (50/50, v/v) + 2% ammonia andthen with 3 mL of ethyl acetate/methanol (50/50, v/v) + 2% formic acid. Thecombined eluates were concentrated to 50 µL using a stream of nitrogen and thenadjusted to 0.5 mL using purified water (Nanopure Diamond, Barnstead).

The serial coupling of RPLC and HILIC with mass spectrometric detectionwas utilized as previously reported (5, 9, 15). An Agilent 1260 HPLC systemconsisting of a degasser, a binary pump, an auto-sampler and a diode arraydetector was amended by a second binary pump (all Agilent Technologies,Waldbronn, Germany). The RPLC column (Poroshell 120 EC-C18, 50.0 × 3.0mm, 2.7 µm; Agilent Technologies) was connected to the first binary pump,while the second binary pump was connected to the zwitterionic HILIC column(ZIC®-HILIC, 150 × 2.1 mm, 5 µm, 200 Å; Merck Sequant, Umea, Sweden).Both columns were connected through a T-piece (Upchurch, IDEX EuropeGmbH, Erlangen, Germany). The system set-up is illustrated in Figure 1a.The mobile phase in the RP column consisted of 10 mM ammonium acetate inACN/water (10/90, v/v) (solvent A) and 10 mM ammonium acetate in ACN/water(90/10, v/v) (solvent B). The second binary pump, serving the HILIC columnutilized ACN (solvent C) and water (solvent D). The injection volume was 10µL. During sample injection, mobile phase composition was 100% solvent Ain RP and 100% C was added to the mobile phase before entering the HILICcolumn. Elution of retained compounds was started in HILIC by increasing thecontent of solvent D from 0 to 40% within 7 minutes. Compounds from the RPcolumn were eluted by subsequent increase of the content of solvent B in themobile phase from 0 to 100% within 25 minutes. The mobile phase compositionadded by binary pump 2 (HILIC) remained unchanged during the elution of RPcompounds. This prevented any further interactions of RP retained compoundswith the stationary phase in HILIC. The chromatographic system was connectedto an Agilent 6230 time-of-flight mass spectrometer (TOF-MS) with a Jet-Streamelectrospray ionization (ESI) ion source (both Agilent Technologies, Santa Clara,CA, U.S.A.). An isocratic pump was additionally connected to the inlet of theESI source, providing a make-up flow for internal mass calibration.

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The analytical SFC system (Figure 1b) consisted of a degasser, a binarypump, an auto-sampler, a temperature controlled column compartment, a diodearray detector and a backpressure regulator (all Agilent Technologies, Waldbronn,Germany). A zwitterionic HILIC column (150 x 2.0 mm, 5 µm, Knauer, Berlin,Germany) was utilized for SFC separations. The mobile phase consisted of CO2and 20 mM ammonium acetate in methanol (modifier). The initial condition of themobile phase was 5% modifier in CO2, which was held constant for two minutes.Modifier proportion was subsequently increased to 40% within 13 minutes, keptconstant for two minutes and reduced to initial conditions within one minute. Theflow rate was set to 1.5 mL/min at a backpressure of 150 bar, a column temperatureof 40°C and a 10 µL injection loop. The outlet of the SFC was connected tothe ESI source of the TOF-MS, as described above for RPLC-HILIC, includingthe make-up flow. ESI parameters of RPLC-HILIC/TOF-MS and SFC/TOF-MSwere applied as shown in Table 1 and described elsewhere (9).

Figure 1. Scheme of the serial RPLC-HILIC coupling (a). Both columns areconnected via T-piece and two high pressure binary pumps are required to

maintain optimal mobile phase conditions. The set-up of the SFC system (b) iscomparable to LC systems, but the high pressure binary pump, the column andthe UV detector are pressurized in SFC separations. Grey regions representthe pressurized parts. Both systems were connected to a time-of-flight mass

spectrometer.

The data was processed using MassHunter Workstation Profinder software(Agilent Technologies, Waldbronn, Germany). Compound search in sampleswas performed on the basis of compound formula. Compounds were importedto databases, using Agilent MassHunter PCDL Manager (Agilent Technologies,

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Waldbronn, Germany). Mass deviations were expected to be lower than 20 ppmand retention times were expected to not shift more than 0.5 minutes, comparedto standard compound measurements. All compound-specific data was obtainedfrom the database STOFF-IDENT, which was designed as part of project RiSKWafunded by the German Federal Ministry of Education and Research (16, 17). Theenvironmental water sample was analyzed in triplicates using each technique.Retention times of the isotope labeled standards were used for the identificationof the corresponding non-labeled compounds in the sample. Standard compoundswere injected once and retention times and masses were used for the search ofcorresponding signals in the water sample.

Table 1. Parameters of the ESI Source, Applied for the Ionization ofCompounds Separated by RPLC-HILIC or SFC

Sheath gastemperature

[°C]

Sheath gasflow [L/min]

Gastemperature

[°C]

Gas flow[L/min]

RPLC-HILIC/TOF-MS 325 7.5 325 10

SFC/TOF-MS 275 6 275 5

Nebulizer gaspressure [psi]

Capillary voltage[kilo volts]

Fragmentorvoltage [volts]

RPLC-HILIC/TOF-MS 45 -3 100

SFC/TOF-MS 45 -4 100

Results and Discussion

The surface water sample investigated in this study was initially subject totarget screening. For this reason 134 reference substances of targeted compoundswere added to the sample as isotope-labeled internal standards (IL-ISTDs) priorto the sample pretreatment procedure. Of these standards, 55 IL-ISTDs were non-polar (log DpH 7 >+1.5), 77 medium polar (log DpH 7 from -2.5 to +1.5) and 2 verypolar (log DpH 7 < -2.5). The water sample was analyzed by RPLC-HILIC/TOF-MS and SFC/TOF-MS. The data was studied for masses of IL-ISTDs, which werepresent in the sample. Subsequently, data were searched for features of unlabeledcounterpart of the IL-ISTDs with matching retention times.

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Figure 2. Extracted ion chromatograms (EICs) of metoprolol acid (1), metoprolol(2), desvenlafaxine (3) and venlafaxine (4), (a) found in the surface water

sample, separated and detected by RPLC-HILIC/TOF-MS. All four compoundswere previously being added to the sample as isotope-labelled internal standard

compound and are shown as EICs in (b) with ′.

Identified features were directly compared to extracted ion chromatogramsof corresponding IL-ISTDs. As an example, masses of metoprolol, itstransformation product metoprolol acid as well as venlafaxine and itstransformation product desvenlafaxine were detected in the surface watersample by RPLC-HILIC/TOF-MS (Figure 2a). Retention times were matchingthose of the corresponding IL-ISTDs present in the sample (Figure 2b). Thechromatographic setup of the RPLC-HILIC technique helped to evaluate thepolarity range of individual separations. Due to the chromatographic setup,HILIC retained compounds eluted before RPLC retained ones. The transitionfrom HILIC to RPLC could be observed at a retention time of 15 minutes. Thetwo transformation products were more polar than the parent compound andeluting earlier during RPLC-HILIC separation. Metoprolol acid was retainedby the HILIC column, which means that the compound was not retained in

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the RP column and only detectable due to the polarity extension of RPLC byHILIC. The same four compounds could also be detected by SFC/TOF-MS(Figure 3). Retention order of compounds during SFC separation was notcomparable to RPLC-HILIC analyses, due to different retention mechanisms.For a final verification of compound identity, the comparison of compoundfragmentation spectra and reference substance would be necessary. The TOFmass spectrometer, utilized in this study did not provide the option for MS/MSmeasurements. The application of RPLC-HILIC and SFC coupled with TOF-MSas two technologically independent separation techniques increased the credibilityof obtained results significantly. As a result, the identity of detected compoundscan be verified with a high degree of certainty, without the requirement to usetandem-mass spectrometric detection.

Suspect target screening was conducted as a second step to identifycompounds in addition to target compounds in the water sample. The basis of thisscreening approach was a list of compounds, which were suspected to be presentin surface water samples. This list was derived from former non-target screeningof various surface water samples. Features were typically extracted from thesurface water data and compared to entries in the database STOFF-IDENT (16).Results from database matching were checked for plausibility and 152 of theproposed compounds were obtained as analytical standard. These compoundswere mainly industrial chemicals, pharmaceuticals, pesticides, transformationproducts and compounds originating from natural organic matter. As a compounddatabase, STOFF-IDENT exclusively contains water relevant molecules, whichcan be expected or have previously been detected in environmental aqueoussamples (17). Database matching with STOFF-IDENT results in a lower numberof matches for a feature, compared to mass spectrometric databases, but since allreceived hits account for molecules, expected in water samples, the quality ofresults is higher. In case of multiple database matches for one feature, compoundinformation like polarity, compound usage or references from literature, whichare contained in STOFF-IDENT, can be used to decide about the most suitablematch. The polarity of the 152 compounds, investigated as suspect compounds inthis study ranged from-7.86 log DpH 7 to +7.67 log DpH 7. All could be separatedand detected by both, RPLC-HILIC/TOF-MS and SFC/TOF-MS. The attainablepolarity ranges of RPLC can be visualized by a mass - retention time plot.In Figure 4a, the analyzed 152 suspect compounds (analytical standards) areplotted, using normalized retention times. The normalization took account of thetransition from HILIC to RPLC after 15 minutes. This time was subtracted fromall retention times. For compounds with retention times lower than 15 minutes,it was set to 1 minute. Using this normalization, data of the serial RPLC-HILICcoupling can be reduced to RPLC data, which could help to identify compoundspreviously detected by other RPLC methods (18). All non-polar compounds wereexclusively retained by RPLC, while the number of medium polar and non-polarcompounds retained by RPLC was limited. As described above, retention timesin RPLC tended to increase with decreasing polarity. Most polar compoundsshowed no retention in RPLC. The addition of HILIC provides the opportunity toseparate these RPLC non-retained compounds and widens the obtainable polarityrange to very polar compounds (Figure 4b).

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Figure 3. Extracted ion chromatograms (EICs) of metoprolol acid (1), metoprolol(2), desvenlafaxine (3) and venlafaxine (4), (a) found in the surface water sample,separated and detected by SFC/TOF-MS. All four compounds were previouslybeing added to the sample as isotope-labelled internal standard compound and

are shown as EICs in (b) with ′.

With only one chromatographic column, the SFC/TOF-MS systemwas also capable of separating and detecting the same compounds like theRPLC-HILIC/TOF-MS system. The attainable polarity range was identical to theserial LC-LC coupling. Elution of compounds occurred mainly from non-polarto polar and is similar to normal phase retention behavior (Figure 5) (19). Polarinteraction, known to occur in HILIC stationary phases (4) can lead to alteredretention behavior for certain compounds, which might explain the high retentionof some non-polar compounds and the low retention of certain polar compounds.The occurrence of polar interactions has previously been described for SFCseparations (20).

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Masses and retention times of the standard compounds, separated and detectedby both techniques, were summarized in an in-house database. The surface watersample was analyzed with both polarity extended separation techniques, using thesame separation and detection setting, as utilized for the standard suspect analyses.

Figure 4. Mass-retention time plots of 152 standard compounds, separated byRPLC-HILIC/TOF-MS, grouped by compound polarity (very polar: log DpH 7<-2.5; medium polar: log DpH 7 from -2.5 to +1.5; non-polar: log DpH 7 >+1.5).Normalized retention times indicated the retention behavior of RPLC (a,). Theserial coupling of RPLC and HILIC (b,) opens the polarity range of separations

to more polar molecules.

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Figure 5. Mass-retention time plot of 152 standard compounds, separated bySFC, grouped by compound polarity (very polar: log DpH 7 <-2.5; medium polar:log DpH 7 from -2.5 to +1.5; non-polar: log DpH 7 >+1.5). The polarity range of

SFC separations covered the full analyte spectrum.

In order to detect emerging or unknown compounds, data of the environmentalsample was compared with data from standard compound analyses. Given thevast number of compounds, which can be present in environmental water samples,acceptable ranges for accuracy and retention times should be set strictly. Thisprevents false positive detection of isomeric or similar compounds containedin the sample. This topic requires even more consideration, since the numberof detectable features is significantly higher when using polarity extendedseparations, compared to commonly used RPLC-MS analyses, due to additionallydetected very polar compounds. However, database matching of features is notsufficient to verify compound identity in an environmental sample. In Figure 6,extracted ion chromatograms of isoniazid, the bisoprolol transformation productdes(isopropoxyethyl) bisoprolol acid, melamine, the transformation productof metformin N-guanylurea (all polar) and tris(2-chloroisopropyl)phosphate(TCPP) (non-polar) are shown. All were obtained from suspect standardcompound analyses (b). Comparable features for all compounds were foundin the surface water sample by RPLC-HILIC/TOF-MS analyses (Figure 6a).Without the application of MS/MS detection this data would not be reliableenough to identify the features as the suspected compounds. The comparisonof RPLC-HILIC/TOF-MS and SFC/TOF-MS data offers the opportunity toincrease knowledge about the identity of a feature. As shown in Figure 7,features of the above mentioned compounds were also found by SFC/TOF-MSanalyses. If features are detected in the surface water sample by both techniques

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and additionally match with data of a standard compound, analyzed by bothtechniques, the probability of mistaking features for isomeric compounds issignificantly decreased and the quality of results increased. Using only one of thetwo separation techniques, the level of compound identification confidence (11)would be rather low, since only a sum formula could be verified. Data comparisonof both independent techniques increases the level of confidence to the fullknowledge of compound identity. Although the application of both separationtechniques expands the polarity range of detectable compounds, the applicationof MS/MS detection will remain a requirement for quantitative analysis, asconducted in target screening. The hyphenation of RPLC-HILIC or SFC withMS/MS detection will be the next step in method development. Even withoutthe use of isotope-labeled reference compounds, both techniques can be usedto provide strong evidence about the presence of compounds in environmentalsamples and SFC/TOF-MS is often less sensitive to matrix suppression (9).

Figure 6. Extracted ion chromatograms of the surface water sample (a) ofmasses corresponding to isoniazid (1), melamine (2), des(isopropoxyethyl)bisoprolol acid (3), a transformation product of bisoprolol, N-guanylurea (4),a transformation product of metformine and TCPP (5) separated and detectedby RPLC-HILIC/TOF-MS. All compounds were analyzed separately as suspect

standard compound and are shown as EICs in (b) with ′.

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Figure 7. Extracted ion chromatograms of the surface water sample (a) ofmasses corresponding to isoniazid (1), melamine (2), des(isopropoxyethyl)bisoprolol acid (3), a transformation product of bisoprolol, N-guanylurea (4),a transformation product of metformine and TCPP (5) separated and detectedby SFC/TOF-MS. All compounds were analyzed separately as suspect standard

compound and are shown as EICs in (b) with ′.

Conclusions

Target screening is and remains the key element of environmental waterquality monitoring. Suspect screening using database tools via retention time andaccurate mass can increase the accuracy of data evaluation. The application ofRPLC-HILIC and SFC offers the opportunity to widen the range of separableand detectable compounds towards very polar compounds. With high robustnessand reliability, RPLC-HILIC and SFC contribute to a better understanding of thepresence of polar compounds in environmental water samples.

The application of both independent separation techniques resulted in morereliable results. The comparison of chromatographic results from RPLC-HILICand SFC coupled with TOF-MS could be used as alternative approach fordata validation if reference materials are available. This is due to the differentchromatographic retention mechanisms for the same molecules. Consequently,

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this leads to a cross-evaluation by chromatography. Tandem mass spectrometrycan further be used to evaluate the results from retention time and accurate mass,adding an additional dimension to data quality.

Using polarity extended chromatographic techniques like RPLC-HILIC andSFC allow retention, detection and identification of so far undetected compounds(by RPLC-MS). The broader separable polarity window is mandatory, whenpolar transformation products are targeted for the identification in environmentalsamples.

Ultimately, the combination of these techniques with tandem massspectrometry will be an effective tool in water analysis. In the future, alsounknown molecules will be classified with more information using the twopolarity extended separation techniques.

Acknowledgments

The authors would like to thank Anja Lechner and Sofia Veloutsou at TUM fortheir contributions, Agilent Technologies for providing the analytical SFC systemas a loan, and Knauer for donating the HILIC column utilized in SFC separations.

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17. Huckele, S.; Track, T. Risk management of emerging compounds andpathogens in the water cycle (RiSKWa). Environ. Sci. Eur. 2013, 25, 1–4.

18. Abate-Pella, D.; Freund, D. M.; Ma, Y.; Simón-Manso, Y.; Hollender, J.;Broeckling, C. D.; Huhman, D. V.; Krokhin, O. V.; Stoll, D. R.; Hegeman, A.D.; Kind, T.; Fiehn, O.; Schymanski, E. L.; Prenni, J. E.; Sumner, L. W.;Boswell, P. G. Retention projection enables accurate calculation of liquidchromatographic retention times across labs and methods. J. Chromatogr.A 2015, 1412, 43–51.

19. Lesellier, E. Retentionmechanisms in super/subcritical fluid chromatographyon packed columns. J. Chromatogr. A 2009, 1216, 1881–1890.

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Chapter 8

Fate of Neonicotinoid Pesticides DuringWastewater and Wetland Treatment

Akash M. Sadaria, Samuel D. Supowit, and Rolf U. Halden*

Biodesign Center for Environmental Security,Biodesign Institute and Global Security Initiative,

School of Sustainable Engineering and the Built Environment,Arizona State University, 781 E. Terrace Mall,Tempe Arizona 85287-5904, United States

*E-mail: [email protected]. Phone: 480-727-0893.

Occurrence and fate of six neonicotinoids (imidacloprid,clothianidin, acetamiprid, thiamethoxam, thiacloprid, anddinotefuran) and one degradate (acetamiprid-N-desmethyl)were studied in a United States municipal wastewater treatmentplant (WWTP) and an engineered wetland downstream.Flow-weighted samples collected in a five-day monitoringcampaign were analyzed by liquid chromatography tandemmass spectrometry (LC-MS/MS) using methods of isotopedilution and standard addition. Three of the six neonicotinoidswere detected. Daily loads of imidacloprid and acetamipridwere stable, whereas those of clothianidin varied. Detected5-day average concentrations in WWTP influent and effluentwere 54.7 ± 9.3 and 48.6 ± 8.4 ng/L for imidacloprid, 3.7 ± 0.8and 1.7 ± 0.5 ng/L for acetamiprid, and 149.7 ± 273.1 and 116.7± 144.9 ng/L for clothianidin, respectively. Concentrationsof neonicotinoids in digested sludge were below the limit ofdetection (<2 μg/kg dry weight). Wetland monitoring revealedlack of removal for imidacloprid and acetamiprid. Hazardquotient (HQ) analysis showed values of larger than unityfor imidacloprid (1.4 ± 0.1) and total neonicotinoids (4.8 ±4.5) in WWTP effluent. Thus, imidacloprid, acetamiprid, andclothianidin were shown to occur in United States wastewater,persist during conventional and wetland treatment, and to posepotential risk in effluent-dominated, receiving surface waters.


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1. IntroductionNeonicotinoids are a class of systemic insecticides registered for use in more

than 120 countries (1). Their neurotoxic properties are exploited for controlof, for example, aphids, thrips, whiteflies, planthoppers, lepidoptera, and somecoleopteran pests. Structurally, neonicotinoids are characterized by having anelectronegative moiety featuring either a nitro or cyano pharmacophore, whichimparts its potency by binding to a cationic subsite of the insect receptor (2).

Studies have shown that neonicotinoids can negatively affect sensitivenon-target aquatic invertebrates and pollinators at concentrations in the lowparts per billion range (3–9). Insectivorous birds also are vulnerable to exposurefrom consumption of contaminated prey (10, 11). During the past decade,environmental contamination with neonicotinoids has been observed in surfacewater at a global scale (12–17). Aside from industrial agriculture, neonicotinoidsare widely used in domestic pest control and horticulture, creating a pathway forthe occurrence of neonicotinoids to wastewater. However, fate and occurrence ofneonicotinoids in sewage are not well studied. As the highest selling neonicotinoidin the U.S., imidacloprid has been detected in 9.8% of effluent samples (n=102)collected at wastewater treatment plants (WWTPs) in Oregon (18). However,corresponding samples of plant influent were not collected, rendering impossiblea determination of the overall removal efficiency of conventional U.S. sewagetreatment infrastructure in said study. Whereas the presence of neonicotinoidsin recycled wastewater discharged into U.S. surface waters has been firmlyestablished, the risk posed to effluent receiving surface waters by these emergingcontaminant has not been quantified in great detail yet.

The goal of the present study was to determine the occurrence and fate ofsix neonicotinoids and a degradates during conventional wastewater and wetlandtreatment and to quantitatively evaluate the risk posed by residues of this class ofpesticides.

2. Materials and Methods2.1. Chemicals and Reagents

HPLC grade organic solvents and water were purchased from Sigma-AldrichCorp., St. Louis, MO and Thermo Fisher Scientific, Waltham, MA, U.S.A.,respectively. Analytical standards for six neonicotinoids (imidacloprid,acetamiprid, clothianidin, thiamethoxam, thiacloprid, and dinotefuran), onedegradate (acetamiprid-N-desmethyl), and the deuterated labeled standards(imidacloprid-d4, acetamiprid-d3, and clothianidin-d3) were obtained fromSigma-Aldrich Corp. (St. Louis, MO).

2.2. Sample Collection

Sampling was conducted in early December 2014 for a period of fiveconsecutive days (3 workweek and 2 weekend days) at an unidentified,conventional sewage treatment plant performing activated sludge treatment,

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chlorine disinfection, and anaerobic digestion for sludge treatment and at awetland located immediately downstream. The plant received sewage comprisedof 94% domestic wastewater and 6% industrial wastewater, and producedClass B+ reclaimed water and Class B biosolids (treated sewage sludge fit forapplication on land). Treated effluent was discharged into an engineering wetlandprior to discharge into a river. Unit processes performed at the WWTP are shownin Figure 1. Primary sludge and waste activated sludge are digested at 35°C withan average solid retention time of 21 days. Seven portable automated samplers(6712 Full-Size Portable Sampler, Teledyne Isco, Lincoln, NE) were programmedto collect 2.5 liters of flow-weighted composite samples. After sample collection,600 mg/L of a preservative (Kathon CG/ICP) and ~100 mg/L sodium thiosulfatewere added and samples were stored at 4°C prior to processing.

Figure 1. Flow diagram of investigated activated sludge treatment plant.Numbers indicate the sampling locations used. Flow-weighted, 24-hour

composite samples were collected with automatic samplers at locations 1, 2, 3, 4,5, 8, and 9. At locations 6 and 7, grab samples were collected. Reproduced withpermission from Reference (19). Copyright 2016 American Chemical Society.

2.3. Sample Extraction

Wastewater samples were spiked with 200 ng each of deuterium-labeledneonicotinoid analogs and then loaded onto reverse-phase, functionalizedpolymeric styrene divinylbenzene sorbent resin cartridges (Strata X & XL, 500mg/3 mL, Phenomenex, Torrance, CA) to concentrate study analytes using anautomatic solid-phase extraction instrument (Dionex AutoTrace 280, ThermoScientific, Waltham, MA). Cartridges were eluted with 8 mL of methanol andformic acid mixture (95:5, v/v). Eluates were evaporated, and reconstituted in awater:methanol mixture (80:20, v/v) containing 0.1% formic acid prior to LC-MSanalysis. Primary and waste activated sludges were centrifuged at 7500 g for 10minutes, and resultant supernatants were analyzed similarly.

Solids from primary and waste activated sludges, and treated dewateredsludges, were dried using a stream of nitrogen and then spiked with 400 ng of

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isotopes per gram dry weight sludge. One gram of solids was extracted with 10mL acetone by shaking for 24 hours followed by 1 hour of sonication. Obtainedextracts were dried, and solvent was exchanged from acetone into 6 mL ofhexane. Florisil cleanup was performed with a sorbent bed featuring a blend ofmagnesium oxide and silica gel (Sep-Pak Vac Florisil Cartridge 6 cc/1 g sorbent,Waters Corporation, Milford, MA). Analytes were eluted sequentially from resincartridges using 4 mL of methylene chloride followed by 4 mL of acetone. Lastly,one mL of serial extracts were mixed, nitrogen dried, and reconstituted with onemL of solution consisting of water, methanol and formic acid (80/20/0.1, v/v/v)prior to LC-MS/MS analysis.

2.4. Liquid Chromatography and Tandem Mass Spectrometry

Simultaneous liquid chromatography separation of neonicotinoids (Figure 2)was carried out using a Shimadzu UPLC system using a reverse phase 4.6 x 150mm C8 column (XBridge, Waters Corporation Milford, MA, U.S.A.). A binarygradient with acidified water and methanol (100:0.1, v/v) at a total flow rate of0.5 mL/min was applied. The mobile phase consisted of 20% organic with aninitial 1-min ramp of 10% min-1, followed by a 6-minute ramp of 10.8% min-1 to 95% organic, where it was held for 3.5 min, resulting in a total run timeof 14 min. An API 4000 tandem mass spectrometer (ABSciex, Framingham,MA, U.S.A.) was operated in positive electrospray (ESI+) mode to monitor thefirst and second most abundant ion transitions for compound quantification andqualification, respectively. Analyst software (v1.5, ABSciex, Framingham, MA,U.S.A.) was used for system control and data analysis.

2.5. Quantitation and Calibration

Imidacloprid, acetamiprid, acetamiprid-N-desmethyl, and clothianidin werequantified using the method of isotope dilution. Thiacloprid, thiamethoxam, anddinotefuran were quantified using the method of standard addition (20). Eight-point calibration curves featuring R2 values of greater than 0.99 were consideredsatisfactory.

2.6. Risk Assessment

Treated wastewater is discharged to the engineered wetlands for additionaltreatment and eventually to the river. Both water bodies contains ecosystem thatwill be exposed to the neonicotinoids present in discharged wastewater. Hazardquotient (HQ) forWWTP and wetlands effluent was determined by taking the ratioof the detected concentration of the neonicotinoids in corresponding effluent andthe level at which no adverse effects are expected. Calculated HQ is a worst-casescenario that does not consider in-stream dilution factor.

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Figure 2. Chromatogram displaying the separation and detection of sixneonicotinoids and degradate acetamiprid-N-desmethyl on C8 column (signalstrength obtained for 0.5 ng of each analyte injected relative to that obtained

for thiacloprid).

3. Results and Discussion

3.1. Analytical Method Performance

The LC-MS/MS method targeted seven analytes simultaneously bymonitoring two ion transitions each, as shown in Figure 2. To ensure the qualityand validity of results, each analytical batch of samples contained correspondingmethod blank, field blank, and sample duplicates. In blank samples, no falsepositives results were obtained for any of the samples analyzed. Average relativepercent difference (RPD) between samples and in their duplicates was 22 ± 20%for detected analytes in wastewater samples.

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3.2. Fate of Neonicotinoids Across Wastewater Treatment Process

Daily loading was observed to be consistent for imidacloprid and acetamiprid(Figure 3), whereas that of clothianidin (Figure 4) showed large variations overthe 5-day sampling period. Three out of six targeted neonicotinoids - thiacloprid,thiamethoxam and dinotefuran, were either absent from daily WWTP processstream samples or present at levels below their respective method detection limits.

3.2.1. Fate of Imidacloprid Across the WWTP

Average daily concentrations of imidacloprid in plant influent varied little(54.7 ± 9.3 ng/L) during the 5-day period of sampling as shown in Figure 3.Primary clarification diverted 1% of total plant flow away as sludge featuring a17 times higher level of suspended solids relative to the primary clarifier effluent.The average concentration in the aqueous phase of primary sludge was 30.7 ± 3.0ng/L and themass of imidacloprid sorbed to sludge particles was below themethoddetection limit (1.1 µg/kg dry weight). Considering the low log octanol-waterpartition coefficient (KOW) value of imidacloprid of <1, absence of imidaclopridin sludge particulates was expected (3). Primary effluent featured 58.4 ± 12.4 ng/Lof imidacloprid, a value that was similar to raw influent, signaling persistence ofthe compound during primary treatment.

Secondary treatment consisted of an activated sludge unit operation relyingon microbial degradation. However, imidacloprid concentration in secondaryeffluent was 48.6 ± 7.8 ng/L. Paired t-test was performed and with p=0.13 andconfidence interval (CI) of 95%, it concluded that difference between influentand secondary effluent was not statistically significant. Analysis of wasteactivated sludge showed imidacloprid concentrations of 22.3 ± 2.5 ng/L in theaqueous phase, with levels on the solid (particulate) fraction registering below thedetection limit; these findings mimicked those obtained for primary sludge.

Figure 3. Daily average concentrations of imidacloprid, acetamiprid (dottedlines), and acetamiprid-N-desmethyl (solid lines) in various wastewater treatment

streams.

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The here examined facility uses a chlorine dosage of 2.5 mg/L to meetmicrobial removal criteria. Although chlorine has the potential to oxidizeorganic compounds, no change in imidacloprid concentration (48.6 ± 8.4 ng/L)was observed, indicating resistance to oxidation under the conditions studied.The detected daily influent and effluent concentrations of imidacloprid werestatistically indistinguishable (p=0.13; CI=95%), implying a lack of removalby conventional sewage treatment. Primary and waste activated sludges werecombined in the plant and subjected to anaerobic digestion at 35°C for 21 daysfollowed by dewatering. Similar to primary and activated sludges, neonicotinoidconcentrations in treated sludge were below the limit of detection.

3.2.2. Fate of Acetamiprid Across the WWTP

During the sampling period, average concentrations of acetamiprid detectedin plant influent and primary clarifier effluent were 3.7 ± 0.8 ng/L and 3.4± 0.6 ng/L, respectively, thereby implying a lack of removal during primarytreatment (Figure 3). In primary sludge, the concentration of acetamiprid in theaqueous phase was 1.0 ± 0.6 ng/L and in particulates <0.7 µg/kg, suggesting noapparent partitioning into sludge particulates. In the secondary treatment effluent,acetamiprid concentration was 1.8 ± 0.4 ng/L (half of the influent), confirmingmicrobial and chemical degradation of acetamiprid in the aeration basin, with theformation of acetamiprid-N-desmethyl (1.3 ± 0.3 ng/L). Prior studies had alsoshown that acetamiprid undergo relatively fast dissipation in neutral environments,with an aqueous dissipation half-life of 4.7 days (21). Concentrations anddistribution of acetamiprid in waste activated sludge mimicked results obtainedfor primary sludge. Similar to imidacloprid, minimal change in acetamipridand acetamiprid-N-desmethyl concentrations were observed after disinfection,indicating a lack of chemical oxidation by chlorine. Concentrations of acetamipridand acetamiprid-N-desmethyl in treated wastewater were 1.7 ± 0.5 ng/L and 1.3± 0.4 ng/L, respectively, whereas dewatered sludge yielded no detections (<0.7µg/kg).

3.2.3. Fate of Clothianidin Across the WWTP

Clothianidin loading during the sampling period was inconsistent, withdaily influent concentrations varying between <0.9 – 666.4 ng/L (80% detectionfrequency) and averaging 149.7 ± 273.1 ng/L. Detected concentrations ofclothianidin in primary, secondary, and disinfection effluent were 163.8 ± 195.9,131.3 ± 170.8, 116.7 ± 144.9 ng/L, respectively (Figure 4). Though aqueous phaseof primary and waste activated sludge featured 115.3 ± 190.9, 47.6 ± 84.8 ng/L,respectively, concentrations of clothianidin sorbed to particulates and dewateredsludge were below MDL (<1.4 µg/kg). Inconsistency in loading made impossiblea determination of the fate of clothianidin; however, considering similar average

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concentrations in treatment streams over the sampling period as well as the lackof partitioning into sludge, results obtained imply a notable persistence of thecompound during conventional wastewater treatment.

Figure 4. Detected concentrations of clothianidin in treatment streams during the5-day sampling period. (inf, influent; 1’ eff, primary effluent; 2’ eff, secondary

effluent; DI’ eff, disinfection effluent.)

3.3. Fate of Neonicotinoids Across Wetland Treatment.

At the study plant treated effluent prior to surface water discharge underwentpolishing in an engineered wetland located immediately downstream thatshowed a hydraulic retention time (HRT) of 4.7 days. Whereas neonicotinoidsin the wetland may experience photodegradation, chemical transformation,biological degradation, accumulation in sediments, leaching into groundwater,and bioaccumulation, no significant transformation was detected for imidacloprid,acetamiprid, and acetamiprid-N-desmethyl. Imidacloprid concentrationsentering and leaving the wetlands were 54.4 ± 3.4 ng/L and 49.9 ± 14.6 ng/L,respectively. Similarly, acetamiprid (2.0 ± 0.0 ng/L to 2.3 ± 0.2 ng/L) andacetamiprid-N-desmethyl (1.7 ± 0.1 ng/L to 2.0 ± 0.5 ng/L) concentrationswere unaffected by wetland treatment. Strong variations in the daily loading ofclothianidin did not allow a determination of its fate during wetland treatment.

The constructed wetland located downstream of the WWTP received treatedeffluent only with no opportunity for dilution. Moreover, as the studied WWTPis located in the southern part of the United States, discharged effluent goesundiluted after wetland treatment for several months of the year. Hazard quotient(HQ) values for neonicotinoids contained in WWTP and wetland effluentwere calculated for sensitive aquatic invertebrates to assess the potential ofadverse effects. A recent review suggested that any long-term neonicotinoid

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concentrations in water exceeding 35 ng/L or short-term peak exposures exceeding200 ng/L can negatively affect sensitive aquatic invertebrate populations (5).These values were derived from a sensitivity distribution analysis of 214 toxicitytests of 48 species (5). We considered 35 ng/L as the benchmark for the HQcalculations of all detected neonicotinoids. For imidacloprid, HQ values forsecondary effluent during the sampling campaign averaged 1.4 ± 0.1, with a valueof greater unity suggesting a statistical probability of harm occurring to sensitivereceptor organisms. The HQ values for all three neonicotinoids combinedaveraged 4.8 ± 4.5 (range 1.5 – 12.3) over the 5-day period.

These HQ values indicate that a dilution of discharged effluent by a factor of2 to 6 will be required to protect biota downstream. Pre-existing neonicotinoidlevels in surface water from agricultural runoff and other WWTP dischargesupstream would further increase these calculated minimum dilution factors inorder to protect freshwater biota.

4. ConclusionThis study adds to prior work on the detection of imidacloprid, thiamethoxam,

clothianidin and acetamiprid in global surface waters and treatment flows (5).Results showed that neonicotinoids are not partitioning into the sewage sludgeparticulates, lack biological degradation during activated sludge treatment, andresist chlorine oxidation during disinfection as well as various potential removalprocesses known to occur in wetlands. While being limited to a single plant in anarid United States location, results further indicate that neonicotinoids may posequantifiable harm to surface waters receiving or dominated by treated wastewater,even after sequential wastewater and wetland treatment.

AcknowledgmentsThis project was supported in part by Award Number R01ES020889 from

the National Institute of Environmental Health Sciences (NIEHS) and by AwardNumber LTR 05/01/12 of the Virginia G. Piper Charitable Trust. The contentis solely the responsibility of the authors and does not necessarily representthe official views of the NIEHS or the National Institutes of Health (NIH). Wethank Heather Finden, Larry Westerman, Ron Elkins, David Epperson, TamaraSaunders, Dr. Arjun Venkatesan, and Edward Reyes for their help with thesampling campaign.

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17. Masiá, A.; Campo, J.; Vázquez-Roig, P.; Blasco, C.; Picó, Y. Screening ofcurrently used pesticides in water, sediments and biota of the GuadalquivirRiver Basin (Spain). J. Hazard. Mater. 2013, 263, 95–104.

18. Hope, B. K.; Pillsbury, L.; Boling, B. A state-wide survey in Oregon (USA)of trace metals and organic chemicals in municipal effluent. Sci. TotalEnviron. 2012, 417, 263–272.

19. Sadaria, A. M.; Supowit, S. D.; Halden, R. U. Mass Balance Assessmentfor Six Neonicotinoid Insecticides During Conventional Wastewaterand Wetland Treatment: Nationwide Reconnaissance in United StatesWastewater. Environ. Sci. Technol. 2016, 50, 6199–6206.

20. Koester, C. J.; Beller, H. R.; Halden, R. U. Analysis of Perchloratein Groundwater by Electrospray Ionization Mass Spectrometry/MassSpectrometry. Environ. Sci. Technol. 2000, 34, 1862–1864.

21. Hladik, M. L.; Kolpin, D. W.; Kuivila, K. M. Widespread occurrence ofneonicotinoid insecticides in streams in a high corn and soybean producingregion, USA. Environ. Pollut. 2014, 193, 189–196.

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Chapter 9

Identifying Toxic Biotransformation Productsof the Insensitive Munitions Compound,2,4-Dinitroanisole (DNAN), Using Liquid

Chromatography Coupledto Quadrupole Time-of-Flight Mass

Spectrometry (LC-QToF-MS)

Christopher I. Olivares,*,1 Leif Abrell,2,3 Jon Chorover,2Michael Simonich,4 Robert L. Tanguay,4 Reyes Sierra-Alvarez,1

and Jim A. Field1

1Chemical and Environmental Engineering, University of Arizona,Tucson, Arizona 85721, United States

2Soil, Water, and Environmental Science, University of Arizona,Tucson, Arizona 85721, United States

3Chemistry and Biochemistry, University of Arizona,Tucson, Arizona 85721, United States

4Environmental and Molecular Toxicology, Sinnhuber Aquatic ResearchLaboratory and the Environmental Health Sciences Center,

Oregon State University, Corvallis, Oregon 97333, United States*E-mail: [email protected].

As insensitive munitions (IMs) replace conventional explosives,releases of 2,4-dinitronanisole (DNAN) –an IM compound–are expected to increase in the environment. DNAN isreadily biotransformed in soils, and while toxicity studieshave evaluated DNAN, little attention has been given to itsbiotransformation products. In this work, we elucidated andsemiquantitated product mixtures formed during anaerobicbiotransformation of DNAN using high-resolution massspectrometry techniques. DNAN underwent nitroreduction,and at later bioconversion stages, formed azo-dimers,accounting for the majority of end-products from DNANanaerobic biotransformation. The chemical analyses


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were complemented by microbial (methanogenesis andAliivibrio fischeri bioluminescence) and zebrafish embryotoxicity assays on mixtures of products formed, as well asindividual compounds (biotransformation products and modelazo-oligomers). Methanogens were severely inhibited duringnitroreduction stages, but recovered at longer incubation timeswhen azo-dimers were predominant. On the other hand, A.fischeri bioluminescence decreased at later biotransformationstages. When tested individually, the most toxic productsto the microbial targets were the azo-oligomer models,while the least toxic species were 2,4-diaminoanisole andits N-acetylated analog. Zebrafish embryos showed fewactive developmental endpoints for the individual compoundstested, but 3-nitro-4-methoxyaniline and the model azo-dimer2,2′-dimethoxy-4,4′-azodianiline had a lowest observableadverse effect level (LOAEL) of 6.4 μM. Overall, theinterdisciplinary experimental design allowed to identify keytransformation processes and products that alter toxicity beyondthe parent compound.

Introduction

2,4-dinitroanisole (DNAN) is a compound used in insensitive munition(IM) formulations. IMs are replacing traditional explosives, such as2,4,6-trinitrotoluene (TNT), due to a lower risk of accidental detonation (1, 2).With increased manufacturing and use in testing sites and battlefields, DNANwill inevitably be released into the environment. DNAN, although slightly morewater soluble than TNT, is expected to remain in soils tainted with the explosivesfor years, while other ingredients in IM formulations are expected to dissolvefaster (3). Therefore, biotic and abiotic reactions, such as biotransformation (4–6)and photolysis (7), are expected to be the main mechanisms affecting the fate ofDNAN in the environment.

Soil catalyzes abiotic and biotic (5, 8) nitro group reductions of DNAN,forming 2-methoxy-5-nitroaniline (MENA), and subsequently 2,4-diaminoanisole(DAAN). The reduction of DNAN to its transformation products increases thecompound mobility because MENA and DAAN are more hydrophilic thanDNAN, the parent compound (9). Moreover, during the course of nitroreduction,reduced amino metabolites can couple forming azo-dimers, that are expected tobe long-term end-products in most natural soil scenarios (5, 6).

While there have been recent reports on DNAN toxicity effects (10, 11) thereis a need to characterize changing adverse effects posed to ecotoxicological targetsfrom its multiple biotransformation products, as well as to identify potential keychemical species that drive toxicity. For instance, preliminary findings reportednitro group reduction in DNAN to occur with decreased inhibition towardsmethanogens (12), as well as reduced mortality to zebrafish after a 48 h exposure

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(13). Moreover, recent research on the ecotoxicity of DNAN have suggestedtransformation reactions occur during toxicity assays (10, 11, 14, 15).

In this study, an integrated chemical and toxicological characterization ofDNAN biotransformation is reported, that correlates the biological activity ofspecific products with their abundance and identifies key drivers of changesin toxicity potency. Our objectives were (1) to develop a high resolutionmass spectrometry method to elucidate and semiquantitate biotransformationproducts, and (2) to integrate the chemical characterization of product mixtureswith quantitative toxicity assays that probe (i) inhibition of methanogens,(ii) bioluminescence of the bacterium Aliivibrio fischeri, and (iii) embryodevelopment in zebrafish.

Materials and MethodsChemicals and Biological Materials

2,4-dinitroanisole (CAS# 119-27-7, DNAN, Alfa-Aesar, 98%), 2-methoxy-5-nitroaniline (CAS# 99-59-2, MENA, Sigma-Aldrich, 98%),3-nitro-4-methoxyaniline (CAS# 577-72-0, denoted “iMENA", Accela ChemBio,97% ), N-(5-amino-2-methoxyphenyl) acetamide (CAS# 64353-88-4, denoted“Ac-DAAN”, Chem-Bridge Corporation, 95%), 2,2′-dimethoxy-4,4′-azodianiline(CAS# 6364-31-4, denoted “dimer L”, MolMall Sarl, >90%), and BismarckBrown Y (m-Bis(2,4-diaminophenylazo)-benzene, CAS# 8005-77-4, denoted“BBY”, Chem-Impex International, 46%) were used in this study without furtherpurification. Camp Navajo (water content = 9.3%), a surface soil with surroundingmilitary activity described previously (5, 16), was used for biotransformationassays. The soil was sieved (2 mm) and stored at 4 °C before use.

Microbial toxicity analyses were performed with methanogenicmicroorganisms in anaerobic sludge and with the marine bioluminescentbacterium Aliivibrio. fischeri (NRRL-B-11177, Modern Water Inc., New Castle,DE, USA). Anaerobic sludge (dry weight (dwt) solids = 11.2 %, volatilesuspended solids (VSS) = 7.9 % per wet weight) was procured from a full-scaleupflow anaerobic sludge blanket reactor treating industrial wastewater from abrewery (Mahou, Guadalajara, Spain). Developmental zebrafish (Danio rerio)embryo toxicity was also evaluated (17). Zebrafish aquaculture was performedat the Sinnhuber Aquatic Research Laboratory (18).

Staggered Anaerobic Biotransformation Assays

The composition of the biotransformation products at different stages ofDNAN (500 μM) bioconversion in soil and sludge was measured in a seriesof anaerobic microcosms that were incubated for up to 50 days to identify andsemiquantitate biotransformation products with high resolution mass spectrometryand to evaluate toxicity (Figure 1). Briefly, 500 μM DNAN was added to a basalmineral medium (19) containing a phosphate buffer (18 mM, pH 7.2) that was

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amended with 10 mM pyruvate. Soil (100 mg) or sludge (75 mg) were addedbefore sealing the tubes and flushing with He/CO2 (80/20%) to achieve anaerobicconditions. Triplicate tubes were incubated for 0, 1, 5, 10, 20, 30, 40, and 50 days,for destructive sampling upon completion of each incubation time. For microbialtoxicity evaluation and LC-QToF-MS analysis, samples from the liquid phasewere retrieved inside an anaerobic glovebox to avoid autoxidation of productsthat were potentially unstable under prolonged air exposure. Liquid samples werecentrifuged (10 min, 9,600×g) to remove suspended particulate matter and thesupernatant solutions were frozen (−20 °C) until chromatographic analysis.

Figure 1. Overview of experimental approach to integrate biotransformationproduct elucidation and characterization with toxicity evaluation. Anaerobicbiotransformation assays were incubated for different time periods (0 to 50days) upon sampling of the liquid phase. Biotransformation products wereelucidated with non-targeted LC-QToF-MS and semiquantified based on masschromatograms for each incubation period. Toxicity of profile mixtures was

evaluated in microbial targets and to zebrafish embryos.

LC-QToF-MS Elucidation of Biotransformation Products

Characterization of bioconversion products soluble in the aqueous phase wasperformed on an UltiMate 3000 UHPLC-DAD (Dionex, Sunnyvale, CA) coupledto a TripleTOF 5600 quadrupole time-of-flight mass spectrometer (Q-ToF-MS)(AB Sciex, Framingham, MA, USA). Samples (10 μL) were run in the UHPLC-with an isocratic 60/40 % H2O/MeOH eluent (0.25 mL min-1, run time 15 min)at room temperature through a reversed phase C18 column, Acclaim RSLCExplosives E2 (2.1 x 100 mm, 2.2 μm) (Thermo Fisher Scientific, Waltham, WA,USA). DNAN, MENA, and DAAN were monitored by UV detection (wavelengthin nm: retention time in min): 300:9, 254:5, and 210:2.3, respectively. TheQToF-MS was operated with an electrospray ionization source (ESI) in positive

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mode kept at 450 °C with a capillary setting of 5.5 kV, and declustering potentialof 80 V. N2 was used as curtain, desolvation, and nebulizer gas at 30, 35, and 35psi, respectively. A non-targeted approach was used to obtain high-resolutionaccurate masses from information dependent acquisition (IDA) mass spectra; 0.1sec cycle time, 6 triggered ions per cycle, mass range 35-1000 Da. Instrumentcalibration was performed by automated infusion of a solution periodically, overa mass range of 35-1000 Da.

Analyst TF 1.6 with PeakView 1.2.0.3 and Formula Finder 1.1.0.0 wereused to process spectral data and to propose molecular formulae for thebiotransformation product candidates. Molecular formulae correspondingto measured molecular ions within 5 ppm of calculated monoisotopic ion(exact) mass were considered, but in most cases measurements were within 1ppm. Once a molecular formula was identified, it was evaluated by manualinspection of product ion mass spectral fragments was conducted. Exact production masses corresponding to proposed fragment losses were calculated andmatched with molecular formulae using ChemCalc (20), and were requiredto be within 5 ppm of calculated (exact) monoisotopic masses. Commonfragmentation reaction patterns emerged among the biotransformation productssuch as: [M-CH3], M-15.0235; [M-NH3], M-17.0265; [M-OCH3], M-31.0164;[M-CH4O], M-32.0262; [M-NO2], M-45.9929; [M-C2H4O2], M-46.0419;[M-C2H3NO2], M-73.0164; and [M-C6H6N2O], M-122.0480. Finally, the cohortof biotransformation products was compared with related studies with DNANand literature on biological reactions with nitroaromatics, aromatic amines, andphenyl-azo compounds

LC-QToF-MS Semiquantitation of Biotransformation Products

In order to study the change in the composition of biotransformation productscorrelated with incubation time, the elucidated products were semiquantitatedby integrating accurate, selected parent ion [M+H]+ and product ion [M+H-Y]+mass chromatogram peaks with reproducible retention times. In the absenceof pure standard calibrants, extracted ion chromatogram peak areas providedsemiquantitative evaluation of the abundance of the biotransformation products.Selected ion chromatograms were extracted using a 10 mDa window that wascentered on the calculated (exact) mass. The same UHPLC and QToF-MSparameters were used for biotransformation product semiquantitation andelucidation (described above).

Microbial and Zebrafish Embryo Toxicity Evaluation of Chemical Library

A chemical library was built for toxicology evaluation based on thebiotransformation products elucidated with LC-QToF-MS. The library wascomprised of DNAN and the commercially available biotransformation products(MENA, iMENA,DAAN,Ac-DAAN), as well as azo-oligomermodel compounds(dimer L, BBY). Microbial activity inhibition assays were performed for

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acetoclastic methanogenesis and bioluminescence, described in detail previously(12), in order to determine concentrations resulting in 50% inhibition (IC50)correlating to inhibition potency of individual compounds in the chemical library.

Zebrafish embryo developmental toxicity was evaluated following apreviously reported high-throughput protocol (18, 21) for exposures of individualtoxicants (0.0064-64 μM, 32 replicates for each concentration and toxicant-freecontrol) to embryos from 6 to 120 hours post-fertilization (hpf), with developmentassessment at 24 and 120 hpf for a total of 22 developmental endpoints andmortality. Lowest observable adverse effect levels (LOAELs) were determinedfor compounds exhibiting developmental toxicity endpoints above thresholdstatistical significance (18, 21).

Toxicity Evaluations of Biotransformation Product Mixtures

Mixtures of biotransformation products were obtained by destructivesampling of the staggered incubations of 500 μM DNAN from the incubationperiod 0-50 days. Mixtures exposed to microbial toxicity targets (methanogensand A. fischeri) were diluted (54-fold for methanogens, 36-fold for A. fischeri)to avoid complete inhibition of methanogenesis or bioluminescence that wouldprevent toxicity changes to be detected.

Results and DiscussionDNAN Biotransformation Pathway

Elucidation of DNAN biotransformation products and pathways was enabledby a non-targeted approach using UHPLC-QToF-MS with IDA experiments.Overall, as seen in Figure 2A, the DNAN biotransformation pathway involvedregioselective nitroreduction of the ortho nitro group in DNAN, yielding MENA.However, nitroreduction of the para nitro group, leading to iMENA, wasalso detected at much lower levels. Further reduction of the remaining nitrogroup resulted in the aromatic diamine product, DAAN. During this process,reactive nitroso groups (not depicted in Figure 2A) condensed with DAAN, andformed azo-dimer m/z 273. The remaining amino moieties remained reactive inazo-dimers undergoing N-methyl substitutions to produce other dimeric productswith m/z 285 and 269. Additionally, a parallel pathway involving acetylation ofDAAN was also found (m/z 181).

DNAN Biotransformation and Microbial Toxicity of Product Mixtures

DNAN underwent rapid nitroreduction in the anaerobic soil incubations.DNAN was removed within ten days of incubation, forming MENA and DAAN,as seen in Figure 3A. However, products formed at longer incubation times(30-50 days) could not be detected by HPLC-DAD, so a gap in mass balance from

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the initial DNAN in solution was evident over this time period. To understandthis imbalance, a semiquantitative mass spectrometric analysis method, basedon results from our initial non-targeted approach with IDA experiments,enabled characterization of additional transformation products monitored toobtain abundance estimates based on integrated peak areas from extracted masschromatograms. As seen in Figure 3B, the majority of products from 30-50 dincubations were comprised of ions > m/z 200 attributed to a suite of azo-dimers,indicative of oligomerization reactions. While the majority of the dominant ionshad been characterized during the non-targeted analyses, m/z 247 (Figure 3A)was putatively assigned the chemical formula C7H9N3O7, and was suspected tobe a transformation product containing a moiety related to humic material (22).

Figure 2. Adapted with permission from reference (22). Copyright 2016.Elsevier. Panel A: Biotransformation pathway of DNAN in anaerobic soilincubations. DNAN undergoes nitroreduction, yielding MENA and DAAN.

Reduced metabolites from nitroreduction lead to azo-dimer formation (m/z 273).The amino moieties in azo-dimers continue to react, forming N-alkyl groups (m/z285, 269). Multiple arrows indicate multiple reaction steps. Panel B: Model

dimer and trimer used for toxicity evaluations.

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Figure 3. DNAN biotransformation started with nitroreduction, while long-termincubation resulted in azo-dimer compounds. The changes in the compositionof the product mixtures altered their toxicity potential to microbial targets.Panel A: HPLC-DAD resolvable products from DNAN (■) nitroreduction:MENA (●) and DAAN (▲). Panel B: LC-QToF-MS enabled semiquantitationof biotransformation products based on peak areas from mass chromatograms.Each shade of gray represents a transformation product monitored in the selectedparent ion list in increasing order from dark (lower m/z) to light (higher m/z) grayshades [M+H]+: 139.0866, 165.0659, 169.0608, 181.0972, 185.0652, 193.0607,228.0768, 243.0877, 243.1241, 245.1300, 247.0425, 259.1190, 267.0975,269.1397, 273.1347, 274.0715, 275.1503, 285.1347, 299.1179, 301.1289,

313.1289, 325.1659, 327.1452, 431.1569. Ions in bold represent most abundantpeak areas measured. Panel C: Normalized methanogenic (○) and A. fischeribioluminescence (□) activity when exposed to transformation product mixtures

formed at different times during DNAN biotransformation.

In the absence of calibrants, this semiquantitative approach still providedvaluable temporal concentration comparisons, since no significant changes inelectrospray ionization of transformation products were anticipated betweendifferent incubation times – because the incubation medium matrix remainedthe same throughout the experiment (same basal medium and soil inoculum).The main limitation of this approach is based on the assumption that identicalionization potentials amongst all the biotransformation products do not exist,therefore an accurate interspecies chemical concentration comparison was notanticipated. In this respect, it is interesting to note that primary amines fromvarious sources, such as polyurethane and azo dyes, measured by ESI LC-MS/MS,showed similar limits of detection (same order of magnitude) (23), suggestingsimilar ionization potentials between structurally similar amines.

Finally, when overlaying microbial toxicity data with transformation productabundances (Figure 3C), it can be noted that methanogenic activity inhibitionincreased during DNAN nitroreduction. However, methanogenic activityrecovered at longer incubation times, when DNAN was depleted and azo-dimerswere predominant. This suggested that reactive intermediates formed duringnitroreduction of DNAN might be responsible for further increases in inhibition,which lasted as long as there was a steady supply of these intermediates fromDNAN. On the other hand, A. fischeri bioluminescence was not affected atthe initial stages of DNAN biotransformation during nitroreduction. However,bioluminescence became inhibited at longer incubation times, when thepredominant species included azo-dimers. Taken together, these results indicatethat the changing anaerobic biotransformation product mixtures of DNAN exhibitfluctuating toxicity potency and mechanisms depending on the microbial target.

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Figure 4. Bioassay chemical library of DNAN, biotransformation products, andmodel compounds. Panel A: Concentrations resulting in 50% inhibition (IC50) ofmethanogenic (○) and A. fischeri bioluminescence (□) for individual chemicalspecies. Gray, shaded area shows DNAN IC50 region as a reference. Panel B:Total number of developmental endpoint hits (out of 640 for 32 replicates with 20possible endpoints each) for each chemical species evaluated at 6.4 μM. Asteriskdenotes total hits above statistical significant threshold. No active endpoints

were detected for MENA. DNAN was not tested (NT) (24). Panel A adapted withpermission from reference (22). Copyright 2016. Elsevier.

Toxicity of Individual Biotransformation Products

Toxicity to microbial targets and the zebrafish embryonic model was furtherevaluated to determine what types of biotransformation products (detected byLCMS) have the greatest changes in toxicity. Figure 4A shows the concentrations

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that result in 50% inhibition (IC50) of methanogenic or bioluminescent activity.The dimer and trimer model compounds (dimer L and BBY in Figure 2B),produced the lowest IC50 values (most toxic) in the chemical library tested.Complete nitroreduction to DAAN, and acetylation of DAAN, resulted in theformation of the least toxic species for both microbial targets. In comparisonwith the microbial toxicity assays, very little zebrafish developmental toxicitywas measured in the tested range of 0.0064-64.0 μM. Only two chemical species(iMENA and dimer L) produced some active developmental toxicity endpointsto rise above a statistical threshold (Figure 4B) at 6.4 μM; the lowest observableadverse effect level (LOAEL) of for both compounds. Overall, zebrafish embryodevelopment and microbial toxicity data suggest that dimers structurally similarto azo-dimers derived from DNAN biotransformation could have toxic effects onboth prokaryotes and eukaryotes.

Conclusion and Implications of Experimental Design

There has been recent growing interest in utilizing chemical analyses ofemerging contaminants and their transformation products to better understandthe biological activity of complex mixtures (25–27). The present work coupledmass spectrometry analyses of biotransformation product mixtures to toxicityevaluation. This interdisciplinary experimental design contributes to the studyof xenobiotic transformation processes in the environment, and to identify keyprocesses and products that alter toxicity beyond the parent compound. Withfurther experimental iterations, biological activity tools could help guide researchefforts on the information-rich data that derives from high-resolution massspectrometry techniques.

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8. Niedwiecka, J. B.; Finneran, K. T. Combined biological and abiotic reactionswith iron and Fe(iii)-reducing microorganisms for remediation of explosivesand insensitive munitions (IM). Environ. Sci. Water Res. Technol. 2015, 1,34–39.

9. Hawari, J.; Monteil-Rivera, F.; Perreault, N. N.; Halasz, A.; Paquet, L.;Radovic-Hrapovic, Z.; Deschamps, S.; Thiboutot, S.; Ampleman, G.Environmental fate of 2,4-dinitroanisole (DNAN) and its reduced products.Chemosphere 2015, 119, 16–23.

10. Dodard, S. G.; Sarrazin, M.; Hawari, J.; Paquet, L.; Ampleman, G.;Thiboutot, S.; Sunahara, G. I. Ecotoxicological assessment of a highenergetic and insensitive munitions compound: 2,4-dinitroanisole (DNAN).J. Hazard. Mater. 2013, 262, 143–50.

11. Kennedy, A. J.; Laird, J. G.; Lounds, C.; Gong, P.; Barker, N. D.;Brasfield, S. M.; Russell, A. L.; Johnson, M. S. Inter- and intraspecieschemical sensitivity: A case study using 2,4-dinitroanisole. Environ.Toxicol. Chem. 2015, 34, 402–411.

12. Liang, J.; Olivares, C.; Field, J. A.; Sierra-Alvarez, R. Microbial toxicityof the insensitive munitions compound, 2,4-dinitroanisole (DNAN), and itsaromatic amine metabolites. J. Hazard. Mater. 2013, 262, 281–287.

13. Ou, C.; Zhang, S.; Liu, J.; Shen, J.; Liu, Y.; Sun, X.; Li, J.; Wang, L.Removal of multi-substituted nitroaromatic pollutants by zero valent iron:a comparison of performance, kinetics, toxicity and mechanisms. Phys.Chem. 2015, 17, 22072–22078.

14. Dumitras-Hutanu, C. A.; Pui, A.; Jurcoane, S.; Rusu, E.; Drochioiu, G.Biological effect and the toxicity mechanisms of some dinitrophenyl ethers.Rom. Biotechnol. Lett. 2009, 14, 4893–4899.

15. Lent, E. M.; Crouse, L. C. B.; Hanna, T.; Wallace, S. M. The subchronicoral toxicity of 2,4-dinitroanisole (DNAN) in rats; U.S. Army Public HealthCommand. Toxicology Portfolio (MCHB-IP-TEP): Aberdeen ProvingGround, MD, 2012.

16. Krzmarzick, M. J.; Khatiwada, R.; Olivares, C. I.; Abrell, L.; Sierra-Alvarez, R.; Chorover, J.; Field, J. A. Biotransformation and degradationof the insensitive munitions compound, 3-nitro-1,2,4-triazol-5-one, by soilbacterial communities. Environ. Sci. Technol. 2015, 49, 5681–5688.

17. Olivares, C. I.; Sierra-Alvarez, R.; Abrell, L.; Chorover, J.; Simonich, M.;Tanguay, R. L.; Field, J. A. Zebrafish embryo toxicity of anaerobicbiotransformation products from the insensitive munitions compound2,4-dinitroanisole (DNAN). Environ. Toxicol. Chem. 2016, 35 (12),2885–3134.

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18. Truong, L.; Reif, D. M.; St Mary, L.; Geier, M. C.; Truong, H. D.;Tanguay, R. L. Multidimensional in vivo hazard assessment using zebrafish.Toxicol. Sci. 2014, 137, 212–33.

19. Olivares, C.; Liang, J.; Abrell, L.; Sierra-Alvarez, R.; Field, J. A. Pathwaysof reductive 2,4-dinitroanisole (DNAN) biotransformation in sludge.Biotechnol. Bioeng. 2013, 110, 1595–1604.

20. Patiny, L.; Borel, A. ChemCalc: A building block for tomorrow’s chemicalInfrastructure. J. Chem. Inf. Model. 2013, 53, 1223–1228.

21. Truong, L.; Harper, S. L.; Tanguay, R. L. InDrug Safety Evaluation; Gautier,J.-C., Ed.; Humana Press: New York, 2011; Vol 691, pp 271−279.

22. Olivares, C. I.; Sierra-Alvarez, R.; Alvarez-Nieto, C.; Abrell, L.;Chorover, J.; Field, J. A. Microbial toxicity and characterization of DNAN(bio)transformation product mixtures. Chemosphere 2016, 154, 499–506.

23. Mortensen, S. K.; Trier, X. T.; Foverskov, A.; Petersen, J. H. Specificdetermination of 20 primary aromatic amines in aqueous food simulants byliquid chromatography–electrospray ionization-tandem mass spectrometry.J. Chromatogr. A 2005, 1091, 40–50.

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Chapter 10

Transformation and Products ofOrganic Micropollutant in Water during

Electro-Enzymatic Catalysis

He Zhao,1 Penghui Du,1 Di Zhang,1 Hongbin Cao,*,1 and Laura Mast2

1Beijing Engineering ResearchCenter of Process Pollution Control, Divisionof Environment Technology and Engineering,

Institute of Process Engineering, Chinese Academy of Sciences,Beijing 100190, China

2Department of Civil Environmental and Sciences,Georgia Institute of Technology, Atlanta 30318, United States

*E-mail:[email protected].

The transformation and products of micropollutant inwaterbodies and water treatment have recently raised greatconcerns. In this chapter, micropollutants with the presenceand absence of humic acid in the electro-enzymatic systemwere studied. Organic pollutants and products were analyzedby ultra performance liquid chromatography coupled withtime-of-flight mass spectrometry (UPLC-TOF-MS). Possibleenzymatic catalyzed transformation intermediates and productsin electro-system were assessed by non-target and suspectscreening. Then, the mechanism of self-polymerization andcross coupling of micropollutant during electro-enzymaticoxidation was proposed.

Introduction

Due to the potential damage on human health and the environment, thetransformation and products of micropollutant during water treatment haveattracted great concerns. To remove micropollutant, several potential technologieshave been studied. One such method, enzyme catalysis, is promising due to itshigh reactivity and selectivity (1–3). Peroxidases, such as horseradish peroxidase(HRP) or polyphenol oxidase, e.g., laccase, can catalyze the conversion of


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phenolic moieties into phenoxy free radicals with the aid of hydrogen peroxide oroxygen. These radicals can couple to form low-solubility polymers that can beremoved from water through precipitation or coagulation (4, 6).

The catalytic cycle of HRP is shown in Scheme 1. This process has beenwidely studied for the treatment of phenolic emergingmicropollutant in waters dueto the high efficiency and specificity of enzyme (7). Humic acid (HA) moleculesalso can be oxidized for polymerization by enzyme-catalyzed oxidative couplingreaction (8).

Scheme 1. The Catalytic Cycle of HRP with Ferulate as Reducing Substrate

However, the practical application of enzyme catalysis is limited bystylishic choice challenges in continuous external H2O2 supply. Given this,an electro-enzymatic process was proposed, which combines the catalysisof oxidoreductases and the electrogeneration of in situ H2O2 (9, 10). Inelectroenzymatic systems, H2O2 is continuously supplied by the two-electronreduction of dioxygen on cathode, which does not require additional chemicals,and electricity is readily available (11), following reaction (1):

In this chapter, two model micropollutant, bisphenol A (BPA) and2,4-dichlorophenol (DCP) were studied in the electro-enzymatic system. Possibleenzymatic catalyzed intermediates and products in electro-system were identifiedby Ultra performance liquid chromatography coupled with time-of-flightmass spectrometry (UPLC-TOF-MS). Targeted and hidden-targeted screening

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methods were used to verify the possible products. The transformation andproducts of BPA and DCP in the presence of HA were examined. Then, themechanism of self-polymerization and cross coupling of micropollutant duringelectro-enzymatic oxidation was proposed.

ExperimentalMaterials

HRP (EC 1.11.1.7) was obtained from Sigma (USA). BPA (97% purity) andDCP (97% purity) was obtained from J&K Scientific Ltd. (China). HA waspurchased from Sinopharm Chemical Reagent Beijing Co., Ltd (China). All otherchemicals used in this study were analytical reagent grade and were obtainedfrom Sinopharm Chemical Reagent Beijing Co., Ltd (China). HRP activity wasmeasured via the 4-amino antipyrine method.

Preparation of Electro-Enzymatic System

For the experiments of self-polymerization, DCP and free enzyme were addedin an electro-system. The electrochemical reactor was arranged in a membranesystem with a pair of Ti electrodes. The effective volumes of the two cells wereboth 150 mL. Oxygen gas was supplied into the bottom of cathode cell at 0.5L/min for the saturation of the dissolved oxygen. CO2 was also provided as abuffer reagent at 0.1 L/min. Effect of free enzyme dose was also examined.

For the experiments of cross-coupling, BPA with the presence of HA wasconducted in an electro-system with immobilized enzyme as cathode. HRP wasimmobilized on graphite felt (GF) as a reported method (12). Briefly, oxidizedGF was fixed onto Ti electrode surface using silver conducting resin (GF/Ti).Then, partially oxidized HRPwas added to hydrazine pretreated GF/Ti to preparedHRP-GF/Ti electrode. The electrochemical reactor was constructed in amembranesystem with a HRP-GF/Ti electrode as cathode and a bare Ti electrode as anode.oxygen gas and CO2 were supplied to the bottom of the cathode cell.

All samples were filtrated by 0.22 μm polytetrafluoroethylene (PTFE)membrane filters (Millipore, Billerica, MA) before analysis. The concentrationsof BPA and DCP residues were determined using an Agilent 1100 HPLC (Agilent,USA) equipped with a UV detector and a C18 reversed-phase (RP) column (150mm × 2.1 mm, 3.5 μm particle, Agilent). Isocratic elution with 50%Milli-Q waterand 50% methanol at a flow rate of 0.25 mL min-1 was used as the mobile phase.

LC-MS Method and Data Processing Workflows for the Analysis ofIntermediates and Products

To identify the intermediates and reaction products, UPLC-TOF-MS analysiswas performed . In a Waters ACQUITY UPLC (Waters, Milford, MA) system,separation was performed on a C18 RP column (130 Å, 1.7 µm, 50 mm × 2.1 mm,3/pkg, Waters), and the injection volume was 5 μL. Initially, methanol componentwas 10%, increased to 50% at 1.5 min, and then maintained 50% for 1 min, then

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increased to 100% at 4 min, reverted to 10% at 4.1 min, and maintained 10% for1 min. The flow-rate was 0.3 mL min-1. In a Waters Xevo G2-XS TOF massspectrometer, full scan mass spectra (m/z 100−800) were recorded in negative ionmode. Capillary voltage was 4500 V of the cone voltage was 40 V and sourcetemperatures was 80 °C. Desolvation gas was nitrogen (Airgas, >99.999% purity)and flow rate was 600 L/h.

Based on these MS analyses, the tentative identification of reaction productswas done according to the following strategy: The molecular formulas of eachspecies present in reaction mixtures and not in control samples were derived fromthe accurate measured mass and isotope patterns. Targeted m/z was selected toform a new peak by Masslynx software, then MS spectrum of the specific peakwas collected. Using Elemental Composition program, the corresponding possiblechemical formula of specific m/z ions could be calculated, with the help of thegeneral reaction mechanisms, we chose the best matched formula and proposedcorresponding structures and reaction pathways.

Results and DiscussionGenerally, enzyme catalyzed oxidation of phenolic compounds involves

several major processes (13): (i) enzymes are activated by O2 or H2O2, (ii)electrons transfer from phenolic substrates to the activated enzyme, formingradicals, and (iii) radical coupling and other reactions occur.

In order to identify clearly the products and transformation of micropollutantsin electro-enzymatic catalysis, we focus on self-polymerization and cross-couplingprocess of micropollutants. Firstly, DCP and free enzyme were added into anelectro-system to explore the products and pathways of enzymatic catalyzedself-polymerization in the electro-system. Furthermore, we investigated themechanism of BPA with the presence of HA in the electro-enzyme system, whereenzyme immobilized on oxygenated cathode.

Enzymatic Catalyzed Products and Transformation of DCPin Electro-Chemical System

In electro-system, with H2O2 serving as an electron acceptor for the activationof HRP, self-polymerization reaction was induced between organic pollutants.Therefore, enzyme dosage and electric current were important factors affectingDCP transformation and products. In the previous study, as shown in Figure 1,DCP removal and transformation was remarkably accelerated with increasingenzyme dosage or current. For example, Increases in HRP dosage provide moreactive sites (7, 14). For 75 U higher HRP addtion, DCP depetion achieved ashigh as 97.4% after only 4 min of reaction. Furthermore, H 2O2 can continuouslygenerate rapidly via O2 reduction with higher electric current. When 8 mA ofelectric current was applied, DCP was removed as high as 98.3% in 6 min ofreaction.

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Figure 1. Effects of experimental parameters on removal of DCP ( [DCP]0 =1 mg L-1) by enzymatic catalysis in electro-system. (a) HRP dosage (current =5 mA); (b) current (HRP dosage = 50 U). (Reproduced with permission from

reference (15). Copyright 2016 Elsevier.)

To evaluate the possible reaction pathways for enzymatic catalyzed DCPtransformation in the electro-system, the reaction products were analyzed byUPLC-TOF-MS technique, and targeted and hidden-targeted screening methodwere used. Detailed possible reaction pathways for the transformation of DCPvia electro-enzymatic catalytic oxidation are presented in Figure 2. In this study,2-chlorohydroxyquinone (2-CHQ, species A, m/z 143) and DCP were identifiedby the targeted screening method. The peaks were well matched with the authenticstandard based on with retention time and mass spectrum. Other products weredetected by hidden-targeted screening method.

Based on the identification of DCP products, we found a large pool of CHQproducts in the electro-system. As reaction I in Figure 2 presents, DCP was firstlyhydroxylated into 2-CHQ and 2-CBQ through the hydroxide substitution underneutral or alkaline pH condition. The hydroxyl substitution of chlorophenols withthe release of chlorine ions is the first step of detoxifying process. Then, 2-CHQcould generate corresponding semiquinone radicals, which further coupled withother different radicals to produce different CHQs . The radical coupling reactionis the second type of dehalogenation reactions. Parts of CHQs were readilyoxididzed to form corresponding CBQs. Due to electron-poor sites, CBQs easilyunderwent nucleophilic reaction and were structurally transformed (16). Withthe presence of nucleophiles OH- and H2O2 (17–19) in this study, OH-CBQsreadily formed via CBQ hydroxylation in the electro-system. Thus, the hydroxylsubstitution of CBQs into OH-CBQs provides another type of dehalogenationprocess.

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Figure 2. Possible reaction pathways of DCP removal in electro-enzymaticsystem. (Reproduced with permission from reference (15). Copyright 2016

Elsevier.)

With increasing incubation time, further enzymatic oxidation and couplingreactions continued due to the presence of phenolic groups in the products.Solid products analyzed by using mass spectrometry (Figure 3), suggests atransformation of DCP dimer to hexamer and their related by-products. Withradical coupling continuing, more hydrophobic oligomer products with highmolecular weights could form, which is similar to natural humification processes.

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Figure 3. Tof-MS spectrum of precipitated sample taken after 30 min ([DCP]0 =10 mg L-1, HRP amount = 150 U, 30 °C, current = 10 mA). (Reproduced with

permission from reference (15). Copyright 2016 Elsevier.)

In the electro-system, CO2 and currents may directionally change theconcentrations of hydroxyl anions and H2O2, and then influence the HRP-drivenoxidation or substitution/addition reactions. An adequate supply of CO2can provide favorable pH conditions and facilitate enzymatic steps, such assubstrate oxidation and radical coupling, to generate precipitable polymerizedproducts. Higher currents facilitated pathway B in our electro-enzymaticsystem. Electron-rich radical anions, generated by deprotonation under alkalineenvironments, are more likely to undergo nucleophilic aromatic substitutionreactions (18, 20). More H2O2 was generated under higher currents. Besidesaccelerating HRP-driven oxidation, H2O2 also encouraged the transformation ofCBQs into less-toxic OH-CBQs. Thus, proper adjustment of the parameters inenzymatic catalysis of electro-system may greatly change the process of DCPtransformation, and facilitate the generation of products with less toxicity.

In this study, 99.7% DCP can be removed in 10-min enzymatic catalyzedoxidation of electro-system. Most of products were 2-CHQ, dimers, oligomers,and the related quinone derivatives. According to the products identified,the mechanism of DCP removal via enzymatic catalyzed oxidation in theelectro-system is proposed (Figure 4), including C-O-C or C-C couplingof chlorophenoxy radicals, hydroxyl substitution, further oxidation, CBQnucleophilic reactions and oligomer formation. Current variations and thepresence of CO2 could significantly affect these reaction pathways. In particular,higher currents enhance the hydroxylation process by promoting alkalineconditions and abundant H2O2 formation. These findings are useful forunderstanding the mechanism of HRP-driven DCP removal in the electro-system.

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Figure 4. Summary mechanism of DCP transformation by electro-enzymatic catalyzed oxidation. (Reproduced with permission fromreference (15). Copyright 2016 Elsevier.)

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Transformation and Products of BPA with the Prensence of HAduring Elecro-Enzymatic Process

In order to reveal the cross-coupling mechanism of micropollutants inelectro-enzymatic system, we further analyzed the polymeric products in theelectro-system with enzyme immobilized on cathode (Figure 5). The studyfound that, H2O2 continuously generated on the cathode through a two-electronreaction. When organic matter was added in the system, the amount of H2O2 wasinfluenced at the beginning. However, H2O2 formation increased quickly withthe time increasing.

Figure 5. Concentration of H2O2 as a function of the electrolytic time in cathodecell of immobilized enzyme system (30 °C, O2 flow-rate = 0.5 L min-1, current =

10 mA).

HA, with abundant phenolic moieties, existed widely in nature and watertreatment process. It can also be oxidized for polymerization by enzyme-catalyzedoxidative coupling reaction. Therefore, the effect of HA on the transformationand product of BPA in enzymatic catalysis of electro-system was investigated.As shown in Figure 6, HA can significantly influence the transformation andproducts of micropollutant BPA during enzymatic catalysis in electro-system(21). After addition of HA, the BPA and dissolved organic carbon (TOC) removalrate increased 47.6% and 15% in 10min enzymatic catalysis of electro-system,respectively.

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Figure 6. Removal of BPA and TOC in the enzymatic catalysis of electro-systemwith HA absent or present.

We have attempted to identify products formed in the electro-enzymaticsystem using UPLC-Tof-MS. As shown in Figure 7, untreated BPA andself-polymerized products (with star) were analyzed by the targeted screeningmethod. With the presence of HA in the electro-enzyme system, the distributionof BPA and its self-polymerized products changed significantly. The obviousdifference was due to the cross-coupling between BPA/BPA self-polymers andHA during the enzymatic catalysis of electro-system. Like BPA free-radicalformation, phenolic moieties in HA can also produce one phenoxyl radical throughHRP-driven oxidation (22). It is noted that in addition to self-polymerization,HA radicals also react with BPA radical via cross-coupling (23). Therefore,the competition between HA radicals and BPA radicals is inhibited the BPAself-coupling, resulting in less BPA dimers formed.

Based on the targeted screening method, BPA and its self-polymerizedproducts can be identified. However, HA and cross-coupling products weretoo complex, it is difficult to analyze them by non-target and suspect screeningmethod.Thus, the chemical and structural features of HA analyzed by combinedLC-MS and FTIR methods. The mechanisms of self-polymerization andcross-coupling processes of BPA in electro-enzymatic system were proposed(Figure 8). Firstly, H2O2 was generated in situ on the cathode by O2 reduction.Then, the HRP on cathode was activated by H2O2 to form intermediate productsand mediated BPA into phenoxygen free radicals followed by self-polymerizationand/or incorporation into HA, leading to 100% BPA removal during the enzymaticcatalysis in electro-system. This process also greatly altered the chemicaland structural features of HA, where hydrophilic moieties transformed intohydrophobic forms.

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Figure 7. TOF-MS spectra of samples after 10-min electro-enzymatic catalysis.(a) Untreated BPA solution (5 mg/L) as a reference control, (b) 5 mg/L BPA butabsence of HA sample, (c) 200 μg/L BPA with the presence of HA (TOC=20 mg/L)sample, (d) 50μg/L BPA with the presence of HA (TOC=20 mg/L) sample and (e)5 μg/L BPA with the presence of HA (TOC=20 mg/L) sample. (Reproduced with

permission from reference (21). Copyright 2015 Elsevier.)

Development and Prospect of Electro-Enzymatic Catalysis

Electro-enzymatic technology was first proposed by Moon S.H.’s group (12).In our study, we summarized the role of electro-enzymatic in promoting organicpollutants removal.

Firstly, enzymatic catalysis can be accelerated through enhanced electrontransfer on the immobilized HRP in electro-chemical process. In situ H2O2 alsocould generate rapidly via reduction of dissolved O2 on a cathode. With the aid ofthe new formed H2O2, HRP can immediately catalyze the conversion of phenolic

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moieties into phenoxy free radicals or other phenoxy active intermediates. Insubsequent reactions, the pathways of organic pollutants in electro-enzymaticcatalysis are similar with those in enzymatic process. On one hand, phenoxyfree radicals of one phenolic micropollutant can self-polymerize into dimmer,trimer, etc. More important, phenoxy active intermediates of different phenolicmicropollutant also can react with each other via cross-coupling. Withpolymerization and coupling continuing, more hydrophobic oligomer productswith high molecular weights could form, which is similar with the naturalhumification process. In fact, the electro-enzymatic catalysis is similar with anenhanced process of natural humification reaction.

Figure 8. The proposed mechanism of BPA removal process in electro-enzymaticsystem in the presence of humic acid. (Reproduced with permission from

reference (24). Copyright 2013 Elsevier.)

Secondly, hydroxylation enhanced by electro-system also affects the productsand pathway of organic pollutants in electro-enzymatic catalysis. In this work,we detected more hydroxyl intermediates and products than enzymatic catalysis,including OH-CBQs and hydroxyl polymers and so on. That indicates electro-process may promote hydroxylation of intermediates during enzymatic catalyzedoxidation. Due to the halides of micropollutants substituted by hydroxyl, electro-enzymatic technology provides a way for detoxifying halogen containing organicmicropollutants.

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Lastly, the electro-enzymatic catalysis was not only effective to reducephenol pollutants, but also could be applied in combined pollutants system. Inaddition to remove phenolic pollutants, electro-enzymatic catalysis could alsoreduce organic micropollutants containing aniline and thiol groups. The anilineand thiol micropollutants as nucleophiles could attack the intermediates of HAvia nucleophilic addition reactions, forming C-S-C/C-N-C covalent bonds in theelectro-enzymatic system. It is an similar cross-coupling mechanism with theC-O-C bond between different phenolic micropollutants.

Furthermore, research on the cross-coupling mechanism of multiplepollutants, especially for different kinds of pollutants, can broaden the applicationsof electro-enzymatic technology in water/wastewater treatment. In our study(21), micropollutants with small molecules like BPA can be enzymatic catalyzedoxidized and integrated into HA structure through cross coupling reaction.The hydrophobicity and molecular size of HA increased in enzyme-catalyzedpolymerization and prone to removal via precipitation separation. Therefore, theelectro-enzyme catalysis could change the properties of dissolved organic matter(DOM) of water/wastewater. It can provide an effective way for DOM removalin combination with other techniques like coagulation or filtration process, inwhich the polymerized micropollutant products can be precipitated along withDOM. For example, the authors had done the research on the micropollutantsremoval approach combining electro-enzyme and electrocoagulation process.Wherein, HRP was immobilized on the cathode and aluminum plate was usedas anode to establish a pair of working electrodes. Compared to individualelectrocoagulation, the combined process showed a higher TOC removal and lessenergy consumption to control certain contaminants (21). In addition, DOM withhigher molecular weight could not only reduce the membrane fouling (25). Thus,it may be potential research on electro-enzyme process to control membranefouling.

Acknowledgments

The authors thank support from the National Natural Science Foundation ofChina (Grant No. 51378487) and Youth Innovation Promotion Association, CAS(2014037).

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Chapter 11

Linking Trace Organic Chemical Attenuationto Microbiome Metabolic Capabilities: Insightsfrom Laboratory- and Full-Scale Managed

Aquifer Recharge Systems

Julia Regnery,*,1 Dong Li,1,2 Simon Roberts,1,3 Christopher Higgins,1Jonathan O. Sharp,1 and Jörg E. Drewes1,4

1Department of Civil and Environmental Engineering,Colorado School of Mines, Golden, Colorado 80401, United States

2Bren School of Environmental Science & Management,University of California Santa Barbara,

Santa Barbara, California 93106, United States3Sciex LLC, Framingham, Massachusetts 01701, United States

4Chair of Urban Water Systems Engineering,Technical University of Munich, 85748 Garching, Germany


Can trace organic chemical biodegradation in complexenvironments be explained by correlating the behavior ofthese water pollutants to the expression and abundance ofspecific enzymes related to their metabolism? In this study,we aim to link trace organic chemical attenuation to themetabolic capability of the microbiome by investigatingintermediate xenobiotic transformation products catalyzed byspecific enzymes. Water samples from both laboratory- andfull-scale managed aquifer recharge systems were analyzedby suspected-target screening using liquid-chromatographytime-of-flight mass spectrometry in ESI negative and positivemode. Identification of transformation products was carriedout by accurate mass and MS/MS spectra. Out of 75potential transformation products for 24 parent compounds,six metabolites for six parent compounds were tentativelyidentified in the analyzed source and groundwater samples.Identified transformation products were compared with the


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expression and abundance of functional genes quantified by 454pyrosequencing. For the majority of functional genes involvedin xenobiotic degradation pathways, no correlation withenvironmental parameters such as depth was observed. Oneimportant finding of our study is that technical guidelines forstandardization of environmental ‘-omics’ research proceduresare crucial and should cover amongst others sampling, technicaldata analysis, and interpretation of results, as well as thedefinition of cut off criteria, reference points, and normalvalues.

Introduction

Themetabolic versatility of certain microorganisms gives them the capacity totransform xenobiotic water pollutants. By definition, xenobiotics are compoundsthat are foreign to an ecological system, e.g., chemicals of anthropogenic originsuch as trace organic chemicals that are introduced to the environment. Thistransformation process can be driven by co-metabolism, which is defined as themicrobial transformation of a non-growth substrate in the presence of a growthsubstrate or another transformable substance (1). The emergence of ‘-omics’technologies over the last decade offered new insights into the complex networkof metabolic and regulatory interactions which frame biodegradation processesin microorganisms. Turnbaugh and Gordon (2) articulated an early scientificargument for uniting metagenomics with metabolomics to shed light on howmicrobial communities function in a variety of environments. Metagenomics seekto characterize the composition of microbial communities, their operations, andtheir dynamically coevolving relationships with the habitats they occupy withouthaving to culture community members, whereas metabolomics characterizemetabolites generated by one or more organisms in a given physiological andenvironmental context using analytical methods such as high-resolution massspectrometry (HR-MS) and nuclear magnetic resonance spectroscopy (2). Sincethen, interest in using meta-omics associated studies to investigate trace organicchemical biotransformation has significantly increased. The microbial capacityto transform xenobiotics can be naturally enhanced by external factors such asthe presence of essential nutrients, adequate electron acceptors, or appropriateenvironmental conditions (e.g., pH, redox potential, humidity, temperature) (3).The general strategy of assessing microbial community function is to characterizethe complete set of genes, transcripts, or enzymes from in situ environmentalmicrobial communities (e.g., by shotgun approaches) and use the abundancesof particular ones to establish associations with the communities’ potentialto biotransform specific trace organic chemicals (4). Recently, studies wereperformed to reveal the microbial community characteristics during simulatedmanaged aquifer recharge (MAR) in soil column systems receiving differentprimary substrate composition and further link these to the biotransformation oftrace organic chemicals (5). Li et al. (5) reported that the metabolic capabilitiesof the microbiome involved in xenobiotic biodegradation (e.g., cytochrome

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P450 (CYP) genes) as well as the attenuation of trace organic chemicals weresignificantly promoted under carbon-limited conditions (i.e., lower biodegradabledissolved organic carbon (BDOC) and higher humic acid content in the feedwater). However, occurrence and fate of transformation products were notmonitored during that study.

By collecting information on xenobiotic biotransformation reactions andpathways, the University of Minnesota pioneered a biocatalysis/biodegradationdatabase (BBD), allowing the prediction of the most likely metabolic pathwayof a given compound based on abstracted chemical reaction processes (6). Atpresent this database is being further developed by the Swiss Federal AquaticResearch Institute in a pathway prediction system (Eawag-PPS) and currentlycomprises 250 biotransformation rules, 1,503 chemical reactions, 219 microbialdegradation pathways, and 993 enzymes. Nevertheless, it should be noted thatbiotransformation pathways established by chemical reactions only providelittle or no relation to the corresponding enzymes and proteins (3). Kern et al.(7) published a study on using high-resolution mass spectrometry to identifytransformation products predicted by the Eawag-PPS (two generations, aerobicand anaerobic) as well as known environmental transformation products andhuman metabolites in natural water samples. Interestingly, only 19 transformationproducts out of 1,794 potential metabolites for 52 parent compounds weretentatively identified. In a different study by Card et al. (8), 14 biotransformationlibraries encoded in eight software packages that predict metabolite structureswere assessed for their sensitivity (proportion of reported metabolites that werepredicted) and selectivity (proportion of predicted metabolites that were reported)toward reported mammalian and microbial metabolites for ten agrochemicals.No library averaged greater than 58% sensitivity or 69% selectivity. With anincreasing number of predicted generations, sensitivity increased and selectivitydecreased.

Most often the information of biodegradation enzymes is only partiallyincluded in biodegradation databases, e.g., provided by enzyme names or theEnzyme Commission (EC) codes. The EC code is a numerical classificationscheme for enzymes based on catalyzed chemical reactions. It is organizedinto four levels of detail, from more general (oxidoreductases, transferases,hydrolases, lyases, isomerases, and ligases) to more specific (the substrate thattransforms). Unfortunately, in many cases, the EC code is not sufficient toidentify the protein complex responsible for the reaction, which is a consequenceof the equivocality in the definition of the codes and the different criteria used byannotators during assignment (3). For example, only 36% of the 993 enzymesincluded in the Eawag-PPS (6) possess a complete EC number. Despite havinga strong potential, it becomes apparent that the approaches currently used bybiotransformation libraries are only able to identify the presence or absenceof previously characterized enzymes and species in environmental samples.Although pure cultures of bacterial strains degrading individual trace organicchemicals have been isolated and reported (9–11), only limited results werepublished for complex mixtures (12). Another limitation is the relevance oflaboratory cultivars to natural conditions as many yet unknown species contributeto biodegradation (3).

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The present study was guided by the question of whether trace organicchemical biotransformation in complex environments such as soil-aquifer systemscan be explained by comparing trace organic chemical attenuation with theexpression and abundance of a targeted subset of putative biotransformation genesextracted from proximal soils. To shed light on this question, both metagenomicsand HR-MS techniques were applied to corresponding soil and water samplesfrom laboratory-scale soil column systems simulating MAR under controlledenvironmental conditions as well as pilot- and full-scale MAR operations inCalifornia and Colorado recharging impaired water via surface spreading.

Methodology

Test Beds and Sample Collection

Recharged source water and groundwater samples from two different MARfield sites in California and Colorado as well as two soil column systems werecollected and analyzed according to Standard Methods (13) during samplingcampaigns in 2012 and 2013 as described in Drewes et al. (14). Water samplesfor advanced chemical analysis were immediately preserved with sodium azide(1 g/L) to prevent further biodegradation. In addition, representative soil sampleswere collected from the soil column systems, the surface of the San GabrielSpreading Grounds test basin in California at 1 cm and 5 cm depths, and over a1 m depth profile from the central infiltration basin at the Prairie Waters Projectaquifer recharge and recovery (ARR) facility in Colorado.

California

The United States Geological Survey/Water Replenishment District ofSouthern California test basin at the Montebello Forebay was constructed inthe early 90’s at the north end of the San Gabriel River Coastal SpreadingGrounds located between Whittier Boulevard and Washington Boulevard in PicoRivera, Los Angeles County, California. Tertiary treated reclaimed water (i.e.,dual-media filtration followed by chlorination and dechlorination) is deliveredfrom the water reclamation facilities to the Spreading Grounds through a culvert.A small percentage of the total flow can be diverted to the 2023 m2 large test basinthrough a pipeline using a submersible pump. The test basin is fully equippedwith a multilevel sampler and monitoring wells and subsurface flow conditionsare very well characterized (15–17). Prior to both sampling campaigns, thefilling of the test basin with reclaimed water to activate/maintain the indigenousmicrobial community was scheduled as detailed in Drewes et al. (14). Duringthis study, DOC, nitrate, and dissolved oxygen concentrations in the rechargedtertiary treated wastewater effluent were in the range of 6.5 ± 1.0 mg/L, 4.3 ±1.4 mg N/L, and 6.8 ± 0.6 mg/L, respectively. Due to the high amount of BDOC(approximately 4 mg/L), subsurface redox conditions at the test basin werecharacterized as oxic/suboxic in the upper aquifer and anoxic conditions in the

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lower aquifer. Suboxic conditions were defined as nitrate reduction of less than0.5 mg N/L and dissolved manganese concentrations of less than 0.05 mg/L.

Colorado

In 2010, the city of Aurora launched the Prairie Waters Project North Campusto supplement its drinking water supply using surface water impaired by treatedwastewater discharges. An MAR system consisting of riverbank filtration (RBF)galleries and an ARR facility as part of an advanced water treatment train wasconstructed along the South Platte River downstream of Denver, Colorado. Thecombined RBF filtrate is delivered via pipes into an array of surface spreadingbasins at the adjacent ARR facility. A continuous slurry wall along the entireperimeter of the ARR facility extending from the surface to the bedrock is usedto isolate the recharged water from the surrounding native groundwater system.Recovery wells and monitoring wells are placed throughout the ARR site. Duringthis study, DOC and nitrate concentrations in the recharged anoxic RBF filtratewere in the range of 3.3 ± 0.3 mg/L and 2.7 ± 1.2 mg N/L, respectively. Due to thelow amount of BDOC in the RBF filtrate (approximately 0.4 mg/L) and re-aerationduring surface spreading, subsurface redox conditions at the ARR site remainedoxic to suboxic. A detailed description of the PrairieWaters Project North Campusis provided elsewhere (14, 18–20).

Soil Column Systems

Two large-scale soil column systems (each 4.5 m in length, 0.15 m innerdiameter), filled with a blend of 50:50 (v/v) technical sand and sandy soilfrom the Prairie Waters Project North Campus (grain size <2 mm, foc <0.3%)were constructed in spring 2012 and were equilibrated with their respectivefeed water type for more than 6 months prior sampling. Both soil columnswere equipped with intermediate soil and water sampling ports at differentdepths and were operated under saturated flow conditions (1 mL/min flow rate).Subsurface travel times in the soil columns were 16 and 20 days, respectively.One soil column received nanofiltration (NF) permeate generated from tap water(alkalinity adjusted) representing carbon-depleted conditions. DOC content inthe nitrogen-purged feed water was 0.3 ± 0.3 mg/L, whereas ammonia was belowdetection limit. The redox condition in this soil column was suboxic to slightlyanoxic (i.e., traces of dissolved manganese detected in soil column effluent). 24trace organic chemicals in aqueous solution were continuously spiked into the feedwater line of this soil column at the 250 ng/L level. The sister column, however,received a blend of secondary treated effluent and nanofiltration permeate 50:50(v/v) with no additional trace organic chemical spiking. DOC and ammoniaconcentrations in the nitrogen-purged feed water were 4.9 ± 0.6 mg/L and 6.5mg/L, respectively with a high amount of BDOC (approximately 2.0 mg/L). Theredox condition in this soil column was predominantly anoxic. Throughout the

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study, water and soil samples were collected from different depths of the soilcolumns representing different travel times.

Metagenomic Analysis

Metagenomic analysis was carried out for four soil column samples and sixMAR infiltration basin samples to assess the relative abundance of sequences thatcode for specific enzymes involved in xenobiotic biodegradation. This shotgunmetagenomic sequencing approach enables comprehensive sampling of all genesextracted from a given sediment sample. DNA was extracted in triplicate for eachsampling location and extractions were pooled. 5 μg DNA of each sample wasobtained and sequenced by 454 pyrosequencing (Saleah Genomics, Inc.). Thedetailed analytical and statistical procedures are described elsewhere (5). In short,metagenomic sequences were demultiplexed, quality filtered and functionallyassigned to the Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology(21) using MG-RAST Version 3.2 (http://metagenomics.nmpdr.org). Only readsthat matched at least one KEGG Ortholog group were included in downstreamstatistical analyses. Percentages of assigned sequences per sample assignedto each group were generated for normalization between samples. The rawpyrosequencing data for all 10 samples are publicly available in MG-RAST underaccession numbers 45481[69, 71, 72, 74, 76, 79, 82, 83, 85, 86].3.

Suspected-Target Screening Approach

Water samples (100 – 200mL)were extracted byWaters Oasis HLB cartridges(500 mg adsorbate) in a pre-concentration and clean up step using an automatedsolid phase extraction (SPE) unit (AutoTrace 280, Thermo Scientific). Isotopestandards were obtained for all target analytes as detailed in Teerlink et al. (22)and spiked into the water samples prior to SPE. SPE cartridges were conditionedwith 5 mLmethyl tertiary butyl ether, 5 mLmethanol, and 5 mL of ultrapure water.The water sample was passed through each cartridge with a flow of 4 mL/min. Thecartridges were then dried under a nitrogen stream for 1 hour, and analytes wereeluted from the cartridges with 5 mLmethanol followed by 5 mL of 10%methanolin methyl tertiary butyl ether. The resulting extract was dried down under a gentlenitrogen stream and resolved in 1 mL methanol. The final sample was prepared ina ratio of 10/90 methanol/water (v/v) for further analysis.

Quantitative analysis of 24 selected wastewater related trace organicchemicals (‘parent compounds’) was performed by liquid chromatographycoupled with tandem mass spectrometry (LC-MS/MS) using an isotope dilutionmethod as reported in Teerlink et al. [22]. In brief, LC-MS/MS analysis wasperformed using an Agilent 1200 LC and a CTC Analytics HTS PAL autosamplerequipped with a 1 mL sample loop for chromatography, coupled with a Sciex3200 QTRAP MS/MS system. Compounds were separated using a Phenomenex150 mm × 4.6 mm Luna C18 column with 5 μm particle size. A binary gradientwith a flow rate of 800 μL/minute was used for both electrospray ionization (ESI)positive and negative methods. Quantification of analytes was carried out inAnalyst (v1.6).

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To identify possible biotransformation pathways of the 24 selected parentcompounds, the biodegradation databases Eawag-PPS (two generations, aerobic)and the KEGG database as well as published experimental data were assessedand potential biotransformation products evaluated. Precedence was given tobiotransformation products for which information about the enzyme-catalyzedreaction was available. Thus, 75 potential metabolites were selected that werelikely to occur in the analyzed surface and groundwater samples. A list of parentcompounds and potential transformation products is provided in Table 1.

A shotgun approach was applied to qualitatively screen for biotransformationproducts in 12 source water and groundwater samples by HR-MS. Sampleswere analyzed by suspected-target screening using a Shimadzu LC-30AD anda CTC Analytics HTS PAL autosampler equipped with a 1 mL sample loopfor chromatography coupled with a time of flight mass spectrometer (SciexTripleTOF 5600+, 5 ppm mass accuracy, 30,000 resolution) in ESI negative andpositive mode with information dependent acquisition (IDA) MS/MS on suitablepeaks. Compounds were separated using a Phenomenex 50 mm × 4.6 mm LunaC18 column with 3 μm particle size. Eluents consisted of 10 mM formic acid inwater (A) and methanol (B). A binary gradient (0% B for 2 min to 100% B over3 min, hold for 3 min, back to 0% for 6 min) at a flow rate of 400 μL/minutewas used for both ESI positive and negative methods. Each sequence run on theTripleTOF 5600+ included ultrapure water blanks and a reference standard mixof parent compounds as a control. Furthermore, mass calibration to less than 5ppm error was performed every five samples. During TOF scans collision energywas set to -5 eV and 5 eV and de-clustering potential to -50 V and 50 V duringESI negative and positive mode, respectively. Ion spray voltage floating was keptat -4.500 eV in ESI negative and 5.500 eV in ESI positive mode. During IDAMS/MS scans collision energy was set to -30 eV (ESI negative) and 30 eV (ESIpositive) while de-clustering potential remained at -50 V and 50 V, respectively.Data processing and identification of transformation products by accurate massand MS/MS spectra were performed using PeakView software (v2.2) with itsextracted ion chromatogram (XIC) manager add-in. The XIC manager consistsof a table for defining a list of masses or formulae to generate extracted ionchromatograms. Hence, an XIC list of the selected 75 potential metabolites withaccurate mass was generated for suspected-target screening of HR-MS/MS data.Ions formed by proton attachment [M+H]+ in ESI positive mode and by protonabstraction [M-H]- in ESI negative mode were checked for metabolites for whichionization was not previously confirmed. Confidence settings for compoundidentification in PeakView were as follows: Mass within 5 ppm error, isotoperatio within 20%, and MS/MS match. To prevent false positives, positive peakfindings from the extracted chromatogram were discarded when the peak intensitywas below a threshold of 103 peak area counts or if a peak of similar retentiontime and intensity was found in the blank. The confidence level scheme proposedby Schymanski et al. (23) was used for tentative identification of transformationproducts. According to this confidence scheme, all of the discussed transformationproducts are either level 3 (i.e., tentative candidate) or level 2 (i.e., probablestructure). Peak areas of tentatively identified transformation products wereintegrated in MultiQuant (v3.0).

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Table 1. List of Parent Compounds and Potential Transformation Products Analyzed by Suspected-Target Screening

Compound Formula Potential transformation product Formula

Acesulfame C4H5NO4S Acesulfame transformation product C4H7NO5S

6-Methyl-1,2,3-oxathiazinan-4-one-2,2-dioxide C4H7NO4S

Amitriptyline C20H23N Nortriptyline C19H21N

3-(10,11-Dihydro-5H-dibenzo[a,d] [7]annulen-5-ylidene)-1-propanone C18H16O

Desmethyl-nortriptyline C18H19N

Amitriptyline C20H23N E-10-Hydroxydesmethyl-nortriptyline C18H19NO

Atenolol C14H22N2O3 Atenolol acid C14H21NO4

Atrazine C8H14ClN5 Hydroxyatrazine C8H15N5O

6-Chloro-N-isopropyl-1,3,5-triazine-2,4-diamine C6H10ClN5

6-Chloro-N-ethyl-1,3,5-triazine-2,4-diamine C5H8ClN5

2-Chloro-4-hydroxy-6-amino-1,3,5-triazine C3H3ClN4O

Caffeine C8H10N4O2 Paraxanthine, theophylline, or theobromine C7H8N4O2

1,3,7-Trimethyluric acid C8H10N4O3

Xanthine C5H4N4O2

Uric acid C5H4N4O3

Carbamazepine C15H12N2O Carbamazepine transformation product C14H9N

DEET C12H17NO m-Methylbenzoate C8H8O2

1,2-Dihydroxy-3-methylcyclohexa-3,5-diene-carboxylate C8H10O4

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3-Methylcatechol C7H8O2

Diazepam C16H13ClN2O Nordazepam C15H11ClN2O

2-Amino-N-(2-benzoyl-4-chlorophenyl)-N-methylacetamide C16H15ClN2O2

2-Amino-N-(2-benzoyl-4-chlorophenyl) acetamide C15H13ClN2O2

Diazepam transformation product C15H10ClNO3

Oxazepam C15H12ClN2O2

Temazepam C16H13ClN2O2

Diclofenac C14H11Cl2NO2 4′-Hydroxydiclofenac C14H11Cl2NO3

1-(2,6-Dichlorophenyl)-1,3-dihydro-2H-indol-2-one C14H9Cl2NO

2,6-Dichloroaniline C6H5Cl2N

(2,6-Dihydroxyphenyl) acetate C8H7O4

4-(2,6-dichlorophenylamino)-1,3-benzene-dimethanol C14H13Cl2NO2

3,5-Dichlorobenzoic acid C7H4Cl2O2

Dilantin C15H12N2O2 4-Hydroxyphenytoin C15H12N2O3

Phenytoin catechol C15H12N2O4

Phenytoin methylcatechol C16H12N2O4

Diphenhydramine C17H21NO Nordiphenhydramine C16H19NO

Dinordiphenhydramine C15H17NO


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Table 1. (Continued). List of Parent Compounds and Potential Transformation Products Analyzed by Suspected-Target Screening


Benzhydryl acetate C15H14O2

Diphenylmethoxyacetic acid C15H14O3

Fluoxetine C17H18F3NO Norfluoxetine C16H16F3NO

4-Trifluoromethylphenol C7H5F3O

N-Benzoylglycine C9H9NO3

Gemfibrozil C15H22O3 3-[(4-Carboxy-4-methylpentyl)oxy]-4-methylbenzoic acid C15H20O5

5-[5-(Hydroxymethyl)-2-methyl-phenoxy]-2,2-dimethylpentanoic acid C15H22O4

Ibuprofen C13H18O2 2-Hydroxy ibuprofen C13H18O3

1-Ethyl-4-isobutylbenzene C12H18

Ibuprofen carboxylic acid C13H16O4

Isobutylcatechol C10H14O2

5-Formyl-2-hydroxy-7-methylocta-2,4-dienoic acid C10H14O4

2-Hydroxy-5-isobutylhexa-2,4-dienedioic acid C10H14O5

Meprobamate C9H18N2O4 Hydroxymeprobamate C9H18N2O5

Naproxen C14H14O3 2-(6-Hydroxynaphthalen-2-yl) propanoic acid C13H12O3

1-(6-Methoxynaphthalen-2-yl) ethanone C13H12O2

Paracetamol C8H9NO2 4-Aminophenol C6H7NO

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N-Acetylimidoquinone C8H7NO2

N-(3,4-Dihydroxyphenyl) acetamide C8H9NO3

Primidone C12H14N2O2 2-Ethyl-2-phenylmalonamide C11H14N2O2

Propylparaben C10H12O3 4-Hydroxybenzoic acid C7H6O3

Sulfamethoxazole C10H11N3O3S 4-Aminobenzenesulfonate C6H6NO3S

3-Amino-5-methylisoxazole C4H6N2O

1,2,4-Benzenetriol C6H6O3

N-4-Acetyl-sulfamethoxazole C12H14N3O4S

3-Amino isoxazole C3H4N2O

TCEP C6H12Cl3O4P Bis-(2-chloroethyl) phosphate C4H9Cl2O4P

TCPP C9H18Cl3O4P Bis-(chloropropyl) phosphate C6H13Cl2O4P

Triclocarban C13H9Cl3N2O 4′,4′-Dichlorocarbanilide C13H10Cl2N2O

1-(3-Chlorophenyl)-3-phenylurea C13H11ClN2O

Carbanilide C13H12N2O

3,3′,4,4′-Tetrachlorocarbanilide C13H8Cl4N2O

3′-Hydroxytriclocarban C13H9Cl3N2O2

3-Chloroaniline C6H6ClN

Trimethoprim C14H18N4O3 α-Hydroxytrimethoprim C14H18N4O4


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Table 1. (Continued). List of Parent Compounds and Potential Transformation Products Analyzed by Suspected-Target Screening


5-(3,4,5-Trimethoxybenzyl)-5-hydroxyl, 6-pyrimidinone-2,4-diamine C14H20N4O5

4-Hydroxytrimethoprim C13H16N4O3

5-(1-Carboxyl, 1-methoxy, 5-methoxy 1-,4-entene) pyrimidine-2,4-diamine, 5-hydroxyl

C13H20N4O5

5-(3,4,5-Trimethoxybenzyl) pyrimidine-2,4-diamine, 5-hydroxyl C14H20N4O4

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Results and DiscussionIdentification of Transformation Products

Out of the 75 potential transformation products for 24 parent compounds, sixmetabolites for six parent compounds were tentatively identified in the analyzedsource and groundwater samples by XIC list search following the suspectscreening approach. These transformation products, their respective parentcompounds, and the potential metabolic conversion pathways are summarized inTable 2. An example of the identification process is displayed for a transformationproduct (3,5-diclorobenzoic acid (24)) of the non-steroidal anti-inflammatorydrug (NSAID) diclofenac (Figure 1).

Figure 1. Structure of the transformation product 3,5-dichlorobenzoic acid in agroundwater sample from California with (a) the corresponding chromatogram,(b) the HR-MS/MS spectrum, (c) the HR-MS/MS spectrum reproduced withpermission from reference (24), copyright (2008) American Chemical Society,and (d) isotope pattern of the ionized molecule. The drawn fragment structures

are those proposed by ACD.

The transformation products theophylline, paraxanthine, theobromine, andE-10-hydroxy-desmethylnortriptyline were detected in ESI positive mode, while3,5-dichlorobenzoic acid, 3-[4(carboxy-4-methylpentyl)oxy]-4-methyl-benzoicacid, m-methylbenzoate, and N-(3,4-dihydroxyphenyl) acetamide weredetected in ESI negative mode. Interestingly, m-methylbenzoate (i.e.,N,N-diethyl-m-toluamide (DEET)), theophylline, theobromine, and paraxanthine(i.e., caffeine) were the only tentatively identified transformation products that

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were predicted by the Eawag-PPS. To some extent this might be related to therestriction that only two generations and aerobic degradation were used for theprediction of transformation products by the Eawag-PPS.

The detected caffeine transformation product is characterized by the lossof one methyl group. Depending on the exact location of the missing methylgroup, the compound with the formula C7H8N4O2 and the exact mass of181.0713 Da for [M+H]+ can be theophylline, theobromine, or paraxanthine,respectively (6). To verify the exact location of the missing methyl group, eitherauthentic standards of the caffeine metabolites should be analyzed or additionalexperiments by HR-MS/MS or nuclear magnetic resonance spectroscopy arerequired. However, caffeine is metabolized extensively (on average 80%)to paraxanthine in humans by CYP1A2, a member of the CYP superfamilyof enzymes (25). The CYP proteins are monooxygenases, which catalyzemany reactions involved in drug metabolism, e.g., the transformation of theanalgesic paracetamol into its metabolite N-(3,4-dihydroxyphenyl) acetamidevia hydroxylation (26) or the hepatic transformation of the antidepressantamitriptyline into E-10-hydroxy-desmethyl-nortriptyline via N-demethylationand (E)-10-hydroxylation (27).

The caffeine metabolite as well as N-(3,4-dihydroxyphenyl) acetamide weredetected at comparably low peak intensities (near the threshold of 103 peakarea counts) in water samples collected from two intermediate sampling portscorresponding to a residence time of 7 and 17 days, respectively of the NF-fedsoil column that received continuous trace organic chemical spiking at the 250ng/L level. In this soil column, the first order decay rate constant and DT50 (i.e.,the time for the dissipation of 50% of the initial concentration) for caffeine weredetermined to be 0.09 d-1 (R2 = 0.755, n = 8) and 7.6 days, respectively withina travel time of 20 days under carbon depleted (low BDOC) and predominantlysuboxic conditions. For paracetamol, the first order decay rate constant and DT50value were in the range of 0.23 d-1 (R2 = 0.802, n = 3) and 3.0 days. However,the presence of the caffeine metabolite and the paracetamol metabolite in the soilcolumn influent sample collected from a valve at the inlet of the column indicatesthat (bio)transformation already occurred in the bottle with spiking solution orduring mixing with the NF feed in the teflon tubing that led to the soil column.This assumption is supported by the fact that concentrations of both parentcompounds were depleted in the respective influent sample compared to spikinglevels. The presence of the metabolites in the NF permeate generated fromtap water prior spiking can be mostly excluded. Results also indicate that bothmetabolites were not stable but further transformed in the soil as the metabolitepeak areas decreased over travel time and distance.

In these water samples, the XIC list search for [M+H]+ in ESI positivemode also suggested the presence of trimethyl uric acid, another transformationproduct of caffeine. But due to very low peak intensities of this potentialtransformation product candidate, no sufficient IDA MS/MS were triggeredto allow for confidence in tentative identification. Besides the caffeine andparacetamol metabolites, the search by XIC list also revealed the occurrenceof E-10-hydroxy-desmethyl-nortriptyline, a transformation product of theantidepressant amitriptyline, in the soil column samples.

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Table 2. Tentatively Identified Biotransformation Products and Their Respective Parent Compounds

Parent compound Formula Tentative metabolite Formula Metabolic conversion Ref.

Amitriptyline C20H23N E-10-hydroxy-desmethyl-nortriptyline

C18H19NO In humans via N-demethyl-ationand (E)-10-hydroxyl-ation by CYPa[EC:1.14.-.-]

(27)

Caffeine C8H10N4O2 Theophylline C7H8N4O2 Via loss of one methyl group bycaffeine de-methylase [EC:1.13.12.-]

(6, 21)

Theobromine C7H8N4O2 Via loss of one methyl group bymethylxanthine N(1)-demethylase[EC: 1.14.13.178]

(6, 21)

Paraxanthine C7H8N4O2 Via loss of one methyl group bymethylxanthine N(3)-demethylase[EC: 1.14.13.179] or CYP1A2 inhumans

(6, 21)

DEET C12H17NO m-Methyl benzoate C8H8O2 Via hydrolysis of the secondary amideby deet hydrolase [EC:3.5.1.-]

(6)

Diclofenac C14H11Cl2NO2 3,5-Dichloro- benzoicacid

C7H4Cl2O2 Possibly via N-dealkyl-ationand carboxylation of thechlorine-containing phenyl ring

(24)

Gemfibrozil C15H22O3 3-[(4-Carboxy-4-methylpentyl)oxy]-4-methylbenzoicacid

C15H20O5 Via carboxylation in humans (29)

Paracetamol C8H9NO2 N-(3,4-dihy-droxyphenyl)acetamide

C8H9NO3 In mice via hydroxylation by CYPa[EC:1.14.-.-]

(26)

a CYP = cytochrome P450

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Caffeine and its transformation products theophylline, theobromine,paraxanthine, and trimethyl uric acid, paracetamol and its metaboliteN-(3,4-dihydroxyphenyl) acetamide, and amitriptyline and its metaboliteE-10-hydroxy-desmethyl-nortriptyline were not detected in the tertiary treatedwastewater effluent used for recharge at the San Gabriel Spreading Grounds testbasin in California and two corresponding groundwater samples representingsubsurface travel times of 0.5 and 2 days, respectively in the oxic to suboxic upperaquifer. At the Prairie Waters Project site in Colorado, caffeine was previouslydetected at very low concentrations (10 ± 6 ng/L) in RBF filtrate recharged atthe ARR infiltration basin. Concentrations of paracetamol and amitriptylinewere always below their respective detection limits of 10 ng/L and 25 ng/L inthe RBF filtrate. However, none of the caffeine, paracetamol, and amitriptylinetransformation products searched for by XIC list were identified in the analyzedRBF filtrate sample and two corresponding groundwater samples representingsubsurface travel times of 3 and 20 days, respectively under oxic conditions.

While biodegradation of the stimulant caffeine and the analgesic paracetamolis less affected by subsurface redox conditions, the NSAID diclofenac andthe fibrate gemfibrozil have demonstrated redox dependent biodegradation inprevious studies. Fast and complete attenuation below the limit of detection wasachieved for both pharmaceuticals under oxic conditions during simulated MAR,whereas almost no attenuation occurred under anoxic conditions during 15 daysof subsurface transit (28). This is corroborated by fast removal of diclofenac andgemfibrozil under oxic conditions at the San Gabriel Spreading Grounds test basinin California and almost no removal in the NF-fed soil column under suboxic toanoxic conditions as illustrated in Figures 2 and 3, respectively. Accordingly, thetentatively identified diclofenac transformation product 3,5-dichlorobenzoic acidwas not detected in the soil column samples (Figure 2a), whereas increasing peakareas were observed for this metabolite at the San Gabriel Spreading Groundstest basin in California (Figure 2b). The formula and structure of the diclofenactransformation product 3,5-dichlorobenzoic acid was previously confirmed in astudy by Peréz and Barceló (24) (Figure 1c), who observed this metabolite in anactivated sludge bioreactor spiked with diclofenac. It has been suggested thatthe metabolic reaction might be related to nitrifying bacteria (24). Interestingly,3,5-dichlorobenzoic acid was not listed in the Eawag-PPS as a potential diclofenacmetabolite.

The tentatively identified gemfibrozil transformation product 3-[(4-carboxy-4-methylpentyl)oxy]-4-methylbenzoic acid in our MAR water sampleswas previously reported as a human metabolite (29). 3-[(4-Carboxy-4-methylpentyl)oxy]-4-methylbenzoic acid was not predicted in the Eawag-PPS.In humans, the hepatic metabolic conversion of gemfibrozil mainly involvesoxidation of a ring methyl group to successively form a hydroxymethyland the observed carboxyl metabolite. However, limited informationis available which specific functional genes catalyze the carboxylationreaction. Less than 2% of an administered dose is excreted in humanurine as unchanged gemfibrozil, while approximately 70% is excreted asthe glucuronide conjugate gemfibrozil 1-beta-glucuronide (27). While thediclofenac transformation product was not detected in samples collected from

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the NF-fed soil column, a small portion of gemfibrozil was transformed to3-[(4-carboxy-4-methylpentyl)oxy]-4-methylbenzoic acid during predominantlysuboxic soil passage (Figure 3a). It is important to note that the gemfibrozilmetabolite was not detected in the soil column influent sample, implying that soilmicrobial processes were required to catalyze the transformation. As expected, themetabolite peak areas in samples collected from the soil columnwere considerablylower than those observed at the San Gabriel Spreading Grounds test basin inCalifornia (Figure 3b). As 3-[(4-carboxy-4-methylpentyl)oxy]-4-methylbenzoicacid is a human metabolite excreted via urine, it was already present in tertiarytreated wastewater effluent used for recharge at the test basin. Despite our limitedsample size, 3-[(4-carboxy-4-methylpentyl)oxy]-4-methylbenzoic acid seemedto be further transformed during soil passage as peak area counts decreasedsubstantially over travel time and distance in the aquifer (Figure 3b).

Figure 2. Redox-dependent attenuation of diclofenac and occurrence of itstransformation product 3,5-dichlorobenzoic acid in samples collected from a)soil column system receiving alkalinity-adjusted nanofiltration permeate (lowBDOC) under suboxic subsurface conditions and b) managed aquifer rechargeinfiltration basin receiving tertiary treated wastewater effluent (high BDOC)

under oxic to suboxic subsurface conditions.

Similar to caffeine, diclofenac and gemfibrozil were only present atlow concentrations of 16 ± 10 ng/L and 46 ± 43 ng/L, respectively in RBFfiltrate recharged at the ARR infiltration basin in Colorado. They wereattenuated below their respective detection limit of 10 ng/L in groundwaterrecovered throughout the ARR site (30). Most likely due to the low initialconcentration of both parent compounds, 3,5-dichlorobenzoic acid and3-[(4-carboxy-4-methylpentyl)oxy]-4-methylbenzoic acid were not identified byXIC list search above the peak intensity threshold in samples from the ColoradoMAR site.

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We also tentatively identified a potential transformation product of the insectrepellent DEET, m-methylbenzoate, with the formula C8H8O2 and the exact massof 136.0536 Da. Transformation of DEET to m-methylbenzoate was predictedby the Eawag-PPS via hydrolysis of the secondary amide (31). As illustrated inFigure 4a, DEET showed almost no removal in the NF-fed soil column system.Nevertheless, peak intensities of m-methylbenzoate increased over travel timeand distance under suboxic subsurface conditions in the soil column. At theARR facility in Colorado, the DEET transformation product was present at lowpeak intensity in the previously anoxic RBF filtrate used for recharge (Figure4b). During infiltration at the ARR infiltration basin, peak intensities decreasedbelow the threshold peak area within three days of subsurface travel under oxicconditions. Although DEET was present at an average concentration of 120 ng/Lin the tertiary treated wastewater effluent recharged at the San Gabriel SpreadingGrounds test basin in California, m-methylbenzoate was neither detected in thetertiary treated wastewater effluent nor in groundwater samples from the upperaquifer.

Figure 3. Redox-dependent attenuation of gemfibrozil and occurrence of itstransformation product 3-[(4-carboxy-4-methylpentyl)oxy]-4-methylbenzoic acidin samples collected from a) soil column system receiving alkalinity-adjustednanofiltration permeate (low BDOC) under suboxic subsurface conditions,and b) managed aquifer recharge infiltration basin receiving tertiary treatedwastewater effluent (high BDOC) under oxic to suboxic subsurface conditions.

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Metabolic Capability of the Soil Microbial Community

Based on the information available about functional genes involved inthe metabolic conversion of transformation products identified in the sourceand groundwater samples (Table 2), MG-RAST annotated metagenomic readsof the soil samples were searched for specific enzyme names and EC codes.Previous studies suggested a relationship of functional gene abundances withenvironmental conditions such as depth, BDOC availability, saturation, orredox condition (5, 32). However, it is important to note that the soil microbialcommunity’s metabolic potential as assessed in this study is interpreted bythe relative abundance of functional genes rather than its real (i.e., expressed)metabolic capability.

Figure 4. Attenuation of DEET and occurrence of its transformation productm-methylbenzoate in samples collected from a) soil column system receivingalkalinity-adjusted nanofiltration permeate (low BDOC) under suboxicsubsurface conditions, and b) managed aquifer recharge infiltration basin

receiving riverbank filtrate (low BDOC) under predominantly oxic subsurfaceconditions.

Researchers previously established the metabolic pathway of caffeine inmicrobes and mammals as a KEGG pathway map (Kanehisa Laboratories,Japan). When the metagenomic reads of all analyzed soil samples were mappedtowards this metabolic pathway, only one of the functional genes, urate oxidase[EC:1.7.3.3], was abundant. Urate oxidase was detected in two soil samples which

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were collected from the NF-fed soil column (relative abundance of 0.014% and0.005%, respectively), the initial soil used for filling the soil columns (0.023%),and the San Gabriel Spreading Grounds test basin surface layer in California(0.057%). Urate oxidase catalyzes a reaction that involves trimethyl uric acid asthe substrate. No sequences were identified that code for functional genes such ascaffeine demethylase or oxidoreductases. Caffeine demethylase [EC:1.13.12.-],methylxanthine N(1)-demethylase [EC: 1.14.13.178], and methylxanthineN(3)-demethylase [EC:1.14.13.179] catalyze the transformation reaction ofcaffeine to theophylline, theobromine, and paraxanthine, respectively. In humans,the transformation of caffeine to paraxanthine is catalyzed by CYP1A2, a memberof the CYP family.

Although transformation of DEET to m-methylbenzoate was proposed to becatalyzed by deet hydrolase [EC:3.5.1.-] via hydrolysis of the secondary amide, nofull EC number had been assigned by Rivera-Cancel et al. (31). Accordingly, itremains unclear, if all hydrolases acting on carbon-nitrogen bonds in linear amides(other than peptide bonds) or just specific ones can catalyze this reaction.

The relative abundance of several functional genes related to xenobioticspathways such as the CYP family are summarized in Table 3. For the majorityof functional genes involved in xenobiotic degradation pathways, no correlationwith depth was observed for the depth profile (0.01 – 1 m) at the Prairie WatersProject ARR infiltration basin (Table 3). However, the relative abundance of allhydrolases with the EC code 3.5.1.- revealed an increasing trend with depth from0.21% (0.01 m) to 0.33% (0.1 m), 0.37% (0.5 m), and 0.46% (1 m). Differences inthe relative abundances of sequences become apparent (Table 3) when comparingthe potential metabolic capability of the microbiome for xenobiotic transformationin the two large-scale soil columns that received different feed water types inexcess of one year (i.e., alkalinity adjusted NF-permeate and secondary treatedwastewater effluent blend, respectively). Presumably this was due to the variableprimary substrate provided by the column feed waters. Functional genes involvedin nitrotoluene biotransformation were enhanced in the soil column that receivedhigh BDOC secondary treated wastewater effluent; in contrast, chlorobenzenedegradation enzymes were depleted (Table 3). The relative abundance of allhydrolases [EC:3.5.1.-] was also substantially lower (0.22%) than abundancesobserved in the initial soil (0.44%) or the NF-fed soil column (0.35% and 0.37%,respectively). While these suggestive trends are interesting, replicate samplesfrom different columns, spatial and temporal locations, and field sites will berequired to establish overarching, statistically significant trends in abundance andcould be enhanced by transcriptomic inquiry as a more accurate proxy of enzymepresence.

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Table 3. Relative Abundance of Functional Genes Related to Metabolic Pathways for Xenobiotics in Soil Samples Collected atDifferent Depths from Managed Aquifer Recharge (MAR) Infiltration Basins in Colorado and California, Respectively and TwoSoil Column Systems Simulating MAR. The Amount of Biodegradable Dissolved Organic Carbon (BDOC) in the RechargedSource Water and Predominant Redox Conditions in the Subsurface during Recharge Are Indicated. Sample ID Numbers Are

Provided for the Raw Sequencing Data in MG-RAST.Relative abundance of functional genes involved in xenobiotics pathways (KEGG pathways)

Sample type Depth[cm]a

Redoxcondition

MG-RA-ST ID

Amino-benzo-ate

Atrazine Benzoate Capro-lactam

Chloro-benzeneb Dioxin Nitro-

toluene Steroid Styrene CYPc

MAR field site CO

Low BDOC influent 1 oxic 4548171.3 0.15% 0.08% 0.25% 0.10% 0.19% 0.06% 0.10% 0.06% 0.00% 0.23%

10 oxic 4548172.3 0.08% 0.03% 0.52% 0.05% 0.21% 0.16% 0.08% 0.08% 0.00% 0.16%

50 oxic 4548174.3 0.11% 0.04% 0.30% 0.04% 0.19% 0.08% 0.04% 0.00% 0.04% 0.23%

100 oxic 4548176.3 0.16% 0.14% 0.22% 0.03% 0.40% 0.03% 0.05% 0.02% 0.06% 0.24%

MAR field site CA

High BDOC influent 1 oxic 4548179.3 0.00% 0.06% 0.32% 0.06% 0.06% 0.00% 0.00% 0.00% 0.00% 0.19%

5 oxic 4548186.3 0.11% 0.05% 0.19% 0.07% 0.15% 0.03% 0.06% 0.02% 0.00% 0.33%

Soil column systems

Initial soil for filling n/a n/a 4548185.3 0.16% 0.07% 0.42% 0.11% 0.26% 0.03% 0.10% 0.04% 0.00% 0.59%

Low BDOC influent 80 suboxic 4548182.3 0.15% 0.06% 0.32% 0.09% 0.25% 0.08% 0.09% 0.05% 0.00% 0.34%

320 suboxic 4548183.3 0.21% 0.03% 0.41% 0.08% 0.28% 0.07% 0.09% 0.04% 0.01% 0.51%

High BDOC influent 200 anoxic 4548169.3 0.14% 0.03% 0.32% 0.02% 0.12% 0.02% 0.31% 0.02% 0.00% 0.32%

a Sampling depth below surface. b And Chlorocyclohexane. c Cytochrome P450.

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Challenges in Uniting Metagenomics with High-Resolution MassSpectrometry

There is immense conceptual promise in uniting genomic and analyticalapproaches for better understanding variability in trace organic chemicalbiotransformation performance. However it is also one fraught with hurdles asexemplified here in addressing the question of whether trace organic chemicalbiotransformation in complex environments can be explained by comparingtrace organic chemical transformation products in aqueous samples with theexpression and abundance of specific functional genes in corresponding soilsamples. One important finding is that technical guidelines for standardizationof environmental ‘-omics’ research procedures are crucial and should coversampling, sequencing depth, technical data analysis, and interpretation of results,as well as the definition of cut off criteria, reference points, and normal values.Lombard et al. (33) studied soil-specific limitations for access and analysis ofsoil microbial communities by metagenomics and emphasized the challenges ofheterogeneity in environmental samples, which can mask trends and shifts inthe activity of functional genes. Furthermore, the sequencing depth using 454pyrosequencing might not be sufficient to cover biodegradation enzymes of lowabundance in sediment or soil ecosystems. It seems less promising to look forindividual specific enzymes for one catalytic step rather than comprehensivepathway maps: relative abundances are low, other yet unknown enzymes arecapable of catalyzing the same reaction, and it is also possible that sequences arenot represented by the shotgun approach due to the shear amount of microbialdiversity (e.g., abundant versus rare taxa). Misclassification of functional genes isanother issue. As discussed earlier, the EC code is often not sufficient to identifythe functional genes that are responsible for the metabolic reaction, for instanceover 60% of the reactions in Eawag-PPS share their EC code with anotherfunctional gene, while most of them cannot carry out the same transformations.

In addition, transformation products with structures such asm-methylbenzoate can be formed from other parent compounds, which inturn makes it difficult to establish associations between the parent compoundand metabolite. Intermediate metabolites can be further biotransformed andconcentrations might be too low to trigger IDA MS/MS during non-target andsuspected-target screenings, whereas too strict cut off criteria for peak intensitiescan lead to false negatives as observed by Kern et al. (7).

Acknowledgments

This work was supported in part by the WateReuse Research Foundation(WRRF) and the Water Replenishment District of Southern California throughgrant WRRF-10-05. Further support was provided through the National ScienceFoundation (NSF) under grant CBET-1055396 and the Engineering ResearchCenter for Reinventing the Nation’s Water Infrastructure (ReNUWIt) grantEEC-1028968.

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Editors’ BiographiesJörg E. Drewes

Prof. Dr.-Ing. Jörg E. Drewes holds Dipl. Ing. and Dr.-Ing. degrees inenvironmental engineering from the Technical University Berlin, Germany. Since 2013,he has been the Chair Professor of Urban Water Systems Engineering at the TechnicalUniversity of Munich (TUM), Germany. At TUM, he serves as the speaker for TUM’sinterdisciplinary Water Cluster. Previously, he served as Full Professor of Civil andEnvironmental Engineering at the Colorado School of Mines, U.S.A. (2001–2013)and Director of Research for the National Science Foundation Engineering ResearchCenter on Reinventing the Nation’s Urban Water Infrastructure (ReNUWIt). ProfessorDrewes’ research and scholarly activities are focusing on energy-efficient engineeredand naturally based water treatment systems, potable water reuse, monitoring strategiesand treatment performance assessments and water recycling, and the fate and transportof trace organic chemicals in engineered and natural water systems. Dr. Dreweshas published more than 300 journal papers, book contributions, and conferenceproceedings. He served on multiple science advisory panels and chaired blue ribbonpanels on topics related to public health, engineering, and reliability of water and waterreuse projects in the U.S., Australia, Africa, and the European Union.

Thomas Letzel

Prof. Dr. rer. nat. habil. Thomas Letzel is an analytical chemist with almost20 years of professional experience in the field of analytical screening techniques usingliquid and gas phase chromatography with mass spectrometric detection. Prof. Letzel ishead of the Analytical Research Group at the Chair of UrbanWater Systems Engineeringat the Technical University of Munich (TUM), Germany. He holds Dipl. and Dr. degreesin chemistry and the license to teach analytical and bioanalytical chemistry from TUM.Currently, the key aspects in his research cover technological, analytical-methodological,and analytical-chemical properties and can be applied in water and wastewater analysisas well as in other relevant environmental matrices, food analysis, beverage and plantextract analysis, among others. New separation techniques, like RPLC-HILIC-MS andSFC-MS, allow the polarity extended separation and identification of organic molecules.A special focus of his is on the chemical analysis with simultaneous functionality analysisusing mass spectrometric detection. Dr. Letzel is author and co-author of more than150 journal papers, book contributions, conference proceedings, and three books. Hehas experience with many national and international research projects, and he activelyparticipates in international environmental initiatives like NORMAN Association andESSEM COST Action ES1307.


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Subject IndexC

CECs in water, international managementstrategies, 11management strategies, principles, 12compound specific toxicityassessments, 15

enforceable MAC values, concept, 13national water quality regulation, 14

management strategies, water qualitymonitoring, 17trace organic chemicals, differentscreening strategies, 19f

mitigation measurescompound specific measures, 16technological measures, 16

Chemicals of emerging concern (CECs)analytical challenges, 5parent compounds and transformationproducts, analytical strategies, 6f

TPs, toxicological relevance, 7water treatment processes, role of TPs, 7

E

Electro-enzymatic catalysisBPA, transformation and products, 155BPA and TOC, removal, 156fH2O2, concentration, 155fsamples, TOF-MS spectra, 157f

DCP, enzymatic catalyzed products andtransformation, 150DCP removal, possible reactionpathways, 152f

DCP transformation, summarymechanism, 154f

experimental parameters on removalof DCP, effects, 151f

precipitated sample, Tof-MSspectrum, 153f

electro-enzymatic catalysis,development and prospect, 157BPA removal process, proposedmechanism, 158f

experimentalelectro-enzymatic system,preparation, 149

LC-MS method and data processingworkflows, 149

materials, 149

results and discussion, 150

I

Insensitive munitions compound, 133materials and methodsbiotransformation product mixtures,toxicity evaluations, 138

biotransformation products,LC-QToF-MS elucidation, 136

biotransformation products,LC-QToF-MS semiquantitation,137

chemical library, microbial andzebrafish embryo toxicityevaluation, 137

chemicals and biological materials,135

experimental approach to integratebiotransformation, overview, 136f

staggered anaerobic biotransformationassays, 135

results and discussionDNAN, bioassay chemical library,142f

DNAN biotransformation pathway,138

experimental design, conclusion andimplications, 143

individual biotransformation products,toxicity, 142

nitroreduction, DNANbiotransformation, 140f

product mixtures, DNANbiotransformation and microbialtoxicity, 138

reference (22), adapted withpermission, 139f

L

LC-HRMS, screening and identification oftransformation productsconclusion, 81environmentally relevant processesbiodegradation batch experiments, 78CBZ, TP252, 80fcombined reduction/oxidation, 75extracted ion chromatogram, 78f

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hydrolysis, 71lamotrigine, selectedphototransformation products,77f

mass chromatograms, comparison, 75foxidation, 73photolysis, 76photolysis of lamotrigine, stability testof TPs, 77f

possible processes, overview, 72freduction, 74simulate phase II metabolism, 79

using lab-based approaches, principle,69identification approach by labexperiments, scheme, 70f

LC-HRMS data, statistical approaches, 45introduction, 46each internal standard, ratio of areas,49f

statistical analysis, considerationsprior, 47

wastewater treatment train, example,46f

metabolomics workflows, 50multivariate analysis, 56examples applying MVA, 57observations, scores plot, 58fstatistical analysis, conjunction, 60target screening results, heat map, 59f

temporal and spatial trend analysis, 52univariate statistics, 53glycol ether sulfate, 54flog2, volcano plot, 55f

LC-MS(/MS) screening, new (practical)strategies, 85materials and methodsanalytical LC-MS systems, 89chemicals and materials, 87lab-scale wastewater treatment plants,87

soil columns, 88transformation products, theoreticalpredictions, 90

wastewater and surface water samples,88

results and discussionbisoprolol degradation, transformationproducts, 96

bisoprolol degradation in LWTP,transformation products, 90

bisoprolol degradation in soilcolumns, transformation products,92

bisoprolol in soil columns,elimination, 95f

LWTP effluent, monitored bisoprololTPs, 93t

three dominating signals, MS/MSspectra, 94f

TPs of bisoprolol, formation, 95fUM-PPS, predictions, 91workflow strategy and furtherlinkages, generalization, 96

M

Microbiome metabolic capabilities, 163methodologybiotransformation, shotgun approach,169

California, 166Colorado, 167metagenomic analysis, 168parent compounds, list, 170tsoil column systems, 167suspected-target screening approach,168

test beds and sample collection, 166results and discussionDEET, attenuation, 181fdiclofenac and occurrence,redox-dependent attenuation,179f

functional genes, relative abundance,183t

gemfibrozil, redox-dependentattenuation, 180f

gemfibrozil transformation product,178

N-(3,4-dihydroxyphenyl) acetamide,caffeine metabolite, 176

several functional genes, relativeabundance, 182

soil microbial community, metaboliccapability, 181

tentatively identifiedbiotransformation products,177t

transformation product3,5-dichlorobenzoic acid, structure,175f

transformation products,identification, 175

uniting metagenomics, challenges,184

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Neonicotinoid pesticides, fate, 121materials and methodschemicals and reagents, 122investigated activated sludgetreatment plant, flow diagram, 123f

liquid chromatography and tandemmass spectrometry, 124

quantitation and calibration, 124risk assessment, 124sample collection, 122sample extraction, 123separation and detection of sixneonicotinoids, chromatogramdisplaying, 125f

results and discussionacetamiprid across the WWTP, fate,127

analytical method performance, 125clothianidin, detected concentrations,128f

clothianidin across the WWTP, fate,127

imidacloprid, daily averageconcentrations, 126f

imidacloprid across the WWTP, fate,126

neonicotinoids across wastewatertreatment process, fate, 126

neonicotinoids across wetlandtreatment, fate, 128

P

Pharmaceuticals, HRMS approaches forevaluating transformations, 25suspect screening approach, 27environmental samples, applyingsuspect screening, 28

lamotrigine, biodegradation studies,35

LMG, workflow sketch approach, 37fneuro-active drugs, attenuationprocesses, 36

pharmaceuticals, fate, 33suspect screening for detection,workflow of the application, 35f

suspect screening for the evaluation,examples, 34

suspect screening methods, examples,29t

X-ray contrast media photolysis study,38

S

Surface water matrices, monitoring of traceorganic compounds, 103material and methods, 105ESI source, parameters, 108tserial RPLC-HILIC coupling, scheme,107f

results and discussion, 108desvenlafaxine, extracted ionchromatograms (EICs), 111f

masses corresponding to isoniazid,extracted ion chromatograms, 115f

metoprolol acid, extracted ionchromatograms (EICs), 109f

152 standard compounds,mass-retention time plots, 112f

152 standard compounds,mass-retention time plots separatedby SFC, 113f

surface water sample, extracted ionchromatograms, 114f

suspect target screening, 110

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Assessing Transformation Products of Chemicals by Non-Target and Suspect Screening : Strategies and...

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