Chemometrics and Cheminformatics in Aquatic Toxicology

Name: Chemometrics and Cheminformatics in Aquatic Toxicology
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Erschienen am 1. Dezember 2021

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Inhalt

Cover
Title Page
Copyright Page
Contents
Preface
Section I Introduction
Chapter 1 Water Quality and Contaminants of Emerging Concern (CECs)
1.1 Introduction: Water Quality and Emerging Contaminants
1.2 Contaminants of Emerging Concern
1.2.1 Pharmaceuticals
1.2.2 Personal Care Products
1.2.3 Nanomaterials
1.2.4 Plasticizers
1.2.5 Surfactants and Metabolites
1.2.6 Flame Retardants
1.2.7 Industrial Additives and Agents
1.2.8 Anticorrosives and Antifouling Agents
1.2.9 Natural Emerging Contaminants: Mycotoxins and Phytotoxins
1.3 Summary and Recommendations for Future Research
References
Chapter 2 The Effects of Contaminants of Emerging Concern on Water Quality
2.1 Introduction
2.1.1 Sources of CECs to the Aquatic Ecosystem
2.1.2 Fate of CECs in Aquatic Environments
2.2 Assessing the Effects of CECs in Aquatic Life
2.2.1 Pharmaceuticals
2.2.2 Personal Care Products
2.2.3 Agricultural Pesticides
2.2.4 Industrial Chemicals
2.3 Multiple Stressors
2.3.1 Mixtures of CECs
2.3.2 Interactions of CECs and Other Environmental Stressors
2.3.3 Climate Change
2.4 Conclusions
Acknowledgments
References
Chapter 3 Chemometrics: Multivariate Statistical Analysis of Analytical Chemical and Biomolecular Data
3.1 Introduction
3.2 Historic Origins
3.3 Applied Statistics
3.4 Analytical and Physical Chemistry
3.5 Scientific Computing
3.6 Development from the 1980s
3.7 A Review of the Main Methods
3.8 Experimental Design
3.9 Principal Components Analysis and Pattern Recognition
3.10 Multivariate Signal Analysis
3.11 Multivariate Calibration
3.12 Digital Signal Processing and Time Series Analysis
3.13 Multiway Methods
3.14 Conclusion
References
Chapter 4 An Introduction to Chemometrics and Cheminformatics
4.1 Brief History of Chemometrics/Cheminformatics
4.2 Current State of Cheminformatics
4.3 Common Cheminformatics Tasks
4.4 Cheminformatics Toolbox
4.5 Conclusion
References
Section II Chemometric and Cheminformatic Tools and Protocols
Chapter 5 An Introduction to Some Basic Chemometric Tools
5.1 Introduction
5.2 Example Datasets
5.2.1 Example 1 - The Mono-Substituted Nitrobenzenes Dataset
5.2.2 Example 2 - The Oil Offshore Production Emission Dataset
5.3 Data Analytical Methods
5.3.1 Pretreatment Methods
5.3.2 Principal Components Analysis (PCA)
5.3.3 Partial Least Squares Projections to Latent Structures (PLS)
5.3.4 Orthogonal Partial Least Squares (OPLS®)
5.3.5 Cross-Validation
5.4 Results
5.4.1 Results for Example 1
5.4.2 Results for Example 2
5.5 Discussion
References
Chapter 6 From Data to Models: Mining Experimental Values with Machine Learning Tools
6.1 Introduction
6.2 Data and Models
6.2.1 Data
6.2.2 Models
6.3 Basic Methods in Model Development with ML
6.3.1 Inputs to the Model
6.3.2 Output of the Model
6.3.3 Basic Algorithms
6.3.4 Evaluating What the Model Has Learned from Data
6.3.5 Model Interpretability
6.4 More Advanced ML Methodologies
6.4.1 Classifiers: from Decision Trees to Ensemble
6.4.2 Mining Datasets to Extract Frequent Subgroups
6.4.3 Kernel Methods and Support Vector Machine (SVM)
6.4.4 From Perceptron to Neural Nets
6.5 Deep Learning
6.5.1 Main DNN Architectures
6.5.2 Interpretation of DNN Models
6.5.3 Consequences of Deep Learning for QSAR
6.6 Conclusions
References
Chapter 7 Machine Learning Approaches in Computational Toxicology Studies
7.1 Introduction
7.1.1 Computer-Based Toxicity Prediction
7.1.2 Brief History of QSAR and Modern Machine Learning Techniques
7.2 Toxicity Data Set Preparation
7.2.1 Data Collection and Chemical Structure Representation
7.2.2 Descriptors and Fingerprints
7.3 Machine-Learning Techniques
7.3.1 Unsupervised Learning
7.3.2 Supervised Learning
7.3.3 Semi-Supervised Learning
7.4 Model Evaluation
7.5 Freely Available Software Tools and Open-Source Libraries Relevant to Machine Learning
7.6 Concluding Remarks
Acknowledgment
References
Chapter 8 Counter-Propagation Neural Networks for Modeling and Read Across in Aquatic (Fish) Toxicity
8.1 Introduction
8.2 Examples of Counter-Propagation Artificial Neural Networks in Fish Toxicity Modeling
8.3 Counter-Propagation Artificial Neural Networks
8.4 Conclusions
References
Chapter 9 Aiming High versus Aiming All: Aquatic Toxicology and QSAR Multitarget Models
9.1 Introduction
9.2 Multitarget QSARS and Aquatic Toxicology
9.2.1 Multitarget QSARS: Basics Overview
9.2.2 Mt-QSAR and the Biotarget Perspective: A Review from Selected Works
9.3 Biotargets and Aqueous Environmental Assessment: Solutions and Recommendations
9.4 Future Perspectives and Conclusion
References
Chapter 10 Chemometric Approaches to Evaluate Interspecies Relationships and Extrapolation in Aquatic Toxicity
10.1 Introduction
10.2 Acute Toxicity Estimation
10.2.1 Quantitative Structure-Activity Relationship (QSAR) Models
10.2.2 Interspecies Correlation Estimation (ICE) Models
10.2.3 Species Sensitivity Distributions (SSDs)
10.2.4 Linking Acute Toxicity Models
10.3 Sublethal Toxicity Extrapolation
10.3.1 Genomics and Sequence-Based Relationships
10.3.2 Chemical Proteomics
10.3.3 Differential Expression and Pathway Analysis
10.4 Discussion
10.5 Conclusions
Disclaimer
References
Section III Case Studies and Literature Reports
Chapter 11 The QSAR Paradigm to Explore and Predict Aquatic Toxicity
11.1 Introduction
11.2 Application of QSAR Methodology to Predict Aquatic Toxicity
11.2.1 Overview
11.2.2 Aquatic Toxicity Endpoints and Relevant Databases
11.2.3 Criteria for Robust QSAR Models
11.2.4 MOA-Based Aquatic Toxicity QSAR (QSTR)
11.2.5 Software Tools for Ecotoxicological Endpoints
11.3 QSAR for Narcosis - The Impact of Hydrophobicity
11.3.1 Linear Solvation Energy Relationships for Narcosis
11.3.2 Application of Chromatographic Systems for Building Narcotic Models
11.4 Excess Toxicity - Overview
11.4.1 QSAR (QSTR) Models for Reactive and Specific Acting Chemicals
11.5 Predictions of Bioconcentration Factor
11.6 Conclusions
References
Chapter 12 Application of Cheminformatics to Model Fish Toxicity
12.1 Introduction
12.2 Fish Toxicities
12.3 Toxicity in Fish Families and Species
12.4 The Fathead Minnow, the Rainbow Trout, and the Bluegill
12.5 Toxicity Variations in FIT Compounds
12.6 Modeling Wide-Range Toxicity Compounds
12.7 Further Evaluations
12.8 Alternative Approaches
12.9 Mechanisms of Action
12.10 Conclusions
Acknowledgments
References
Chapter 13 Chemometric Modeling of Algal and Daphnia Toxicity
13.1 Introduction
13.2 Algae Class
13.2.1 Short Characterization of Algae Class
13.2.2 QSAR Models Developed Using the Algae
13.3 Daphniidae Family
13.3.1 Short Characterization of Daphniidae Family
13.3.2 QSAR Models Developed Using Daphnia magna
13.4 Interspecies Correlation Estimation for Algal and Daphnia Aquatic Toxicity
13.4.1 Algal and Daphnia Toxicity Correlation
13.4.2 Algal, Daphnia and Other Species Toxicity Correlation
13.4.3 Daphnia and Other Species Toxicity Correlation
13.4.4 Algae Species Toxicity Correlations
13.4.5 Algal and Other Species Toxicity Correlation
13.5 Conclusions
Abbreviations List
References
Chapter 14 Chemometric Modeling of Algal Toxicity
14.1 Introduction
14.1.1 Environmental Importance of Algae
14.1.2 OECD Principles
14.1.3 Brief Summary of Algal QSAR Models
14.2 Criteria Set for the Comparison of Selected QSAR Models
14.2.1 The Modeled Endpoints
14.2.2 Descriptors
14.2.3 Model Performance
14.2.4 Applicability Domain
14.2.5 Software Used for QSAR Modeling
14.3 Literature MLR Studies on Algae
14.4 Conclusion
References
Chapter 15 Chemometric Modeling of Daphnia Toxicity
15.1 Introduction
15.2 QSTR and QSTTR Analyses
15.3 QSTR/QSTT/QSTTR Modeling of Daphnia Toxicity
15.3.1 Categorized Chemicals
15.3.2 Non-categorized Chemicals
15.4 Mechanistic Interpretations of Chemometric Models
15.5 Conclusive Remarks and Future Directions
Acknowledgment
References
Chapter 16 Chemometric Modeling of Daphnia Toxicity: Quantum-Mechanical Insights
16.1 Introduction
16.2 Quantum-Mechanical Methods
16.3 Quantum-Mechanical Descriptors for Daphnia Toxicity
16.4 Concluding Remarks and Future Outlook
References
Chapter 17 Chemometric Modeling of Toxicity of Chemicals to Tadpoles
17.1 Introduction
17.2 Overview and Morphology of Tadpoles
17.2.1 Tadpole as a Target for Ecotoxicity Testing
17.3 Reports of Tadpole Toxicity Due Various Environmental Contaminants: What Do We Know So Far?
17.4 In silico Models Reported for Tadpole Ecotoxicity: A Literature Review
17.5 Application of QSARs or Related Approaches in Modeling Tadpole Toxicity: A Future Perspective
17.6 Conclusion
Acknowledgment
References
Chapter 18 Chemometric Modeling of Toxicity of Chemicals to Marine Bacteria
18.1 Introduction
18.1.1 Marine Bacteria: A Source of Ocean's Wealth
18.1.2 Morphology of Marine Bacteria
18.1.3 Marine Bacteria in Symbiotic Association with Other Species
18.1.4 Marine Bacteria as Nitrogen Fixers
18.2 Marine Bacteria and Their Role in Nitrogen Fixing
18.2.1 Marine Bacteria That Actually "Fix" Nitrogen
18.2.2 Marine Bacteria Which Are Involved in Nitrification
18.2.3 Primary Producers Marine Bacteria Those Who Do Not Fix Nitrogen
18.3 Marine Bacteria as Biomarkers for Ecotoxicity Estimation
18.4 Chemometric Tools Applied in Ecotoxicity Evaluation of Marine Bacteria
18.4.1 Ecotoxicity Evaluations of Organic Compounds
18.4.2 Ecotoxicity Evaluations Using Capacity Factors (k)
18.4.3 Ecotoxicity Evaluations of Shale Oil Components
18.4.4 Ecotoxicity Evaluations of Human Pharmaceuticals
18.4.5 Ecotoxicity Evaluations of Ionic Liquids (ILs)
18.5 Conclusion
Acknowledgment
References
Chapter 19 Chemometric Modeling of Pesticide Aquatic Toxicity
19.1 Introduction
19.2 QSARs Models
19.2.1 QSAR Models Developed Using Fish Species
19.2.2 QSAR Models Developed Using Zebrafish Embryos
19.2.3 QSAR Models Developed Using Algae Species
19.2.4 QSAR Models Developed Using Americamysis bahia Species
19.2.5 QSAR Models Developed Using Daphnia magna
19.2.6 QSAR/QAAR Models Developed Using Interspecies Correlations
19.3 Conclusions
References
Chapter 20 Contribution of Chemometric Modeling to Chemical Risks Assessment for Aquatic Plants: State-of-the-Art
20.1 Introduction
20.2 Definition and Classification
20.3 Advantage of Aquatic Plants
20.3.1 Ecosystems Benefits
20.3.2 Economic Benefits
20.3.3 Phytoremediation Using Aquatic Plants
20.4 Contaminants and Their Toxicity
20.5 Chemometrics for Aquatic Plants Toxicity
20.6 Review of Literature on Chemometrics for Aquatic Plants Toxicity
20.6.1 Toxicity of Pharmaceuticals
20.6.2 Toxicity of Pesticides
20.6.3 Toxicity of Nanoparticles
20.6.4 Toxicity of Heavy Metal and Metalloids
20.6.5 Toxicity of Others Pollutants
20.7 Conclusions
References
Chapter 21 Application of 3D-QSAR Approaches to Classification and Predictionof Aquatic Toxicity
21.1 Introduction
21.1.1 Environmental Risk Assessment of Chemicals
21.1.2 In silico Models in Environmental Risk Assessment
21.1.3 Introduction and Limitation of the Previous QSAR Approaches
21.1.4 Challenges and Improvement Through 3D-QSAR
21.2 Principles of CAPLI 3D-QSAR
21.2.1 Docking Protocols
21.2.2 Data Preparation
21.2.3 Structure-based Pharmacophore and 3D-fingerprint Descriptors
21.2.4 CAPLI 3D-QSAR Development and Validation
21.2.5 Prediction of Binding Mode and Affinity
21.3 Applications in Chemical Classification and Toxicity Prediction
21.3.1 Mechanism-Based Classification of OP Inhibitors
21.3.2 Species Susceptibility Prediction
21.3.3 Structure-Toxicity Relationship Analysis
21.4 Limitation and Potential Improvement
21.4.1 Convolutional Neural Network
21.5 Conclusions and Recommendations
Acknowledgments
References
Chapter 22 QSAR Modeling of Aquatic Toxicity of Cationic Polymers
22.1 Introduction
22.2 Materials and Methods
22.2.1 Polymers
22.2.2 Dataset
22.2.3 Descriptor Calculation
22.2.4 Dataset Division
22.2.5 Model Development
22.2.6 Model Validation
22.3 Results and Discussion
22.3.1 QSTR Modeling for Fish Toxicity 96 h Dataset
22.3.2 QSTR Modeling for Daphnia magna Toxicity 48 h Dataset
22.3.3 QSTR Modeling for Green Algae Toxicity 96 h Dataset
22.3.4 QSTR Modeling for Chronic Toxicity Against Green Algae
22.3.5 Interspecies Modeling of Polymers
22.4 Conclusions
Acknowledgments
References
Section IV Tools and Databases
Chapter 23 In Silico Platforms for Predictive Ecotoxicology: From Machine Learning to Deep Learning
23.1 Introduction
23.2 Machine Learning and Deep Learning
23.2.1 Support Vector Machines
23.2.2 Random Forest
23.2.3 Deep Neural Networks
23.3 Toxicity Prediction Modeling
23.3.1 General Procedure of Modeling
23.3.2 QSAR
23.3.3 Molecular Docking
23.3.4 Read-Across
23.3.5 Structural Alerts
23.3.6 Adverse Outcome Pathway
23.4 Challenges and Future Directions
References
Chapter 24 The Use and Evolution of Web Tools for Aquatic Toxicology Studies
24.1 Introduction
24.2 Methodologies Used in Aquatic Toxicology Tests
24.2.1 Database
24.2.2 Toxicity
24.2.3 Quantitative Structure-Activity Relationships (QSARs) Between Chemical Structures and Biological Activity in Aquatic Toxicity Studies
24.3 Web Tools Used in Aquatic Toxicology
24.3.1 Aggregated Computational Toxicology Online Resource (ACToR)
24.3.2 ECOTOXicology (ECOTOX)
24.3.3 OASIS
24.3.4 TOXMATCH
24.3.5 OSIRIS
24.3.6 BIOWIN Models
24.3.7 AdmetSar
24.3.8 Chembench
24.3.9 Ecological Structure-Activity Relationships (ECOSAR)
24.3.10 OECD QSAR Toolbox
24.3.11 PASS
24.3.12 Applications of in silico Techniques to Aquatic Toxicology Tests
24.4 Perspectives
References
Chapter 25 The Tools for Aquatic Toxicology within the VEGAHUB System
25.1 Introduction
25.2 The VEGA Models
25.2.1 The VEGA Models for Aquatic Toxicity
25.2.2 The Example of the Fish Acute Toxicity Model Developed Using Neural Networks
25.2.3 The Differences Between the Aquatic Toxicity Models
25.2.4 The Components of the Applicability Domain Index
25.2.5 The Evaluation of the Results of the VEGA Models
25.3 ToxRead and Read-Across Within VEGAHUB
25.4 Prometheus and JANUS
25.5 The Future Developments
25.5.1 The VERMEER Project
25.5.2 The toDIVINE Project
25.6 Conclusions
References
Chapter 26 Aquatic Toxicology Databases
26.1 Introduction
26.2 Aquatic Toxicity
26.2.1 Aquatic Toxicity Test
26.2.2 Aquatic Test Species
26.3 Importance of Aquatic Toxicity Databases
26.4 Characteristic of an Ideal Aquatic Toxicity Database
26.5 Aquatic Toxicology Databases
26.5.1 Acute Toxicity Database
26.5.2 Aquatic Toxicity Information Retrieval (AQUIRE)
26.5.3 Ecotoxicology Database (ECOTOX)
26.5.4 Environmental Residue Effects Database (ERED)
26.5.5 EnviroTox
26.5.6 MOAtox
26.5.7 Toxicity/Residue Database
26.6 Overview and Conclusion
Acknowledgments
Conflicts of Interest
References
Chapter 27 Computational Tools for the Assessment and Substitution of Biocidal Active Substances of Ecotoxicological Concern: The LIFE-COMBASE Project
27.1 Introduction
27.1.1 Biocides Regulation
27.1.2 Alternative Methods
27.1.3 Computational Approaches on Biocides: State of the Art
27.1.4 The LIFE-COMBASE Project
27.2 Database Compilation
27.2.1 Criteria Definition for the Selection of Biocidal Active Substances
27.2.2 Sources of Data
27.3 Development of the QSAR Models
27.3.1 Preparation of the Data Sets
27.3.2 QSAR Models for Microorganisms
27.3.3 QSAR Models for Algae
27.3.4 QSAR Models for Daphnia magna
27.3.5 QSAR Models on Fish
27.4 Prediction of Metabolites and their Associated Toxicity
27.5 Implementation of the In Silico QSARs Within VEGA and Integration with Read Across Models in ToxRead
27.5.1 Implementation of the QSAR Models Within VEGA
27.5.2 Implementation of the Rules for Read-Across and Grouping Within ToxRead
27.5.3 Integration of QSARs and Read-Across Within a Weight-of-evidence Strategy
27.6 Implementation of the LIFE-COMBASE Decision Support System
27.6.1 Database Search Engine
27.6.2 Biocides' Chemical Space
27.6.3 Metabolites Prediction
27.6.4 Calculation of Aquatic Ecotoxicity
27.6.5 Generation of Alternative Biocide Structures
27.7 Implementation of the LIFE-COMBASE Mobile App
27.8 Concluding Remarks
Acknowledgments
References
Chapter 28 Image Analysis and Deep Learning Web Services for Nano informatics
28.1 Introduction
28.2 NanoXtract
28.2.1 NanoXtract Environment and Image Uploading
28.2.2 Computational Workflow and Available Settings
28.2.3 Produced Results
28.3 DeepDaph
28.3.1 DeepDaph Environment
28.3.2 Produced Results
28.4 Conclusions
Acknowledgments
References
Index
EULA

1
Water Quality and Contaminants of Emerging Concern (CECs)

Antonio Juan García-Fernández1, Silvia Espín1, Pilar Gómez-Ramírez1, Pablo Sánchez-Virosta2, and Isabel Navas1

1 Toxicology and Risk Assessment Group, Department of Health Sciences, Faculty of Veterinary, Biomedical Research Institute of Murcia (IMIB-Arrixaca), University of Murcia, Murcia, Spain

2 Toxicology Group, Department of Health Sciences, Faculty of Veterinary, University of Murcia, Murcia, Spain

1.1 Introduction: Water Quality and Emerging Contaminants

Water is a scarce resource globally and the pollution-driven deterioration of its quality further limits its availability. Freshwater pollution is mainly caused by the discharge of large amounts of untreated or insufficiently treated wastewater into aquatic environments, like aquifers, rivers, lakes, and coastal waters. Therefore, water quality is an important concern around the planet and during the twenty-first century will represent one of the main challenges for our societies. This challenge will be of huge proportions in both industrialized and developing countries. However, water quality is actually not just a health issue. According to UNESCO, public health, sustainable livelihoods, gender equality, poverty reduction, conservation of the ecosystems, and equitable social and economic developments are highly dependent on water quality [1]. This should justify why in 2010 the United Nations General Assembly (RES/64/292) recognized access to clean and safe drinking water and sanitation as a human right for our health and well-being.

The water quality survey carried out in 2002 by the US Geological Survey revealed the omnipresence of pharmaceuticals and hormones in the environment [2]. Almost 30?years before, organic compounds including pharmaceuticals had already been identified in domestic wastewaters by gas chromatography/mass spectrometry (GC/MS) [3]. However, the significant increase in the number of studies on the so-called emerging contaminants (ECs) has only been possible thanks to the technological advances in high-performance liquid chromatography/mass spectrometry (HPLC/MS) during the last two decades, achieving sufficient sensitivity to quantify organic compounds at concentrations below µg?l-1 [4].

Broadly speaking, ECs could be defined as any chemical product, whether synthetic or natural, or any microorganism with potential to induce adverse health effects on human and the environment, and for which there are no controls or regulations on their presence in the environment. Therefore, ECs constitute a heterogeneous class of pollutants, which includes different groups of contaminants such as pharmaceuticals, personal care products (PCPs), pesticides, flame retardants, industrial additives, surfactants, plasticizers, nanomaterials, and mycotoxins, among others (Table 1.1). Metabolites and natural hormones should also be included. In addition, it should be kept in mind that ECs suffer different processes (biodegradation, oxidation, hydrolysis, chemical reduction, and photolysis) generating other transformed products that, in some cases, are more toxic or more persistent than the parent compound [4, 5]. There are many technological obstacles to remove them with immediate effectiveness from wastewater treatment plants (WWTPs), releasing them at residual concentrations into surface and groundwater. This is a cause of concern because some of them may trigger the development of antibiotic resistance in pathogen bacteria, chronic toxicity, and endocrine disruption in humans and aquatic wildlife [1, 6].

Table 1.1 List of emerging contaminants of concern in water environments.

Group of contaminant Type of compound Examples Pharmaceuticals Human and veterinary antibiotics Trimethoprim, ciprofloxacin, enrofloxacin, erythromycin, amoxicillin, lincomycin, sulfamethoxazole, chloramphenicol, triclosan Analgesics, anti-inflammatory drugs Ibuprofen, diclofenac, paracetamol, codein, acetaminophen, acetylsalicylic acid, fenoprofen, ketoprofen, paracetamol, phenazone, and propyphenazone Psychiatric drugs Diazepam, carbamazepine, primidone, salbutamol, fluoxetine ß-Blockers Metoprolol, propranolol, timolol, atenolol, sotalol Lipid regulators Bezafibrate, clofibric acid, fenofibric acid, etofibrate, gemfibrozil X-ray contrasts Iopromide, iopamidol, diatrizoate Hormones and steroids Estradiol, estrone, estriol, diethylstilbestrol (DES) Personal care products (PCPs) UV filters 4-Methyl-benzylidene-camphor (4MBC), ethylhexyl-methoxycinnamate (EHMC), benzophenone-3 (BP-3), octocrylene (OC) Fragrances Nitro (e.g. ketone or xylene), polycyclic (e.g. galaxolide, tonalide, celestolide, phantolide), macrocyclic, and alicyclic musks Preservatives Parabens (alkyl esters of p-hydroxybenzoic acid) Antiseptics Triclosan, triclocarban, chlorophene Insect repellents N,N-Diethyl-m-toluamide (DEET) Nanomaterials Metals and metal oxides, carbonaceous, silicon nanomaterials, organics, ceramics, polymers Silver (Ag), titanium dioxide (TiO2), zinc oxide (ZnO), carbon-based nanomaterials Plasticizers Phthalates, bisphenol A (BPA) Surfactants and surfactant metabolites Alkylphenol ethoxylates, 4-nonylphenol, 4-octylphenol, alkylphenol carboxylates Flame retardants Chlorinated flame retardants Polychlorinated biphenyls, dechlorane plus, polychlorinated dibenzodioxins, and polychlorinated dibenzofurans Brominated flame retardants Polybrominated diphenyl ethers (PBDEs): polybrominated biphenyls (PBBs), TBBPA, HBCD, decabromodiphenyl ethane or 1,2-bis (pentabromodiphenyl) ethane (DBDPE), 1,2-bis (2,4,6-tribromophenoxy) ethane (BTBPE), 2-ethylhexyl-2,3,4,5-tetrabromobenzoate (TBB or EHTBB), bis(2-ethylhexyl)-3,4,5,6-tetrabromo-phthalate (TBPH or BEHTBP), tetrabromobisphenol A-bis (2,3-dibromopropylether) (TBBPA-DBPE), and hexachlorocyclopentadienyldibromo-cyclooctane (HCDBCO) Fluorinated flame retardants Perfluorotoctane sulfonate (PFOS), perfluoroctanoic acid (PFOA) Organophosphate flame retardants and plastizicers Tris(2-chloroethyl) phosphate (TCEP), tris(2-chloro-1-methylethyl) phosphate (TCPP), and tris(1,3-dichloro-2-propyl) phosphate (TDCP) Industrial additives and agents Volatile organic carbons (VOCs) CHCl3, trichloroethylene (TCE) and tetrachloroethylene (PERC) Polycyclic aromatic hydrocarbons (PAHs) Indeno[1,2,3-cd]pyrene, benz[a]anthracene, dibenz[a,h]anthracene, benzo[a]pyrene (BaP), chrysene, benzo[b]fluoranthene, and benzo[k]fluoranthene Siloxanes (silicones) Octamethylcyclotetrasiloxane (D4) and decamethylcyclopentasiloxane (D5) Gasoline additives Dialkyl ethers, methyl-t-butyl ether (MTBE) Anticorrosives and antifouling agents Benzotriazoles (1H-benzotriazole (BTri), (4-methyl-1H-benzotriazole (4-TTri), 5-methyl-1H-benzotriazole (5-TTri), the mixture of 4- and 5-methyl-1H-benzotriazole (TTri), 5,6-dimethyl-1H-benzotriazole (XTri) and 5-chloro-1H-benzotriazole (CBT) and benzothiazoles (benzothiazole (BTH), 2-hydroxy-benzothiazole (2-OH-BTH), 2-amino-benzothiazole (2-NH2-BTH), 2-methyl-benzothiazole (2-Me-BTH), 2-methyl-thio-benzothiazole (2-Me-S-BTH), 2-mercapto-benzothiazole(2-SH-BTH), 2-thiocyanomethylthio-benzothiazole (2-SCNMeS-BTH), and 2-benzothiazole-sulfonic acid (2-SO3H-BTH), organotins, zinc pyrithione (ZnPT), antifouling biocides (irgarol, diuron, sea-nine 211, dichlofluanid, chlorothalonil, thiram, busan, densil, pyridine-triphenylborane, capsaicin, econea, medetomidine, and tolylfluanid) Chelating agents (EDTA), aromatic sulfonates Natural emerging...

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1 Water Quality and Contaminants of Emerging Concern (CECs)

1.1 Introduction: Water Quality and Emerging Contaminants

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Water Quality and Contaminants of Emerging Concern (CECs)