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Record W4402439998 · doi:10.1002/etc.5994

Transformation of Environmental Contaminants: Uncovering Reaction Mechanisms, Identifying Novel Products, and Understanding Environmental Implications

2024· editorial· en· W4402439998 on OpenAlexaff
Carrie A. McDonough, Shira Joudan, Natalia Quinete, Xiaomeng Wang

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2024
Typeeditorial
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsNatural Resources CanadaUniversity of Alberta
Fundersnot available
KeywordsTransformation (genetics)ContaminationEnvironmental chemistryEnvironmental scienceBiochemical engineeringComputer scienceChemistryEcologyBiologyEngineering

Abstract

fetched live from OpenAlex

Thousands of synthetic substances are released into the environment through industrial processes, waste disposal, product usage, and other human activities, presenting a serious challenge for environmental risk assessors (Persson et al., 2022). Chemicals that are persistent, bioaccumulative, and toxic (PBT) or persistent, mobile, and toxic (PMT) are often prioritized as potential contaminants of concern (Arp & Hale, 2019). However, some chemicals are not persistent outright, but rather transform in the environment or in biota with poorly understood implications for PBT/PMT (Chen et al., 2015; Cwiertny et al., 2014; Zahn et al., 2024). The potential for chemicals to transform into unknown products that are similarly or more toxic or persistent than the parent is often not considered in environmental risk assessment. In many cases, the disappearance of a chemical is taken to mean that risks associated with the parent substance have been attenuated, with no consideration of the potential for harmful transformation products. Predicting chemical reactivity, describing transformation reactions, and identifying transformation products are all essential to truly understand risks posed by environmental contaminants. Many previous studies have demonstrated the formation of unexpected transformation products in indoor and outdoor environments and in engineered systems (e.g., wastewater and drinking water treatment facilities). These products are typically overlooked because they are not targeted by traditional chemical analyses. For example, formation of novel chlorinated byproducts from various organic compounds during drinking water treatment can result in novel toxic chemicals in treated water, posing a human health risk (Cochran et al., 2024; Wong et al., 2019). In addition, transformation of organophosphates from plastics via oxidation and hydrolysis formed several novel products that were tentatively identified in an indoor environment using nontarget analysis (Kutarna et al., 2023). Conversely, unknown parent chemicals can transform into known toxic products and are also often overlooked in environmental risk assessment; this is often (although not always) the case for per/polyfluoroalkyl substances (PFASs; Joudan et al., 2022; Xiao et al., 2018). Chemical transformation also occurs through a variety of biological processes. Microbial communities cause transformations in natural and engineered systems (Cook et al., 2022; Fenner et al., 2021). Metabolic reactions occurring in vivo also transform chemicals, often enhancing their solubility, their mobility, and potentially their reactivity, with implications for toxicity and for biomonitoring in humans and wildlife (Joudan et al., 2017; Phillips et al., 2020; Rand & Mabury, 2013). Rates and pathways of transformation are species specific, contributing to differences in body burdens and biomarkers among species (Letcher et al., 2014; Roberts et al., 2011). A diverse array of known and unknown PFAS transform via biological processes to ultimately form toxic and highly persistent perfluoroalkyl acids (PFAAs), potentially acting as an indirect source of PFAAs in humans (D'Eon & Mabury, 2011; McDonough et al., 2022). One reason that transformation of environmental contaminants is often overlooked is the lack of appropriate analytical techniques to detect and identify unexpected transformation products. Recent advances in untargeted high‐resolution mass spectrometry (HRMS) and nuclear magnetic resonance (NMR) spectroscopy as well as technologies to map and predict potential metabolites, have expediated discovery of previously unknown transformation products (Abdallah et al., 2015; Djoumbou‐Feunang et al., 2019; Han et al., 2021). In addition, analyses that measure bioactivity (e.g., receptor‐mediated activity) rather than targeting specific chemicals have become a useful integrative technique to highlight the toxicity of unknown transformation products (Cwiertny et al., 2014). A hybrid toxicity identification evaluation and effect‐directed analysis approach coupled with HRMS identified a highly toxic and mobile transformation product formed after ozonation of an antioxidant used in tires [N‐(1,3‐dimethylbutyl)‐N′‐phenyl‐p‐phenylenediamine‐quinone] as likely responsible for acute mortality in coho salmon (Tian et al., 2021). In this special series, we highlight recent research employing innovative analytical techniques coupled with field sampling strategies and laboratory experiments to uncover the formation, occurrence, and environmental implications of transformation products in a variety of contexts. These studies explore how the structures of parent molecules determine their lability and fate, providing essential information for predicting environmental impacts of chemicals based on their molecular structure. They also demonstrate strategies to tackle the complexity of environmental mixtures containing thousands of trace organic contaminants. They showcase the use of novel approaches for risk‐based chemical prioritization and untargeted chemical analysis and describe structure‐based relationships that could be used to predict transformation products for novel chemicals. Three of the studies focus on various aspects of PFAS transformation, highlighting how much is still unknown about this chemical class, particularly when novel, potentially labile structures beyond the highly persistent PFAAs are under consideration. All four studies in this series demonstrate the application of untargeted, integrative, and effects‐based screening methods that can capture unexpected transformation products. Cardenas Perez et al. (2024) used transcriptomic analysis to generate comprehensive insights into the impacts on aquatic biota of micropollutant mixtures from environmental water samples. By combining this integrative effects‐based approach with HRMS suspect screening and predicted effects based on the CompTox Chemicals Dashboard of the US Environmental Protection Agency, they prioritized contaminants and biological pathways of concern for environmental risk assessment. Dukes and McDonough (2024) used HRMS to screen for possible biological transformation products in urine collected from mice dosed with a complex aqueous film‐forming foam mixture. They built their suspect screening list based on previous literature and predictive tools (BioTransformer) and confirmed identifications by generating transformation products in vitro. Mundhenke et al. (2023) used quantitative fluorine NMR spectroscopy and HRMS to identify intermediate and final products formed via photolysis of four fluorinated pharmaceuticals, providing insights into how molecular structure relates to the formation of organofluorine byproducts. Folkerson and Mabury (2023) combined the total oxidizable precursor assay, mass spectrometry, and ion chromatography to achieve a detailed understanding of transformation products generated by novel hydrofluoroether alcohols in aerobic wastewater treatment plant microcosms and to investigate how the structure of these chemicals determines their ultimate fate in sunlit surface waters. The research presented in this series contributes to our understanding of the transformation and environmental implications of emerging and novel organic contaminants. These studies also highlight promising paths forward to improve environmental risk assessment by incorporating transformation processes and their expected products, including 1) generating new knowledge on how molecular structure relates to reactivity to inform predictive models for risk assessment and inform the design of safer chemicals, 2) using integrative chemical and biological techniques to get a much more comprehensive understanding of contaminant burdens that includes overlooked transformation products, and 3) analyzing real environmental samples and commercial products to capture unexpected, exposure‐relevant substances. Carrie A. McDonough: Writing—original draft; Writing—review & editing. Shira Joudan: Writing—review & editing. Natalia Soares Quinete: Writing—review & editing. Xiaomeng Wang: Writing—review & editing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.017
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.002
Science and technology studies0.0030.003
Scholarly communication0.0080.005
Open science0.0040.001
Research integrity0.0170.021
Insufficient payload (model declined to judge)0.0080.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.227
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2024
Admission routes1
Has abstractno

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