Impact of long-term temperature shifts on activated sludge microbiome dynamics and micropollutant removal.

Micropollutants removal efficiency strongly vary across different aerobic wastewater treatment plants, resulting in their frequent detection in surface and ground waters. Seasonal temperature variation is a major factor influencing plant performance, but it is still unclear how prolonged periods of temperature change impact microbiome and micropollutant biotransformation. This work investigates the effect of long-term temperature variation on the microbial dynamics in an activated sludge system, and the impact on micropollutant biotransformation. Sequencing batch reactors were used as model system and 4-40 °C temperature range was studied. 16S rRNA amplicon sequencing showed that temperature drives microbial structure (gDNA) and activity (RNA), rather than time, and this was stronger below 15 °C and above 25 °C. The microbial community was richest and more diverse at 20 °C, while rarer and more specific taxa became predominant over time, at more extreme temperatures. This suggested that less abundant taxa might be responsible for maintaining the biotransformation capability in the activated sludge at extreme temperatures. Micropollutant biotransformation rates mostly deviated from the classic Arrhenius model below 15 °C and above 25 °C, indicating that prolonged exposure to temperature changes leads to temperature-induced taxonomic shifts, resulting in the emerging of different sets of biotransformation pathways over different temperature ranges.

Substrate promiscuity of xenobiotic-transforming hydrolases from stream biofilms impacted by treated wastewater.

Organic contaminants enter aquatic ecosystems from various sources, including wastewater treatment plant effluent. Freshwater biofilms play a major role in the removal of organic contaminants from receiving water bodies, but knowledge of the molecular mechanisms driving contaminant biotransformations in complex stream biofilm (periphyton) communities remains limited. Previously, we demonstrated that biofilms in experimental flume systems grown at higher ratios of treated wastewater (WW) to stream water displayed an increased biotransformation potential for a number of organic contaminants. We identified a positive correlation between WW percentage and biofilm biotransformation rates for the widely-used insect repellent, N,N-diethyl-meta-toluamide (DEET) and a number of other wastewater-borne contaminants with hydrolyzable moieties. Here, we conducted deep shotgun sequencing of flume biofilms and identified a positive correlation between WW percentage and metagenomic read abundances of DEET hydrolase (DH) homologs. To test the causality of this association, we constructed a targeted metagenomic library of DH homologs from flume biofilms. We screened our complete metagenomic library for activity with four different substrates, including DEET, and a subset thereof with 183 WW-related organic compounds. The majority of active hydrolases in the metagenomic library preferred aliphatic and aromatic ester substrates while, remarkably, only a single reference enzyme was capable of DEET hydrolysis. Of the 626 total enzyme-substrate combinations tested, approximately 5% were active enzyme-substrate pairs. Metagenomic DH family homologs revealed a broad substrate promiscuity spanning 22 different compounds when summed across all enzymes tested. We biochemically characterized the most promiscuous and active enzymes identified based on metagenomic analysis from uncultivated Rhodospirillaceae and Planctomycetaceae. In addition to characterizing new DH family enzymes, we exemplified a framework for linking metagenome-guided hypothesis generation with experimental validation. Overall, this study expands the scope of known enzymatic contaminant biotransformations for metagenomic hydrolases from WW-receiving stream biofilm communities.

Should Transformation Products Change the Way We Manage Chemicals?

When chemical pollutants enter the environment, they can undergo diverse transformation processes, forming a wide range of transformation products (TPs), some of them benign and others more harmful than their precursors. To date, the majority of TPs remain largely unrecognized and unregulated, particularly as TPs are generally not part of routine chemical risk or hazard assessment. Since many TPs formed from oxidative processes are more polar than their precursors, they may be especially relevant in the context of persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances, which are two new hazard classes that have recently been established on a European level. We highlight herein that as a result, TPs deserve more attention in research, chemicals regulation, and chemicals management. This perspective summarizes the main challenges preventing a better integration of TPs in these areas: (1) the lack of reliable high-throughput TP identification methods, (2) uncertainties in TP prediction, (3) inadequately considered TP formation during (advanced) water treatment, and (4) insufficient integration and harmonization of TPs in most regulatory frameworks. A way forward to tackle these challenges and integrate TPs into chemical management is proposed.

Influence of Season on Biodegradation Rates in Rivers.

Biodegradation plays a key role in the fate of chemicals in the environment. The variability of biodegradation in time can cause uncertainty in evaluating the environmental persistence and risk of chemicals. However, the seasonality of biodegradation in rivers has not yet been the subject of environmentally relevant testing and systematic investigation for large numbers of chemicals. In this work, we studied the biodegradation of 96 compounds during four seasons at four locations (up- and downstream of WWTPs located on two Swedish rivers). Significant seasonality (ANOVA, p < 0.05) of the first-order rate constant for primary biodegradation was observed for most compounds. Variations in pH and total bacterial cell count were not the major factors explaining the seasonality of biodegradation. Deviation from the classical Arrhenius-type behavior was observed for most of the studied compounds, which calls into question the application of this relationship to correct biodegradation rate constants for differences in environmental temperature. Similarities in magnitude and seasonality of biodegradation rate constants were observed for some groups of chemicals possessing the same functional groups. Moreover, reduced seasonality of biodegradation was observed downstream of WWTPs, while biodegradation rates of most compounds were not significantly different between up- and downstream.

Coupling pathway prediction and fluorescence spectroscopy to assess the impact of auxiliary substrates on micropollutant biodegradation.

Some bacteria can degrade organic micropollutants (OMPs) as primary carbon sources. Due to typically low OMP concentrations, these bacteria may benefit from supplemental assimilation of natural substrates present in the pool of dissolved organic matter (DOM). The biodegradability of such auxiliary substrates and the impacts on OMP removal are tightly linked to biotransformation pathways. Here, we aimed to elucidate the biodegradability and effect of different DOM constituents for the carbofuran degrader Novosphingobium sp. KN65.2, using a novel approach that combines pathway prediction, laboratory experiments, and fluorescence spectroscopy. Pathway prediction suggested that ring hydroxylation reactions catalysed by Rieske-type dioxygenases and flavin-dependent monooxygenases determine the transformability of the 11 aromatic compounds used as model DOM constituents. Our approach further identified two groups with distinct transformation mechanisms amongst the four growth-supporting compounds selected for mixed substrate biodegradation experiments with the pesticide carbofuran (Group 1: 4-hydroxybenzoic acid, 4-hydroxybenzaldehyde; Group 2: p-coumaric acid, ferulic acid). Carbofuran biodegradation kinetics were stable in the presence of both Group 1 and Group 2 auxiliary substrates. However, Group 2 substrates would be preferable for bioremediation processes, as they showed constant biodegradation kinetics under different experimental conditions (pre-growing KN65.2 on carbofuran vs. DOM constituent). Furthermore, Group 2 substrates were utilisable by KN65.2 in the presence of a competitor (Pseudomonas fluorescens sp. P17). Our study thus presents a simple and cost-efficient approach that reveals mechanistic insights into OMP-DOM biodegradation.

Early Assessment of Biodegradability of Small Molecules to Support the Chemical Design in Agro & Pharma R&D.

The use of agrochemical and pharmaceutical active ingredients is essential in our modern society. Given the increased concern and awareness of the potential risks of some chemicals, there is a growing need to align with 'green chemistry' and 'safe and sustainable by design' principles and thus to evaluate the hazards of agrochemical and pharmaceutical active ingredients in early stages of R&D. We give an overview of the current challenges and opportunities to assess the principle of biodegradability in the environment. Development of new medium/high-throughput methodologies, combining predictive tools and wet-lab experimentation are essential to design biodegradable chemicals early in the active ingredient discovery and selection process.

Can AI Help Improve Water Quality? Towards the Prediction of Degradation of Micropollutants in Wastewater.

Micropollutants have become a serious environmental problem by threatening ecosystems and the quality of drinking water. This account investigates if advanced AI can be used to find solutions for this problem. We review background, the challenges involved, and the current state-of-the-art of quantitative structure-biodegradation relationships (QSBR). We report on recent progress combining experiment, quantum chemistry (QC) and chemoinformatics, and provide a perspective on potential future uses of AI technology to help improve water quality.

Making waves: Enhancing pollutant biodegradation via rational engineering of microbial consortia.

Biodegradation holds promise as an effective and sustainable process for the removal of synthetic chemical pollutants. Nevertheless, rational engineering of biodegradation for pollutant remediation remains an unfulfilled goal, while chemical pollution of waters and soils continues to advance. Efforts to (i) identify functional bacteria from aquatic and soil microbiomes, (ii) assemble them into biodegrading consortia, and (iii) identify maintenance and performance determinants, are challenged by large number of pollutants and the complexity in the enzymology and ecology of pollutant biodegradation. To overcome these challenges, approaches that leverage knowledge from environmental bio-chem-informatics and metabolic engineering are crucial. Here, we propose a novel high-throughput bio-chem-informatics pipeline, to link chemicals and their predicted biotransformation pathways with potential enzymes and bacterial strains. Our framework systematically selects the most promising candidates for the degradation of chemicals with unknown biotransformation pathways and associated enzymes from the vast array of aquatic and soil bacteria. We substantiated our perspective by validating the pipeline for two chemicals with known or predicted pathways and show that our predicted strains are consistent with strains known to biotransform those chemicals. Such pipelines can be integrated with metabolic network analysis built upon genome-scale models and ecological principles to rationally design fit-for-purpose bacterial communities for augmenting deficient biotransformation functions and study operational and design parameters that influence their structure and function. We believe that research in this direction can pave the way for achieving our long-term goal of enhancing pollutant biodegradation.

Systematic Handling of Environmental Fate Data for Model Development-Illustrated for the Case of Biodegradation Half-Life Data.

The assessment of environmental hazard indicators such as persistence, mobility, toxicity, or bioaccumulation of chemicals often results in highly variable experimental outcomes. Persistence is particularly affected due to a multitude of influencing environmental factors, with biodegradation experiments resulting in half-lives spanning several orders of magnitude. Also, half-lives may lie beyond the limits of reliable half-life quantification, and the number of available data points per substance may vary considerably, requiring a statistically robust approach for the characterization of data. Here, we apply Bayesian inference to address these challenges and characterize the distributions of reported soil half-lives. Our model estimates the mean, standard deviation, and corresponding uncertainties from a set of reported half-lives experimentally obtained for a single substance. We apply our inference model to 893 pesticides and pesticide transformation products with experimental soil half-lives of varying data quantity and quality, and we infer the half-life distribution for each compound. By estimating average half-lives, their experimental variability, and the uncertainty of the estimations, we provide a reliable data source for building predictive models, which are urgently needed by regulatory authorities to manage existing chemicals and by industry to design benign, nonpersistent chemicals. Our approach can be readily adapted for other environmental hazard indicators.

Benchmarking the Persistence of Active Pharmaceutical Ingredients in River Systems.

Assessing the persistence of organic micropollutants from field data has been notoriously laborious, requiring extensive data including emissions and chemical properties, and the application of detailed mass-balance models, which often contain parameters that are impossible to measure. To overcome some of these obstacles, we developed the concept of persistence benchmarking for large rivers that receive numerous emissions and provide enough residence time to observe the dissipation of compounds. We estimated the dissipation rate constants of 41 compounds (mostly active pharmaceutical ingredients) from five measurement campaigns in the Rhine and Danube rivers using concentration rate profiles with respect to carbamazepine. Dissipation rates clearly distinguished between known fast- and slow-degrading compounds, and campaign-specific boundary conditions had an influence on a minor subset of compounds only. Benchmarking provided reasonable estimates on summer total system half-lives in the Rhine compared to previous laboratory experiments and a mass-balance modeling study. Consequently, benchmarking can be a straightforward persistence assessment method of continuously emitted organic micropollutants in large river systems, especially when it is supported by field monitoring campaigns of proper analytical quality and spatial resolution.

Combining predictive and analytical methods to elucidate pharmaceutical biotransformation in activated sludge.

While man-made chemicals in the environment are ubiquitous and a potential threat to human health and ecosystem integrity, the environmental fate of chemical contaminants such as pharmaceuticals is often poorly understood. Biodegradation processes driven by microbial communities convert chemicals into transformation products (TPs) that may themselves have adverse ecological effects. The detection of TPs formed during biodegradation has been continuously improved thanks to the development of TP prediction algorithms and analytical workflows. Here, we contribute to this advance by (i) reviewing past applications of TP identification workflows, (ii) applying an updated workflow for TP prediction to 42 pharmaceuticals in biodegradation experiments with activated sludge, and (iii) benchmarking 5 different pathway prediction models, comprising 4 prediction models trained on different datasets provided by enviPath, and the state-of-the-art EAWAG pathway prediction system. Using the updated workflow, we could tentatively identify 79 transformation products for 31 pharmaceutical compounds. Compared to previous works, we have further automatized several steps that were previously performed by hand. By benchmarking the enviPath prediction system on experimental data, we demonstrate the usefulness of the pathway prediction tool to generate suspect lists for screening, and we propose new avenues to improve their accuracy. Moreover, we provide a well-documented workflow that can be (i) readily applied to detect transformation products in activated sludge and (ii) potentially extended to other environmental studies.

enviRule: an end-to-end system for automatic extraction of reaction patterns from environmental contaminant biotransformation pathways.

MOTIVATION: Transformation products (TPs) of man-made chemicals, formed through microbially mediated transformation in the environment, can have serious adverse environmental effects, yet the analytical identification of TPs is challenging. Rule-based prediction tools are successful in predicting TPs, especially in environmental chemistry applications that typically have to rely on small datasets, by imparting the existing knowledge on enzyme-mediated biotransformation reactions. However, the rules extracted from biotransformation reaction databases usually face the issue of being over/under-generalized and are not flexible to be updated with new reactions. RESULTS: We developed an automatic rule extraction tool called enviRule. It clusters biotransformation reactions into different groups based on the similarities of reaction fingerprints, and then automatically extracts and generalizes rules for each reaction group in SMARTS format. It optimizes the genericity of automatic rules against the downstream TP prediction task. Models trained with automatic rules outperformed the models trained with manually curated rules by 30% in the area under curve (AUC) scores. Moreover, automatic rules can be easily updated with new reactions, highlighting enviRule's strengths for both automatic extraction of optimized reactions rules and automated updating thereof. AVAILABILITY AND IMPLEMENTATION: enviRule code is freely available at https://github.com/zhangky12/enviRule.

From market to environment - consumption-normalised pharmaceutical emissions in the Rhine catchment.

Direct and indirect threats by organic micropollutants can only be reliably assessed and prevented if the exposure to these chemicals is known, which in turn requires a confident estimate of their emitted amounts into the environment. APIs (Active Pharmaceutical Ingredients) enter surface waters mostly through the sewer system and wastewater treatment plants (WWTPs). However, their effluent fluxes are highly variable and influenced by several different factors that challenge robust emission estimates. Here, we defined a dimensionless, theoretically consumption-independent 'escape factor' (kesc) for estimating the amount of APIs (expected to be) present in WWTP effluents. The factor is determined as the proportion of marketed and actually emitted amounts of APIs. A large collection of German and Swiss monitoring datasets were analyzed to calculate stochastic kesc values for 31 APIs, reflecting both the magnitude and uncertainty of consumption-normalised emissions. Escape factors provide an easy-to-use tool for the estimation of average API emissions and expected variability from numerous WWTPs given that consumption data are provided, thereby supporting simulation modeling of the fate of APIs in stream networks or exposure assessments.

Do biotransformation data from laboratory experiments reflect micropollutant degradation in a large river basin?

Identifying a chemical's potential for biotransformation in the aquatic environment is crucial to predict its fate and manage its potential hazards. Due to the complexity of natural water bodies, especially river networks, biotransformation is often studied in laboratory experiments, assuming that study outcomes can be extrapolated to compound behavior in the field. Here, we investigated to what extent outcomes of laboratory simulation studies indeed reflect biotransformation kinetics observed in riverine systems. To determine in-field biotransformation, we measured loads of 27 wastewater treatment plant effluent-borne compounds along the Rhine and its major tributaries during two seasons. Up to 21 compounds were detected at each sampling location. Measured compound loads were used in an inverse model framework of the Rhine river basin to derive k'bio,field values - a compound-specific parameter describing the compounds' average biotransformation potential during the field studies. To support model calibration, we performed phototransformation and sorption experiments with all the study compounds, identifying 5 compounds that are susceptible towards direct phototransformation and determining Koc values covering four orders of magnitude. On the laboratory side, we used a similar inverse model framework to derive k'bio,lab values from water-sediment experiments run according to a modified OECD 308-type protocol. The comparison of k'bio,lab and k'bio,field revealed that their absolute values differed, pointing towards faster transformation in the Rhine river basin. Yet, we could demonstrate that relative rankings of biotransformation potential and groups of compounds with low, moderate and high persistence agree reasonably well between laboratory and field outcomes. Overall, our results provide evidence that laboratory-based biotransformation studies using the modified OECD 308 protocol and k'bio values derived thereof bear considerable potential to reflect biotransformation of micropollutants in one of the largest European river basins.

Modernizing persistence-bioaccumulation-toxicity (PBT) assessment with high throughput animal-free methods.

The assessment of persistence (P), bioaccumulation (B), and toxicity (T) of a chemical is a crucial first step at ensuring chemical safety and is a cornerstone of the European Union's chemicals regulation REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals). Existing methods for PBT assessment are overly complex and cumbersome, have produced incorrect conclusions, and rely heavily on animal-intensive testing. We explore how new-approach methodologies (NAMs) can overcome the limitations of current PBT assessment. We propose two innovative hazard indicators, termed cumulative toxicity equivalents (CTE) and persistent toxicity equivalents (PTE). Together they are intended to replace existing PBT indicators and can also accommodate the emerging concept of PMT (where M stands for mobility). The proposed "toxicity equivalents" can be measured with high throughput in vitro bioassays. CTE refers to the toxic effects measured directly in any given sample, including single chemicals, substitution products, or mixtures. PTE is the equivalent measure of cumulative toxicity equivalents measured after simulated environmental degradation of the sample. With an appropriate panel of animal-free or alternative in vitro bioassays, CTE and PTE comprise key environmental and human health hazard indicators. CTE and PTE do not require analytical identification of transformation products and mixture components but instead prompt two key questions: is the chemical or mixture toxic, and is this toxicity persistent or can it be attenuated by environmental degradation? Taken together, the proposed hazard indicators CTE and PTE have the potential to integrate P, B/M and T assessment into one high-throughput experimental workflow that sidesteps the need for analytical measurements and will support the Chemicals Strategy for Sustainability of the European Union.

Concerted Evaluation of Pesticides in Soils of Extensive Grassland Sites and Organic and Conventional Vegetable Fields Facilitates the Identification of Major Input Processes.

The intensive use of pesticides and their subsequent distribution to the environment and non-target organisms is of increasing concern. So far, little is known about the occurrence of pesticides in soils of untreated areas─such as ecological refuges─as well as the processes contributing to this unwanted pesticide contamination. In this study, we analyzed the presence and abundance of 46 different pesticides in soils from extensively managed grassland sites, as well as organically and conventionally managed vegetable fields (60 fields in total). Pesticides were found in all soils, including the extensive grassland sites, demonstrating a widespread background contamination of soils with pesticides. The results suggest that after conversion from conventional to organic farming, the organic fields reach pesticide levels as low as those of grassland sites not until 20 years later. Furthermore, the different pesticide composition patterns in grassland sites and organically managed fields facilitated differentiation between long-term persistence of residues and diffuse contamination processes, that is, short-scale redistribution (spray drift) and long-scale dispersion (atmospheric deposition), to offsite contamination.

To be or not to be degraded: in defense of persistence assessment of chemicals.

Characterizing the degradation behavior of chemicals in the environment is a key component of chemical hazard and risk assessment. Persistence has been successfully characterized for readily and for slowly degradable chemicals using standardized tests, but for the third group of chemicals with intermediate degradability ("middle group"), the assessment is less straightforward. Whether chemicals of this group behave as persistent or not in a given environment depends on environmental factors such as the presence of sorbents that can limit the bioavailability of chemicals. Uncertainties associated with current persistence assessments of chemicals in the middle group do not imply that persistence assessment is generally inconsistent, too ambiguous for regulatory use, and not useful in chemical hazard and risk assessment. Given the complexity of the environmental factors influencing chemical degradation, and the diversity of commercial chemicals, it has to be accepted though that for chemicals in the middle group even improved testing methods will not remove all of the immanent heterogeneity in their persistence data. For cases with widely different but technically valid persistence data, a weight-of-evidence approach is necessary and the "benefit of the doubt" should follow the precautionary principle in order to protect human and ecosystem health. We maintain that technically valid persistence data, although they might be considered dissatisfying from a scientific point of view because of high variability or even inconclusiveness, can well be sufficient for regulatory purposes. As with anything, also in persistence assessment, the scientific logic aims for a mechanistic description of the processes involved, low uncertainty, and a comprehensive understanding derived from a broad empirical basis. If the scientific logic is used as a benchmark in the regulatory context, this may easily lead to "paralysis by analysis". While regulatory decisions should be based on sound science, discrepancies between scientific goals and regulatory needs and, consequently, different levels of requirements (must-have versus nice-to-have) for degradation studies need to be recognized and appreciated. We further advocate for enhancing consistency between regulatory persistence assessments ("one substance-one assessment"), which is currently not the case.

Wastewater microorganisms impact the micropollutant biotransformation potential of natural stream biofilms.

Biotransformation is the most important process removing manmade chemicals from the environment, yet mechanisms governing this essential ecosystem function are underexplored. To understand these mechanisms, we conducted experiments in flow-through systems, by colonizing stream biofilms under different conditions of mixing river water with treated (and ultrafiltered) wastewater. We performed biotransformation experiments with those biofilms, using a set of 75 micropollutants, and could disentangle potential mechanisms determining the biotransformation potential of stream biofilms. We showed that the increased biotransformation potential downstream of wastewater treatment plants that we observed for specific micropollutants contained in household wastewaters (downstream effect) is caused by microorganisms released with the treated effluent, rather than by the in-stream exposure to those micropollutants. Complementary data from 16S rRNA amplicon-sequencing revealed 146 amplicon sequence variants (ASVs) that followed the observed biotransformation patterns. Our results align with findings for community tolerance, and provide clear experimental evidence that microorganisms released with treated wastewater integrate into downstream biofilms and impact crucial ecosystem functions.

Large-scale assessment of organic contaminant emissions from chemical and pharmaceutical manufacturing into Swiss surface waters.

This study presents a nation-wide assessment of the influence of chemical and pharmaceutical manufacturing (CPM) wastewaters on synthetic organic contaminant (SOC) emissions to Swiss surface waters. Geographic Information System (GIS) based analysis of the presence of CPM in wastewater treatment plant (WWTP) catchments revealed wide distribution of this industrial sector across Switzerland, suggesting that one-third of the 718 Swiss WWTPs may be influenced by CPM wastewaters. To reflect the diversity of this type of wastewaters, we investigated the effluents of 11 WWTPs of diverse sizes and technologies, which treated 0-100% wastewater from a variety of CPM activities. In an extensive sampling campaign, we collected temporally high resolved (i.e., daily) samples for 2-3 months to capture the dynamics of CPM discharges. The > 850 samples were then measured with liquid chromatography high-resolution mass spectrometry (LC-HRMS). Non-target characterization of the LC-HRMS time series datasets revealed that CPM wastewaters left a highly variable and site-specific signature in the effluents of the WWTPs. Particularly, compared to WWTPs with purely domestic input, a larger variety of substances (up to 15 times more compounds) with higher maximum concentrations (1-2 orders of magnitude) and more uncommon substances were found in CPM-influenced effluents. Moreover, in the latter, highly fluctuating discharges often contributed to a substantial fraction of the overall emissions. The largely varying characteristics of CPM discharges between different facilities were primarily related to the type of activities at the industries (i.e., production versus processing of chemicals) as well as to the pre-treatment and storage of CPM wastewaters. Eventually, for one WWTP, LC-HRMS time series were correlated with ecotoxicity time series obtained from bioassays and major toxic components could be identified. Overall, in view of their potential relevance to water quality, a strong focus on SOC discharges from CPM is essential, including the design of situation-specific monitoring, as well as risk assessment and mitigation strategies that consider the variability of industrial emissions.

Scientific concepts and methods for moving persistence assessments into the 21st century.

The evaluation of a chemical substance's persistence is key to understanding its environmental fate, exposure concentration, and, ultimately, environmental risk. Traditional biodegradation test methods were developed many years ago for soluble, nonvolatile, single-constituent test substances, which do not represent the wide range of manufactured chemical substances. In addition, the Organisation for Economic Co-operation and Development (OECD) screening and simulation test methods do not fully reflect the environmental conditions into which substances are released and, therefore, estimates of chemical degradation half-lives can be very uncertain and may misrepresent real environmental processes. In this paper, we address the challenges and limitations facing current test methods and the scientific advances that are helping to both understand and provide solutions to them. Some of these advancements include the following: (1) robust methods that provide a deeper understanding of microbial composition, diversity, and abundance to ensure consistency and/or interpret variability between tests; (2) benchmarking tools and reference substances that aid in persistence evaluations through comparison against substances with well-quantified degradation profiles; (3) analytical methods that allow quantification for parent and metabolites at environmentally relevant concentrations, and inform on test substance bioavailability, biochemical pathways, rates of primary versus overall degradation, and rates of metabolite formation and decay; (4) modeling tools that predict the likelihood of microbial biotransformation, as well as biochemical pathways; and (5) modeling approaches that allow for derivation of more generally applicable biotransformation rate constants, by accounting for physical and/or chemical processes and test system design when evaluating test data. We also identify that, while such advancements could improve the certainty and accuracy of persistence assessments, the mechanisms and processes by which they are translated into regulatory practice and development of new OECD test guidelines need improving and accelerating. Where uncertainty remains, holistic weight of evidence approaches may be required to accurately assess the persistence of chemicals. Integr Environ Assess Manag 2022;18:1454-1487. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).