Staff

Kathrin Fenner

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Prof. Dr. Kathrin Fenner

Senior scientist / group leader

Department Environmental Chemistry

About Me

I am passionate about learning how chemicals degrade in the environment and whether any hazardous transformation products are formed during degradation. The ultimate goal of my research is to develop more accurate methods to assess persistence and risk from transformation product formation in regulatory risk assessment procedures. My research focuses on the following three areas in particular.

  1. Prediction of biodegradation pathways and rates
    Current (Q)SBRs tools to predict biodegradation half-lives produce highly uncertain results and available pathway prediction tools suffer from combinatorial explosion due to a lack of methods to prioritize possible pathways. In my research, I mine biotransformation pathway and rate information to develop novel algorithms for biotransformation prediction. In parallel, my team performs bioreactor experiments with pertinent microbial communities and employ high-resolution mass spectrometry to identify transformation products to use that data to further improve biotransformation prediction
  2. Hazard and risk assessment of transformation products
    The goal of this aspect of my research is to develop models and indicators to predict and assess the environmental fate of transformation products. In collaboration with the team of Prof. B. Escher (UFZ Leipzig, Universität Tübingen) we are developing methods to estimate mixture toxicity of transformation products and their parent compound, using the strong structural resemblance to the parent compounds and what is known about parent toxicity as a guiding principle.
  3. Improved tools for persistence assessment
  4. Persistence assessment is key to all chemical legislations in Europe. In my research, I try to contribute to a critical reflection of current persistence assessment paradigms and methods. For instance, I introduced the concept of joint persistence as novel persistence indicator that accounts for transformation product formation. Currently, I am also working on a project that evaluates the value of the OECD 308 guideline for evaluation of persistence of chemicals at the sediment-water interface.

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Projects

Chemical pollution is a major threat to ecosystems and human health and current regulations seem insufficient to prevent widespread chemical contamination...

High-throughput experimental and computational tools for safe-by-design chemicals
Chemicals are released to the environment through a variety of pathways...

High-throughput biodegradation screening
Understanding and predicting the fate of synthetic chemicals under different environmental conditions is essential to evaluate their potential risk for humans and ecosystems...

enviPath plus
The increasing pollution of the environment by chemicals is a major problem.

KlarA_Prediction of the degradation of pollutants in wastewater treatment plants based on structural degradation relationships
Pesticides are major environmental pollutants. For this reason, the European Commission (EC) has imposed a stringent pesticide regulatory scheme...

ARISTO
At the Rhine monitoring station, the Basel-Stadt Environment and Energy Office (AUE Basel-Stadt) is measuring water contamination with organic micropollutants on a daily basis…

RÜS Data 2.0 - Extended data analysis of LC-HRMS time series
A large number and variety of chemicals used in households, healthcare, industry or agriculture enter our wastewater treatment plants with the domestic and industrial wastewater....

Molecular mechanisms of microbiological pollutant degradation in wastewater treatment and natural waters
When pharmaceuticals end up in the environment...

PREMIER - Prioritisation and Risk Evaluation of Medicines in the EnviRonment

enviPath

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Curriculum Vitae

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Publications

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Fenner, K., Cousins, I., Gardea-Torresdey, J., Hollender, J., Scheringer, M., Tratnyek, P., … Wang, Z. (2026). 60 years of persistence science in ES&T: from foundational insights to demonstrated impact. Environmental Science and Technology, 60(15), 11139-11140. doi:10.1021/acs.est.6c04872, Institutional Repository
Fenner, K., Zahn, D., Jöhncke, U., Sigmund, G., Mayer, P., Hughes, C., … Reemtsma, T. (2026). Persistence assessment of chemicals: trajectories toward New Approach Methodologies (P-NAMs). Environmental Science and Technology, 60(15), 11243-11252. doi:10.1021/acs.est.6c00444, Institutional Repository

Chemical persistence has long been recognized as a critical determinant of ecosystem and human exposure, exemplified by legacy pollutants such as DDT, PCBs, and, more recently, PFAS. Despite decades of regulation and research, robust experimental half-life data are available for only a fraction of chemicals in use, hampering their persistence assessment. Current testing frameworks, while refined, lack the efficacy to address these large data gaps, underscoring the need for innovative approaches. We argue that new approach methodologies for persistence assessment (P-NAMs)─including high-throughput (HT) experimental systems and advanced in silico models─are needed. HT-testing can bridge the gap between biodegradability screening tests and resource-intensive simulation studies. Simultaneously, HT-testing can generate large, consistent data sets needed to improve the mechanistic understanding of biotransformation and train more accurate predictive models. Integration of transformation product analysis and FAIR (findable, accessible, interoperable, and reusable) data repositories will further enhance mechanistic understanding and model reliability. We call for coordinated efforts across academia, industry, and regulatory bodies to establish standardized reporting, expand accessible data sets, and validate predictive tools. By advancing P-NAMs, the scientific community can ensure that persistence assessment evolves from a regulatory bottleneck into a driver of innovation, safeguarding human and ecosystem health and promoting safe chemical design.
Kalt, M., Ceppi, E., Robinson, S. L., & Fenner, K. (2026). Breaking it down: unveiling the roles of chemical structure, concentrations and technology on micropollutant biotransformation in wastewater treatment plants. Water Research, 292, 125283 (12 pp.). doi:10.1016/j.watres.2025.125283, Institutional Repository

Complex mixtures of anthropogenic micropollutants (MPs) pose risks to ecosystems and health by entering the environment through multiple pathways. Microbial biotransformation is a major removal process, yet the key drivers remain poorly understood. Activated sludge (AS) treatment in wastewater treatment plants (WWTPs) acts as a partial barrier and reduces MP emissions into aquatic ecosystems, yet removal efficiencies vary widely across MPs. Here, we studied the biotransformation of 189 MPs in AS from six Swiss WWTPs operated at three treatment conditions: carbon elimination (Celim), nitrification–denitrification (DeNit), and moving bed biofilm reactors (MBBR). MPs were spiked into lab-scale assays with AS sampled from those WWTPs and analyzed by high-resolution mass spectrometry to derive biotransformation rate constants. Overall, biotransformation efficiency followed the order of solids retention time (MBBR DeNit Celim), and, across MPs, their average influent concentration (Cinf) was the most decisive factor for increasing observed transformation. Using a multivariate analysis approach, we further identified structural features that were key determinants of MP biotransformation. Presence of functional groups amenable to broadly co-metabolic processes (e.g., amides, esters, sulfonamides) resulted in fast biotransformation and consistently low variability across treatment technologies. In contrast, we found ample evidence for catabolic degradation of many of the MPs, which mostly exhibited high variance across treatment technologies, and identified specific structural features that increased the likelihood of catabolic degradation. Contrary to prevailing assumptions, catabolic degradation was found to be widespread, especially in MBBR, and not limited to high-Cinf MPs. These findings provide new insights into the fate of MPs during microbial biotransformation, and highlight the need to distinguish co-metabolic from catabolic pathways when further investigating the structural determinants that lead to increased biotransformation. As such, our study is in line with the EU's Safe and Sustainable by Design (SSbD) framework, emphasizing proactive chemical design for full biodegradability.
Salz, M., Cordero Solano, J. A., Fenner, K., & Hafner, J. (2026). Confidently uncertain: probabilistic machine learning to predict soil biotransformation half-lives. Environmental Science and Technology, 60(14), 11077-11086. doi:10.1021/acs.est.6c03516, Institutional Repository

Predicting environmental persistence of chemicals from molecular structure is an open challenge, yet indispensable in regulatory screenings for potentially harmful substances and to advance the development of safe-and-sustainable-by-design chemicals. Limited availability of biotransformation half-life data makes persistence prediction difficult, and models typically struggle to generalize beyond their training data. Therefore, reliable estimates of prediction confidence are key. Here, we propose a probabilistic model for the prediction of soil biotransformation half-lives. A Gaussian Process Regressor was trained on 867 mean pesticide half-lives with data uncertainty estimates. Instead of single half-life values, our model predicts well-calibrated probability distributions that can be used to calculate a compound's probability of being persistent. Although the overall model performance remains moderate, the predictions are reliable when the confidence in the prediction is high. We applied our model to pesticide transformation products with unknown half-lives, and to a database of globally marketed chemicals. We show that our model is able to identify chemicals that are known, or suspected to be, persistent in the environment. The model is available as an online app (https://pepper-app.streamlit.app/) and as a Python library (pepper-lab) to meet diverse user needs.
Seller-Brison, C., Weissbach, F., Jorner, K., Scheringer, M., & Fenner, K. (2026). Hazard assessment of antioxidants as contaminants of concern. Environmental Science and Technology Letters, 13(4), 560-567. doi:10.1021/acs.estlett.5c01217, Institutional Repository

Antioxidants (AOs) are increasingly detected in the environment, in aquatic organisms, and in human biosamples. Therefore, we performed a comprehensive hazard assessment of more than 500 natural and synthetic AOs by exploring their recalcitrance toward mineralization (not readily biodegradable, NRB), persistence (P), bioaccumulation (B), toxicity (T), mobility (M), and formation of toxic transformation products (TPs). Literature experimental data complemented with in silico predictions revealed that 60% of the AOs classify as NRB and T, out of which 31.6% classify as P and T. Of highest concern according to EU regulations are PBT or PMT compounds, with 4.8% and 2.1% of the AOs studied, respectively, falling into these categories. All AOs classified as PBT or PMT are of synthetic origin and, by majority, primary AOs, i.e., phenolic or amine compounds. Further, most predicted toxic TPs stem from phenolic and amine structures. Natural or nature-identical primary AOs or secondary AOs, e.g., ester or organic sulfur compounds, are less often classified as hazardous. Only 19 AOs were identified as not fulfilling any of the hazard criteria, highlighting the need for more experimental data on AO hazards for regulatory purposes, as well as for further research on safe AO alternatives.
Tian, R., Weir, L. M., Posselt, M., Fenner, K., & McLachlan, M. S. (2026). Can Benchmarking increase the accuracy of predicting biodegradation rates across aquatic ecosystems?. Environmental Science and Technology, 60(13), 10234-10241. doi:10.1021/acs.est.6c00470, Institutional Repository

Describing and dealing with the large temporal and spatial variability in biodegradation rate constants is a requirement for robust persistence and exposure assessment. Chemical benchmarking uses a well-characterized reference chemical in a manner analogous to an internal standard in analytical chemistry; its measured biodegradation rate constant captures environmental-specific information that is used to predict the variability of the rate constants of other chemicals. We compiled 1656 biodegradation rate constants for 97 chemicals in European and Australian aquatic ecosystems, all measured with the same modified OECD 309 test protocol. Two benchmarking approaches were assessed for their ability to reduce the spatiotemporal variability in the data: (i) normalizing all chemicals to a single benchmark chemical (universal benchmarking); and (ii) grouping chemicals and normalizing the chemicals within each group to a benchmark chemical chosen from within that group (group-specific benchmarking). Universal benchmarking did not reduce the measured variability, while group-specific benchmarking did when the grouping of chemicals was optimized using the data. However, when chemical grouping was predicted based on the molecular fingerprint MACCS (Molecular ACCess System) or initial biotransformation rules, there was no reduction in variability for most chemicals. Group-specific benchmarking has promise as a tool to predict spatiotemporal variability in biodegradation rate constants when an appropriate chemical grouping is possible, but our current understanding of the key chemical features associated with biodegradability is insufficient to reliably group chemicals a priori.
Zhang, K., Marti, T. D., Probst, S. I., Robinson, S. L., & Fenner, K. (2026). Enzyme association for environmental biotransformation reactions through contrastive learning of reaction center-specific fingerprints. Bioinformatics, 42(4), btag142 (12 pp.). doi:10.1093/bioinformatics/btag142, Institutional Repository

Motivation: Microbial biotransformation plays a central role in the environmental degradation of chemical contaminants, driven by the catalytic activities of diverse enzymes. However, linking specific enzymes to contaminant removal and predicting associated transformation products (TPs) under real-world conditions remain a major challenge. In this study, we present a self-supervised, contrastive fine-tuning strategy for reaction fingerprint learning, designed to improve the chemical relevance of BERT-based reaction embeddings for environmental biotransformation reactions. Specifically, we fine-tuned a BERT encoder such that the cosine similarity between its reaction fingerprints aligns with the Tanimoto similarity of traditional structure-based fingerprints.

Results: The resulting compact, 256-dimensional fingerprints, termed crxnfp, showed an improved ability to cluster reactions according to transformation type and focus attention on chemically meaningful reaction centers. Our crxnfp fingerprints were further validated in reaction classification tasks across multiple datasets, achieving superior or comparable performance relative to existing methods. Importantly, they enabled a similarity-based association of biotransformation rules and reactions from enviPath with enzyme annotations from the Rhea and UniProt databases, offering a scalable approach to enrich environmental biotransformation datasets with enzymatic information. Additionally, crxnfp was employed to identify specific enzyme classes involved in contaminant biotransformation, which were subsequently validated through experiments conducted in this study, achieving 91.3% accuracy at the third-level enzyme classification. The crxnfp fingerprints offer a promising solution to advance the understanding of contaminant biotransformation and guide the development of enzyme-informed strategies for contaminant management across diverse environmental contexts. Availability and implementation: Code is available at https://github.com/zhangky12/crxnfp and https://github.com/zhangky12/crxnfp_knn.
Chonova, T., Ruppe, S., Langlois, I., Griesshaber, D. S., Loos, M., Honti, M., … Singer, H. (2025). Unveiling industrial emissions in a large European river: insights from data mining of high-frequency measurements. Water Research, 268, 122745 (13 pp.). doi:10.1016/j.watres.2024.122745, Institutional Repository

Despite the tremendous efforts to improve river water quality, chemical contamination remains a significant issue. Besides well-known contaminants, in recent years, pollutants of industrial origin received increasing attention because of the huge knowledge gap regarding their occurrence, fate and environmental risks. Moreover, such pollutants often exhibit high concentration fluctuations over time, which makes them less predictable and measurable with classical short-time campaigns.
This study provides insights into the different sources of chemical contamination of the Rhine River based on temporal high-frequency LC-HRMS monitoring data from a single location. A newly developed prioritization strategy selected nearly 3000 substances as potentially major contaminants. A novel classification analysis based on temporal behavior identified 53 % of these compounds (accounting for 62 % of the time-integrated intensity recorded in the dataset) as originating from irregular emission sources. Irregular emissions can originate from industrial production cycles. After delimiting other potential irregular sources, we have strong evidence indicating that a considerable share of the irregular emissions likely comes from industrial activities. This finding is supported by the structural elucidation of sixteen irregularly emitted substances, for which the industrial origin was successfully confirmed. Those compounds include 3-chloro-5-(trifluoromethyl)pyridine-2-carboxylic acid and 4-(dimethylamino)-2,2-diphenylpentanenitrile. In addition, 40 other compounds exhibited temporal emission patterns similar to the sixteen industrial compounds, which strongly suggests a common contamination source. Finally, 100 top-ranking compounds were selected for further structural elucidation and emission reduction measures. The computational approach outlined within this study can be effectively applied in other large river catchments to identify unknown contaminants stemming from industrial sources.
Coll, C., Screpanti, C., Hafner, J., Zhang, K., & Fenner, K. (2025). Read-across of biotransformation potential between activated sludge and the terrestrial environment: toward making it practical and plausible. Environmental Science and Technology, 59(3), 1790-1800. doi:10.1021/acs.est.4c09306, Institutional Repository

Recent emphasis on the development of safe-and-sustainable-by-design chemicals highlights the need for methods facilitating the early assessment of persistence. Activated sludge experiments have been proposed as a time- and resource-efficient way to predict half-lives in simulation studies. Here, this persistence "read-across" approach was developed to be more broadly and robustly applicable. We evaluated 21 previously used reference plant protection products (PPPs) for their broader applicability in calibrating regression and classification models for predicting half-lives in soil (DT50OECD307) and water-sediment systems (DT50OECD308) based on their half-life in sludge and the organic carbon-water partition coefficient KOC as predictors. The calibrated regression models showed satisfactory predictions of DT50OECD307 for another 22 test PPPs. Performance was less satisfying for the prediction of DT50OECD308 for 46 active pharmaceutical ingredients (APIs), suggesting a need for expanding the set of calibration substances and more experimental KOC values. The classification models mostly correctly classified persistent and non-persistent test compounds for both PPPs and APIs, which is relevant for early-stage screening of persistence. Transformation products of the reference compounds in activated sludge samples were consistent with the reported degradation pathways in soil, particularly with respect to major aerobic, enzyme-catalyzed transformation reactions. Overall, "reading across" biotransformation in environmental compartments such as soils or sediments from experiments with activated sludge outperformed three widely used in silico approaches for estimating half-lives and hence has immediate potential to support early assessment of biodegradability when aiming to develop chemicals that are safe and sustainable by design.
Cordero Solano, J. A., Hafner, J., McLachlan, M. S., Singer, H., & Fenner, K. (2025). Predicting micropollutant removal in wastewater treatment based on molecular structure: benchmark data and models. Environmental Science and Technology, 59(41), 22020-22028. doi:10.1021/acs.est.5c09314, Institutional Repository

Models to predict the environmental fate of micropollutants are needed for alternatives assessment and safe-by-design efforts. Wastewater treatment plants (WWTPs) are the main barrier to prevent micropollutants from entering receiving water bodies, and WWTP breakthrough is an important indicator of chemical persistence. State-of-the-art models to predict breakthrough are limited by their need for first-order degradation rate constants, a metric that is often unavailable. Here, we build models that predict removal in conventional treatment directly from the chemical structure using data from field-scale monitoring for over 1000 chemicals. The best predictions were achieved using substructure-based fingerprints (i.e., MACCS) and random forests, and identified influential substructures agree with structural moieties relevant for biotransformation. We show that our models are more reliable than existing process-based models used in EU and US regulatory contexts, making them important contributions to the in silico toolbox for alternatives assessment, the design of more benign chemicals in industrial research and development, and even exposure modeling in a risk assessment context. Moreover, our data sets along with our extensive systematic evaluation of different curation criteria and the scripts to reproduce it are key for future model advancement. Our model is publicly available (pepper-app) along with the training data and the scripts to reproduce the data curation process (github.com/FennerLabs/pepper).

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Address

E-Mail: kathrin.fenner@eawag.ch
Phone: +41 58 765 5085
Fax: +41 58 765 5802
Address: Eawag
Überlandstrasse 133
8600 Dübendorf
Office: BU F18
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Expertise

biological degradation, mass spectrometry, micropollutants, organic pollutants

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Research Group

See my team page

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