Understanding transformation products formation and microbial diversity for drinking water aquifer management
Organic micropollutants (OMPs) are pervasive across all portions of the water cycle. This includes groundwater aquifers, a major source for drinking water production. Targeted measurements of specific OMPs show that various pesticides, pharmaceuticals, industrial compounds, and per- and polyfluoroalkyl substances (PFAS) are present in the ng/L to µg/L range. However, such standard OMP monitoring only focuses on known compounds, neglecting the countless unknown OMP and their transformation products (TPs) that can potentially pollute groundwater aquifers. Recent evidence showed that OMPs are effectively removed during soil passage (Ore, et al, 2025). However, this is not the case for all compounds, such as PFAS. Furthermore, the formation of TPs from PFAS and other OMPs in groundwater remain poorly understood. Presence, fate, and accumulation of these compounds is determined by their physicochemical properties such as hydrophilicity, charge, and biodegradability. At the same time, groundwater conditions are important too, including availability of electron acceptors and donors such as dissolved organic carbon (DOC), as these shape biodegradation conditions. Finally, microbes are required for biodegradation activity, and hence their community composition and functional activity plays an important role in OMPs fate and TPs formation. Mimicking these complex set of conditions and simulating the long retention times present in the field remains challenging in the lab
Research challenges
This PhD project focuses on exploiting large datasets available at the drinking water companies on groundwater geochemistry (electron acceptors, DOC etc), OMP and TP presence, and microbial community composition, to first provide a comprehensive understanding of real aquifer conditions. These datasets will be augmented with non-targeted screening (NTS) data of groundwater samples to detect unknown TPs, paired with metagenomic (and possibly -transcriptomic) sequence data of groundwater microbial communities. These large datasets will be analysed using a range of (big) data-science tools (e.g., multivariate statistical analysis, shallow and deep learning) to identify key covariates that explain the fate and behaviour of OMPs and TPs. These covariates will include relevant groundwater conditions as well as characteristics of microbial communities and/or active genes. The project will provide a deeper understanding of groundwater quality, prioritise the most informative descriptors for predicting water quality to support aquifer management, and help identify conditions that could be leveraged to stimulate biodegradation.
Your assignment
You will mine existing groundwater quality datasets from drinking water companies and complement these with targeted sampling campaigns. Using advanced analytical tools, such as non-target screening, metagenomics and metatranscriptomics, you will prioritise and where possible identify OMP/TPs, characterize microbial communities, and identify microbial processes related to TP formation. By applying advanced data science tools, you will uncover key predictors of OMP and TP occurrence and accumulation. Your work will translate complex data into actionable insights, advancing our understanding of groundwater quality and the environmental conditions that promote natural biodegradation.
Your profile
You hold a master’s degree in environmental sciences, bio- or cheminformatics or a closely related field, with demonstrated experience and affinity with data science. You have an affinity with several of the following areas: organic micropollutants, microbiology, geohydrology, analytical chemistry, and data science. At least intermediate programming skills in Python, R and/or Bash are desired, and workflow organisation (e.g., SnakeMake, Nextflow) on a high-performance computing (HPC) system is considered favourable. You are an independent and motivated researcher with a strong interest in multidisciplinary work and a willingness to explore topics beyond your current expertise. You enjoy working in a dynamic environment and are comfortable tackling challenges as they arise. Possession of a driver’s license is a plus.
Keywords: Organic micropollutants; Groundwater, Biodegradation; Microbial community; Machine learning
Professor/University group/Wetsus supervisor(s):
University promotor: Dr. Nora B. Sutton (Associated professor at Environmental Technology, Wageningen University);
University co-promotor: Frederic Been (Assistant Professor at Chemistry for Environment & Health, Vrije Universiteit Amsterdam)
Wetsus supervisor: Dr. Pieter van Veelen (Head of the Laboratory of Microbiology)
Project partners: Groundwater Technology
Only applications that are complete, in English, and submitted via the application webpage before the deadline will be considered eligible.
Guidelines for applicants: https://phdpositionswetsus.eu/guide-for-applicants/
