Department Environmental Chemistry

Possible starting date: February 2023 or later

Description:

Synthetic antioxidants (SAOs) are chemicals of emerging concern, since they are ubiquitously present in everyday life products, such as plastics. With the outbreak of the Covid-19 pandemic, they are of even more interest, as they are contained in facemasks. Consequently, it is expected that human take them up. Upon entrance into the human body, metabolization is expected, in order to increase their polarity and to enhance their excretion. After excretion, they end up in wastewater treatment plants, whose wastewater can be analyzed after sample preparation with high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (HPLC-HRMS/MS).

As their name says, SAOs possess antioxidant characteristics, as they can undergo reactions with radicals, to prevent oxidation of the product they protect. The resulting oxidized products may be identical to the human metabolites, as many biotransformation reactions are of oxidative nature. The aim of this project is to identify human metabolites of SAOs in wastewater water and thereby indirectly human exposure. In a second step, the goal is to develop an experiment to identify oxidation products of these compounds in order to ensure that the identified metabolites exclusively originate from human metabolism.

This project will start with a data analysis part of previously acquired HPLC-HRMS/MS data on wastewater samples. Several software tools will be used for screening SAO. The results will be used in the second part to design and execute an experiment for the identification of oxidized products followed by hands on HPLC-HRMS/MS measurements.

We are looking for a chemistry or environmental science student with some background in biogeochemistry or any other natural science student with some laboratory experience and who is motivated to learn more about environmental chemistry. The start of the project is flexible. If you are interested, please contact

Corina Meyer or

Prof. Dr. Juliane Hollender

Possible starting date: January/February 2023, but an earlier starting date could be discussed

Description:

The presence of fluorine-containing chemicals in the aquatic environment is of increasing concern, due to their stability and potential impact to organisms in receiving waters and drinking water resources downstream of wastewater treatment plants (WWTPs). Per- and polyfluoroalkyl substances (PFAS) include many compounds with different chain length and functional groups, and are used in industry and different consumer products like textiles, electronics, coatings, and fire fighting foams. Beside the PFAS, organofluorine compounds, which include fluorine-containing pharmaceuticals and pesticides, also contribute to the total fluorine content. However, little is known about their relative importance or fraction.  

In this project we want to evaluate the occurrence and contribution of the organofluorine compounds in wastewater by screening for characteristic MS2 fragments to find known and suspected compounds, but also transformation products of them.  Influents and effluents of WWTP will be sampled and analyzed with liquid chromatography coupled to high resolution mass spectrometry (HRMS).

We are looking for a person with a background in chemical analysis and with interest in programming (scripting with R).

Keywords: LC-HRMS, organofluorine micropollutants, programming

Supervisors: Christa McArdell, Heinz Singer & Kathrin Fenner.

Possible starting date: From mid September 2022

Background:

With the development of high-resolution mass spectrometry and nontargeted screening workflows, we are able to detect and process tends of thousands of unknown and potentially harmful environmental pollutants in aquatic samples. However, since even the most sophisticated data analytics workflows require manual verification for tentative identification and quantification, we have to prioritize which compounds are most relevant in different studies. Such prioritization strategies are often based on the measured areas or frequencies of occurrence, both of which lack toxicological relevance so critical in the context of environmental pollution. In order to append toxicological relevance to HRMS analysis, machine learning methods (ML) are being developed to predict the toxicity of unknown features based on their MSMS spectra and thus prioritize the compounds based on their potential toxic effect. 

Study goal:

In this study, we want to focus on the analytical validation of MLinvitroTox, an ML tool under development at Eawag for the prediction of toxicity fingerprints for unknown HRMS features based on their MSMS spectra. We aim to deploy MLinvitroTox on wastewater samples for prioritization of potentially toxic species towards tentative identification and quantification, followed by confirmation/rejection with authentic standards. In this work, we will collect and analyze wastewater samples as well as use the “digitally frozen” sample repository already present at Eawag. This work will help to establish the validity of the proposed approach as part of the EXPECTmine project (Mining toxicity and HRMS data for linking exposures to Effects: https://www.eawag.ch/en/department/uchem/projects/translate-to-english-expect). 

Tasks:

The student will gain skills in working with analytical techniques in general and in particular ultra-high-performance liquid chromatography-tandem high-resolution mass spectrometry (UHPLC-HRMS/MS). More specifically, the student supported by the supervisor will collect and analyze wastewater samples, perform targeted, suspect, as well as nontargeted analyses, deploy MLinvitroTox on the results, identify potentially toxic features of interest, and attempt to confirm their identity analytically. The work will combine fieldwork, analytics, machine learning, and cheminformatics.
Supervised by Dr. Kasia Arturi, Prof. Dr. Juliane Hollender.

Contact: kasia.arturi@cluttereawag.ch, juliane.hollender@cluttereawag.ch