Department Environmental Chemistry

Increasing contamination of the environment with chemicals is a major concern. Wastewater treatment plants are one of the main barriers to prevent chemicals entering then environment, however, most chemicals are only partially removed. Estimating the degree to which these chemicals break through wastewater treatment is an urgent matter in order to:

• Facilitate chemical risk assessment
• Support the development of better degradable chemicals
• Assist industries in their wastewater management

One reason why satisfactory models to make such estimations do not exist, is the lack of a sufficiently large and consistently measured dataset. Fortunately, recent advances in high resolution mass spectrometry now allow monitoring a large number of chemicals at once. As part of the KlarA project we have compiled and carefully curated monitoring data of hundreds of individual chemical substances from wastewater treatment plants in Switzerland, Sweden and Australia. We have built a dataset that we are now utilizing to develop models that predict removal of chemicals during wastewater treatment based on their chemical structure. Our main strategy consists of using machine learning algorithms to build quantitative structure-activity relationship models.

The main outcomes of this project will be both the high-quality datasets produced, and the open-source models and tools that will be available for researchers and officials alike.