Project Details
Projekt
Sorption of legacy pollutants onto natural colloids in surface waters under field conditions
Applicant
Dr. Allan Philippe
Subject Area
Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 458047880
The fate and toxicity of non-biodegradable pollutants, particularly per- and polyfluoroalkyl substances (PFAS) and toxic metals, in surface waters are significantly influenced by their sorption onto natural mineral surfaces. While numerous studies have investigated sorption mechanisms, most have been conducted under controlled laboratory conditions. This approach fails to account for the complex interplay between natural coatings, water composition, and particle surface properties present in real environmental systems. We propose that a more accurate assessment of sorption coefficients for persistent pollutants in surface waters could be achieved by shifting from systematic experimental designs to field experiments in representative water bodies combined with multivariate data analysis. This approach can better capture the dynamic nature of pollutant-particle interactions in situ, which is essential for developing more realistic models to predict the environmental fate and potential ecological impacts of these persistent contaminants. Following the methodology established in our previous project, we propose to apply this approach to study the sorption behaviour of persistent pollutants (PFAS and toxic metals) on natural colloids in surface waters. We plan to use the dialysis bag method to produce environmentally coated natural particles (clays and Fe-oxides) in situ, characterize their coatings, and determine effective sorption coefficients for a selection of toxic metals and PFAS by spiking the exposed particle suspensions with known amounts of pollutants. By conducting experiments in 20 representative water bodies across three different seasons, we will generate a robust dataset to develop multivariate predictive models. We will determine the standard parameters of the water and its dissolved organic matter composition using by liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry and fluorescence spectroscopy for a complete state of the art characterization. The exposed particles’ coating will be characterized using infrared spectroscopy, X-ray photoelectron spectroscopy, secondary ion time-of-flight mass spectrometry, and ζ-potentiometry. Machine learning models will enable us to explore the characteristics of environmental coatings on common natural particles, the influence of water composition on these coatings, and how the coatings affect pollutant sorption. Ultimately, the project aims to use these models for predicting accurately sorption coefficients under environmental conditions and to explore these models to gain a deeper understanding of the sorption mechanisms of legacy and emerging pollutants on natural particles.
DFG Programme
Research Grants
