With increasingly powerful analytical techniques, we are generating more extensive data on human and environmental chemical exposures. The growing use of comprehensive methods, such as non-target screening approaches, presents the challenge of extracting relevant chemical information from vast datasets. Addressing this challenge requires the development and implementation of advanced data analysis pipelines and machine learning tools to efficiently prioritise and identify hazardous contaminants. At the same time, measurements will always provide, at most, a (geographically and temporally) patchy and incomplete view due to practical and resource limitations. This is especially true for resource-poor regions in the world that are already disproportionally exposed, and will be even more so in the future.
With increasingly powerful analytical techniques, we are generating more extensive data on human and environmental chemical exposures. The growing use of comprehensive methods, such as non-target screening approaches, presents the challenge of extracting relevant chemical information from vast datasets. Addressing this challenge requires the development and implementation of advanced data analysis pipelines and machine learning tools to efficiently prioritise and identify hazardous contaminants. At the same time, measurements will always provide, at most, a (geographically and temporally) patchy and incomplete view due to practical and resource limitations. This is especially true for resource-poor regions in the world that are already disproportionally exposed, and will be even more so in the future. In this research theme, we develop and apply advanced process-based models, statistical data analyses, and machine learning tools, to support exposure and risk assessments that make optimal use of the extensive data generated by our analytical techniques. We use our models and expertise in comprehensive predictive and diagnostic risk assessments, for example in regulatory settings or for scenario analyses of interventions and mitigation strategies.
Projects:
B2E (Frederic Béen, Marja Lamoree) “From source to effect (B2E): Integrated approach to address the emission of industrial chemicals to surface waters”
HolyTAP (Frederic Béen, Marja Lamoree, Jan Post (Wetsus), Jan Willem Schoonen (Wetsus)) “Development of a holistic (bio)analytical platform to assess the hazard of transformation products formed during water treatment”
MAGIcIAN (Rik Oldenkamp)
“Modelling Approaches to Guide Intelligent surveillance for the sustainable Introduction of novel ANtibiotics”
MLIdent (Frederic Béen, Marja Lamoree) “Harnessing machine learning to improve identification and quantification of micro- and nanoplastics”
PARC (Marja Lamoree, Frederic Béen, Sicco Brandsma, Pim Leonards) “Partnership for the Assessment of Risks from Chemicals”
SpectralQ (Frederic Béen) “Machine Learning and High-Resolution Mass Spectrometry for Enhanced Monitoring of Environmental Contaminants”
SPRINGS and @SPRINGS_EU (Rik Oldenkamp; Bas Teusink) “Supporting Policy Regulations and Interventions to Negate aggravated Global diarrheal disease due to future climate Shocks”
SWIM (Rik Oldenkamp) “Safe drinking Water production from AMR polluted surface water by developing, Improving and testing innovative Membrane technology”
TULIP and @tulipph (Rik Oldenkamp; Matti Gralka; Frank Bruggeman) “Community-based engagement and intervenTions to stem the tide of antimicrobial resistance spread in the aqUatic environments catalysed by cLImate change and Plastic pollution interactions”