Development a smart forewarning system to assess the occurrence, fate and behaviour of contaminants of emerging concern and pathogens, in waters
FOREWARN will assess the occurrence, fate and behaviour of contaminants of emerging concern (CECs) and pathogens and develop machine-learning methods to model their transfer and behaviour and build a decision support system (DSS) for predicting risks and propose mitigation strategies. FOREWARN will be focussed on CECs such as antibiotics and pathogens such as antibiotic-resistant bacteria (ARB), antibiotic resistance genes (ARG) and emerging viruses, such as SARS-CoV-2.
The project will consider 2 types of case studies:
- In-silico case studies will be selected from previous results, and dataset obtained in past or ongoing EU projects. Data will be used to develop the models and algorithms to feed and develop the DSS system to better understanding the sources, transport, degradation of CECs and pathogens and modelling their behaviour.
- The adaptive DSS system will be refined and tested under real environmental conditions (6 months) to achieve TRL5 in real environment case studies.
Contaminants of emerging concern, pathogens, antibiotics resistant genes, antibiotic-resistant bacteria, machine-learning
Dr. Esteban Abad & Marinella Farré,
Institute of Environmental Assessment and Water Research (IDAEA) – Spanish National Research Council (CSIC), Spain
E-Mail: & firstname.lastname@example.org
University of Helsinki – Finland
French Agency for Food, Environmental and Occupational Health & Safety (ANSES) – France
Dublin City University – Ireland
Attikon University Hospital – Greece