Project: Development a smart forewarning system to assess the occurrence, fate and behaviour of contaminants of emerging concern and pathogens, in waters

Acronym	FOREWARN (Reference Number: ID 400)
Duration	01/09/2021 - 31/08/2024
Project Topic	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: 1) 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. 2) The adaptive DSS system will be refined and tested under real environmental conditions (6 months) to achieve TRL5 in real environment case studies.
Project Results (after finalisation)	1. To establish plug-flow models of the fate and behaviour of CECs on sewage-impacted aquatic systems and their dynamics under different hydrological and climatic conditions based on the impact of the flow changes on the concentrations, circulating flows, the average upstream emissions and an overall decay constant assessing the retention and degradation capacity of aquatic ecosystems; 2. To assess and model the biodegradation of antibiotics to estimate and anticipate antibiotic resistance transmission rates and assessing gene transfer; 3. To evaluate the transmission and changes of AMR and pathogens including emerging viruses such as SARS-CoV-2 in aquatic ecosystems. 4. To assess the efficiency of WW treatments and when needed to identify requirements and propose tailored WW treatments to achieve safety levels 5. To develop a DSS system that will use the collected data to attempt to predict the emergence and impact of CECs and pathogens. This will include analysis of feature importance and development of interpretable calibrated models for effective decision support. 6. To evaluate the performance of the DSS to achieve a TRL 5.
Network	AquaticPollutants
Call	1st AquaticPollutants Joint Call 2020

Project partner

Number	Name	Role	Country
1	CSIC	Coordinator	Spain
2	University of Helsinki	Partner	Finland
3	Anses	Partner	France
4	Dublin City University	Partner	Ireland
5	Attikon University Hospital	Partner	Greece