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


Achievements so far

FOREWARN focuses on the assessment of emerging contaminants (CECs) and pathogens such as antibiotics, antibiotic resistant bacteria (ARBs), resistance genes and viruses such as SARS-CoV-2, using large datasets to link aquatic conditions, human impacts and the presence of these contaminants. The project uses two types of case studies: 1) historical EU project data for in silico studies to develop a Decision Support System (DSS) and 2) real case studies under different environmental conditions.  The in silico studies have compiled data from scientific publications and public databases to create a database to develop the first version of the DSS.

Real case studies from different locations, including Finland, France, Greece and Spain, include sampling campaigns in different seasons, considering surface waters, sediments and wastewater treatment plant effluents. Initial results show the presence of antibiotics in most samples, with differences in microbial load between influent and effluent. The data collected from real case studies is crucial for feeding the FOREWARN database and developing models and algorithms for the DSS.

If you're interested in scientific publication associated with FORWARN, click here.




Project Coordinator:
Dr. Esteban Abad & Marinella Farré,
Institute of Environmental Assessment and Water Research (IDAEA) – Spanish National Research Council (CSIC), Spain

E-Mail: &

Partner Institutions:
University of Helsinki – Finland

French Agency for Food, Environmental and Occupational Health & Safety (ANSES) – France

Dublin City University – Ireland

Attikon University Hospital – Greece