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FOREWARN

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

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Project coordinators: Esteban ABAD & Marinella FARRÉ

Institute of Environmental Assessment and Water Research (IDAEA-CSIC) - Spain

Communication contact:

Esteban ABAD - eahqam(at)cid.csic.es / Marinella FARRÉ - mfuqam(at)cid.csic.es

Partners:
Leena Maunula University of Helsinki (UH) Finland
Sandra Martin-Latil French Agency for Food, Environmental and
Occupational Health & Safety (ANSES)
France
Kevin McGuinness Dublin City University (DCU) Ireland
Spyros Pournaras Attikon University Hospital (UA) Greece

Abstract:

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.
Using large datasets from previous research, FOREWARN will establish the basis of the relationships between the conditions of a particular aquatic environment such as the hydrological and geological conditions, clime, drought and floods, the anthropogenic stressors such as water supply, wastewater discharges, type of wastewater discharges, among many others, and the emerging pollution that is CECs and emerging pathogens.
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 to achieve TRL5 in real environment case studies.

WP structure

WP number

Led by

WP number

Led by

WP1

IDAEA-CSIC

WP4 ANSES
WP2 UA WP5 IDAEA-CSIC
WP3 DCU WP6 IDAEA-CSIC
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Expected research results

  • Occurrence fate and behavior of CECs and pathogens of case study areas.
  • Machine-learning methods to model CESs and pathogens transfer and behavior, and a decision support system (DSS) for predicting risks and propose mitigation strategies.


For more details on the work plan and impact of the project consult the AquaticPollutants booklet.

Keywords: contaminants of emerging concern, pathogens, antibiotics resistant genes, antibiotic-resistant bacteria, machine-learning

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published on 2021/10/15 10:29:00 GMT+1 last modified 2022-04-07T13:53:30+01:00