The deterioration of water quality, caused by climatic/seasonal changes, global warming, or industrial waste etc. is a major global concern. Water quality directly impacts our lives as drinks or food made from it affect our health. Over the last decade, water quality observing technology has risen to the challenge of scientists to identify and mitigate poor water quality by providing them with cost-effective tools that can take measurements of essential biogeochemical variables autonomously. Yet, despite these options becoming more readily available, there is a gap between the technology and the end-user (including the investigators and technicians that deploy these technologies) due to a collective lack of training, in-depth knowledge, and skilled workers who can meet new and emerging challenges.
There is also a disconnect between data quality, data gathering by autonomous sensors and data analysis, which is a major obstacle, as the sensors are already being deployed (e.g. through buoys, boats etc.) to broaden data coverage in space and time. AQUASENSE will resolve these challenges while providing 15 early stage researchers (ESRs) the unparalleled multidisciplinary training in the field of water quality through autonomous sensors and autonomous deployment. The ESRs trained through AQUASENSE programme will fill the skill-gap and contribute towards strengthening of Europe’s human resources and industry competitiveness in the strategic fields of aqua/agriculture and sensing technologies.
AQUASENSE is a multi-site Innovative Training Network (ITN) comprising 14 internationally reputed research teams (from academia, research centres and industry) from 9 European countries (UK, Germany, Ireland, Serbia, Sweden, Italy, Poland, Austria, Estonia).
AQUASENSE will bring a step change in the field of water and aqua-food quality monitoring, while training future research leaders. To this end, AQUASENSE brings together an excellent multidisciplinary consortium with world leading complementary expertise.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No: H2020- MSCA-ITN-2018-813680