AquaCop

Project summary and findings

Monitoring the health of aquatic ecosystems is a vital activity to ensure the sustainable management of surface water bodies. The AquaCop project aims to improve the efficiency, accuracy, and implementation of coastal water monitoring programmes by developing and deploying a reliable, remote sensing-based water quality monitoring system.

The project has developed state-of-the-art machine learning (ML) tools to assess surface water quality in coastal and transitional waters using a range of satellite ocean colour multispectral data. The system monitors key water quality indicators, including optically active chlorophyll concentrations, as well as optically inactive parameters such as DIN, TON, phosphates, BOD, and dissolved oxygen. The project focuses on local and shared aquatic ecosystems, particularly those associated with the University of Galway, such as Galway Bay.

In-situ and remote sensing monitoring results demonstrate that satellite products can effectively support the assessment of water quality in marine environments and complement the EPA’s sampling programme. The results of the AquaCop project also show that combining in-situ sampling with advanced remote sensing techniques provides an optimal and comprehensive monitoring programme, in line with the requirements of the EU Water Framework Directive.

AquaCop is a three-year project funded by the Environmental Protection Agency (EPA), running from 2023 to 2026. It represents a close collaboration between the University of Galway, the EPA, and the Marine Institute, alongside international partners from the Netherlands and Australia, ensuring that its outcomes contribute both to scientific knowledge and to practical water management in Ireland’s coastal regions.

Abstract

Since mid-20th century there has been observed a continuous deterioration of water quality across Europe. Irish transitional and coastal (TraC) waters are threatened by the synergistic effects of multiple environmental pressures such as nutrient enrichment, oxygen depletion and acidification among others. An implementation of the Water Framework Directive (WFD) requires all surface waters in the EU to achieve at least good status; with just over 30% of transitional waters and 79% of coastal waters at good or high status, this requirement has not been achieved in Ireland. In this light, the monitoring of surface water environments, as required by WFD, is immensely important to understand and disentangle the effects of pressures and environmental change on aquatic ecosystems. However, conventional monitoring approaches based on in-situ data collection and laboratory analysis can be very expensive and time consuming while limited in terms of spatial coverage and temporal frequency. Additional sources of data such as remote sensing data, which allow frequent surveys over large areas, can help to address problems associated with these complex interactions. Thus, as the Earth observation records become more available and accurate, the RS data have now the potential to provide an invaluable, cost-effective complementary dataset for operational monitoring, optimization and assessment of surface waters.

The overarching aim of the proposed AquaCop research project is to exploit Copernicus data to improve efficiency, accuracy and implementation of coastal water monitoring programmes. This will be achieved in a multiple-step process by assessing the accuracy of available CMEMS products and developing the location-specific optically active and inactive RS products to complement and optimize the existing monitoring programme, and ultimately by developing the coastal water quality index model for TraC waters (by using advanced artificial intelligence and machine learning techniques) to further support the monitoring programme and draw inference about water quality and trophic statuses. This project will bring forward the data collection into the 21st century and improve the capacity of monitoring programmes to take account of the highly complex spatial and temporal dynamics of TraC waters. The project will also generate a significant step forward in our understanding of nutrient cycling, feedbacks, water quality problems and environmental stressors/pressures on aquatic environments related to human activities including climate change. AquaCop will integrate optimized in-situ data with remotely sensed data streams to facilitate more accurate assessment of water quality and cost-effective management of WQ monitoring. The project will also propose the RS data-driven CWQI model that can support assessment of water quality in TraC waterbodies.