The global pollution of water bodies is continuously increasing and requires new, reliable approaches to monitoring water quality. Continuous, site-specific, and robust measurement data are particularly crucial for assessing remediation measures. Conventional measurement methods reach their limits in this context, as natural water bodies are subject to strong temporal and spatial fluctuations and can often only be sampled at discrete points.

The IBAIA project addresses this challenge by developing a portable, modular multisensor system for in-situ real-time monitoring of water bodies. The aim is to record water quality parameters directly on site over long periods of time, thereby creating a sound data basis for environmental management, regulation, and remediation strategies.

At the core of the project is the development of four innovative sensor modules that combine complementary photonic and electrochemical technologies with microfluidic and microelectronic components. This integrative approach achieves higher selectivity, increased sensitivity, and a wide measurable concentration range — all key requirements for deployment in complex natural water environments.

The individual sensors are designed as interchangeable modules and are integrated into a single, compact system. This modular design enables flexible adaptation to different application scenarios and user requirements, both in terms of cost and analytical functionality. The overall system is designed for autonomous operation at remote locations, is powered energy-efficiently by solar cells and batteries, and requires only minimal human intervention.

Long-term operation of the system generates large volumes of data, the acquisition, processing, and interpretation of which are an integral part of the project. To this end, IBAIA pursues an innovative data-driven approach that places the measured values in an environmentally relevant context and increases their informative value for assessing the condition of water bodies and the effectiveness of remediation measures.

Funded by the European Union’s Horizon Europe research and innovation programme (HORIZON-CL4-2022-DIGITAL-EMERGING-01) under grant agreement No. GA 101092723.