UrbanM2O’s first AI and data science result

As the first UrbanM2O monitoring campaign approaches its final stage, we are seeing the project’s first concrete AI and data science result from the work on developing and benchmarking AI-enhanced monitoring technologies, led by Wetsus European Centre of Excellence for Sustainable Water Technology.

At the Zurich case study, sensors from aqa.earth have been operating alongside Eawag’s MS2Field, combining continuous water quality and environmental measurements with high-resolution micropollutant data.

The first analyses are now showing encouraging relationships between sensor signals and measured micropollutants. This is where Sensor Fusion becomes powerful. High quality reference measurements can help continuous sensors extract much more information from water, at a much higher frequency.

aqa.earth is a Dutch water technology company developing connected monitoring systems that combine water, soil and environmental sensing with data science and AI. In UrbanM2O, its Diver, EcoSniffer and supporting sensors are being used to capture complementary physicochemical, optical and environmental signals and connect them with advanced reference measurements.

The result is an important first step from collecting monitoring data to learning from it and building smarter, more scalable ways to understand urban water quality. And this is still only the first monitoring campaign! With the next development phase now beginning, we are looking forward to further progress and more AI-enabled monitoring solutions emerging from the project.