Imagine placing a few smart thermometers and chemical sensors in underground wells to listen to the Earth's breathing. When certain gases like radon spike, it's like a warning light flashing before an earthquake or eruption. By using AI to track these patterns across many sites, we can create a real-time map of underground changes to warn people sooner.
Current earthquake forecasting is limited, and monitoring groundwater for precursors like radon is often too expensive or power-hungry for wide-scale use. This creates a gap in early warning capabilities for high-risk municipalities.
A low-power, affordable sensor device for real-time monitoring of radon, temperature, and acidity in water, coupled with an AI-driven data analysis system.
If you are an infrastructure monitoring firm dealing with high-risk seismic zones — this project developed a low-power sensor system that monitors radon and acidity in real time. This allows for better risk awareness and the creation of mitigation strategies for critical sites.
If you are a municipal agency dealing with public safety in earthquake-prone areas — this project developed a scalable system of 100-200 affordable sensors. These devices provide real-time geochemical maps to help protect citizens and urban assets.
If you are a water quality company dealing with radon-related health issues in groundwater — this project developed a sensor that monitors radon content and flow patterns. This provides a multi-purpose tool for both health safety and geological monitoring.
Based on available project data, the project focuses on creating a 'cheap' and 'affordable' sensor design to enable large-scale deployment, though specific unit prices are not listed.
Yes, the system is designed for scalability with a target production of 100-200 sensors for deployment in sensitive sites.
Based on available project data, the project is coordinated by KTH Royal Institute of Technology, but specific licensing terms are not provided in the summary.
The sensors use mobile network and w-lan connections to send data to a database for AI-based analysis.
The project runs from 2022-10-01 to 2026-09-30, with prototypes already placed in Italy and Greece.
The consortium is heavily research-oriented, consisting of 6 universities and 4 research centers, with only 2 SMEs and 2 larger industrial partners (13% industry ratio). This suggests the technology is currently in a high-validation phase rather than a commercial rollout phase, though the presence of SMEs indicates a path toward marketization.
Contact KUNGLIGA TEKNISKA HOEGSKOLAN (KTH) in Sweden
Contact us to explore licensing for the radon sensor hardware or AI analysis models.
