Digital Environmental Intelligence for Stress, Crowding and Nature-Based Health Benefits

An integrated AI system merging environmental observations, crowding metrics, noise/air sensors, biosignal stress detection and EMA (Ecologicam Momentary Assessment) wellbeing data. The platform identifies hotspots of tourism-induced stress and the restorative effects of green and natural areas, enabling cities to design evidence-based policies and resilient tourism governance.

This project develops a unified digital platform that integrates environmental observations, physiological sensing (biosensors), ecological momentary assessment, noise and air-quality monitoring, crowding metrics, GIS, and citizen-science data to understand how tourism pressures shape stress, health and wellbeing in urban and natural settings. Building on experimental stress research and Single-Case Experimental Design methodologies from protected sites , and on validated noise/air/stress measurement protocols , the platform models both negative impacts of overtourism and positive ecosystem services of nature exposure. Using AI and ML tools, it provides real-time, interoperable indicators and predictive analytics for local authorities, supporting more inclusive, transparent and adaptive governance aligned with Horizon Europe Destination 7: Innovative governance, environmental observations and digital solutions.

We seek partners to co-develop an AI-driven environmental observation platform combining biosensors, EMA, noise/air monitoring, crowd metrics and citizen science to assess tourism-induced stress and nature-based wellbeing. The solution supports adaptive, data-driven governance in UNESCO cities, urban parks and protected areas.