Geo-Intelligence for improved air quality monitoring and analysis (GeoAIr)

GEO-AIr will develop a “downstream” service for monitoring air quality by integrating frontier research on three technologies: satellite-based and model-based Earth Observation (EO), ground-based low-cost sensors using Internet of Things (IoT)  and data representation via extended reality (XR). The rationale is that the considerable availability of satellite data has led to the development of numerous related services that are part of the end of the chain of value, but which allow to realize the preponderant part of the economic turnover. The area where methods will be tested is the Po Valley; it is a well-known fact that in this densely-populated and built-up area stagnation of atmospheric pollutants has a high impact on human and ecosystem health.

Novel methods will be investigated to integrate these three technologies. The project will use geomatic approaches in “geo-based” artificial-intelligence to model reliable air quality information by integrating satellite-based open data from EO space missions, but also from services such as the Copernicus Atmosphere Monitoring Service (CAMS) or the Copernicus Climate Change Service (C3S) which provide space-time resolved estimates and forecasts of the atmospheric composition. A number of ground-based low-cost IoT sensors, that will be developed ad hoc in the project and rigorously calibrated with higher-end devices from the regional environmental agency (ARPA), will be used for model training, validation and testing. The third aspect addressed by the project will be the spatial mapping in space and time of air quality information. Access to mapped results will be displayed via novel geo–visualization tools that use XR solutions.

The impact is expected to be multi-fold. The air quality information will be shared with experts from other disciplines to foster improvements in other scientific fields, e.g. biostatistics and medical epidemiologists that study respiratory disease. The IoT low-cost sensors will be replicated and thus branch to citizen science projects3. Social impact will come from increased public awareness of concentration of invisible pollutants in the air at specific geo-locations using the XR tools; the issue solved by the project in this field, will be to build and test an XR-interface modelling layer that translates invisible data in the real context, enhancing contextual awareness by the wide public.  Overall the project will bring better understanding of spatial and temporal factors that affect air quality, supporting actions towards a more robust, resilient and ultimately healthy society.

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