Climate and Environmental Digital Twins for Human Health: Leveraging Earth Observation for Compound Climate and Air Quality Extremes Early Warning

Climate resilience is a defining challenge of the 21st century, yet public health authorities continue to face difficulties in operationalising state-of-the-art geospatial and environmental science. In Portugal, as in Europe more broadly, extreme temperatures have already increased in frequency and severity, contributing to measurable studies reporting impacts on excess mortality and morbidity. Of relevance, these impacts are often amplified by the simultaneous degradation of air quality. However, evidence on the compounding effect of temperatures and atmospheric composition has largely been event-specific, fragmented across case studies of individual heatwaves, cold waves, or air-quality exceedance episodes, limiting our ability to implement dedicated compound events early-warning systems. The ESA-funded AIR4health project, developed under the Early Digital Twin Components initiative, addresses these gaps by designing innovative algorithms and a user-driven climate service focused on predicting human mortality and morbidity excesses in Portugal, during compound extreme temperature and pollutant exceedance events. The project has developed two Machine Learning (ML)–based AIR4health Downscaling Algorithms and the corresponding Incidence Risk Ratio (IRR) Models depicting, in a 1 km x 1 km spatial resolution, and the daily mortality and hospital admissions excesses per municipality that are attributable to (i) Heat & Ozone (O3) and (ii) Cold & Nitrogen Dioxide (NO2) compound events, using a long (2000-2018) healthcare database for mainland Portugal. These indicators integrate EO data, in-situ air-quality records from the EEA, and CAMS/C3S model outputs to improve the spatial resolution. Results achieved included diverse ML architectures (random forest, XGboost, Neural Network, using linear regression as the non-ML benchmarking model) which were validated against observational records on withheld data, proving the ML models' superiority in spatially depicting O3 and NO2 exceedances, in space and time, proving the added-value of such architectures for hazard mapping, with implications for the health impact assessment, with mean absolute error metrics inferior to 10µg/m3 in almost all cases, including during extreme events. Furthermore, impact models showed the relevance of the combined effect, particularly concerning heat and O3 compound events during which the IRR may increase by 50%, compared to considering heat alone. In complement, users have been involved in defining the requirements for a graphical interface that resonates with the currently existing seasonal surveillance system, with the goal of delivering an interactive dashboard that automatically conveys risk indices into useful geospatial information for the public health sector. With these actions, AIR4health advances beyond current country-level systems by implementing fully spatiotemporal exposure–response modelling. Its dynamic and continuous framework will deliver a prototype DTC capable of providing fine-scale early warning for combined climate and air-quality extremes. By benchmarking results against European-level datasets, AIR4health will support scalable pathways towards relevant practices in planetary health and climate-preparedness, while contributing to the broader European Digital Twin ecosystem.