ENVESOME: The Environmental Exposome and Health

ENVESOME addresses the impact of air pollution, noise, light, and hazardous waste on non-communicable diseases through the development of an integrative exposome framework. The project combines advanced human biomonitoring and new approach methodologies (NAMs) (in vitro systems, multi-omics, computational toxicology) to elucidate adverse outcome pathways (AOPs) linking complex pollutant mixtures to disease. We collect detailed external exposure data (ambient sensors, wearables, remote sensing) and biological measurements (genomics, epigenomics, proteomics, metabolomics, cytokine panels) in population cohorts and experimental models. A physiologically based biokinetic (PBBK) modelling platform combined with Bayesian inversion methods links external exposures to internal doses and back-calculates realistic in vitro test concentrations via quantitative in vitro–in vivo extrapolation (QIVIVE). Machine learning and advanced statistics map high-dimensional omics signatures onto health endpoints and derive exposure–effect dose-response relationships (Benchmark Doses). Parallel in vitro studies (human cell/organoid models of liver, brain, immune, cardiac, respiratory systems) characterize mechanistic toxicity pathways for real-life chemical mixtures. These mechanistic insights and omics-based biomarkers are integrated with socio-behavioral data (agent-based models of activity patterns, socioeconomic context) to account for vulnerability. The project conducts interventional case studies in Urban and Waste contexts: co-designing and testing exposure-reduction strategies (e.g. traffic mitigation, green infrastructure, waste management) via Urban Living Labs with stakeholders. Continuous exposure and health monitoring (wearables, questionnaires, biomarkers) evaluate intervention efficacy. Finally, project findings are translated into practical tools. These include updated exposure limit values derived from pathway-based dose–response analysis; a city-scale Decision Support System (DSS) for policy simulation that integrates air quality, noise, light, and waste exposure models with population data; a user-focused mobile application (for education, exposure tracking, and health coaching) with an integrated chatbot to communicate risks and promote healthy behaviors; and a fuzzy-logic risk stratification engine that combines multifactorial data for personalized prevention. Overall, ENVESOME employs a systems-level exposome approach that merges detailed exposure assessment, multi-omics profiling, computational modeling, and stakeholder engagement to uncover causal links between environmental stressors and health across the life course. It delivers targeted, science-driven interventions for public health protection.