The climate exposome: a new tool for addressing the health impacts of climate change

Abstract

BACKGROUND AND AIM: The climate policy debate currently focuses on two key aspects: the relevance and proper extent of mitigation measures in order to avoid crossing the point of no return; and the necessity for adaptation measures considering the very different socio-economic state and dynamics across the globe. Assessing the health benefits of policy measures geared towards climate change adaptation is key for accurate impact assessment of the measures envisaged. Reliable quantification of direct and indirect impacts related to both climate change and to climate mitigation policies and measures is a sine qua non for further climate action. METHODS: The exposome accounts for the totality of exposures over an individual’s life course, focusing inevitably on age windows of increased susceptibility. Rendering it operational requires development and adaptation of novel tools for exposure assessment (both external and internal). Making use of the exposome for comprehensive health risk assessment on the population scale requires development of advanced statistical and biochemical/pathology models based on a combination of environmental and high dimensional biological data, enhanced by machine learning and big data analytics. In addition, agent-based models help capture the changing socioeconomic dynamics that influence societal vulnerability to climate-induced health stress. RESULTS:Considering the change in environmental pressure and human exposure to health stressors linked to climate change would allow us to construct the climate exposome: namely, the exposome of human population subgroups considering the climate change aspects relevant to the ca. 80 years of the human life course. CONCLUSIONS:The methodological framework for unraveling the climate exposome is presented and examples demonstrating its applicability and usefulness in climate decision-making are given. Novel integrated assessment models entails a schema based on enhanced data fusion and ensemble modelling, supported by big data analytics for filling data gaps. This methodological framework should support science-based decision-making in the climate action arena, notwithstanding the uncertainties. KEYWORDS: Climate, Exposome, Metabolomics