Search for a command to run...
Quantifying exposures to environmental factors such as pollution, temperature, noise, coastal flooding or green space is essential in determining the human exposome, i.e. the totality of human environmental exposures. This is subsequently crucial for quantifying the contribution of the exposome to human health. Major challenges in determining convincing exposure estimates are i) using a multitude of harmonised environmental factors, often originating from different disciplines such as hydrology, ecology or atmospheric sciences ii) using high spatial and temporal resolution datasets and incorporating mobility proxies to appropriately represent human activities and iii) datasets with continental or global extent to evaluate spatial patterns and to incorporate large-scale impacts, for example of climate change. For a rational and convenient exposure assessment performed by epidemiologists it is desired that estimates are easily accessible without the burden of performing the computations themselves.To address these challenges, we developed an exposure assessment workflow to process a set of environmental factors. These include factors beneficial for human wellbeing, such as accessibility to green space, as well as factors with negative health impacts, such as high temperature or earthquake risks. The workflow uses open-source software and datasets. The processed environmental factor datasets are on global scale at 1km or 100m resolution. Human mobility, represented by buffer calculations on each cell, and aggregations to e.g. administrational units were calculated in a processing workflow implemented in LUE (https://zenodo.org/records/16792016) and computed on the Dutch national supercomputer Snellius. The data sets were then combined with global population density maps to estimate the human exposome for each grid cell. In our presentation we illustrate the exposure assessment workflow and show spatial patterns of exposure estimates. The datasets are made accessible via a SpatioTemporal Asset Catalog in the Global Environmental Exposure Dataspace (GEESE), a subproject of the SAGE European Green Deal Data Space (https://www.greendealdata.eu/).