[Big data in environmental epidemiology. Satellite and land use data for the estimation of environmental exposures at national level].

Objectives: to define a national geographic domain, with high spatial (1 km²) and temporal (daily) resolution, and to build a list of georeferenced environmental and temporal indicators useful for environmental epidemiology applications at national level.
Design: geographic study.
Setting and Participants: study domain: Italian territory divided into 307,635 1-km² grid cells; study period: 2006-2012, divided into 2,557 daily time windows.
Main Outcome Measures: for each grid cell and day, an extensive number of indicators has been computed. These indicators include spatial (administrative layers, resident population, presence of water bodies, climatic zones, land use variables, impervious surfaces, orography, viability, point and areal emissions of air pollutants) and spatio-temporal predictors (particulate matter data from monitoring stations, meteorological parameters, desert dust advection episodes, aerosol optical depth, normalized difference vegetation index, planetary boundary layer) potentially useful to characterize population environmental exposures and to estimate their health effects, at national level.
Results and Conclusions: this study represents the first example of relational big data in environmental epidemiology at national level, where multiple sources of data (satellite, environmental, meteorology, land use, population) have been linked on a common spatial and temporal domain, aimed at promoting environmental epidemiology applications at national and local level.