On the Use of Satellite Data to Estimate Spatially Referenced Health Risk of Air Pollution

Background/Aims: The state of the art in air quality assessment comprises information and data processing tools using only data from ground-based measurement and atmospheric modelling. Ground measurements are not taken from dense enough monitoring networks around the world to permit a satisfactory analysis of the influence of air pollution on the health of vulnerable population groups. Attempts to improve our estimation of atmospheric pollutant concentrations at the urban and regional scale from combining ground data with numerical modeling are hampered by the need for high quality and up-to-date emissions inventories, as well as accurate estimates of initial and boundary conditions of the models. Information derived from earth observation satellites can bridge the gap between models, simulating the transport and chemical transformation of atmospheric pollutants and analytical observations. Methods: A data fusion methodology was developed to integrate satellite data with ground-based information and atmospheric modeling to derive particulate matter and ozone loading at the ground level. Physical properties of tropospheric aerosol and ozone are linked with the atmospheric physical\ufffdchemical processes that determine the total mass concentration and size distribution of particulate matter and the concentration of ozone. Coupling these with spatially explicitly exposure\ufffdresponse functions and population data, it results in refined maps of health risk attributable to air pollution. Results: The methodology was implemented in Athens, Greece and Rome, Italy, 2 capitals characterized by intense photochemical pollution and long-range transport of dust. Maps of health risk were produced. The spatially scalar nature of the approach allowed us to evaluate the impact of risk modifiers such as the existence of urban vegetation and population susceptibility. Conclusion: Satellite data can be used efficiently to improve the spatial link between environmental pollution and human health. The data fusion method proposed in the present study opens the way toward the enhanced use of this valuable information in spatial epidemiology and environmental health science