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

Abstract

O-31A5-4 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 modeling. Ground measurements of air pollutants are not taken from dense enough monitoring networks around the world to permit a satisfactory analysis of the actual influence of fine urban aerosol and ozone on the health of vulnerable population groups, such as the elderly, children under the age of 15, asthmatics, people with cardiovascular problems. Introduction of information derived from Earth Observation satellite data can be used to bridge the gap between models simulating the transport and chemical transformation of ambient air pollutants, and analytical observations. Methods: A data and model fusion methodology has been developed to integrate the 3 information data sources (i.e., Earth Observation [EO], ground-based information and atmospheric modeling) to derive PM10, PM2.5 and ozone loading at the ground level. The resulting pollution maps are coupled to epidemiologically derived exposure-response functions and population data, resulting in high resolution morbidity and mortality indicator maps. Comparison of these maps with actual health outcome statistics reveals new insight into the spatial link between air pollution exposure and public health risk. Results: The data assimilation methodology was applied in Athens, Greece and Rome, Italy, 2 of the largest capitals in Southern Europe, characterized by increased photochemical pollution and long-range transport of PM. Results showed that the proposed methodology improved significantly the spatial accuracy of health risk estimates. Given the scalar nature of the approach, refined risk estimates can be made in areas populated by susceptible sub-groups taking into account risk modifiers such as the existence of urban vegetation and socioeconomic condition. Conclusion: Satellite-based atmosphere observation can be a key contributor to the determination of the spatial relationship between air pollution and public health risk. Efficient data and model fusion is the optimal way to achieving this.