Comparison between ozone monitoring data and modelling data, in Italy, from the perspective of health indicator assessments

• Published in: Transactions on ecology and the environment Vol. 116; pp. 125 - 134
• Main Authors: DE MARCO, A, SCREPANTI, A, RACALBUTO, S, PIGNATELLI, T, VIALETTO, G, MONFORTI, F, ZANINI, G
• Format: Conference Proceeding Journal Article
• Language: English
• Published: Southampton WIT 2008; W I T Press
• Subjects: Applied sciences; Atmospheric pollution; Comparative analysis; Equipment costs; Exact sciences and technology; Health risks; Modelling; Ozone; Pollution; Pollution monitoring; Risk analysis; Stations; Territory; Italy; Cartography; Rain; Health and environment; Surveillance; Risk assessment; Health hazards; Ozone; Measuring network; Modeling; Indicator; Public health
• ISBN: 9781845641276; 1845641272
• ISSN: 1746-448X; 1743-3541
• DOI: 10.2495/AIR080141

The need for comparison between monitoring data and modelling data on ozone comes both from the qualitatively and quantitatively scarce outcome of the Italian ozone monitoring network and, at the same time, from the necessity for assessment and validation of the modelling methodology. Indeed, the distribution of the monitoring stations in Italy is not uniform and a dramatic lack of data is observed in all of the southern Italian areas. A number of different strategies can be applied to obtain a uniform distribution of data within the territory. The methodology of \“spatialization” is described in the paper and applied to the health exposure indicator SOMO35 (developed by the WHO), pursuing the ultimate objective of identifying risk areas for the population. Such areas are then compared with similar areas from the analysis carried out by the Italian Integrated Assessment model RAINS Italy. The comparative analysis reported in this paper highlighted the differences, deepening the background rationale and ultimately increasing the robustness of the health risk analysis. Moreover, maps generated by the model could also be used to identify critical areas not covered by monitoring stations, so driving a more cost efficient allocation of expensive equipment.