Abstract
This paper describes the design and application of a modeling system capable of rapidly supporting decision-makers regarding urban air quality strategies, in particular, providing emission and concentration maps, as well as external costs (mortality and morbidity) due to air pollution, and total implementation costs of improvement measures. Results from a chemical transport model are used to train artificial neural networks and link emission of pollutant precursors and urban air quality. A ranking of different emission scenarios is done based on multi-criteria decision analysis (MCDA), which includes economic and social aspects. The Integrated Urban Air Pollution Assessment Model (IUAPAM) was applied to the Porto city (Portugal) and results show that it is possible to reduce the number of premature deaths per year attributable to particulate matter (PM10), from 1300 to 1240 (5%), with an investment of 0.64 M€/year, based on fireplace replacements.
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