| Resumo: | Currently, air pollution represents one of the main environmental causes of mortality. It is also responsible by cutting lives short, reducing productivity through working days lost across the economy, increasing medical costs, and by considerable economic impacts. Europe's most serious air pollutants in terms of harm to human health are particulate matter, nitrogen dioxide and ground-level ozone. The principal objective of this thesis is to explore the capabilities of Integrated Assessment Modelling tools to cost-efficiently evaluate measures to improve the air quality, and furthermore to develop an urban Integrated Assessment Model (IAM). For this purpose a review of current integrated assessment methodologies to improve air quality, from simple (e.g. scenario approach) to more comprehensive ones (e.g. optimization approach) was done and some application tests were performed. Based on identified advantages of the revised approaches the Integrated Urban Air Pollution Assessment Model (IUAPAM) was designed and evaluated through its application to a selected urban case study (Porto Urban Area) considering different emission scenarios. The developed model is able to reproduce rapidly emission reduction scenarios and to estimate health impacts, making use of Artificial Neural Networks. Moreover, the use of Multi-Criteria Decision Analysis (MCDA) allows including social aspects and ranking air quality measures/scenarios. This research work contributes to a better understanding of the utility of IAM tools that are available to support the air quality decision-making process. IUPAM revealed to be useful to quickly evaluate the effect of local and regional policies focused on air pollution improvement
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