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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Funding
This study received a financial support from CESAM (UID/AMB/50017 - POCI-01-0145-FEDER-007638), FCT/MCTES through national funds (PIDDAC), and the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020. This study also received support from Enrico Turrini and Marialuisa Volta from the University of Brescia (Italy). This study received another financial support from FEDER through the COMPETE Programme and the national funds from FCT—Science and Technology Portuguese Foundation for the Ph.D. grant of H. Relvas (SFRH/BD/101660/2014).
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Relvas, H., Miranda, A.I. An urban air quality modeling system to support decision-making: design and implementation. Air Qual Atmos Health 11, 815–824 (2018). https://doi.org/10.1007/s11869-018-0587-z
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DOI: https://doi.org/10.1007/s11869-018-0587-z