An urban air quality modeling system to support decision-making: design and implementation
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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.
KeywordsDecision-making Air quality management Artificial neural networks Multi-criteria decision analysis Integrated assessment modeling
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).
- Amann M, Bertok I, Borken-Kleefeld J, Cofala J, Heyes C, Höglund-Isaksson L, Klimont Z, Nguyen B, Posch M, Rafaj P, Sandler R, Schöpp W, Wagner F, Winiwarter W (2011) Cost-effective control of air quality and greenhouse gases in Europe: modeling and policy applications. Environ Model Softw 26:1489–1501. https://doi.org/10.1016/j.envsoft.2011.07.012 CrossRefGoogle Scholar
- Carnevale C, Finzi G, Pisoni E, Volta M, Guariso G, Gianfreda R, Maffeis G, Thunis P, White L, Triacchini G (2012a) An integrated assessment tool to define effective air quality policies at regional scale. Environ Model Softw 38:306–315. https://doi.org/10.1016/j.envsoft.2012.07.004 CrossRefGoogle Scholar
- CCDR-LVT (2006) Plans and programmes to improve air quality in the region of Lisbon and Tagus Valley. Lisbon Regional Coordination and Development Commission. (pp. 234). LisbonGoogle Scholar
- Costa S, Ferreira J, Silveira C, Costa C, Lopes D, Relvas H, Borrego C, Roebeling P, Miranda AI, Teixeira JP (2014) Integrating health on air quality assessment—review report on health risks of two major European outdoor air pollutants: PM and NO2. J Toxicol Environ Health Part B 17:307–340. https://doi.org/10.1080/10937404.2014.946164 CrossRefGoogle Scholar
- Desaigues B, Ami D, Bartczak A, Braun-Kohlová M, Chilton S, Czajkowski M, Farreras V, Hunt A, Hutchison M, Jeanrenaud C, Kaderjak P, Máca V, Markiewicz O, Markowska A, Metcalf H, Navrud S, Nielsen JS, Ortiz R, Pellegrini S, Rabl A, Riera R, Scasny M, Stoeckel ME, Szántó R, Urban J (2011) Economic valuation of air pollution mortality: a 9-country contingent valuation survey of value of a life year (VOLY). Ecol Indic 1:902–910. https://doi.org/10.1016/j.ecolind.2010.12.006 CrossRefGoogle Scholar
- EEA (2011) The application of models under the European Union’s Air Quality Directive: A technical reference guide, EEA Technical report No 10/2011, European Environment AgencyGoogle Scholar
- EEA (2017) Air quality in Europe—2017 report, EEA Report No 13/2017, European Environment AgencyGoogle Scholar
- Karvosenoja N, Kangas L, Kupiainen K, Kukkonen J, Karppinen A, Sofiev M, Tainio M, Paunu VV, Ahtoniemi P, Tuomisto JT, Porvari P (2010) Integrated modeling assessments of the population exposure in Finland to primary PM2.5 from traffic and domestic wood combustion on the resolutions of 1 and 10 km. Air Qual Atmos Health 4:179–188. https://doi.org/10.1007/s11869-010-0100-9 CrossRefGoogle Scholar
- Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, AlMazroa MA, Amann M, Anderson HR, Andrews KG, Aryee M, Atkinson C, Bacchus LJ, Bahalim AN, Balakrishnan K, Balmes J, Barker-Collo S, Baxter A et al (2012) A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the global burden of disease study 2010. Lancet 380:2224–2260. https://doi.org/10.1016/S0140-6736(12)61766-8 CrossRefGoogle Scholar
- Thokala P, Devlin N, Marsh K, Baltussen R, Boysen M, Kalo Z, Longrenn T, Mussen F, Peacock S, Watkins J, Ijzerman M (2016) Multiple criteria decision analysis for health care decision making—an introduction: report 1 of the ISPOR MCDA emerging good practices task force. Value Health 19:1–13. https://doi.org/10.1016/j.jval.2015.12.003 CrossRefGoogle Scholar
- WHO (2013) Health risks of air pollution in Europe—HRAPIE project recommendations for concentration–response functions for cost–benefit analysis of particulate matter, ozone and nitrogen dioxide. World Health Organization Regional Office for Europe. World Health Organization, GenevaGoogle Scholar