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In the last years, research efforts have been addressed on the effects of single and multiple pollutants on human health, in particular in densely populated areas. Modelling tools, integrating atmospheric science with the latest evidence available from air pollution epidemiology and exposure science, represent a valuable support to health impact assessment. This article considers the latest developments of the DIATI Dispersion and Externalities Model (DIDEM). To extend DIDEM’s scope of analysis, the inte- gration with different pollutant dispersion models was recently implemented. Particularly, in this arti- cle, a comparative evaluation between CALPUFF (California Puff) Lagrangian puff model and SPRAY Lagrangian particle model is presented. To help reaching this objective, the case study of Turin’s district heating system, presented in previous publications, was re-considered and deepened. CALPUFF and SPRAY models were compared on the same emission scenario. NOx and total PM concentrations result- ing from the simulations were of the same magnitude, with some difference in the spatial distribution. Total health damage costs differed between 8.5% and 9.7%, with lower values corresponding to SPRAY simulations. This difference mostly corresponds to the different spatial distribution of pollutant con- centrations which, in turn, correspond to different exposure levels. The possibility of selecting different modelling tools extends the usability of DIDEM to a larger set of applications, including a wider scope of application and a larger range of users. The results provide important information in the view of the characterization of the overall uncertainty of the impact pathway approach methodology.
air pollution, impact pathway, dispersion modelling, health, external costs, heating network
[1] Wesson, K., Fann, N., Morris, M., Fox, T., Hubbell, B. A multipollutant risk-based approach to air quality management: Case study for Detroit. Atmospheric Pollution Research, 1, pp. 296–304, 2010.
[2] Fann, N., Wesson, K., Hubbell, B. Characterizing the confluence of air pollution risks in the United States. Air Quality Atmosphere, and Health, 2015. DOI: 1007/s11869-015-0340-9.
[3] Anenberg, S.C., Belova, A., Bramdt, J., Fann, N., Greco, S., Guttikunda, S., Heroux, M.E., Hurley, F., Krzyzanowski, M., Medina, S., Miller, B., Pandey, K., Roos, J., Van Dingenen, R. Survey of ambient air pollution health risk assessment tools. Risk Analysis, 36(9), pp. 1718–1736, 2016. DOI: 10.1111/risa.12540.
[4] European Commission. Externalities of Energy – Vol. 2: Methodology – method for estimation of physical impacts and monetary valuation for priority impact pathways. European Commission DG XII “Science, Research and Development”, JOULE: Luxembourg, 1995
[5] Ravina, M., Panepinto, D., Zanetti, M.C. DIDEM – An integrated model for comparative health damage costs calculation of air pollution. Atmospheric Environment, 173, pp. 81–95, 2018a. DOI: https://doi.org/10.1016/j.atmosenv.2017.11.010.
[6] Ravina, M., Panepinto, D., Zanetti, M.C. A dispersion and externalities model supporting energy systems planning: development and case study. WIT Transactions on Ecology and the Environment, Vol. 230, WIT Press, 2018b. ISSN 1743-3541
[7] Boldo, E., Linares, C., Aragones, N., Lumbreras, J., Borge, R., de la Paz, D., PerezGomez, B., Fernandez-Navarro, P., Garcia-Perez, J., Pollan, M., Ramis, R., Moreno, T., Karanasiou, A., Lopez-Abente, G. Air quality modeling and mortality impact of fine particulate matter reduction policies in Spain. Environmental Research, 128, pp. 15–26, 2014. DOI:10.1016/j.envres.2013.10.009.
[8] WHO. 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: Copenhagen, Denmark, p. 54, 2013a.
[9] WHO. Review of evidence on health aspects of air pollution − REVIHAAP Project. Technical report, The WHO European Centre for Environment and Health: Bonn, Germany, p. 302, 2013b.
[10] Holland, M. Cost–Benefit Analysis of Final Policy Scenarios for the EU Clean Air Package – Version 2, Corresponding to IIASA TSAP Report 11, Version 2a. EMRC, 2014.
[11] Tinarelli, G., Anfossi, D., Bider, M., Ferrero, E. & Trini Castelli, S. A new high performance version of the Lagrangian particle dispersion model SPRAY, some case studies. Air Pollution Modelling and its Application XIII, Vol. 23, eds. Gryning S.E. & E. Batchvarova, Plenum Press: New York, pp. 499-506, 2000. ISBN: 0-306-46188-9
[12] U.S. EPA. AERMOD implementation guide. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Air Quality Assessment Division, AERMOD Implementation Workgroup, Research Triangle Park: North Carolina, 2018.
[13] U.S. Environmental Protection Agency (US EPA), CALPUFF modeling system user’s manual, version 6, 2011.
[14] Tinarelli, G., Anfossi, D., Brusasca, G., Ferrero, E., Giostra, U., Morselli, M.G., Moussafir, J., Tampieri, F. & Trombetti, F., Lagrangian particle simulation of tracer dispersion in the lee of a schematic two-dimensional hill. Journal of Applied Meteorology, 33(N. 6), pp. 744–756, 1994.
[15] Tinarelli, G., Anfossi, D., Bider, M., Ferrero, E., Trini Castelli, S., A new high performance version of the Lagrangian particle dispersion model SPRAY, some case studies. Air Pollution Modelling and its Application XIII, Vol. , 23, eds. Gryning S.E. & E. Batchvarova, Plenum Press: New York, pp. 499–506, 2000. ISBN: 0-306-46188-9.
[16] Arianet company http://www.aria-net.it/ (accessed September 20).
[17] Aria Technologies http://www.aria.fr/ (accessed September 20).
[18] Tinarelli, G., Brusasca, G., Oldrini, O., Anfossi, A., Trini Castelli, S. & Moussafir, J. Micro-Swift-Spray (MSS) a new modeling system for the simulation of dispersion at microscale. General description and validation. Air Pollution Modeling and its Applications XVII, eds., C. Borrego & A.N. 10 Norman, Springer, pp. 449–458, 2007.
[19] ISAC Istituto di Scienze dell’Atmosfera e del Clima http://www.isac.cnr.it/it/content/torino (accessed September 20).
[20] Trini Castelli, S., Ferrero, E., Anfossi, D. & Ohba, R. Turbulence closure models and their application in RAMS. Environmental Fluid Mechanics 5(Number 1-2), pp. 169–192, 2005.
[21] Nieuwstadt, F.T.M., van Dop H. Turbulence and Air Pollution Modeling – Reidel Publishing Company, 1982.
[22] Venkatram, A., Wyngaard, J.C. Lectures on Air Pollution Modeling – American Meteorological Society, 1988.
[23] Seinfeld, J.H. & Pandis, S.N. 2006. Atmospheric Chemistry and Physics 2nd ed, J. Wiley & Sons
[24] Iren Energia company website. http://www.irenenergia.it/. (accessed September 20).
[25] Piedmont Region. Decree of the Regional Council of 4 August 2009, no. 46-11968. Update of the Regional Plan for the Rehabilitation and Protection of Air Quality - Draft plan for environmental heating and conditioning and implementing provisions on energy performance in construction pursuant to Article 21, paragraph 1, letters a) b) q) of Regional Law 28 May 2007, no. 13. In Italian, 2009.
[26] Ravina, M., Panepinto, D., Zanetti, M.C., Genon, G. 2017. Environmental analysis of a potential district heating network powered by a large-scale cogeneration plant. Environmental Science and Pollution Research, 24, pp. 13424–13436, 2009, DOI: 10.1007/s11356-017-8863-2.
[27] Ravina, M., Panepinto, D. & Zanetti, M.C., District heating system: evaluation of environmental and economic aspects. International Journal of Environmental Impacts, 1(4), pp. 420–432, 2018. DOI: 10.2495/EI-V1-N4-420-432.
[28] van der Kamp, J. & Bachmann, T.M., Health-related external cost assessment in Europe: methodological developments from ExternE to the 2013 clean air policy package. Environmental Science & Technology, 49, pp. 2929–2938, 2015. DOI: 10.1021/es5054607.
[29] Rodean, H.C., Stochastic Lagrangian Models of Turbulent Diffusion, American Meteorological Society. 1996.
[30] Scire, J.S., Lurmann, F.W., Bass, A. & Hanna, S.R., User’s guide to the MESOPUFF II model and related processor programs. EPA-600/8–84-013. U.S., Environmental Protection Agency, Research Triangle Park. 1984.
[31] Fracastoro, G.V. & Serraino, M., A methodology for assessing the energy performance of large scale building stocks and possible applications. Energy and Buildings, 43, pp. 844–852, 2011. DOI: https://doi.org/10.1016/j.enbuild.2010.12.004.
[32] Chang, M.C.O., Chow, J.C., Watson, J.G., Hopke, P.K., Seung-Muk, Y. & England, G.C., Measurement of ultrafine particle size distributions from coal-, oil-, and gas-fired stationary combustion sources, Journal of the Air & Waste Management Association, 54(12), pp. 1494–1505, 2004. DOI: 10.1080/10473289.2004.10471010.
[33] Piedmont’s Regional Agency for the Environmental Protection. http://www.arpa.piemonte.it/ (last accessed 2019-05-10)
[34] Briggs, G.A., Plume rise predictions. Lectures on Air Pollution and Environmental Impact Analyses. ed. D. Haugen, American Meteorological Society: Boston, MA, pp. 59–111, 1982.
[35] Souto, M.J., Souto, J.A., Pérez-Muñuzuri, V., Casares, J.J. & Bermudez, J.L., A comparison of operational Lagrangian particle and adaptive puff models for plume dispersion forecasting. Atmospheric Environment, 35, pp. 2349–2360. 2001.