Neuro-fuzzy and neural network systems for air quality control |
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| Authors: | Claudio Carnevale Giovanna Finzi Enrico Pisoni Marialuisa Volta |
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| Institution: | 1. Center for Geophysics, IDL, University of Lisbon, 1749-016 Lisboa, Portugal;2. Center for Theoretical and Computational Physics, University of Lisbon, Av. Prof. Gama Pinto 2, 1649-003 Lisbon, Portugal;3. TWIST – Turbulence, Wind Energy and Stochastics, Institute of Physics, Carl-von-Ossietzky University of Oldenburg, DE-26111 Oldenburg, Germany;4. ForWind – Center for Wind Energy Research, Institute of Physics, Carl-von-Ossietzky University of Oldenburg, DE-26111 Oldenburg, Germany;1. Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India;2. Department of Mining Engineering, National Institute of Technology, Rourkela, Odisha 769008, India;3. Centre for Atmospheric Sciences, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi 110016, India;1. European Commission, Joint Research Centre, Institute for Environment and Sustainability, Air and Climate Unit, Ispra, Italy;2. Enviroware Srl, Concorezzo, MB, Italy;3. Earth Sciences Department, Barcelona Supercomputing Center (BSC-CNS), Barcelona, Spain;4. Ricerca sul Sistema Energetico (RSE SpA), Milano, Italy;5. University of Murcia, Department of Physics, Physics of the Earth, Campus de Espinardo, Ed. CIOyN, 30100 Murcia, Spain;6. Laboratory for Air Pollution and Environmental Technology, Empa, Dubendorf, Switzerland;7. Centre for Atmospheric & Instrumentation Research, University of Hertfordshire, College Lane, Hatfield AL10 9AB, United Kingdom;8. Department of Physical and Chemical Sciences, Center of Excellence for the Forecast of Severe Weather (CETEMPS), University of L''Aquila, L''Aquila, Italy;9. ECMWF, Shinfield Park, RG2 9AX Reading, United Kingdom;10. Karlsruher Institut für Technologie (KIT), Institut für Meteorologie und Klimaforschung, Atmosphärische Umweltforschung (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany;11. Section Environmental Meteorology, Division Customer Service, ZAMG – Zentralanstalt für Meteorologie und Geodynamik, 1190 Wien, Austria;12. National Center for Atmospheric Research, Boulder, CO, US;13. Center of Excellence SPACE-SI, Ljubljana, Slovenia;14. Netherlands Organization for Applied Scientific Research (TNO), Utrecht, The Netherlands;15. Air Quality Research Section, Atmospheric Science and Technology Directorate, Environment Canada, 4905 Dufferin Street, Toronto, Ontario, Canada;p. Met Office, FitzRoy Road, Exeter EX1 3PB, United Kingdom;q. Environmental Software and Modelling Group, Computer Science School, Technical University of Madrid (UPM), Campus de Montegancedo, Boadilla del Monte, Madrid 28660, Spain;r. Emissions and Model Evaluation Branch, Atmospheric Modeling and Analysis Division/NERL/ORD, Research Triangle Park, NC, USA;s. Leibniz Institute for Tropospheric Research, Permoserstr. 15, D-04318 Leipzig, Germany;t. National Institute of Meteorology and Hydrology, Bulgarian Academy of Sciences, 66 Tzarigradsko shaussee Blvd., Sofia 1784, Bulgaria;u. Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, USA;v. University of Ljubljana, Faculty of Mathematics and Physics, Ljubljana, Slovenia;1. Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan, ROC;2. Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City, 25137, Taiwan, ROC |
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| Abstract: | In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source–receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source–receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source–receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source–receptor models are able to accurately reproduce the simulation of the 3D modelling system. |
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