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Predicting Air Quality by Integrating a Mesoscopic Traffic Simulation Model and Simplified Air Pollutant Estimation Models

Abstract

Continuous growth in traffic demand has led to a decrease in the air quality in various urban areas. More than ever, local authorities for environmental protection and urban planners are interested in performing detailed investigations using traffic and air pollution simulations for testing various urban scenarios and raising citizen awareness where necessary. This article is focused on the traffic and air pollution in the eco-neighbourhood “Nancy Grand Cœur”, located in a medium-size city from north-eastern France. The main objective of this work is to build an integrated simulation model which would predict and visualize various environmental changes inside the neighbourhood such as: air pollution, traffic flow or meteorological information. Firstly, we conduct a data profiling analysis on the received data sets together with a discussion on the daily and hourly traffic patterns, average nitrogen dioxide concentrations and the regional background concentrations recorded in the eco-neighbourhood for the study period. Secondly, we build the 3D mesoscopic traffic simulation model using real data sets from the local traffic management centre. Thirdly, by using reliable data sets from the local air-quality management centre, we build a regression model to predict the evolution of nitrogen dioxide concentrations, as a function of the simulated traffic flow and meteorological data. We then validate the estimated results through comparisons with real data sets, with the purpose of supporting the traffic engineering decision-making and the smart city sustainability. The last section of the paper is reserved for further regression studies applied to other air pollutants monitored in the eco-neighbourhood, such as sulphur dioxide and particulate matter and a detailed discussion on benefit and challenges to conduct such studies.

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References

  1. UN.: World Urbanizatin Prospects: The 2014 Revision, Highlights (ST/ESA/SER.A/352). Department oof Economic and Social Affairs, Poulation Division, New York: United Nations (2014)

  2. TomTom.: [Online]. Available: https://bit.ly/1RxyKAl (2018) Accessed 4 4 2018

  3. U. EPA.: What Are the Six Common Air Pollutants? [Online]. Available: http://www3.epa.gov/airquality/urbanair/ (2016). Accessed 30 03 2017

  4. U. EPA.: International Decontamination Research and Development Conference. National Homeland Security Research Center, Durham, NC (2015)

  5. EEA.: Air quality in Europe — 2017 report. European Environment Agency, Copenhagen, Denmark (2017)

  6. OMS.: Plus sain, plus juste, plus sur: l'itinéraire de la santé dans le monde 2007-2017. Organisation mondiale de la Santé ; Licence : CC BY-NC-SA, Genève (2017)

  7. W. B. Group and IHME.: The Cost of Air Pollution, Strengthening the Economic Case for Action. The World Bank and Institute for Health Metrics and Evaluation, University of Washington, Seattle (2016)

  8. OCDE.: Les conséquences économiques de la pollution de l'air extérieur. Éditions OCDE, Paris (2016)

  9. Xuesong, Z., Shams, T., Hao, L., Taylor, J., Bin, L., Nagui, M.: Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic manageent strategies. 37, 123–136.

  10. T. P. S. F. P. B. J. &. C. M. Fontes.: How to combine different microsimulation tools to assess the environmental impacts of road traffic? Lessons and directions. 34, 293–306 (2015)

  11. Shorshani, M.F., André, M., Bonhomme, C., Seigneur, C.: Modelling chain for the effect of road traffic on air and water quality: Techniques, current status and future prospects. 64, 102-123 (2015)

  12. Nancy, G.: [Online]. Available: www.grand-nancy.org/grands-projets/nancy-grand-coeur/ . [Accessed 30 3 2017].

  13. Mihaita, A., Camargo, M., Lhoste, P.: Optimization of a complex urban intersection using discrete event simulation and evolutionary algorithms. Cape Town, South Africa (2014)

  14. Mihaita, A., Dupont, L., Camargo, M.: An urban traffic signal optimization using a 3D mesoscopic simulation approach and evolutionary algorithms (submitted) (2016)

  15. MEDE.: Bilan de la qualité de l'air en France en 2012. Ministère de l'écologie et du développement durable, Direction Générale de l’Energie et du Climat, Paris (2012)

  16. Lorraine, A.: Caractérisation de la qualité de l’air ambiant à Nancy en 2015 en contexte de proximité trafic. Air Lorraine, Nancy (2016)

  17. van den Elshout, S.: Citeair II, common information to European air. European Union, Bruxelles (2012)

  18. Ni, D.: Multiscale modeling of traffic flow. Mathematica Aeterna. 1(1), 27–54 (2011)

  19. Maerivoet, S., De Moor, B.: Transportation planning and traffic flow models. Katholieke Universiteit Leuven (2005)

  20. Hoogendoorn, S.P., Bovy, P.H.L.: State-of-the-art of vehicular traffic flow modelling. Journal of Systems and Control Engineering. 215(4), (2001)

  21. Barceló, J.: Fundamentals of traffic simulation. Springer-Verlag, New York (2010)

    Book  Google Scholar 

  22. Wang, Y., Prevedouros, P. D.: Synopsis of traffic simulation models. University of Hawaii, Manoa (1996)

  23. Aimsun.: TSS Barcelona. [Online]. Available: https://bit.ly/2G174KH (2017) Accessed 24 03 2018

  24. Archer, J., Hogskolan, K.: Indicators for traffic safety assessment and prediction and their application in microsimulation modelling: a study of urban and suburban intersections. KTH, Stockholm (2005)

  25. ASPA.: Modélisation de la qualité de l’air sur le futur éco-quartier Danube. Association pour la surveillance et l'étude de la Pollution Athmosphérique en Alsace., Alsace (2012)

  26. Galineau, J.: Bilan des émissions atmosphériques du transport routier en Lorraine. Air Lorraine, Nancy (2012)

  27. Liao, D., Valliant, R.: Variance inflation factors in the analysis of complex survey data. 38(1), 53–62 (2012)

  28. Schlotzhauer, S.D.: Elementary statistics using JMP. SAS Institute Inc, Cary (2007)

    Google Scholar 

  29. Makridakis, S., Wheelwright, S.C., Hyndman, R.J.: Forecasting methods and applications (1998)

  30. Barlas, Y.: Model validation in system dynamics. pp. 1–10 (1994)

  31. H. A., K. H., S. S.L., J. J., J. H., P. T., A. F., N. T., K. M., S. J.N.: The role of relative humidity in continental new particle formation. J. Geophys. Res. Atmos 116, 909–926, (2011)

  32. Hussein, T., Karppinen, A., Kukkonen, J., Härkönen, J., Aalto, P., Hämeri, K., Kerminen, V.-M., Kulmala, M.: Meteorological dependence of size-fractionated number concentrations of urban aerosol particles. Atmos. Environ. 40(1427–1440), (2006)

  33. Mihăiţă, A.-S., Dupont, L., Chery, O., Camaego, M., Cai, C.: Air quality monitoring using stationary versus mobile sensing units: a case study from Lorraine, France. vol. Special Number of ITS World COngress 2018, no. (submitted) (2018)

  34. Mihăiţă, A.-S., Dupont, L., Chery, O., Camaego, M., Cai, C.: Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling. vol. (submitted) (2018)

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Acknowledgements

This work has been developed in the ERPI laboratory, from Nancy France, under the Chaire REVES project funding. The final writing and submission of the paper has been done in the DATA61|CSIRO research laboratory from Sydney, Australia, with further work on the analysis of SO2 and PM10 pollutants. The authors of this work are grateful for the data and support provided by Grand Nancy, Air Lorraine and FlexSim Conseil.

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Correspondence to Adriana Simona Mihăiţă.

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Mihăiţă, A.S., Ortiz, M.B., Camargo, M. et al. Predicting Air Quality by Integrating a Mesoscopic Traffic Simulation Model and Simplified Air Pollutant Estimation Models. Int. J. ITS Res. 17, 125–141 (2019). https://doi.org/10.1007/s13177-018-0160-z

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  • DOI: https://doi.org/10.1007/s13177-018-0160-z

Keywords

  • Mesoscopic traffic simulation
  • Air pollution
  • Concentration estimation
  • Eco-neighbourhood