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

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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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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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Authors and Affiliations

  1. DATA61, 13 Garden Str., Eveleigh, 2015, Australia

    Adriana Simona Mihăiţă & Chen Cai

  2. ERPI laboratory EA6737, 8 Rue Bastien Lepage, 54010, Nancy, France

    Mirian Benavides Ortiz & Mauricio Camargo

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  1. Adriana Simona Mihăiţă
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  2. Mirian Benavides Ortiz
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  3. Mauricio Camargo
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  4. Chen Cai
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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

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