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Measuring and Modelling the Concentration of Vehicle-Related PM2.5 and PM10 Emissions Based on Neural Networks

Author

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  • Vladimir Shepelev

    (Department of Automobile Transportation, South Ural State University (National Research University), 454080 Chelyabinsk, Russia)

  • Aleksandr Glushkov

    (Department of Mathematical and Computer Modeling, South Ural State University (National Research University), 454080 Chelyabinsk, Russia)

  • Ivan Slobodin

    (Department of Automobile Transportation, South Ural State University (National Research University), 454080 Chelyabinsk, Russia)

  • Yuri Cherkassov

    (Department of Transport and Service, M. Dulatov Kostanay Engineering and Economic University, Kostanay 110000, Kazakhstan)

Abstract

The urban environment near the road infrastructure is particularly affected by traffic emissions. This problem is exacerbated at road junctions. The roadside concentration of particulate (PM2.5 and PM10) emissions depends on traffic parameters, meteorological conditions, the characteristics and condition of the road surface, and urban development, which affects air flow and turbulence. Continuous changes in the structure and conditions of the traffic flow directly affect the concentration of roadside emissions, which significantly complicates monitoring and forecasting the state of ambient air. Our study presents a hybrid model to estimate the amount, concentration, and spatio-temporal forecasting of particulate emissions, accounting for dynamic changes in road traffic structure and the influence of meteorological factors. The input module of the model is based on data received from street cameras and weather stations using a trained convolutional neural network. Based on the history of emission concentration data as input data, we used a self-learning Recurrent Neural Network (RNN) for forecasting. Through micromodeling, we found that the order in which vehicles enter and exit an intersection affects the concentration of vehicle-related emissions. Preliminary experimental results showed that the proposed model provides higher accuracy in forecasting emission concentration (83–97%) than existing approaches.

Suggested Citation

  • Vladimir Shepelev & Aleksandr Glushkov & Ivan Slobodin & Yuri Cherkassov, 2023. "Measuring and Modelling the Concentration of Vehicle-Related PM2.5 and PM10 Emissions Based on Neural Networks," Mathematics, MDPI, vol. 11(5), pages 1-23, February.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1144-:d:1080082
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    References listed on IDEAS

    as
    1. Viacheslav Morozov & Vladimir Shepelev & Viktor Kostyrchenko, 2022. "Modeling the Operation of Signal-Controlled Intersections with Different Lane Occupancy," Mathematics, MDPI, vol. 10(24), pages 1-24, December.
    2. Vladimir Shepelev & Alexandr Glushkov & Tatyana Bedych & Tatyana Gluchshenko & Zlata Almetova, 2021. "Predicting the Traffic Capacity of an Intersection Using Fuzzy Logic and Computer Vision," Mathematics, MDPI, vol. 9(20), pages 1-19, October.
    3. Vladimir Shepelev & Sultan Zhankaziev & Sergey Aliukov & Vitalii Varkentin & Aleksandr Marusin & Alexey Marusin & Aleksandr Gritsenko, 2022. "Forecasting the Passage Time of the Queue of Highly Automated Vehicles Based on Neural Networks in the Services of Cooperative Intelligent Transport Systems," Mathematics, MDPI, vol. 10(2), pages 1-22, January.
    4. Noli Brazil, 2022. "Environmental inequality in the neighborhood networks of urban mobility in US cities," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(17), pages 2117776119-, May.
    5. José Balsa-Barreiro & Alfredo J. Morales & Rubén C. Lois-González & Ãtila Bueno, 2021. "Mapping Population Dynamics at Local Scales Using Spatial Networks," Complexity, Hindawi, vol. 2021, pages 1-14, May.
    6. Vladimir Shepelev & Alexandr Glushkov & Olga Fadina & Aleksandr Gritsenko, 2022. "Comparative Evaluation of Road Vehicle Emissions at Urban Intersections with Detailed Traffic Dynamics," Mathematics, MDPI, vol. 10(11), pages 1-19, May.
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    Cited by:

    1. Vladimir Shepelev & Aleksandr Glushkov & Ivan Slobodin & Mohammed Balfaqih, 2023. "Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions," Mathematics, MDPI, vol. 11(16), pages 1-30, August.
    2. Maksymilian Mądziel, 2024. "Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies," Energies, MDPI, vol. 17(19), pages 1-18, October.

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