IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i19p4924-d1490720.html
   My bibliography  Save this article

Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies

Author

Listed:
  • Maksymilian Mądziel

    (Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszow, Poland)

Abstract

In response to increasing environmental demands, modeling emissions from older vehicles presents a significant challenge. This paper introduces an innovative methodology that takes advantage of advanced AI and machine learning techniques to develop precise emission models for older vehicles. This study analyzed data from road tests and the OBDII diagnostic interface, focusing on CO 2 , CO, THC, and NOx emissions under both cold and warm engine conditions. The key results showed that random forest regression provided the best predictions for THC in a cold engine (R 2 : 0.76), while polynomial regression excelled for CO 2 (R 2 : 0.93). For warm engines, polynomial regression performed best for CO 2 (R 2 : 0.95), and gradient boosting delivered results for THC (R 2 : 0.66). Although prediction accuracy varied by emission compound and engine state, the models consistently demonstrated high precision, offering a robust tool for managing emissions from aging vehicle fleets. These models offer valuable information for transportation policy and pollution reduction strategies, particularly in urban areas.

Suggested Citation

  • Maksymilian Mądziel, 2024. "Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies," Energies, MDPI, vol. 17(19), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4924-:d:1490720
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/19/4924/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/19/4924/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhenggang Huo & Xiaoting Zha & Mengyao Lu & Tianqi Ma & Zhichao Lu, 2023. "Prediction of Carbon Emission of the Transportation Sector in Jiangsu Province-Regression Prediction Model Based on GA-SVM," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    2. Vidyattama, Yogi & Tanton, Robert & Nakanishi, Hitomi, 2021. "Investigating Australian households’ vehicle ownership and its relationship with emission tax policy options," Transport Policy, Elsevier, vol. 114(C), pages 196-205.
    3. Yordan P Raykov & Alexis Boukouvalas & Fahd Baig & Max A Little, 2016. "What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-28, September.
    4. 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.
    5. Don Fullerton & Li Gan, 2005. "Cost-Effective Policies to Reduce Vehicle Emissions," American Economic Review, American Economic Association, vol. 95(2), pages 300-304, May.
    6. Zhichao Li, 2022. "Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil," IJERPH, MDPI, vol. 19(20), pages 1-16, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marco Percoco, 2010. "Urban Transport Policies And The Environment: Evidence From Italy," Articles, International Journal of Transport Economics, vol. 37(2).
    2. Xiao, Junji & Ju, Heng, 2011. "The impacts of air-pollution motivated automobile consumption tax adjustments of China," MPRA Paper 27743, University Library of Munich, Germany.
    3. Wu Bo & Kunming Zhao & Gang Cheng & Yaping Wang & Jiazhe Zhang & Mingkai Cheng & Can Yang & Wa Da, 2024. "Study on Transportation Carbon Emissions in Tibet: Measurement, Prediction Model Development, and Analysis," Sustainability, MDPI, vol. 16(19), pages 1-26, September.
    4. Alice Ciccone & Emilia Soldani, 2021. "Stick or Carrot? Asymmetric Responses to Vehicle Registration Taxes in Norway," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 80(1), pages 59-94, September.
    5. McManus, Walter, 2007. "Economic analysis of feebates to reduce greenhouse gas emissions from light vehicles for California," MPRA Paper 3461, University Library of Munich, Germany.
    6. Francisco Gallego & Juan-Pablo Montero & Christian Salas, 2011. "The Effect of Transport Policies on Car Use: Theory and Evidence from Latin American Cities," Documentos de Trabajo 407, Instituto de Economia. Pontificia Universidad Católica de Chile..
    7. Jafar Hussain & Chien‐Chiang Lee, 2022. "A green path towards sustainable development: Optimal behavior of the duopoly game model with carbon neutrality instruments," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(6), pages 1523-1541, December.
    8. De Borger, Bruno & Mulalic, Ismir & Rouwendal, Jan, 2016. "Substitution between cars within the household," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 135-156.
    9. Hernán Barahona & Francisco Gallego & Jeanne Juan-Pablo Montero, 2016. "Adopting a Cleaner Technology: The Effect of Driving Restrictions on Fleet Turnover," Working Papers ClioLab 24, EH Clio Lab. Instituto de Economía. Pontificia Universidad Católica de Chile.
    10. Vidyattama, Yogi & Tanton, Robert & Sinclair, Darren & Schirmer, Jacki, 2024. "Simulating electric vehicle policy in the Australian capital territory," Transport Policy, Elsevier, vol. 149(C), pages 91-99.
    11. Wenjie Li & Chun Luo & Yiwei He & Yu Wan & Hongbo Du, 2023. "Estimating Inter-Regional Freight Demand in China Based on the Input–Output Model," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
    12. Lorentziadis, Panos L. & Vournas, Stylianos G., 2011. "A quantitative model of accelerated vehicle-retirement induced by subsidy," European Journal of Operational Research, Elsevier, vol. 211(3), pages 623-629, June.
    13. Yoshifumi Konishi & Meng Zhao, 2017. "Can Green Car Taxes Restore Efficiency? Evidence from the Japanese New Car Market," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(1), pages 51-87.
    14. West, Jeremy & Hoekstra, Mark & Meer, Jonathan & Puller, Steven L., 2017. "Vehicle miles (not) traveled: Fuel economy requirements, vehicle characteristics, and household driving," Journal of Public Economics, Elsevier, vol. 145(C), pages 65-81.
    15. Eskeland, Gunnar S. & Mideksa, Torben K., 2008. "Transportation fuel use, technology and standards: The role of credibility and expectations," Policy Research Working Paper Series 4695, The World Bank.
    16. Omer Ajmal & Shahzad Mumtaz & Humaira Arshad & Abdullah Soomro & Tariq Hussain & Razaz Waheeb Attar & Ahmed Alhomoud, 2024. "Enhanced Parameter Estimation of DENsity CLUstEring (DENCLUE) Using Differential Evolution," Mathematics, MDPI, vol. 12(17), pages 1-46, September.
    17. Tscharaktschiew, Stefan, 2014. "Shedding light on the appropriateness of the (high) gasoline tax level in Germany," Economics of Transportation, Elsevier, vol. 3(3), pages 189-210.
    18. Maksymilian Mądziel, 2023. "Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones," Energies, MDPI, vol. 16(19), pages 1-16, October.
    19. Joaquín Pérez-Ortega & Nelva Nely Almanza-Ortega & David Romero, 2018. "Balancing effort and benefit of K-means clustering algorithms in Big Data realms," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-19, September.
    20. Bruno De Borger & Jan Rouwendal, 2014. "Car User Taxes, Quality Characteristics, and Fuel Efficiency Household Behaviour and Market Adjustment," Journal of Transport Economics and Policy, University of Bath, vol. 48(3), pages 345-366, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4924-:d:1490720. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.