Climate change and increasing greenhouse gas emissions are the main environmental issues that have a direct effect on society, of which road transport is the largest contributor of emissions. Last years, road transport was responsible for about three quarters of total greenhouse gas emissions from transport. The Carbon dioxide CO2 emissions from highway vehicles constitute the major component of anthropogenic greenhouse gas emissions. Modeling of vehicle exhaust CO2 emissions becomes an essential issue for researchers. The main objective of this paper was modeling of vehicle CO2 emissions by development of a statistical model. Twelve heavy-duty trucks emissions were collected with their average speed over their distances travelled. Data was recorded using on-board diagnostics equipment for freight transport trucks. Multiple linear regressions MLR was implemented to build emission models from the collected data, using predictor variables of distances travelled, average speeds and their powers. Results demonstrated that MLR are powerful tools for predicting vehicle exhaust CO2 emissions with a high correlation coefficient, indicating the accuracy and the efficiency of predicted models. These results can be used as premature warning of the population to respect air quality standards and ensure a sustainable environment.


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    Titel :

    Statistical Model for Estimating Exhaust CO2 Emissions from Heavy-Duty Trucks


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Walha, Lassaad (Herausgeber:in) / Jarraya, Abdessalem (Herausgeber:in) / Djemal, Fathi (Herausgeber:in) / Chouchane, Mnaouar (Herausgeber:in) / Aifaoui, Nizar (Herausgeber:in) / Chaari, Fakher (Herausgeber:in) / Abdennadher, Moez (Herausgeber:in) / Benamara, Abdelmajid (Herausgeber:in) / Haddar, Mohamed (Herausgeber:in) / Helali, Ali (Autor:in)

    Kongress:

    International Conference Design and Modeling of Mechanical Systems ; 2021 ; Hammamet, Tunisia December 20, 2021 - December 22, 2021



    Erscheinungsdatum :

    20.08.2022


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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