Background and Objective: Transportation sector generates the largest share of greenhouse gas emissions (CO2 and CH4) which causes global warming. “Stop-and-go” driving and congested traffic flow results in a decrease in average car speeds, an increase in traffic incidents, and finally escalates GHGs emissions. Hence, congestion is directly related to carbon emissions. The objective of this study was to estimate the GHGs emissions of urban transportation sector using the congestion index. Materials and Methods: The traffic flow in the main street of Hamadan city was continuously monitored in 8 time periods from 8 am to 12 pm in August 2018. Then, a traffic congestion map was prepared. Subsequently the surface area and weight coefficient of each road were obtained based on the traffic assignment model. Finally, the amount of GHGs emissions from transportation sector, CO2 emission per capita, and annual amount of CO2 and CH4 release in each street were calculated and estimated. Results: Results showed that the total CO2 and CH4 emissions from the transportation sector of Hamadan city are 776000 and 396 ton/year, respectively. Imam Khomeini Highway (29166 ton CO2/year) and Mirzadeh Eshghi Street (24453 ton CO2/year) recorded the highest level of carbon emission due to the highespeed and more width “stop and go” driving conditions. Conclusion: The conformity of the congestion index with the traffic assignment model can be used to estimate the amount of urban traffic pollution. Finally, because of the high growth rate of CO2 and CH4 and the role of forests in maximizing carbon sequestration, it is imperative to manage and increase green spaces to reduce CO2.


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    Titel :

    Estimation of greenhouse gases emissions from urban traffic: a case study of Hamadan city


    Beteiligte:
    Kamran Shayesteh (Autor:in) / Shiva Gharibi (Autor:in) / Behnaz Attaiean (Autor:in)


    Erscheinungsdatum :

    2021



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt





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