The high number of vehicles crossing between islands through the ferry port has an impact on vehicle exhaust emissions during the queuing process for entering ships. This study aims to observe and analyze the prediction of motor vehicle emission loads at the Amolengo ferry port, South Konawe Regency, Southeast Sulawesi with analytical prediction studies and simple statistical data on possible vehicle emission estimates resulting from loading and unloading activities. Collecting research data by calculating the volume of vehicles and calculating the emission load with a percentage in terms of the density of the number of vehicles at the Amolengo Ferry Port. Statistical approach by calculating the arithmetic mean and standard deviation over day variables to obtain cumulative vehicle passing data. Based on these results, we calculated the volume of vehicle traffic passing through the Amolengo Ferry Port, South Konawe, as many as 147 vehicles/hour, 1411 vehicles/day, 515088 vehicles/year. Types of motorized vehicles carry out more activities and bus vehicles carry out fewer activities. The estimated annual emission load which is calculated based on the emission factor is known that the largest CO emission load is generated by gasoline-fueled cars of 5.05 tons/year and motorcycles of 4.37 tons/year. Meanwhile, the largest N2O emission load is generated by trucks, which is 0.0013 tons/year. This research provides that the statistical approach to determining the emission load certainly needs to be reviewed from the density of the number of vehicles to obtain an estimate of CO gas emissions.


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    Title :

    Beban Emisi Kendaraan Bermotor di Pelabuhan Ferry Amolengo Konawe Selatan, Sulawesi Tenggara


    Contributors:

    Publication date :

    2023-07-26


    Remarks:

    doi:10.22236/jgel.v7i2.8194
    Jurnal Geografi, Edukasi dan Lingkungan (JGEL); Vol. 7 No. 2 (2023): Edisi Bulan Juli; 109-117 ; 2579-8510 ; 2579-8499 ; 10.22236/jgel.v7i2



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




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