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Estimation of aircraft emissions at Georgian international airport

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  • Tokuslu, Aydin

Abstract

This study is the first which focuses on the estimation of air pollutants including nitrogen oxides, carbon monoxide, and hydrocarbons from aircraft during landing and take-off (LTO) cycles for the year 2018 at Tbilisi International Airport in Georgia. The estimation model is based on flight data recorded by TAV Airports Holding Corporations in Georgia, including type and number of aircraft, engine type, number of passengers, and emission factors from the International Civil Aviation Organization Engine Exhaust Emission Databank were used for calculating the emissions. The total aircraft emissions during the LTO cycle were estimated as 428.78 t/y (247.33 t/y for NOx, 161.21 t/y for CO, and 20.24 t/y for HC) at Tbilisi international airport. International flights were responsible for 99% of the total LTO emissions from all flights in 2018. The results showed that NOx was mainly emitted during the take-off and climb-out modes, accounting for 27% and 37% of the total emissions. CO and HC were mostly produced during the taxi mode, accounting for 77% and 70% of the total emissions. The estimation presents that a decrease of 2 min in taxiing time will result in a decrease of approximately 5% in LTO emissions. To forecast future emissions, it was assessed that an increase of 50% in LTO cycles might cause a rise of between 55 and 60% in emissions. This study will help to create aircraft emission inventory in the Caucasus region.

Suggested Citation

  • Tokuslu, Aydin, 2020. "Estimation of aircraft emissions at Georgian international airport," Energy, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:energy:v:206:y:2020:i:c:s0360544220313268
    DOI: 10.1016/j.energy.2020.118219
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. He, Honglin & Yang, Xiaojun & Li, Chunyang & Teng, Jinfang, 2024. "Numerical analysis of the landing and take-off cycle standard for supersonic engines based on pollutant emission characteristics," Energy, Elsevier, vol. 299(C).
    3. Aygun, Hakan & Dursun, Omer Osman & Toraman, Suat, 2023. "Machine learning based approach for forecasting emission parameters of mixed flow turbofan engine at high power modes," Energy, Elsevier, vol. 271(C).
    4. Cao, Feng & Tang, Tie-Qiao & Gao, Yunqi & You, Feng & Zhang, Jian, 2023. "Calculation and analysis of new taxiing methods on aircraft fuel consumption and pollutant emissions," Energy, Elsevier, vol. 277(C).
    5. Xiurui Guo & Chunxiao Ning & Yaqian Shen & Chang Yao & Dongsheng Chen & Shuiyuan Cheng, 2023. "Projection of the Co-Reduced Emissions of CO 2 and Air Pollutants from Civil Aviation in China," Sustainability, MDPI, vol. 15(9), pages 1-23, April.
    6. Cheng Li & Yangzhou Li & Jian Xing, 2023. "Multivariate Grey Prediction Model Application in Civil Aviation Carbon Emission Based on Fractional Order Accumulation and Background Value Optimization," Sustainability, MDPI, vol. 15(11), pages 1-19, June.
    7. Lyu, Chen & Liu, Xiaoman & Wang, Zhen & Yang, Lu & Liu, Hao & Yang, Nan & Xu, Shaodong & Cao, Libin & Zhang, Zhe & Pang, Lingyun & Zhang, Li & Cai, Bofeng, 2023. "An emissions inventory using flight information reveals the long-term changes of aviation CO2 emissions in China," Energy, Elsevier, vol. 262(PB).
    8. Wang, Yu & Sun, Huimin & Lin, Yiming & Cui, Qiang & Shen, Yanxin & Li, Xin, 2024. "An interval-valued estimation method of aircraft route carbon emission: A function of aircraft seat capacity and route flight time," Energy, Elsevier, vol. 294(C).

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