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Optimization Method for Reducing the Air Pollutant Emission and Aviation Noise of Arrival in Terminal Area

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  • Yong Tian

    (Air Traffic Department, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Lili Wan

    (Air Traffic Department, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Bojia Ye

    (Air Traffic Department, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Runze Yin

    (Terminal Radar Control Center, Jiangsu Air Traffic Management Sub-bureau, Nanjing 210016, China)

  • Dawei Xing

    (Air Traffic Department, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

In order to reduce the environmental impact of aircraft operation in the terminal area, this paper researched the route optimization method. Firstly, this paper constructed the air pollutant emission and noise assessment model, and the flight performance model. Secondly, aiming at reducing air emissions and noise level, the multi-objective terminal area route optimization model is established based on the principles of flight safety and flight procedure construction. Then this paper puts forward the path optimization method of emission and noise reduction of terminal area route network, through the research on the priority setting method of terminal area approach and departure route planning. The route segmentation method and NSGA-II algorithm are employed to solve the problem. Finally, a numerical case study is carried out for the Shanghai terminal area, and yields the following results: (1) Compared with the original route network, the optimized route network in the terminal area can significantly reduce emission and noise by reducing pollutant emission by 51.4% and noise influence by 21.5%; (2) The method can also reduce fuel consumption by 60.5% and the total route length by 21.1%.

Suggested Citation

  • Yong Tian & Lili Wan & Bojia Ye & Runze Yin & Dawei Xing, 2019. "Optimization Method for Reducing the Air Pollutant Emission and Aviation Noise of Arrival in Terminal Area," Sustainability, MDPI, vol. 11(17), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4715-:d:262175
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    References listed on IDEAS

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    1. Grampella, Mattia & Martini, Gianmaria & Scotti, Davide & Zambon, Giovanni, 2016. "The factors affecting pollution and noise environmental costs of the current aircraft fleet: An econometric analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 310-325.
    2. Sidiropoulos, Stavros & Majumdar, Arnab & Han, Ke, 2018. "A framework for the optimization of terminal airspace operations in Multi-Airport Systems," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 160-187.
    3. Nourelhouda Dougui & Daniel Delahaye & Stéphane Puechmorel & Marcel Mongeau, 2013. "A light-propagation model for aircraft trajectory planning," Journal of Global Optimization, Springer, vol. 56(3), pages 873-895, July.
    4. Fabio Carlucci & Andrea Cirà & Paolo Coccorese, 2018. "Measuring and Explaining Airport Efficiency and Sustainability: Evidence from Italy," Sustainability, MDPI, vol. 10(2), pages 1-17, February.
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    Cited by:

    1. Luca Fredianelli & Marco Nastasi & Marco Bernardini & Francesco Fidecaro & Gaetano Licitra, 2020. "Pass-by Characterization of Noise Emitted by Different Categories of Seagoing Ships in Ports," Sustainability, MDPI, vol. 12(5), pages 1-12, February.
    2. Marco Nastasi & Luca Fredianelli & Marco Bernardini & Luca Teti & Francesco Fidecaro & Gaetano Licitra, 2020. "Parameters Affecting Noise Emitted by Ships Moving in Port Areas," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    3. Fangzi Liu & Zihong Li & Hua Xie & Lei Yang & Minghua Hu, 2021. "Predicting Fuel Consumption Reduction Potentials Based on 4D Trajectory Optimization with Heterogeneous Constraints," Sustainability, MDPI, vol. 13(13), pages 1-33, June.

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