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<p>Air transport has become the fastest-growing carbon/air pollutant emission sources. Landing-takeoff (LTO) management is a cost-effective way to address aviation’s low-emission, decarbonization and energy-conservation challenges. Accurate estimation of LTO fuel and emissions is crucial. However, the widely-used International Civil Aviation Organization (ICAO) method with constant time-in-mode resulted in huge uncertainties. We established the Aircraft Landing-takeoff Time, Fuel, and Emission Model (ALTFEM), substantially improving the capability of dynamically capturing time-in-mode, to estimate LTO fuel consumption and emissions for each flight. The time-in-mode estimation errors of ALTFEM-estimated taxi in/out durations were reduced by 30.2% and 118% compared to ICAO-suggested defaults (taxi-in:420 s, taxi-out:1140 s). Our work improved the accuracy of airport-specific estimates for fuel, HC, NOx, and CO<sub>2</sub> by 14%–40%, compared to ICAO-based results. Unexpected higher (1.1–1.2 times) energy-saving potentials during low-traffic periods were found in busy airports with longer taxi durations (e.g., the Shanghai-Pudong-International-Airport), implying a potential effective mitigation direction.</p>

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Refined aircraft landing-takeoff activity modeling to improve the estimation of aviation CO2 and pollutants emissions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月27日

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  • Chaoyu Wen,
  • Jianlei Lang,
  • Yunya Fu,
  • Zekang Yang,
  • Xiaoqing Cheng,
  • Ying Zhou,
  • Shaojun Zhang,
  • Dongsheng Chen,
  • Shuiyuan Cheng

摘要

Air transport has become the fastest-growing carbon/air pollutant emission sources. Landing-takeoff (LTO) management is a cost-effective way to address aviation’s low-emission, decarbonization and energy-conservation challenges. Accurate estimation of LTO fuel and emissions is crucial. However, the widely-used International Civil Aviation Organization (ICAO) method with constant time-in-mode resulted in huge uncertainties. We established the Aircraft Landing-takeoff Time, Fuel, and Emission Model (ALTFEM), substantially improving the capability of dynamically capturing time-in-mode, to estimate LTO fuel consumption and emissions for each flight. The time-in-mode estimation errors of ALTFEM-estimated taxi in/out durations were reduced by 30.2% and 118% compared to ICAO-suggested defaults (taxi-in:420 s, taxi-out:1140 s). Our work improved the accuracy of airport-specific estimates for fuel, HC, NOx, and CO2 by 14%–40%, compared to ICAO-based results. Unexpected higher (1.1–1.2 times) energy-saving potentials during low-traffic periods were found in busy airports with longer taxi durations (e.g., the Shanghai-Pudong-International-Airport), implying a potential effective mitigation direction.