Accurate modeling of aircraft fuel consumption, emissions, and noise is crucial in evaluating new air transportation operational procedures and policies to abate negative environmental impacts. The Aviation Environmental Design Tool (AEDT) is a comprehensive software package developed to address this requirement. Although the modeling of departure operations around airports is of great interest to policy makers and communities, AEDT’s default departure procedures, assumptions of maximum takeoff thrust, and current weight estimations do not fully represent real-world operational flight conditions. With more access to flight data, this paper first presents the development of improved departure profiles that fine-tune the previous modeling assumptions to more accurately reflect real-world operations. We then present a systematic analysis of the new departure profiles through a large-scale computer experiment and follow-up statistical analysis. The result provides comprehensive and valuable insights on the sensitivity of assumptions, quantification of the new profiles’ impacts on estimating aviation environmental impacts, and variability among different aircraft models. Lastly, the statistical learning method elastic net is used to find the driving factors behind the complex patterns observed in the noise results.

飞机燃料消耗，排放和噪声的准确建模对于评估新的航空运输运营程序和政策以减轻负面环境影响至关重要。航空环境设计工具（AEDT）是为满足这一要求而开发的综合软件包。尽管决策者和社区对机场周围的离场运营建模非常感兴趣，但AEDT的默认离场程序，最大起飞推力的假设以及当前的重量估算并不能完全代表现实世界中的运行飞行状况。通过更多地访问航班数据，本文首先介绍了改进的离场资料的开发，这些资料可以对以前的建模假设进行微调，以更准确地反映实际操作。然后，我们通过大规模的计算机实验和后续的统计分析，对新的出发航线进行系统的分析。结果为假设的敏感性，新配置文件对估计航空环境影响的影响进行量化以及不同飞机模型之间的可变性提供了全面而有价值的见解。最后，使用统计学习方法弹性网来找到在噪声结果中观察到的复杂模式背后的驱动因素。