美国西海岸枢纽国内航空的三维排放制图与社会成本估算
3-D Emissions Mapping and Social Cost Estimation for US Domestic Aviation at West Coast Hubs
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中文总结 AI 辅助
本研究利用物理信息自编码器重建美国西海岸枢纽的飞行轨迹，生成三维排放图并估算社会成本，发现NOx虽质量占比小但主导健康成本，为航空环境影响评估提供依据。
中文摘要 AI 辅助
现有的航空排放清单缺乏高分辨率社会成本和健康影响评估所需的精确轨迹数据。本文通过重建美国西海岸枢纽的飞行轨迹，开发了三维排放图，以估算区域环境和机场附近的健康影响。将物理信息自编码器（AE）应用于2025年1月覆盖美国西海岸枢纽的ADS-B轨迹记录。编码器结合了卷积神经网络（CNN）、双向门控循环单元（Bi-GRU）和带跳跃连接的三维CNN；解码器为时间卷积网络（TCN）。将其与基线自编码器和三次样条插值进行基准比较。排放通过EUROCONTROL飞机性能数据（BADA）性能表和ICAO发动机排放数据库（EEDB）排放指数进行映射，并使用波音燃油流量方法2（BFFM2）进行高度修正。对所有飞行阶段的社会成本进行量化，并对每个枢纽50公里范围内的着陆和起飞循环进行健康影响评估。所提出的自编码器模型在5%至50%的缺失率下均优于TCN自编码器和三次样条插值。对排放清单进行货币化显示，NOx在直接气候强迫中产生较小的净冷却效应，尽管其质量不到CO2的0.4%，却占货币化空气质量和健康成本的99.7%，使其成为主要的健康成本驱动因素。据我们所知，这是首批将基于自编码器的轨迹重建与独立的空间-时间特征编码和基于高度的排放建模相结合的研究之一，以生成用于空气质量、气候影响和人口暴露的区域航空排放清单。所得的排放图和社会成本估算为美国国内航空的环境影响评估和机场附近健康政策评估提供了定量背景。
英文摘要
Existing aviation emissions inventories lack accurate trajectory data for high-resolution social cost and health impact assessment. This paper develops a 3-D emissions map by reconstructing flight trajectories for US west coast hubs to estimate regional environmental and near-airport health impacts. A physics-informed autoencoder (AE) is applied to ADS-B trajectory records for January 2025 covering US west coast hubs. The encoder combines a Convolutional Neural Network (CNN), a Bi-GRU, and a 3-D CNN with skip connection; the decoder is a Temporal Convolutional Network (TCN). It is benchmarked against a baseline-AE and cubic spline interpolation. Emissions are mapped via EUROCONTROL Base of Aircraft Data (BADA) performance tables and ICAO Engine Emissions Databank (EEDB) emission indices, with altitude corrections via Boeing Fuel Flow Method 2 (BFFM2). Social costs are quantified for all flight phases, with health impacts assessed for Landing and Takeoff cycles within 50 km of each hub. The proposed AE model outperforms both a TCN-AE and cubic spline interpolation across 5% to 50% missing rates. Monetizing the emissions inventory shows NOx produces a small net cooling effect in direct climate forcing, while accounting for 99.7% of monetized air-quality and health cost despite being under 0.4% of CO2 by mass, making it the dominant health-cost driver. To our knowledge, this is among the first studies combining AE-based trajectory reconstruction with separate spatial-temporal feature encoding and altitude-based emissions modeling to produce a regional aviation emissions inventory for air quality, climate impact and population exposure. The resulting emissions map and social cost estimates provide quantitative context for environmental impact assessment and near-airport health policy evaluation for US domestic aviation.
