Localized correction model for airport aircraft emissions based on operational simulation and its application
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摘要:
交通工具的污染物排放量及分布是交通系统优化的重要依据之一，鉴于机场实际运行条件对飞机污染物排放具有直接影响，且各影响因素之间存在复杂的交互作用，构建了基于运行仿真的机场飞机排放本地化修正模型，以实现排放的精准定量。基于全空域及机场仿真模型开展面向机场排放的“多场景-多因素”仿真试验，获取了运行时间、燃油流率等排放参数的多元数据集；利用k近邻互信息算法和夏普利加性解释模型识别影响排放的关键因素，并量化了各因素对燃油消耗和污染物排放量的贡献程度，据此构建了排放本地化修正参数集；结合福州长乐国际机场的历史航班数据，开展实例应用研究，建立了2022年该机场飞机精细化排放清单。分析结果表明：大气温度、地面风速风向、飞机进离场速度、管制间隔和天气现象是影响机场飞机排放的主要因素；HC和CO排放在滑行阶段占比较高，分别占总排放的97.4%和94.2%，而NOx和PM2.5排放在爬升和起飞阶段占比较高，两阶段占比之和分别为52.9%和66.1%；随垂直高度的增加，各污染物排放总体均呈现先增大后减小再逐渐趋于平稳的态势，HC、CO、SO2和PM2.5的排放峰值出现在200~350 m范围内，而NOx和CO2的排放峰值则集中于300~500 m范围内，计算结果与基于选定航班机载实时记录数据的排放计算结果的相对偏差仅为0.6%~1.3%，而与基于国际民用航空组织基准排放模型估算结果的相对偏差为11%~23%。建立的基于运行仿真的机场飞机排放本地化修正模型可为评估机场运行污染排放效应及制定科学减排对策提供技术支撑。
Abstract:The emission and distribution of transportation pollutants are an important basis for transportation system optimization. Given that the actual operating conditions of airports directly affect airport aircraft emissions and that complex interactions exist among various factors, a localized correction model for airport aircraft emissions based on operational simulation was developed to achieve accurate emission quantification. Through the total airspace & airport modeller (TAAM), "multi-scenario-multi-factor" simulation experiments for airport aircraft emissions were conducted to obtain a multivariate dataset of emission parameters including operational time and fuel flow rate. The k-nearest neighbor mutual information algorithm and SHAP model were employed to identify the key factors affecting emissions and to quantify the contribution of each factor to fuel consumption and pollutant emissions. Consequently, a localized emission correction parameter set was constructed. Using historical flight data from Fuzhou Changle International Airport, a case study was conducted and a refined emission inventory was established for aircraft at this airport in 2022. Analysis results indicate that atmospheric temperature, surface wind speed and direction, aircraft approach and departure speeds, air traffic control separation, and weather phenomena are the primary factors influencing airport aircraft emissions. During the taxiing phase, HC and CO represent a substantial proportion of total emissions, accounting for 97.4% and 94.2%, respectively. NOx and PM2.5 emissions are predominantly observed during the climb and takeoff phases, and the sums of proportions of the two stages are 52.9% and 66.1%, respectively. As altitude increases, emissions of various pollutants generally exhibit a trend of initially rising, followed by a decline, and ultimately stabilizing. The emission peak of HC, CO, SO2, and PM2.5 occurs within the altitude range of 200-350 m, whereas that of NOx and CO2 occurs within the 300-500 m range. The relative deviation between the calculated results and those based on selected flight onboard real-time recorded data ranges from 0.6% to 1.3%, whereas the relative deviation from the estimation results derived from the International Civil Aviation Organization (ICAO) baseline emission model estimation results varies from 11% to 23%. The localized correction model for airport aircraft emissions based on operational simulation can provide technical support for assessing the pollution emission effects of airport operations and formulating scientific emission reduction strategies.
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图 2 福州机场实际与仿真运行中航班时刻、流量及延误
Figure 2. Flight timetable, traffic flow, and delays in real and simulated operations at Fuzhou Airport
图 3 特征变量组合高维互信息图和总体特征航班样本的SHAP分析
Figure 3. High-dimensional mutual information map of combinations of feature variables and the SHAP analysis of flight samples with whole characteristic variables
图 4 福州机场典型繁忙日管制指令统计
Figure 4. Statistics of control instructions on typical busy days of Fuzhou Airport
图 5 2022年福州机场排放时间分布
Figure 5. Temporal distribution of emissions at Fuzhou Airport in 2022
图 6 2022年福州机场排放空间分布
Figure 6. Spatial distribution of emissions at Fuzhou Airport in 2022
图 7 本地化修正模型与其他方法的燃油消耗量与排放量估算对比
Figure 7. Comparison of fuel consumption and estimated emissions by the localized correction model and other methods
表 1 福州机场运行控制情景设置
Table 1. Scenarios setting of operational control at Fuzhou Airport
间隔情景 S1 S2 S3 S4 S5 S6 连续起飞间隔/min 2.0 2.1 2.2 2.3 2.4 2.5 连续落地间隔/km 6.4 7.0 8.0 9.0 10.0 11.0 一起两落间隔(插飞机间隔)/km 10.8 11.5 12.0 13.0 14.0 15.0 
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表 2 福州机场水平调速试验设置
Table 2. Horizontal speed control experiment settings at Fuzhou Airport
进离场指示空速情景 V1 V2 V3 V4 V5 V6 V7 V8 V9 最后进近速度仿真试验组/(km·h-1) 215 225 235 245 255 265 275 285 295 离场速度仿真试验组/ (km·h-1) 490 500 510 520 530 540 550 560 570
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表 3 福州机场气象条件试验设置
Table 3. Meteorological condition setting at Fuzhou Airport
气象条件类型 情景参数设置 特殊天气现象 典型温度为20.8 ℃，天气变量为降雨 典型温度为5.0 ℃，天气变量为霜冻 典型温度为5.0 ℃，天气变量为降雪 典型温度为20.8 ℃，天气变量为恶劣天气 大气温度 天气现象恒量为干燥，年平均温度为20.8 ℃ 天气现象恒量为干燥，年平均最高温度为26.8 ℃ 天气现象恒量为干燥，年平均最低温度为17.5 ℃ 天气现象恒量为干燥，年极端最高温度为41.9 ℃ 天气现象恒量为干燥，年极端最低温度为2.6 ℃ 地面风速风向 天气现象恒量为干燥，年平均温度为20.8 ℃ 风向为0°、30°、60°、90°、120°、150°、180°、210°、240°、270°、300°、330° 风速为0、9.3、18.5、27.8、37.0、46.3 km·h-1 云底高 模型默认值 能见度/m 模型默认值
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表 4 基于关键参考指标误差分析的仿真模型标定
Table 4. Calibration of simulation model based on error analysis of key reference indicators
仿真模型标定关键参考指标 MRE RMSE MAE 起降航班时刻分布/(架次·h-1) 0.006 2.000 2.000 各走廊口方向流量分布/架次 0.095 0.026 0.020 各跑道方向运行平均延误时间/min 0.168 0.926 0.925
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表 5 基于管制指令的飞机排放本地化修正系数集
Table 5. Localized correction coefficient set for aircraft emissions based on air traffic control instructions
管制指令类型 日均频次 排放本地化修正系数(1+Δ) 等待指令 登机口等待 11 1.028 92 滑行道等待 13 1.055 52 跑道排序等待 3 1.075 78 航路点等待 1 1.046 59 调速指令 进离场加速/减速 37 1.085 32
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表 6 基于气象条件的飞机排放本地化修正系数集
Table 6. Localized correction coefficient set for aircraft emissions based on meteorological conditions
不同气象条件 2022年出现天数/d 排放本地化修正系数(1+Δ) 典型天气现象 降雨 188 1.051 67 降雪 0 1.075 05 龙卷风 0 1.084 23 冰雹 1 1.011 54 暴雨 11 1.084 25 大气温度区间 2.6 ℃ 年极端最低温度 1 0.847 13 (2.6, 17.5] ℃ 年平均最低温度 115 1.154 42 (17.5, 20.8] ℃ 年平均温度 60 1.212 31 (20.8, 26.8] ℃ 年平均最高温度 83 1.333 25 (26.8, 41.9] ℃ 年极端最高温度 106 1.482 74 风速区间 (0, 9.3] km·h-1 顺风 1 0.921 71 斜顺风 1 0.962 24 正侧风 0 1.059 50 (9.3, 18.5] km·h-1 顺风 43 1.053 56 斜顺风 28 1.091 48 正侧风 20 1.155 92 (18.5, 27.8] km·h-1 顺风 47 1.182 11 斜顺风 56 1.269 89 正侧风 9 1.356 73 (27.8, 37.0] km·h-1 顺风 41 1.375 24 斜顺风 24 1.406 98 正侧风 18 1.446 63 (37.0, 46.3] km·h-1 顺风 6 1.456 68 斜顺风 22 1.443 23 正侧风 12 1.478 23
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[1] 中国民用航空局. 2024年民航行业发展统计公报[R]. 北京: 中国民用航空局, 2025.Civil Aviation Administration of China. The civil aviation industry development statistical bulletin in 2024[R]. Beijing: Civil Aviation Administration of China, 2025. [2] 中华人民共和国生态环境部. 中国移动源环境管理年报2021[R]. 北京: 生态环境部, 2021.Ministry of Ecology and Environment of the People's Republic of China. China mobile sources environmental management annual report 2021[R]. Beijing: Ministry of Ecology and Environment of the People's Republic of China, 2021. [3] EASTHAM S D, CHOSSIERE G P, SPETH R L, et al. Global impacts of aviation on air quality evaluated at high resolution[J]. Atmospheric Chemistry and Physics, 2024, 24(4): 2687-2703. doi: 10.5194/acp-24-2687-2024 [4] ZHANG J R, JIANG Y L, WANG Y J, et al. Increased impact of aviation on air quality and human health in China[J]. Environmental Science & Technology, 2023, 57(48): 19575-19583. [5] 韩博, 孔魏凯, 谭宏志, 等. 飞机LTO排放影响评估耦合模型研究与应用[J]. 中国环境科学, 2020, 40(6): 2409-2417.HAN Bo, KONG Wei-kai, TAN Hong-zhi, et al. A coupling model and its application on impact assessment of the aircraft emissions during LTO cycle[J]. China Environmental Science, 2020, 40(6): 2409-2417. [6] MA S M, WANG X, HAN B, et al. Exploring emission spatiotemporal pattern and potential reduction capacity in China's aviation sector: Flight trajectory optimization perspective[J]. Science of the Total Environment, 2024, 951: 175558. doi: 10.1016/j.scitotenv.2024.175558 [7] ZHANG J R, ZHANG S J, ZHANG X L, et al. Developing a high-resolution emission inventory of China's aviation sector using real-world flight trajectory data[J]. Environmental Science & Technology, 2022, 56: 5743-5752. [8] ZHU C X, HU R, LIU B W, et al. Uncertainty and its driving factors of airport aircraft pollutant emissions assessment[J]. Transportation Research Part D: Transport and Environment, 2021, 94: 102791. doi: 10.1016/j.trd.2021.102791 [9] 韩博, 孔魏凯, 姚婷玮, 等. 京津冀机场群飞机LTO大气污染物排放清单[J]. 环境科学, 2020, 41(3): 1143-1150.HAN Bo, KONG Wei-kai, YAO Ting-wei, et al. Air pollutant emission inventory from LTO cycles of aircraft in the Beijing-Tianjin-Hebei airport group, China[J]. Environmental Science, 2020, 41(3): 1143-1150. [10] 王兵, 冯培佳, 彭瑛. 基于QAR数据和改进iTransformer的民用航空器燃油流量估算方法[J/OL]. 交通运输工程学报, 2025, https://doi.org/10.19818/j.cnki.1671-1637.2026.067 .WANG Bing, FENG Pei-jia, PENG Ying. Estimation method for civil aircraft fuel flow based on QAR data and improved iTransformer[J/OL]. Journal of Traffic and Transportation Engineering, 2025,https://doi.org/10.19818/j.cnki.1671-1637.2026.067 .[11] MA S M, LIN S Z, HAN B, et al. Revealing considerable emissions reduction potential in flight operations: A real-time emission perspective[J]. Transportation Research Part D: Transport and Environment, 2025, 143: 104745. doi: 10.1016/j.trd.2025.104745 [12] 任强, 王立婕, 姚婷玮. 基于机载数据的飞行过程大气污染排放研究[J]. 安全与环境学报, 2022, 22(6): 3395-3402.REN Qiang, WANG Li-jie, YAO Ting-wei. Study on air pollution emission of aircraft during flight based on flight data[J]. Journal of Safety and Environment, 2022, 22(6): 3395-3402. [13] 邢志伟, 李世皎, 唐云霄, 等. 基于Agent-元胞自动机的机场场面交通仿真[J]. 系统仿真学报, 2018, 30(3): 857-865.XING Zhi-wei, LI Shi-jiao, TANG Yun-xiao, et al. Airport surface traffic simulation based on agent-cellular automaton[J]. Journal of System Simulation, 2018, 30(3): 857-865. [14] 张启钱, 王中叶, 张洪海, 等. 基于SMILO-VTAC模型的复杂低空多机冲突解脱方法[J]. 交通运输工程学报, 2019, 19(6): 125-136.ZHANG Qi-qian, WANG Zhong-ye, ZHANG Hong-hai, et al. SMILO-VTAC model based multi-aircraft conflict resolution method in complex low-altitude airspace[J]. Journal of Traffic and Transportation Engineering, 2019, 19(6): 125-136. [15] 陈正磊, 种小雷, 刘超佳, 等. 基于多智能体建模和蒙特卡洛仿真的跑道容量评估[J]. 交通运输工程学报, 2023, 23(6): 244-256. doi: 10.19818/j.cnki.1671-1637.2023.06.016CHEN Zheng-lei, CHONG Xiao-lei, LIU Chao-jia, et al. Runway capacity evaluation based on multi-agent modeling and Monte Carlo simulation[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 244-256. doi: 10.19818/j.cnki.1671-1637.2023.06.016 [16] JIA Z H, TANG H L, JIN D H, et al. Research on the volume-based fully coupled method of the multi-fidelity engine simulation[J]. Aerospace Science and Technology, 2022, 123: 107429. doi: 10.1016/j.ast.2022.107429 [17] 李明捷, 黄欣宁, 吕晨辉, 等. 基于排队网络模型的机场航班延误预测[J]. 科学技术与工程, 2023, 23(27): 11886-11891.LI Ming-jie, HUANG Xin-ning, LV Chen-hui, et al. Airport flight delay prediction based on queuing network model[J]. Science Technology and Engineering, 2023, 23(27): 11886-11891. [18] COVER T, HART P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory, 1967, 13(1): 21-27. doi: 10.1109/TIT.1967.1053964 [19] AHA D W, KIBLER D, ALBERT M K. Instance-based learning algorithms[J]. Machine Learning, 1991, 6(1): 37-66. doi: 10.1023/A:1022689900470 [20] STOGBAUER H, KRASKOV A, ASTAKHOV S A, et al. Least-dependent component analysis based on mutual information[J]. Physical Review E, 2004, 70(6): 066123. doi: 10.1103/PhysRevE.70.066123 [21] 唐铁桥, 曹峰, 王芃, 等. 飞机可持续滑行技术理论进展与应用挑战[J]. 交通运输工程学报, 2026, 26(1): 8-30. doi: 10.19818/j.cnki.1671-1637.2026.01.001TANG Tie-qiao, CAO Feng, WANG Peng, et al. Theoretical advances and application challenges of sustainable aircraft taxiing technology[J]. Journal of Traffic and Transportation Engineering, 2026, 26(1): 8-30. doi: 10.19818/j.cnki.1671-1637.2026.01.001 [22] AP-93-TM-2017-01, 机场时刻容量评估技术规范[S].AP-93-TM-2017-01, Technical specifications for airport capacity assessment of flight schedules[S]. [23] International Civil Aviation Organization. Doc9889, airport air quality manual[R]. Montreal: International Civil Aviation Organization, 2011. [24] European Union Aviation Safety Agency. ICAO aircraft engine emissions databank[R]. Cologne: European Union Aviation Safety Agency, 2023. [25] International Civil Aviation Organization. Doc8168, aircraft Operations[M]. Montreal: International Civil Aviation Organization, 2020. [26] 胡荣, 吴文洁, 陈琳, 等. 气象因素对飞机进近飞行燃油效率的影响[J]. 北京航空航天大学学报, 2018, 44(4): 677-683.HU Rong, WU Wen-jie, CHEN Lin, et al. Influence of meteorological factors on aircraft fuel efficiency in approach flight[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 677-683. [27] 苏佳明, 胡明华, 尹嘉男, 等. 复杂机场"机-车-场道"交通仿真模型研究[J]. 南京航空航天大学学报, 2024, 56(6): 994-1001.SU Jia-ming, HU Ming-hua, YIN Jia-nan, et al. An "aircraft-vehicle-surface" traffic simulation model in complex airports[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2024, 56(6): 994-1001. [28] ZHAO J B, MAO Z X, HAN B, et al. Characterizing aircraft exhaust emissions and impact factors at Tianjin Binhai International Airport via open-path fourier-transform infrared spectrometer[J]. Toxics, 2024, 12(11): 782. doi: 10.3390/toxics12110782 -
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