Calculation and analysis of pollutants during takeoff and landing based on airborne data

Abstract

This study adopts the calculation method of reducing pollutant emission during flight based on the flight record data from Nantong to Chengdu. More accurate flight record data can be obtained by interpolating, filtering, and weighting QAR (airborne quick access recorder) data in order to recover specific flight conditions. BMMF2 and P3‐T3 flow correction method are used to correct the fuel flow value in the QAR data and analyze the exact consumption time of each phase of the LTO (landing take‐off cycle) based on the key parameter. The actual pollutant emission results calculated using QAR data are compared with the reference data of International Civil Aviation Organization under the same conditions, and then changes of pollutant emission in each flight stage are analyzed. The results show that in the four modes of approach, taxi, take‐off, and climb of standard landing and take‐off (LTO) cycle, the pollutants emitted in the taxi section account for about 60% of the whole cycle, the taxi time is reduced by 6.78 min, the total emission is reduced by 11.4%, and the fuel consumption is reduced by 11.96%. The estimated nitrogen oxide emissions of LTO in stages using the QAR measured data after accurately dividing the flight phase, which is over 28% different from the original estimation method. The QAR data can analyze the pollutant emissions of aircraft in different stages. Methods for calculating the engine emissions of the LTO phase can provide a more accurate reference for assessing pollution of near‐ground aircraft and airports in different regions.

Risk assessment method for controlled flight into terrain of airlines based on QAR data

“…Currently, scholars use QAR data to conduct research primarily in the following domains: Mathematical analysis and processing methods to estimate data parameters that are not directly recorded in QAR data (Sembiring et al , 2013; Sembiring et al , 2018), determination of energy dissipation rate (EDR) through wind component of QAR data and pollutant emissions during flight (Kim et al , 2022; Huang et al , 2019; Pan et al , 2021), identification of normal and abnormal flights during operation (Li et al , 2011), identification of anomalous in-flight data (Chen et al , 2022; Jesse et al , 2008), consideration of the tradeoff between accuracy and complexity in Engine Health Management (Ren et al , 2022), resolution of the similarity issue in QAR data (Feng et al , 2012), diagnosis of aircraft exceedance event (Gao et al , 2014) and enhancement of maintenance fault diagnosis and prevention efficiency (Yang and Dong, 2012; Yang and Meng, 2012). Diverse data analysis systems are developed to explore operational risks, evaluate and assess pilot flight technology, identify technical training problems, optimize training processes, enhance flight training quality, monitor flight quality, engine status and elevate the underutilized QAR data rate (Haverdings and Chan, 2010; Sun, 2003; Li, 2010).…”

“…Mathematical analysis and processing methods to estimate data parameters that are not directly recorded in QAR data (Sembiring et al , 2013; Sembiring et al , 2018), determination of energy dissipation rate (EDR) through wind component of QAR data and pollutant emissions during flight (Kim et al , 2022; Huang et al , 2019; Pan et al , 2021), identification of normal and abnormal flights during operation (Li et al , 2011), identification of anomalous in-flight data (Chen et al , 2022; Jesse et al , 2008), consideration of the tradeoff between accuracy and complexity in Engine Health Management (Ren et al , 2022), resolution of the similarity issue in QAR data (Feng et al , 2012), diagnosis of aircraft exceedance event (Gao et al , 2014) and enhancement of maintenance fault diagnosis and prevention efficiency (Yang and Dong, 2012; Yang and Meng, 2012).…”