Uncertainty and its driving factors of airport aircraft pollutant emissions assessment. (May 2021)

• Record Type: Journal Article
• Authors: Zhu, Changxin; Hu, Rong; Liu, Bowen; Zhang, Junfeng
• Is Part Of: Transportation research. Volume 94(2021)
• Publication Date: 2021-05
• Subjects: Emission model -- Uncertainty analysis -- QAR data -- Aircraft emissions -- Sensitivity analysis -- Interaction of factors; Transportation -- Research -- Periodicals; Transportation -- Environmental aspects -- Periodicals; 354.76
• DOI: 10.1016/j.trd.2021.102791
• Languages: English
• ISSNs: 1361-9209

Abstract

Highlights: Emission estimation results using QAR data is 47% lower than ICAO standard values. TIM, EI, TOW and WS are top four key driving factors of assessment uncertainty. 6 sets of factor combinations are identified as significant contributors to estimation deviation. Abstract: To improve the accuracy of emission estimation for decision-making, the uncertainty analysis of aircraft pollutant emissions is a necessary fundamental work. This paper concentrates on the sources and degree of assessment uncertainty of aircraft pollutant emissions (i.e., HC, CO, and NOx) in the landing-and-take-off (LTO) cycle. The key uncertain parameters and their interactions are identified by Monte Carlo simulation and sensitivity analysis, respectively, using a case study involving Guangzhou Baiyun International Airport. Our analysis shows that: (1) emission estimation results using Quick Access Recorder (QAR) data are 47% lower than ICAO standard values and differ for the various operational phases; (2) the key factors driving uncertainty (i.e., emission index (EI), time-in-mode (TIM), take-off weight (TOW), and wind speed (WS)) are isolated; (3) interactions of TIM-EI, TOW-TIM, TOW-EI, TOW-Temperature, TOW-Pressure, and WS-TIM are identified as the essential combinations contributing to bias error of emission estimation. Finally, we also provide optimization suggestions for emission calculation modeling to improve assessment accuracy.