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Optical Remote Sensing Analysis of Exhaust Emissions During Aircraft Taxiing at Hefei Xinqiao International Airport
- Source :
- Remote Sensing, Vol 17, Iss 4, p 664 (2025)
- Publication Year :
- 2025
- Publisher :
- MDPI AG, 2025.
-
Abstract
- The taxiing stage of an aircraft is characterized by its long duration, low operating thrust, and low combustion efficiency, resulting in substantial emissions of CO, CO2, and VOCs, which adversely affect air quality near airports. This study has developed an open-path Fourier transform infrared spectroscopy (OP-FTIR) monitor with second-level time resolution to enable the optical remote monitoring of pollutants during taxiing. Measurements of CO, CO2, and VOCs were conducted over one month at Hefei Xinqiao International Airport (HXIA). The generalized additive model (GAM) is used for data analysis to reveal complex nonlinear relationships between aircraft emission concentrations and meteorological factors, aircraft models, and their corresponding registration numbers. The GAM analysis shows that among meteorological factors, humidity, and atmospheric pressure have the most significant impact on aircraft exhaust monitoring, with a relative average contribution value as high as approximately six. The explanatory power of aircraft models for emissions is low (R2 < 0.18), whereas that of registration numbers is high (R2 > 0.6), suggesting that individual differences between aircrafts play a crucial role in emission concentration variations. Furthermore, a noticeable correlation was found between the CO/CO2 ratio and volatile organic compound (VOC) concentrations (R2 > 0.63), indicating that combustion efficiency significantly affects VOC emissions. This study not only advances the real-time remote sensing monitoring of pollutants during aircraft taxiing but also underscores the crucial role of the GAM in identifying the key drivers of emissions, providing a scientific basis for precise environmental protection management and policy-making.
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 17
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.238337c6da1f4fe59a18b2b794094518
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/rs17040664