In this paper, we analyze two months of trajectory data for aircraft landing in five major European airports. Based on open ADS-B data from the OpenSky Network and open performance models, we enrich all trajectories with automatically detected procedure information, fuel consumption, and emissions for supported aircraft types. To assess the inefficiencies associated with holding patterns, point merges, and continuous descent operations across different airports, we propose methodologies to quantify and compare these environmental inefficiencies. Holding patterns are found to have a higher negative impact on the environment than point merge and continuous descent operations. Furthermore, the paper provides recommendations for procedure evaluations of future airports, which could help policymakers and relevant stakeholders to evaluate the environmental performances of arrival procedures based on open data and open models@en

The environmental impact of aviation has become the focus of increased concerns for policymakers around the world. The recent pandemic provided many interesting case studies on the impact of aviation on the environment. Following the initial COVID-19 containment measures and hard lockdowns, the sharp decrease in aircraft movements caused a measurably improved air quality worthy of further study.The OpenSky Network has acted as an important open data source for aviation research since 2013. In this paper, we analyze one year of fine-grained pre-COVID air traffic trajectories (comprising the entire year 2018) to estimate fuel consumption and pollutant emissions in the aviation industry. We compare this large-scale big data processing approach to a reduced model approach based solely on global commercial aircraft movement schedules collected from airlines and airports, aggregated by a commercial provider.Our study quantifies the impact of commercial aviation on global emissions. The numbers reveal that aviation's CO₂ emissions contribute to 2% of global emissions and that commercial aviation contribution remains a proxy for countries' wealth.

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The large amount of aircraft trajectory data publicly available through open data sources like the OpenSky Network presents a wide range of possibilities for monitoring and post-operational analysis of air traffic performance. This contribution addresses the automatic identification of operational events associated with trajectories. This is a challenging task that can be tackled with both empirical, rule-based methods and statistical, data-driven approaches. In this paper, we first propose a taxonomy of significant events, including usual operations such as take-off, Instrument Landing System (ILS) landing and holding, as well as less usual operations like firefighting, in-flight refuelling and navigational calibration. Then, we introduce different rule-based and statistical methods for detecting a selection of these events. The goal is to compare candidate methods and to determine which of the approaches performs better in each situation. @en