Whereas commercial aviation is attempting to achieve the reduction of its substantial carbon footprint, general aviation’s (GA) climate change contribution is negligibly small, which is why the sector is facing other sustainability challenges mainly entailing the operation of dated technology and aircraft, increasing regulatory constraints, rising costs, noise emissions, and popular discontent, as well as remaining the last mobility sector in the world to still use leaded fuels. Throughout recent years, there have been remarkable sustainability trends in GA as well as heightened efforts to improve its emissions profile (noise, pollutants, CO2) and environmental reputation, for instance by the increased use of electric aircraft, especially for private pilot training. From a sociotechnical perspective, this mixed-methods study highlights current sustainability challenges and trends in GA as well as potential pathways towards more sustainable GA and private pilot training. Eight in-depth semi-structured interviews with Swiss and international GA stakeholders were complemented with a bilingual representative quantitative online survey (N=427) among Swiss GA stakeholders, a comparative CO2 analysis showing the emissions advantages and feasibility limits of supplementing private pilot training with lessons using electric aircraft, as well as participant observation. The data show that most Swiss GA stakeholders have increased environmental awareness and are concerned about sustainability and the environment both, in flight and other activities. Although the majority advocates for sustainable development in GA there are not one but many challenges and obstacles to a more sustainable GA. The largest challenges are the abatement of noise emissions and the facilitation of the leaded aviation gasoline (AVGAS 100LL) phaseout. The most pertinent obstacles towards sustainable GA innovation are said to be bureaucracy, overregulation and reluctance in the civil aviation authorities, high costs, averseness to risk and innovation, as well as a trend of decline in GA activity due to continuous demographic change. No single sustainability pathway but rather a mix of immediate and long-term sustainability measures was identified. Despite its current limitations, electric aviation proves to be one of the most feasible pathways to sustainable private pilot training. For more sustainable GA, the use of more fuel-efficient planes and available unleaded fuels, propeller, and muffler retrofits, as well as is feasible short- and midterm measures. In the long run, electric and hybrid aviation as well as bio- and synfuels are likely to become attractive options for GA. The study shows the importance of sustainable development in GA and private pilot training, not because it will majorly contribute to climate change mitigation, but because it will ensure the improvement of its negative environmental reputation and societal acceptance, which will be vital to ensuring the survival of the GA sector.

<p>  </p> <p>Aircraft noise is one of the most significant environmental concerns for the aviation industry, and it adversely affects the physical and mental health of community members who are in close proximity to airports. The operations and expansion of airports and land use planning are affected because of the community’s adverse reaction to such annoyances. Aircraft operations and fleet mix information are required when airport managers and stakeholders execute the Aviation Environmental Design Tool (AEDT) to compute the noise metrics; however, these data are unavailable from over 2,000 United States non-primary General Aviation (GA) airports that lack full-time air traffic control facilities or personnel. </p> <p>This study developed a low-cost noise assessment technology for non-towered GA airports. The Automatic Dependent Surveillance-Broadcast (ADS-B) messages were obtained using an inexpensive ADS-B receiver. A barometric pressure calibration was applied to improve the aircraft operations estimation. A fleet mix database was created by linking the collected ADS-B data to an FAA-registered aircraft database containing U.S.-registered aircraft information (such as types of aircraft and engines). Specific aircraft information was obtained by filtering the International Civil Aviation Organization (ICAO) identification code from the obtained ADS-B records. A set of 20 advanced aircraft performance parameters was constructed to determine the operation mode and corresponding power setting. The corresponding noise levels were determined using the EUROCONTROL Aircraft Noise and Performance (ANP) database.</p> <p>The testing and validation results from the case study at the Purdue University Airport (ICAO Code: KLAF) demonstrated the developed low-cost approach could identify aircraft noise events, and the accuracy of modeled noise data was assessed with an average error of 4.50 dBA. Therefore, the developed approach appears to be an affordable means of monitoring aircraft noise at non-towered GA airports.  </p>

<p dir="ltr">The absence or limited availability of operational statistics at general aviation airports restricts airport managers and operators from assessing comprehensive operational data. The traditional manual compilation of operational statistics is labor-intensive and lacks the depth and accuracy to depict a holistic picture of a general aviation airport’s operations. This research developed a reliable and efficient approach to address the problem by providing a comprehensive and versatile flight status identification framework. </p><p dir="ltr">Leveraging the BlueSky flight simulation module, the research can generate a synthetic flight database to emulate real-world general aviation aircraft’s flight scenarios. Two neural network architectures, namely, an RNN-GAN network and a refined Seq2Seq network, were explored to examine their capability to reconstruct flight trajectories. The Seq2Seq network, which demonstrated better performance, was further employed to estimate the simulated aircraft’s different metrics, such as internal mechanical metrics and flight phase. Additionally, this research undertook an array of diverse tailored evaluation techniques to assess the efficacy of flight status predictions and conducted comparative analyses between various configurations. </p><p dir="ltr">Furthermore, the research concluded by discussing the future development of the framework, emphasizing its potential for generalization across various flight data applications and scenarios. The enhanced methodology for collecting operational statistics and the analysis tool will enable airport managers and regulators to better receive a comprehensive view of the airport’s operations, facilitating airport planning and development.</p>

<p dir="ltr">Weather conditions such as thunderstorms, wind shear, snowstorms, turbulence, icing, and fog can create potentially hazardous flying conditions in the National Airspace System (NAS) (FAA, 2021). In general aviation (GA), hazardous weather conditions are most likely to cause accidents with fatalities (FAA, 2013). Therefore, it is critical to communicate weather conditions to pilots and controllers to increase awareness of such conditions, help pilots avoid weather hazards, and improve aviation safety (NTSB, 2017b). Pilot Reports (PIREPs) are one way to communicate pertinent weather conditions encountered by pilots (FAA, 2017a). However, in a hazardous weather situation, communication adds to pilot workload and GA pilots may need to aviate and navigate to another area before feeling safe enough to communicate the weather conditions. The delay in communication may result in PIREPs that are both inaccurate and untimely, potentially misleading other pilots in the area with incorrect weather information (NTSB, 2017a). Therefore, it is crucial to enhance the PIREP submission process to improve the accuracy, timeliness, and usefulness of PIREPs, while simultaneously reducing the need for hands-on communication.</p><p dir="ltr">In this study, a potential method to incrementally improve the performance of an automated spoken-to-coded-PIREP system is explored. This research aims at improving the information extraction model within the spoken-to-coded-PIREP system by using underlying structures and patterns in the pilot spoken phrases. The first part of this research is focused on exploring the structural elements, patterns, and sub-level variability in the Location, Turbulence, and Icing pilot phrases. The second part of the research is focused on developing and demonstrating a structured two-level Named Entity Recognition (NER) model that utilizes the underlying structures within pilot phrases. A structured two-level NER model is designed, developed, tested, and compared with the initial single level NER model in the spoken-to-coded-PIREP system. The model follows a structured approach to extract information at two levels within three PIREP information categories – Location, Turbulence, and Icing. The two-level NER model is trained and tested using a total of 126 PIREPs containing Turbulence and Icing weather conditions. The performance of the structured two-level NER model is compared to the performance of a comparable single level initial NER model using three metrics – precision, recall, and F1-Score. The overall F1-Score of the initial single level NER model was in the range of 68% – 77%, while the two-level NER model was able to achieve an overall F1-Score in the range of 89% – 92%. The two-level NER model was successful in recognizing and labelling specific phrases into broader entity labels such as Location, Turbulence, and Icing, and then processing those phrases to segregate their structural elements such as Distance, Location Name, Turbulence Intensity, and Icing Type. With improvements to the information extraction model, the performance of the overall spoken-to-coded-PIREP system may be increased and the system may be better equipped to handle the variations in pilot phrases and weather situations. Automating the PIREP submission process may reduce the pilot’s hands-on task-requirement in submitting a PIREP during hazardous weather situations, potentially increase the quality and quantity of PIREPs, and share accurate weather-related information in a timely manner, ultimately making GA flying safter.</p>