A Methodology for Conditioning ADS-B Helicopter Trajectories for Noise and Emissions Assessment

Helicopter operations are often underrepresented in environmental assessments due to their relatively low number of movements and the use of aggregated indicators that do not capture their localised impacts. At the same time, rotorcraft activity typically occurs at low altitude within urban environments, where noise and emissions are directly perceptible and spatially concentrated. This creates a need for assessment approaches based on observed operations and capable of providing spatially resolved results. Automatic Dependent Surveillance-Broadcast (ADS-B) data provide high-resolution observations of aircraft trajectories and are increasingly used to analyse real-world aviation activity. However, existing approaches to ADS-B data processing have largely been developed for fixed-wing operations and do not address the specific challenges of rotorcraft activity, including low-altitude signal loss, positional artefacts, and incomplete trajectories. As a result, ADS-B data for helicopters are generally not suitable for direct use in applications requiring physically consistent and operationally defined inputs. This study proposes a methodology to condition ADS-B helicopter trajectories into a physically consistent and operationally characterised dataset suitable for downstream analysis. The approach integrates trajectory correction, reconstruction of incomplete operations, and the derivation of flight modes and associated parameters. The resulting dataset provides a complete, operation-level description of helicopter activity derived from observed data. The methodology is demonstrated through its application to helicopter operations in the Zurich area and its integration with established environmental modelling approaches, including a rotorcraft-specific noise model (NORAH2) and a flight-mode-based emissions estimation method (Rindlisbacher and Chabbey). The results produce spatially resolved maps and tabulated outputs describing environmental impacts over a defined period, enabling the identification of localised hotspots. The contribution of this work lies in providing a reproducible and integrated framework that bridges the gap between raw ADS-B rotorcraft observations and application-ready datasets for spatially explicit environmental assessment.