The contribution of aircraft operations to ambient ultrafine particle (UFP) concentration at and around airports can be significant. This review article considers the volatile and non-volatile elements of particulate matter emissions from aircraft engines, their characteristics and quantification and identifies gaps in knowledge. The current state of the art emission inventory methods and dispersion modelling approaches are reviewed and areas for improvement and research needs are identified. Quantification of engine non-volatile particulate matter (nvPM) is improving as measured certification data for the landing and take-off cycle are becoming available. Further work is needed: to better estimate nvPM emissions during the full-flight; to estimate non-regulated (smaller) engines; and to better estimate the emissions and evolution of volatile particles (vPM) in the aircraft exhaust plume. Dispersion modelling improvements are also needed to better address vPM. As the emissions inventory data for both vPM and nvPM from aircraft sources improve, better estimates of the contribution of aircraft engine emissions to ambient particulate concentrations will be possible.

Estimating the environmental impact of aviation, in terms of noise and emissions, requires accurate knowledge of flight parameters such as departure/arrival procedure, aircraft mass, thrust and flap settings. However, these parameters are not available from radar/ADS-B data, which are the main sources of multiple flight-specific information including aircraft position over time. This paper introduces a novel approach to estimating these parameters from radar/ADS-B data using three neural network models trained on flight simulator data for the Boeing 747-400 airplane. The models are tested on 2204 simulated flights that are not used for training and that are transformed into radar/ADS-B data formats. The models are capable of predicting the unknown parameters for the ADS-B data format, with the weight prediction error being 2.63% of maximum takeoff weight, the average R2 score for the thrust profile prediction being 89.90% and the flap setting profile having an average R2 score of 84.68%, while for the radar-like data format, the values are 2.11%, 95.74% and 88.24%, respectively. The predicted parameters can be used to improve the environmental impact assessment of individual flights and to support policy-making and management decisions. This approach is a proof of concept based on simulation data that will need to be validated on real data before being applied in practice.