Aircraft emissions can pose a health threat by degrading air quality. The actual pollution footprint of aircraft is however not well understood. This research therefore explores the potential of performing near-runway measurements with low-cost sensor nodes to analyse the plume composition and pollutant contribution of individual aircraft. The results showed significant differences in pollutant concentrations between departing- and landing aircraft and among species. The majority of CO plumes of landing aircraft and most NO and NO2 plumes of departures could be analysed while SO2 did not yield meaningful signals. Analysis revealed the cumulative NOx plume concentration of departures to be 3.5 times higher for large aircraft than for medium-sized ones, which could not be observed in terms of CO for landing aircraft. Between sensor nodes the cumulative plume concentrations differed ~8 - 37%. The results of this work demonstrate the use of low-cost sensors for better understanding the emissions footprint of individual aircraft.

Aircraft noise is a significant problem for communities surrounding airports. Accurate prediction models are needed to estimate noise levels from aircraft operations. In this research, the accuracy of the sonAIR aircraft noise model is evaluated in predicting noise levels around Schiphol airport by comparison to measurement data from NOMOS and the current best-practice modelling approach Doc29. Results show a significant but consistent underestimation of noise levels by sonAIR, mainly due to a generalisation of emission models. The standard deviation of differences between model results and measurements is lower for sonAIR than for Doc29 by up to 1 dB. Differences between measurement and model results were found in the relation between N1 and noise levels, maximum noise levels and frequency spectra. These results demonstrate that sonAIR provides more reliable predictions of noise levels on the single flight event level than Doc29. Additionally, this study shows agreement with results from a previous validation study in Zürich, thereby confirming the applicability of sonAIR to another airport. This research contributes to better aircraft noise predictions, which will have implications ultimately leading to a better quality of life for communities affected by aircraft noise.

Since the pre-industrial era, methane has contributed as much as 0.5°C to global warming. With a global warming potential of 28-34 times that of CO2 over 100 years, and with 80 times the warming power of CO2 in the first 20 years, methane has a large impact on climate change. Measurements from the satellite instrument TROPOMI help inform on the global atmospheric content of methane. We frequently observe methane plumes in TROPOMI data. These plumes come from super-emitters, and those provide a significant but uncertain contribution to global methane emissions, and they are relatively easy to mitigate. The TROPOMI automated plume detection algorithm makes plume detection and emission quantification possible around the world, which is infeasible without automation. However, the current TROPOMI plume emission quantification algorithm makes use of mass balance methods to estimate the methane emissions of point sources. These approaches provide reasonable but highly uncertain estimates. Hence, in this thesis, we aim to improve the accuracy of the automated emission quantification algorithm with the help of more sophisticated techniques based on atmospheric transport models.

First, this study analysed mass balance methods by quantifying emissions from synthetic plumes having known emission rates. Synthetic plumes were generated using the WRF and FLEXPART atmospheric transport models. A classification algorithm was developed to segregate synthetic plumes into different categories based on their geometries to determine challenging plume emission quantification scenarios for the mass balance methods. We found that mass balance methods produce uncertain emission estimates due to several inherent limitations like missing plume pixels, performance under low wind speed conditions, and no utilisation of three-dimensional wind speeds. Next, the atmospheric transport model based plume scaling approach was analysed by quantifying emissions from synthetic plumes. This analysis revealed that the plume scaling approach could overcome several inherent limitations of the mass balance methods and reduce the uncertainty of plume emission quantification by nearly 10%. Finally, the plume scaling approach was applied to TROPOMI plumes. The FLEXPART model was observed to be most suitable for replicating TROPOMI plumes. With the help of results obtained from this research, a decision tree algorithm was developed. This decision tree can choose the most suitable plume emission quantification method between the mass balance methods and the plume scaling approach for a given TROPOMI plume, maintaining a balance between ease of use of the mass balance methods and accuracy of the plume scaling approach.

In this thesis research, dutch aircraft noise model (NRM) calculations are compared to measurements to analyze the aircraft classification used in the model. To do so, a new implementation of the model has been set up to calculate with LVNL trackdata and NOMOS measurement data. First, the classification approach is analyzed as prescribed, which shows clustering of aircraft types when compared to measurements. After, the theoretical input data as prescribed for the model is assessed and replaced by real data for each type specific. This leads to improvements of the model calculations when compared to measurements and shows that aircraft-specific flight-information would improve the model significantly. Concluding, it is shown that aircraft noise models strongly rely on the quality of the input data. Current input data shows certain mismatches with respect to reality, resulting in differences between calculations and measurements. However, the calculations can give valuable insights in the effect of operations on the overall noise exposure.

The sound of fly-over aircraft is measured with the use of an acoustic camera to gain more information on aircraft noise. This camera can be optimised to gain data of a higher quality. Within the ANCE section this optimisation was required mainly to diminish the influence of the ground effect in the data and to increase the resolution of the eventual source plots. The increase in resolution was required to be able to separate engine and airframe noise also for the smaller aircraft. The research started with a quantification of the previous fly-over camera of the ANCE section. It appeared that the fringes from Lloyds’s mirror pattern were present, but different for each microphone. This was due to the edge diffraction and should be solved by placing the microphones at least 30 cm from the edge. The increase in resolution should be solved by doubling the array aperture to 3.4 m. These and many other findings were implemented in a new design. This design consists out of a de-attachable rigid structure of 4x4m placed on adjustable legs to which 64 microphones could be clipped in an Underbrink spiral array configuration. The design was built and tested for fly-over aircraft at Schiphol Airport. The measured data was analysed and it appeared that the fringes from the ground effect were gone. Furthermore, the resolution was improved and the engines and main landing could be separated for smaller aircraft as the B737 and A321.