The space industry is undergoing rapid growth, but unlike many other industries remains unregulated in terms of emissions. As the number of space missions is set to increase even faster, understanding the environmental impact of different aspects of spaceflight becomes increasingly important. We develop a four-dimensional emission inventory for spaceflight activities in 2022, incorporating the effects of re-entry and afterburning, and simulate their effects on stratospheric composition, radiative forcing and air quality using the GEOS-Chem chemical transport model. We show that spaceflight emissions lead to a global column ozone loss of 0.086 DU and a net radiative forcing of 6.3 mW/m$^{2}$. Our results suggest that the global air quality impacts are limited, with a population-weighted \ch{PM_{2.5}} concentration increase of 2.082 \cdot 10$^{-8}$ $\mu$g/m$^{3}$ per Tg of propellant burned. We find that \ch{NO_{x}} emissions from re-entry account for 83.9\% of the column ozone depletion and we are the first to show that the inclusion of afterburning reduces the ozone depletion by 12.2\% and the net radiative forcing by 30.5\%. Among individual propellant types, solid propellant has the greatest impact on ozone depletion, causing a reduction of 0.015 DU, while RP1-fueled rockets contribute the most to radiative forcing, with a value of 5.56 mW/m$^{2}$. If the number of launches and re-entries increases sixfold, the radiative forcing would become \textasciitilde 38 mW/m$^{2}$ and the column ozone depletion would reach 0.45 DU, negating the current recovery of the ozone layer. Our results highlight the need to consider and accurately model re-entry emissions and show that the impact from afterburning is non-negligible.

This report discusses urban air quality measurements carried out in a park in Aigaleo (Athens), located adjacent to a busy road. The measurements were taken using the Sniffer4D Mini 2 sensor box with a drone to investigate how Urban Air Mobility can aid in measuring urban air quality. CO, NO₂, O₃, SO2, PM_2.5 and PM₁₀ concentrations were recorded through point measurements at varying locations, altitudes and times. Several validation measurements were carried out, showing that temperature heavily influenced the outcome of the recordings. Due to the high number of variables, no conclusion has been found on the wind effect. During rush hour, NO₂ and CO concentrations were highest close to the traffic; however, NO₂ concentration showed non-linear behaviour with increase in altitude when further from the traffic or out of rush hour. SO₂ concentration always decreased with an increase in altitude; highest concentrations were observed further from the busy road. Highest O₃ concentrations were obtained in the afternoon at higher altitudes, due to its reaction under sunlight. PM_2.5 and PM₁₀ concentrations had a high variability due to environmental conditions. Additionally, continuous measurements were carried out, in which the drone flew a path through several locations and altitudes. The results from these were interpolated to enable the detection of areas with higher concentrations of air pollutants.

Over recent decades, global emissions have surged, raising environmental concerns. Academics have developed models to predict transport patterns, regional sensitivities, and global impacts. This research addresses gaps in understanding global civil aviation emissions by enhancing the accuracy of current models, particularly during climb and descent phases. Using ADS-B data and key airport parameters, the study improves the emission estimation model by Quadros et al. The revised approach, which more accurately estimates distances flown during climb and descent, showed a close correlation with real distances at specific airports, enhancing global and local emissions predictions. Results indicate an increase in NOX emissions and a decrease in CO for both climb and descent phases. Despite some operational inefficiencies and the need for further refinement, the new method provides a more representative approach to estimating aircraft positions concerning flight distance in the emission model.

Addressing the increasingly urgent need for sustainable aviation solutions, this study explores operational innovations as a quicker and more scalable addition to novel zero-emission propulsion systems. Through the use of regression-based causal inference methods, this study aims to understand the relationship between flight fuelburn inefficiency and the factors causing these inefficiencies. Such an approach allows for the attribution of inefficiencies to factors on an overall scale, requiring less specific domain knowledge for initial results. A case study, involving a sample of 100,000 flights, representative of European operations, reveals that airspace structure (3.2% increase in inefficiency) and turbulence along the flight plan (2.5% increase) are the leading causes, while variations in average airspeed, congestion, and crosswind contribute the least to flight inefficiency. A compilation of the results shows that the performed analysis leaves 61% of the observed flight inefficiency unaccounted for. Future work would include the exploration of different metrics even closer to actual climate and air quality effects, as well as detailed uncertainty quantification. The developed flight inefficiency prediction model allows experimentation with counterfactual scenarios, contributing to the global transition towards more sustainable air transport networks.

With renewed interest in the development of civil supersonic aircraft, their return in the future is becoming more ever more likely. The environmental impact of emissions in the stratosphere on climate and the ozone layer therefore needs to be explored. The stratospheric ozone levels determine the amount of harmful ultraviolet radiation reaching the Earth's surface and thus the level of risk to human health and ecosystems. Ozone response is complex, varying with emission altitude and latitude and we are currently reliant on computationally expensive chemistry-transport models to calculate chemical species concentration changes resulting from supersonic aviation emissions. This paper takes a novel approach to reduce the dependency on these models, creating data-driven dynamical systems that model the global spatiotemporal atmospheric ozone response for different emission scenarios. The dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD) methods are applied to atmospheric ozone data obtained from the GEOS-Chem model, and the evolution of the dominant POD spatial modes are modelled using sparse identification of nonlinear dynamics algorithm (SINDy). We show that DMD models can reconstruct monthly global column ozone changes with root mean square errors less than 0.05 Dobson unit (DU) for a period of three years. Predicting the global mean column ozone changes for the years beyond the period used to construct the models, results in errors less than 0.12 DU. Independent DMD models at two different altitudes can be interpolated to produce estimates for ozone response at an intermediate altitude. These methods can serve as a basis for low dimensional surrogate models that can be used to evaluate chemical species concentrations changes as a result of supersonic aviation emissions.

Air quality at airports is a critical concern, directly impacting public health and environmental sustainability. This research involves developing and validating a jet plume model designed to quantify air quality near runways using COMSOL multiphysics engine. The model replicates experimental setups conducted at Schiphol Airport, simulating high Mach number flows from the jet engine with the Spalart-Allmaras turbulence model to analyze the dispersion of pollutants, specifically NO, NO2 and CO. Key operational and environmental variables were established as input parameters, and a time-dependent study ensured the simulated plume was fully developed. The plume analysis consisted of the examination of plume width and peak pollutant concentration. Sensitivity analysis revealed that the dominant factor influencing plume width in the model was the ambient wind speed and direction. Comparisons between model outputs and monitoring site data indicated that mean plume widths for arrivals and departures were relatively aligned for both departures and arrivals, with some discrepancies likely stemming from translating variable real-world parameters into constants within the model. Concentration results highlighted limitations in input methods for larger aircraft, while the model effectively reflected pollutant levels for medium and smaller aircraft, showcasing its potential for quantifying emissions during landing and take-off cycles.

At speeds roughly between five and ten thousand km/h, hypersonic aircraft offer the promise of an extremely fast means of transport. Growing concerns about climate warming, however, direct attention to sustainability. This thesis focuses on atmospheric composition and radiation changes by considering a range of individual hypersonic aircraft designs on trajectory and route network level.

State-of-the-art Earth system models are used for simulations, and results calculated with the EMAC model are subsequently compared with simulations performed elsewhere with the LMDZ-INCA model. The comparison to a third model, i.e. WACCM, with a very similar – but independent – model setup allows even further clarification. For model validation satellite measurements (ozone, water vapor) and aircraft measurements (ozone, water vapor, temperature) are taken into account.

After the introduction in the first chapter, the second chapter is a general description of the Earth system including anthropogenic perturbations, in particular perturbations from subsonic, supersonic and hypersonic aircraft emissions followed by a detailed explanation of methods and the EMAC model setup in the third chapter. A new research finding in the context of middle atmospheric chemistry is the increased methane and nitric acid oxidation following hypersonic emissions. This effect results in a (photo-)chemical net production of water vapor and eventually increases water vapor perturbations further, which is described in detail in chapter 4. In chapter 5 an analysis of atmospheric dynamics and transport of emitted trace gases in the middle atmosphere underlines the importance of the Brewer-Dobson circulation and shows the impact of polar stratospheric clouds on water vapor perturbations during polar winter. The evaluation of multiple hypersonic aircraft designed for different cruise altitudes shows that their climate impact increases with cruise altitude and can be approximately 10-20 times as much as a conventional aircraft (chapter 6). Emissions at different hypersonic cruise altitude and latitude regions show that the climate impact can vary more with latitude of emission than with altitude of emission (chapter 7). With rf_of_hypersonic_trajectories() a software was developed to estimate the climate impact of aircraft design and flight trajectory/network options in seconds based on robust results from Earth system modelling. Using the software it is shown that a cruise altitude optimization loop can reduce the overall climate impact of a state-of-the-art aircraft design (chapter 8).

There are two methodological highlights to mention in the context of the EMAC model. The first is a new MESSy submodel H2OEMIS, which was created as part of this thesis. H2OEMIS is an interface to include water vapor emissions in EMAC model simulations, which was not possible before. This submodel will generally be of interest for future evaluations of e.g. any vehicles emitting water vapor and the impact of volcanic eruptions with EMAC. The secondmethodological highlight is the application of a novel speed-up technique during simulation runs, which reduces the simulated years by twothirds. To conclude the summary, the four following points are important to take away. This thesis brought

• A new research finding on middle atmospheric chemistry: The identification of a chemical feedback that enhances the water vapor perturbation lifetime albeit an increasing chemical water vapor destruction
• A robust estimate of the climate impact of hypersonic aircraft for both specific aircraft designs and general atmospheric and radiative sensitivities showing a large altitude and latitude dependence
• An easily accessible tool for researchers and companies to estimate the climate impact of new hypersonic aircraft designs with low cost and low time
• An estimate how the development of hypersonic aircraft would contribute to a road map to a climate optimal aircraft industry compared to conventional aircraft
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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.

Ammonia shows great potential for decarbonizing the road transportation sector because it does not produce CO₂ emissions during combustion. On the other hand, ammonia-fueled engines emit gases such as (unburned) ammonia (NH₃), and nitrogen oxides (NO)_x, which contribute to air pollution and adversely impact human health, among other environmental effects. The aim of this work is to investigate the air quality impacts and human health trade-offs associated with potential emissions from an ammonia-fueled road transportation fleet of different vehicle categories. Specifically, we create a simplified emissions tool to quantify the NH₃ and NO_x spatial emissions from state-of-the-art ammonia engine developments. Next, the emissions are combined with spatial sensitivities, obtained from the adjoint of GEOS-Chem, to estimate the corresponding air quality impacts in terms of PM_2.5 formation and human health impacts with respect to premature mortalities.
The research findings indicate that the ammonia-fueled sector generally exhibits higher air quality and human health impacts compared to conventional transport. We estimate that the implementation of various ammonia engine configurations into the road transportation fleet in the USA in 2011 could lead to approximately 23 000 to 250 000 premature mortalities.
However, there is potential for mitigating air quality impacts of ammonia emissions through the use of post-combustion treatment methods or the adoption of ammonia-fueled heavy-duty vehicles. These approaches could help achieve air pollution levels comparable to those of fossil fuel vehicles while reducing the carbon footprint, offering future possibilities for an ammonia-fueled road transportation sector.

Previous studies have estimated that over 16,000 annual early deaths are induced by global aviation emissions. Combined with a growing industry, this calls for sustainable developments to reduce the negative impact on human health. However, the potential application of more sustainable technologies varies amongst aircraft size and range. Thus, an understanding of the impacts on air quality for different aircraft types and flight distances is required to estimate the impact mitigation potential of sustainable aviation alternatives. In this study, we compare the impact of aircraft size and flight distance on emissions intensity, associated changes in air quality, and aviation-attributable premature mortality to evaluate these potential mitigation options. Specifically, we use the openAVEM emissions inventory to estimate aircraft emissions and the GEOS-Chem atmospheric chemistry-transport model to evaluate their impacts on ground-level concentrations of ozone (O₃), nitrogen dioxide (NO₂), and fine particulate matter (PM_2.5) including black carbon (BC).
We estimate that aviation activities in 2019 were responsible for nearly 80,000 early deaths, of which 65% are found in Asia. We find that widebody aircraft are associated with 57% of aviation-attributable premature mortality, and that this type has a 66% higher impact per revenue passenger kilometre (RPK) than narrowbody aircraft. Furthermore, O3 impacts are largest for long-haul flights, while intra-European flights yield 50% larger impacts per RPK than similar flights globally. We conclude that only 5% of all aviation-attributable premature mortality is associated with regional aircraft and that the 5% longest flights are responsible for almost half of the impact. Results of this work provide insights on the potential air quality impacts of the sustainable aviation mitigation options relevant for different aircraft categories and flight distances.

In this work the possibility for use of the satellite remote sensing instrument TROPOMI to detect NOx emissions from en-route aviation is assessed. Analysis of aviation intensity, quality of satellite retrievals, and emissions from non-aviation anthropogenic sources showed that the North Atlantic, including the North Atlantic Flight Corridor, would have high detection potential. Over this corridor, emissions from telemetric observed aircraft are computed under assumption of stable meteorology leading to an upper bound for the average accumulation of emissions for the months of April 2020 and April 2021. These emissions are compared with TROPOMI NO2 observations retrieved over all sky and cloudy sky scenes. In order to reduce advection influences, TROPOMI measurements at locations of aviation observations close to the overpass time are analysed separately.

General conclusions of this first assessment are not optimistic for detection of en-route aviation emissions by means of TROPOMI observations. The maximum NOx emissions attributable to en-route aviation over the North Atlantic Flight Corridor during April 2020 and April 2021 were found to be lower than detection limits associated with TROPOMI for NO2 retrieval. Neither did inspection of TROPOMI measurements at locations of recent aircraft observations retrieve any evidence of aviation-attributable emissions. Therefore detection, documentation and quantification of actual NOx emissions from en-route aviation remains a
challenge.

In recent years, the aviation industry has faced escalating environmental scrutiny, prompted by the global shift towards heightened environmental awareness. This study explores a collaboration framework set up by airlines which envision to partially or totally shoulder the environmental impact costs generated by their airborne emissions. The methodology developed includes the production of detailed emissions data to be employed during the airline’s planning strategy. The model conceived introduces a taxation approach exploited by airlines to include and incentive more environmentally sustainable solutions and, a collaboration mechanism based on the possibility to share passenger demand between collaborating airlines. The environmental incentive and collaboration are implemented in a Linear Programming Network and Frequency Planning model with the goal of maximising airlines profitability. A case study is analysed focusing on two airlines, KLM and TAP, collaborating to achieve improved environmental and economic performance. The airlines engage in a scenario where the environmental impact externalities generated by airborne operations are shouldered by the airlines themselves. Employing the model developed, the airlines explore solutions that either reduce the environmental impact or that solely maximise profit of the two airlines. The results underscore the potential benefits of including collaboration in the airline’s planning strategy, with a total 1.33% reduction in environmental impact when prioritising environmental cost abatement. Collaboration also demonstrated to be a consistent tool to achieve a profit recovery, showing up to a total anticipated profit increase of 2.21% when prioritising profitability or 0.71% when the priority is given to environmental impact reduction. In conclusion, this study highlights the potential efficacy of collaboration in addressing aviation’s environmental and economic challenges.

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.

The approach to sustainable aviation requires effective policy and regulation, informed by accurate environmental research. Aviation emissions inventories form the basis of any environmental assessment, however they are based on simplifications and assumptions that introduce a degree of uncertainty. In terms of robust model development, it is necessary to identify significant sources of uncertainty to determine priorities for improvement.

The results of a global sensitivity analysis identifies 5 key contributors to uncertainty across fuelburn and emissions estimates: BADA Drag and Fuel Flow corrective multipliers, Takeoff Weight, Taxi Thrust and Arrival Inefficiency. Across the emitted species, uncertainty is driven by their respective EI.

EUROCONTROL flight track data allows for an analysis of lateral inefficiencies, and improved distribution fitting. The resulting distributions are more representative of actual flight behavior. The introduction of these improved distributions into the original model result in an overall reduction in fuelburn and emissions estimates.

Excessive nitrogen deposition from anthropogenic emissions has a global impact on natural biodiversity. While aviation emissions have been studied in the context of climate change and air quality, this is not true for nitrogen deposition. Meanwhile the growth of the aviation industry is likely to increase the relative contribution of aviation emissions to nitrogen deposition. Current regulatory policies aiming to mitigate nitrogen deposition are limited to local scale, and are inadequately applicable to the global nature of the aviation industry.
In this research the impact of aviation emissions from both landing and take-off and non-landing and take-off emissions are studied by perturbing emission scenarios in the GEOS-Chem chemistry transport model for 2005. Results indicate that the aviation industry is responsible for 0.71% of total nitrogen deposition worldwide. While regionally contributing up to 30%, on average only 7.7% of aviation attributable nitrogen deposition is attributable to landing and take-off emissions globally , indicating that local regulatory policies do not take into account upwards of 70% of aviation attributable nitrogen deposition.

A lowcost multipollutant sensor box is developed, calibrated and deployed to quantify aviationattributable air quality impacts at Amsterdam Schiphol airport. The sensor box contains four lowcost electrochemical gas sensors that respond to CO, NO, NO2 and O3 concentrations in ambient air. Additional sensors in the sensor box allow the determination of pressure, temperature, relative humidity, sound intensity, UVlight, visible light and IRlight. The pressure, temperature and relative humidity sensors assist in calibrating the gas measurements obtained by the gas sensors. Sound intensity measurements and ADSB data assist in the detection of nearby aircraft activity. Calibration of the sensor box was done by collocating it for ∼74 hours with an air quality monitor of the Dutch air quality network in Wijk aan Zee, the Netherlands. Regulatory air quality data could be obtained at 10 s resolution for CO, NO and NO2. The absence of regulatory O3 measurements results in discarding the corresponding lowcost sensor in further analysis. Next, multivariate linear regression and random forest models were constructed to transform the raw sensor box measurements into meaningful concentrations. The random forest model has a strong tendency to overfit, even though it performs better in absolute numbers. The final calibration models for each species are multivariate linear regression models with an RMSE for CO, NO and NO2 of 118.13 μgm−3, 2.38 μgm−3 and 3.96 μgm−3, respectively. Corresponding correlation coefficients are 0.94, 0.99 and 0.98. Three different types of aircraft activity have been evaluated by deploying the sensor box at Amsterdam Schiphol airport. On 22 January 2021, the air quality impacts from landings on the Polderbaan were investigated by deploying the sensor box ∼200 m from the runway. On both 17 and 19 February 2021, the impact of aircraft departures on the Aalsmeerbaan and Kaagbaan were investigated by measuring ∼300 m from the runway. Finally, on 30 March 2021, air quality impacts from taxiing aircraft were obtained by taking measurements on the airport apron with aircraft coming as close as 43 m to the sensor box. The obtained sensor box measurements represent total air quality concentrations, which contain impacts from multiple sources. Quantifying the proportion of aviationattributable concentrations is achieved via a datadriven approach in which a baseline signal helps to extract the local aviationattributable signal. It was found that this strategy results in local signals that can be linked to activity from aircraft.

As poor air quality poses a risk to human health, this study aims to estimate the impact of Schiphol airport on local concentrations of various air pollutants from January 2014 to August 2020. The pollutants of interest are nitrogen monoxide, nitrogen dioxide, ozone and particulate matter. Three separate methods were applied: a comparison of air quality measurements from different locations, an analysis about the effect of wind on ambient concentrations and finally meteorology and road and air traffic activity-based regression modelling. Based on these methods it was estimated that the airport is responsible for between 24 to 31% of local concentrations of nitrogen dioxide. During the COVID-19 situation these effects decreased by ~ 4 - 5 µg/m3, which is 17 - 21% of the average concentrations measured in the years before. For the other pollutants the results were less robust and quantitative estimates include large uncertainties.

The aim of this thesis project is to investigate the temporal changes of vegetation in Europe using statistical analysis and machine learning. A methodology is used consisting of five main steps. The first is data pre-processing, which is used to reduce the noise within the data set. This is followed by classification, which divides the data into its various land cover types. Next, the time series are decomposed into their trend and seasonal components. The fourth step is to forecast these time series components, and finally the generated trend and seasonal components are analysed. Annual land cover results for the entire domain have been produced, with an over-all classification accuracy exceeding 80%. Furthermore, a statistically significant slow degree of greening was determined for the majority of the domain over the twenty year time span and the growing season shows lengthening for the areas that transitioned from browning to greening.