Chemistry transport models play a crucial role in the evaluation of the effect of anthropogenic emissions on the atmosphere and climate, but they come with high computational costs and require specialized know-how. This renders them impractical for applications in multidisciplinary optimisation, or regulatory and operational decision-making processes where environmental effects are to be considered. Such applications require computationally efficient surrogate models of the complex chemistry transport models. Here we investigate the use of data-driven discovery and reduced-order modelling methods for this purpose. Specifically, we examine the dynamic mode decomposition (DMD) and proper orthogonal decomposition coupled with the sparse identification of non-linear dynamics (POD-SINDy). We evaluate their ability to reconstruct and forecast changes in the distribution of ozone in response to the introduction of supersonic aircraft as modelled by the GEOS-Chem chemistry transport model. Of the tested methods, we find that optimized DMD and bagging optimized DMD with constrained eigenvalues perform best. These methods can reconstruct and forecast full-atmospheric ozone responses for up to several years without losing stability, at smaller errors than estimates using the spatio-temporal mean of the data. On average, the constrained optimized DMD method reduces the reconstruction error by 63.5 % and that of forecasting by 25.8 % compared to the spatio-temporal mean. For the constrained bagging optimized DMD these reductions are 45.0 % and 23.1 %, respectively. The resulting change in global ozone column, calculated from the reconstructed atmospheres, has an error smaller than 10 %. This is achieved while reducing the computational and storage requirements by several orders of magnitude, which may be a worthwhile tradeoff for some applications.

The normalized fan rotational speed per aircraft engine (N1%) is an essential input parameter to noise prediction models, but is often confidential and not directly accessible to researchers. The aircraft acoustic signal characteristics, and specifically the tonal component, can be used to extract this parameter. However, existing methodologies estimate N1% parameters from whole-aircraft spectra, which can lead to inaccurate estimations. This research aims at investigating the various tonal contributions by isolating and reconstructing spectrograms of individual noise sources using acoustic arrays. Using such arrays, it is possible to discriminate between the various components that contribute to the noise emitted by the aircraft, especially between the engines, but also the nose landing gear. From the resulting engine-specific spectrograms the N1% of individual engines for 24 aircraft were obtained. For the A321neo and the B737NG, it is found that, for 80% of the analyzed aircraft, additional engine tones accompany the higher harmonics of the engine blade passage frequency, with these additional tones corresponding to twice the shaft frequency. In addition, it was found that N1% differences between the two engines are reflected in the spectrograms and that a tone stemming from the nose landing gear can be present, resulting in a complex pattern of tones in the whole-aircraft spectrogram. The insights on the various tonal contributions to the received signal are of importance regarding the further development of methods that aim to extract the engine setting from aircraft noise measurements and as such for enabling more accurate noise calculations.

Aviation-induced aerosols, particularly composed of sulfate (SO4), can interact with liquid clouds by enhancing their reflectivity and lifetime, thereby exerting a cooling effect. The magnitude of these interactions, however, remains highly uncertain and may even offset the combined warming from aviation’s other climate forcers depending on spatiotemporal factors such as emission altitude and season. Here, we introduce AIRTRAC v2.0, the latest advancement of the Lagrangian tagging submodel within the Modular Earth Submodel System (MESSy), and the first submodel to provide aviation-specific sulfate tagging in this framework. AIRTRAC contributes to lowering uncertainty by tracking global contributions of aviation-emitted sulfur dioxide (SO2) and sulfuric acid (H2SO4) to SO4 formation. Using a sulfur-species tagging approach for SO2, H2SO4 and SO4, it enables the characterization of transport patterns and highlights atmospheric regions with enhanced potential for aerosol–cloud interactions. In contrast to some of the existing sulfate tagging models, AIRTRAC considers a full range of microphysical processes along trajectories. To investigate sulfate transport from aviation, two global simulations were performed for January–March and July–September 2015, using pulse emissions of SO2 and H2SO4 distributed across a cruise altitude of 240 hPa (~10.6 km) based on the aviation SO2 inventory of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Comparisons of AIRTRAC-derived SO4 distributions with perturbation-based simulations under analogous conditions show reasonable agreement. Using AIRTRAC v2.0, we estimate median SO2 and SO4 lifetimes of 22 d and 2.1 months, respectively, in northern winter, and 14 d and 2.2 months in summer, consistent with volcanic eruption modeling and observational benchmarks involving high-altitude SO2 injection. The median SO4 production efficiency during summer was found to be statistically significantly larger by 144 % compared to winter, due to a more efficient oxidation of SO2. Large-scale circulation patterns may contribute to enhancing SO4 lifetimes, especially when injected in the Tropics, where emissions could ascend into the stratosphere, past 100 hPa (~16 km). AIRTRAC v2.0 currently excludes SO2 oxidation from aviation nitrogen oxides (NOx) and does not tag other species such as black carbon. Owing to its flexible design, however, the approach can be readily extended to additional aerosols. Overall, AIRTRAC v2.0 offers the novel capability to track the atmospheric transport of aviation-emitted SO2, H2SO4 and SO4, providing critical insights into one of aviation’s most uncertain climate impacts.

Understanding acoustic characteristics of aircraft is critical for designing optimal fleet compositions in terms of noise and improved airport operations. This study investigates acoustic signatures across different aircraft types, engine designs, and operational conditions. A dataset consisting of 457 field acoustic measurements of commercial turbofan aircraft landing and taking-off from Amsterdam Airport Schiphol was used. To unveil meaningful patterns, we focused on dimensionality reduction techniques—Principal Component Analysis (PCA) and tdistributed Stochastic Neighbour Embedding (t-SNE)— to analyse this high-dimensional acoustic data. These methods are complemented by clustering algorithms and supervised machine learning models, such as K-Means, random forests for feature importance, and multilayer perceptrons (MLP) to classify aircraft types, engine configurations, and operations. Results reveal a strong loudness axis in the first principal component, overshadowing subtle spectral and timebased differences across aircraft families, especially for takeoffs. Nonetheless, focusing on higher-order components and alternative embeddings (t-SNE) highlights additional spectral and temporal markers. Operation classification (landing vs. takeoff) achieves 98% accuracy, but aircraft and engine family classification remain challenging, with accuracy capped below 50% using these feature sets. These findings suggest that advanced feature selection and dimensionality reduction while considering amplitude characteristics are essential for disentangling nuanced design-based acoustic traits.

Aircraft emissions of (ultra)fine particles during landing and take-off operations pose increasing human health hazards for airport employees and near-airport communities. Measurements of in-operation aircraft are therefore crucial for characterizing real-world aircraft emissions, and their variability. In this work, we develop an approach that enables the gathering of large quantities of data on real-world aircraft-specific emissions. We use three types of portable PM sensors located ca. 200 m downwind of an operational runway at Amsterdam Airport Schiphol, over different seasons, to characterize the plumes from ca. 500 specific operations covering most aircraft types of the global flying fleet. High concentration peaks (in the order of 10⁶ particles/cm³) of sub-25 nm particles are observed in the near field. While departure plumes exhibit higher particle number concentrations than arrival plumes, the values do not necessarily scale with aircraft size or engine thrust rating. We find large variability among aircraft types and engine models, highlighting the importance of incorporating real-world observations when assessing the impacts of aviation on the atmospheric composition and human health.

Commercial supersonic aircraft may return in the near future, offering reduced travel time while flying higher in the atmosphere than subsonic aircraft, thus displacing part of the passenger traffic and associated emissions to higher altitudes. For the first time since 2007, we present a comprehensive multi-model assessment of the atmospheric and radiative effect of this displacement. We use four models (EMAC, GEOS-Chem, LMDz–INCA, and MOZART-3) to evaluate three scenarios in which subsonic aviation is partially replaced with supersonic aircraft. Replacing 4 % of subsonic traffic with Mach 2 aircraft that have a NOx emissions index of 13.8 g (NO2) kg−1 leads to ozone column loss of −0.3 % (−0.9 DU; model range from −0.4 % to −0.1 %), and it increases radiative forcing by 19.1 mW m−2 (model range from 16.7 to 28.1). This forcing is driven by water vapor (18.2 mW m−2), ozone (11.4 mW m−2), and aerosol emissions (−10.5 mW m−2). The use of a Mach 2 concept with low-NOx emissions (4.6 g (NO2) kg−1) reduces the effect on forcing and ozone to 13.4 mW m−2 (model range from 2.4 to 23.4) and −0.1 % (−0.3 DU; model range from −0.2 % to +0.0 %), respectively. If a Mach 1.6 aircraft with a lower cruise altitude and NOx emissions of 4.6 g (NO2) kg−1 is used instead, we find a near-net-zero effect on the ozone column and an increase in the radiative forcing of 3.7 mW m−2 (model range from 0.5 to 7.1). The supersonic concepts have up to 185 % greater radiative effect per passenger kilometer from non-CO2 emissions compared to subsonic aviation (excluding contrail impacts).

Climate and justice are interconnected. However, simply raising ethical issues associated with the links between climate change, technology, and health is insufficient. Rather, policies and practices need to consider ethics ahead of time. If it is only added “after the fact,” policy will be less efficient and opportunities for carbon minimization will be lost. This will require the cooperation of people at many levels and can be guided by two essential ethical principles: distributive justice and environmental sustainability.

Flight altitude is relevant to the climate effects resulting from aircraft emissions. Other research has shown that flying higher within the troposphere leads to larger warming from O ₃ production. Aircraft NO _x emissions are of particular interest, as they lead to warming via the short-term production of O ₃, but also to reduced warming via processes like CH ₄ depletion. We focus on short-term O ₃ production, as it constitutes one of aviation’s largest warming components. Understanding how O ₃ formation varies altitudinally throughout the upper troposphere/lower stratosphere is essential for designing climate-compatible aircraft and routing. We quantify this variation by performing simulations with a global atmospheric chemistry model for three representative cruise altitudes, five regions and two seasons using three methods: Eulerian tagging, perturbation and Lagrangian tagging. This multi-method, regional approach overcomes limitations of previous studies that utilize only one of these methods and apply global emission inventories biased towards present-day flight distributions, thus limiting their applicability to future aviation scenarios. Our results highlight that underrepresenting emissions in areas with growing flight activity (e.g. Asia Pacific) may lead to significant, regional underestimations of the altitudinal sensitivity of short-term NO _x -related O ₃ warming effects in certain cases. We find that emitting in Southern regions, like Australasia, leads to warming larger by a factor of two when compared to global averages. Our findings also suggest that flying lower translates to lower warming from short-term O ₃ production and that this effect is strongest during the local summer. We estimate differences ranging from a factor of 1.2-2.6 between tagging and perturbation results that are attributable to non-linearities of NO _x -O ₃ chemistry, and derived regional correction factors for a widely-used sub-model. Overall, we stress that a combination of all three methods is necessary for a robust assessment of aviation climate effects as they address fundamentally different questions.

Atmospheric sensitivities (gradients), quantifying the atmospheric response to emissions or other perturbations, can provide meaningful insights on the underlying atmospheric chemistry or transport processes. Atmospheric adjoint modeling enables the calculation of receptor-oriented sensitivities of model outputs of interest to input parameters (e.g., emissions), overcoming the numerical cost of conventional (forward) modeling. The adjoint of the GEOS-Chem atmospheric chemistry-transport model is a widely used such model, but prior to v36 it lacked extensive stratospheric capabilities. Here, we present the development and evaluation of the discrete adjoint of the global chemistry-transport model (CTM) GEOS-Chem unified chemistry extension (UCX) for stratospheric applications, which extends the existing capabilities of the GEOS-Chem adjoint to enable the calculation of sensitivities that include stratospheric chemistry and interactions. This development adds 37 new tracers, 273 kinetic and photolysis reactions, an updated photolysis scheme, treatment of stratospheric aerosols, and all other features described in the original UCX paper. With this development the GEOS-Chem adjoint model is able to capture the spatial, temporal, and speciated variability in stratospheric ozone depletion processes, among other processes. We demonstrate its use by calculating 2-week sensitivities of stratospheric ozone to precursor species and show that the adjoint captures the Antarctic ozone depletion potential of active halogen species, including the chlorine activation and deactivation process. The spatial variations in the sensitivity of stratospheric ozone to NOx emissions are also described. This development expands the scope of research questions that can be addressed by allowing stratospheric interactions and feedbacks to be considered in the tropospheric sensitivity and inversion applications.

Reliable prediction of aviation’s environmental impact, including the effect of nitrogen oxides on ozone, is vital for effective mitigation against its contribution to global warming. Estimating this climate impact however, in terms of the short-term ozone instantaneous radiative forcing, requires computationally-expensive chemistry-climate model simulations that limit practical applications such as climate-optimised planning. Existing surrogates neglect the large uncertainties in their predictions due to unknown environmental conditions and missing features. Relative to these surrogates, we propose a high-accuracy probabilistic surrogate that not only provides mean predictions but also quantifies heteroscedastic uncertainties in climate impact estimates. Our model is trained on one of the most comprehensive chemistry-climate model datasets for aviation-induced nitrogen oxide impacts on ozone. Leveraging feature selection techniques, we identify essential predictors that are readily available from weather forecasts to facilitate the implementation therein. We show that our surrogate model is more accurate than homoscedastic models and easily outperforms existing linear surrogates. We then predict the climate impact of a frequently-flown flight in the European Union, and discuss limitations of our approach.

Civil supersonic aviation may return in the near future. Their emissions have been found to lead to changes in the composition of the stratosphere, affecting the ozone layer and climate. To keep up with the rapid developments in supersonic aircraft technology and alternative fuels there is an increasing need for the development of surrogate modeling methods, which requires knowledge of the sensitivities to these emissions. We present a parametric study which evaluates the first- and second-order sensitivities of the ozone column and radiative forcing (RF) to supersonic emissions across two flight corridors and three altitudes. For a given increase in global fuel burn, we find that the increase in emission of (Formula presented.) is the main driver of both the changes in the global ozone column and RF, the latter of which is linked through changes in the ozone distribution. Followed by the increase in the emission of (Formula presented.), which leads to (Formula presented.) loss and has a cooling effect. The ozone column and climate are least sensitive to increases in (Formula presented.) emissions. We also show that interactions between (Formula presented.), (Formula presented.), and (Formula presented.) emissions lead to non-linear behavior in the atmospheric response. The effect of these interactions can lead to (Formula presented.) 5% differences in the ozone column impacts and up to 7.3% increases in RF. Our results demonstrate that the majority of second-order sensitivities may be neglected in surrogate models for small errors, which could greatly simplify their development. Our results also indicate that reductions in flight altitude and fleetwide (Formula presented.) emissions may effectively reduce the environmental footprint of supersonic aviation emissions.

To reduce the growing distrust in aircraft noise models felt by communities around the airport, it is imperative to ensure accurate modelling methodologies validated by appropriately measured noise metrics. This is especially crucial in regions farther from the airport where Lden = 45 - 55 dBA because the amount of affected residents in these areas is large. Currently, there is a lack of measured noise levels at such distances and uncertainty about the assumed procedures, such as the aircraft thrust settings. Regarding the latter, before comparing the model and measured noise levels, it’s thus crucial to first create a robust workflow for obtaining accurate input data for the noise predictions. In this contribution, as a first step, audio files from the noise monitoring stations around Rotterdam The Hague airport (RTHA), combined with dedicated array and single microphone measurements, are considered for extracting fan rotational speed, N1. The 64-microphone array and the single microphone system were co-located with one of the monitoring stations at a distance of 1.14 km away from the RTHA runway. The engine settings are retrieved from the intensity-averaged spectrograms obtained from the microphone array. Using the derived thrust settings, the noise levels measured by the monitoring stations are compared with the single-event noise level prediction made by the European Noise model, Doc.29. The aircraft position, i.e., input for the model, is obtained from ADS-B data, which contains the position vector and velocity of the aircraft at 1-second intervals. In the framework of this study, noise predictions for both arrival and take-off procedures for three aircraft types are presented. Finally, this case study aims to investigate the applicability of the data from monitoring stations for the aim of model-data predictions at the mentioned regions.

Aviation’s contribution to anthropogenic global warming is estimated to be between 3 – 5% [1]. This assessment comprises two contributions: the well understood atmospheric impact of carbon dioxide (CO2) and the more uncertain non-CO2 effects. The latter pertain to persistent contrails and pollutants like nitrogen oxides (NOx), water vapor (H2O), sulfur oxides (SOx) and soot particles. NOx emissions are involved in non-linear processes that result in the short-term production of ozone (O3) and longer-term destruction of methane (CH4), stratospheric water vapor (SWV), and primary mode ozone (PMO). The aviation-attributable impacts arising from this short-term increase in O3 can vary by more than a factor of 1.5 depending on the selected modelling approach. This O3 increase is associated with the second largest warming effect across aviation’s main climate forcers [1]. We therefore quantify this figure using three modelling approaches (an Eulerian and a Lagrangian tagging scheme as well as a perturbation approach) at three potential aircraft cruise altitudes (200, 250 and 300 hPa) at which NOx pulse emissions are introduced in the Americas, Africa, Eurasia and Australasia. In general, the tagging method computes the contribution by an emission source to the concentration of a chemical species while a perturbation approach consists in calculating the total impact of an emission to the concentration of a species by means of subtracting two simulations: one with all emissions and a second without the specific source’s emissions. We compare results from Eulerian and Lagrangian simulations using the same climate-chemistry code: the ECHAM5/MESSy Atmospheric Chemistry (EMAC) model. With the Eulerian setup, we are able to capture non-linear processes and feedback effects, but not track the transport of emitted species in detail. The Lagrangian setup [2], on the other hand, allows for the accompaniment of thousands of air parcel trajectories, but at the cost of assuming a simplified linear chemistry mechanism. We find that the Lagrangian tagging approach provides the largest estimates for O3 production and radiative forcing (RF), followed by the Eulerian tagging scheme and lastly by the perturbation method. We therefore investigate the appropriateness of each of these in quantifying aviation’s total and marginal climate effects by addressing the following research questions: 1) By how much are the estimates for the short-term NOx-induced O3 perturbation and consequent RF varying across the three modelling approaches and why? 2) How does this RF vary with emission altitude within the upper Troposphere/lower Stratosphere (UTLS)?

[1] Lee, D.S., Fahey, D.W., Skowron, A., Allen, M.R., Burkhardt, U., Chen, Q., Doherty, S.J., Freeman, S., Forster, P.M., Fuglestvedt, J., Gettelman, A., De León, R.R., Lim, L.L., Lund, M.T., Millar, R.J., Owen, B., Penner, J.E., Pitari, G., Prather, M.J., Sausen, R., and Wilcox, L.J.: The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018, Atmos. Environ., 244, 117834, https://doi.org/10.1016/j.atmosenv.2020.117834, 2021.

[2] Maruhashi, J., Grewe, V., Frömming, C., Jöckel, P., and Dedoussi, I. C.: Transport patterns of global aviation NOx and their short-term O3 radiative forcing – a machine learning approach, Atmos. Chem. Phys., 22, 14253–14282, https://doi.org/10.5194/acp-22-14253-2022, 2022.

Aviation produces a net climate warming contribution that comprises multiple forcing terms of mixed sign. Aircraft NOx emissions are associated with both warming and cooling terms, with the short-term increase in O3 induced by NOx emissions being the dominant warming effect. The uncertainty associated with the magnitude of this climate forcer is amongst the highest out of all contributors from aviation and is owed to the nonlinearity of the NOx-O3 chemistry and the large dependency of the response on space and time, i.e., on the meteorological condition and background atmospheric composition. This study addresses how transport patterns of emitted NOx and their climate effects vary with respect to regions (North America, South America, Africa, Eurasia and Australasia) and seasons (January-March and July-September in 2014) by employing global-scale simulations. We quantify the climate effects from NOx emissions released at a representative aircraft cruise altitude of 250 hPa (∼10400 m) in terms of radiative forcing resulting from their induced short-term contributions to O3. The emitted NOx is transported with Lagrangian air parcels within the ECHAM5/MESSy Atmospheric Chemistry (EMAC) model. To identify the main global transport patterns and associated climate impacts of the 14 000 simulated air parcel trajectories, the unsupervised QuickBundles clustering algorithm is adapted and applied. Results reveal a strong seasonal dependence of the contribution of NOx emissions to O3. For most regions, an inverse relationship is found between an air parcel's downward transport and its mean contribution to O3. NOx emitted in the northern regions (North America and Eurasia) experience the longest residence times in the upper midlatitudes (40 %-45 % of their lifetime), while those beginning in the south (South America, Africa and Australasia) remain mostly in the Tropics (45 %-50 % of their lifetime). Due to elevated O3 sensitivities, emissions in Australasia induce the highest overall radiative forcing, attaining values that are larger by factors of 2.7 and 1.2 relative to Eurasia during January and July, respectively. The location of the emissions does not necessarily correspond to the region that will be most affected - for instance, NOx over North America in July will induce the largest radiative forcing in Europe. Overall, this study highlights the spatially and temporally heterogeneous nature of the NOx-O3 chemistry from a global perspective, which needs to be accounted for in efforts to minimize aviation's climate impact, given the sector's resilient growth.

Excess nitrogen deposition from anthropogenic sources of atmospheric emissions, such as agriculture and transportation, can have negative effects on natural environments. Designing effective conservation efforts requires knowledge of the contribution of individual sectors. This study utilizes a global atmospheric chemistry-transport model to quantify, for the first time, the contribution of global aviation NO_x emissions to nitrogen deposition for 2005 and 2019. We find that aviation led to an additional 1.39 Tg of nitrogen deposited globally in 2019, up 72 % from 2005, with 67 % of each year's total occurring through wet deposition. In 2019, aviation was responsible for an average of 0.66 %, 1.13 %, and 1.61 % of modeled nitrogen deposition from all sources over Asia, Europe, and North America, respectively. These impacts are spatially widespread, with 56 % of deposition occurring over water. Emissions during the landing, taxi and takeoff (LTO) phases of flight are responsible for 8 % of aviation's nitrogen deposition impacts on average globally, and between 16 and 32 % over most land in regions with high aviation activity. Despite currently representing less than 1.2 % of nitrogen deposition globally, further growth of aviation emissions would result in increases in aviation's contribution to nitrogen deposition and associated critical loads.

Aside from the climate impacts from carbon dioxide (CO2) emissions, civil aircraft currently in operation also emit nitrogen oxides (NOx), water vapor (H2O) and other non-CO2 pollutants whose combined primary and secondary effects account for almost 70% of aviation's net contribution towards anthropogenic global warming. NOx emissions, in the short-run, actuate the second largest aviation warming effect via indirect ozone (O3) formation [1], and, unlike CO2, the altitude, geographic location and time of emission are all significant drivers of their resulting climate impact. Past studies [2] have shown a positive correlation between emission altitude and warming from NOx emitted within the troposphere, but such analyses are frequently limited by emission inventories that largely focus on flights across the North Atlantic Flight Corridor (NAFC), thereby disregarding future shifts and alternate patterns in civil aviation traffic. We bridge this gap via global, Lagrangian and Eulerian simulations using the EMAC model wherein equal amounts of NO are released across five regions during two seasons, as was done in [3], but now for two additional flight levels that together cover typical subsonic cruise ranges of 10–12 km. The NOx-induced O3 production was calculated using two modelling methods, tagging and perturbation, as both are vastly used for climate effects estimations. The former is used to compute the contribution of a source to a whole while the latter is useful, for instance, in quantifying the total impact from variations in the strength of a source. We therefore characterize the relation between emission altitude and warming from short-term O3 on a global scale. These results and analyses then form an integral part of the development of next-generation surrogate models that will make climate-optimal routing a reality [4].

References
[1] Lee, D. S., Fahey, D. W., Skowron, A., Allen, M. R., Burkhardt, U., Chen, Q., Doherty, S. J., Freeman, S., Forster, P. M., Fuglestvedt, J., Gettelman, A., De León, R. R., Lim, L. L., Lund, M. T., Millar, R. J., Owen, B., Penner, J. E., Pitari, G., Prather, M. J., Sausen, R., and Wilcox, L. J.: The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018, Atmos. Environ., 244, 117834, https://doi.org/10.1016/j.atmosenv.2020.117834, 2021.
[2] Matthes, S., Lim, L., Burkhardt, U., Dahlmann, K., Dietmüller, S., Grewe, V., Haslerud, A. S., Hendricks, J., Owen, B., Pitari, G., Righi, M., and Skowron, A.: Mitigation of Non-CO2 Aviation's Climate Impact by Changing Cruise Altitudes, Aerospace, 8, 36, https://doi.org/10.3390/aerospace8020036, 2021.
[3] Maruhashi, J., Grewe, V., Frömming, C., Jöckel, P., and Dedoussi, I. C.: Transport patterns of global aviation NOx and their short-term O3 radiative forcing – a machine learning approach, Atmos. Chem. Phys., 22, 14253–14282, https://doi.org/10.5194/acp-22-14253-2022, 2022.
[4] Rao, P., Dwight, R., Singh, D., Maruhashi, J., Dedoussi, I., Grewe, V., and Frömming, C.: Towards a new surrogate model for predicting short-term NOx-O3 effects from aviation using Gaussian processes, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4337, https://doi.org/10.5194/egusphere-egu23-4337, 2023.

Acknowledgements
This research is part of the ACACIA (Advancing the Science for Aviation and Climate; http://www.acacia-project.eu) project, which is funded by the European Commission, Horizon 2020 Framework program under the grant agreement no. 875036.
This study used the Dutch national e-infrastructure with the support of the SURF Cooperative (grant nos. EINF-441 and EINF-2734).

While efforts have been made to curb CO2 emissions from aviation, the more uncertain non-CO2 effects that contribute about two-thirds to the warming in terms of radiative forcing (RF), still require attention. The most important non-CO2 effects include persistent line-shaped contrails, contrail-induced cirrus clouds and nitrogen oxide (NOx) emissions that alter the ozone (O3) and methane (CH4) concentrations, both of which are greenhouse gases, and the emission of water vapour (H2O). The climate impact of these non-CO2 effects depends on emission location and prevailing weather situation; thus, it can potentially be reduced by advantageous re-routing of flights using Climate Change Functions (CCFs), which are a measure for the climate effect of a locally confined aviation emission. CCFs are calculated using a modelling chain starting from the instantaneous RF (iRF) measured at the tropopause that results from aviation emissions. However, the iRF is a product of computationally intensive chemistry-climate model (EMAC) simulations and is currently restricted to a limited number of days and only to the North Atlantic Flight Corridor. This makes it impossible to run EMAC on an operational basis for global flight planning. A step in this direction lead to a surrogate model called algorithmic Climate Change Functions (aCCFs), derived by regressing CCFs (training data) against 2 or 3 local atmospheric variables at the time of emission (features) with simple regression techniques and are applicable only in parts of the Northern hemisphere. It was found that in the specific case of O3 aCCFs, which provide a reasonable first estimate for the short-term impact of aviation NOx on O3 warming using temperature and geopotential as features, can be vastly improved [1]. There is aleatoric uncertainty in the full-order model (EMAC), stemming from unknown sources (missing features) and randomness in the known features, which can introduce heteroscedasticity in the data. Deterministic surrogates (e.g. aCCFs) only predict point estimates of the conditional average, thereby providing an incomplete picture of the stochastic response. Thus, the goal of this research is to build a new surrogate model for iRF, which is achieved by : 1. Expanding the geographical coverage of iRF (training data) by running EMAC simulations in more regions (North & South America, Eurasia, Africa and Australasia) at multiple cruise flight altitudes, 2. Following an objective approach to selecting atmospheric variables (feature selection) and considering the importance of local as well as non-local effects, 3. Regressing the iRF against selected atmospheric variables using supervised machine learning techniques such as homoscedastic and heteroscedastic Gaussian process regression. We present a new surrogate model that predicts iRF of aviation NOx-O3 effects on a regular basis with confidence levels, which not only improves our scientific understanding of NOx-O3 effects, but also increases the potential of global climate-optimised flight planning.

Policy-makers seeking to limit the impact of coal electricity-generating units (EGUs, also known as power plants) on air quality and climate justify regulations by quantifying the health burden attributable to exposure from these sources. We defined “coal PM2.5” as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM2.5 from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM2.5 from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years. Exposure to coal PM2.5 was associated with 2.1 times greater mortality risk than exposure to PM2.5 from all sources. A total of 460,000 deaths were attributable to coal PM2.5, representing 25% of all PM2.5-related Medicare deaths before 2009 and 7% after 2012. Here, we quantify and visualize the contribution of individual EGUs to mortality.

The aircraft’s environmental performance on fleet level is so far completely decoupled from the design process. The climate impact from aviation arising from non-CO2 effects are largely independent from CO2 emissions, but rather depend on the atmospheric state. Previously complex climate-chemistry models were used to evaluate the non-CO2 emissions impact on climate. This is far too computationally demanding for a multidisciplinary design optimisation (MDO) process, requiring a multitude of climate impact evaluations. The question then is, how to efficiently design the next generation climate optimal aircraft? In this paper, a new concept for designing aircraft with minimum climate impact using Climate Functions for Aircraft Design (CFAD) is presented. The content of this paper provides an overview of the development of these innovative CFAD and demonstrates the ability to be integrated in an existing MDO framework. The mitigation potential by optimising aircraft design using CFAD is analysed with respect to different cruise conditions and by minimizing the overall climate impact. To validate the CFAD, a higher fidelity assessment is carried out. Finally, the key performance indicators, i.e. fuel consumption, flight time and operating cost, of the optimised aircraft design are compared to that of the reference aircraft.

Sustainability is the biggest challenge facing the aerospace industry today. With the global number of flights expected to rise, the climate impact of aviation will continue to increase. Current research states that the rerouting of aircraft through wind-optimisation for the purpose of fuel usage minimisation and emission reduction is an effective sustainability contribution. However, these routing models only optimize for minimum fuel burn, not necessarily minimum climate impact. Flying efficiently through wind fields could mean flying through regions with higher climate impact, for example, where warming contrails are formed. This potentially forfeits the advantage of the reduced emissions from the wind-optimized route. By bringing together fields such as satellite remote sensing, atmospheric science and aircraft surveillance data, a climate optimized free routing model can be made. This paper creates a climate optimized free routing airspace model by incorporating knowledge from the aforementioned fields and existing wind-optimization models with AI and open-source tools.