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The strong growth rate of the aviation industry in recent years has created significant challenges in terms of environmental impact. Air traffic contributes to climate change through the emission of carbon dioxide (CO2) and other non-CO2 effects, and the associated climate impact is expected to soar further. The mitigation of CO2 contributions to the net climate impact can be achieved using novel propulsion, jet fuels, and continuous improvements of aircraft efficiency, whose solutions lack in immediacy. On the other hand, the climate impact associated with non-CO2 emissions, being responsible for two-thirds of aviation radiative forcing, varies highly with geographic location, altitude, and time of the emission. Consequently, these effects can be reduced by planning proper climate-aware trajectories. To investigate these possibilities, this paper presents a survey on operational strategies proposed in the literature to mitigate aviation’s climate impact. These approaches are classified based on their methodology, climate metrics, reliability, and applicability. Drawing upon this analysis, future lines of research on this topic are delineated.

Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO2 emissions and non-CO2 impacts, such as NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NOx emitted, or per flown kilometre.In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NOx-induced ozone and H2O effects are well represented by the ACCF model output. The NOx-induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NOx-induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NOx-induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO2 contribution observed in this study is low compared with the non-CO2 effects, which requires further diagnosis.

Aviation aims to reduce its climate effect by adopting trajectories that avoid regions of the atmosphere where aviation emissions have a large impact. To that end, prototype algorithmic climate change functions (aCCFs) can be used, which provide spatially and temporally resolved information on aviation's climate effect in terms of future near-surface temperature change. These aCCFs can be calculated with meteorological input data obtained from, e.g., numerical weather prediction models. We present here the open-source Python library called CLIMaCCF, an easy-to-use and flexible tool which efficiently calculates both the individual aCCFs (i.e., aCCF of water vapor, nitrogen oxide (NOx)-induced ozone production and methane depletion, and contrail cirrus) and the merged non-CO2 aCCFs that combine all these individual contributions. To construct merged aCCFs all individual aCCFs are converted to the same physical unit. This unit conversion needs the technical specification of aircraft and engine parameters, i.e., NOx emission indices and flown distance per kilogram of burned fuel. These aircraft- and engine-specific values are provided within CLIMaCCF version V1.0 for a set of aggregated aircraft and engine classes (i.e., regional, single-aisle, wide-body). Moreover, CLIMaCCF allows the user to choose from a range of physical climate metrics (i.e., average temperature response for pulse or future scenario emissions over the time horizons of 20, 50, or 100 years). Finally, we demonstrate the abilities of CLIMaCCF through a series of example applications.

The climate impact of non-CO2 emissions, which are responsible for two-thirds of aviation radiative forcing, highly depends on the atmospheric chemistry and weather conditions. Hence, by planning aircraft trajectories to reroute areas where the non-CO2 climate impacts are strongly enhanced, called climate-sensitive regions, there is a potential to reduce aviation-induced non-CO2 climate effects. Weather forecast is inevitably uncertain, which can lead to unreliable determination of climate-sensitive regions and aircraft dynamical behavior and, consequently, inefficient trajectories. In this study, we propose robust climate-optimal aircraft trajectory planning within the currently structured airspace considering uncertainties in standard weather forecasts. The ensemble prediction system is employed to characterize uncertainty in the weather forecast, and climate-sensitive regions are quantified using the prototype algorithmic climate change functions. As the optimization problem is constrained by the structure of airspace, it is associated with hybrid decision spaces. To account for discrete and continuous decision variables in an integrated and more efficient manner, the optimization is conducted on the space of probability distributions defined over flight plans instead of directly searching for the optimal profile. A heuristic algorithm based on the augmented random search is employed and implemented on graphics processing units to solve the proposed stochastic optimization computationally fast. An open-source Python library called ROOST (V1.0) is developed based on the aircraft trajectory optimization technique. The effectiveness of our proposed strategy to plan robust climate-optimal trajectories within the structured airspace is analyzed through two scenarios: a scenario with a large contrail climate impact and a scenario with no formation of persistent contrails. It is shown that, for a nighttime flight from Frankfurt to Kyiv, a 55 % reduction in climate impact can be achieved at the expense of a 4 % increase in the operating cost.

The optimization of aircraft trajectories involves balancing operating costs and climate impact, which are often conflicting objectives. To achieve compromised optimal solutions, higher-level information such as preferences of decision-makers must be taken into account. This paper introduces the SolFinder 1.0 module, a decision-making tool designed to identify eco-efficient aircraft trajectories, which allow for the reduction of the flight's climate impact with limited cost penalties compared to cost-optimal solutions. SolFinder 1.0 offers flexible decision-making options that allow users to select trade-offs between different objective functions, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel, which optimizes trajectories under atmospheric conditions simulated by the ECHAM/MESSy Atmospheric Chemistry model. This paper focuses on the ability of the module to identify eco-efficient trajectories while solving a bi-objective optimization problem that minimizes climate impact and operating costs. SolFinder 1.0 enables users to explore trajectory properties at varying locations of the Pareto fronts without prior knowledge of the problem results and to identify solutions that limit the cost of reducing the climate impact of a single flight.

Tumour-associated macrophages (TAMs) play key roles in tumour progression. Recent evidence suggests that TAMs critically modulate the efficacy of anticancer therapies, raising the prospect of their targeting in human cancer. In a large retrospective cohort study involving 110 patients with pancreatic ductal adenocarcinoma (PDAC), we assessed the density of CD68-TAM immune reactive area (%IRA) at the tumour-stroma interface and addressed their prognostic relevance in relation to postsurgical adjuvant chemotherapy (CTX). In vitro, we dissected the synergism of CTX and TAMs. In human PDAC, TAMs predominantly exhibited an immunoregulatory profile, characterised by expression of scavenger receptors (CD206, CD163) and production of interleukin 10 (IL-10). Surprisingly, while the density of TAMs associated to worse prognosis and distant metastasis, CTX restrained their protumour prognostic significance. High density of TAMs at the tumour-stroma interface positively dictated prognostic responsiveness to CTX independently of T-cell density. Accordingly, in vitro, gemcitabine-treated macrophages became tumoricidal, activating a cytotoxic gene expression programme, inhibiting their protumoural effect and switching to an antitumour phenotype. In patients with human PDAC, neoadjuvant CTX was associated to a decreased density of CD206(+) and IL-10(+) TAMs at the tumour-stroma interface. Overall, our data highlight TAMs as critical determinants of prognostic responsiveness to CTX and provide clinical and in vitro evidence that CTX overall directly re-educates TAMs to restrain tumour progression. These results suggest that the quantification of TAMs could be exploited to select patients more likely to respond to CTX and provide the basis for novel strategies aimed at re-educating macrophages in the context of CTX.

Purpose Eliciting antitumor T-cell response by targeting the PD-1/PD-L1 axis with checkpoint inhibitors has emerged as a novel therapeutic strategy in non-small cell lung cancer (NSCLC). The identification of predictors for sensitivity or resistance to these agents is, therefore, needed. Herein, we investigate the correlation of metabolic information on FDG-PET with tissue expression of immune-checkpoints and other markers of tumor-related immunity in resected NSCLC patients. Materials and methods All patients referred to our institution for upfront surgical resection of NSCLC, who were investigated with FDG-PET prior to surgery, were consecutively included in the study. From January 2010 to May 2014, 55 patients (stage IA-IIIB; M:F = 42:13; mean age 68.9 years) were investigated. Sampled surgical tumor specimens were analyzed by immunohistochemistry (IHC) for CD68-TAMs (tumor-associated macrophages), CD8-TILs (tumor infiltrating lymphocytes), PD-1-TILs, and PD-L1 tumor expression. Immunoreactivity was evaluated, and scores were compared with imaging findings. FDG-PET images were analyzed to define semi-quantitative parameters: SUVmax and SUVmean. Metabolic information on FDG-PET was correlated with tissue markers expression and disease-free survival (DFS) considering a median follow-up of 16.2 months. Results Thirty-six adenocarcinomas (ADC), 18 squamous cell carcinomas (SCC), and one sarcomatoid carcinoma were analyzed. All tumors resulted positive at FDG-PET: median SUVmax 11.3 (range: 2.3–32.5) and SUVmean 6.4 (range: 1.5–13) both resulted significantly higher in SCC compared to other NSCLC histotypes ( p  = 0.007 and 0.048, respectively). IHC demonstrated a median immunoreactive surface covered by CD68-TAMs of 5.41 % (range: 0.84–14.01 %), CD8-TILs of 2.9 % (range: 0.11–11.92 %), PD-1 of 0.65 % (range: 0.02–5.87 %), and PD-L1 of 0.7 % (range: 0.03–10.29 %). We found a statistically significant correlation between SUVmax and SUVmean with the expression of CD8 TILs (rho = 0.31; p  = 0.027) and PD-1 (rho = 0.33; p  = 0.017 and rho = 0.36; p  = 0.009, respectively). The other tissue markers correlated as follows: CD8 TILs and PD-1 (rho = 0.45; p  = 0.001), CD8 TILs and PD-L1 (rho = 0.41; p  = 0.003), CD68-TAMs and PD-L1 (rho = 0.30; p  = 0.027), PD-1 and PD-L1 (rho = 0.26; p  = 0.059). With respect to patients’ outcome, SUVmax, SUVmean, and disease stage showed a statistically significant correlation with DFS ( p  = 0.002, 0.004, and <0.001, respectively). Conclusions The present study shows a direct association between metabolic parameters on FDG-PET and the expression of tumor-related immunity markers, suggesting a potential role for FDG-PET to characterize the tumor microenvironment and select NSCLC patients candidate to checkpoint inhibitors.