Abstract. In addition to CO2, the climate impact of aviation is strongly influenced by non-CO2 emissions, such as nitrogen oxides, influencing ozone and methane, and water vapour, which can lead to the formation of persistent contrails in ice-supersaturated regions. Because these non-CO2 emission effects are characterised by a short lifetime, their climate impact largely depends on emission location and time; that is to say, emissions in certain locations (or times) can lead to a greater climate impact (even on the global average) than the same emission in other locations (or times). Avoiding these climate-sensitive regions might thus be beneficial to climate. Here, we describe a modelling chain for investigating this climate impact mitigation option. This modelling chain forms a multi-step modelling approach, starting with the simulation of the fate of emissions released at a certain location and time (time-region grid points). This is performed with the chemistry–climate model EMAC, extended via the two submodels AIRTRAC (V1.0) and CONTRAIL (V1.0), which describe the contribution of emissions to the composition of the atmosphere and to contrail formation, respectively. The impact of emissions from the large number of time-region grid points is efficiently calculated by applying a Lagrangian scheme. EMAC also includes the calculation of radiative impacts, which are, in a second step, the input to climate metric formulas describing the global climate impact of the emission at each time-region grid point. The result of the modelling chain comprises a four-dimensional data set in space and time, which we call climate cost functions and which describes the global climate impact of an emission at each grid point and each point in time. In a third step, these climate cost functions are used in an air traffic simulator (SAAM) coupled to an emission tool (AEM) to optimise aircraft trajectories for the North Atlantic region. Here, we describe the details of this new modelling approach and show some example results. A number of sensitivity analyses are performed to motivate the settings of individual parameters. A stepwise sanity check of the results of the modelling chain is undertaken to demonstrate the plausibility of the climate cost functions.

Opening the sky to new classes of airspace user is a political and economic imperative for the European Union. Drone industries have a significant potential for economical growth according to the latest estimations. To enable this growth safely and efficiently, the CORUS project has developed a concept of operations for drones flying in Europe in very low-level airspace, which they have to share that space with manned aviation, and quite soon with urban air mobility aircraft as well. U-space services and the development of smart, automated, interoperable, and sustainable traffic management solutions are presented as the key enabler for achieving this high level of integration. In this paper, we present the U-space concept of operations (ConOps), produced around three new types of airspace volume, called X, Y, and Z, and the relevant U-space services that will need to be supplied in each of these. The paper also describes the reference high-level U-space architecture using the European air traffic management architecture methodology. Finally, the paper proposes the basis for the aircraft separation standards applicable by each volume, to be used by the conflict detection and resolution services of U-space.

The geographical characteristics of Taiwan have made the country heavily dependent on air transport and this, combined with the island's high population density, has augmented the importance of aircraft noise issues. Nine of the country's seventeen airports are on the main island, with eight being on remote islands. Following the corporatization of Taiwan Taoyuan International Airport, the Taiwanese Civil Aeronautics Administration (CAA) is in charge of sixteen airports, of which six are of joint civil-military use. This paper presents the current aircraft noise management measures at Taiwanese airports: noise limits for aircraft, regulations on the reduction of aircraft noise at source, aircraft noise charges, land use planning of airport neighbourhoods, improvements to airport layouts, and noise monitoring systems. After consideration of various aspects of environmental impacts due to aircraft and airport operations, the CAA has been planning the implementation of an airport environmental management system, using Taipei Songshan Airport, in the heart of the city, as the first pilot case. In addition, having reviewed work done by the international Aircraft Noise Non-Acoustic factors (ANNA) discussion group, the paper suggests methods for handling Taiwan's noise issues in a resident-friendly manner.

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In addition to CO₂, the climate impact of aviation is strongly influenced by non-CO₂ emissions, such as nitrogen oxides, influencing ozone and methane, and water vapour, forming contrails. Because these non-CO₂ emission effects are characterised by a short lifetime, their climate impact largely depends on emission location and time, i.e. emissions in certain locations (or times) can lead to a greater climate impact (even on the global average) than the same emission in other locations (or times). Avoiding these climate sensitive regions might thus be beneficial to climate. Here, we describe a modelling chain for investigating this climate impact mitigation option. It forms a multi-step modelling approach, starting with the simulation of the fate of emissions released at a certain location and time (time-region). This is performed with the chemistry–climate model EMAC, extended by the two submodels AIRTRAC 1.0 and CONTRAIL 1.0, which describe the contribution of emissions to the composition of the atmosphere and the contrail formation. Numerous time-regions are efficiently calculated by applying a Lagrangian scheme. EMAC also includes the calculation of radiative impacts, which are, in a second step, the input to climate metric formulas describing the climate impact of the time-region emission. The result of the modelling chain comprises a four dimensional dataset in space and time, which we call climate cost functions, and which describe at each grid point and each point in time, the climate impact of an emission. In a third step, these climate cost functions are used in a traffic simulator (SAAM), coupled to an emission tool (AEM) to optimise aircraft trajectories for the North Atlantic region. Here, we describe the details of this new modelling approach and show some example results. A number of sensitivity analyses are performed to motivate the settings of individual parameters. A stepwise sanity check of the results of the modelling chain is undertaken to demonstrate the plausibility of the climate cost functions