Sustainable aviation fuel (SAF) can reduce aviation's CO 2 and non-CO 2 impacts. We quantify the change in contrail properties and climate forcing in the North Atlantic resulting from different blending ratios of SAF and demonstrate that intelligently allocating the limited SAF supply could multiply its overall climate benefit by factors of 9−15. A fleetwide adoption of 100% SAF increases contrail occurrence (+5%), but lower nonvolatile particle emissions (−52%) reduce the annual mean contrail net radiative forcing (−44%), adding to climate gains from reduced life cycle CO 2 emissions. However, in the short term, SAF supply will be constrained. SAF blended at a 1% ratio and uniformly distributed to all transatlantic flights would reduce both the annual contrail energy forcing (EF contrail ) and the total energy forcing (EF total , contrails + change in CO 2 life cycle emissions) by ∼0.6%. Instead, targeting the same quantity of SAF at a 50% blend ratio to ∼2% of flights responsible for the most highly warming contrails reduces EF contrail and EF total by ∼10 and ∼6%, respectively. Acknowledging forecasting uncertainties, SAF blended at lower ratios (10%) and distributed to more flights (∼9%) still reduces EF contrail (∼5%) and EF total (∼3%). Both strategies deploy SAF on flights with engine particle emissions exceeding 10 12 m −1 , at nighttime, and in winter.

Abstract. Aviation emissions that are dispersed into the Earth's atmosphere affect the climate and air pollution, with significant spatiotemporal variation owing to heterogeneous aircraft activity. In this paper, we use historical flight trajectories derived from Automatic Dependent Surveillance–Broadcast (ADS-B) telemetry and reanalysis weather data for 2019–2021 to develop the Global Aviation emissions inventory based on ADS-B (GAIA). In 2019, 40.2 million flights collectively travelled 61 billion kilometres using 283 Tg of fuel, leading to CO2, NOX, non-volatile particulate matter (nvPM) mass and number emissions of 893 Tg, 4.49 Tg, 21.4 Gg, and 2.8×1026, respectively. Global responses to COVID-19 led to reductions in the annual flight distance flown, CO2, and NOX emissions in 2020 (-43 %, -48 % and -50 %, respectively relative to 2019) and 2021 (-31 %, -41 % and -43 %, respectively) with significant regional variability. Short-haul flights with duration < 3 h accounted for 83 % of all flights, yet only for 35 % of the 2019 CO2 emissions, while long-haul flights with duration > 6 h (5 % of all flights) were responsible for t43 % of CO2 and 49 % of NOX emissions. Globally, actual flight trajectories flown are, on average, ~5 % greater than the great-circle path between the origin-destination airport but this varies by region and flight distance. An evaluation of 8,705 unique flights between London and Singapore showed large variabilities in the flight trajectory profile, fuel consumption and emission indices. GAIA captures the spatiotemporal distribution of aviation activity and emissions and is provided for use in future studies to evaluate the negative externalities arising from global aviation.

Abstract. In this paper we consider a discrete version of the Bernoulli convolution problem traditionally studied via functional analysis. We discuss several innovative algorithms for computing the sequences with this new approach. In particular, these algorithms assist us in gathering data regarding the maximum values. By looking at a family of associated polynomials, we gain insight on the local behavior of the sequence itself. This work was completed as part of the Clemson University REU, an NSF funded program 1 .

Climate impact models of the non-CO2 emissions of aviation are still subject to significant uncertainties. Condensation trails, or contrails, are one of these non-CO2 effects. In order to validate the contrail simulation models, a dataset of observations covering the entire lifetime of the contrails will be required, as well as the characteristics of the aircraft which produced them. This study carries on the work on contrail observation from geostationary satellite by proposing a new way to track contrails and identify the flight that produced it using geostationary satellite infrared images, weather data as well as air traffic data. It solves the tracking and the identification problem as one, each process leveraging information from the other to achieve a better overall result. This study is a new step towards a consistent contrail dataset that could be used to validate contrail models.

Abstract. In this paper we consider a discrete version of the Bernoulli convolution problem traditionally studied via functional analysis. We develop an algorithm which bounds the Bernoulli sequences, and we give a significant improvement on the best known bound.