Aviation emissions impact surface air quality at multiple scales-from near-airport pollution peaks associated with airport landing and take off (LTO) emissions, to intercontinental pollution attributable to aircraft cruise emissions. Previous studies have quantified aviation's air quality impacts around a specific airport, in a specific region, or at the global scale. However, no study has assessed the air quality and human health impacts of aviation, capturing effects on all aforementioned scales. This study uses a multi-scale modeling approach to quantify and monetize the air quality impact of civil aviation emissions, approximating effects of aircraft plume dynamics-related local dispersion (∼1 km), near-airport dispersion (∼10 km), regional (∼1000 km) and global (∼10 000 km) scale chemistry and transport. We use concentration-response functions to estimate premature deaths due to population exposure to aviation-attributable PM 2.5 and ozone, finding that aviation emissions cause ∼16 000 (90% CI: 8300-24 000) premature deaths per year. Of these, LTO emissions contribute a quarter. Our estimate shows that premature deaths due to long-term exposure to aviation-attributable PM 2.5 and O 3 lead to costs of ∼$21 bn per year. We compare these costs to other societal costs of aviation and find that they are on the same order of magnitude as global aviation-attributable climate costs, and one order of magnitude larger than aviation-attributable accident and noise costs.

Aviation emissions are not on a trajectory consistent with Paris Climate Agreement goals. We evaluate the extent to which fuel pathways-synthetic fuels from biomass, synthetic fuels from green hydrogen and atmospheric CO 2 , and the direct use of green liquid hydrogen-could lead aviation towards net-zero climate impacts. Together with continued efficiency gains and contrail avoidance, but without offsets, such an energy transition could reduce lifecycle aviation CO 2 emissions by 89-94% compared with year-2019 levels, despite a 2-3-fold growth in demand by 2050. The aviation sector could manage the associated cost increases, with ticket prices rising by no more than 15% compared with a no-intervention baseline leading to demand suppression of less than 14%. These pathways will require discounted investments on the order of US$0.5-2.1 trillion over a 30 yr period. However, our pathways reduce aviation CO 2 -equivalent emissions by only 46-69%; more action is required to mitigate non-CO 2 impacts.

SUMMARY ObjectiveScheduled air transport services connect airports throughout the world and thereby enable interaction on a global scale. By doing so, they spur globalization (Hummels, 2007) as well as social and economic development (Lakshmanan, 2011). In order to facilitate integration of regions into global value chains, planners, scholars and policymakers therefore need to understand as to how scheduled air transport services link a region to other markets. For this purpose, connectivity metrics have been developed, which measure the degree of connections between airports (Burghouwt, Redondi, 2013). In particular, the 'connection quality-weighting' approach (Veldhuis, 1997;Burghouwt, de Wit, 2005) has been used to compute the aggregate quality of all available connections at an airport with regard to their properties in quickly bridging distances. However, such a metric has neither been calibrated on the basis of observed passenger behavior nor been computed for the world's airports across a multi-decade time series. This paper sets out to develop the first such metric and to discuss global airline network development between 1990 and 2012 from a connectivity perspective. How Air Transport Connects the World 4 MethodologyThe Global Connectivity Index (GCI) for each airport is computed by summing the connectionquality of each available flight connection weighted by the interaction potential, to which the connection provides access. This requires three levels of analysis. First, on the link-identification level, we identify from OAG flight schedules all scheduled nonstop and onestop connections, which are available to passengers at each airport. Second, on the link-quality level, we compute each connection's frequency and relative connectivity value as compared to (hypothetical) nonstop flights. The relative connectivity value is derived from flight duration and layover time and calibrated through observed routing data for US passengers. Third, on the destination-quality level, we model the interaction potential, to which each worldwide airport provides access. For this purpose, we use gridded wealth-adjusted population data and a distance-decay function. Transaction-specific idiosyncrasies such as tastes or fares, which vary among potential passengers and impact on each passenger's itinerary choice, are not considered since they cannot be aggregated to the route level, yet. Results By computing yearly GCITo date, no analysis exists which evaluates 'quality-weighted' connectivity and/or centrality at the world's airports with the help of an empirically calibrated model. Such a model is developed in this paper. We compute these metrics to analyze worldwide connectivity and centrality trends The remainder of this paper proceeds as follows: In Section 2, the building blocks of 'connection quality-weighted' connectivity and centrality are outlined. Section 3 develops the connectivity and the centrality metrics. Global and world-region trends in connectivity and centrality between 1990 and 2012 are analyzed in Sect...