Nitrogen deposition from aviation emissions.

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 NOx 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.

Annual satellite-based NDVI-derived land cover of Europe for 2001-2019.

Land cover plays an important role in the Earth's climate as it affects multiple biochemical cycles and is critical for food security and biodiversity. As land cover is continuously evolving, influenced by anthropogenic and other factors, the availability of temporally varying land cover data sets of large spatial domains is integral to understanding, monitoring, and informing environmental management efforts. Here we use classification trees to generate annual land cover maps of the European continent for 2001 to 2019 on a ∼250 m resolution. The classification trees are trained using gap-filled and smoothed MODIS normalised difference vegetation index (NDVI) satellite data, as well as CORINE reference land cover data. We apply the bagging ensemble technique on oversampled NDVI data, with an additional majority vote for overlapping segments over the continent-wide domain. We distinguish between 39 land cover classes, with a total classification accuracy of 75% and average precision of 76%. The accuracy varies between the classes, with common classes (e.g. agricultural and forest classes) performing better than rarer ones (e.g. artificial land cover). Over the entire continent, we find that artificial land cover, wetlands, and forests have increased on average by 0.76, 0.50 and 0.22%/year respectively, while the agricultural area has decreased by 0.21%/year. We also quantify these changes in land cover on a national and metropolitan level. Given the near-real-time availability of global NDVI data, we note the potential of the presented approach for generating 'near-real-year' annual land cover data sets of large geographic domains, for the continuous monitoring of land cover change and the effects of interventions.

Comparisons of simple and complex methods for quantifying exposure to individual point source air pollution emissions.

Expanded use of reduced complexity approaches in epidemiology and environmental justice investigations motivates detailed evaluation of these modeling approaches. Chemical transport models (CTMs) remain the most complete representation of atmospheric processes but are limited in applications that require large numbers of runs, such as those that evaluate individual impacts from large numbers of sources. This limitation motivates comparisons between modern CTM-derived techniques and intentionally simpler alternatives. We model population-weighted PM2.5 source impacts from each of greater than 1100 coal power plants operating in the United States in 2006 and 2011 using three approaches: (1) adjoint PM2.5 sensitivities calculated by the GEOS-Chem CTM; (2) a wind field-based Lagrangian model called HyADS; and (3) a simple calculation based on emissions and inverse source-receptor distance. Annual individual power plants' nationwide population-weighted PM2.5 source impacts calculated by HyADS and the inverse distance approach have normalized mean errors between 20 and 28% and root mean square error ranges between 0.0003 and 0.0005 µg m-3 compared with adjoint sensitivities. Reduced complexity approaches are most similar to the GEOS-Chem adjoint sensitivities nearby and downwind of sources, with degrading performance farther from and upwind of sources particularly when wind fields are not accounted for.

Premature mortality related to United States cross-state air pollution.

Outdoor air pollution adversely affects human health and is estimated to be responsible for five to ten per cent of the total annual premature mortality in the contiguous United States1-3. Combustion emissions from a variety of sources, such as power generation or road traffic, make a large contribution to harmful air pollutants such as ozone and fine particulate matter (PM2.5)4. Efforts to mitigate air pollution have focused mainly on the relationship between local emission sources and local air quality2. Air quality can also be affected by distant emission sources, however, including emissions from neighbouring federal states5,6. This cross-state exchange of pollution poses additional regulatory challenges. Here we quantify the exchange of air pollution among the contiguous United States, and assess its impact on premature mortality that is linked to increased human exposure to PM2.5 and ozone from seven emission sectors for 2005 to 2018. On average, we find that 41 to 53 per cent of air-quality-related premature mortality resulting from a state's emissions occurs outside that state. We also find variations in the cross-state contributions of different emission sectors and chemical species to premature mortality, and changes in these variations over time. Emissions from electric power generation have the greatest cross-state impacts as a fraction of their total impacts, whereas commercial/residential emissions have the smallest. However, reductions in emissions from electric power generation since 2005 have meant that, by 2018, cross-state premature mortality associated with the commercial/residential sector was twice that associated with power generation. In terms of the chemical species emitted, nitrogen oxides and sulfur dioxide emissions caused the most cross-state premature deaths in 2005, but by 2018 primary PM2.5 emissions led to cross-state premature deaths equal to three times those associated with sulfur dioxide emissions. These reported shifts in emission sectors and emission species that contribute to premature mortality may help to guide improvements to air quality in the contiguous United States.