We evaluate public health and climate impacts of low-sulphur fuels in global shipping. Using high-resolution emissions inventories, integrated atmospheric models, and health risk functions, we assess ship-related PM_2.5 pollution impacts in 2020 with and without the use of low-sulphur fuels. Cleaner marine fuels will reduce ship-related premature mortality and morbidity by 34 and 54%, respectively, representing a ~ 2.6% global reduction in PM_2.5 cardiovascular and lung cancer deaths and a ~3.6% global reduction in childhood asthma. Despite these reductions, low-sulphur marine fuels will still account for ~250k deaths and ~6.4 M childhood asthma cases annually, and more stringent standards beyond 2020 may provide additional health benefits. Lower sulphur fuels also reduce radiative cooling from ship aerosols by ~80%, equating to a ~3% increase in current estimates of total anthropogenic forcing. Therefore, stronger international shipping policies may need to achieve climate and health targets by jointly reducing greenhouse gases and air pollution.

This paper presents a state-of-the-art anthropogenic emission inventory developed for the European domain for an 18-year time series (2000–2017) at a 0.05^∘ × 0.1^∘ grid resolution, specifically designed to support air quality modelling. The main air pollutants are included: NO_x, SO₂, non-methane volatile organic compounds (NMVOCs), NH₃, CO, PM₁₀ and PM_2.5, and also CH₄. To stay as close as possible to the emissions as officially reported and used in policy assessment, the inventory uses the officially reported emission data by European countries to the UN Framework Convention on Climate Change, the Convention on Long-Range Transboundary Air Pollution and the EU National Emission Ceilings Directive as the basis where possible. Where deemed necessary because of errors, incompleteness or inconsistencies, these are replaced with or complemented by other emission data, most notably the estimates included in the Greenhouse gas Air pollution Interaction and Synergies (GAINS) model. Emissions are collected at the high sectoral level, distinguishing around 250 different sector–fuel combinations, whereafter a consistent spatial distribution is applied for Europe. A specific proxy is selected for each of the sector–fuel combinations, pollutants and years. Point source emissions are largely based on reported facility-level emissions, complemented by other sources of point source data for power plants. For specific sources, the resulting emission data were replaced with other datasets. Emissions from shipping (both inland and at sea) are based on the results from a separate shipping emission model where emissions are based on actual ship movement data, and agricultural waste burning emissions are based on satellite observations. The resulting spatially distributed emissions are evaluated against earlier versions of the dataset as well as against alternative emission estimates, which reveals specific discrepancies in some cases. Along with the resulting annual emission maps, profiles for splitting particulate matter (PM) and NMVOCs into individual components are provided, as well as information on the height profile by sector and temporal disaggregation down to the hourly level to support modelling activities. Annual grid maps are available in csv and NetCDF format (https://doi.org/10.24380/0vzb-a387, Kuenen et al., 2021).

A method is presented for the evaluation of the exhaust emissions of marine traffic, based on the messages provided by the Automatic Identification System (AIS), which enable the identification and location determination of ships. The use of the AIS data facilitates the positioning of ship emissions with a high spatial resolution, which is limited only by the inaccuracies of the Global Positioning System (typically a few metres) that is used in vessel navigation. The emissions are computed based on the relationship of the instantaneous speed to the design speed, and the detailed technical information of the engines of the ships. The modelling of emissions is also based on a few basic principles of ship design, including the modelling of the propelling power of each vessel in terms of its speed. We have investigated the effect of waves on the consumption of fuel, and on the emissions to the atmosphere. The predictions of fuel consumption were compared with the actual values obtained from the shipowners. For a Roll on – Roll off cargo/passenger ship (RoPax), the predicted and reported values of annual fuel consumption agreed within an accuracy of 6%. According to the data analysis and model computations, the emissions of NO_x, SO_x and CO₂ originating from ships in the Baltic Sea during the full calendar year of 2007 were in total 400 kt, 138 kt and 19 Mt, respectively. A breakdown of emissions by flag state, the type of ship and the year of construction is also presented. The modelling system can be used as a decision support tool in the case of issues concerning, e.g., the health effects caused by shipping emissions or the construction of emission-based fairway dues systems or emissions trading. The computation of emissions can be automated, which will save resources in constructing emission inventories. Both the methodologies and the emission computation program can be applied in any sea region in the world, provided that the AIS data from that specific region are available.

A method is presented for the evaluation of the exhaust emissions of marine traffic, based on the messages provided by the Automatic Identification System (AIS), which enable the positioning of ship emissions with a high spatial resolution (typically a few tens of metres). The model also takes into account the detailed technical data of each individual vessel. The previously developed model was applicable for evaluating the emissions of NO_x, SO_x and CO₂. This paper addresses a substantial extension of the modelling system, to allow also for the mass-based emissions of particulate matter (PM) and carbon monoxide (CO). The presented Ship Traffic Emissions Assessment Model (STEAM2) allows for the influences of accurate travel routes and ship speed, engine load, fuel sulphur content, multiengine setups, abatement methods and waves. We address in particular the modeling of the influence on the emissions of both engine load and the sulphur content of the fuel. The presented methodology can be used to evaluate the total PM emissions, and those of organic carbon, elemental carbon, ash and hydrated sulphate. We have evaluated the performance of the extended model against available experimental data on engine power, fuel consumption and the composition-resolved emissions of PM. We have also compared the annually averaged emission values with those of the corresponding EMEP inventory, As example results, the geographical distributions of the emissions of PM and CO are presented for the marine regions of the Baltic Sea surrounding the Danish Straits.

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC). Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open-data, open-science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.11388387, Smith et al., 2024a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that, for the 2014–2023 decade average, observed warming was 1.19 [1.06 to 1.30] °C, of which 1.19 [1.0 to 1.4] °C was human-induced. For the single-year average, human-induced warming reached 1.31 [1.1 to 1.7] °C in 2023 relative to 1850–1900. The best estimate is below the 2023-observed warming record of 1.43 [1.32 to 1.53] °C, indicating a substantial contribution of internal variability in the 2023 record. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.26 [0.2–0.4] °C per decade over 2014–2023. This high rate of warming is caused by a combination of net greenhouse gas emissions being at a persistent high of 53±5.4 Gt CO₂e yr⁻¹ over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO₂ emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for some of the indicators presented here.