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 PM2.5 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 PM2.5 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.

Abstract. This paper investigates a potential of two remotely sensed wild-land fire characteristics: 4-µm Brightness Temperature Anomaly (TA) and Fire Radiative Power (FRP) for the needs of operational chemical transport modelling and short-term forecasting of atmospheric composition and air quality. The treatments of the TA and FRP data are presented and a methodology for evaluating the emission fluxes of primary aerosols (PM 2.5 and total PM) is described. The method does not include the complicated analysis of vegetation state, fuel load, burning efficiency and related factors, which are uncertain but inevitably involved in approaches based on burnt-area scars or similar products. The core of the current methodology is based on the empirical emission factors that are used to convert the observed temperature anomalies and fire radiative powers into emission fluxes. These factors have been derived from the analysis of several fire episodes in Europe (28.4-5.5.2006, 15.8-25.8.2006 and in August 2008. These episodes were characterised by: (i) well-identified FRP and TA values, and (ii) available groundbased observations of aerosol concentrations, and optical thickness for the regions where the contribution of the fire smoke to the concentrations of PM 2.5 was dominant, in comparison with those of other pollution sources. The emission factors were determined separately for the forested and grassland areas; in case of mixed-type land use, an intermediate scaling was assumed. Despite significant differences between the TA and FRP methodologies, an accurate nonlinear fitting was found between the predictions of these approaches. The agreement was comparatively weak only for small fires, for which the accuracy of both products is exCorrespondence to: M. Sofiev (mikhail.sofiev@fmi.fi) pected to be low. The applications of the Fire Assimilation System (FAS) in combination with the dispersion model SILAM showed that both the TA and FRP products are suitable for the evaluation of the emission fluxes from wild-land fires. The fire-originated concentrations of aerosols (PM 2.5 , PM 10 , sulphates and nitrates) and AOD, as predicted by the SILAM model were mainly within a factor of 2-3 compared with the observations. The main challenges of the FAS improvement include refining of the emission factors globally, determination of the types of fires (smouldering vs flaming), evaluation of the injection heights of the plumes, and predicting the temporal evolution of fires.

[1] A parameterization for the emission of sea-salt aerosol (SSA) particles is presented and its application in the SILAM dispersion modeling system for regional and global SSA simulations is discussed. The SSA production term is based on the parameterization of Monahan et al. and on experimental data from Mårtensson et al., and Clarke et al. The observational data were used to extend the Monahan et al. SSA emission flux to particles as small as 10 nm (dry particle diameter D P ) and to account for water temperature and salinity. The result is an analytical formulation describing the SSA production fluxes for particles with D p between 10 nm and 10 mm. This source function is implemented in the dispersion model SILAM and applied to compute the distribution of sea salt over the North Atlantic and Western Europe for the years 2000, 2003, 2007, 2009 and 2010, as well as globally for 2001 and 2008. The computed annual global production of SSA is between 6700 and 7400 Tg/year. Comparison of the SILAM near-surface SSA concentrations and its wet deposition with the in situ EMEP observations showed good agreement for summer periods while in winter time the model tends to under-estimate the wet deposition by a factor of ∼3. The underestimation is attributed to the coarse fraction (D p > 10 mm) and the spume production mechanism, which were excluded from the analysis, to the wet deposition parameterization in SILAM and to the under-estimated precipitation amount in the input meteodata. The predicted vertically integrated aerosol optical depth (AOD) showed a close match with satellite observations over SSA-dominated areas.Citation: Sofiev, M., J. Soares, M. Prank, G. de Leeuw, and J. Kukkonen (2011), A regional-to-global model of emission and transport of sea salt particles in the atmosphere,

More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting

Plastic pollution is one of the most pressing environmental and social issues of the 21st century. Recent work has highlighted the atmosphere’s role in transporting microplastics to remote locations [S. Allen et al., Nat. Geosci. 12, 339 (2019) and J. Brahney, M. Hallerud, E. Heim, M. Hahnenberger, S. Sukumaran, Science 368, 1257–1260 (2020)]. Here, we use in situ observations of microplastic deposition combined with an atmospheric transport model and optimal estimation techniques to test hypotheses of the most likely sources of atmospheric plastic. Results suggest that atmospheric microplastics in the western United States are primarily derived from secondary re-emission sources including roads (84%), the ocean (11%), and agricultural soil dust (5%). Using our best estimate of plastic sources and modeled transport pathways, most continents were net importers of plastics from the marine environment, underscoring the cumulative role of legacy pollution in the atmospheric burden of plastic. This effort uses high-resolution spatial and temporal deposition data along with several hypothesized emission sources to constrain atmospheric plastic. Akin to global biogeochemical cycles, plastics now spiral around the globe with distinct atmospheric, oceanic, cryospheric, and terrestrial residence times. Though advancements have been made in the manufacture of biodegradable polymers, our data suggest that extant nonbiodegradable polymers will continue to cycle through the earth’s systems. Due to limited observations and understanding of the source processes, there remain large uncertainties in the transport, deposition, and source attribution of microplastics. Thus, we prioritize future research directions for understanding the plastic cycle.

Ten state-of-the-science regional air quality (AQ) modeling systems have been applied to continental-scale domains in North America and Europe for full-year simulations of 2006 in the context of Air Quality Model Evaluation International Initiative (AQMEII), whose main goals are model intercomparison and evaluation. Standardised modeling outputs from each group have been shared on the web-distributed ENSEMBLE system, which allows statistical and ensemble analyses to be performed. In this study, the one-year model simulations are inter-compared and evaluated with a large set of observations for ground-level particulate matter (PK10 and PM2.5) and its chemical components. Modeled concentrations of gaseous PM precursors, SO2 and NO2, have also been evaluated against observational data for both continents. Furthermore, modeled deposition (dry and wet) and emissions of several species relevant to PM are also inter-compared. The unprecedented scale of the exercise (two continents, one full year, fifteen modeling groups) allows for a detailed description of AQ model skill and uncertainty with respect to PM. Analyses of PM10 yearly time series and mean diurnal cycle show a large underestimation throughout the year for the AQ models included in AQMEII. The possible causes of PM bias, including errors in the emissions and meteorological inputs (e.g., wind speed and precipitation), and the calculated deposition are investigated. Further analysis of the coarse PM components, PM2.5 and its major components (SO4, NH4, NO3, elemental carbon), have also been performed, and the model performance for each component evaluated against measurements. Finally, the ability of the models to capture high PM concentrations has been evaluated by examining two separate PM2.5 episodes in Europe and North America. A large variability among models in predicting emissions, deposition, and concentration of PM and its precursors during the episodes has been found. Major challenges still remain with regards to identifying and eliminating the sources of PM bias in the models. Although PM2.5 was found to be much better estimated by the models than PM10, no model was found to consistently match the observations for all locations throughout the entire yea

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continents. The focus for this particular evaluation was meteorological parameters relevant to air 51 quality processes such as transport and mixing, chemistry, and surface fluxes. The unprecedented 52 scale of the exercise (one year, two continents) allowed us to examine the general characteristics of 53 meteorological models' skill and uncertainty. In particular, we found that there was a large 54 variability between models or even model versions in predicting key parameters such as surface 55 shortwave radiation. We also found several systematic model biases such as wind speed 56 overestimations, particularly during stable conditions. We conclude that major challenges still remain 57 in the simulation of meteorology, such as nighttime meteorology and cloud/radiation processes, for 58 air quality simulation. 59 60 61 -3 -