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. The paper presents the transport module of the System for Integrated modeLling of Atmospheric coMposition SILAM v.5 based on the advection algorithm of Michael Galperin. This advection routine, so far weakly presented in the international literature, is positively defined, stable at any Courant number, and efficient computationally. We present the rigorous description of its original version, along with several updates that improve its monotonicity and shape preservation, allowing for applications to long-living species in conditions of complex atmospheric flows. The scheme is connected with other parts of the model in a way that preserves the sub-grid mass distribution information that is a cornerstone of the advection algorithm. The other parts include the previously developed vertical diffusion algorithm combined with dry deposition, a meteorological preprocessor, and chemical transformation modules.The quality of the advection routine is evaluated using a large set of tests. The original approach has been previously compared with several classic algorithms widely used in operational dispersion models. The basic tests were repeated for the updated scheme and extended with real-wind simulations and demanding global 2-D tests recently suggested in the literature, which allowed one to position the scheme with regard to sophisticated state-of-the-art approaches. The advection scheme performance was fully comparable with other algorithms, with a modest computational cost.

Abstract. We have measured spectral albedo, as well as ancillary parameters, of seasonal European Arctic snow at Sodankylä, Finland (67°22' N, 26°39' E). The springtime intensive melt period was observed during the Snow Reflectance Transition Experiment (SNORTEX) in April 2009. The upwelling and downwelling spectral irradiance, measured at 290–550 nm with a double monochromator spectroradiometer, revealed albedo values of ~0.5–0.7 for the ultraviolet and visible range, both under clear sky and variable cloudiness. During the most intensive snowmelt period of four days, albedo decreased from 0.65 to 0.45 at 330 nm, and from 0.72 to 0.53 at 450 nm. In the literature, the UV and VIS albedo for clean snow are ~0.97–0.99, consistent with the extremely small absorption coefficient of ice in this spectral region. Our low albedo values were supported by two independent simultaneous broadband albedo measurements, and simulated albedo data. We explain the low albedo values to be due to (i) large snow grain sizes up to ~3 mm in diameter; (ii) meltwater surrounding the grains and increasing the effective grain size; (iii) absorption caused by impurities in the snow, with concentration of elemental carbon (black carbon) in snow of 87 ppb, and organic carbon 2894 ppb, at the time of albedo measurements. The high concentrations of carbon, detected by the thermal–optical method, were due to air masses originating from the Kola Peninsula, Russia, where mining and refining industries are located.

Since the first International Cooperative for Aerosol Prediction (ICAP) multi‐model ensemble (MME) study, the number of ICAP global operational aerosol models has increased from five to nine. An update of the current ICAP status is provided, along with an evaluation of the performance of ICAP‐MME over 2012–2017, with a focus on June 2016–May 2017. Evaluated with ground‐based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality MODerate‐resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAP‐MME AOD consensus remains the overall top‐scoring and most consistent performer among all models in terms of root‐mean‐square error (RMSE), bias and correlation for total, fine‐ and coarse‐mode AODs as well as dust AOD; this is similar to the first ICAP‐MME study. Further, over the years, the performance of ICAP‐MME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of ICAP‐MME can be predicted is also examined. Leading predictors are found to be the consensus mean and spread. Regression models of absolute forecast errors were built for AOD forecasts of different lengths for potential applications. ICAP‐MME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 2012–2017 suggests a general tendency for model improvements in fine‐mode AOD, especially over Asia. No significant improvement in coarse‐mode AOD is found overall for this time period.

[1] A new approach to treatment of vertical dispersion and dry deposition of atmospheric aerosols is suggested for primary application in mesoscale to global atmospheric transport models. The vertical exchange scheme extends the resistance analogy formulated earlier for gaseous species and fine aerosols. The approach is based on the exact solution of the steady-state equation for aerosol flux within a finite layer. The flux is expressed as a linear function of concentrations at the layer boundaries and accounts for the vertical inhomogeneity of the diffusion coefficient and the regular vertical velocity. The new dry deposition scheme accounts for physical properties of the air flow, surface and depositing particles. The flow is given by the vertical profile of exchange coefficient and characteristic velocity at the surface. The deposition rate to smooth surfaces is obtained via solution of the budget equation for particle mass. The transition from smooth to rough flow regime is considered. Rough surfaces are characterized by two length scales: the aerodynamic roughness and the "collection scale", introduced in this paper. The collection scale incorporates the effective size of collectors and a ratio of the airflow velocity at the top of the roughness elements to the friction velocity. The particles are described by their physical size, relaxation time and Brownian diffusivity. The scheme was developed basing exclusively on wind-tunnel and numerical experiments available from the literature, and reproduces them well. The data of outdoor experiments have noticeably larger uncertainties, which allowed only general evaluation of their agreement with the predictions.Citation: Kouznetsov, R., and M. Sofiev (2012), A methodology for evaluation of vertical dispersion and dry deposition of atmospheric aerosols,

Abstract:The aim of the research project "Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR)" is to substantially increase the understanding of the stable atmospheric boundary layer (SBL) through a combination of well-established and innovative observation methods as well as by models of different complexity. During three weeks in February 2017, a first field campaign was carried out over the sea ice of the Bothnian Bay in the vicinity of the Finnish island of Hailuoto. Observations were based on ground-based eddy-covariance (EC), automatic weather stations (AWS) and remote-sensing instrumentation as well as more than 150 flight missions by several different Unmanned Aerial Vehicles (UAVs) during mostly stable and very stable boundary layer conditions. The structure of the atmospheric boundary layer (ABL) and above could be resolved at a very high vertical resolution, especially close to the ground, by combining surface-based measurements with UAV observations, i.e., multicopter and fixed-wing profiles up to 200 m agl and 1800 m agl, respectively. Repeated multicopter profiles provided detailed information on the evolution of the SBL, in addition to the continuous SODAR and LIDAR windAtmosphere 2018, 9, 268; doi:10.3390/atmos9070268www.mdpi.com/journal/atmosphereAtmosphere 2018, 9, 268 2 of 29 measurements. The paper describes the campaign and the potential of the collected data set for future SBL research and focuses on both the UAV operations and the benefits of complementing established measurement methods by UAV measurements to enable SBL observations at an unprecedented spatial and temporal resolution.

Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

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