The EURODELTA III exercise has facilitated a comprehensive intercomparison and evaluation of chemistry transport model performances. Participating models performed calculations for four 1-month periods in different seasons in the years 2006 to 2009, allowing the influence of different meteorological conditions on model performances to be evaluated. The exercise was performed with strict requirements for the input data, with few exceptions. As a consequence, most of differences in the outputs will be attributed to the differences in model formulations of chemical and physical processes. The models were evaluated mainly for background rural stations in Europe. The performance was assessed in terms of bias, root mean square error and correlation with respect to the concentrations of air pollutants (NO₂, O₃, SO₂, PM₁₀ and PM_2.5), as well as key meteorological variables. Though most of meteorological parameters were prescribed, some variables like the planetary boundary layer (PBL) height and the vertical diffusion coefficient were derived in the model preprocessors and can partly explain the spread in model results. In general, the daytime PBL height is underestimated by all models. The largest variability of predicted PBL is observed over the ocean and seas. For ozone, this study shows the importance of proper boundary conditions for accurate model calculations and then on the regime of the gas and particle chemistry. The models show similar and quite good performance for nitrogen dioxide, whereas they struggle to accurately reproduce measured sulfur dioxide concentrations (for which the agreement with observations is the poorest). In general, the models provide a close-to-observations map of particulate matter (PM_2.5 and PM₁₀) concentrations over Europe rather with correlations in the range 0.4–0.7 and a systematic underestimation reaching −10 µg m⁻³ for PM₁₀. The highest concentrations are much more underestimated, particularly in wintertime. Further evaluation of the mean diurnal cycles of PM reveals a general model tendency to overestimate the effect of the PBL height rise on PM levels in the morning, while the intensity of afternoon chemistry leads formation of secondary species to be underestimated. This results in larger modelled PM diurnal variations than the observations for all seasons. The models tend to be too sensitive to the daily variation of the PBL. All in all, in most cases model performances are more influenced by the model setup than the season. The good representation of temporal evolution of wind speed is the most responsible for models' skillfulness in reproducing the daily variability of pollutant concentrations (e.g. the development of peak episodes), while the reconstruction of the PBL diurnal cycle seems to play a larger role in driving the corresponding pollutant diurnal cycle and hence determines the presence of systematic positive and negative biases detectable on daily basis.

In central Europe, ammonium sulphate and ammonium nitrate make up a large fraction of fine particles which pose a threat to human health. Most studies on air pollution through particulate matter investigate the influence of emission reductions of sulphur- and nitrogen oxides on aerosol concentration. Here, we focus on the influence of ammonia (NH3) emissions. Emission scenarios have been created on the basis of the improved ammonia emission parameterization implemented in the SMOKE for Europe and CMAQ model systems described in part I of this study. This includes emissions based on future European legislation (the National Emission Ceilings) as well as a dynamic evaluation of the influence of different agricultural sectors (e.g. animal husbandry) on particle formation. The study compares the concentrations of NH3, View the MathML sourceNH4+, NO3 -, sulphur compounds and the total concentration of particles in winter and summer for a political-, technical- and behavioural scenario. It was found that a reduction of ammonia emissions by 50% lead to a 24% reduction of the total PM2.5 concentrations in northwest Europe. The observed reduction was mainly driven by reduced formation of ammonium nitrate. Moreover, emission reductions during winter had a larger impact than during the rest of the year. This leads to the conclusion that a reduction of the ammonia emissions from the agricultural sector related to animal husbandry could be more efficient than the reduction from other sectors due to its larger share in winter ammonia emissions

Current understanding of mercury (Hg) behavior in the atmosphere contains significant gaps. Some key characteristics of Hg processes, including anthropogenic and geogenic emissions, atmospheric chemistry, and air–surface exchange, are still poorly known. This study provides a complex analysis of processes governing Hg fate in the atmosphere involving both measured data from ground-based sites and simulation results from chemical transport models. A variety of long-term measurements of gaseous elemental Hg (GEM) and reactive Hg (RM) concentration as well as Hg wet deposition flux have been compiled from different global and regional monitoring networks. Four contemporary global-scale transport models for Hg were used, both in their state-of-the-art configurations and for a number of numerical experiments to evaluate particular processes. Results of the model simulations were evaluated against measurements. As follows from the analysis, the interhemispheric GEM gradient is largely formed by the prevailing spatial distribution of anthropogenic emissions in the Northern Hemisphere. The contributions of natural and secondary emissions enhance the south-to-north gradient, but their effect is less significant. Atmospheric chemistry has a limited effect on the spatial distribution and temporal variation of GEM concentration in surface air. In contrast, RM air concentration and wet deposition are largely defined by oxidation chemistry. The Br oxidation mechanism can reproduce successfully the observed seasonal variation of the RM ∕ GEM ratio in the near-surface layer, but it predicts a wet deposition maximum in spring instead of in summer as observed at monitoring sites in North America and Europe. Model runs with OH chemistry correctly simulate both the periods of maximum and minimum values and the amplitude of observed seasonal variation but shift the maximum RM ∕ GEM ratios from spring to summer. O₃ chemistry does not predict significant seasonal variation of Hg oxidation. Hence, the performance of the Hg oxidation mechanisms under study differs in the extent to which they can reproduce the various observed parameters. This variation implies possibility of more complex chemistry and multiple Hg oxidation pathways occurring concurrently in various parts of the atmosphere.

The North Sea is one of the areas with the highest ship traffic densities worldwide. At any time, about 3000 ships are sailing its waterways. Previous scientific publications have shown that ships contribute significantly to atmospheric concentrations of NO_x, particulate matter and ozone. Especially in the case of particulate matter and ozone, this influence can even be seen in regions far away from the main shipping routes. In order to quantify the effects of North Sea shipping on air quality in its bordering states, it is essential to determine the emissions from shipping as accurately as possible. Within Interreg IVb project Clean North Sea Shipping (CNSS), a bottom-up approach was developed and used to thoroughly compile such an emission inventory for 2011 that served as the base year for the current emission situation. The innovative aspect of this approach was to use load-dependent functions to calculate emissions from the ships' current activities instead of averaged emission factors for the entire range of the engine loads. These functions were applied to ship activities that were derived from hourly records of Automatic Identification System signals together with a database containing the engine characteristics of the vessels that traveled the North Sea in 2011. The emission model yielded ship emissions among others of NO_x and SO₂ at high temporal and spatial resolution that were subsequently used in a chemistry transport model in order to simulate the impact of the emissions on pollutant concentration levels. The total emissions of nitrogen reached 540 Gg and those of sulfur oxides 123 Gg within the North Sea – including the adjacent western part of the Baltic Sea until 5° W. This was about twice as much of those of a medium-sized industrialized European state like the Netherlands. The relative contribution of ships to, for example, NO₂ concentration levels ashore close to the sea can reach up to 25 % in summer and 15 % in winter. Some hundred kilometers away from the sea, the contribution was about 6 % in summer and 4 % in winter. The relative contribution of the secondary pollutant NO₃⁻ was found to reach 20 % in summer and 6 % in winter even far from the shore.

The impact of air pollution on human health and the associated external costs in Europe and the United States (US) for the year 2010 are modeled by a multi-model ensemble of regional models in the frame of the third phase of the Air Quality Modelling Evaluation International Initiative (AQMEII3). The modeled surface concentrations of O₃, CO, SO₂ and PM_2.5 are used as input to the Economic Valuation of Air Pollution (EVA) system to calculate the resulting health impacts and the associated external costs from each individual model. Along with a base case simulation, additional runs were performed introducing 20 % anthropogenic emission reductions both globally and regionally in Europe, North America and east Asia, as defined by the second phase of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP2). Health impacts estimated by using concentration inputs from different chemistry–transport models (CTMs) to the EVA system can vary up to a factor of 3 in Europe (12 models) and the United States (3 models). In Europe, the multi-model mean total number of premature deaths (acute and chronic) is calculated to be 414 000, while in the US, it is estimated to be 160 000, in agreement with previous global and regional studies. The economic valuation of these health impacts is calculated to be EUR 300 billion and 145 billion in Europe and the US, respectively. A subset of models that produce the smallest error compared to the surface observations at each time step against an all-model mean ensemble results in increase of health impacts by up to 30 % in Europe, while in the US, the optimal ensemble mean led to a decrease in the calculated health impacts by ∼ 11 %. A total of 54 000 and 27 500 premature deaths can be avoided by a 20 % reduction of global anthropogenic emissions in Europe and the US, respectively. A 20 % reduction of North American anthropogenic emissions avoids a total of ∼ 1000 premature deaths in Europe and 25 000 total premature deaths in the US. A 20 % decrease of anthropogenic emissions within the European source region avoids a total of 47 000 premature deaths in Europe. Reducing the east Asian anthropogenic emissions by 20 % avoids ∼ 2000 total premature deaths in the US. These results show that the domestic anthropogenic emissions make the largest impacts on premature deaths on a continental scale, while foreign sources make a minor contribution to adverse impacts of air pollution.