Atmospheric deposition of nitrogen to the Baltic Sea in the period 1995–2006

The EMEP/MSC-W model has been used to compute atmospheric nitrogen deposition into the Baltic Sea basin for the period of 12 yr: 1995–2006. The level of annual total nitrogen deposition into the Baltic Sea basin has changed from 230 Gg N in 1995 to 199 Gg N in 2006, decreasing 13 %. This value corresponds well with the total nitrogen emission reduction (11 %) in the HELCOM Contracting Parties. However, inter-annual variability of nitrogen deposition to the Baltic Sea basin is relatively large, ranging from −13 % to +17 % of the averaged value. It is mainly caused by the changing meteorological conditions and especially precipitation in the considered period. The calculated monthly deposition pattern is similar for most of the years showing maxima in the autumn months October and November. The source allocation budget for atmospheric nitrogen deposition to the Baltic Sea basin was calculated for each year of the period 1997–2006. The main emission sources contributing to total nitrogen deposition are: Germany 18–22 %, Poland 11–13 % and Denmark 8–11 %. There is also a significant contribution from distant sources like the United Kingdom 6–9 %, as well as from the international ship traffic on the Baltic Sea 4–5 %

Comparison of modelled and monitored deposition fluxes of sulphur and nitrogen to ICP-forest sites in Europe

The EMEP MSC-W Eulerian chemical transport model, and its predictions of deposition of acidifying and eutrophying pollutants over Europe, play a key role in the development of emission control strategies for Europe. It is important that this model is tested against observational data. Here we compare the results of the EMEP model with measured data from 160 sites of the European Union/ICP Forest (Level II) monitoring network, for the years 1997 and 2000. This comparison comprises: (a) Precipitation amount, (b) Total deposition of SO42- to coniferous and deciduous forests, (c) Wet deposition of SO42-, NO3- and NH4+ in open field sites, and (d) Concentrations of SO42-, NO3- and NH4+ in precipitation. Concerning precipitation, the EMEP model and ICP network showed very similar overall levels (within 4% for 1997 and 11% for 2000). The correlation was, however, poor (r2=0.15-0.23). This can be attributed largely to the influence of a few outliers, combined with a small range of rainfall amounts for most points. Correlations between modelled and observed deposition values in this study were rather high (r2 values between 0.4-0.8 for most components and years), with mean values across all sites being within 30%. The EMEP model tends to give somewhat lower values for SO42-, NO3- and NH4+ wet deposition to ICP, but differences in mean values were within 20% in 1997 and 30% in 2000. Modelled and observed concentrations of SO 42-, NO3- and NH4+ in precipitation are very similar on average (differences of 0-14%), with good correlation between modelled and observed data (r 2=0.50-0.78). Differences between the EMEP model and ICP measurements are thought to arise from a mixture of problems with both the observations and model. However, the overall conclusion is that the EMEP model performs rather well in reproducing patterns of S and N deposition to European forests

Evaluating the ecological realism of plant species distribution models with ecological indicator values

Species distribution models (SDMs) are routinely applied to assess current as well as future species distributions, for example to assess impacts of future environmental change on biodiversity or to underpin conservation planning. It has been repeatedly emphasized that SDMs should be evaluated based not only on their goodness of fit to the data, but also on the realism of the modelled ecological responses. However, possibilities for the latter are hampered by limited knowledge on the true responses as well as a lack of quantitative evaluation methods. Here we compared modelled niche optima obtained from European-scale SDMs of 1,476 terrestrial vascular plant species with empirical ecological indicator values indicating the preferences of plant species for key environmental conditions. For each plant species we first fitted an ensemble SDM including three modeling techniques (GLM, GAM and BRT) and extracted niche optima for climate, soil, land use and nitrogen deposition variables with a large explanatory power for the occurrence of that species. We then compared these SDM-derived niche optima with the ecological indicator values by means of bivariate correlation analysis. We found weak to moderate correlations in the expected direction between the SDM-derived niche optima and ecological indicator values. The strongest correlation occurred between the modelled optima for growing degree days and the ecological indicator values for temperature. Correlations were weaker for SDM-derived niche optima with a more distal relationship to ecological indicator values (notably precipitation and soil moisture). Further, correlations were consistently highest for BRT, followed by GLM and GAM. Our method gives insight into the ecological realism of modelled niche optima and projected core habitats and can be used to improve SDMs by making a more informed selection of environmental variables and modeling techniques

Screening of the EMEP source receptor relationships: application to five European countries

In this work, a methodology based on the calculation of potencies and potentials is used to screen modeled emission reduction scenarios performed with the European Monitoring and Evaluation Programme/Meteorological Synthesizing Centre-West (EMEP/MSC-W) air quality model. Specific indicators are proposed to look at the results in terms of model processes (potencies) as well as in terms of their impacts on policy (potentials). A specific template to screen the results is also developed and applied. The EMEP/MSC-W model results obtained for 5 EU countries for 5 precursors and 2 levels of emission reductions (15 and 40 %) are analyzed with the following purposes: (i) build confidence in the processes implemented in the model, (ii) identify potential for national abatement versus transboundary transport, (iii) assess the relative importance of various precursor emissions, and (iv) estimate the importance of non-linearity with respect to the level of emission reduction chosen and among the precursor emissions. The proposed methodology proves to be very useful for comparing the responses across countries and precursors in a uniform way. The results confirm our knowledge in terms of processes implemented in the EMEP/MSC-W model. The validity of the linear assumption made during the derivation of the EMEPbased source receptor relationships is generally valid although minor non-linearities with respect to NH3 (all countries) and NOx (in Italy) are observed. Because no true reference can be used to assess the quality of the model results in scenario mode, it is important to consider this screening as a benchmark to which other models or updated versions of the EMEP/MSC-W model can be compared to in the future

Can we explain the trends in European ozone levels?

International audienc

Modelling street level PM10 concentrations across Europe: source apportionment and possible futures

Despite increasing emission controls, particulate matter (PM) has remained a critical issue for European air quality in recent years. The various sources of PM, both from primary particulate emissions as well as secondary formation from precursor gases, make this a complex problem to tackle. In order to allow for credible predictions of future concentrations under policy assumptions, a modelling approach is needed that considers all chemical processes and spatial dimensions involved, from long-range transport of pollution to local emissions in street canyons. Here we describe a modelling scheme which has been implemented in the GAINS integrated assessment model to assess compliance with PM10 (PM with aerodynamic diameter <10 um) limit values at individual air quality monitoring stations reporting to the AirBase database. The modelling approach relies on a combination of bottom up modelling of emissions, simplified atmospheric chemistry and dispersion calculations, and a traffic increment calculation wherever applicable. At each monitoring station fulfilling a few data coverage criteria, measured concentrations in the base year 2009 are explained to the extent possible and then modelled for the past and future. More than 1850 monitoring stations are covered, including more than 300 traffic stations and 80% of the stations which exceeded the EU air quality limit values in 2009. As a validation, we compare modelled trends in the period 2000-2008 to observations, which are well reproduced. The modelling scheme is applied here to quantify explicitly source contributions to ambient concentrations at several critical monitoring stations, displaying the differences in spatial origin and chemical composition of urban roadside PM10 across Europe. Furthermore, we analyse the predicted evolution of PM10 concentrations in the European Union until 2030 under different policy scenarios. Significant improvements in ambient PM10 concentrations are expected assuming successful implementation of already agreed legislation; however, these will not be large enough to ensure attainment of PM10 limit values in hot spot locations such as Southern Poland and major European cities. Remaining issues are largely eliminated in a scenario applying the best available emission control technologies to the maximal technically feasible extent

Spatial and temporal variations in ammonia emissions – a freely accessible model code for Europe

Deriving a parameterisation of ammonia emissions for use in chemistry-transport models (CTMs) is a complex problem as the emission varies locally as a result of local climate and local agricultural management. In current CTMs such factors are generally not taken into account. This paper demonstrates how local climate and local management can be accounted for in CTMs by applying a modular approach for deriving data as input to a dynamic ammonia emission model for Europe. Default data are obtained from information in the RAINS system, and it is demonstrated how this dynamic emission model based on these input data improves the NH&lt;sub&gt;3&lt;/sub&gt; calculations in a CTM model when the results are compared with calculations obtained by traditional methods in emission handling. It is also shown how input data can be modified over a specific target region resulting in even further improvement in performance over this domain. The model code and the obtained default values for the modelling experiments are available as supplementary information to this article for use by the modelling community on similar terms as the EMEP CTM model: the GPL licencse v3

Airborne nitrogen deposition to the Baltic Sea: past trends, source allocation and future

Despite significant reductions in nitrogen emissions achieved in Europe during the last three decades, eutrophication remains an environmental concern in the Baltic Sea basin. Recently, a number of comprehensive modelling studies have been conducted for the HELCOM Commission to inform the 2021 update of the Baltic Sea Action Plan. The calculations have focused on trends in airborne nitrogen deposition to the Baltic Sea and its nine sub-basins during the 2000-2017 period, the identification and ranking of the main contributors to deposition, as well as future projections for 2030, assuming compliance with the Gothenburg Protocol and the EU NEC Directive. This paper synthesizes the main results from these studies and puts them into the context of maximum allowable nutrient inputs to the Baltic Sea. According to our results, the airborne annual deposition to the Baltic Sea in 2017 amounted to 122.6 Gg(N) of oxidized nitrogen and 105.3 Gg(N) of reduced nitrogen, corresponding to a decrease since 2000 by, respectively, 39% and 11%. In order to filter out the large inter-annual variability due to meteorology and to better reflect trends in emissions, weather-normalized depositions of nitrogen have been calculated as well, according to which the decreases since 2000 amount to 35%, 7% and 25% for oxidized, reduced and total nitrogen, respectively. In 2017, Germany, Poland and Denmark were the most important contributors to airborne deposition of total (oxidized + reduced) nitrogen to the Baltic Sea. Agriculture contributed most to reduced nitrogen deposition, while the transport sector contributed most to oxidized nitrogen deposition. Agriculture in Germany was the single-most important contributor to nitrogen deposition to the Baltic Sea basin in 2017 (accounting for about 15% of the total), but there are numerous other important sectoral contributions. Emissions of nitrogen from the nine HELCOM Contracting Parties contributed 49%, 76% and 61% to oxidized, reduced and total nitrogen deposition, respectively. Assuming full compliance with the EU NEC Directive and the Gothenburg Protocol, significant further reductions in nitrogen deposition can be achieved by 2030, down to an annual deposition of 72.7 Gg(N) and 84.7 Gg(N) of oxidized and reduced nitrogen, respectively

Good Agreement Between Modeled and Measured Sulfur and Nitrogen Deposition in Europe, in Spite of Marked Differences in Some Sites

Atmospheric nitrogen and sulfur deposition is an important effect of atmospheric pollution and may affect forest ecosystems positively, for example enhancing tree growth, or negatively, for example causing acidification, eutrophication, cation depletion in soil or nutritional imbalances in trees. To assess and design measures to reduce the negative impacts of deposition, a good estimate of the deposition amount is needed, either by direct measurement or by modeling. In order to evaluate the precision of both approaches and to identify possible improvements, we compared the deposition estimates obtained using an Eulerian model with the measurements performed by two large independent networks covering most of Europe. The results are in good agreement (bias &lt;25%) for sulfate and nitrate open field deposition, while larger differences are more evident for ammonium deposition, likely due to the greater influence of local ammonia sources. Modeled sulfur total deposition compares well with throughfall deposition measured in forest plots, while the estimate of nitrogen deposition is affected by the tree canopy. The geographical distribution of pollutant deposition and of outlier sites where model and measurements show larger differences are discussed

Impact of regional climate change and future emission scenarios on surface O3 and PM2.5 over India

Eleven of the world\u27s 20 most polluted cities are located in India and poor air quality is already a major public health issue. However, anthropogenic emissions are predicted to increase substantially in the short-term (2030) and medium-term (2050) futures in India, especially if no further policy efforts are made. In this study, the EMEP/MSC-W chemical transport model has been used to predict changes in surface ozone (O3) and fine particulate matter (PM 2.5 ) for India in a world of changing emissions and climate. The reference scenario (for present-day) is evaluated against surface-based measurements, mainly at urban stations. The evaluation has also been extended to other data sets which are publicly available on the web but without quality assurance. The evaluation shows high temporal correlation for O 3 (r = 0.9) and high spatial correlation for PM 2.5 (r = 0.5 and r = 0.8 depending on the data set) between the model results and observations. While the overall bias in PM 2.5 is small (lower than 6%), the model overestimates O 3 by 35%. The underestimation in NO x titration is probably the main reason for the O 3 overestimation in the model. However, the level of agreement can be considered satisfactory in this case of a regional model being evaluated against mainly urban measurements, and given the inevitable uncertainties in much of the input data. For the 2050s, the model predicts that climate change will have distinct effects in India in terms of O 3 pollution, with a region in the north characterized by a statistically significant increase by up to 4% (2 ppb) and one in the south by a decrease up to -3% (-1.4 ppb). This variation in O 3 is assumed to be partly related to changes in O 3 deposition velocity caused by changes in soil moisture and, over a few areas, partly also by changes in biogenic non-methane volatile organic compounds. Our calculations suggest that PM 2.5 will increase by up to 6.5% over the Indo-Gangetic Plain by the 2050s. The increase over India is driven by increases in dust, particulate organic matter (OM) and secondary inorganic aerosols (SIAs), which are mainly affected by the change in precipitation, biogenic emissions and wind speed. The large increase in anthropogenic emissions has a larger impact than climate change, causing O 3 and PM 2.5 levels to increase by 13 and 67% on average in the 2050s over the main part of India, respectively. By the 2030s, secondary inorganic aerosol is predicted to become the second largest contributor to PM 2.5 in India, and the largest in the 2050s, exceeding OM and dust