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      Modeled deposition of nitrogen and sulfur in Europe estimated by 14 air quality model systems: evaluation, effects of changes in emissions and implications for habitat protection

      1 , 1 , 1 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 8 , 10 , 11 , 5 , 5 , 12 , 11 , 9 , 9 , 13 , 5 , 14 , 15 , 11 , 11 , 16 , 11 , 17 , 18 , 19 , 5 , 20 , 17 , 21 , 21 , 22 , 23 , 22 , 24 , 21

      Atmospheric chemistry and physics

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          The evaluation and intercomparison of air quality models is key to reducing model errors and uncertainty. The projects AQMEII3 and EURODELTA-Trends, in the framework of the Task Force on Hemispheric Transport of Air Pollutants and the Task Force on Measurements and Modelling, respectively (both task forces under the UNECE Convention on the Long Range Transport of Air Pollution, LTRAP), have brought together various regional air quality models to analyze their performance in terms of air concentrations and wet deposition, as well as to address other specific objectives.

          This paper jointly examines the results from both project communities by intercomparing and evaluating the deposition estimates of reduced and oxidized nitrogen (N) and sulfur (S) in Europe simulated by 14 air quality model systems for the year 2010. An accurate estimate of deposition is key to an accurate simulation of atmospheric concentrations. In addition, deposition fluxes are increasingly being used to estimate ecological impacts. It is therefore important to know by how much model results differ and how well they agree with observed values, at least when comparison with observations is possible, such as in the case of wet deposition.

          This study reveals a large variability between the wet deposition estimates of the models, with some performing acceptably (according to previously defined criteria) and others underestimating wet deposition rates. For dry deposition, there are also considerable differences between the model estimates. An ensemble of the models with the best performance for N wet deposition was made and used to explore the implications of N deposition in the conservation of protected European habitats. Exceedances of empirical critical loads were calculated for the most common habitats at a resolution of 100 × 100 m 2 within the Natura 2000 network, and the habitats with the largest areas showing exceedances are determined.

          Moreover, simulations with reduced emissions in selected source areas indicated a fairly linear relationship between reductions in emissions and changes in the deposition rates of N and S. An approximate 20 % reduction in N and S deposition in Europe is found when emissions at a global scale are reduced by the same amount. European emissions are by far the main contributor to deposition in Europe, whereas the reduction in deposition due to a decrease in emissions in North America is very small and confined to the western part of the domain. Reductions in European emissions led to substantial decreases in the protected habitat areas with critical load exceedances (halving the exceeded area for certain habitats), whereas no change was found, on average, when reducing North American emissions in terms of average values per habitat.

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          HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution

          The mandate of the Task Force Hemispheric Transport of Air Pollution (TF HTAP) under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) is to improve the scientific understanding of the intercontinental air pollution transport, to quantify impacts on human health, vegetation and climate, to identify emission mitigation options across the regions of the Northern Hemisphere, and to guide future policies on these aspects. The harmonization and improvement of regional emission inventories is imperative to obtain consolidated estimates on the formation of global-scale air pollution. An emissions data set has been constructed using regional emission grid maps (annual and monthly) for SO 2 , NO x , CO, NMVOC, NH 3 , PM 10 , PM 2.5 , BC and OC for the years 2008 and 2010, with the purpose of providing consistent information to global and regional scale modelling efforts. This compilation of different regional gridded inventories – including that of the Environmental Protection Agency (EPA) for USA, the EPA and Environment Canada (for Canada), the European Monitoring and Evaluation Programme (EMEP) and Netherlands Organisation for Applied Scientific Research (TNO) for Europe, and the Model Inter-comparison Study for Asia (MICS-Asia III) for China, India and other Asian countries – was gap-filled with the emission grid maps of the Emissions Database for Global Atmospheric Research (EDGARv4.3) for the rest of the world (mainly South America, Africa, Russia and Oceania). Emissions from seven main categories of human activities (power, industry, residential, agriculture, ground transport, aviation and shipping) were estimated and spatially distributed on a common grid of 0.1° × 0.1° longitude-latitude, to yield monthly, global, sector-specific grid maps for each substance and year. The HTAP_v2.2 air pollutant grid maps are considered to combine latest available regional information within a complete global data set. The disaggregation by sectors, high spatial and temporal resolution and detailed information on the data sources and references used will provide the user the required transparency. Because HTAP_v2.2 contains primarily official and/or widely used regional emission grid maps, it can be recommended as a global baseline emission inventory, which is regionally accepted as a reference and from which different scenarios assessing emission reduction policies at a global scale could start. An analysis of country-specific implied emission factors shows a large difference between industrialised countries and developing countries for acidifying gaseous air pollutant emissions (SO 2 and NO x ) from the energy and industry sectors. This is not observed for the particulate matter emissions (PM 10 , PM 2.5 ), which show large differences between countries in the residential sector instead. The per capita emissions of all world countries, classified from low to high income, reveal an increase in level and in variation for gaseous acidifying pollutants, but not for aerosols. For aerosols, an opposite trend is apparent with higher per capita emissions of particulate matter for low income countries.
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                Author and article information

                Role: Edited by:
                Atmos Chem Phys
                Atmos Chem Phys
                Atmospheric chemistry and physics
                13 August 2018
                18 July 2018
                15 November 2018
                : 18
                : 14
                : 10199-10218
                [1 ]Environmental Department, CIEMAT, Madrid, 28040, Spain
                [2 ]Finnish Meteorological Institute, Helsinki, FI00560, Finland
                [3 ]Cornell University, Ithaca, NY, 14850, USA
                [4 ]NILU-Norwegian Institute for Air Research, Kjeller, 2007, Norway
                [5 ]ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Via Martiri di Monte Sole 4, 40129 Bologna, Italy
                [6 ]Bahcesehir University Engineering and Natural Sciences Faculty. 34353 Besiktas Istanbul, Turkey
                [7 ]SMHI, Swedish Meteorological and Hydrological Institute Norrköping, Norrköping, Sweden
                [8 ]Enviroware srl, Concorezzo, MB, Italy
                [9 ]INERIS, Institut National de l’Environnement Industriel et des Risques, Parc Alata, 60550 Verneuil-en-Halatte, France
                [10 ]Institute of Coastal Research, Chemistry Transport Modelling Group, Helmholtz-Zentrum Geesthacht, Germany
                [11 ]Department of Environmental Science, Aarhus University, Roskilde, 4000, Denmark
                [12 ]Department of Physical and Chemical Sciences, University of L’Aquila, L’Aquila, Italy
                [13 ]Ex European Commission, Joint Research Centre (JRC), 21020 Ispra (Va), Italy
                [14 ]European Centre for Medium-Range Weather Forecasts, Reading, UK
                [15 ]Ricardo Energy & Environment, Gemini Building, Fermi Avenue, Harwell, Oxon, OX11 0QR, UK
                [16 ]Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC, USA
                [17 ]BSC, Barcelona Supercomputing Center, Centro National de Supercomputacidn, Nexus II Building, Jordi Girona, 29, 08034 Barcelona, Spain
                [18 ]Environmental Research Group, Kings’ College London, London, UK
                [19 ]Netherlands Organization for Applied Scientific Research (TNO), Utrecht, the Netherlands
                [20 ]IASS, Institute for Advanced Sustainability Studies, Potsdam, Germany
                [21 ]European Commission, Joint Research Centre (JRC), Ispra (VA), Italy
                [22 ]Climate Modelling and Air Pollution Division, Research and Development Department, Norwegian Meteorological Institute (MET Norway), P.O. Box 43, Blindern, 0313 Oslo, Norway
                [23 ]Eurasia Institute of Earth Sciences, Istanbul Technical University, Turkey
                [24 ]Faculty of Science and Technology, University of Tromsø, Tromsø, Norway
                Author notes

                Author contributions. MGV prepared the paper with contributions from all coauthors; MGV, MRT, and JLG worked on the analysis of all the model results from all the groups. HGG performed the discussion on impacts on ecosystems. MGV and ACo carried out the FRES1 simulations; MP carried out the FI1 simulations. ACo, FC, and BB contributed to the ED_CHIM simulations. UI, JHC, CG, KMH, and JBr carried out the DK1 simulations; RBi and RBe carried out the data upload and management in the ENSEMBLE system for the AQMEII3 project; JBi carried out the DE1 simulations; AF carried out the UK2 simulations; JF carried out the C-IFS model simulations for the boundary conditions of the AQMEII3 simulations; NK carried out the UK1 simulations; UA, LP, and AU contributed with TR1 simulations; SG and CH coordinated AQMEII3 activities; ST and PW were involved in ED_EMEP simulations and the preparation of boundary conditions for the EURODELTA project simulations; CA performed ED_MATCH simulations; MA, GB, Aca, MDI, and MM contributed to ED_MINNI simulations. MTP and OJ participated in ED_CMAQ simulations. AM performed ED_LOTO simulations. WA contributed with the observational dataset, Sect. 2.2, and the related discussion. CC coordinated the data management for the ED-Trends project.

                Correspondence: Marta G. Vivanco ( m.garcia@ )

                This work is distributed under the Creative Commons Attribution 4.0 License.



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