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      Influence of anthropogenic emissions and boundary conditions on multi-model simulations of major air pollutants over Europe and North America in the framework of AQMEII3

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          Abstract

          In the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3), and as contribution to the second phase of the Hemispheric Transport of Air Pollution (HTAP2) activities for Europe and North America, the impacts of a 20 % decrease of global and regional anthropogenic emissions on surface air pollutant levels in 2010 are simulated by an international community of regional-scale air quality modeling groups, using different state-of-the-art chemistry and transport models (CTMs). The emission perturbations at the global level, as well as over the HTAP2-defined regions of Europe, North America and East Asia, are first simulated by the global Composition Integrated Forecasting System (C-IFS) model from European Centre for Medium-Range Weather Forecasts (ECMWF), which provides boundary conditions to the various regional CTMs participating in AQMEII3. On top of the perturbed boundary conditions, the regional CTMs used the same set of perturbed emissions within the regional domain for the different perturbation scenarios that introduce a 20 % reduction of anthropogenic emissions globally as well as over the HTAP2-defined regions of Europe, North America and East Asia.

          Results show that the largest impacts over both domains are simulated in response to the global emission perturbation, mainly due to the impact of domestic emission reductions. The responses of NO 2, SO 2 and PM concentrations to a 20 % anthropogenic emission reduction are almost linear (~ 20 % decrease) within the global perturbation scenario with, however, large differences in the geographical distribution of the effect. NO 2, CO and SO 2 levels are strongly affected over the emission hot spots. O 3 levels generally decrease in all scenarios by up to ~ 1 % over Europe, with increases over the hot spot regions, in particular in the Benelux region, by an increase up to ~ 6 % due to the reduced effect of NO x titration. O 3 daily maximum of 8 h running average decreases in all scenarios over Europe, by up to ~ 1 %. Over the North American domain, the central-to-eastern part and the western coast of the US experience the largest response to emission perturbations. Similar but slightly smaller responses are found when domestic emissions are reduced. The impact of intercontinental transport is relatively small over both domains, however, still noticeable particularly close to the boundaries. The impact is noticeable up to a few percent, for the western parts of the North American domain in response to the emission reductions over East Asia. O 3 daily maximum of 8 h running average decreases in all scenarios over north Europe by up to ~ 5 %. Much larger reductions are calculated over North America compared to Europe.

          In addition, values of the Response to Extra-Regional Emission Reductions (RERER) metric have been calculated in order to quantify the differences in the strengths of nonlocal source contributions to different species among the different models. We found large RERER values for O 3 (~ 0.8) over both Europe and North America, indicating a large contribution from non-local sources, while for other pollutants including particles, low RERER values reflect a predominant control by local sources. A distinct seasonal variation in the local vs. non-local contributions has been found for both O 3 and PM 2.5, particularly reflecting the springtime long-range transport to both continents.

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          Most cited references40

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          The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions

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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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              Asian dust events of April 1998

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                Author and article information

                Journal
                101214388
                38670
                Atmos Chem Phys
                Atmos Chem Phys
                Atmospheric chemistry and physics
                1680-7316
                1680-7324
                27 July 2018
                2018
                22 August 2018
                : 18
                : 12
                : 8929-8952
                Affiliations
                [1 ]Aarhus University, Department of Environmental Science, Frederiksborgvej 399, Roskilde, Denmark
                [2 ]European Commission, Joint Research Centre (JRC), Ispra, Italy
                [3 ]Eurasia Institute of Earth Sciences, Istanbul Technical University, Istanbul, Turkey
                [4 ]Ricerca sul Sistema Energetico (RSE SpA), Milan, Italy
                [5 ]University of Murcia, Department of Physics, Physics of the Earth, Campus de Espinardo, Facultad de Química, Murcia, Spain
                [6 ]Enviroware srl, Concorezzo, Italy
                [7 ]Institute of Coastal Research, Chemistry Transport Modelling Group, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany
                [8 ]INERIS, Institut National de l’Environnement Industriel et des Risques, Parc Alata, Verneuil-en-Halatte, France
                [9 ]Dept. Physical and Chemical Sciences, University of L’Aquila, L’Aquila, Italy
                [10 ]Center of Excellence CETEMPS, University of L’Aquila, L’Aquila, Italy
                [11 ]Centre for Atmospheric and Instrumentation Research (CAIR), University of Hertfordshire, Hatfield, UK
                [12 ]European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
                [13 ]Ricardo Energy & Environment, Gemini Building, Fermi Avenue, Harwell, Oxon, UK
                [14 ]Environmental Research Group, Kings’ College London, London, UK
                [15 ]NRC Research Associate at Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC, USA
                [16 ]Ramboll Environ, 773 San Marin Drive, Suite 2115, Novato, CA, USA
                [17 ]Finnish Meteorological Institute, Atmospheric Composition Research Unit, Helsinki, Finland
                [18 ]Cornell University, Department of Earth and Atmospheric Sciences, Ithaca, NY, USA
                [19 ]CIEMAT, Avda. Complutense 40, Madrid, Spain
                [20 ]Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC, USA
                [a ]now at: Section Environmental Meteorology, Division Customer Service, ZAMG e Zentralanstalt für Meteorologie und Geodynamik, Vienna, Austria
                Author notes
                Correspondence: Ulas Im ( ulas@ 123456envs.au.dk )

                Author contributions. UI, JHC, CG, KMH and JBr carried out the DK1 simulations and analyses of the data from all groups; UI prepared the manuscript with contributions from all co-authors; ES and SG carried out the error optimization work and data upload to the ENSEMBLE system; UA, LP and AU carried out the DE1 simulations; AB and GP carried out the IT2 simulations; RBa, PJG and LPP carried out the ES1 simulations; RBi and RBe carried out the data upload and management in the ENSEMBLE system; JBi carried out the DE1 simulations; AC and MGV carried out the FRES1 simulations; GC and PT carried out the IT1 simulations; AF and RS carried out the UK1 simulations; JF carried out the C-IFS model simulations extraction of the boundary conditions and extraction of C-IFS data for model evaluation; AF and RR carried out the UK2 simulations; NK carried out the UK3 simulations; PL and CH carried out the US3 simulations; UN and GY carried out the US1 simulations; and MP carried out the FI1 simulations. SG and CH coordinated and designed the experimental setup of the AQMEII3 exercise.

                Article
                EPAPA982662
                10.5194/acp-18-8929-2018
                6104647
                4ab2b8d3-3b67-4727-a8a0-303ed0b70f5a

                This work is distributed under the Creative Commons Attribution 4.0 License http//creativecommons.org/licenses/by/4.0/.

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