{"facets":[{"admin":false,"displayName":"Record type","fieldName":"recordType","hierarchical":false,"results":[{"active":false,"count":14,"name":"Dataset","url":"http://data.inms.international:443/inms/documents?facet=recordType%7CDataset"},{"active":false,"count":15,"name":"Model","url":"http://data.inms.international:443/inms/documents?facet=recordType%7CModel"},{"active":false,"count":3,"name":"Third-party dataset","url":"http://data.inms.international:443/inms/documents?facet=recordType%7CThird-party%20dataset"}]},{"admin":false,"displayName":"Pollutant","fieldName":"inmsPollutant","hierarchical":false},{"admin":false,"displayName":"Model Type","fieldName":"modelType","hierarchical":false},{"admin":false,"displayName":"Demonstration Region","fieldName":"inmsDemonstrationRegion","hierarchical":false},{"admin":false,"displayName":"Project","fieldName":"inmsProject","hierarchical":false}],"nextPage":"http://data.inms.international:443/inms/documents?page=2","numFound":32,"order":"asc","page":1,"results":[{"catalogue":"inms","description":"The EMEP4Earth model framework consists of an atmospheric chemistry transport model which simulates hourly to annual average atmospheric composition and deposition of pollutants, and the weather research and forecast model. Pollutants simulated include PM10, PM2.5, secondary organic aerosols, elemental carbon, secondary inorganic aerosols, SO2, NH3, NOx, and O3. Dry and wet deposition of pollutants are also calculated. \nEMEP4Earth operates at horizontal resolutions ranging from 1°×1° for the global domain down to ~1.1 km2 for specific regional domains (i.e. UK) The model setup is very flexible and can be changed for utilisation in scientific and/or policy applications. The default vertical domain ranges from ~50 m (thickness of the first layer at the surface) up to ~16 km (at the top of the vertical domain - 100 hPa), however setting a more flexible horizontal and vertical resolution is also possible.","documentType":"CEH_MODEL","identifier":"9b8b52fe-df65-48e6-a9f3-25c0a47ff636","incomingCitationCount":0,"keyword":["INMS"],"metadataDate":"2024-11-04T09:28:46.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/e30f10ed-2147-45b6-be94-b94f19487de8","https://data.inms.international/id/9b8b52fe-df65-48e6-a9f3-25c0a47ff636"],"resourceType":"Model","shortenedDescription":"The EMEP4Earth model framework consists of an atmospheric chemistry transport model which simulates hourly to annual average atmospheric composition and deposition of pollutants, and the weather research and forecast model. Pollutants simulated include PM10, PM2.5,…","state":"published","title":"EMEP4Earth (WRF-EMEP global)","version":1.0,"view":["public","leav","inms-users"]},{"catalogue":"inms","description":"GLOBIOM is a global economic bottom-up agricultural and forest sector model. The model is based on a detailed spatially explicit grid and estimates economic and environmental impacts, incl. nutrient balances, tightly linked with bio-physical process based models like EPIC. The model is typically used for scenario analysis in medium (2030), long (2050), and very long (2100) time horizon.","documentType":"CEH_MODEL","identifier":"681deb6d-25f4-45e1-86bc-74935aa29fdf","incomingCitationCount":0,"keyword":["INMS"],"metadataDate":"2024-11-04T09:24:25.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/5e6e20a8-db5f-4aa0-b11a-44e5838827b1","https://data.inms.international/id/681deb6d-25f4-45e1-86bc-74935aa29fdf"],"resourceType":"Model","shortenedDescription":"GLOBIOM is a global economic bottom-up agricultural and forest sector model. The model is based on a detailed spatially explicit grid and estimates economic and environmental impacts, incl. nutrient balances, tightly linked with bio-physical process based models like…","state":"published","title":"GLOBIOM","version":1.0,"view":["inms-users","public","leav","leclere@iiasa.ac.at"]},{"authorAffiliation":["National Oceanography Centre"],"authorOrcid":["0000-0002-2081-2693"],"availability":"Unknown","catalogue":"inms","dataFormat":["NetCDF"],"description":"Model concentrations of winter nitrate, growing season chlorophyll-a and monthly minimum oxygen for the Northwest European Shelf Seas derived from simulations of the Nucleus for a European Model of the Ocean (NEMO) coupled to the European Regional Seas Ecosystem Model (ERSEM). \nData for mean conditions between 1998 and 2007 and for 2010 are derived from a reanalysis (https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011, accessed May 2021). \nClimate projections for mean conditions from 2005 to 2015 and from 2045 to 2055 are for the RCP8.5 climate scenario (https://doi.org/10.5281/zenodo.3953801). ","documentType":"GEMINI_DOCUMENT","identifier":"b6a14d00-8ec0-4936-a4d6-e175766cd319","incomingCitationCount":0,"keyword":["INMS"],"keywordsProject":["INMS"],"licence":"Non-Open government licence","lineage":"This dataset is based on two NEMO-ERSEM simulations for the Northwest European Continental shelf.  \n\nThe reanalysis study was conducted using E.U. Copernicus Marine Service Information: https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011. Validation of the reanalysis data is in https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-011.pdf  (accessed May 2021). An evaluation of the RCP8.5 climate projection is described by Wakelin et al. (2020) (https://doi.org/https://doi.org/10.1016/j.pocean.2020.102400). ","locations":["POLYGON((-14.093 44.089, -14.093 63.234, 12.274 63.234, 12.274 44.089, -14.093 44.089))"],"metadataDate":"2025-12-15T15:57:46.000Z","orcid":["0000-0002-2081-2693"],"organisation":["National Oceanography Centre"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/b6a14d00-8ec0-4936-a4d6-e175766cd319"],"resourceType":"Dataset","shortenedDescription":"Model concentrations of winter nitrate, growing season chlorophyll-a and monthly minimum oxygen for the Northwest European Shelf Seas derived from simulations of the Nucleus for a European Model of the Ocean (NEMO) coupled to the European Regional Seas Ecosystem Model…","state":"published","temporalExtentText":"Data collected from 1998 to 2055","title":"Concentrations of Nitrate, Chlorophyll-a and Oxygen for the Northwest European Shelf Seas (1998-2007, 2010, 2005-2015 and 2045-2055)  ","version":1.0,"view":["public","ccaporusso@gmx.com","slwa@noc.ac.uk"]},{"authorAffiliation":["European Commission, Joint Research Centre"],"authorFamilyName":["Crippa","Oreggioni","Guizzardi","Muntean","Schaaf","Solazzo"],"authorFullName":["Crippa, M.","Oreggioni, G.","Guizzardi, D.","Muntean, M.","Schaaf, E.","Solazzo, E."],"authorGivenName":["Monica","Gabriel","Diego","Marilena","Edwin","Efisio"],"availability":"Unknown","catalogue":"inms","dataFormat":["Comma-separated values (CSV)","NetCDF"],"description":"It includes emissions time series (1970-2015)  and gridmaps for Carbon Monoxide (CO), Nitrogen Oxides (NOx), Non-Methane Volatile Organic Compounds (NMVOC), Methane (CH4), Ammonia (NH3), Nitrogen oxides (NOx), Sulfur Dioxide (SO2), Fine Particulate Matter (PM10 and PM2.5) and Carbonaceous speciation (BC, OC).\nEmission gridmaps are expressed in ton substance / 0.1degree x 0.1degree / year for the .txt files and in kg substance /m2 /s for the .nc files.\nEDGAR aims to inform scientists and policy makers on the evolution of the emission inventories over time for all world countries and to provide the scientific community 0.1degX0.1deg gridmaps representing the emissions sources. ","documentType":"GEMINI_DOCUMENT","familyName":["Crippa","Oreggioni","Guizzardi","Muntean","Schaaf","Solazzo"],"fullName":["Crippa, M.","Oreggioni, G.","Guizzardi, D.","Muntean, M.","Schaaf, E.","Solazzo, E."],"givenName":["Monica","Gabriel","Diego","Marilena","Edwin","Efisio"],"identifier":"0466dd11-0b69-4c66-b82b-a017d9714bf9","incomingCitationCount":0,"licence":"Non-Open government licence","lineage":"The emissions in EDGAR are estimated based on the activity data from the international statistics (e.g. IEA, USGS, IFA, FAO etc.) and emissions factors from the official guidebooks such as IPCC and EEA/EMEP, and scientific literature. For emissions distribution on global gridmaps we use proxy data such as population, location of the point sources etc.","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2026-03-23T10:13:35.000Z","organisation":["European Commission, Joint Research Centre"],"recordType":"Third-party dataset","resourceIdentifier":["https://data.inms.international/id/0466dd11-0b69-4c66-b82b-a017d9714bf9"],"resourceType":"Third-party dataset","shortenedDescription":"It includes emissions time series (1970-2015)  and gridmaps for Carbon Monoxide (CO), Nitrogen Oxides (NOx), Non-Methane Volatile Organic Compounds (NMVOC), Methane (CH4), Ammonia (NH3), Nitrogen oxides (NOx), Sulfur Dioxide (SO2), Fine Particulate Matter (PM10 and…","state":"published","temporalExtentText":"Data collected from 1970 to 2015","title":"Emissions Database for Global Atmospheric Research (EDGAR) Global Air Pollutant Emissions, 1970-2015","version":1.0,"view":["public","inms-users","crimar"]},{"authorAffiliation":["NASA"],"availability":"Unknown","catalogue":"inms","description":"The NASA Goddard Institute for Space Studies (GISS) Earth System Model (ModelE) dataset comprises outputs from a coupled atmosphere-ocean general circulation model used to simulate past, present, and future climate conditions. ModelE integrates key components of the Earth system, including atmosphere, ocean, sea ice, and land surface, with optional modules for atmospheric chemistry, aerosols, and the carbon cycle.\n\nThe dataset typically includes variables such as surface and atmospheric temperature, precipitation, radiation fluxes, winds, humidity, ocean temperature and circulation, sea ice extent, and biogeochemical tracers (e.g., aerosols and greenhouse gases), depending on the specific model configuration and experiment (e.g., CMIP simulations).\n\nMethodologically, ModelE is a physically based, process-driven numerical model that solves equations governing fluid dynamics, thermodynamics, and radiative transfer on a global grid. Simulations are driven by prescribed boundary conditions (e.g., greenhouse gas concentrations, solar forcing) or fully coupled interactions between system components. Many outputs are produced as part of international intercomparison projects such as CMIP, enabling standardized evaluation of climate model performance and projections.","documentType":"GEMINI_DOCUMENT","identifier":"86f223bd-88ab-4a49-af0d-2e8e899e93f8","incomingCitationCount":0,"licence":"Non-Open government licence","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2026-03-24T13:16:32.000Z","organisation":["NASA"],"recordType":"Third-party dataset","resourceIdentifier":["https://data.inms.international/id/86f223bd-88ab-4a49-af0d-2e8e899e93f8"],"resourceType":"Third-party dataset","shortenedDescription":"The NASA Goddard Institute for Space Studies (GISS) Earth System Model (ModelE) dataset comprises outputs from a coupled atmosphere-ocean general circulation model used to simulate past, present, and future climate conditions. ModelE integrates key components of the…","state":"published","title":"GISS Earth System Model: ModelE","version":1.0,"view":["public","inms-users","ccaporusso@gmx.com"]},{"catalogue":"inms","documentType":"CEH_MODEL","identifier":"d65a5816-585d-4899-9723-bdacd24246db","incomingCitationCount":0,"keyword":["INMS"],"metadataDate":"2024-11-04T09:26:46.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/2788461a-3a34-4350-8b9c-9e2b6025bbba","https://data.inms.international/id/d65a5816-585d-4899-9723-bdacd24246db"],"resourceType":"Model","state":"published","title":"TM5","version":1.0,"view":["public","leav","inms-users"]},{"catalogue":"inms","description":"The Common Agricultural Policy Regional Impact Analysis (CAPRI) modelling system is a large-scale comparative static, global multi-commodity,  partial equilibrium model for the agricultural sector. It has been developed for policy impact assessment of the European Common Agricultural Policy (CAP) and other policies affecting agriculture from global to regional and farm type scale, focusing on Europe .\n\nCAPRI simulates changes in global agricultural trade and EU supply of agricultural commodities under given technological, economic and policy constraints. Strengths of CAPRI include the possibility of good representation of EU policies, the detailed description of farm management in EU supply models, and the bio-physical approach based on nutrient mass-flow approach, including life-cycle assessment with regard to GHGs (operational) and nitrogen (operational end 2017) for agricultural commodities.\n\nThe code for the model is stored at Bonn University. For access contact Adrian Leip who will forward the request. \n","documentType":"CEH_MODEL","identifier":"dbab4238-3736-4797-859a-0fe8c41f28e9","incomingCitationCount":0,"keyword":["INMS"],"metadataDate":"2024-11-04T09:22:18.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/0f717c5c-653f-4992-8dc5-56bf4d880d3c","https://data.inms.international/id/dbab4238-3736-4797-859a-0fe8c41f28e9"],"resourceType":"Model","shortenedDescription":"The Common Agricultural Policy Regional Impact Analysis (CAPRI) modelling system is a large-scale comparative static, global multi-commodity,  partial equilibrium model for the agricultural sector. It has been developed for policy impact assessment of the European Common…","state":"published","title":"CAPRI Modelling System (Common Agricultural Policy Regionalised Impact)","version":1.0,"view":["public","leav","inms-users"]},{"catalogue":"inms","description":"Main drivers are population, economic development (income), land use, livestock and crop production. Main inputs are land use, use of synthetic and organic fertilizers, atmospheric N deposition, biological N2 fixation, total population, population with sewage connection, nutrient removal during treatment, water discharges, runoff, dams.  Inputs for land use, population, diffuse and point sources of nutrients are from IMAGE; inputs for hydrology are from WBM. Inputs for future years were derived from IMAGE and WBM as well. These models quantitatively interpreted Millennium Ecosystem Assessment scenarios to produce the required inputs for Global NEWS-2","documentType":"CEH_MODEL","identifier":"6ca8d695-7e1e-4008-bedc-26ef2ab541d0","incomingCitationCount":0,"inmsScale":["Basin"],"keyword":["Basin","Water Quality","INMS"],"metadataDate":"2024-11-04T09:39:20.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/26db611a-351b-4eea-851e-922d1409061b","https://data.inms.international/id/6ca8d695-7e1e-4008-bedc-26ef2ab541d0"],"resourceType":"Model","shortenedDescription":"Main drivers are population, economic development (income), land use, livestock and crop production. Main inputs are land use, use of synthetic and organic fertilizers, atmospheric N deposition, biological N2 fixation, total population, population with sewage connection,…","state":"published","title":"Global NEWS (Nutrient Export from WaterSheds)","version":1.0,"view":["public","leav"]},{"catalogue":"inms","description":"GLOBIO uses all biophysical information from IMAGE, such as climate, vegetation, agricultural land use, and it uses soil information. Atmospheric deposition is from an ensemble of chemistry transport models","documentType":"CEH_MODEL","identifier":"177e2331-17a6-49c6-9825-f6af3d6ccef3","incomingCitationCount":0,"inmsScale":["Global"],"keyword":["Impact Model","Global","Biodiversity","Agricultural Practices","INMS"],"metadataDate":"2024-11-04T09:32:44.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/31f92f89-9c1b-475f-9b04-303020907bbf","https://data.inms.international/id/177e2331-17a6-49c6-9825-f6af3d6ccef3"],"resourceType":"Model","shortenedDescription":"GLOBIO uses all biophysical information from IMAGE, such as climate, vegetation, agricultural land use, and it uses soil information. Atmospheric deposition is from an ensemble of chemistry transport models","state":"published","title":"IMAGE-GLOBIO terrestrial","version":1.0,"view":["public","leav","inms-users"]},{"authorAffiliation":["National Oceanography Centre","Plymouth Marine Laboratory"],"authorOrcid":["0000-0002-2081-2693","0000-0002-1587-4996","0000-0002-8949-4785","0000-0003-1813-056X"],"availability":"Unknown","catalogue":"inms","dataFormat":["NetCDF"],"description":"Model concentrations of July-to-September mean nitrate and August-to-October mean chlorophyll-a for South Asia seas (62.4°E to 98.3°E, 2.0°N to 26.2°N) derived from a hindcast of the Nucleus for a European Model of the Ocean (NEMO) coupled to the European Regional Seas Ecosystem Model (ERSEM). Data are averaged over the period 1998 to 2007 and for the year 2010. \nNEMO-ERSEM climate projection of nitrate for Southeast Asia seas (77.8°E to 133.0°E, 19.3°S to 24.0°N) under the RCP8.5 climate scenario; average July-to-September concentrations over the periods 2005 to 2015 and 2045 to 2055. ","documentType":"GEMINI_DOCUMENT","identifier":"36bff017-345e-43c8-a470-6d605e15c1dc","incomingCitationCount":0,"keyword":["INMS"],"keywordsProject":["INMS"],"licence":"Non-Open government licence","lineage":"This dataset is based on two NEMO-ERSEM simulations: a hindcast of South Asia Seas and an RCP8.5 climate projection for Southeast Asia. The model setups are described by Polton et al. (2023) (labelled SANH for South Asia and SEAsia for Southeast Asia in https://doi.org/10.5194/gmd-16-1481-2023). ","locations":["POLYGON((62.4 -19.3, 62.4 26.2, 133 26.2, 133 -19.3, 62.4 -19.3))"],"metadataDate":"2025-12-15T15:57:46.000Z","orcid":["0000-0002-2081-2693","0000-0002-1587-4996","0000-0002-8949-4785","0000-0003-1813-056X"],"organisation":["National Oceanography Centre","Plymouth Marine Laboratory"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/36bff017-345e-43c8-a470-6d605e15c1dc"],"resourceType":"Dataset","shortenedDescription":"Model concentrations of July-to-September mean nitrate and August-to-October mean chlorophyll-a for South Asia seas (62.4°E to 98.3°E, 2.0°N to 26.2°N) derived from a hindcast of the Nucleus for a European Model of the Ocean (NEMO) coupled to the European Regional Seas…","state":"published","temporalExtentText":"Data collected from 1998 to 2055","title":"Concentrations of Nitrate (1998-2007, 2010, 2005-2015 and 2045-2055) and Chlorophyll-a (1998-2007 and 2010) for South Asia Seas","version":1.0,"view":["public","ccaporusso@gmx.com","slwa@noc.ac.uk"]},{"authorAffiliation":["UK Centre for Ecology & Hydrology"],"availability":"Unknown","catalogue":"inms","dataFormat":["NetCDF"],"description":"This dataset is a model output from the European Monitoring and Evaluation Programme (EMEP) model driven by Weather and Research Forecast model meteorology (WRF) . It provides global estimates daily averaged atmospheric composition and deposition for nitrogen, sulphur, particulate organic matter and ozone on a 1.0x1.0 degrees grid for the year 2010 in seven different experiments. The experiments are:\n\n•\tAll 2010 emissions are included\n•\tA 20% reduction of anthropogenic NOx emission\n•\tA 20% reduction of anthropogenic NH3 emissions\n•\tA 100% reduction of anthropogenic NH3 and NOx emissions \n•\tA 20% reduction of anthropogenic NH3 and NOx emissions\n•\tA 20% reduction of ALL anthropogenic\n•\tA 100% reduction of ALL anthropogenic\n\nThe EMEP version used is 4.33 and the WRF version is 3.9.1.1.\nThe data was produced as part of the INMS (International Nitrogen Managements System) project that is implemented by the UN Environment with founding through the Global Environment Facility (GEF) and executed trough UK Centre for Ecology & Hydrology (UKCEH).\n","documentType":"GEMINI_DOCUMENT","identifier":"1a5dcd3c-1e65-4d56-b88c-a1b2b9f2ffe0","incomingCitationCount":0,"inmsScale":["Global"],"keyword":["INMS","Global"],"keywordsOther":["Global"],"keywordsProject":["INMS"],"licence":"Non-Open government licence","lineage":"The EMEP-WRF global meteorology is evaluated with the global WMO AWS network, and over the UK the AURN monitoring stations are used to evaluate the EMEP-WRF atmospheric composition. No further QA/QC is carried out. Your use of the content of this dataset is entirely at your own risk. We make no warranty or guarantee that the content is error free or fit for your intended use","locations":["POLYGON((-174.511 -56.745, -174.511 84.162, 136.973 84.162, 136.973 -56.745, -174.511 -56.745))"],"metadataDate":"2025-12-15T15:57:54.000Z","organisation":["INMS","UK Centre for Ecology & Hydrology"],"recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/a28d4e46-1b8b-4d53-bcfb-4a80a6968a34","https://data.inms.international/id/1a5dcd3c-1e65-4d56-b88c-a1b2b9f2ffe0"],"resourceType":"Dataset","shortenedDescription":"This dataset is a model output from the European Monitoring and Evaluation Programme (EMEP) model driven by Weather and Research Forecast model meteorology (WRF) . It provides global estimates daily averaged atmospheric composition and deposition for nitrogen, sulphur,…","state":"published","temporalExtentText":"Data collected from 2010 to 2010","title":"European Monitoring and Evaluation Program Model (EMEP) driven by Weather and Research Forecast model meteorology (WRF) global simulation of atmospheric composition and deposition for the year 2010","version":1.0,"view":["public","leav","inms-users"]},{"catalogue":"inms","documentType":"CEH_MODEL","identifier":"6b24ce1c-8a2d-4d9b-817f-804fa16007e7","incomingCitationCount":0,"keyword":["INMS","Hydrological","Water","Land"],"metadataDate":"2024-11-04T09:27:51.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/4b0ad695-7335-4355-ae4e-7d1cd33f86d1","https://data.inms.international/id/6b24ce1c-8a2d-4d9b-817f-804fa16007e7"],"resourceType":"Model","state":"published","title":"WBM/VIC","version":1.0,"view":["public","leav","inms-users"]},{"authorAffiliation":["PBL – Netherlands Environmental  Assessment Agency"],"availability":"Unknown","catalogue":"inms","dataFormat":["TIFF"],"description":"The datasets display the loss of plant biodiversity due to nitrogen deposition in the world in two past years (1970 and 2010) and six modelled scenarios for 2050. \nThe datasets have been created by the GLOBIO 4 model.","documentType":"GEMINI_DOCUMENT","identifier":"3fb43167-0d9b-43ba-bea4-230c64701f10","incomingCitationCount":1,"inmsScale":["Global"],"keyword":["INMS","Global"],"keywordsOther":["Global"],"keywordsProject":["INMS"],"licence":"Non-Open government licence","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2025-12-15T15:57:46.000Z","organisation":["UK Centre for Ecology & Hydrology","INMS","PBL – Netherlands Environmental  Assessment Agency"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/3fb43167-0d9b-43ba-bea4-230c64701f10"],"resourceType":"Dataset","shortenedDescription":"The datasets display the loss of plant biodiversity due to nitrogen deposition in the world in two past years (1970 and 2010) and six modelled scenarios for 2050. \nThe datasets have been created by the GLOBIO 4 model.","state":"published","temporalExtentText":"Data collected from 1970 to 2050","title":"GLOBIO-terrestrial nitrogen deposition impacts on plant mean species abundance (MSA) for the years 1970, 2010 and 2050","version":1.0,"view":["public","ccaporusso@gmx.com"]},{"catalogue":"inms","description":"The Emissions Database for Global Atmospheric Research (EDGAR) provides independent estimates of the global anthropogenic emissions and emission trends, based on publicly available statistics, for the atmospheric modeling community as well as for policy makers. This scientific independent emission inventory is characterized by a coherent world historical trend from 1970 to 2012, including emissions of all greenhouse gases, air pollutants, aerosols and toxic pollutants (Hg). Data are presented for all countries, with emissions provided per main source category, and spatially allocated on a 0.1x0.1 grid over the globe.","documentType":"CEH_MODEL","identifier":"43a222b0-b7dc-4b54-90e9-855d2f52c950","incomingCitationCount":0,"keyword":["INMS"],"metadataDate":"2024-11-04T09:27:02.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/41178be8-1daa-4511-8648-011e4277549a","https://data.inms.international/id/43a222b0-b7dc-4b54-90e9-855d2f52c950"],"resourceType":"Model","shortenedDescription":"The Emissions Database for Global Atmospheric Research (EDGAR) provides independent estimates of the global anthropogenic emissions and emission trends, based on publicly available statistics, for the atmospheric modeling community as well as for policy makers. This…","state":"published","title":"Emissions Database for Global Atmospheric Research (EDGAR)","version":1.0,"view":["public","leav","inms-users"]},{"catalogue":"inms","description":"GAINS estimates emissions of air pollutants and greenhouse gases in future scenarios based on (1) projections of activity data and (2) rate of implementation of emission reducing technologies. An optimization algorithm allows to minimize costs of measures when intending to arrive at a given ecological “endpoint” (human health, biodiversity, GHG level etc.). \n\nMore than 200 individual abatement technologies and abatement costs are individually defined and implemented in connection with specific “target” emitted compound, interference with other compounds considered. Costs are computed as a function of investments, interest rates and country specific labour/energy/ commodity costs; co-benefits can be integrated in cost factors (e.g. negative energy costs).\n","documentType":"CEH_MODEL","identifier":"289b4b74-3284-4c5d-a9f7-1b22bf51421a","incomingCitationCount":0,"inmsScale":["Global"],"keyword":["Global","Greenhouse Gases","Air Pollution","INMS"],"metadataDate":"2024-11-04T09:36:31.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/951d2c32-3d30-4e87-958a-65378ec1858c","https://data.inms.international/id/289b4b74-3284-4c5d-a9f7-1b22bf51421a"],"resourceType":"Model","shortenedDescription":"GAINS estimates emissions of air pollutants and greenhouse gases in future scenarios based on (1) projections of activity data and (2) rate of implementation of emission reducing technologies. An optimization algorithm allows to minimize costs of measures when intending…","state":"published","title":"GAINS Global","version":1.0,"view":["public","leav","inms-users"]},{"authorAffiliation":["European Commission, Joint Research Centre"],"availability":"Unknown","catalogue":"inms","dataFormat":["Comma-separated values (CSV)","NetCDF"],"description":"It includes emissions time series and gridmaps for CO2, CH4 and N2O. Carbon dioxide emissions are provided for the period 1970-2018, and CH4 and N2O for 1970-2015. \nEDGAR aims to inform scientists and policy makers on the evolution of the emission inventories over time for all world countries and to provide the scientific community 0.1degX0.1deg gridmaps representing the emissions sources. \nEmission gridmaps are expressed in ton substance / 0.1degree x 0.1degree / year for the .txt files and in kg substance /m2 /s for the .nc files.","documentType":"GEMINI_DOCUMENT","identifier":"78701875-cfa2-48d6-b8bb-9fd7c4beb966","incomingCitationCount":0,"licence":"Non-Open government licence","lineage":"The emissions in EDGAR are estimated based on the activity data from the international statistics (e.g. IEA, USGS, IFA, FAO etc.) and emissions factors from the official guidebooks such as IPCC and EEA/EMEP, and scientific literature. For emissions distribution on global gridmaps we use proxy data such as population, location of the point sources etc.","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2024-11-01T10:28:15.000Z","organisation":["European Commission, Joint Research Centre"],"recordType":"Third-party dataset","resourceIdentifier":["https://data.inms.international/id/78701875-cfa2-48d6-b8bb-9fd7c4beb966"],"resourceType":"Third-party dataset","shortenedDescription":"It includes emissions time series and gridmaps for CO2, CH4 and N2O. Carbon dioxide emissions are provided for the period 1970-2018, and CH4 and N2O for 1970-2015. \nEDGAR aims to inform scientists and policy makers on the evolution of the emission inventories over time…","state":"published","temporalExtentText":"Data collected from 1970 to 2018; Data collected from 1970 to 2015","title":"Emissions Database for Global Atmospheric Research (EDGAR) Global Greenhouse Gas Emissions, 1970-2015","version":1.0,"view":["public","inms-users","crimar"]},{"authorAffiliation":["PBL – Netherlands Environmental  Assessment Agency"],"authorOrcid":["0000-0001-6162-9943"],"availability":"Unknown","catalogue":"inms","dataFormat":["Comma-separated values (CSV)"],"description":"The dataset comprises the simulated loss of freshwater biodiversity intactness (expressed as MSA; scale 0-1) in lakes and rivers worldwide, as well as the simulated concentration of cyanobacteria (harmful algal blooms) in lakes (in range classes related to the WHO standards; Chorus & Welker, 2021), due to nutrient emissions to surface water.\nThe effects have been modelled by the model GLOBIO-Aquatic (Janse et al., 2015; Janse et al., 2023). The data are global at a 30x30 arc minutes resolution, and cover the years 1970, 2010, and 2050 for six INMS scenarios, representing different combinations  of SSPs, RCPs and nitrogen policy options.\n","documentType":"GEMINI_DOCUMENT","identifier":"a349e3ca-fe95-448c-b14d-6a488392bc77","incomingCitationCount":0,"keyword":["INMS"],"keywordsProject":["INMS"],"licence":"Non-Open government licence","lineage":"The nutrient emissions and concentrations in surface water have been modelled by the Global Nutrient Model (GNM; Beusen et al., 2022) combined with the global hydrological model PCR-GLOBWB (Sutanudjaja et al., 2018). The missions are the sum of leaching from (agricultural) land, urban emissions, aquaculture and atmospheric deposition.\nLand-use and climate projections were based on the IMAGE model (Stehfest et al.,  2014).","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2025-12-15T15:57:46.000Z","orcid":["0000-0001-6162-9943"],"organisation":["PBL – Netherlands Environmental  Assessment Agency"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/a349e3ca-fe95-448c-b14d-6a488392bc77"],"resourceType":"Dataset","shortenedDescription":"The dataset comprises the simulated loss of freshwater biodiversity intactness (expressed as MSA; scale 0-1) in lakes and rivers worldwide, as well as the simulated concentration of cyanobacteria (harmful algal blooms) in lakes (in range classes related to the WHO standards;…","state":"published","supplementalDescription":["\n"],"supplementalName":["GLOBIO | Global biodiversity model for policy support","Beusen AHW, Doelman JC, Van Beek LPH, et al (2022) Exploring river nitrogen and phosphorus loading and export to global coastal waters in the Shared Socio-economic pathways. Glob Environ Change 72:102426. ","Chorus, I, Welker M; eds. (2021). Toxic Cyanobacteria in Water, 2nd edition. CRC Press, Boca Raton (FL), on behalf of the World Health Organization, Geneva, CH.","Janse, J.H., Kuiper, J.J., Weijters, M.J., Westerbeek, E.P., Jeuken, M.H.J.L., Bakkenes, M.Alkemade, R., Verhoeven, J.T.A., 2015. GLOBIO-aquatic, a global model of human impact on the biodiversity of inland aquatic ecosystems. Env. Sci. Pol. 48: 99-114.","Janse, J.H., Bakkenes, M. and Meijer, J.R. (2016). GLOBIO-AQUATIC, Technical Model Description, Model version 1.3 (Aug. 2014). PBL, Den Haag, publ. nr. 2829.","Stehfest, E., van Vuuren, D., Kram, T., Bouwman, L., Alkemade, R., Bakkenes, M., Biemans, H., Bouwman, A., den Elzen, M., Janse, J., Lucas, P., van Minnen, J., Muller, M., Prins, A., 2014. Integrated assessment of global environmental change with IMAGE 3.0. Model description and policy applications. PBL Netherlands Environmental Assessment Agency.","Janse JH, Bakkenes M, Barbarossa V, Gernaat DEHJ, Giesen P (2023). Future pathways to restore ecological quality of freshwater ecosystems  within SSP2/RCP6.0 boundary conditions. Background report Future Water Challenges. PBL, Den Haag."],"temporalExtentText":"Data collected from 1970 to 2050","title":"GLOBIO-AQUATIC. Effects of nutrient emissions on freshwater biodiversity and algal blooms for the years 1970, 2010 and 2050.","version":1.0,"view":["jh.janse@gmail.com","public","ccaporusso@gmx.com"]},{"authorAffiliation":["European Commission,  Joint Research Centre"],"authorFamilyName":["Van Dingenen"],"authorFullName":["Van Dingenen, R."],"authorGivenName":["Rita"],"authorOrcid":["0000-0003-2521-4972"],"availability":"Unknown","catalogue":"inms","dataFormat":["NetCDF","Comma-separated values (CSV)"],"description":"This dataset is a model output from the TM5-Fast Scenario Screening Tool (TM5-FASST) which computes the wet and dry annual deposition of NOy, NHx and the sum of both, and monthly AOT40 and total surface ozone gridded on the global domain at 0.5°x0.5° resolution. In this instance TM5-FASST was run for IMAGE historical emissions for years 1970 and 2010.","documentType":"GEMINI_DOCUMENT","familyName":["Van Dingenen"],"fullName":["Van Dingenen, R."],"givenName":["Rita"],"identifier":"98a0718b-81bb-491f-8152-c451dd6c211c","incomingCitationCount":0,"licence":"Non-Open government licence","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2026-04-02T08:32:06.000Z","orcid":["0000-0003-2521-4972"],"organisation":["European Commission,  Joint Research Centre","UK Centre for Ecology & Hydrology"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/98a0718b-81bb-491f-8152-c451dd6c211c"],"resourceType":"Dataset","ror":["https://ror.org/00pggkr55"],"shortenedDescription":"This dataset is a model output from the TM5-Fast Scenario Screening Tool (TM5-FASST) which computes the wet and dry annual deposition of NOy, NHx and the sum of both, and monthly AOT40 and total surface ozone gridded on the global domain at 0.5°x0.5° resolution. In…","state":"published","title":"Global simulation of NOy and NHx deposition, AOT40 and total ozone (TM5-FASST Scenario Screening Tool) for the years 1970 and 2010","version":1.0,"view":["public","inms-users","ccaporusso@gmx.com"]},{"catalogue":"inms","description":"Miterra-Global is an environmental impact assessment model at global scale. The model can be used to assess the effects of the implementation of ammonia (NH3) and nitrate (NO3) measures and policies on the emissions of NH3, (nitrous oxide) N2O, N oxides (NOx), and methane (CH4) to the atmosphere, leaching of N (including nitrate) to ground water and surface waters, and on the phosphorus (P) balance at regional level. The emission and leaching factors are used to calculate greenhouse gas emissions (CO2, CH4, N2O) in a deterministic and annual basis. Model inputs refer to: (1) activity data, such as animal numbers, crop yields and N fertilizer amounts, and (2) spatial environmental data, such as climate and land use.","documentType":"CEH_MODEL","identifier":"5765d43a-458d-48ed-a7b2-5bf1d208ba73","incomingCitationCount":0,"inmsScale":["Global"],"keyword":["Global","Greenhouse Gases","INMS","mitigation practices"],"locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2024-11-04T09:37:50.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/bb33db1a-324e-4be6-8114-9d7f5bd18c08","https://data.inms.international/id/5765d43a-458d-48ed-a7b2-5bf1d208ba73"],"resourceType":"Model","shortenedDescription":"Miterra-Global is an environmental impact assessment model at global scale. The model can be used to assess the effects of the implementation of ammonia (NH3) and nitrate (NO3) measures and policies on the emissions of NH3, (nitrous oxide) N2O, N oxides (NOx), and methane…","state":"published","title":"MITERRA Global","version":1.0,"view":["inms-users","chantal.hendriks@wur.nl","public","leav"]},{"altTitle":["MARINA-Global model for nutrients in water"],"authorAffiliation":["Wageningen University & Research"],"authorOrcid":["0000-0002-8063-7743","0000-0002-2543-4871","0000-0002-8954-6629"],"availability":"Unknown","catalogue":"inms","dataFormat":["Comma-separated values (CSV)","Shapefile"],"description":"River export of nitrogen and phosphorus is provided for 2010 and 2050. This data is computed by the MARINA-Global model: Model to Assess River Inputs of pollutants to seas. Nitrogen and phosphorus are in dissolved inorganic and organic forms. \nThe future follows Shared Socio-economic Pathways (SSP) 1 (sustainability-oriented) and 5 (economic-oriented). SSP1 is combined with Representative Concentration Pathway (RCP) 2.6. SSP5 is combined with RCP8.5 to create the scenarios:\n\n1) Business as usual (SSP5-RCP8.5 Low N ambition)\n2) Bioenergy (SSP1-RCP2.6 High N ambition with low meat & dairy diet)\n\nRiver export of the nutrients is calculated from around ten thousands sub-basins as a function of human activities on the land (e.g., agriculture, sewage) and sub-basin characteristics (e.g., hydrology, terrestrial and aquatic retentions). \n\nThe modelling approaches of MARINA-Global are published and evaluated in earlier studies, please see the documentation below.","documentType":"GEMINI_DOCUMENT","identifier":"933f9d5f-07e1-4197-a201-c0867d949c76","incomingCitationCount":2,"keyword":["INMS","MARINA-Global","Nutrients","River Export","Sub-basins","Globe "],"keywordsOther":["MARINA-Global","Nutrients","River Export","Sub-basins","Globe "],"keywordsTheme":["INMS"],"licence":"Non-Open government licence","lineage":"Sources of the data are indicated in Chapter 11 of the INI book. \n\nAdditional sources\n-\tfor point-source inputs of nutrients to rivers: Strokal, Maryna; Bai, Zhaohai; Franssen, Wietse; Hofstra, Nynke; Koelmans, Albert A.; Ludwig, Fulco; et al. Metadata supporting the article: Urbanization: an increasing source of multiple pollutants to rivers in the 21st century. https://doi.org/10.6084/m9.figshare.13333796  (2020)\n\n-\tHydrological data: https://vic.readthedocs.io/en/master/Overview/ModelOverview/ and ISIMIP2b https://www.isimip.org/\n","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2025-12-15T15:57:54.000Z","orcid":["0000-0002-8063-7743","0000-0002-2543-4871","0000-0002-8954-6629"],"organisation":["Wageningen University & Research","INMS ","INMS"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/933f9d5f-07e1-4197-a201-c0867d949c76"],"resourceType":"Dataset","shortenedDescription":"River export of nitrogen and phosphorus is provided for 2010 and 2050. This data is computed by the MARINA-Global model: Model to Assess River Inputs of pollutants to seas. Nitrogen and phosphorus are in dissolved inorganic and organic forms. \nThe future follows Shared…","state":"published","supplementalName":["The MARINA model (Model to Assess River Inputs of Nutrients to seAs): Model description and results for China "],"temporalExtentText":"Data collected from 2010 to 2050","title":"Modelled river export of nutrients from sub-basins globally for scenarios SSP1 and SSP5 and for the years 2010 and 2050","version":1.0,"view":["public","inms-users","crimar"]}],"rows":20,"semanticEnabled":false,"url":"http://data.inms.international:443/inms/documents"}