{"type": "FeatureCollection", "features": [{"id": "10.1016/j.cosust.2018.11.002", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:16:12Z", "type": "Journal Article", "created": "2018-11-28", "title": "Models for assessing engineered nanomaterial fate and behaviour in the aquatic environment", "description": "Engineered nanomaterials (ENMs, material containing<br/>particles with at least one dimension less than 100 nm) are<br/>present in a range of consumer products and could be<br/>released into the environment from these products during<br/>their production, use or end-of-life. The high surface to<br/>volume ratio of nanomaterials imparts a high reactivity,<br/>which is of interest for novel applications but may raise<br/>concern for the environment. In the absence of<br/>measurement methods, there is a need for modelling to<br/>assess likely concentrations and fate arising from current<br/>and future releases. To assess the capability that exists to<br/>do such modelling, progress in modelling ENM fate since<br/>2011 is reviewed. ENM-specific processes represented in<br/>models are mainly limited to aggregation and, in some<br/>instances, dissolution. Transformation processes (e.g.<br/>sulphidation), the role of the manufactured coatings,<br/>particle size distribution and particle form and state are still<br/>usually excluded. Progress is also being made in modelling<br/>ENMs at larger scales. Currently, models can give a<br/>reasonable assessment of the fate of ENMs in the<br/>environment, but a full understanding will likely require<br/>fuller inclusion of these ENM-specific processes.", "keywords": ["RELEASE", "transformation", "aggregation", "Urbanisation", "METALLIC NANOPARTICLES", "QUANTIFICATION", "SILVER NANOPARTICLES", "01 natural sciences", "6. Clean water", "modelling", "engineered nanomaterials", "NanoFASE", "TIO2 NANOPARTICLES", "Life Science", "WATER", "NANO-SILVER", "EXPOSURE", "RISK-ASSESSMENT", "105906 Environmental geosciences", "ZINC-OXIDE", "aquatic environment", "105906 Umweltgeowissenschaften", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.cosust.2018.11.002"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Current%20Opinion%20in%20Environmental%20Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.cosust.2018.11.002", "name": "item", "description": "10.1016/j.cosust.2018.11.002", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.cosust.2018.11.002"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-02-01T00:00:00Z"}}, {"id": "10.1039/c8en00501j", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:18:30Z", "type": "Journal Article", "created": "2018-08-29", "title": "On the application of spectral corrections to particle flux measurements", "description": "<p>An altered empirical method to estimate attenuation correction factors in particle flux measurements.</p>", "keywords": ["SCALAR SIMILARITY", "Environmental sciences", "Physical sciences", "EDDY COVARIANCE METHOD", "DEPOSITION VELOCITIES", "AEROSOL FLUXES", "13. Climate action", "TURBULENCE", "Urbanisation", "SCOTS PINE FOREST", "EXCHANGE", "SOUTHERN FINLAND", "7. Clean energy"]}, "links": [{"href": "http://pubs.rsc.org/en/content/articlepdf/2018/EN/C8EN00501J"}, {"href": "https://doi.org/10.1039/c8en00501j"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%3A%20Nano", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1039/c8en00501j", "name": "item", "description": "10.1039/c8en00501j", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1039/c8en00501j"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-01T00:00:00Z"}}, {"id": "10.5194/acp-23-8081-2023", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:22:38Z", "type": "Journal Article", "created": "2023-07-19", "title": "Towards near-real-time air pollutant and greenhouse  gas emissions: lessons learned from multiple  estimates during the COVID-19 pandemic", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. The 2020 COVID-19 crisis caused an unprecedented drop in anthropogenic emissions of air pollutants and greenhouse gases. Given that emissions estimates from official national inventories for the year 2020 were not reported until 2 years later, new and non-traditional datasets to estimate near-real-time emissions became particularly relevant and widely used in international monitoring and modelling activities during the pandemic. This study investigates the impact of the COVID-19 pandemic on 2020 European (the 27 EU member states and the UK) emissions by comparing a selection of such near-real-time emission estimates, with the official inventories that were subsequently reported in 2022 under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) and the United Nations Framework Convention on Climate Change (UNFCCC). Results indicate that annual changes in total 2020 emissions reported by official and near-real-time estimates are fairly in line for most of the chemical species, with NOx and fossil fuel CO2 being reported as the ones that experienced the largest reduction in Europe in all cases. However, large discrepancies arise between the official and non-official datasets when comparing annual results at the sector and country level, indicating that caution should be exercised when estimating changes in emissions using specific near-real-time activity datasets, such as time mobility data derived from smartphones. The main examples of these differences are observed for the manufacturing industry NOx (relative changes ranging between \u221221.4\u2009% and \u22125.4\u2009%) and road transport CO2 (relative changes ranging between \u221229.3\u2009% and \u22125.6\u2009%) total European emissions. Additionally, significant discrepancies are observed between the quarterly and monthly distribution of emissions drops reported by the various near-real-time inventories, with differences of up to a factor of 1.5 for total NOx during April\u00a02020, when restrictions were at their maximum. For residential combustion, shipping and the public energy industry, results indicate that changes in emissions that occurred between 2019 and 2020 were mainly dominated by non-COVID-19 factors, including meteorology, the implementation of the Global Sulphur Cap and the shutdown of coal-fired power plants as part of national decarbonization efforts, respectively. The potential increase in NMVOC emissions from the intensive use of personal protective equipment such as hand sanitizer gels is considered in a heterogeneous way across countries in officially reported inventories, indicating the need for some countries to base their calculations on more advanced methods. The findings of this study can be used to better understand the uncertainties in near-real-time emissions and how such emissions could be used in the future to provide timely updates to emission datasets that are critical for modelling and monitoring applications.                     </p></article>", "keywords": ["330", "550", "Physics", "QC1-999", "Air pollution", "Near-real-time emissions", "Urbanisation", "Covid-19 pandemic", "7. Clean energy", "3. Good health", "[SDU] Sciences of the Universe [physics]", "Chemistry", "Greenhouse gasses", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "11. Sustainability", "QD1-999"]}, "links": [{"href": "https://acp.copernicus.org/articles/23/8081/2023/acp-23-8081-2023.pdf"}, {"href": "https://doi.org/10.5194/acp-23-8081-2023"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/acp-23-8081-2023", "name": "item", "description": "10.5194/acp-23-8081-2023", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/acp-23-8081-2023"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-28T00:00:00Z"}}, {"id": "10.3390/atmos8050084", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:46Z", "type": "Journal Article", "created": "2017-05-05", "title": "Emissions and Possible Environmental Implication of Engineered Nanomaterials (ENMs) in the Atmosphere", "description": "<p>In spite of the still increasing number of engineered nanomaterial (ENM) applications, large knowledge gaps exist with respect to their environmental fate, especially after release into air. This review aims to summarize the current knowledge of emissions and behavior of airborne engineered nanomaterials. The whole ENM lifecycle is considered from the perspective of possible releases into the atmosphere. Although in general, emissions during use phase and end-of-life seem to play a minor role compared to entry into soil and water, accidental and continuous emissions into air can occur especially during production and some use cases such as spray application. Implications of ENMs on the atmosphere as e.g., photo-catalytic properties or the production of reactive oxygen species are reviewed as well as the influence of physical processes and chemical reactions on the ENMs. Experimental studies and different modeling approaches regarding atmospheric transformation and removal are summarized. Some information exists especially for ENMs, but many issues can only be addressed by using data from ultrafine particles as a substitute and research on the specific implications of ENMs in the atmosphere is still needed.</p>", "keywords": ["Aerosols", "RELEASE", "ULTRAFINE PARTICLES", "Engineered nanomaterials", "660", "[SDE.IE]Environmental Sciences/Environmental Engineering", "Atmospheric transport", "Urbanisation", "ENMs", "ENMS", "Physik (inkl. Astronomie)", "01 natural sciences", "Ultrafine particles", "AEROSOLS", "13. Climate action", "Release", "ENGINEERED NANOMATERIALS", "8. Economic growth", "ATMOSPHERIC TRANSPORT", "TRANSFORMATION PROCESSES", "[SDE.IE] Environmental Sciences/Environmental Engineering", "Transformation processes", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.mdpi.com/2073-4433/8/5/84/pdf"}, {"href": "https://doi.org/10.3390/atmos8050084"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmosphere", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/atmos8050084", "name": "item", "description": "10.3390/atmos8050084", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/atmos8050084"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-05-05T00:00:00Z"}}, {"id": "10.5194/egusphere-2023-186", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:22:43Z", "type": "Journal Article", "created": "2023-07-19", "title": "Towards near-real-time air pollutant and greenhouse  gas emissions: lessons learned from multiple  estimates during the COVID-19 pandemic", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. The 2020 COVID-19 crisis caused an unprecedented drop in anthropogenic emissions of air pollutants and greenhouse gases. Given that emissions estimates from official national inventories for the year 2020 were not reported until 2 years later, new and non-traditional datasets to estimate near-real-time emissions became particularly relevant and widely used in international monitoring and modelling activities during the pandemic. This study investigates the impact of the COVID-19 pandemic on 2020 European (the 27 EU member states and the UK) emissions by comparing a selection of such near-real-time emission estimates, with the official inventories that were subsequently reported in 2022 under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) and the United Nations Framework Convention on Climate Change (UNFCCC). Results indicate that annual changes in total 2020 emissions reported by official and near-real-time estimates are fairly in line for most of the chemical species, with NOx and fossil fuel CO2 being reported as the ones that experienced the largest reduction in Europe in all cases. However, large discrepancies arise between the official and non-official datasets when comparing annual results at the sector and country level, indicating that caution should be exercised when estimating changes in emissions using specific near-real-time activity datasets, such as time mobility data derived from smartphones. The main examples of these differences are observed for the manufacturing industry NOx (relative changes ranging between \u221221.4\u2009% and \u22125.4\u2009%) and road transport CO2 (relative changes ranging between \u221229.3\u2009% and \u22125.6\u2009%) total European emissions. Additionally, significant discrepancies are observed between the quarterly and monthly distribution of emissions drops reported by the various near-real-time inventories, with differences of up to a factor of 1.5 for total NOx during April\u00a02020, when restrictions were at their maximum. For residential combustion, shipping and the public energy industry, results indicate that changes in emissions that occurred between 2019 and 2020 were mainly dominated by non-COVID-19 factors, including meteorology, the implementation of the Global Sulphur Cap and the shutdown of coal-fired power plants as part of national decarbonization efforts, respectively. The potential increase in NMVOC emissions from the intensive use of personal protective equipment such as hand sanitizer gels is considered in a heterogeneous way across countries in officially reported inventories, indicating the need for some countries to base their calculations on more advanced methods. The findings of this study can be used to better understand the uncertainties in near-real-time emissions and how such emissions could be used in the future to provide timely updates to emission datasets that are critical for modelling and monitoring applications.</p></article>", "keywords": ["330", "550", "Physics", "QC1-999", "Air pollution", "Near-real-time emissions", "Urbanisation", "Covid-19 pandemic", "7. Clean energy", "3. Good health", "[SDU] Sciences of the Universe [physics]", "Chemistry", "Greenhouse gasses", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "11. Sustainability", "QD1-999"]}, "links": [{"href": "https://acp.copernicus.org/articles/23/8081/2023/acp-23-8081-2023.pdf"}, {"href": "https://doi.org/10.5194/egusphere-2023-186"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-2023-186", "name": "item", "description": "10.5194/egusphere-2023-186", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-2023-186"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-28T00:00:00Z"}}, {"id": "10.5194/acp-21-773-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:37Z", "type": "Journal Article", "created": "2021-01-20", "title": "Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Our estimates are provided in the form of a dataset of reduction factors varying per country and day that will allow the modelling and identification of the associated impacts upon air quality. The country- and daily-resolved reduction factors are provided for each of the following source categories: energy industry (power plants), manufacturing industry, road traffic and aviation (landing and take-off cycle). We computed the reduction factors based on open-access and near-real-time measured activity data from a wide range of information sources. We also trained a machine learning model with meteorological data to derive weather-normalized electricity consumption reductions. The time period covered is from 21\u00a0February, when the first European localized lockdown was implemented in the region of Lombardy (Italy), until 26\u00a0April 2020. This period includes 5\u00a0weeks (23\u00a0March until 26\u00a0April) with the most severe and relatively unchanged restrictions upon mobility and socio-economic activities across Europe. The computed reduction factors were combined with the Copernicus Atmosphere Monitoring Service's European emission inventory using adjusted temporal emission profiles in order to derive time-resolved emission reductions per country and pollutant sector. During the most severe lockdown period, we estimate the average emission reductions to be \u221233\u2009% for NOx, \u22128\u2009% for non-methane volatile organic compounds (NMVOCs), \u22127\u2009% for SOx and \u22127\u2009% for PM2.5 at the EU-30 level (EU-28 plus Norway and Switzerland). For all pollutants more than 85\u2009% of the total reduction is attributable to road transport, except SOx. The reductions reached \u221250\u2009% (NOx), \u221214\u2009% (NMVOCs), \u221212\u2009% (SOx) and \u221215\u2009% (PM2.5) in countries where the lockdown restrictions were more severe such as Italy, France or Spain. To show the potential for air quality modelling, we simulated and evaluated NO2 concentration decreases in rural and urban background regions across Europe (Italy, Spain, France, Germany, United-Kingdom and Sweden). We found the lockdown measures to be responsible for NO2 reductions of up to \u221258\u2009% at urban background locations (Madrid, Spain) and \u221244\u2009% at rural background areas (France), with an average contribution of the traffic sector to total reductions of 86\u2009% and 93\u2009%, respectively. A clear improvement of the modelled results was found when considering the emission reduction factors, especially in Madrid, Paris and London where the bias is reduced by more than 90\u2009%. Future updates will include the extension of the COVID-19 lockdown period covered, the addition of other pollutant sectors potentially affected by the restrictions (commercial and residential combustion and shipping) and the evaluation of other air quality pollutants such as O3 and PM2.5. All the emission reduction factors are provided in the Supplement.                     </p></article>", "keywords": ["Atmospheric chemistry", "330", "550", "QC1-999", "Lockdowns", "Air pollution", "Urbanisation", "Environment", "COVID-19 (Malaltia)", "7. Clean energy", "01 natural sciences", "COVID-19 (Malaltia) -- Aspectes ambientals", "COVID-19 (Disease)", "11. Sustainability", "QD1-999", "0105 earth and related environmental sciences", "Physics", "Atmospheric emissions", "COVID-19", "Atmospheric chemistry modelling", "3. Good health", "Chemistry", "13. Climate action", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", "Confinament", "Europa", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]"]}, "links": [{"href": "https://acp.copernicus.org/articles/21/773/2021/acp-21-773-2021.pdf"}, {"href": "https://doi.org/10.5194/acp-21-773-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/acp-21-773-2021", "name": "item", "description": "10.5194/acp-21-773-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/acp-21-773-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-22T00:00:00Z"}}, {"id": "10.5194/essd-13-367-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:47Z", "type": "Journal Article", "created": "2021-02-13", "title": "Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling", "description": "<p>Abstract. We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx; SOx; NMVOC, non-methane volatile organic compound; NH3; CO; PM10; and PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1\uffe2\uff80\uff89\uffc3\uff97\uffe2\uff80\uff890.1\uffe2\uff88\uff98 and regional European 0.1\uffe2\uff80\uff89\uffc3\uff97\uffe2\uff80\uff890.05\uffe2\uff88\uff98 grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS programme. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production and traffic counts) at national and local levels were collected and combined with existing meteorology-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily and hourly) the resulting profiles are pollutant-dependent, year-dependent (i.e. time series from 2010 to 2017) and/or spatially dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Guevara et al., 2020a) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68, Guevara et al., 2020b) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: February 2021).                     </p>", "keywords": ["China", "Atmospheric chemistry", "550", "Anthropogenic emissions", "Ammonia emissions", "Urbanisation", "Environment", "7. Clean energy", "[SDU] Sciences of the Universe [physics]", "11. Sustainability", "Air-pollution", "GE1-350", "Gridded emissions", "Fuel use", "QE1-996.5", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Inventory", "Geology", "Environmental sciences", "Data product", "Qu\u00edmica atmosf\u00e8rica", "13. Climate action", "Air quality", "Transport model", "Data sets", "Bottom-up", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Air pollutants"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/367/2021/essd-13-367-2021.pdf"}, {"href": "https://doi.org/10.5194/essd-13-367-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-13-367-2021", "name": "item", "description": "10.5194/essd-13-367-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-13-367-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-12T00:00:00Z"}}, {"id": "10.5194/essd-16-337-2024", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:22:47Z", "type": "Journal Article", "created": "2024-01-15", "title": "A global catalogue of CO2 emissions and co-emitted species from power plants, including high-resolution vertical and temporal profiles", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We present a high-resolution global emission catalogue of CO2 and co-emitted species (NOx, SO2, CO, CH4) from thermal power plants for the year 2018. The construction of the database follows a bottom-up approach, which combines plant-specific information with national energy consumption statistics and fuel-dependent emission factors for CO2 and emission ratios for co-emitted species (e.g. the amount of NOx emitted relative to CO2: NOx/CO2). The resulting catalogue contains annual emission information for more than 16\u2009000 individual facilities at their exact geographical locations. Each facility is linked to a country- and fuel-dependent temporal profile (i.e. monthly, day of the week and hourly) and a plant-level vertical profile, which were derived from national electricity generation statistics and plume rise calculations that combine stack parameters with meteorological information. The combination of the aforementioned information allows us to derive high-resolution spatial and temporal emissions for modelling purposes. Estimated annual emissions were compared against independent plant- and country-level inventories, including Carbon Monitoring for Action (CARMA), the Global Infrastructure emission Database (GID) and the Emissions Database for Global Atmospheric Research (EDGAR), as well as officially reported emission data. Overall good agreement is observed between datasets when comparing the CO2 emissions. The main discrepancies are related to the non-inclusion of auto-producer or heat-only facilities in certain countries due to a lack of data. Larger inconsistencies are obtained when comparing emissions from co-emitted species due to uncertainties in the fuel-, country- and region-dependent emission ratios and gap-filling procedures. The temporal distribution of emissions obtained in this work was compared against traditional sector-dependent profiles that are widely used in modelling efforts. This highlighted important differences and the need to consider country dependencies when temporally distributing emissions. The resulting catalogue (https://doi.org/10.24380/0a9o-v7xe, Guevara et al., 2023) is developed in the framework of the Prototype System for a Copernicus CO2 service (CoCO2) European Union (EU)-funded project to support the development of the Copernicus CO2 Monitoring and Verification Support capacity (CO2MVS).                     </p></article>", "keywords": ["QE1-996.5", "550", "Atmospheric carbon dioxide", "Heating plants", "Urbanisation", "Geology", "Environment", "7. Clean energy", "12. Responsible consumption", "Emission", "Environmental sciences", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Enginyeria ambiental", "Centrals t\u00e8rmiques", "13. Climate action", "11. Sustainability", "GE1-350", "Anh\u00eddrid carb\u00f2nic atmosf\u00e8ric"]}, "links": [{"href": "https://doi.org/10.5194/essd-16-337-2024"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-16-337-2024", "name": "item", "description": "10.5194/essd-16-337-2024", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-16-337-2024"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-15T00:00:00Z"}}, {"id": "2117/342462", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:26:56Z", "type": "Journal Article", "created": "2021-02-13", "title": "Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx; SOx; NMVOC, non-methane volatile organic compound; NH3; CO; PM10; and PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1\u2009\u00d7\u20090.1\u2218 and regional European 0.1\u2009\u00d7\u20090.05\u2218 grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS programme. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production and traffic counts) at national and local levels were collected and combined with existing meteorology-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily and hourly) the resulting profiles are pollutant-dependent, year-dependent (i.e. time series from 2010 to 2017) and/or spatially dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Guevara et al., 2020a) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68, Guevara et al., 2020b) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: February 2021).</p></article>", "keywords": ["China", "Atmospheric chemistry", "550", "Anthropogenic emissions", "Ammonia emissions", "Urbanisation", "Environment", "7. Clean energy", "[SDU] Sciences of the Universe [physics]", "11. Sustainability", "Air-pollution", "GE1-350", "Gridded emissions", "Fuel use", "QE1-996.5", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Inventory", "Geology", "Environmental sciences", "Data product", "Qu\u00edmica atmosf\u00e8rica", "13. Climate action", "Air quality", "Transport model", "Data sets", "Bottom-up", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Air pollutants"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/367/2021/essd-13-367-2021.pdf"}, {"href": "https://doi.org/2117/342462"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/342462", "name": "item", "description": "2117/342462", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/342462"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-12T00:00:00Z"}}, {"id": "2117/405068", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:26:56Z", "type": "Journal Article", "created": "2024-01-15", "title": "A global catalogue of CO2 emissions and co-emitted species from power plants, including high-resolution vertical and temporal profiles", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We present a high-resolution global emission catalogue of CO2 and co-emitted species (NOx, SO2, CO, CH4) from thermal power plants for the year 2018. The construction of the database follows a bottom-up approach, which combines plant-specific information with national energy consumption statistics and fuel-dependent emission factors for CO2 and emission ratios for co-emitted species (e.g. the amount of NOx emitted relative to CO2: NOx/CO2). The resulting catalogue contains annual emission information for more than 16\u2009000 individual facilities at their exact geographical locations. Each facility is linked to a country- and fuel-dependent temporal profile (i.e. monthly, day of the week and hourly) and a plant-level vertical profile, which were derived from national electricity generation statistics and plume rise calculations that combine stack parameters with meteorological information. The combination of the aforementioned information allows us to derive high-resolution spatial and temporal emissions for modelling purposes. Estimated annual emissions were compared against independent plant- and country-level inventories, including Carbon Monitoring for Action (CARMA), the Global Infrastructure emission Database (GID) and the Emissions Database for Global Atmospheric Research (EDGAR), as well as officially reported emission data. Overall good agreement is observed between datasets when comparing the CO2 emissions. The main discrepancies are related to the non-inclusion of auto-producer or heat-only facilities in certain countries due to a lack of data. Larger inconsistencies are obtained when comparing emissions from co-emitted species due to uncertainties in the fuel-, country- and region-dependent emission ratios and gap-filling procedures. The temporal distribution of emissions obtained in this work was compared against traditional sector-dependent profiles that are widely used in modelling efforts. This highlighted important differences and the need to consider country dependencies when temporally distributing emissions. The resulting catalogue (https://doi.org/10.24380/0a9o-v7xe, Guevara et al., 2023) is developed in the framework of the Prototype System for a Copernicus CO2 service (CoCO2) European Union (EU)-funded project to support the development of the Copernicus CO2 Monitoring and Verification Support capacity (CO2MVS).</p></article>", "keywords": ["QE1-996.5", "550", "Atmospheric carbon dioxide", "Heating plants", "Urbanisation", "Geology", "Environment", "7. Clean energy", "12. Responsible consumption", "Emission", "Environmental sciences", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Enginyeria ambiental", "Centrals t\u00e8rmiques", "13. Climate action", "11. Sustainability", "GE1-350", "Anh\u00eddrid carb\u00f2nic atmosf\u00e8ric"]}, "links": [{"href": "https://doi.org/2117/405068"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/405068", "name": "item", "description": "2117/405068", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/405068"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-15T00:00:00Z"}}, {"id": "2117/339455", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:26:56Z", "type": "Journal Article", "created": "2021-01-20", "title": "Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Our estimates are provided in the form of a dataset of reduction factors varying per country and day that will allow the modelling and identification of the associated impacts upon air quality. The country- and daily-resolved reduction factors are provided for each of the following source categories: energy industry (power plants), manufacturing industry, road traffic and aviation (landing and take-off cycle). We computed the reduction factors based on open-access and near-real-time measured activity data from a wide range of information sources. We also trained a machine learning model with meteorological data to derive weather-normalized electricity consumption reductions. The time period covered is from 21\u00a0February, when the first European localized lockdown was implemented in the region of Lombardy (Italy), until 26\u00a0April 2020. This period includes 5\u00a0weeks (23\u00a0March until 26\u00a0April) with the most severe and relatively unchanged restrictions upon mobility and socio-economic activities across Europe. The computed reduction factors were combined with the Copernicus Atmosphere Monitoring Service's European emission inventory using adjusted temporal emission profiles in order to derive time-resolved emission reductions per country and pollutant sector. During the most severe lockdown period, we estimate the average emission reductions to be \u221233\u2009% for NOx, \u22128\u2009% for non-methane volatile organic compounds (NMVOCs), \u22127\u2009% for SOx and \u22127\u2009% for PM2.5 at the EU-30 level (EU-28 plus Norway and Switzerland). For all pollutants more than 85\u2009% of the total reduction is attributable to road transport, except SOx. The reductions reached \u221250\u2009% (NOx), \u221214\u2009% (NMVOCs), \u221212\u2009% (SOx) and \u221215\u2009% (PM2.5) in countries where the lockdown restrictions were more severe such as Italy, France or Spain. To show the potential for air quality modelling, we simulated and evaluated NO2 concentration decreases in rural and urban background regions across Europe (Italy, Spain, France, Germany, United-Kingdom and Sweden). We found the lockdown measures to be responsible for NO2 reductions of up to \u221258\u2009% at urban background locations (Madrid, Spain) and \u221244\u2009% at rural background areas (France), with an average contribution of the traffic sector to total reductions of 86\u2009% and 93\u2009%, respectively. A clear improvement of the modelled results was found when considering the emission reduction factors, especially in Madrid, Paris and London where the bias is reduced by more than 90\u2009%. Future updates will include the extension of the COVID-19 lockdown period covered, the addition of other pollutant sectors potentially affected by the restrictions (commercial and residential combustion and shipping) and the evaluation of other air quality pollutants such as O3 and PM2.5. All the emission reduction factors are provided in the Supplement.</p></article>", "keywords": ["Atmospheric chemistry", "330", "550", "QC1-999", "Lockdowns", "Air pollution", "Urbanisation", "Environment", "COVID-19 (Malaltia)", "01 natural sciences", "7. Clean energy", "COVID-19 (Malaltia) -- Aspectes ambientals", "COVID-19 (Disease)", "11. Sustainability", "QD1-999", "0105 earth and related environmental sciences", "Physics", "Atmospheric emissions", "COVID-19", "Atmospheric chemistry modelling", "3. Good health", "Chemistry", "13. Climate action", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", "Confinament", "Europa", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]"]}, "links": [{"href": "https://acp.copernicus.org/articles/21/773/2021/acp-21-773-2021.pdf"}, {"href": "https://doi.org/2117/339455"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/339455", "name": "item", "description": "2117/339455", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/339455"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-22T00:00:00Z"}}, {"id": "3045302210", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:27:31Z", "type": "Journal Article", "created": "2021-01-20", "title": "Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Our estimates are provided in the form of a dataset of reduction factors varying per country and day that will allow the modelling and identification of the associated impacts upon air quality. The country- and daily-resolved reduction factors are provided for each of the following source categories: energy industry (power plants), manufacturing industry, road traffic and aviation (landing and take-off cycle). We computed the reduction factors based on open-access and near-real-time measured activity data from a wide range of information sources. We also trained a machine learning model with meteorological data to derive weather-normalized electricity consumption reductions. The time period covered is from 21\u00a0February, when the first European localized lockdown was implemented in the region of Lombardy (Italy), until 26\u00a0April 2020. This period includes 5\u00a0weeks (23\u00a0March until 26\u00a0April) with the most severe and relatively unchanged restrictions upon mobility and socio-economic activities across Europe. The computed reduction factors were combined with the Copernicus Atmosphere Monitoring Service's European emission inventory using adjusted temporal emission profiles in order to derive time-resolved emission reductions per country and pollutant sector. During the most severe lockdown period, we estimate the average emission reductions to be \u221233\u2009% for NOx, \u22128\u2009% for non-methane volatile organic compounds (NMVOCs), \u22127\u2009% for SOx and \u22127\u2009% for PM2.5 at the EU-30 level (EU-28 plus Norway and Switzerland). For all pollutants more than 85\u2009% of the total reduction is attributable to road transport, except SOx. The reductions reached \u221250\u2009% (NOx), \u221214\u2009% (NMVOCs), \u221212\u2009% (SOx) and \u221215\u2009% (PM2.5) in countries where the lockdown restrictions were more severe such as Italy, France or Spain. To show the potential for air quality modelling, we simulated and evaluated NO2 concentration decreases in rural and urban background regions across Europe (Italy, Spain, France, Germany, United-Kingdom and Sweden). We found the lockdown measures to be responsible for NO2 reductions of up to \u221258\u2009% at urban background locations (Madrid, Spain) and \u221244\u2009% at rural background areas (France), with an average contribution of the traffic sector to total reductions of 86\u2009% and 93\u2009%, respectively. A clear improvement of the modelled results was found when considering the emission reduction factors, especially in Madrid, Paris and London where the bias is reduced by more than 90\u2009%. Future updates will include the extension of the COVID-19 lockdown period covered, the addition of other pollutant sectors potentially affected by the restrictions (commercial and residential combustion and shipping) and the evaluation of other air quality pollutants such as O3 and PM2.5. All the emission reduction factors are provided in the Supplement.                     </p></article>", "keywords": ["Atmospheric chemistry", "330", "550", "QC1-999", "Lockdowns", "Air pollution", "Urbanisation", "Environment", "COVID-19 (Malaltia)", "7. Clean energy", "01 natural sciences", "COVID-19 (Malaltia) -- Aspectes ambientals", "COVID-19 (Disease)", "11. Sustainability", "QD1-999", "0105 earth and related environmental sciences", "Physics", "Atmospheric emissions", "COVID-19", "Atmospheric chemistry modelling", "3. Good health", "Chemistry", "13. Climate action", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", "Confinament", "Europa", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]"]}, "links": [{"href": "https://acp.copernicus.org/articles/21/773/2021/acp-21-773-2021.pdf"}, {"href": "https://doi.org/3045302210"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3045302210", "name": "item", "description": "3045302210", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3045302210"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-22T00:00:00Z"}}, {"id": "3129610671", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:27:37Z", "type": "Journal Article", "created": "2021-02-13", "title": "Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx; SOx; NMVOC, non-methane volatile organic compound; NH3; CO; PM10; and PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1\u2009\u00d7\u20090.1\u2218 and regional European 0.1\u2009\u00d7\u20090.05\u2218 grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS programme. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production and traffic counts) at national and local levels were collected and combined with existing meteorology-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily and hourly) the resulting profiles are pollutant-dependent, year-dependent (i.e. time series from 2010 to 2017) and/or spatially dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Guevara et al., 2020a) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68, Guevara et al., 2020b) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: February 2021).                     </p></article>", "keywords": ["China", "Atmospheric chemistry", "550", "Anthropogenic emissions", "Ammonia emissions", "Urbanisation", "Environment", "7. Clean energy", "[SDU] Sciences of the Universe [physics]", "11. Sustainability", "Air-pollution", "GE1-350", "Gridded emissions", "Fuel use", "QE1-996.5", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Inventory", "Geology", "Environmental sciences", "Data product", "Qu\u00edmica atmosf\u00e8rica", "13. Climate action", "Air quality", "Transport model", "Data sets", "Bottom-up", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Air pollutants"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/367/2021/essd-13-367-2021.pdf"}, {"href": "https://doi.org/3129610671"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3129610671", "name": "item", "description": "3129610671", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3129610671"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-12T00:00:00Z"}}, {"id": "54b8ec0dc751df78895fa5a2", "type": "Feature", "geometry": null, "properties": {"updated": "2016-02-25T23:35:43.799", "type": "Dataset", "title": "Use of space 1999 (shapefile/Lambert93)", "description": "The EUTM (space usage mode) is based on a data manufacturing and validation process designed with and for elected officials and technicians, both in the input and in the basic treatments carried out for the entire interscot space. The data comes from a photo-interpretation carried out with a UMC (minimum collection unit) at the 1/1000th. The analysis of the evolutions was made from two photo-interpretations, one of 1999 and the other of 2009, according to a nomenclature in 17 posts inspired by the MOS realized in 2008 by the Master Plan Rouen Elbeuf and the State.", "formats": [{"name": "ZIP"}], "keywords": ["artificialisation", "espace", "fr", "mode-occupation", "mue", "occupation", "occupation-du-sol", "sol", "urbanisation", "usage"], "contacts": [{"organization": "https://www.data.gouv.fr/organizations/54b8eab7c751df78885fa5a2/", "roles": ["publisher"]}]}, "links": [{"href": "https://www.data.gouv.fr/api/1/datasets/r/39b78acc-fff1-4a23-8201-df92583ebd75"}, {"href": "https://www.data.gouv.fr/api/1/datasets/r/997b21e7-0e07-4457-a995-2a03af1978ec"}, {"href": "https://www.data.gouv.fr/api/1/datasets/r/9d9d7a5a-b721-4edc-a554-f09a56eca013"}, {"href": "https://www.data.gouv.fr/api/1/datasets/r/cb383ae4-ec2c-4acd-96ef-17f6e36be135"}, {"href": "https://www.data.gouv.fr/datasets/mode-dusage-de-lespace-1999-shapefile-lambert93/"}, {"href": "http://data.europa.eu/88u/dataset/54b8ec0dc751df78895fa5a2"}, {"rel": "self", "type": "application/geo+json", "title": "54b8ec0dc751df78895fa5a2", "name": "item", "description": "54b8ec0dc751df78895fa5a2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/54b8ec0dc751df78895fa5a2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "54b8f873c751df04185fa5a3", "type": "Feature", "geometry": null, "properties": {"updated": "2015-12-28T14:33:39.513", "type": "Dataset", "title": "Use of space 2009 (shapefile/Lambert93)", "description": "The EUTM (space usage mode) is based on a data manufacturing and validation process designed with and for elected officials and technicians, both in the input and in the basic treatments carried out for the entire interscot space. The data comes from a photo-interpretation carried out with a UMC (minimum collection unit) at the 1/1000th. 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