{"type": "FeatureCollection", "facets": {"type": {"type": "terms", "property": "type", "buckets": [{"value": "Journal Article", "count": 19}, {"value": "Dataset", "count": 2}]}, "soil_chemical_properties": {"type": "terms", "property": "soil_chemical_properties", "buckets": [{"value": "carbon", "count": 1}]}, "soil_biological_properties": {"type": "terms", "property": "soil_biological_properties", "buckets": []}, "soil_physical_properties": {"type": "terms", "property": "soil_physical_properties", "buckets": [{"value": "water", "count": 2}, {"value": "drainage", "count": 1}]}, "soil_classification": {"type": "terms", "property": "soil_classification", "buckets": []}, "soil_functions": {"type": "terms", "property": "soil_functions", "buckets": [{"value": "ecosystem services", "count": 1}]}, "soil_threats": {"type": "terms", "property": "soil_threats", "buckets": [{"value": "urbanisation", "count": 21}, {"value": "anthropogenic erosion", "count": 2}]}, "soil_processes": {"type": "terms", "property": "soil_processes", "buckets": []}, "soil_management": {"type": "terms", "property": "soil_management", "buckets": []}, "ecosystem_services": {"type": "terms", "property": "ecosystem_services", "buckets": []}}, "features": [{"id": "10.1002/rnc.4288", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:14:21Z", "type": "Journal Article", "created": "2018-08-07", "title": "Quantization effects and convergence properties of rigid formation control systems with quantized distance measurements", "description": "Summary<p>In this paper, we discuss quantization effects in rigid formation control systems when target formations are described by interagent distances. Because of practical sensing and measurement constraints, we consider in this paper distance measurements in their quantized forms. We show that under gradient\uffe2\uff80\uff90based formation control, in the case of uniform quantization, the distance errors converge locally to a bounded set whose size depends on the quantization error, while in the case of logarithmic quantization, all distance errors converge locally to zero. A special quantizer involving the signum function is then considered with which all agents can only measure coarse distances in terms of binary information. In this case, the formation converges locally to a target formation within a finite time. Lastly, we discuss the effect of asymmetric uniform quantization on rigid formation control.</p", "keywords": ["0209 industrial biotechnology", "0203 mechanical engineering", "Quantization", "FOS: Electrical engineering", " electronic engineering", " information engineering", "formation control", "Systems and Control (eess.SY)", "02 engineering and technology", "quantization effect", "rigid formation control", "Electrical Engineering and Systems Science - Systems and Control", "binary measurement"]}, "links": [{"href": "https://openresearch-repository.anu.edu.au/bitstream/1885/202815/5/01_Sun_Quantization_effects_and_2018.pdf.jpg"}, {"href": "https://openresearch-repository.anu.edu.au/bitstream/1885/202815/8/quantization-effects-convergence.pdf.jpg"}, {"href": "https://doi.org/10.1002/rnc.4288"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Robust%20and%20Nonlinear%20Control", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/rnc.4288", "name": "item", "description": "10.1002/rnc.4288", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/rnc.4288"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-08-07T00:00:00Z"}}, {"id": "10.1016/j.cosust.2018.11.002", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:16:02Z", "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": "10.1016/j.envpol.2017.06.102", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:16:14Z", "type": "Journal Article", "created": "2017-07-13", "title": "Using nitrogen concentration and isotopic composition in lichens to spatially assess the relative contribution of atmospheric nitrogen sources in complex landscapes", "description": "Reactive nitrogen (Nr) is an important driver of global change, causing alterations in ecosystem biodiversity and functionality. Environmental assessments require monitoring the emission and deposition of both the amount and types of Nr. This is especially important in heterogeneous landscapes, as different land-cover types emit particular forms of Nr to the atmosphere, which can impact ecosystems distinctively. Such assessments require high spatial resolution maps that also integrate temporal variations, and can only be feasibly achieved by using ecological indicators. Our aim was to rank land-cover types according to the amount and form of emitted atmospheric Nr in a complex landscape with multiple sources of N. To do so, we measured and mapped nitrogen concentration and isotopic composition in lichen thalli, which we then related to land-cover data. Results suggested that, at the landscape scale, intensive agriculture and urban areas were the most important sources of Nr to the atmosphere. Additionally, the ocean greatly influences Nr in land, by providing air with low Nr concentration and a unique isotopic composition. These results have important consequences for managing air pollution at the regional level, as they provide critical information for modeling Nr emission and deposition across regional as well as continental scales.", "keywords": ["2. Zero hunger", "Air Pollutants", "Lichens", "Nitrogen Isotopes", "Portugal", "Atmosphere", "Nitrogen", "Urbanization", "Geographic Mapping", "Agriculture", "15. Life on land", "01 natural sciences", "6. Clean water", "Reactive nitrogen", "13. Climate action", "Nitrogen Fixation", "11. Sustainability", "Industry", "Isoscapes", "14. Life underwater", "Polution - Eutrophication", "Ecosystem", "Environmental Monitoring", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.envpol.2017.06.102"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Pollution", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.envpol.2017.06.102", "name": "item", "description": "10.1016/j.envpol.2017.06.102", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.envpol.2017.06.102"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-01T00:00:00Z"}}, {"id": "10.1016/j.scitotenv.2014.11.004", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:16:56Z", "type": "Journal Article", "created": "2014-11-20", "title": "Impacts Of Lucc On Soil Properties In The Riparian Zones Of Desert Oasis With Remote Sensing Data: A Case Study Of The Middle Heihe River Basin, China", "description": "Large-scale changes in land use and land cover over long timescales can induce significant variations in soil physicochemical properties, particularly in the riparian zones of arid regions. Frequent reclamation of wetlands and grasslands and intensive agricultural activity have induced significant changes in both land use/cover and soil physicochemical properties in the riparian zones of the middle Heihe River basin of China. The present study aims to explore whether land use/land cover change (LUCC) can well explain the variations in soil properties in the riparian zones of the middle Heihe River basin. To achieve this, we mapped LUCC and quantified the type of land use change using remote sensing images, topographic maps, and GIS analysis techniques. Forty-two sites were selected for soil and vegetation sampling. Then, physical and chemical experiments were employed to determine soil moisture, soil bulk density, soil pH, soil organic carbon, total nitrogen, total potassium, total phosphorous, available nitrogen, available potassium, and available phosphorous. The Independent-Samples Kruskal-Wallis Test, principal component analysis, and a scatter matrix were used to analyze the effects of LUCC on soil properties. The results indicate that the majority of the parameters investigated were affected significantly by LUCC. In particular, soil moisture and soil organic carbon can be explained well by land cover change and land use change, respectively. Furthermore, changes in soil moisture could be attributed primarily to land cover changes. Changes in soil organic carbon were correlated closely with the following land use change types: wetlands-arable, forest-grasslands, and grasslands-desert. Other parameters, including pH and total K, were also found to exhibit significant correlations with LUCC. However, changes in soil nutrients were shown to be induced most probably by human agricultural activity (i.e. fertilize, irrigation, tillage, etc.), rather than by simple conversions from one land use/cover types to the others.", "keywords": ["2. Zero hunger", "China", "Conservation of Natural Resources", "Nitrogen", "Urbanization", "Agriculture", "Phosphorus", "04 agricultural and veterinary sciences", "Environment", "15. Life on land", "01 natural sciences", "6. Clean water", "3. Good health", "Soil", "Rivers", "13. Climate action", "Remote Sensing Technology", "0401 agriculture", " forestry", " and fisheries", "Desert Climate", "Ecosystem", "Environmental Monitoring", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.scitotenv.2014.11.004"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.scitotenv.2014.11.004", "name": "item", "description": "10.1016/j.scitotenv.2014.11.004", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2014.11.004"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-02-01T00:00:00Z"}}, {"id": "10.1016/j.scitotenv.2021.149346", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:16:59Z", "type": "Journal Article", "created": "2021-07-31", "title": "Characterization of the main land processes occurring in Europe (2000-2018) through a MODIS NDVI seasonal parameter-based procedure", "description": "The identification and recognition of the land processes are of vital importance for a proper management of the ecosystem functions and services. However, on-ground land uses/land covers (LULC) characterization is a time-consuming task, often limited to small land areas, which can be solved using remote sensing technologies. The objective of this work is to investigate how the different MODIS NDVI seasonal parameters responded to the main land processes observed in Europe in the 2000-2018 period; characterizing their temporal trend; and evaluating which one reflected better each specific land process. NDVI time-series were evaluated using TIMESAT software, which extracted eight seasonality parameters: amplitude, base value, length of season, maximum value, left and right derivative values and small and large integrated values. These parameters were correlated with the LULC changes derived from COoRdination of INformation on the Environment Land Cover (CLC) for assessing which parameter better characterized each land process. The temporal evolution of the maximum seasonal NDVI was the parameter that better characterized the occurrence of most of the land processes evaluated (afforestation, agriculturalization, degradation, land abandonment, land restoration, urbanization; R2 from 0.67-0.97). Large integrated value also presented significant relationships but they were restricted to two of the three evaluated periods. On the contrary, land processes involving CLC categories with similar NDVI patterns were not well captured with the proposed methodology. These results evidenced that this methodology could be combined with other classification methods for improving LULC identification accuracy or for identifying LULC processes in locations where no LULC maps are available. Such information can be used by policy-makers to draw LULC management actions associated with sustainable development goals. This is especially relevant for areas where food security is at stake and where terrestrial ecosystems are threatened by severe biodiversity loss.", "keywords": ["Land cover", "Urbanization", "CORINE", "Biodiversity", "15. Life on land", "01 natural sciences", "Europe", "Normalized difference vegetation index", "13. Climate action", "Land use", "11. Sustainability", "Seasons", "TIMESAT", "Ecosystem", "Environmental Monitoring", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.iris.unict.it/bitstream/20.500.11769/511362/1/Ramirez-Cuesta%20et%20al%202021.pdf"}, {"href": "https://doi.org/10.1016/j.scitotenv.2021.149346"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.scitotenv.2021.149346", "name": "item", "description": "10.1016/j.scitotenv.2021.149346", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2021.149346"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-01T00:00:00Z"}}, {"id": "10.1039/c8en00501j", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:18:05Z", "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": "10.1111/gcb.12238", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:18:59Z", "type": "Journal Article", "created": "2013-04-30", "title": "Winter Climate Change Effects On Soil C And N Cycles In Urban Grasslands", "description": "Abstract<p>Despite growing recognition of the role that cities have in global biogeochemical cycles, urban systems are among the least understood of all ecosystems. Urban grasslands are expanding rapidly along with urbanization, which is expected to increase at unprecedented rates in upcoming decades. The large and increasing area of urban grasslands and their impact on water and air quality justify the need for a better understanding of their biogeochemical cycles. There is also great uncertainty about the effect that climate change, especially changes in winter snow cover, will have on nutrient cycles in urban grasslands. We aimed to evaluate how reduced snow accumulation directly affects winter soil frost dynamics, and indirectly greenhouse gas fluxes and the processing of carbon (C) and nitrogen (N) during the subsequent growing season in northern urban grasslands. Both artificial and natural snow reduction increased winter soil frost, affecting winter microbial C and N processing, accelerating C and N cycles and increasing soil\uffc2\uffa0:\uffc2\uffa0atmosphere greenhouse gas exchange during the subsequent growing season. With lower snow accumulations that are predicted with climate change, we found decreases in N retention in these ecosystems, and increases inN2OandCO2flux to the atmosphere, significantly increasing the global warming potential of urban grasslands. Our results suggest that the environmental impacts of these rapidly expanding ecosystems are likely to increase as climate change brings milder winters and more extensive soil frost.</p>", "keywords": ["2. Zero hunger", "Nitrogen", "Climate Change", "Urbanization", "04 agricultural and veterinary sciences", "15. Life on land", "Poaceae", "Carbon", "Soil", "13. Climate action", "11. Sustainability", "0401 agriculture", " forestry", " and fisheries", "Seasons", "Ecosystem"]}, "links": [{"href": "https://doi.org/10.1111/gcb.12238"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/gcb.12238", "name": "item", "description": "10.1111/gcb.12238", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcb.12238"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-05-29T00:00:00Z"}}, {"id": "10.3390/atmos8050084", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:21:18Z", "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": {"updated": "2026-04-04T16:22:11Z", "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": "10.5281/zenodo.10888463", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:22:36Z", "type": "Dataset", "title": "Urban Riparian Wetland Water Quality Dataset_Stormwater Capture in Beaver-mediated Wetlands along Walnut Creek, Raleigh, North Carolina, USA", "description": "This is the initial release of a\u00a0water quality\u00a0dataset pertaining to the\u00a0riparian floodplain wetlands\u00a0alongside Walnut Creek in Raleigh, North Carolina USA.\u00a0 Walnut Creek is the main drainage channel in an\u00a0urbanized watershed\u00a0(HUC-12: 030202011101) in central North Carolina.\u00a0 There are several riparian floodplain wetlands along the creek which are largely supplied by\u00a0urban stormwater\u00a0runoff including directed\u00a0storm sewer flows\u00a0and regular\u00a0overbank flooding\u00a0events. In many of these wetlands local water retention and residence time in the surface ponds is mediated by the damming activity of\u00a0North American beavers (Castor canadensis).\u00a0 This dataset contains data specific to the water quality values of Walnut Creek, its tributary Little Rock Creek, and the surface ponds and groundwater at the\u00a0Walnut Creek Wetland Park\u00a0which is actively influenced by resident beavers.\u00a0 The period of this dataset is from\u00a0January 5, 2023 through October 28, 2023.\u00a0  This dataset includes a variety of common water quality parameters measured in situ by use of a YSI Pro water quality meter, as well as dissolved nutrient values determined by laboratory analysis of collected water samples.\u00a0 YSI data was collected on a weekly basis and water samples were collected for laboratory analysis on a monthly basis. Additional measurements and collection took place during six large rainfall events to allow comparison between baseflow and stormflow conditions across the site.\u00a0 This dataset aims to provide a comprehensive look at the water quality of Walnut Creek in comparison with the surface ponds and groundwater in the Walnut Creek Wetland Park, which are all ultimately sourced from urban stormwater runoff.   This water quality dataset is intended to accompany the separate hydrology dataset published on Zenodo at URL: https://doi.org/10.5281/zenodo.10709630. Together, these datasets are meant to support an improved understanding of the water availability and water quality found in connection with beaver-mediated stormwater capture in an urbanized watershed in the North Carolina Piedmont.  \u00a0This dataset resulted from research supported with a Graduate Student Research Grant awarded by the\u00a0North Carolina Water Resources Research Institute (WRRI), under Project Number 23-10-W: 'Stormwater Diversion, Storage, and Treatment by Beaver-enhanced Floodplain Wetlands in Piedmont Urban Watersheds'. \u00a0  This material is based upon work supported by the\u00a0National Science Foundation (NSF)\u00a0Graduate Research Fellowship Program (GRFP) under Grant No. (DGE 2137100). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.  Special thanks to\u00a0Raleigh Parks\u00a0and\u00a0Walnut Creek Wetland Park\u00a0for making this work possible.  Laboratory analysis support for evaluation of dissolved nutrients (nitrate+nitrite, TKN, total phosphorus, and total organic carbon) was provided by the NC State Environmental and Agricultural Testing Services (EATS) laboratory, Department of Crop and Soil Sciences.  \u00a0Additional laboratory analysis support for evaluation of dissolved nutrients (TKN and total phosphorus) was provided by the NC State Environmental Analysis Laboratory (EAL), Department of Biological and Agricultural Engineering (BAE).  \u00a0Usage of and technical support for the YSI Pro water quality meter used in this study was made possible by the Osburn Lab, Department of Marine, Earth and Atmospheric Sciences (MEAS), NC State University.", "keywords": ["beaver", "Piedmont", "Castor canadensis", "stormwater", "urbanization", "water quality", "wetlands"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10888463"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10888463", "name": "item", "description": "10.5281/zenodo.10888463", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10888463"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-27T00:00:00Z"}}, {"id": "10044/1/97038", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:25:00Z", "type": "Journal Article", "created": "2022-04-01", "title": "Terahertz Metastructures for Noninvasive Biomedical Sensing and Characterization in Future Health Care [Bioelectromagnetics]", "description": "According to a recent report [1] from the Cancer Research Agency of the World Health Organization, cancer is a dominant cause of mortality worldwide, leading to 10 million deaths in 2020 alone. Diagnosing a patient from the early stages tremendously raises the chance of survival. Current clinical cancer detection approaches including X-ray, magnetic resonance imaging (MRI), and biomarker analysis not only fail to provide a precise border of the malignant tissue, especially in the early stages of cancer, but also can be invasive and lead to tissue damage. Recent progress in EM biosensor technologies has the potential to deliver a point-of-care diagnosis and surpass conventional methods regarding accuracy, time, and cost.", "keywords": ["Technology", "Organizations", "Science & Technology", "Sensors", "Tissue damage", "610", "Engineering", " Electrical & Electronic", "02 engineering and technology", "Cancer detection", "Costs", "Point of care", "ARRAYS", "3. Good health", "0906 Electrical and Electronic Engineering", "Engineering", "Magnetic resonance imaging", "CELLS", "Telecommunications", "PLASMONS", "1005 Communications Technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "Electrical & Electronic", "Networking & Telecommunications"]}, "links": [{"href": "https://eprints.gla.ac.uk/263337/1/263337.pdf"}, {"href": "http://xplorestaging.ieee.org/ielx7/74/9747968/09748039.pdf?arnumber=9748039"}, {"href": "https://doi.org/10044/1/97038"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Antennas%20and%20Propagation%20Magazine", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10044/1/97038", "name": "item", "description": "10044/1/97038", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10044/1/97038"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-01T00:00:00Z"}}, {"id": "1885/202815", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:25:43Z", "type": "Journal Article", "created": "2018-08-07", "title": "Quantization effects and convergence properties of rigid formation control systems with quantized distance measurements", "description": "Summary<p>In this paper, we discuss quantization effects in rigid formation control systems when target formations are described by interagent distances. Because of practical sensing and measurement constraints, we consider in this paper distance measurements in their quantized forms. We show that under gradient\uffe2\uff80\uff90based formation control, in the case of uniform quantization, the distance errors converge locally to a bounded set whose size depends on the quantization error, while in the case of logarithmic quantization, all distance errors converge locally to zero. A special quantizer involving the signum function is then considered with which all agents can only measure coarse distances in terms of binary information. In this case, the formation converges locally to a target formation within a finite time. Lastly, we discuss the effect of asymmetric uniform quantization on rigid formation control.</p", "keywords": ["0209 industrial biotechnology", "Digital control/observation systems", "Agent technology and artificial intelligence", "formation control", "Lyapunov and other classical stabilities (Lagrange", " Poisson", "  (L^p", " l^p )", " etc.) in control theory", "Systems and Control (eess.SY)", "02 engineering and technology", "Decentralized systems", "quantization effect", "Electrical Engineering and Systems Science - Systems and Control", "binary measurement", "0203 mechanical engineering", "Quantization", "FOS: Electrical engineering", " electronic engineering", " information engineering", "rigid formation control"]}, "links": [{"href": "https://openresearch-repository.anu.edu.au/bitstream/1885/202815/5/01_Sun_Quantization_effects_and_2018.pdf.jpg"}, {"href": "https://openresearch-repository.anu.edu.au/bitstream/1885/202815/8/quantization-effects-convergence.pdf.jpg"}, {"href": "https://doi.org/1885/202815"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Robust%20and%20Nonlinear%20Control", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1885/202815", "name": "item", "description": "1885/202815", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1885/202815"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-08-07T00:00:00Z"}}, {"id": "20.500.11769/511362", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:25:52Z", "type": "Journal Article", "created": "2021-07-30", "title": "Characterization of the main land processes occurring in Europe (2000-2018) through a MODIS NDVI seasonal parameter-based procedure", "description": "The identification and recognition of the land processes are of vital importance for a proper management of the ecosystem functions and services. However, on-ground land uses/land covers (LULC) characterization is a time-consuming task, often limited to small land areas, which can be solved using remote sensing technologies. The objective of this work is to investigate how the different MODIS NDVI seasonal parameters responded to the main land processes observed in Europe in the 2000-2018 period; characterizing their temporal trend; and evaluating which one reflected better each specific land process. NDVI time-series were evaluated using TIMESAT software, which extracted eight seasonality parameters: amplitude, base value, length of season, maximum value, left and right derivative values and small and large integrated values. These parameters were correlated with the LULC changes derived from COoRdination of INformation on the Environment Land Cover (CLC) for assessing which parameter better characterized each land process. The temporal evolution of the maximum seasonal NDVI was the parameter that better characterized the occurrence of most of the land processes evaluated (afforestation, agriculturalization, degradation, land abandonment, land restoration, urbanization; R2 from 0.67-0.97). Large integrated value also presented significant relationships but they were restricted to two of the three evaluated periods. On the contrary, land processes involving CLC categories with similar NDVI patterns were not well captured with the proposed methodology. These results evidenced that this methodology could be combined with other classification methods for improving LULC identification accuracy or for identifying LULC processes in locations where no LULC maps are available. Such information can be used by policy-makers to draw LULC management actions associated with sustainable development goals. This is especially relevant for areas where food security is at stake and where terrestrial ecosystems are threatened by severe biodiversity loss.", "keywords": ["Land cover", "Urbanization", "CORINE", "Biodiversity", "15. Life on land", "01 natural sciences", "Europe", "Normalized difference vegetation index", "13. Climate action", "Land use", "11. Sustainability", "Seasons", "TIMESAT", "Ecosystem", "Environmental Monitoring", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.iris.unict.it/bitstream/20.500.11769/511362/1/Ramirez-Cuesta%20et%20al%202021.pdf"}, {"href": "https://doi.org/20.500.11769/511362"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11769/511362", "name": "item", "description": "20.500.11769/511362", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11769/511362"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-01T00:00:00Z"}}, {"id": "2117/339455", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:26:06Z", "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": "2117/342462", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:26:06Z", "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": "2026-04-04T16:26:06Z", "type": "Journal Article", "created": "2024-01-15", "title": "A global catalogue of CO                     2                     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": "3045302210", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:26:38Z", "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": "2026-04-04T16:26:44Z", "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": "3164960866", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:26:47Z", "type": "Journal Article", "created": "2021-05-27", "title": "A spatiotemporal ensemble machine learning framework for generating land use / land cover time-series maps for Europe (2000 \u2013 2019) based on LUCAS, CORINE and GLAD Landsat", "description": "Abstract         <p>A seamless spatiotemporal machine learning framework for automated prediction, uncertainty assessment, and analysis of land use / land cover (LULC) dynamics is presented. The framework includes: (1) harmonization and preprocessing of high-resolution spatial and spatiotemporal covariate datasets (GLAD Landsat, NPP/VIIRS) including 5 million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and uncertainty per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model was fitted by combining random forest, gradient boosted trees, and artificial neural network, with logistic regressor as meta-learner. The results show that the most important covariates for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with 62%, 70%, and 87% accuracy when predicting 33 (level-3), 14 (level-2), and 5 classes (level-1); with artificial surface classes such as 'airports' and 'railroads' showing the lowest match with validation points. The spatiotemporal model outperforms spatial models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest gradual deforestation trends in large parts of Sweden, the Alps, and Scotland. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict land cover for years prior to 2000 and beyond 2020. The generated land cover time-series data stack (ODSE-LULC), including the training points, is publicly available via the Open Data Science (ODS)-Europe Viewer.</p", "keywords": ["Time Factors", "Spatiotemporal", "QH301-705.5", "Data Mining and Machine Learning", "Urbanization", "Uncertainty", "Spatial analysis", "R", "Environmental monitoring", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "Europe", "Big data", "Machine learning", "Medicine", "0401 agriculture", " forestry", " and fisheries", "Biology (General)", "Landsat", "Ensemble", "Land use/land cover", "Environmental Monitoring", "Probability", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/3164960866"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PeerJ", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3164960866", "name": "item", "description": "3164960866", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3164960866"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-27T00:00:00Z"}}, {"id": "PMC9308969", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:29:06Z", "type": "Journal Article", "created": "2021-05-27", "title": "A spatiotemporal ensemble machine learning framework for generating land use / land cover time-series maps for Europe (2000 \u2013 2019) based on LUCAS, CORINE and GLAD Landsat", "description": "<title>Abstract</title>                 <p>A seamless spatiotemporal machine learning framework for automated prediction, uncertainty assessment, and analysis of land use / land cover (LULC) dynamics is presented. The framework includes: (1) harmonization and preprocessing of high-resolution spatial and spatiotemporal covariate datasets (GLAD Landsat, NPP/VIIRS) including 5 million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and uncertainty per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model was fitted by combining random forest, gradient boosted trees, and artificial neural network, with logistic regressor as meta-learner. The results show that the most important covariates for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with 62%, 70%, and 87% accuracy when predicting 33 (level-3), 14 (level-2), and 5 classes (level-1); with artificial surface classes such as 'airports' and 'railroads' showing the lowest match with validation points. The spatiotemporal model outperforms spatial models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest gradual deforestation trends in large parts of Sweden, the Alps, and Scotland. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict land cover for years prior to 2000 and beyond 2020. The generated land cover time-series data stack (ODSE-LULC), including the training points, is publicly available via the Open Data Science (ODS)-Europe Viewer.</p>", "keywords": ["Time Factors", "Spatiotemporal", "QH301-705.5", "Data Mining and Machine Learning", "Urbanization", "Uncertainty", "Spatial analysis", "R", "Environmental monitoring", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "Europe", "Big data", "Machine learning", "Medicine", "0401 agriculture", " forestry", " and fisheries", "Biology (General)", "Landsat", "Ensemble", "Land use/land cover", "Environmental Monitoring", "Probability", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/PMC9308969"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PeerJ", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC9308969", "name": "item", "description": "PMC9308969", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC9308969"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-27T00:00:00Z"}}, {"id": "r_lombar:22214824-4535-4d3d-bce6-e7ca4896b7d8", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[8.29, 44.31], [8.29, 46.89], [11.85, 46.89], [11.85, 44.31], [8.29, 44.31]]]}, "properties": {"themes": [{"concepts": [{"id": "environment"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Zone a rischio naturale"}, {"id": "Idrografia"}, {"id": "Suolo"}, {"id": "Siti protetti"}, {"id": "Geologia"}, {"id": "Copertura del suolo"}], "scheme": "https://www.eionet.europa.eu/gemet/it/inspire-themes"}, {"concepts": [{"id": "Regional"}], "scheme": "Spatial scope"}], "updated": "2023-12-14", "type": "Dataset", "language": "ita", "title": "Strategic Project of the Seveso Stream Sub-basin", "description": "Contenuti cartografici del Progetto Strategico di Sottobacino del torrente Seveso (approvato con DGR. n. 7563 del 18 dicembre 2017), pubblicato sul sito dei Contratti di fiume e consultabile alla pagina http://www.contrattidifiume.it/it/azioni/seveso/progetto-di-sottobacino-seveso/index.html Il Progetto fornisce un servizio di mappa che si compone di una parte conoscitiva e di una interpretativa, lavorando all'interno del Geoportale, sia in termini di input che di output: di input poich\u00e9 la cartografia si basa su strati informativi che per la maggior parte sono gi\u00e0 disponibili (ad esempio dati DUSAF, Data base topografici, reti ecologiche, previsioni di piani comunali e sovracomunali, ecc.) e che definiscono il quadro conoscitivo dell'ambito di progetto, di output in quanto gli elaborati cartografici e i loro futuri aggiornamenti, sono condivisi e fruibili sul Geoportale. In particolare il quadro interpretativo di Progetto si articola nei seguenti elaborati cartografici: 1. Ambito di analisi, che delimita il territorio sul quale sono state condotte le analisi territoriali. 2. Ambito di applicazione delle misure, che delimita l'ambito sul quale sono state impostate misure generali e localizzate finalizzate alla risoluzione delle criticit\u00e0. 3. Carte delle criticit\u00e0 e delle misure: 3.1 Criticit\u00e0, che riporta le 15 criticit\u00e0 prioritarie individuate nell'ambito di analisi, a partire dalla combinazione/interazione degli elementi di sensibilit\u00e0 territoriale individuati \u2022 1-Interferenza urbanizzato e rete mobilit\u00e0 con corpi idrici \u2022 2-Ridotta capacit\u00e0 di drenaggio \u2022 3-Potenziali fonti di pressione puntuale \u2022 4-Artificializzazione alveo fluviale e sponde \u2022 5-Potenziali pressioni legate all\u2019uso agricolo del suolo \u2022 6-Interferenze antropizzato con RER e discontinuit\u00e0 rete ecologica \u2022 7-Suolo e sottosuolo non ottimali per gestione acque meteoriche Insufficiente azione locale di prevenzione dei rischi \u2022 9-Fenomeni di dismissione \u2022 10-Qualit\u00e0 morfologica e funzionalit\u00e0 fluviale non buone \u2022 11-Stato chimico del corpo idrico non buono \u2022 12-Stato ecologico del corpo idrico non buono \u2022 13-Pericolosit\u00e0 per fenomeni idraulici e idrogeologici \u2022 14-Rischio idraulico medio alto \u2022 15-Rischio idrogeologico medio alto Interrogando le singole celle, nel campo scheda Misure generali .pdf, aprendo il collegamento ipertestuale si pu\u00f2 accedere all'elenco delle misure generali previste nel progetto per la risoluzione di ogni specifica criticit\u00e0. 3.2 Compresenza di criticit\u00e0, che rappresenta la presenza e la numerosit\u00e0 delle 15 criticit\u00e0 prioritari individuate. La rappresentazione grafica \u00e8 articolata in cinque classi (da 0 a 1, 2, 3, da 4 a 6, da 7 a 12) in funzione della loro compresenza. 3.3 Misure generali, interrogando il poligono che rappresenta l'ambito di applicazione delle misure \u00e8 possibile aprire tramite collegamento ipertestuale l'elenco delle misure generali previste nel progetto per la risoluzione di ogni specifica criticit\u00e0. 3.4 Misure localizzate, che mostra la localizzazione dei punti delle misure localizzate complessive e suddivise per singola Criticit\u00e0. Le misure generali e localizzate rispondono ai Macro obbiettivi: Q - qualit\u00e0, R - rischio, SE - servizi ecosistemici e G - governace, ed ai temi del Progetto: Q - qualit\u00e0, R - rischio idraulico, SE - servizi ecosistemici, G - governance, D - drenaggio urbano, P - paesaggio, C - connessioni ecologiche e RF - riqualificazione fluviale. 3.5 Compresenza degli elementi di sensibilit\u00e0, che rappresenta la presenza e la numerosit\u00e0 dei seguenti elementi sensibilit\u00e0 esistenti o potenziali sull'ambito di analisi: - Interferenza urbanizzato e corpi idrici - Interferenza infrastrutture e corpi idrici - Interferenza urbanizzato e laghi - Interferenza infrastrutture e laghi - Rischio idraulico R3 R4 - Rischio frane R3 R4 - Ponti - Opere di difesa - Pericolosit\u00e0 esondazione alta - Pericolosit\u00e0 frana alta - Classe fattibilit\u00e0 incongrua - Stato chimico corpi idrici non buono - Stato ecologico corpi idrici non buono - Indice Qualit\u00e0 Morfologica non buono - Indice Funzionalit\u00e0 Fluviale non buono - Scarichi - Tratti tombinati - Impianti di depurazione - Siti contaminati - Impianti gestione rifiuti - Corridoi primari alta antropizzazione - Corridoi primari medio bassa antropizzazione - RER Elementi primari - RER Elementi secondari - RER Varchi - Cave cessate - Aree dismesse - Insediamenti produttivi - Aziende a Rischio di Incidente Rilevante - Ridotta capacit\u00e0 drenante - Ridotta permeabilit\u00e0 suoli - Bassa soggiacenza falda - Aree agricole Agli elementi di sensibilit\u00e0 del territorio, complessivamente 33, \u00e8 stato assegnato un peso 2 se esistenti e un pero 1 se potenziali, un peso 0 se assenti. La rappresentazione grafica \u00e8 articolata per classi (bassa, medio bassa, media, medio alta, alta) in funzione della loro compresenza. Sia il livello informativo della Compresenza degli elementi di sensibilit\u00e0, che il livello informativo delle Carte delle criticit\u00e0, consentono di territorializzare nell\u2019ambito di analisi del Progetto (discretizzato con una maglia di 100 x 100 metri), la presenza/compresenza/assenza di fattori eterogenei (indipendentemente dalla loro estensione), altrimenti non confrontabili in una rappresentazione tradizionale. Le Carte si avvalgono sia di strati informativi gi\u00e0 disponibili sul Geoportale di Regione Lombardia che di altri livelli informativi elaborati durante le fasi di costruzione del Progetto. La loro costruzione consente una lettura simultanea delle diverse forme di degrado e rischio a supporto di politiche e misure integrate finalizzate all\u2019innalzamento della complessiva qualit\u00e0 del territorio del sottobacino. Sono strumenti di orientamento e di valutazione che permettono di definire e localizzare le misure del Progetto e supportare gli enti territoriali nella redazione di piani e progetti. Le date dell'aggiornamento pi\u00f9 recente relativo alle banche dati utilizzate per realizzare gli strati informativi della cartografia sono indicate nel documento allegato. La rappresentazione delle aree dismesse deriva dalla banca dati AGISCO (Anagrafe e Gestione Integrata dei Siti Contaminati) che non riporta l'aggiornamento rispetto al riutilizzo urbanistico dell'area, rappresentando quindi solo uno storico di tutte le aree per le quali l'Ufficio Bonifiche di Regione Lombardia ha ricevuto segnalazione di potenziale contaminazione, ha avviato e/o completato la caratterizzazione, ha avviato e/o completato la bonifica/messa in sicurezza. Di conseguenza lo strato informativo dovr\u00e0 essere oggetto di verifiche puntuali da parte dei Comuni. 4. Mappa dell\u2019acqua del Seveso, rappresenta gli effetti degli usi del suolo sulle acque e le numerose funzioni idrologiche: - sia in termini positivi, per quel che riguarda le funzioni di alimentazione del corso d\u2019acqua, infiltrazione, regolazione delle piene, espansione e fitodepurazione, protezione e filtro; - che in termini negativi, individuando le pressioni che derivano dalle aree edificate, dalle aree interessate da pratiche agro-colturali che implicano l'impoverimento della componente organica del suolo e l'utilizzo di prodotti chimici e fertilizzanti, da trattamenti di manutenzione e gestione degli impianti sportivi inerbiti (es. golf) e da scarichi puntiformi in corpo idrico. La mappa si compone dei seguenti strati informativi \u2022 Alimentazione artificiale corsi d'acqua \u2022 Alimentazione naturale corsi d'acqua \u2022 Scarichi puntuali \u2022 Protezione degli acquiferi \u2022 Impianti produttivi, tecnologici, sportivi e ospedali \u2022 Aree di infiltrazione estesa \u2022 Aree di infiltrazione locale \u2022 Elementi di pressione derivanti da destinazione per impianti sportivi inerbiti \u2022 Elementi di pressione derivanti da destinazione agricola \u2022 Elementi di pressione derivanti da destinazione estrattiva \u2022 Destinazione residenziale e assimilabile \u2022 Reti stradali e ferroviarie \u2022 Vegetazione con funzione di fascia tampone e filtro \u2022 Tratti idrici sottobacino Seveso \u2022 Ridotta permeabilit\u00e0 suoli La rappresentazione grafica della permeabilit\u00e0 dei suoli \u00e8 articolata per classi (bassa, media, alta). 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