{"type": "FeatureCollection", "facets": {"type": {"type": "terms", "property": "type", "buckets": [{"value": "Journal Article", "count": 6}]}, "soil_chemical_properties": {"type": "terms", "property": "soil_chemical_properties", "buckets": [{"value": "soil organic carbon", "count": 1}]}, "soil_biological_properties": {"type": "terms", "property": "soil_biological_properties", "buckets": []}, "soil_physical_properties": {"type": "terms", "property": "soil_physical_properties", "buckets": []}, "soil_classification": {"type": "terms", "property": "soil_classification", "buckets": []}, "soil_functions": {"type": "terms", "property": "soil_functions", "buckets": []}, "soil_threats": {"type": "terms", "property": "soil_threats", "buckets": [{"value": "anthropogenic erosion", "count": 6}, {"value": "urbanisation", "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": "10.1016/j.ecss.2017.05.009", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:16:06Z", "type": "Journal Article", "created": "2017-05-12", "title": "Changes In Organic Carbon Accumulation Driven By Mangrove Expansion And Deforestation In A New Zealand Estuary", "description": "Abstract   Mangroves are rapidly being lost to deforestation in many locations while expanding their areal extent in other subtropical and temperate regions. Currently, there is a paucity of information on how these changes may alter the carbon accumulation capacity of coastal areas. Here, sediment cores were collected from two areas and used to determine the influence of mangrove migration and deforestation on sediment carbon stocks and accumulation rates. The deforested area contained lower sedimentary organic carbon stocks (2767\u00a0\u00b1\u00a0580\u00a0g\u00a0m \u22122 ) compared to the preserved area (6949\u00a0\u00b1\u00a084\u00a0g\u00a0m \u22122 ). Sediment accumulation rates, derived from excess  210 Pb and  239+240 Pu depositional signatures, ranged from 0.19 to 0.35\u00a0cm\u00a0yr \u22121 . The total sedimentary organic carbon (TOC) accumulation rates for the period after mangrove deforestation (2005\u20132011) exhibited significant differences between preserved areas (Core C: 43.9\u00a0\u00b1\u00a06.9\u00a0g\u00a0m \u22122  yr \u22121 ; Core D: 83.1\u00a0\u00b1\u00a05.9\u00a0g\u00a0m \u22122  yr \u22121 ) and the deforested area (Core B: 25.8\u00a0\u00b1\u00a06.0\u00a0g\u00a0m \u22122  yr \u22121 ), suggesting a decline after deforestation. For the preserved area, the TOC accumulation under mangrove dominance (65.5\u00a0\u00b1\u00a016.3\u00a0g\u00a0m \u22122  yr \u22121 , after 1944) was higher than under saltmarsh dominance (23.5\u00a0\u00b1\u00a015.9\u00a0g\u00a0m \u22122  yr \u22121 , before 1944), as revealed by carbon isotopic signatures (\u03b4 13 C). The increase in the TOC accumulation due to mangrove expansion in this New Zealand estuary was conservatively estimated as three-fold higher, and two-fold higher in stocks in comparison to the period when this ecosystem was dominated by non-mangrove vegetation.", "keywords": ["580", "0106 biological sciences", "Organic carbon burial", "550", "Anthropogenic deforestation", "Geology", "15. Life on land", "01 natural sciences", "13. Climate action", "210Pb 239\u00fe240Pu", "Mangroves", "Geochronologies", "14. Life underwater", "Mangrove expansion", "Organic carbon", "Environmental Sciences", "210Pb", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.ecss.2017.05.009"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Estuarine%2C%20Coastal%20and%20Shelf%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.ecss.2017.05.009", "name": "item", "description": "10.1016/j.ecss.2017.05.009", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.ecss.2017.05.009"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-06-01T00:00:00Z"}}, {"id": "10.1016/j.microc.2019.05.050", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:16:48Z", "type": "Journal Article", "created": "2019-05-21", "title": "The capability of rare earth elements geochemistry to interpret complex archaeological stratigraphy", "description": "In this study rare earth elements (REE) signatures (REE ratios, cerium and europium anomalies) are applied to a complex soil stratigraphic sequence from the site of Konso, Ethiopia, with the aim of determining whether REE can distinguish the strata observed in the field. Forty soil samples were taken from a depositional sequence that includes overlapping human induced and \u2018natural\u2019 erosional and depositional processes. The samples were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to determine trace elements and REE, with concentrations of major elements determined using X-ray fluorescence (XRF). Cluster Analyses (CA) were used to observe differences between strata. The mechanisms that influenced REE values and fractionations were related to OM accumulation, pyrogenic SOM, redox, secondary CaCO3 precipitation, suggesting the addition of proxies to the REE, Sc and Y data processing. This produced a clustering of samples that more accurately reflected the stratigraphic field observations. It is expected that this approach, combining the analysis of REE concentrations with an understanding of the mechanisms driving them in a given site or profile, will be replicable for other stratigraphic sequences. The results demonstrate that REE signatures are not just able to detect stratigraphic differences defined through field observations but also highlight variations within the same deposits. REE analysis could therefore become a powerful geoarchaeological tool, even for studies of complex stratigraphies.", "keywords": ["2300", "1200", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.microc.2019.05.050"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Microchemical%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.microc.2019.05.050", "name": "item", "description": "10.1016/j.microc.2019.05.050", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.microc.2019.05.050"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-01T00:00:00Z"}}, {"id": "2117/385368", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:26:06Z", "type": "Journal Article", "created": "2022-10-19", "title": "A New NMVOC Speciated Inventory for a Reactivity-Based Approach to Support Ozone Control Strategies in Spain", "description": "Ozone (O3) pollution is a persistent problem in many regions of Spain, so understanding O3 precursor emissions and trends is essential to design effective control strategies. We estimated the impact of Non-Methane Volatile Organic Compounds (NMVOC) species upon O3 formation potential (OFP) using the maximum incremental reactivity approach. For this, we developed a speciated NMVOC emission inventory for Spain from 2010 to 2019 combining national reported emissions with state-of-the-art speciation profiles, which resulted in a database of emissions for over 900 individual NMVOC species and 153 individual sectors. Additionally, we analysed 2030 emission projections to quantify the expected impact of planned measures on future OFP levels. Overall, the main activities contributing to OFP in Spain are paint manufacturing and applications (20 %), manure management (16 %), and domestic solvent use (6 %). These activities contribute unevenly across regions. The more urbanised areas report a larger contribution from the solvent sector (64 % in Madrid), while in rural areas, manure management and agricultural waste burning gain importance (24 % in Extremadura), indicating that local control measures should be implemented. The top 10 NMVOC species contributing to OFP are ethanol, ethene, xylenes, propene, toluene, formaldehyde, 1,3-butadiene, styrene, n-butane, and cyclopentane, which together are responsible for 54 % of the total OFP. Our trend analysis indicates a reduction of NMVOC emissions and OFP of -5 % and -10 % between 2010 and 2019, respectively. The larger decrease in OFP is driven by a bigger reduction in xylenes (-29 %) and toluene (-28 %) from paint application industries and the road transport sector. By 2030 a significant increase (+37 %) in the OFP from the public electricity sector is expected due to the planned increase in biomass use for power generation. Our results indicate that policies should focus on paint reformulation, limiting aerosol products, and implementing NMVOC control devices in future biomass power plants.", "keywords": ["Anthropogenic emissions", "15. Life on land", "Tropospheric ozone", "7. Clean energy", "01 natural sciences", "Emission control strategies", "Article", "12. Responsible consumption", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Enginyeria ambiental", "Atmospheric ozone -- Spain", "OFP Tropospheric ozone", "NMVOC speciation", "OFP", "13. Climate action", "11. Sustainability", "Oz\u00f3 atmosf\u00e8ric -- Espanya", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/2117/385368"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/SSRN%20Electronic%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/385368", "name": "item", "description": "2117/385368", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/385368"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5194/gmd-2017-222", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:22:17Z", "type": "Journal Article", "created": "2018-08-30", "title": "CTDAS-Lagrange v1.0: A high-resolution data assimilation system for regional carbon dioxide observations", "description": "<p>Abstract. We have implemented a regional carbon dioxide data assimilation system based on the CarbonTracker Data Assimilation Shell (CTDAS) and a high-resolution Lagrangian transport model, the Stochastic Time-Inverted Lagrangian Transport model driven by the Weather Forecast and Research meteorological fields (WRF-STILT). With this system, named CTDAS-Lagrange, we simultaneously optimize terrestrial biosphere fluxes and four parameters that adjust the lateral boundary conditions (BCs) against CO2 observations from the NOAA ESRL North America tall tower and aircraft programmable flask packages (PFPs) sampling program. Least-squares optimization is performed with a time-stepping ensemble Kalman smoother, over a time window of 10\uffc2\uffa0days and assimilating sequentially a time series of observations. Because the WRF-STILT footprints are pre-computed, it is computationally efficient to run the CTDAS-Lagrange system. To estimate the uncertainties in the optimized fluxes from the system, we performed sensitivity tests with various a\uffc2\uffa0priori biosphere fluxes (SiBCASA, SiB3, CT2013B) and BCs (optimized mole fraction fields from CT2013B and CTE2014, and an empirical dataset derived from aircraft observations), as well as with a variety of choices on the ways that fluxes are adjusted (additive or multiplicative), covariance length scales, biosphere flux covariances, BC parameter uncertainties, and model\uffe2\uff80\uff93data mismatches. In pseudo-data experiments, we show that in our implementation the additive flux adjustment method is more flexible in optimizing net ecosystem exchange (NEE) than the multiplicative flux adjustment method, and our sensitivity tests with real observations show that the CTDAS-Lagrange system has the ability to correct for the potential biases in the lateral BCs and to resolve large biases in the prior biosphere fluxes. Using real observations, we have derived a range of estimates for the optimized carbon fluxes from a series of sensitivity tests, which places the North American carbon sink for the year\uffc2\uffa02010 in a range from \uffe2\uff88\uff920.92 to \uffe2\uff88\uff921.26\uffe2\uff80\uff89PgC\uffe2\uff80\uff89yr\uffe2\uff88\uff921. This is comparable to the TM5-based estimates of CarbonTracker (version CT2016, -0.91\uffc2\uffb11.10\uffe2\uff80\uff89PgC\uffe2\uff80\uff89yr\uffe2\uff88\uff921) and CarbonTracker Europe (version CTE2016, -0.91\uffc2\uffb10.31\uffe2\uff80\uff89PgC\uffe2\uff80\uff89yr\uffe2\uff88\uff921). We conclude that CTDAS-Lagrange can offer a versatile and computationally attractive alternative to these global systems for regional estimates of carbon fluxes, which can take advantage of high-resolution Lagrangian footprints that are increasingly easy to obtain.                     </p>", "keywords": ["QE1-996.5", "SATELLITE-OBSERVATIONS", "TECHNICAL NOTE", "Geology", "NORTH-AMERICA", "15. Life on land", "AIR-SAMPLING-NETWORK", "01 natural sciences", "MODEL", "13. Climate action", "ATMOSPHERIC CO2 INVERSIONS", "Life Science", "ANTHROPOGENIC EMISSIONS", "GREENHOUSE-GAS MEASUREMENTS", "INTERANNUAL VARIABILITY", "EXCHANGE", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.geosci-model-dev-discuss.net/gmd-2017-222/gmd-2017-222.pdf"}, {"href": "https://doi.org/10.5194/gmd-2017-222"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/gmd-2017-222", "name": "item", "description": "10.5194/gmd-2017-222", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gmd-2017-222"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-05T00:00:00Z"}}, {"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": "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"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?facets=true&soil_threats=anthropogenic+erosion&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?facets=true&soil_threats=anthropogenic+erosion&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?facets=true&soil_threats=anthropogenic+erosion&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?facets=true&soil_threats=anthropogenic+erosion&offset=6", "hreflang": "en-US"}], "numberMatched": 6, "numberReturned": 6, "distributedFeatures": [], "timeStamp": "2026-04-04T17:53:07.907790Z"}