{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.14027088", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:10Z", "type": "Dataset", "created": "2024-11-01", "title": "Per- and Polyfluoroalkyl Substances (PFAS) Concentrations in the Upper Danube Catchment: Integrated Dataset from H2020 Project PROMISCES - Case Study 2", "description": "Dataset Description  This dataset was produced within the framework of\u00a0Horizon 2020 Framework Programme, Project PROMISCES (Preventing Recalcitrant Organic Mobile Industrial chemicalS for Circular Economy in the Soil-sediment-water system). Project website: https://promisces.eu/  The dataset contains information on the environmental concentrations of Per- and Polyfluoroalkyl substances (PFASs) collected as part of the PROMISCES project's Case Study #2,\u00a0Subtask 2.2.4 \u2013 Large catchment scale monitoring in different environmental compartments. It also includes data gathered from various external sources.  Abstract  PFASs are a group of synthetic chemicals widely used in various household and industrial applications (Gl\u00fcge et al., 2020). Due to their high chemical stability, PFASs are resistant to natural degradation processes, leading to their accumulation in different environmental matrices and ultimately posing potential health risks to humans (Sunderland et al., 2019). PROMISCES CS#2 focused on understading the fate and transport of PFASs in the upper Danube catchment, covering the Danube from its source to the city of Budapest. Over approximately 1.5 years, a comprehensive monitoring campaign was conducted in this study area, across multiple environmental compartments:\u00a0    Atmopsheric Deposition:\u00a0  River water: including Danube mainstream and its tributaries.  Groundwater: including bank-filtered water from the Danube, and groundwater directly influenced by the landfills  Landfill leachate  Surface Runoff  Wastewater: Influent and effluent from municipal waterwater treatment plants (WWTPs) and direct industrial dischargers   Particularly, the case study placed a special focus on the Danube and its bank filtration sites at two major cities in the Upper Danube, Vienna and Budapest.  The dataset primarily consists results from targeted analysis of 32 individual PFAS substances. In addition, available data for these 32 PFASs in the study area were collected from various online resources or provided directly by project partners. For confidentiality reasons, some external data have been anonymized on names and locations.\u00a0  Partial of this dataset have already contributed to a 2023 publication (Liu et al.), which was based on preliminary data before the completion of the full monitoring campaign and external data collection.  The full dataset was analysed and discussed in the publication Liu et al. (2025): https://www.doi.org/10.1186/s12302-025-01141-6  Technical Details  This dataset includes:    A Zip file containing .accdb Microsoft Access database  A ZIP file containing .csv files structured to match the database   Notice that the .accdb version is out of maintance and removed in version 3.0. The only changes compared to version 2.0 was the substance short-names for two compounds:    substance with CAS number 2355-31-9 updated from \u201cMeFOSAA\u201d to \u201cN-MeFOSAA\u201d  substance with CAS number 2991-50-6 updated from \u201cEtFOSAA\u201d to \u201cN-EtFOSAA   Database structure  One query is created to show most important information:    Concentrations_PFAS: contains all PFAS concentration data.\u00a0   In addition, tables were provided with more infomation on the metadata:    Table1_measurements: concentrations data with units, values, limit of quantifications (LOQs); keys indicating relationships with other tables.  Table2_samplings: sample codes, sampling times (if available), sampling type, sampling techniques; key indicating relationships with Table7_analytical_methods.  Table3_samples: sample names, sample sites, coordinates and coordinate systems (if available).  Table4_compartments: sample matrices/compartments, more detailed sample types.  Table5_compounds: CAS numbers, substance short names, Sus Dat IDs, substance names in NORMAN database, substance group short names and long names.  Table6_datasources: data source names, organisations, countries, references, links.  Table7_analytical_methods: laboratories, preparation methods, analytical methods, analytical method standards.   References  Gl\u00fcge, J., Scheringer M., Cousins I., DeWitt J., Goldenman G., Herzke D., Lohmann R., Ng A., Trier X., Wang Z (2020) An Overview of the Uses of Per- and Polyfluoroalkyl Substances (PFAS). Environmental Science: Processes & Impacts 12. https://doi.org/10.1039/D0EM00291G  Liu, M., Saracevic, E., Kittlaus, S., Oudega, T., Obeid, A., Nagy-Kov\u00e1cs, Z., L\u00e1szl\u00f3, B., Krlovic, N., Saracevic, Z., Lindner, G., Rab, R., Derx, J., Zoboli, O., Zessner, M. (2023) PFAS-Belastungen im Einzugsgebiet der oberen Donau. \u00d6sterr Wasser- und Abfallw 75, 503\u2013514 . https://doi.org/10.1007/s00506-023-00973-x\u00a0  Sunderland, Elsie M., Xindi C. Hu, Clifton Dassuncao, Andrea K. Tokranov, Charlotte C. Wagner, and Joseph G. Allen. (2019) A Review of the Pathways of Human Exposure to Poly- and Perfluoroalkyl Substances (PFASs) and Present Understanding of Health Effects. Journal of Exposure Science & Environmental Epidemiology 29, no. 2 : 131\u201347. https://doi.org/10.1038/s41370-018-0094-1", "keywords": ["Water management", "Environmental sciences", "water pollution", "emerging pollutants", "PFAS", "hazardous substances", "Danube", "water quality", "Pollution", "environmental monitoring"], "contacts": [{"organization": "Liu, Meiqi", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14027088"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14027088", "name": "item", "description": "10.5281/zenodo.14027088", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14027088"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-01T00:00:00Z"}}, {"id": "10.1016/j.envint.2019.03.060", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:15:28Z", "type": "Journal Article", "created": "2019-04-05", "title": "Characterization of wastewater effluents in the Danube River Basin with chemical screening, in vitro bioassays and antibiotic resistant genes analysis", "description": "Averaged 7-day composite effluent wastewater samples from twelve wastewater treatment plants (WWTPs) in nine countries (Romania, Serbia, Hungary, Slovenia, Croatia, Slovakia, Czechia, Austria, Germany) in the Danube River Basin were collected. WWTPs' selection was based on countries' dominant technology and a number of served population with the aim to get a representative holistic view of the pollution status. Samples were analyzed for 2248 chemicals of emerging concern (CECs) by wide-scope target screening employing LC-ESI-QTOF-MS. 280 compounds were detected at least in one sample and quantified. Spatial differences in the concentrations and distribution of the compounds classes were discussed. Additionally, samples were analyzed for the possible agonistic/antagonistic potencies using a panel of in vitro transactivation reporter gene CALUX\u00ae bioassays including ER\u03b1 (estrogenics), anti-AR (anti-androgens), GR (glucocorticoids), anti-PR (anti-progestins), PPAR\u03b1 and PPAR\u03b3 (peroxisome proliferators) and PAH assays. The potency of the wastewater samples to cause oxidative stress and induce xenobiotic metabolism was determined using the Nrf2 and PXR CALUX\u00ae bioassays, respectively. The signals from each of the bioassays were compared with the recently developed effect-based trigger values (EBTs) and thus allowed for allocating the wastewater effluents into four categories based on their measured toxicity, proposing a putative action plan for wastewater operators. Moreover, samples were analyzed for antibiotics and 13 antibiotic-resistant genes (ARGs) and one mobile genetic element (intl1) with the aim to assess the potential for antibiotic resistance. All data collected from these various types of analysis were stored in an on-line database and can be viewed via interactive map at https://norman-data.eu/EWW_DANUBE.", "keywords": ["0211 other engineering and technologies", "500", "Drug Resistance", " Microbial", "02 engineering and technology", "Wide-scope target screening", "Wastewater", "01 natural sciences", "Bioassays", "6. Clean water", "Anti-Bacterial Agents", "Environmental sciences", "Rivers", "13. Climate action", "Emerging substances", "Antibiotic resistant genes", "Effluent wastewater", "GE1-350", "Biological Assay", "Danube River Basin", "Emerging substances Wide-scope target screening Effluent wastewater Bioassays Antibiotic resistant genes Danube River Basin", "Water Pollutants", " Chemical", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.envint.2019.03.060"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environment%20International", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.envint.2019.03.060", "name": "item", "description": "10.1016/j.envint.2019.03.060", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.envint.2019.03.060"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-01T00:00:00Z"}}, {"id": "10.48436/dej9y-j2703", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:24Z", "type": "Other", "created": "2024-11-03", "title": "Regionalised Emission Model (MoRE) for PFAS from H2020 Project PROMISCES - Case Study 2", "description": "MoRE model for PFAS emissions into surface waters in the upper Danube basin  This record contains a SQLite database driven emission model for modelling PFAS emissions into surface waters of the upper Danube basin, and a collection of flowcharts in PDF (and PDF/A) format demonstrating the model setup processes.  Description of the model  The model system MoRE (Modeling of Regionalized Emissions) was initially developed by the Karlsruhe Institute of Technology (KIT) in cooperation with the German Federal Environment Agency. It is based on the MONERIS model system. MoRE was developed as a tool in an open source environment for modelling substance emissions into surface waters for a wide range of substances with relevance for water quality (Fuchs et al. 2017).  The modelling in MoRE is carried out as a regionalized pathway analysis. The substance emissions are modelled with temporal and spatial differentiation via various emission pathways, as indicated in the EU Guidance Document No 28 (EC 2012) for tier 3 for establishing an inventory of emissions. The temporal resolution of the model are annual time steps and the spatial resolution is 526 sub-catchments with a size of 354 \u00b1 352 km\u00b2.  In the PROMISCES project the model was adapted for modelling of PFAS, which means additional emission pathways were implemented, which might be significant for PFAS and other pathways with less significance for this substance group were simplified and grouped together. Thus, the model contains now the following pathways:  Point pathways:    Municipal wastewater treatment plants  Industrial direct dischargers   Diffuse pathways:    direct atmospheric deposition onto water surface  surface runoff from unsealed areas  soil erosion  groundwater with contribution from    legacy pollution from PFAS production site (in case of PROMISCES cs#2, the industrial park at Gendorf, Germany)  legacy pollution from aerodromes caused by fire-fighting training activities  legacy pollution from municipal landfills    sewer systems   Due to the flexible structure of MoRE, new substances and emission pathways can be integrated at any time, provided that the necessary input data are available and modelling can be carried out in a reasonable way. In addition, MoRE offers the possibility to modify existing calculation approaches and to test different input data sets by comparing them. For this purpose, different variants can be created. In the PROMISCES project three model variants for the current state were implemented to represent the uncertainty in the model input data:    Base variant: Based on the median evaluation of environmental concentrations this variant should present the most likely model outcome. If more than 80% of the environmental concentrations were measured as below the analytical limit of quantitation (LOQ), or less than 3 concentrations were observed above the LOQ, half value of the LOQ was used as input data.  Best-Case: This variant is based on the 25th percentile of environmental concentrations and represents a best-case evaluation with rather low pollution. If more than 80% of the environmental concentrations were measured as below the LOQ, or less than 3 concentrations were observed above the LOQ, 0 was used as input data.  Worst-Case: This variant is based on the 75th percentile of environmental concentrations and represents a worst-case evaluation with rather high pollution. If more than 80% of the environmental concentrations were measured as below the LOQ, or less than 3 concentrations were observed above the LOQ, the value of the LOQ was used as input data.   The PROMISCES modelling guidance document (D2.4) in Chapter XX provides an example of the application of the model in the Upper Danube region.  References  EC (2012), European Commission: Guidance Document No. 28. Technical guidance on the preparation of an inventory of emissions, discharges and losses of priority and priority hazardous substances, 1st edn. Common implementation strategy for the Water Framework Directive (2000/60/EC), vol 058. ISBN: 978-92-79-23823-9. European Commission, Brussels  Fuchs S, Kaiser M, Kiemle L, Kittlaus S, Rothvo\u00df S, Toshovski S, Wagner A, Wander R, Weber T, Ziegler S (2017): Modeling of Regionalized Emissions (MoRE) into Water Bodies: An Open-Source River Basin Management System. Water 9:239. https://doi.org/10.3390/w9040239  Technical details  The MoRE model system is based on an open source PostgreSQL or SQLite database, a generic calculation engine and the MoRE Developer user interface, which can be used to read, modify and extend the contents of the database. All computations are performed by the calculation engine, which is controlled via the user interface. The modelling results can be exported as tables via the MoRE Developer user interface and the results can be used in GIS for mapping. Users can work with MoRE in two different ways: on the basis of a multi-user access in a PostgreSQL database via the Internet or as a stand-alone application on the PC.  Here the SQLite based version is provided as an executable in a zip file. After extraction from the zip file the executable (.exe file) can be started on a Windows operating system (Windows 10 and 11 tested).  More information on how to use MoRE can be found in the\u00a0MoRE documentation wiki  Licensing  The MoRE-Developer graphical user interface is property of COS Geoinformatik GmbH & Co. KG. Redistribution is only allowed with the permission of COS Geoinformatik GmbH & Co. KG, Karlsruher Str. 10b, 76275 Ettlingen, Germany,\u00a0www.cosgeo.de, Phone: +49 7243 3241-11, email: armin.canzler@cosgeo.de.  The MoRE calculation engine (MoRE Rechenkern.dll) is licensed under a GNU Affero General Public License, Version 3 (AGPL V3.0 http://www.gnu.org/licenses/agpl.html).  The content of the database, if not differently stated in the data itself is licensed under a Creative Commons Attribution Share Alike 4.0 International license (CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/).", "keywords": ["water pollution", "regionalized pathway analysis", "per and polyfluorinated substances", "PFAS", "Other", "emission model", "Danube", "catchment", "MoRE model", "per- and polyfluorinated substances"]}, "links": [{"href": "https://doi.org/10.48436/dej9y-j2703"}, {"rel": "self", "type": "application/geo+json", "title": "10.48436/dej9y-j2703", "name": "item", "description": "10.48436/dej9y-j2703", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.48436/dej9y-j2703"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.48436/wg9dy-r9r31", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:24Z", "type": "Other", "created": "2024-11-03", "title": "Regionalised Emission Model (MoRE) for PFAS from H2020 Project PROMISCES - Case Study 2", "description": "MoRE model for PFAS emissions into surface waters in the upper Danube basin  This record contains a SQLite database driven emission model for modelling PFAS emissions into surface waters of the upper Danube basin, and a collection of flowcharts in PDF (and PDF/A) format demonstrating the model setup processes.  Description of the model  The model system MoRE (Modeling of Regionalized Emissions) was initially developed by the Karlsruhe Institute of Technology (KIT) in cooperation with the German Federal Environment Agency. It is based on the MONERIS model system. MoRE was developed as a tool in an open source environment for modelling substance emissions into surface waters for a wide range of substances with relevance for water quality (Fuchs et al. 2017).  The modelling in MoRE is carried out as a regionalized pathway analysis. The substance emissions are modelled with temporal and spatial differentiation via various emission pathways, as indicated in the EU Guidance Document No 28 (EC 2012) for tier 3 for establishing an inventory of emissions. The temporal resolution of the model are annual time steps and the spatial resolution is 526 sub-catchments with a size of 354 \u00b1 352 km\u00b2.  In the PROMISCES project the model was adapted for modelling of PFAS, which means additional emission pathways were implemented, which might be significant for PFAS and other pathways with less significance for this substance group were simplified and grouped together. Thus, the model contains now the following pathways:  Point pathways:    Municipal wastewater treatment plants  Industrial direct dischargers   Diffuse pathways:    direct atmospheric deposition onto water surface  surface runoff from unsealed areas  soil erosion  groundwater with contribution from    legacy pollution from PFAS production site (in case of PROMISCES cs#2, the industrial park at Gendorf, Germany)  legacy pollution from aerodromes caused by fire-fighting training activities  legacy pollution from municipal landfills    sewer systems   Due to the flexible structure of MoRE, new substances and emission pathways can be integrated at any time, provided that the necessary input data are available and modelling can be carried out in a reasonable way. In addition, MoRE offers the possibility to modify existing calculation approaches and to test different input data sets by comparing them. For this purpose, different variants can be created. In the PROMISCES project three model variants for the current state were implemented to represent the uncertainty in the model input data:    Base variant: Based on the median evaluation of environmental concentrations this variant should present the most likely model outcome. If more than 80% of the environmental concentrations were measured as below the analytical limit of quantitation (LOQ), or less than 3 concentrations were observed above the LOQ, half value of the LOQ was used as input data.  Best-Case: This variant is based on the 25th percentile of environmental concentrations and represents a best-case evaluation with rather low pollution. If more than 80% of the environmental concentrations were measured as below the LOQ, or less than 3 concentrations were observed above the LOQ, 0 was used as input data.  Worst-Case: This variant is based on the 75th percentile of environmental concentrations and represents a worst-case evaluation with rather high pollution. If more than 80% of the environmental concentrations were measured as below the LOQ, or less than 3 concentrations were observed above the LOQ, the value of the LOQ was used as input data.   The PROMISCES modelling guidance document (D2.4) in Chapter XX provides an example of the application of the model in the Upper Danube region.  References  EC (2012), European Commission: Guidance Document No. 28. Technical guidance on the preparation of an inventory of emissions, discharges and losses of priority and priority hazardous substances, 1st edn. Common implementation strategy for the Water Framework Directive (2000/60/EC), vol 058. ISBN: 978-92-79-23823-9. European Commission, Brussels  Fuchs S, Kaiser M, Kiemle L, Kittlaus S, Rothvo\u00df S, Toshovski S, Wagner A, Wander R, Weber T, Ziegler S (2017): Modeling of Regionalized Emissions (MoRE) into Water Bodies: An Open-Source River Basin Management System. Water 9:239. https://doi.org/10.3390/w9040239  Technical details  The MoRE model system is based on an open source PostgreSQL or SQLite database, a generic calculation engine and the MoRE Developer user interface, which can be used to read, modify and extend the contents of the database. All computations are performed by the calculation engine, which is controlled via the user interface. The modelling results can be exported as tables via the MoRE Developer user interface and the results can be used in GIS for mapping. Users can work with MoRE in two different ways: on the basis of a multi-user access in a PostgreSQL database via the Internet or as a stand-alone application on the PC.  Here the SQLite based version is provided as an executable in a zip file. After extraction from the zip file the executable (.exe file) can be started on a Windows operating system (Windows 10 and 11 tested).  More information on how to use MoRE can be found in the\u00a0MoRE documentation wiki  Licensing  The MoRE-Developer graphical user interface is property of COS Geoinformatik GmbH & Co. KG. Redistribution is only allowed with the permission of COS Geoinformatik GmbH & Co. KG, Karlsruher Str. 10b, 76275 Ettlingen, Germany,\u00a0www.cosgeo.de, Phone: +49 7243 3241-11, email: armin.canzler@cosgeo.de.  The MoRE calculation engine (MoRE Rechenkern.dll) is licensed under a GNU Affero General Public License, Version 3 (AGPL V3.0 http://www.gnu.org/licenses/agpl.html).  The content of the database, if not differently stated in the data itself is licensed under a Creative Commons Attribution Share Alike 4.0 International license (CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/).", "keywords": ["water pollution", "regionalized pathway analysis", "per and polyfluorinated substances", "PFAS", "Other", "emission model", "Danube", "catchment", "MoRE model", "per- and polyfluorinated substances"], "contacts": [{"organization": "Liu, Meiqi, Kittlaus, Steffen, Zessner-Spitzenberg, Matthias,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.48436/wg9dy-r9r31"}, {"rel": "self", "type": "application/geo+json", "title": "10.48436/wg9dy-r9r31", "name": "item", "description": "10.48436/wg9dy-r9r31", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.48436/wg9dy-r9r31"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.15474678", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:30Z", "type": "Dataset", "created": "2024-11-01", "title": "Per- and Polyfluoroalkyl Substances (PFAS) Concentrations in the Upper Danube Catchment: Integrated Dataset from H2020 Project PROMISCES - Case Study 2", "description": "Dataset Description  This dataset was produced within the framework of\u00a0Horizon 2020 Framework Programme, Project PROMISCES (Preventing Recalcitrant Organic Mobile Industrial chemicalS for Circular Economy in the Soil-sediment-water system). Project website: https://promisces.eu/  The dataset contains information on the environmental concentrations of Per- and Polyfluoroalkyl substances (PFASs) collected as part of the PROMISCES project's Case Study #2,\u00a0Subtask 2.2.4 \u2013 Large catchment scale monitoring in different environmental compartments. It also includes data gathered from various external sources.  Abstract  PFASs are a group of synthetic chemicals widely used in various household and industrial applications (Gl\u00fcge et al., 2020). Due to their high chemical stability, PFASs are resistant to natural degradation processes, leading to their accumulation in different environmental matrices and ultimately posing potential health risks to humans (Sunderland et al., 2019). PROMISCES CS#2 focused on understading the fate and transport of PFASs in the upper Danube catchment, covering the Danube from its source to the city of Budapest. Over approximately 1.5 years, a comprehensive monitoring campaign was conducted in this study area, across multiple environmental compartments:\u00a0    Atmopsheric Deposition:\u00a0  River water: including Danube mainstream and its tributaries.  Groundwater: including bank-filtered water from the Danube, and groundwater directly influenced by the landfills  Landfill leachate  Surface Runoff  Wastewater: Influent and effluent from municipal waterwater treatment plants (WWTPs) and direct industrial dischargers   Particularly, the case study placed a special focus on the Danube and its bank filtration sites at two major cities in the Upper Danube, Vienna and Budapest.  The dataset primarily consists results from targeted analysis of 32 individual PFAS substances. In addition, available data for these 32 PFASs in the study area were collected from various online resources or provided directly by project partners. For confidentiality reasons, some external data have been anonymized on names and locations.\u00a0  Partial of this dataset have already contributed to a 2023 publication (Liu et al.), which was based on preliminary data before the completion of the full monitoring campaign and external data collection.  The full dataset was analysed and discussed in the publication Liu et al. (2025): https://www.doi.org/10.1186/s12302-025-01141-6  Technical Details  This dataset includes:    A Zip file containing .accdb Microsoft Access database  A ZIP file containing .csv files structured to match the database   Notice that the .accdb version is out of maintance and removed in version 3.0. The only changes compared to version 2.0 was the substance short-names for two compounds:    substance with CAS number 2355-31-9 updated from \u201cMeFOSAA\u201d to \u201cN-MeFOSAA\u201d  substance with CAS number 2991-50-6 updated from \u201cEtFOSAA\u201d to \u201cN-EtFOSAA   Database structure  One query is created to show most important information:    Concentrations_PFAS: contains all PFAS concentration data.\u00a0   In addition, tables were provided with more infomation on the metadata:    Table1_measurements: concentrations data with units, values, limit of quantifications (LOQs); keys indicating relationships with other tables.  Table2_samplings: sample codes, sampling times (if available), sampling type, sampling techniques; key indicating relationships with Table7_analytical_methods.  Table3_samples: sample names, sample sites, coordinates and coordinate systems (if available).  Table4_compartments: sample matrices/compartments, more detailed sample types.  Table5_compounds: CAS numbers, substance short names, Sus Dat IDs, substance names in NORMAN database, substance group short names and long names.  Table6_datasources: data source names, organisations, countries, references, links.  Table7_analytical_methods: laboratories, preparation methods, analytical methods, analytical method standards.   References  Gl\u00fcge, J., Scheringer M., Cousins I., DeWitt J., Goldenman G., Herzke D., Lohmann R., Ng A., Trier X., Wang Z (2020) An Overview of the Uses of Per- and Polyfluoroalkyl Substances (PFAS). Environmental Science: Processes & Impacts 12. https://doi.org/10.1039/D0EM00291G  Liu, M., Saracevic, E., Kittlaus, S., Oudega, T., Obeid, A., Nagy-Kov\u00e1cs, Z., L\u00e1szl\u00f3, B., Krlovic, N., Saracevic, Z., Lindner, G., Rab, R., Derx, J., Zoboli, O., Zessner, M. (2023) PFAS-Belastungen im Einzugsgebiet der oberen Donau. \u00d6sterr Wasser- und Abfallw 75, 503\u2013514 . https://doi.org/10.1007/s00506-023-00973-x\u00a0  Sunderland, Elsie M., Xindi C. Hu, Clifton Dassuncao, Andrea K. Tokranov, Charlotte C. Wagner, and Joseph G. Allen. (2019) A Review of the Pathways of Human Exposure to Poly- and Perfluoroalkyl Substances (PFASs) and Present Understanding of Health Effects. 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