Investigating the extent of PFAS contamination in the Upper Danube Basin across environmental compartments

Liuetal. Environmental Sciences Europe (2025) 37:99 https://doi.org/10.1186/s12302-025-01141-6 RESEARCH Open Access © The Author(s) 2025, corrected publication 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Environmental Sciences Europe Investigating theextent ofPFAS contamination intheUpper Danube Basin acrossenvironmental compartments Meiqi Liu1*, Ernis Saracevic1, Thomas J. Oudega2,3, Ali A. A. Obeid2,3, Zsuzsanna Nagy‑Kovács4, Balázs László4, Steffen Kittlaus1, Ottavia Zoboli1, Jörg Krampe1, Julia Derx2,3 and Matthias Zessner1,3 Abstract Background Per‑ and polyfluoroalkyl substances (PFAS) are emerging organic pollutants widely detected in environ‑ mental systems, posing risks to human health and the ecosystem. Despite increasing efforts to monitor PFAS in river systems, knowledge gaps remain regarding sources and emissions via different pathways. This study investigates PFAS contamination across multiple environmental compartments in the Upper Danube Basin, including surface water, groundwater, wastewater, landfill leachate, surface runoff, and atmospheric deposition. The primary objectives are to assess the extent of PFAS contamination, identify key emission sources and transport pathways, and evaluate associated risks in terms of the potential exceedance of current and proposed environmental regulatory thresholds in the European Union. Results The findings reveal a widespread presence of PFAS, with PFOA, PFOS and short‑chain compounds being predominant. The Alz River and Gendorf chemical park emerge as hotspots with far‑reaching effects downstream, contributing significantly to diffuse legacy contamination of PFOA and being a significant source of two industrial PFOA substitutes, ADONA and GenX. Wastewater treatment plants, old municipal landfills, and sites with a history of fire‑fighting foam application are identified as key pathways or sources of legacy pollution, exhibiting higher concentrations compared to the other matrices. Notably, no significant removal is observed when comparing influ‑ ent and effluent samples from conventional WWTPs. The study further demonstrates that groundwater is vulnerable to contamination from point sources and to infiltration from rivers, with bank filtration proving largely ineffective in preventing PFAS contamination. Conclusions The study underscores the necessity for source and pathway control measures to mitigate PFAS pollu‑ tion, the implementation of advanced treatment technologies to safeguard drinking water and surface water quality, and targeted remediation for legacy soil and groundwater contamination. Additionally, strong use regulations should be explored to minimize ongoing emissions. The multi‑compartment monitoring proves to be a crucial approach to under‑ stand the complexity of PFAS distribution at the catchment scale. Comparative analysis and risk assessment highlight chal‑ lenging situations for water management, offering an indispensable basis for emission modeling as a next step for quanti‑ tative assessment of the relevance of different sources and pathways for surface water pollution. Keywords Water pollution, Emerging contaminants, Catchment monitoring, Source identification, Watershed management, Water Framework Directive, Drinking Water Directive, Environmental Quality Standards *Correspondence: Meiqi Liu [email protected] Full list of author information is available at the end of the article Page 2 of 20 Liuetal. Environmental Sciences Europe (2025) 37:99 Background Perand polyfluoroalkyl substances (PFAS) have gained increasing attention at the worldwide level in recent years, as a growing number of studies have revealed links between these synthetic chemicals and adverse effects on human health [97, 116]. Once produced, PFAS are distributed and accumulated in the environment, leading to human exposure through pathways such as drinking water, food, aerosols, and indoor dust [26, 107, 119, 123, 128, 135]. PFAS have been used in a wide range of industrial and household applications since the 1950 s due to their desirable properties such as chemical stability, hydrophobicity, oleophobicity, and the ability to lower surface tension [14, 41]. However, most PFAS are either environmentally and microbiologically non-degradable or ultimately transformed into terminal products that are still PFAS [23, 127]. Extensive studies have found positive associations between PFAS exposure and immunotoxic, neuro-developmental toxicity, thyroid and kidney disorders, hormonal effects, carcinogenic potency, infertility, and cancers [98, 111, 119]. PFAS can enter environmental media throughout their life cycle, resulting in a continuous exchange of an ever increasing amount of PFAS between the environmental compartments [5]. Surface waters, in particular, serve as conduits for the transport and accumulation of PFAS [103]. Understanding the fate and transport of PFAS through different routes to surface water is crucial for identifying emission sources and pathways, as well as developing effective management strategies to control pollution and protect public health [63]. Surface water contamination occurs through both the point source and diffuse pathways [63]. In rural and non-industrial catchments, diffuse inputs can be significant due to wet and dry atmospheric deposition [84, 99]. PFAS can sorb to particulate matter in aerosols, allowing atmospheric transport over long distances [35]. PFAS deposited on soils can reach groundwater and surface water during precipitation events, and PFAS have been widely detected in runoff water samples [22, 57, 133]. In addition, urban storm water can contain not only PFAS accumulated on sealed surfaces through atmospheric deposition, but also PFAS washed-out from various materials applied in the built environment [9]. Major pollution pathways or identifiable emission points include certain industrial facilities, wastewater treatment plants (WWTPs) and the use of sludge generated from PFAS-contaminated WWTPs, the application of aqueous film-forming foam (AFFF) for firefighting-related activities, and landfills [8, 16, 41, 85, 100]. Interactions between surface water and groundwater can transport PFAS from point sources to groundwater and contribute to further diffuse contamination [12, 49, 109]. In addition, the river itself can also diffusely affect groundwater quality [72]. Despite increasing efforts to document the contamination of PFAS in rivers, a systematic understanding of the relative contributions of different environmental pathways remains lacking. Multi-compartment monitoring can provide the necessary information basis for a more comprehensive understanding of PFAS sources, fate, and transport within aquatic systems [55]. Among the different environmental compartments, bank-filtrated water represents a critical but underexplored one. As a sustainable and cost-effective drinking water supply method, riverbank filtration is widely used across Europe, particularly in cities such as Berlin and Budapest [78, 86], as well as in developing regions [53, 106]. However, little is known about the levels of PFAS contamination and transport mechanisms within the water that flows through the river bank, raising concerns about its effectiveness in removing these persistent pollutants. Similarly, while numerous studies have investigated PFAS contamination in wastewater in Europe, relatively few have systematically examined both the influent and effluent. Such investigations are essential to evaluate the removal efficiencies of PFAS and to understand the source of PFAS contamination from the profile of influent samples [61]. The Upper Danube Basin (UDB) presents an important case for understanding PFAS contamination on a large catchment scale. This region has been the focus of micropollutant monitoring [7, 73, 74], providing opportunities to integrate external data sets into a harmonized assessment of contamination profiles. However, previous studies have focused mainly on a limited subset of PFAS, such as PFOA, PFOS, and short-chain perfluoroalkyl acids (PFAA). Recent non-target and suspect screening studies have revealed a broader spectrum of PFAS in the Danube [88, 124], highlighting the need for expanded targeted analysis to enhance our understanding of their occurrence and distribution in the region. Given the extensive presence of PFAS and their diverse pathways into aquatic systems [102, 103], regulatory efforts have been implemented to control their environmental impact. The Stockholm Convention on Persistent Organic Pollutants [117], the US National Primary Drinking Water Regulation [125], and the European Union (EU) Drinking Water Directive(DWD) [33] have established guidelines to limit PFAS pollution. More recently, within the EU, a draft version of Environmental Quality Standards (EQS) [30] has proposed updated regulatory limits for PFAS in surface and groundwater. Assessing PFAS contamination levels in the Danube in Page 3 of 20 Liuetal. Environmental Sciences Europe (2025) 37:99 the context of these evolving standards would provide valuable insights for water management. To address knowledge gaps, a comprehensive monitoring campaign was conducted in the UDB, targeting 31 individual PFAS in multiple environmental compartments, including atmospheric deposition, surface water, groundwater, surface runoff, landfill leachate, and wastewater. Specific efforts were made to collect samples from the Danube and its bank-filtered water in two cities, as well as influent and effluent from the same WWTPs. Furthermore, additional external PFAS monitoring data from countries within the basin were integrated into a harmonized database [71], providing a basis for a more comprehensive assessment of PFAS concentrations and distribution. By adopting a holistic approach to multi-compartment monitoring, this study provides the first comprehensive assessments of PFAS contamination in a large part of the Danube region. It offers insights into PFAS transport via different pathways, identifies key emission sources and contamination hotspots, evaluates the efficacy of traditional removal mechanisms, and assesses the risks of exceedance of current and proposed regulatory thresholds. These findings advance an integrated understanding of PFAS dynamics at the catchment level, and offer a transferable approach that can support micropollutant monitoring and management efforts in other catchments beyond the Danube. Methods Study area description The Danube is Europe’s second largest river, providing resources for numerous human activities along its course. This study focuses on the UDB, which spans approximately 186,059 km 2 and extends to Budapest, Hungary. The region is home to over 27.5 million inhabitants in five major countries: Germany, Austria, Czech Republic, Slovakia, and Hungary. Figure1 illustrates the location of the UDB in relation to the entire Danube basin in a global context. Sampling A comprehensive monitoring campaign was conducted between 2021 and 2023 within the UDB, targeting various environmental compartments, including river water, groundwater, wastewater, landfill leachate, surface runoff and atmospheric deposition. River water grab samples were collected under base flow conditions in nine main tributaries of the Danube. Along the Danube, Vienna and Budapest were selected as representative sites, where base flow samples and groundwater samples were taken bimonthly. Groundwater samples were collected from nearby bank-filtration sites, as bank filtration serves as primary and backup drinking water sources in Budapest [86] and Vienna [20], respectively. In addition, depending on the number of high-flow events and the feasibility of sampling on site, one to three high-flow samples were also collected at surface water sites. More details on sampling at bank-filtration sites and related analyzes are available in [93]. For wastewater, weekly composite samples were taken at seven municipal wastewater treatment plants and four industrial wastewater treatment plants, from both inflow and outflow points. At four legacy municipal landfills— remnants of former waste disposal practices no longer allowed under current EU regulations—samples of leachate or groundwater directly beneath the landfill were collected. Surface runoff samples from unsealed soil were collected during precipitation events from three sites representing agricultural, forest, and pasture land uses. Composite atmospheric bulk deposition samples were collected at three sites over three periods of 4 months. All sampling activities followed the recommendations of multiple PFAS sampling guide documents [17, 81]. The samples were collected and stored in 1-l high-density polyethylene (HDPE) bottles. After collection, the samples were transported to the laboratory in 48 h under cooled conditions (0–10 ◦ C) and stored at 6 ◦ C upon arrival. Detailed information on sampling sites is available in the concentration database [71]. Chemical analysis andquality control For quality control, at least one laboratory blank sample and one field blank sample was prepared for each environmental matrix. To prevent cross-contamination, all sampling materials and equipment have been thoroughly cleaned before use. The targeted analysis of these compounds was performance by liquid chromatography tandem mass spectrometry (LC–MS/MS), following EPA method 1633 [126]. The sample preparation included concentration steps using either automated inline solidphase extraction (SPE) or manual SPE, depending on the sample matrix, to ensure the highest analytical accuracy. During laboratory processing, extracted internal standards were added during the SPE step to evaluate recovery rates, while non-extracted internal standards were included to establish the initial calibration and ensure the precision during LC–MS analysis. For sample injection, a PAL RTC sampler was used. Separation was achieved using an Agilent 1290 Infinity II HPLC pump, coupled with a Sciex Qtrap 6500+ mass spectrometer equipped with an electrospray ionization source (EIS). A Phenomenex Luna Omega 3 µm PS C18 (100 × 3.0 mm, 100 Å) analytical column was maintained at 40 ◦ C, with an injection volume of 40 µL. Furthermore, a Phenomenex Luna C18 (50 × 3 mm, 110 Å) delay Page 4 of 20 Liuetal. Environmental Sciences Europe (2025) 37:99 column was installed. Chromatographic separation was performed using a mobile binary gradient phase, and the gradient conditions are provided in Tables1. The ESI of the mass spectrometer operated in negative ion mode, using multiple reaction monitoring for the target components listed in Table1. The limit of quantification (LOQ) for each substance was determined by direct injection, following the DIN 32645 guideline [25]. Detailed LOQ values are provided in Tables2. The validity of the LC–MS/MS analysis results has been initially verified internally by the data management team. Questionable results have undergone repeated analysis before being stored in the designated PFAS database. Additional data collection Based on the monitoring results collected during our sampling campaign, we have further expanded the dataset by incorporating PFAS concentration data from other surveys, covering the study area as comprehensively as possible. Several monitoring campaigns have been conducted in the Danube region, such as the EU Project ”DHm3c” [59] and the Joint Danube Survey 4 [52], which provide valuable data that enhanced the temporal resolution at some sampling sites, and extended spatial coverage beyond the scope of our own campaign. Furthermore, some national-level studies extensively focuses on specific environmental compartments, contributing substantial additional data, for example, groundwater measurements from the Austrian National Groundwater Monitoring Campaign [15] and wastewater measurements from Germany [92]. In addition, certain data are not publicly available but obtainable through direct requests to local environmental ministries. In general, these efforts have led to substantial increases in the volume of concentration data, improvement in spatial and temporal coverage, and increased reliability of the Fig. 1 Map of the study area—Upper Danube (red boundary), its relative position to the whole Danube Basin (black boundary) and in the world (small map upper right corner). Danube and its major tributaries are outlined in blue; administrative regions for major countries in the Upper Danube are shaded in different colors; major large cities in the study area are shown in red points and labeled with names Page 5 of 20 Liuetal. Environmental Sciences Europe (2025) 37:99 results, which helps provide a more complete picture of PFAS contamination patterns across the UDB. To systematically manage and store the collected data, we have constructed a harmonized database [71], which is used to present the results in this study. This comprehensive database is publicly available and will provide support for future studies and analysis in the UDB. It not only includes essential measurements information, such as sample matrices, concentration values, units and LOQ, but also provides extensive metadata, such as sampling site coordinates, sampling type, sampling techniques, the laboratory conducting the chemical analysis, and analytical methods applied. Details on data sources, environmental matrices and the number of measurements included in the database are provided in Supplementary Material 2. Statistical analysis Concentration data collected from the database are first classified into different types based on environmental compartments and specific characteristics. Data collected from the Danube tributaries are further classified to assess the impact of wastewater. The Table 1 Overview of the PFAS compounds investigated in this study Columns are also given to indicate whether the compound has been included in the lists of calculating PFAS sum from the Drinking Water Directive (DWD, 2020) [33] and the proposed Environmental Quality Standards (EQS, 2022) [30]. For compounds included in the proposed EQS (2022), relative potency factors (RPFs) as PFOAequivalents are given Classifications of PFAS group is according to the EPA Method 1633 [126] Substance CAS number PFAS group In DWD (2020) In proposed EQS (2022) RPF PFBA 375‑22‑4 Perfluoroalkyl carboxylic acids (PFCA) Yes Yes 0.05 PFPeA 2706‑90‑3 Perfluoroalkyl carboxylic acids (PFCA) Yes Yes 0.03 PFHxA 307‑24‑4 Perfluoroalkyl carboxylic acids (PFCA) Yes Yes 0.01 PFHpA 375‑85‑9 Perfluoroalkyl carboxylic acids (PFCA) Yes Yes 0.505 PFOA 335‑67‑1 Perfluoroalkyl carboxylic acids (PFCA) Yes Yes 1 PFNA 375‑95‑1 Perfluoroalkyl carboxylic acids (PFCA) Yes Yes 10 PFDA 335‑76‑2 Perfluoroalkyl carboxylic acids (PFCA) Yes Yes 7 PFUdA 2058‑94‑8 Perfluoroalkyl carboxylic acids (PFCA) Yes Yes 4 PFDoDA 307‑55‑1 Perfluoroalkyl carboxylic acids (PFCA) Yes Yes 3 PFTrDA 72629‑94‑8 Perfluoroalkyl carboxylic acids (PFCA) Yes Yes 1.7 PFTeDA 376‑06‑7 Perfluoroalkyl carboxylic acids (PFCA) No Yes 0.3 PFBS 375‑73‑5 Perfluoroalkyl sulfonic acids (PFSA) Yes Yes 0.001 PFPeS 2706‑91‑4 Perfluoroalkyl sulfonic acids (PFSA) Yes Yes 0.3005 PFHxS 355‑46‑4 Perfluoroalkyl sulfonic acids (PFSA) Yes Yes 0.6 PFHpS 375‑92‑8 Perfluoroalkyl sulfonic acids (PFSA) Yes Yes 1.3 PFOS 1763‑23‑1 Perfluoroalkyl sulfonic acids (PFSA) Yes Yes 2 PFNS 68259‑12‑1 Perfluoroalkyl sulfonic acids (PFSA) Yes No – PFDS 335‑77‑3 Perfluoroalkyl sulfonic acids (PFSA) Yes Yes 2 GenX 13252‑13‑6 Per‑ and Polyfluoroether carboxylic acids (PFECA) No Yes 0.06 ADONA 919005‑14‑4 Per‑ and Polyfluoroether carboxylic acids (PFECA) No Yes 0.03 4:2 FTS 757124‑72‑4 Fluorotelomer sulfonic acids (FTS) No No – 6:2 FTS 27619‑97‑2 Fluorotelomer sulfonic acids (FTS) No No – 8:2 FTS 39108‑34‑4 Fluorotelomer sulfonic acids (FTS) No No – PFOSA 754‑91‑6 Perfluorooctane sulfonamides (FOSA) No No – N‑MeFOSA 31506‑32‑8 Perfluorooctane sulfonamides (FOSA) No Yes 0.02 N‑EtFOSA 4151‑50‑2 Perfluorooctane sulfonamides (FOSA) No No – N‑MeFOSAA 2355‑31‑9 Perfluorooctane sulfonamidoacetic acids (FOSAA) No No – N‑EtFOSAA 2991‑50‑6 Perfluorooctane sulfonamidoacetic acids (FOSAA) No No – 9Cl‑PF3ONS 73606‑19‑6 Ether sulfonic acids (ESA) No No – 11Cl‑PF3OUdS 763051‑92‑9 Ether sulfonic acids (ESA) No No – N‑MeFOSE 24448‑09‑7 Perfluorooctane sulfonamide ethanols (FOSE) No No – N‑EtFOSE 1691‑99‑2 Perfluorooctane sulfonamide ethanols (FOSE) No No – Page 6 of 20 Liuetal. Environmental Sciences Europe (2025) 37:99 average annual river discharge values (2015–2021) were calculated using data from official hydrological gauges. Similarly, the average annual discharge of treated municipal wastewater during the same period was obtained by aggregating data from the Urban Wastewater Treatment Directive (UWWTD) Waterbase [31] at the catchment level using QGIS software [104]. The proportion of municipal wastewater treatment effluent in river water at each sampling site was then calculated as the ratio of annual effluent discharge to annual river discharge. Based on these results, the tributary sites are classified into three groups, with the share of effluent in river water below 1%, between 1% and 3% and above 3%. Although the database includes comprehensive PFAS measurements from Danube samples along the entire river stretch up to Budapest, considering the varying characteristics of Danube at different sections, a selective subset is employed for further analysis and visualizations. Specifically, only samples from Vienna and Budapest are included in this focused dataset. This approach excludes data from upstream of a known hotspot region (the Alz river) in the UDB, thereby minimizing potential confounding influences. Additionally, as both surface water and bank-filtered groundwater were sampled in parallel at Vienna and Budapest, this selection improves the comparability of results between compartments. Groundwater data are categorized into three groups. Sampling points located within 5 km of areas where AFFF was potentially applied or near legacy municipal landfills are classified as potentially impacted by hotspot contamination, respectively. All other sampling points are classified as groundwater without known influences from hotspot. PFAS occurrence is visualized using heatmaps, displaying detection frequencies across different sample types. Boxplots are employed to illustrate concentration distributions, where the box dimensions represent the interquartile range (IQR), spanning from the first to the third quartile. A line inside the box indicates the median value, while whiskers extend to the largest or smallest values within 1.5 times the IQR from the quartiles. Values beyond the whiskers are considered outliers and are shown as individual points [131]. For some PFAS concentration boxplots, the regression on order statistics (ROS) method is applied [67]. This semiparametric approach assumes a log-normal distribution for environmental concentration data and imputes censored values accordingly [47, 48]. It is important to note that these estimates are used solely for statistical analysis and do not represent true measured values. In addition, the variability and uncertainty of the estimated values may increase as the proportion of censored measurements increases. In this study, if the censored values exceed 80% or fewer than three values are detected above LOQ for a specific substance/ type combination, a substitution method is applied, replacing measurements below the LOQ with half the LOQ value. Consequently, boxplots may display boxes based on varying half-LOQ values. If a single LOQ dominates the dataset, this may appear as a line at half its value, while “outliers” in the plot may represent either half-LOQ values from less common LOQs, or actual measurements above LOQ. Careful evaluation is necessary when interpreting these results, and it is recommended to always consider the accompanying information on the percentage of measurements above LOQ for each substance that given in the boxplots. Nevertheless, the combination of ROS and substitution methods is adopted as it ensures the most unbiased and robust way to estimate summary statistics. Boxplots have also been used to visualize the levels of the PFAS sum parameters. The sums of the compounds of PFAS are calculated according to the EU Drinking Water Directive (DWD, 2020/2184) [33], which considers the sum of 20 PFAS, and the proposed Environmental Quality Standard Directive (EQS, 2022) [30], which considers the sum of 24 PFAS adjusted to PFOA equivalent toxicity levels, respectively. It is important to note that only 17 of the 20 PFAS listed in DWD (2020) and 20 of the 24 PFAS listed in the proposed EQS (2022) are within the scope of this study (Table1), and not all samples contain measurements for all listed PFAS. To avoid excessive omissions, only samples that contain at least 10 of the substances appearing on both lists are included. Additionally, all values below LOQ are treated as zero, as required by regulations, although this does not imply the absence of non-quantifiable concentrations. To facilitate visualization on a logarithmic scale, values of the sum parameters equal to zero are adjusted to 0.0001 ng/l. This replacement represents the minimum potential true value, calculated as the product of the lowest LOQ and the lowest RPF among the PFAS compounds from the drafted EQS. Therefore, the estimates in the boxplots represent the minimum likelihood of exceeding the threshold levels. Due to the proportion of censored data and the likelihood of non-normal distributions, nonparametric statistical tests are used to assess concentration differences between environmental compartments and sample types. The Wilcoxon signed rank test [130] is applied for pairwise comparisons, while the Kruskal–Wallis test [62] is used for multi-group comparisons. If intragroup differences are detected, Dunn’s test [27] is performed for post hoc pairwise analysis. The significance level (p-value) for all statistical tests is set at 0.05. All data analyses were performed with the statistical software R (v4.1.0)[105]. The following R packages were Page 7 of 20 Liuetal. Environmental Sciences Europe (2025) 37:99 used for data processing: the odbc package [50] for database import, the tidyverse collection [129] and the ggsci package [134] for data manipulation and visualization, the NADA package [66] for the implementation of the ROS method and the FSA package [94] for the Dunn test. Results anddiscussion PFAS occurrence At least one PFAS compound listed in Table1 is present in 60% of all samples (referred as ‘sample-level detection’ below). The PFCA and PFSA are the most frequently detected compound groups, with sample-level detections of 50% and 46%, respectively. This is followed by the FTS (11%) group. The detection rates for the remaining groups encompassed by this study are all below 10%. Among individual compounds, PFOA exhibits the highest sample-level detection rate (40%), while for PFOS the level is 33%. Except for these two long-chain compounds, short-chain compounds are predominant, comprising PFBA (38%), PFBS (37%), PFHxA (36%), PFPeA (29%), PFHpA (27%) and PFHxS (23%). This observation is consistent with global findings [85, 90, 101], and can be attributed to the widespread distribution and application of these short-chain PFCA and PFSA compounds in the industrial and commercial activities. Furthermore, they are the degradation products of other PFAS precursor compounds, and are persistent in the environment. In contrast, the presence of the other substances is detected in less than 20% of the samples. Specifically, N-EtFOSAA and N-EtFOSE are not detected in any of the samples. As these two compounds have only limited fields of application and are reported without contemporary usage [41], it is possible that both substances are not present in the water environment of UDB. However, the detection rates can vary significantly by environmental matrix, as shown in other cross-compartment studies from Europe [4] and the United States [46]. In our study, among all leachate and surface runoff samples, the rate with at least one PFAS being detected is 100%, followed by samples of wastewater (98%), surface water (83%), atmospheric deposition (73%) and groundwater (50%). Figure2 further illustrates the variability of dominant PFAS across different sample types. The heatmap presents measurement-level detection for each substancetype combination, which refers to the detection rate of measurements above LOQ for a specific compound, with numerical data provided in Supplementary Materials 3. For example, groundwater samples potentially impacted by AFFF applications or landfills have significantly higher detections of many PFAS compounds compared to groundwater sample without known source of contamination. This could be explained by the transfer of PFAS from contaminated lands to the groundwater [13, 19, 49]. To summarize, the results of PFAS occurrences demonstrate the widespread presence of PFAS contamination, with noticeable differences observed across environmental compartments. Furthermore, variations in detections across different types within the same compartments reveal the relationships between point-source emissions and contamination degrees, as surface water samples collected from hotspot regions or groundwater samples impacted by contaminated lands show clearly higher detections than others. PFAS spatial distribution Surface water Figures3 and 4 illustrate the concentration distributions of commonly detected PFAS in surface water samples. The analysis includes the Alz River, an indirect tributary of the Danube via the Inn, the main Danube at Vienna and Budapest, and other Danube tributaries, which are classified based on the proportion of treated wastewater discharges relative to their total annual river discharge. The Alz River exhibits a distinct contamination pattern compared to other surface water groups, particularly for PFCAs and PFECAs. Not only the detections of these compounds in Alz are 100% or close to it, but also their concentrations are much higher, ranging from hundreds to even thousands of nanograms per liter. Dunn’s tests confirm the observation (Tables3), revealing that for selected PFCAs concentrations in the Alz are significantly higher than all the other surface water groups. For PFECAs, the differences between the Alz and other tributary groups are significant, but not for the Danube. For PFSAs, Alz does not exhibit higher concentrations than the other groups. In contrast, the level of PFHxS is significantly lower in the Alz compared to other tributaries. The Gendorf industrial park is a recognized contamination hotspot within the Alz catchment. This area hosts several facilities for the production of plastic and PFAS, some with a history of fluoropolymer production dating back to the 1960s [29]. Reports from the Bavarian State Office for the Environment indicate extensive contamination by PFOA in soils and groundwater over an area of approximately 230 km 2 near Gendorf [6, 13]. According to the local environmental authority [6], PFOA concentrations in the Alz River were reported at 5000–8000 ng/L in 2006, decreasing to below the LOQ of 20 ng/L by 2016. However, our results with samples collected from 2019 to 2023 show that PFOA concentrations in the Alz downstream range from 14 to 220 ng/L. This is similar to the findings of Joerss etal. [54], who measured PFOA levels at three sites downstream of Gendorf along the Alz in Page 8 of 20 Liuetal. Environmental Sciences Europe (2025) 37:99 2018, reporting concentrations from 20 ng/L to 180 ng/L. Although PFOA production at this site was phased out in 2008 [6], both our study and Joerss etal. [54] highlight the persistence of legacy PFOA emissions from contamination in the surrounding soil and groundwater. For other compounds, our study observes similar levels of PFCA, PFSA and FTS compared to Joerss etal. [54] in the Alz. However, for GenX, our measured mean concentration (38 ng/L) is much lower than the previously reported 2600 ng/L. ADONA concentrations have decreased from a mean level of 2100 ng/L to 1501 ng/L, still remaining at high levels. As replacement compounds for PFOA, ADONA and GenX have been detected in surface waters in China, Europe, and North America [96]. It is of particular interest that within the UDB, the presence of these two compounds is found to be almost exclusively confined to the Alz and the Danube section downstream of Alz. This finding provides strong evidence that the Gendorf chemical park is a major source of these specific PFAS contaminants. Previous studies on PFAS contamination in the Danube report average PFOS concentrations of 7 ng/L (2010) [73], 5.9 ng/L (2017) [74], and approximately 2.1 ng/L (2023) [7], while our study detects an average level of 1.8 ng/L. For PFOA, historical average concentrations in the Danube are 16.4 ng/L (2007) [79], 20 ng/L (2010) [73], 4.9 ng/L (2017)[74], and 2.8 ng/L (2023) [7], whereas our study finds an average level of 2.3 ng/L. In general, a decreasing trend is observed in PFOS and PFOA concentrations after their gradual phase-out and regulatory restrictions, consistent with observations in North America [102]. However, the persistence of legacy contamination remains an issue due to the prolonged use of PFOS-containing products, the degradation and transformation of precursor compounds [68, 89], and the long environmental residence time of longchain PFAS [45]. Our findings related to the Gendorf site highlight how such factors continue to contribute to contamination in the Danube. PFCA PFSA PFECA FTS FOSA FOSAA ESA FOSE PFBA PFPeA PFHxA PFHpA PFOA PFNA PFDA PFUdA PFDoDA PFTrDA PFTeDA PFBS PFPeS PFHxS PFHpS PFOS PFNS PFDS GenX ADONA 4:2 FTS 6:2 FTS 8:2 FTS PFOSA N-MeFOSA N-EtFOSA N-MeFOSAA N-EtFOSAA 9Cl-PF3ONS 11Cl-PF3OUd S N-MeFOSE N-EtFOSE Industrial Effluent Industrial Influent Municipal Effluent Municipal Influent Surface Runoff Landfill Leachate Groundwater Potentially Impacted by Landfills Groundwater Potentially Impacted by AFFF Applications Groundwater Without Known Source of Contamination Danube Bank-filtered Water (Vienna and Budapest) Main Danube (Vienna and Budapest) Tributary Alz Danube Tributary Atmospheric Deposition Detected Rate (%) 020406080 100 Fig. 2 Heatmap showing the measurement‑level detection rates of each PFAS substance–sample type combinations analyzed in this study. The X‑axis lists the substance name, while the Y‑axis shows the sample type. Darker shades indicate higher detections, as shown by the gradient color bar from light‑gray to black (0–100%). The LOQ values for each compound are provided in Table s2 Page 9 of 20 Liuetal. Environmental Sciences Europe (2025) 37:99 27 (100%), 38 (100%) 27 (100%), 38 (97%) 27 (100%), 38 (89%) 27 (96%), 38 (97%) 27 (100%), 38 (97%) 23 (43%), 38 (89%) 24 (29%), 38 (100%) 27 (81%), 38 (97%) 8 (100%), 38 (95%) 8 (100%), 38 (95%) 8 (12%), 38 (50%) 0.1 1 10 100 1000 10000 PFBA PFPeA PFHxA PFHpAPFOAPFBS PFHxS PFOS GenX ADONA6:2 FTS Concentration (ng/L) Sample Type Tributary Alz Main Danube (Vienna and Budapest) LOQs Range Fig. 3 PFAS concentrations on a logarithmic scale for samples from Danube mainstream (Vienna and Budapest) and river Alz. Box in red dashed outline represents the range of LOQ values among measurements; for different compounds, sample sizes and detection above the LOQ (%) of each type are listed on top of the graph, in a sequence of their appearance on the graph. Values below LOQ are semi‑quantitative 104 (23%), 587 (35%), 532 (48%) 110 (19%), 599 (15%), 546 (26%) 110 (25%), 598 (25%), 531 (39%) 110 (8%), 600 (8%), 546 (14%) 109 (15%), 605 (28%), 560 (34%) 110 (7%), 601 (16%), 546 (28%) 110 (14%), 601 (9%), 546 (22%) 110 (43%), 612 (60%), 564 (73%) 65 (0%), 118 (15%), 118 (2%) 65 (0%), 118 (36%), 118 (8%) 25 (24%), 60 (10%), 86 (33%) 0.1 1 10 100 1000 10000 PFBA PFPeA PFHxA PFHpAPFOAPFBS PFHxS PFOS GenX ADONA6:2 FTS Concentration (ng/L) LOQs Range Sample Type Tributary with Wastewater Discharge <1% Tributary with Wastewater Discharge 1-3% Tributary with Wastewater Discharge > 3% Fig. 4 PFAS concentrations on a logarithmic scale for samples from Danube tributaries classified into three groups according to the proportion of wastewater dilution. Box in red dashed outline represents the range of LOQ values among measurements. For different compounds, sample sizes and detection above the LOQ (%) of each type are listed on top of the graph, in a sequence of their appearance on the graph. Values below LOQ are semi‑quantitative Page 16 of 20 Liuetal. Environmental Sciences Europe (2025) 37:99 are particularly valuable for quantifying and comparing emissions through multiple pathways, especially diffuse emissions, which are often underrepresented in compartment-specific assessments. The comparative analysis highlights key areas that require enhanced monitoring efforts and further reveals variability in risk levels between environmental compartments, providing crucial information to prioritize resource allocation to meet environmental quality targets. Although this study focuses on the UDB, PFAS contamination is a global concern. The insights gained on the distribution of PFAS in our study can contribute to a better understanding of the patterns of contamination, with implications for water management strategies in other river systems facing similar challenges. Abbreviations 4:2 FTS 4:2 Fluorotelomer sulfonic acid 6:2 FTS 6:2 Fluorotelomer sulfonic acid 8:2 FTS 8:2 Fluorotelomer sulfonic acid AFFF Aqueous film‑forming foam ADONA 4,8‑Dioxa‑3H‑perfluorononanoic acid DWD Drinking Water Directive EU European Union EIS Electrospray ionization source EQS Environmental Quality Standards ESA Ether sulfonic acids FOSE Perfluorooctane sulfonamide ethanols FOSA Perfluorooctane sulfonamides FOSAA Perfluorooctane sulfonamidoacetic acids FTCA Fluorotelomer carboxylic acids FTS Fluorotelomer sulfonic acids GenX Perfluoro‑2‑methyl‑3‑oxahexanoic acid IQR Interquartile range LC–MS/MS Liquid chromatography–tandem mass spectrometry LOQ Limit of quantification N‑EtFOSA N‑Ethylperfluorooctane sulfonamide N‑EtFOSAA 2‑(N‑Ethylperfluorooctanesulfonamido)acetic acid N‑EtFOSE N‑Ethyl‑N‑(2‑hydroxyethyl)perfluorooctanesulfonamide N‑MeFOSA N‑Methylperfluorooctanesulfonamide N‑MeFOSAA 2‑(N‑Methylperfluorooctanesulfonamido)acetic acid N‑MeFOSE N‑Methyl‑N‑(2‑hydroxyethyl)perfluorooctanesulfonamide PFAA Perfluoroalkyl acids PFBA Perfluorobutanoic acid PFBS Perfluorobutanesulfonic acid PFCA Perfluoroalkyl carboxylic acids PFDA Perfluorodecanoic acid PFDS Perfluorodecanesulfonic acid PFHxA Perfluorohexanoic acid PFHxS Perfluorohexanesulfonic acid PFHpA Perfluoroheptanoic acid PFHpS Perfluoroheptanesulfonic acid PFOA Perfluorooctanoic acid PFNA Perfluorononanoic acid PFOS Perfluorooctanesulfonic acid PFPeA Perfluoropentanoic acid PFPeS Perfluoropentanesulfonic acid PFTrDA Perfluorotridecanoic acid PFTeDA Perfluorotetradecanoic acid PFUdA Perfluoroundecanoic acid PFOSA Perfluorooctanesulfonamide ROS Regression on order statistics SPE Solid‑phase extraction UDB Upper Danube Basin WFD Water Framework Directive WWTP Wastewater treatment plant Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s12302‑ 025‑ 01141‑6. Supplementary material 1. Supplementary material 2. Supplementary material 3. Acknowledgements Open access funding provided by TU Wien (TUW). The authors thank the water chemistry laboratory of the TU Wien, especially the team members Zdravka Saracevic and Nedim Sahovic, for their assistance with the chemi‑ cal analysis. In addition, we acknowledge the contributions of Elsa Eder and Severin Osten, for their assistance during the sampling campaign. Author contributions M.L.: data curation, formal analysis, visualization, writing—original draft; E.S.: methodology, investigation, writing—review and editing; T.J.O.: investigation, resources; A.A.A.O.: investigation, resources, writing—review and editing; Z.NK.: resources; B.L.: resources; S.K.: validation, writing—review and editing; O.Z.: validation, writing—review and editing; J.K.: writing—review and editing; J.D.: conceptualization, methodology, validation, writing—review and editing, supervision, funding acquisition; M.Z.: conceptualization, methodology, valida‑ tion, writing—review and editing, supervision, funding acquisition. Funding Open access funding provided by TU Wien (TUW). The research leading to these results has been supported by the PROMISCES project, funded by the European Union H2020 Programme (H2020/2014‑2020) under grant agree‑ ment n ◦ 101036449. Availability of data and materials The dataset generated and analyzed during the current study is available in the repository [Perand Polyfluoroalkyl Substance (PFAS) Concentrations in the Upper Danube Catchment: Integrated data set from the H2020 Project PROMISCES—Case Study 2],https:// doi. org/ 10. 5281/ zenodo. 14027 087[71]. The R code supporting the data analysis and computational environmental setup of the current study is available in the Gitlab project, [https:// gitlab. tuwien. ac. at/ meiqi. liu/ promi sces_ cs2_ monit oring]. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no conflict of interest. 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