Occurrence of Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) in the Transboundary Guarani Aquifer System within a Highly Urbanized Context in the Sinos River Basin

Occurrence of Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) in the Transboundary Guarani Aquifer System within a Highly Urbanized Context in the Sinos River Basin | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Occurrence of Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) in the Transboundary Guarani Aquifer System within a Highly Urbanized Context in the Sinos River Basin Matheus Beretta Duarte, Ari Roisenberg, José André Teixeira Azevedo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6261164/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Guarani Aquifer System (GAS) is a transboundary sedimentary aquifer source of public water supply for millions of people in Brazil, Argentina, Uruguay, and Paraguay, known for the high quality of its groundwaters. However, under surface conditions in an urban environment with poor sanitation and intense industrial activity, water quality can degrade due to the input of a wide range of contaminants, including per and polyfluoroalkyl substances (PFAS). PFAS are anthropogenic substances widely used in industry for their unique properties, such as hydrophobicity and chemical stability. However, in the environment, they exhibit high persistence and potential health risks to humans and other organisms. In this study, 28 PFAS were investigated in the GAS within the context of the Sinos River Basin, located in southern Brazil, a region known for its lack of sewage treatment, uncontrolled urbanization, and strong leather, metallurgical and electroplating industries. Fifteen groundwater samples were collected from tubular wells and springs, and five samples were taken from the Sinos River. In five groundwater samples least two PFAS were detected, with the total PFAS (ΣPFAS) up to 16.78 ng/L, with PFOA and PFBA being the most frequent. In surface waters of the Sinos River, ΣPFAS ranged from 0.98 to 71.09 ng/L, with 6:2 FTS and PFOSA being the most frequently detected. Our study is the first one to identify the background of PFAS in the GAS and suggests the need for long-term monitoring of the aquifer, as its characteristics may promote the retention of these highly persistent compounds, likely associated with the use of pesticides and the leather and electroplating industries. Guarani Aquifer System PFAS Sinos River Southern Brazil Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Brazil has experienced numerous drought episodes over the past decade, with a trend towards increasing frequency. In addition, the quality of surface waters in the country has been subject to intense degradation due to the untreated discharge of domestic sewage, industrial effluents, and agriculture nutrients and pesticides (Cunha et al., 2019 ; Marmontel et al., 2018 ; Albuquerque et al., 2016 ; Froehner et al., 2010 ). The growing frequency of these situations indicates a likely future surge in groundwater demand, driven by the need for human consumption, industrial activities, and agriculture, particularly in densely populated urban areas (Hirata et al., 2019 ; Hirata and Foster, 2015). As the main source of groundwater in Brazil, the Guarani Aquifer System (GAS), one of the largest porous sandstone aquifer systems on the planet, provides water resources to millions of inhabitants also in Argentina, Uruguay, and Paraguay (Ribeiro, 2008 ; Silva and Hussein, 2019 ). Spanning an area of approximately 1.2 million km², this heterogeneous aquifer system exhibits varying conditions of confinement, porosity, permeability, and recharge capacity, potentially receiving a wide range of organic and inorganic chemical contaminants (Soares et al., 2008 ; Costa et al., 2019 ). Among the broad spectrum of potential contaminants are the perfluoroalkyl and polyfluoroalkyl substances (PFAS), synthetic industrial chemicals, classified as emerging contaminants. These substances, produced since the 1940’s, have attracted the attention of the scientific community and governments due to their bioaccumulation in organisms, persistence, chemical stability, and presence in various environmental matrices, including surface waters, air, treated water, food, and groundwater (Tang et al., 2023 ; Morales-McDevitt et al., 2021 ; Stoiber et al., 2020 ; Cui et al., 2020 ). Studies also point to adverse health effects associated with PFAS exposure, such as thyroid alterations, reproductive issues, and cancer (Fenton et al., 2021 ). PFAS are globally used in numerous applications, such as surfactants, flame retardants, fire extinguishing foams, pesticide formulations, and in the textile, electroplating, metallurgical, and leather industries, as well as in the manufacture of aqueous film-forming foams (Donley et al., 2024 ; Vo et al., 2021; Xiao, 2017 ; Kotthoff et al., 2015 ). Compounds such as PFOA and PFOS have had their use restricted in countries like the United States; however, in Brazil, there is no legislation, and the debate remains limited (Podder et al., 2021 ; Wee and Aris, 2023 ; Pontius, 2019 ). The sources of PFAS contamination in aquatic environments are diverse and often difficult to define due to their pervasive use in society and persistence in the environment, acting as legacy pollutants. High concentrations of PFAS in water matrices can be associated with landfills, wastewater treatment plant effluents, sewage sludge, and industries that use PFAS in their processes. Contamination can occur both as point source and diffuse pollution, associated with processes such as urban runoff and atmospheric transport (Zhou et al., 2024 ; Faust, 2023 ; Lenka et al., 2022 ; Stoiber et al., 2020 ; Codling et al., 2020 ). The presence of PFAS in groundwater has been identified worldwide, with concentrations ranging from ng/L to mg/L, in both shallow and deep aquifers, composed of different lithologies, structures, sources, and land uses (Currell et al., 2024 ; Sadia et al., 2023 ; Johnson, 2022 ; Johnson et al., 2022 ). Few studies on PFAS in water matrices have been conducted in Brazil, with even fewer involving groundwater (Fig. 1 ). Specifically, in the Guarani Aquifer System, there is no literature on the occurrence of PFAS, highlighting an immediate need to understand the presence of these compounds. Given the strong link between PFAS contamination and sewage, wastewater treatment plants, and industrial activities, along with the potential for groundwater degradation in urban aquifers, we selected the highly polluted Sinos River Basin (SRB) in southern Brazil to investigate the occurrence of 28 PFAS in the Guarani Aquifer System (GAS). This basin is among the most contaminated in Brazil and suffers primarily from inadequate sewage collection and treatment, alongside a leather and electroplating industry with minimal environmental regulations (Plano Sinos, 2014 ). Materials and methods Study area The Sinos River Basin covers an area of 3,680 km² and is home to approximately 1.5 million inhabitants, 94% of whom reside in urban area (Fig. 2 ). The region’s primary economic activities include metallurgical, leather-footwear, food, and mechanical industries (Plano Sinos, 2014 ). Specifically, the study area is situated in the final stretch of the São Leopoldo River Basin (SRB), located within the Municipality of São Leopoldo, which is part of the Metropolitan Region of Porto Alegre, the most densely populated area in southern Brazil. The climate is classified as humid subtropical, with relatively well-distributed rainfall throughout the year, averaging 1,397 mm annually (Alvares et al., 2013 ). A significant feature of the Sinos River Basin is the severe degradation of its surface water quality due to the discharge of untreated domestic and industrial waste. This condition culminated in a well-known environmental disaster in 2006, which resulted in the death of 100 tons of fish, one of the most significant environmental disasters in Brazil (Pedde et al., 2015 ). In the study area, only 12.21% of sewage is collected and treated before being discharged into the Sinos River (SNIS, 2023 ). The river’s sediments, aquatic organisms, and waters are contaminated by metals such as Cr, Cd, and Pb, primarily from the leather and metallurgical industries, as well as nitrates and other byproducts from the degradation of domestic sewage such Bisphenol A (Becker et al., 2017 ; Plano Sinos, 2014 ; Rodrigues and Formoso, 2006 ; Vargas et al., 2001 ; Hatje et al., 1998 ). The Guarani Aquifer System in the study area is prevalently unconfined and composed of fine to medium-grained sandstones or derived soils, with variable clay and organic matter contents. Surface levels are overlay by a thin layer of recent alluvial and colluvial unconsolidated sediments. Due to the poor quality and low productivity of the water in these unconsolidated sediments, water is generally extracted from the GAS or deeper aquifers. In general, GAS waters are of good quality, classified as calcium bicarbonate or sodium bicarbonate with low mineralization. However, in the study area, metal contamination has been observed in both surface and groundwater (Abreu and Roisenberg, 2018 ; Kuhn and Roisenberg, 2017 ; GSB, 2005). The specific capacity of tubular wells in the study area ranges from below 0.05 m³/h/m to 2 m³/h/m, with higher yields associated with wells drilled near tectonic lineaments (Duarte et al., 2020 ). In a more limited area, a hydrogeological unit known as the Permian Aquitards (PA) occurs, consisting mostly of fine-grained rocks, such as shales, with low water transmission capacity, producing wells with specific capacities below 0.1 m³/h/m. Higher specific capacities can be achieved when these shales are intensely fractured (GSB, 2005; Plano Sinos, 2014 ). Sampling Surface water samples were collected from the main course of the Sinos River within the study area at various sites, including locations used for public water supply and areas where secondary tributaries (i.e., Luiz Rau and João Corrêa streams) flow into the main river. Five samples were collected on the same day in April 2024. Groundwater sampling sites included 12 tubular wells used for human consumption and other activities without prior treatment, one monitoring well, and two springs. The depths of the sampled wells ranged from 6 to 202 meters and included both unregulated wells and those officially licensed by the government. It should be emphasized that the vast majority of wells in Brazil are unregulated, constructed without technical expertise, maintenance, or prior chemical and microbiological analysis of water quality before consumption (Hirata et al., 2015 ). Samples were taken directly from the well’s water outlet, without any treatment or alteration of the groundwater properties. Analytical method The analytical method used was Method 1633 – Analysis of Per- and Polyfluoroalkyl Substances (PFAS) in Aqueous, Solid, Biosolids, and Tissue Samples by LC-MS/MS, with quantification by isotope dilution, as established by the United States Environmental Protection Agency (USEPA, 2021). The analyses were conducted by the commercial laboratory Eurofins Brazil (Rio Claro, São Paulo, Brazil), accredited under ISO 17025. All samples were stored in 250 mL high-density polyethylene bottles, refrigerated at 4°C after collection, and analyzed within a maximum period of 9 days. Details of each analytical parameter and the compounds analyzed can be found in Table 1 . Systematic Literature Review Approach This study follows a systematic literature review methodology to assess the occurrence and environmental impact of per- and polyfluoroalkyl substances in groundwater and surface water in Brazil. The review was conducted to ensure the collection of data that is reproducible and comparable to the reporting in this study. A comprehensive search was performed in the ScienceDirect and PubMed using the keywords “PFAS,” “Brazil”, “water”. All studies published were considered. Table 1 PFAS analyzed and their parameters obtained in LC-MS/MS using the Method 1633 – Analysis of Per- and Polyfluoroalkyl Substances (PFAS) in Aqueous, Solid, Biosolids, and Tissue Samples by LC-MS/MS (USEPA, 2021). The limit of quantification (LOQ) determined for all the analyzed chemical compounds was 2 ng/L Compound CAS NUMBER Uncertainty LOD (ng/L) Reference Perfluorobutanoic acid (PFBA) 375-22-4 1.1580 0.826 USEPA 1633 07/2023 Perfluoropentanoic acid (PFPeA) 2706-90-3 0.1218 0.295 USEPA 1633 07/2024 Perfluorohexanoic acid (PFHxA) 307-24-4 0.2064 0.242 USEPA 1633 07/2025 Perfluoroheptanoic acid (PFHpA) 375-85-9 0.1798 0.299 USEPA 1633 07/2026 Perfluorooctanoic acid (PFOA) 335-67-1 0.1478 0.345 USEPA 1633 07/2027 Perfluorononanoic acid (PFNA) 375-95-1 0.1842 0.420 USEPA 1633 07/2028 Perfluoro(2-methyl-3-oxahexanoic) Acid (HFPO-DA) (Also known GenX) 13252-13-6 0.1938 0.796 USEPA 1633 07/2029 Perfluoroundecanoic acid (PFUnA) 2058-94-8 0.3166 0.320 USEPA 1633 07/2030 Perfluorododecanoic acid (PFDoA) 307-55-1 0.1486 0.196 USEPA 1633 07/2031 Perfluorotridecanoic acid (PFTrDA) 72629-94-8 0.1920 0.827 USEPA 1633 07/2032 Perfluorotetradecanoic acid (PFTreA) 376-06-7 0.2672 0.567 USEPA 1633 07/2033 Perfluorohexadecanoic acid (PFHxDA) 67905-19-5 0.2300 0.368 USEPA 1633 07/2034 Perfluorooctadecanoic acid (PFODA) 16517-11-6 0.1722 0.284 USEPA 1633 07/2035 Perfluorooctanesulfonamide (PFOSA) 754-91-6 0.2578 0.236 USEPA 1633 07/2036 N-methylperfluoro-1-octanesulfonamidoacetic acid (NMeFOSAA) 2355-31-9 0.2052 0.811 USEPA 1633 07/2037 N-Ethyl-N-[(heptadecafluorooctyl)sulphonyl]glycine (NEtFOSAA) 2991-50-6 0.1654 0.548 USEPA 1633 07/2038 1H,1H,2H,2H-Perfluorohexanesulphonic acid (4:2 FTS) 757124-72-4 0.2528 0.339 USEPA 1633 07/2039 1H,1H,2H,2H-Tridecafluorooctane-1-sulphonic acid (6:2 FTS) 27619-97-2 0.2062 0.750 USEPA 1633 07/2040 1H,1H,2H,2H-Perfluorodecanesulfonic acid (8:2 FTS) 39108-34-4 0.2568 0.174 USEPA 1633 07/2041 1H,1H,2H,2H-Perfluorododecane sulfonic acid (10:2 FTS) 120226-60-0 0.2274 0.599 USEPA 1633 07/2042 Nonafluorobutane-1-sulfonic acid (PFBS) 375-73-5 0.1490 0.688 USEPA 1633 07/2043 Perfluorohexanesulfonic acid (PFHxS) 355-46-4 0.2278 0.653 USEPA 1633 07/2044 Perfluorooctanesulfonic acid (PFOS) 1763-23-1 0.1380 0.175 USEPA 1633 07/2045 Perfluoropentanesulfonic acid (PFPeS) 2706-91-4 0.1538 0.777 USEPA 1633 07/2046 Perfluoroheptanesulfonic acid (PFHpS) 375-92-8 0.1354 0.578 USEPA 1633 07/2047 Perfluorononanesulfonic acid (PFNS) 68259-12-1 0.2216 0.280 USEPA 1633 07/2048 Perfluorodecane sulfonic acid (PFDS) 335-77-3 0.1950 0.255 USEPA 1633 07/2049 Perfluorododecanesulfonic acid (PFDoS) 79780-39-5 0.2568 0.281 USEPA 1633 07/2050 Results and discussion Data analysis Of the 28 PFAS analyzed, 7 were detected in groundwater and 8 in surface waters (Tables 2 and 3 ). The detection frequency for groundwater indicated a higher presence of PFOA (33.3%) and PFBA (20%), while in surface waters, 6:2 FTS (80%), PFOSA (80%) and PFOS (60%) were predominant (Fig. 3 ). Per and polyfluoroalkyl substances can be classified into different substance groups based on the functional groups attached to their perfluoroalkyl chains, with a carboxylate group for perfluoroalkyl carboxylic acids (PFCA) and a sulfonate group for perfluorosulfonic acids (PFSA) (Buck et al., 2011 ). There was a higher detection frequency of PFCA substances in groundwater from the Guarani Aquifer System and Permian Aquitards, whereas PFSA compounds and 6:2 FTS dominated in the Sinos River. PFAS are also categorized as either short- or long-chain based on the length of their carbon chains, with seven or fewer carbons for PFCA and six or fewer for PFSA (Buck et al., 2011 ). Long-chain PFAS (i.e PFOA and PFNA) are considered legacy pollutants due to the phasing out of their production in Western Europe, the United States, and Japan (Wang et al., 2014 ). However, despite the lack of legislation or practical initiatives in Brazil to replace these compounds, PFOA was detected more frequently in groundwater (33.33%) than in surface water (20%). Both groundwater and surface water samples showed the presence of the same substances, except for 6:2 FTS, which was detected only in surface water. The 6:2 FTS compound is widely used in the production of paints and coatings and in the metallurgical and leather industries, functioning as a PFAS alternative. Under aerobic conditions, it can undergo significant biotransformation and degradation, mainly producing short-chain PFCA (Yan et al., 2024 ; Hamid et al., 2020 a, b; Field and Seow, 2017 ; Yang et al., 2014 ). PFOSA has been described as a degradation product of the bioactive pesticide sulfluramid, used for controlling ants and termites, and is widely applied in South America, with Brazil being one of the world's largest producers. Additionally, sulfluramid can contain PFOA impurities (Brazil, 2023 ; Zabaleta et al., 2018 ). PFOSA was detected more frequently in the Sinos River (80%) than in its underlying groundwater from the GAS and AP (6.67%). Ahrens et al. ( 2011 ), in an experimental study, found that PFOSA had up to 20% adsorption potential by sediments. Therefore, it can be inferred that PFOSA, like 6:2 FTS, undergoes a concentration reduction in the Guarani Aquifer System due to surface or shallow processes such as biodegradation and adsorption. Further investigation is needed to determine whether a connection exists between groundwater and surface water. A new PFAS alternative, HFPO-DA (trade name GenX), has been used as a replacement for long-chain PFAS, more associated with health risks, particularly PFOA, and is now being detected in various countries in different water and soil matrices (Li et al., 2022 ; Zhou et al., 2021 ; Xu et al., 2021 ; Galloway et al., 2020 ). In this study, HFPO-DA was not detected in either the Sinos River or in the groundwater from the Guarani Aquifer System and Permian Aquitards. Other PFAS compounds that were not detected include PFHpA, PFNA, PFUnA, PFDoA, PFTrDA, PFTreA, PFHxDA, PFODA, NMeFOSAA, NEtFOSAA, 4:2 FTS, 8:2 FTS, 10:2 FTS, PFHxS, PFPeS, PFHpS, PFNS, PFDS and PFDoS. Table 2 Concentration (ng/L) of PFAS in groundwater from the Sinos River Basin, with 14 samples from the Guarani Aquifer System and 1 sample from the Permian Aquitards, along with their respective detection frequencies Sample Deep (m) PFBA PFPeA PFHxA PFOA ΣPFCA PFOSA PFBS PFOS ΣPFSA ΣPFAS P01 52 <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD P02 100 <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD P03 100 8.41* <LOD 1.15* 0.75* 10.30 <LOD <LOD 0.58* 0.58 10.89 P04 112 1.30* <LOD <LOD 1.41* 2.71 <LOD <LOD <LOD <LOD 2.71 P05 6 7.06 6.31 1.22* 1.11* 15.70 1.08* <LOD <LOD 1.08 16.78 P06 100 <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD P07 50 <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD P08 202 <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD P09 9 <LOD <LOD <LOD 1.00* 1.00 <LOD 1.36* <LOD 1.36 2.36 P10 ** <LOD <LOD <LOD 0.66* 0.66 <LOD 0.90* <LOD 0.90 1.56 P11 93 <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD P12 33 <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD P13 21 <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD P14 ** <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD P15 56 <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD Detection Frequency (%) - 20.0 6.7 13.3 33.3 - 6.7 13.3 6.7 - - Minimum 6.00 <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD Maximum 202.00 8.41 6.31 1.22 1.41 15.70 1.08 1.36 0.58 1.36 16.78 * Concentrations between the limit of quantification (LOQ) (2 ng/L) and the limit of detection (LOD) (specified in Table 1 for each compound) should be interpreted as qualitative data . ** Spring . Table 3 PFAS concentration (ng/L) in the Sinos River, along with its respective detection frequency Sample PFBA PFPeA PFHxA PFOA ΣPFCA PFOSA PFBS PFOS ΣPFSA 6:2FTS ΣPFAS S16 <LOD <LOD <LOD <LOD <LOD 0.98* <LOD <LOD 0.98 <LOD 0.98 S17 5.81 5.51 1.90* 0.83* 14.05 <LOD 1.11* 11.09 12.20 1.33* 27.58 S18 <LOD <LOD <LOD <LOD <LOD 1.02* <LOD <LOD 1.02 0.80* 1.82 S19 <LOD <LOD <LOD <LOD <LOD 0.99* <LOD 1.74* 2.74 26.47 29.21 S20 <LOD <LOD <LOD <LOD <LOD 0.65* <LOD 1.59* 2.23 68.86 71.09 Detection Frequency (%) 20 20 20 20 - 80 20 60 - 80 - Minimum <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD <LOD Maximum 5.81 5.51 1.90 0.83 14.05 1.02 1.11 11.09 12.20 68.86 71.09 * Concentrations between the limit of quantification (LOQ) (2 ng/L) and the limit of detection (LOD) (specified in Table 1 for each compound) should be interpreted as qualitative data . Groundwater The ΣPFAS concentration ranged from below the LOD to 16.77 ng/L, with four samples from the Guarani Aquifer System in surface or subsurface conditions and one from the Permian Aquitards. Among the samples collected from wells, PFAS was detected at depths ranging from 6 to 112 meters. Of the two springs sampled, PFAS was detected in only one (Sample P10), with a ΣPFAS concentration of 1.56 ng/L. All samples are in urbanized neighborhoods, generally with uncontrolled urbanization and limited sanitation services. In the recent urban expansion areas of the study region, located in the north and southeast, no PFAS were detected. Most of the samples in which PFAS were detected are in the older neighborhoods near the Sinos River. In all groundwater samples where PFAS were detected, at least two different compounds were identified, with PFOA always present, ranging from 0.66 to 1.41 ng/L (Fig. 4 ). PFOA, widely used as a surfactant in the manufacture of teflon, leather, and pipe-sealing tapes, is frequently found in both surface and groundwater and has even been identified in human tissues (Li et al., 2022 ; Xiao et al., 2015 ; Post et al., 2012 ; Steenland et al., 2010 ). Its long residence time in soil and its persistence make it highly likely to infiltrate aquifers (Zareitalabad et al., 2023). Long-chain PFAS exhibit higher adsorption in the presence of organic matter and are more likely to form aggregates, while short-chain PFAS tend to be more stable and pose a greater risk for aquifer contamination (Bai and Son, 2021 ; Gagliano et al., 2020 ;). Among the PFAS analyzed in this study, four short-chain compounds (PFBA, PFPeA, PFHxA, PFBS) and three long-chain compounds (PFOA, PFOSA, PFOS) were found in samples from the Guarani Aquifer System and Permian Aquitards. In a study on the detection of PFAS in everyday products, Kotthoff et al. ( 2015 ) reported concentrations of up to 200 µg/kg for PFBA and 120 µg/kg for PFBS in the leather. Sample P5, collected from a very shallow well, 6 meters of depth, exhibited significant concentrations of PFBA and PFPeA, 7.06 and 6.31 ng/L, respectively. This may suggest a strong link to the leather and footwear industries, which dominate the Sinos River Basin. Moreover, PFBA and PFBS may also act as legacy compounds originating from tannery activities, particularly in decommissioned industries within the study area. These industries, however, have left behind numerous sites with environmental liabilities, primarily associated with heavy metal contamination, due to the absence of environmental damage mitigation practices. Stefano et al. (2022), in a study on PFAS occurrence in river waters, fractured and porous aquifers in Porto Alegre, the state capital and part of the metropolitan area that includes this study area, detected twelve PFAS in groundwater and nine in surface waters. In groundwater, ΣPFAS concentrations reached up to 718 ng/L, with PFHpA detected in approximately 70% of the samples. Porto Alegre, however, has a population ten times larger and a more intense history of urbanization, characterized by fractured aquifers and unconsolidated Cenozoic sedimentary aquifers, which generally exhibit a higher potential for contamination compared to the Guarani Aquifer System. PFAS infiltration and sorption processes through sediments are complex, depending on local variables such as soil constituents (i.e., silty/clay ratio, organic matter, soil structure) and climate (Zhong et al., 2021 ; Wallis et al., 2022 ). Further studies on PFAS migration through local soils are needed to better understand contamination processes in the GAS. However, a tendency to identify persistent compounds like PFOA and those related to specific regional activities, such as PFBA, can be observed. Another likely scenario is that PFAS infiltrates the GAS through leaks in sewer pipes, septic tanks, and cesspits, with the latter two being the predominant forms of wastewater management in the region (Plano Sinos, 2014 ). Surface Water The ΣPFAS concentration in surface waters ranged from 0.97 to 71.09 ng/L, with the lowest concentration recorded before the Sinos River enters the urban area (Sample S16). This lower concentration may be due to reduced wastewater input, as PFAS presence is associated with local characteristics, the degree of urbanization, and the intensity of industrial activities (Liddie et al., 2023 ; Viticoski et al., 2022 ; Wei et al., 2018 ). At the connection between the Luiz Rau Stream (Sample S17), there was a significant increase in ΣPFAS (27.58 ng/L), marked by the presence of PFOS, PFBA, and PFPeA. The Luiz Rau Stream shows severe water quality degradation due to industrial and domestic sewage discharge, with concentrations of total phosphorus, biochemical oxygen demand, and heavy metals (Cd and Pb) above the limits set by the Brazilian legislation (Petry et al., 2016 ). Other sewage discharge indicators, such as Caffeine and Bisphenol A, were also found in concentrations up to 28.439 and 498.2 ng/L, respectively (Machado et al., 2018; Peteffi et al., 2018 ). Sample S18, collected near a public water supply watershed plant, had a ΣPFAS concentration of 1.81 ng/L. This sampling site is located near the largest natural urban wetland in southern Brazil. The understanding of PFAS bioaccumulation, degradation, and sorption capacity in wetlands remains limited and is dependent on the organisms, plants, and sediments present (Arslan and El-Din, 2021). Lott et al. ( 2023 ), in an experimental study in a constructed wetland, observed a reduction of PFAS precursors (6:3 FTCA, 7:3 FTCA, N-MeFOSAA, and N-EtFOSAA) associated with an increase in PFBA, PFNA, PFBS and PFOS. The highest ΣPFAS concentration, 71.09 ng/L, was found at the connection with the João Corrêa Stream, a waterbody predominantly composed of untreated sewage, which intersects densely urbanized areas and receives industrial effluents, mainly from metallurgy and electroplating industries. The primary PFAS found in this sample was 6:2 FTS, possibly linked to metal coating industries common in the adjacent neighborhood (Yang et al., 2013). Madeira et al. ( 2023 ), in a study of the background in surface waters in São Paulo State, southeastern Brazil, an area with more intensive agricultural and industrial activities than the study area, detected eight PFAS. ΣPFAS ranged from 50 ng/L to below the limit of quantification, showing results similar to those obtained in the Sinos River Basin. Risk Assessment In April 2024, the United States Environmental Protection Agency (USEPA) published the PFAS National Primary Drinking Water Regulation (NPDWR), establishing regulations, guidelines, monitoring, and Maximum Contaminant Levels (MCL) for PFOA, PFOS, PFHxS, PFNA, HFPO-DA (GenX), and mixtures of two or more of these, including PFHxS, PFNA, HFPO-DA and PFBS, in drinking water. The USEPA also set non-enforceable Maximum Contaminant Level Goals (MCLGs) for these PFAS, based on potential health risks from exposure, with a notification for public water systems that fail to reduce PFAS concentrations below the MCL by 2029 (Table 4 ) (USEPA, 2024). Table 4 Maximum Contaminant Levels (MCL) and non-enforceable Maximum Contaminant Level Goals (MCLGs) for five PFAS defined in the National Primary Drinking Water Regulation of The United States (USEPA, 2024) PFAS MCL MCLG PFOA 4 ng/L 0 PFOS 4 ng/L 0 PFHxS 10 ng/L 10 ng/L PFNA 10 ng/L 10 ng/L HFPO-DA (GenX) 10 ng/L 10 ng/L Mixtures containing two or more of PFHxS, PFNA, HFPO-DA, and PFBS 1 (unitless) Hazard Index 1 (unitless) Hazard Index In Europe, the recast of the Drinking Water Directive (2020) (DWD) sets limits for PFAS, though it is less stringent than the standards established by the NPDWR. The DWD regulates PFAS in drinking water under two criteria: Total PFAS and Sum of PFAS. Total PFAS refers to any substance classified within this group, with a maximum allowable limit of 4500 ng/L. The Sum of PFAS applies only to specific compounds, including PFBA, PFHxA, PFNA, and PFOS, with a cumulative limit of 100 ng/L (EU, 2020). Currently, Brazil lacks federal and state-level regulations for PFAS. The Brazilian Federal Legislation for drinking water regulates only heavy metals, some hydrocarbons, few pesticides, and disinfectant-related products (Brazil, 2021 ). The Environmental Agency of São Paulo State (CETESB), responsible for environmental licensing and management of contaminated areas, has included some PFAS (PFOA, PFOS, PFBS, and related substances) in its toxicological information sheets. However, it does not enforce mandatory monitoring. All groundwater samples in this study are within the limits established by the NPDWR and DWD, indicating good water quality in the local Guarani Aquifer System, which is often used for drinking water, frequently without prior treatment and through informal means (Hirata et al., 2015 and 2019 ). Nevertheless, in the long term, PFAS contamination in aquifers formed by fine-grained sediments such as clay and fine sand, which have low permeability, as in the study area, tends to increase the persistence of these contaminants and complicate removal processes (Sale et al., 2023; Abou-Khalil et al., 2023 ). Studies on PFAS occurrence in Brazilian surface waters have already identified samples with concentrations exceeding the limits set by the USEPA for PFOA and/or PFOS. However, for the surface waters of the Sinos River Basin, none of the samples showed concentrations above the limits set by the European Union or U.S. regulations. It is important to highlight that the ΣPFAS concentration ranges observed in these other Brazilian studies are comparable to those reported in the present study (Fig. 5 ) (Starling et al., 2024 ; Rodrigues et al., 2024 ; Madeira et al., 2023 ; Stefano et al., 2023 ; Baabish et al., 2021 ; Nascimento et al., 2018 ; Schwanz et al., 2016 ; Quinete et al., 2009 ). At present, the local GAS groundwater and surface waters of Sinos River do not pose an immediate health risk due to PFAS exposure. However, when considering the detected concentrations in both groundwater and surface water, and future goals set by the USEPA, 5 out of 15 groundwater samples and 3 out of 5 surface water samples exhibit values above the Maximum Contaminant Level Goals. Additionally, the characteristics of the aquifer, along with inadequate domestic and industrial sewage treatment, may lead to the accumulation of PFAS over time, underscoring the need for monitoring measures to be implemented for these substances. Conclusion This study represents the first recorded occurrence of PFAS in the transboundary Guarani Aquifer within an urban context, where this aquifer system is at its most vulnerable condition. A total of 28 PFAS were analyzed, with 7 compounds detected in wells and springs, ranging from shallow to deep well depths, located in older neighborhoods near the Sinos River. PFOA was predominant in the groundwater, likely due to its strong persistence and widespread use in consumer products, as well as PFBA, which is characteristic of the leather and footwear industries prevalent in the Sinos River Basin. In the surface waters of the Sinos River, 7 PFAS were detected, a high detection frequency for 6:2 FTS and PFOSA, associated with electroplating and pesticide use, respectively. Greater detection of FPSA in surface water than groundwater. These results may indicate attenuation by surface processes for PFSA such degradation and sedimental sorption. Although the detected PFAS concentrations do not currently raise immediate concerns for human health risks, there is a projected increase in these compounds due to poor sanitation, uncontrolled urbanization, and the characteristics of the GAS, which may retain PFAS and increase the cost of future removal efforts. PFAS studies conducted on surface and groundwater in Brazil have reported ΣPFAS concentration levels similar to those found in this study; however, data on groundwater remain extremely limited. A comprehensive understanding of PFAS infiltration and migration processes within the Guarani Aquifer System, including potential hydraulic linkages with surface waters, necessitates additional research. Declarations Acknowledgements The first author thanks theNational Council for Scientific and Technological Development (CNPq) for the fellowship (grant CNPq 141143/2020-7). Funding This study was financially supported by the National Institute of Advanced Analytical Sciences and Technologies (CNPq grant #465768/2014-8, and FAPESP grant #2014/50951-4). Author Contribution Matheus Beretta Duarte: conceptualization, data collection, analysis and interpretation of results, writing; Ari Roisenberg: data collection, supervision, review of the manuscript; Cassiana Carolina Montagner: data analysis, supervision, review of the manuscript; José André Teixeira Azevedo and Vladimir Oliveira Elias: data analysis, resources, methodology. Data availability Data will be made available on request. Ethics approval all authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors Competing interests The authors declare no competing interests. References Abou-Khalil, C., Kewalramani, J., Zhang, Z., Sarkar, D., Abrams, S., & Boufadel, M.C. (2023). 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Science of The Total Environment, 836, 155524. https://doi.org/10.1016/j.scitotenv.2022.155524 Vo, H.N.P., Ngo, H.H., Guo, W., Nguyen, T.M.H., Li, J., Liang, H., Deng, L., Chen, Z., & Nguyen, T.A.H. (2020). Poly‐and perfluoroalkyl substances in water and wastewater: A comprehensive review from sources to remediation. Journal of Water Process Engineering , 36, 101393. https://doi.org/10.1016/j.jwpe.2020.101393 Wallis, I., Hutson, J., Davis, G., Kookana, R., Rayner, J., & Prommer, H. (2022). Model-based identification of vadose zone controls on PFAS mobility under semi-arid climate conditions. Water Research , 225, 119096. https://doi.org/10.1016/j.watres.2022.119096 Wang, Z., Cousins, I.T., Scheringer, M., Buck, R.C., & Hungerbühler, K. (2014). Global emission inventories for C4–C14 perfluoroalkyl carboxylic acid (PFCA) homologues from 1951 to 2030, Part I: production and emissions from quantifiable sources. Environment International , 70, 62-75. https://doi.org/10.1016/j.envint.2014.04.006 Wee, S.Y., & Aris, A.Z. (2023). Revisiting the “forever chemicals”, PFOA and PFOS exposure in drinking water. NPJ Clean Water , 6, 57. http://doi.org/10.1038/s41545-023-00274-6 Wei, C., Wang, Q., Song, X., Chen, X., Fan, R., Ding, D., & Liu, Y. (2018). Distribution, source identification and health risk assessment of PFASs and two PFOS alternatives in groundwater from non-industrial areas. Ecotoxicology and Environmental Safety , 152, 141–150. https://doi.org/10.1016/j.ecoenv.2018.01.039 Xiao, F., Simcik, M.F., Halbach, T.R., & Gulliver, J.S. (2015). Perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) in soils and groundwater of a US metropolitan area: Migration and implications for human exposure. Water Research , 72, 64-74. https://doi.org/10.1016/j.watres.2014.09.052 Xiao, F. (2017). Emerging poly-and perfluoroalkyl substances in the aquatic environment: a review of current literature. Water Research, 124, 482-495. https://doi.org/10.1016/j.watres.2017.07.024 Xu, B., Liu, S., Zhou, J.L., Zheng, C., Jin, W., Chen, B., Zhang, T., & Qiu, W. (2021). PFAS and their substitutes in groundwater: Occurrence, transformation and remediation. Journal of Hazardous Materials , 412, 125159. https://doi.org/10.1016/j.jhazmat.2021.125159 Yan, P.F., Dong, S., Manz, K.E., Woodcock, M.J., Liu, C., Mezzari, M.P., Abriola, L.M., Pennell, K.D., & Cápiro, N.L. (2024). Aerobic biotransformation of 6:2 fluorotelomer sulfonate in soils from two aqueous film-forming foam (AFFF)-impacted sites. Water Research, 249, 120941. https://doi.org/10.1016/j.watres.2023.120941 Yang, X., Huang, J., Zhang, K., Yu, G., Den, S., & Wang, B. (2014). Stability of 6:2 fluorotelomer sulfonate in advanced oxidation processes: degradation kinetics and pathway. Environmental Science and Pollution Research, 21, 4634-4642. http://doi.org/10.1007/s11356-013-2389-z Zabaleta, I., Bizkarguenaga, E., Nunoo, D.B., Schultes, L., Leonel, J., Prieto, A., Zuloaga, O., & Benskin, J.P. (2018). Biodegradation and uptake of the pesticide sulfluramid in a soil–carrot mesocosm. Environmental Science & Technology , 52(5), 2603-2611. https://doi.org/10.1021/acs.est.7b03876 Zareitalabad, P., Siemens, J., Hamer, M., & Amelung, W. (2013). Perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) in surface waters, sediments, soils and wastewater – A review on concentrations and distribution coefficients. Chemosphere , 91(6), 725–732. https://doi.org/10.1016/j.chemosphere.2013.02.024 Zhong, H., Zheng, M., Liang, Y., Wang, Y., Gao, W., Wang, Y., & Jiang, G. (2021). Legacy and emerging per-and polyfluoroalkyl substances (PFAS) in sediments from the East China Sea and the Yellow Sea: Occurrence, source apportionment and environmental risk assessment. Chemosphere , 282, 131042. https://doi.org/10.1016/j.chemosphere.2021.131042 Zhou, J., Li, S., Liang, X., Feng, X., Wang, T., Li, Z., & Zhu, L. (2021). First report on the sources, vertical distribution and human health risks of legacy and novel per-and polyfluoroalkyl substances in groundwater from the Loess Plateau, China. Journal of Hazardous Materials, 404, 124134. https://doi.org/10.1016/j.jhazmat.2020.124134 Zhou, T., Li, X., Liu, H., Dong, S., Zhang, Z., Wang, Z., Li, J., Nghiem, L.D., Khan, S.J., & Wang, Q. (2024). Occurrence, fate, and remediation for per-and polyfluoroalkyl substances (PFAS) in sewage sludge: A comprehensive review. Journal of Hazardous Materials , 466, 133637. https://doi.org/10.1016/j.jhazmat.2024.133637 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6261164","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440529988,"identity":"bddceac1-79c9-4959-b759-afccff47d455","order_by":0,"name":"Matheus Beretta Duarte","email":"","orcid":"","institution":"Universidade Federal do Rio Grande do Sul","correspondingAuthor":false,"prefix":"","firstName":"Matheus","middleName":"Beretta","lastName":"Duarte","suffix":""},{"id":440529989,"identity":"fdee599a-470b-4c15-85be-b8ee23c194d2","order_by":1,"name":"Ari Roisenberg","email":"","orcid":"","institution":"Universidade Federal do Rio Grande do Sul","correspondingAuthor":false,"prefix":"","firstName":"Ari","middleName":"","lastName":"Roisenberg","suffix":""},{"id":440529997,"identity":"b9f01446-01f1-4b72-87b4-0be7d499a321","order_by":2,"name":"José André Teixeira Azevedo","email":"","orcid":"","institution":"Universidade Estadual de Campinas, Cidade Universitária","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"André Teixeira","lastName":"Azevedo","suffix":""},{"id":440530000,"identity":"d82d893a-c9bb-4f52-8581-bb9cde0f7ca7","order_by":3,"name":"Vladimir Oliveira Elias","email":"","orcid":"","institution":"Universidade Estadual de Campinas, Cidade Universitária","correspondingAuthor":false,"prefix":"","firstName":"Vladimir","middleName":"Oliveira","lastName":"Elias","suffix":""},{"id":440530002,"identity":"f4d66e58-0486-444b-bd86-95ad560d1e72","order_by":4,"name":"Cassiana Carolina Montagner","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBADOQYGxgYwy4A4DQkMxqRrSWyAsQlq4W8/nfi48Idd+obbzY0ffubcYTCXPoBfi8SZ3M3GMxKSczfcOdgs2bvtGYNlXwJ+LQYSvNukeRKYczfcSGxj4N12mMHgDAGHQbXUpxsAtTD+JUHL4QSQFmaibAH7hSftuOHMG4nN0rLbnvFY9hDQwt9+duNjHptqeb4b6Q8/vt12R86ch4AWdHCAVA1ALSTrGAWjYBSMguEPAJXdQzcmLyoiAAAAAElFTkSuQmCC","orcid":"","institution":"Universidade Estadual de Campinas, Cidade Universitária","correspondingAuthor":true,"prefix":"","firstName":"Cassiana","middleName":"Carolina","lastName":"Montagner","suffix":""}],"badges":[],"createdAt":"2025-03-19 11:39:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6261164/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6261164/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80881823,"identity":"d8bbe510-4bc1-4012-b6d1-9e665cd92a1f","added_by":"auto","created_at":"2025-04-18 07:52:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44368,"visible":true,"origin":"","legend":"\u003cp\u003eStudies on PFAS in Brazil with a distinction between groundwater and surface water matrices (Starling et al., 2024; Rodrigues et al., 2024; Madeira et al., 2023; Stefano et al., 2023; Baabish et al., 2021; Nascimento et al., 2018; Schwanz et al., 2016; Quinete et al., 2009)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6261164/v1/66d2dc60f1b9fce423d65b0b.jpg"},{"id":80881826,"identity":"a2f1a5e2-43ba-4047-8dbf-9e5bea87909d","added_by":"auto","created_at":"2025-04-18 07:52:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159498,"visible":true,"origin":"","legend":"\u003cp\u003eHydrogeological map with sampling points, indicating the study area within the Sinos River Basin. All surface water samples were taken from the Sinos River, while all but one groundwater sample (P6) were collected from the Guarani Aquifer System. A) Study area within São Leopoldo City and sample sites; B) Location of the Sinos River Basin in Brazil and the Guarani Aquifer System distribution; C) Location of the study area within the Sinos River Basin\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6261164/v1/14c4c962b91210cf85c89714.jpg"},{"id":80881818,"identity":"c7778991-c060-4e8c-9eef-86565bbad3f6","added_by":"auto","created_at":"2025-04-18 07:52:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33739,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of detection frequency of eight PFAS in groundwater from the Guarani Aquifer System, Permian aquitards, and surface waters of the Sinos River. There is little difference in the detection frequency of perfluoroalkyl carboxylic acids (PFCAs) between the two aqueous matrices; however, there is a predominance of perfluorosulfonic acids (PFSA) and 6:2 FTS in surface waters compared to groundwater\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6261164/v1/f31b3cb08ef0aca3448c8406.jpg"},{"id":80882508,"identity":"e5f94d56-bf2c-42a5-a0f0-173bea8101f6","added_by":"auto","created_at":"2025-04-18 08:00:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34338,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots depicting the concentrations (ng/L) of PFAS detected in groundwater from the Guarani Aquifer System and Permian Aquitards in the Sinos River Basin. In all samples where PFAS were detected, PFOA was present, while PFBA exhibited the greatest variation\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6261164/v1/76c5dcf33bec6a068349e5ed.jpg"},{"id":80881817,"identity":"3e2b9099-afa4-488d-982c-229b93f0555b","added_by":"auto","created_at":"2025-04-18 07:52:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":64082,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot showing ΣPFAS concentration ranges found in all PFAS studies conducted in Brazil, identified through a systematic literature review, with differentiation between groundwater, surface or tap water (Starling et al., 2024; Rodrigues et al., 2024; Madeira et al., 2023; Stefano et al., 2023; Baabish et al., 2021; Nascimento et al., 2018; Schwanz et al., 2016; Quinete et al., 2009)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6261164/v1/599e4b67283c14ad5160cd84.jpg"},{"id":81657379,"identity":"caf2578f-ef49-4615-afad-2328a214e20f","added_by":"auto","created_at":"2025-04-29 19:01:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1274122,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6261164/v1/d694fa0d-653d-4328-9063-9d27a0b9e961.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Occurrence of Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) in the Transboundary Guarani Aquifer System within a Highly Urbanized Context in the Sinos River Basin","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBrazil has experienced numerous drought episodes over the past decade, with a trend towards increasing frequency. In addition, the quality of surface waters in the country has been subject to intense degradation due to the untreated discharge of domestic sewage, industrial effluents, and agriculture nutrients and pesticides (Cunha et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Marmontel et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Albuquerque et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Froehner et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The growing frequency of these situations indicates a likely future surge in groundwater demand, driven by the need for human consumption, industrial activities, and agriculture, particularly in densely populated urban areas (Hirata et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hirata and Foster, 2015).\u003c/p\u003e \u003cp\u003eAs the main source of groundwater in Brazil, the Guarani Aquifer System (GAS), one of the largest porous sandstone aquifer systems on the planet, provides water resources to millions of inhabitants also in Argentina, Uruguay, and Paraguay (Ribeiro, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Silva and Hussein, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Spanning an area of approximately 1.2\u0026nbsp;million km\u0026sup2;, this heterogeneous aquifer system exhibits varying conditions of confinement, porosity, permeability, and recharge capacity, potentially receiving a wide range of organic and inorganic chemical contaminants (Soares et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Costa et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the broad spectrum of potential contaminants are the perfluoroalkyl and polyfluoroalkyl substances (PFAS), synthetic industrial chemicals, classified as emerging contaminants. These substances, produced since the 1940\u0026rsquo;s, have attracted the attention of the scientific community and governments due to their bioaccumulation in organisms, persistence, chemical stability, and presence in various environmental matrices, including surface waters, air, treated water, food, and groundwater (Tang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Morales-McDevitt et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Stoiber et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cui et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies also point to adverse health effects associated with PFAS exposure, such as thyroid alterations, reproductive issues, and cancer (Fenton et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePFAS are globally used in numerous applications, such as surfactants, flame retardants, fire extinguishing foams, pesticide formulations, and in the textile, electroplating, metallurgical, and leather industries, as well as in the manufacture of aqueous film-forming foams (Donley et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vo et al., 2021; Xiao, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kotthoff et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Compounds such as PFOA and PFOS have had their use restricted in countries like the United States; however, in Brazil, there is no legislation, and the debate remains limited (Podder et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wee and Aris, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pontius, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sources of PFAS contamination in aquatic environments are diverse and often difficult to define due to their pervasive use in society and persistence in the environment, acting as legacy pollutants. High concentrations of PFAS in water matrices can be associated with landfills, wastewater treatment plant effluents, sewage sludge, and industries that use PFAS in their processes. Contamination can occur both as point source and diffuse pollution, associated with processes such as urban runoff and atmospheric transport (Zhou et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Faust, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lenka et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Stoiber et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Codling et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe presence of PFAS in groundwater has been identified worldwide, with concentrations ranging from ng/L to mg/L, in both shallow and deep aquifers, composed of different lithologies, structures, sources, and land uses (Currell et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sadia et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Johnson, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Johnson et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Few studies on PFAS in water matrices have been conducted in Brazil, with even fewer involving groundwater (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, in the Guarani Aquifer System, there is no literature on the occurrence of PFAS, highlighting an immediate need to understand the presence of these compounds.\u003c/p\u003e \u003cp\u003eGiven the strong link between PFAS contamination and sewage, wastewater treatment plants, and industrial activities, along with the potential for groundwater degradation in urban aquifers, we selected the highly polluted Sinos River Basin (SRB) in southern Brazil to investigate the occurrence of 28 PFAS in the Guarani Aquifer System (GAS). This basin is among the most contaminated in Brazil and suffers primarily from inadequate sewage collection and treatment, alongside a leather and electroplating industry with minimal environmental regulations (Plano Sinos, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy area\u003c/p\u003e \u003cp\u003eThe Sinos River Basin covers an area of 3,680 km\u0026sup2; and is home to approximately 1.5\u0026nbsp;million inhabitants, 94% of whom reside in urban area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The region\u0026rsquo;s primary economic activities include metallurgical, leather-footwear, food, and mechanical industries (Plano Sinos, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Specifically, the study area is situated in the final stretch of the S\u0026atilde;o Leopoldo River Basin (SRB), located within the Municipality of S\u0026atilde;o Leopoldo, which is part of the Metropolitan Region of Porto Alegre, the most densely populated area in southern Brazil. The climate is classified as humid subtropical, with relatively well-distributed rainfall throughout the year, averaging 1,397 mm annually (Alvares et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA significant feature of the Sinos River Basin is the severe degradation of its surface water quality due to the discharge of untreated domestic and industrial waste. This condition culminated in a well-known environmental disaster in 2006, which resulted in the death of 100 tons of fish, one of the most significant environmental disasters in Brazil (Pedde et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In the study area, only 12.21% of sewage is collected and treated before being discharged into the Sinos River (SNIS, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The river\u0026rsquo;s sediments, aquatic organisms, and waters are contaminated by metals such as Cr, Cd, and Pb, primarily from the leather and metallurgical industries, as well as nitrates and other byproducts from the degradation of domestic sewage such Bisphenol A (Becker et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Plano Sinos, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rodrigues and Formoso, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Vargas et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Hatje et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Guarani Aquifer System in the study area is prevalently unconfined and composed of fine to medium-grained sandstones or derived soils, with variable clay and organic matter contents. Surface levels are overlay by a thin layer of recent alluvial and colluvial unconsolidated sediments. Due to the poor quality and low productivity of the water in these unconsolidated sediments, water is generally extracted from the GAS or deeper aquifers. In general, GAS waters are of good quality, classified as calcium bicarbonate or sodium bicarbonate with low mineralization. However, in the study area, metal contamination has been observed in both surface and groundwater (Abreu and Roisenberg, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kuhn and Roisenberg, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; GSB, 2005). The specific capacity of tubular wells in the study area ranges from below 0.05 m\u0026sup3;/h/m to 2 m\u0026sup3;/h/m, with higher yields associated with wells drilled near tectonic lineaments (Duarte et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a more limited area, a hydrogeological unit known as the Permian Aquitards (PA) occurs, consisting mostly of fine-grained rocks, such as shales, with low water transmission capacity, producing wells with specific capacities below 0.1 m\u0026sup3;/h/m. Higher specific capacities can be achieved when these shales are intensely fractured (GSB, 2005; Plano Sinos, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSampling\u003c/p\u003e \u003cp\u003eSurface water samples were collected from the main course of the Sinos River within the study area at various sites, including locations used for public water supply and areas where secondary tributaries (i.e., Luiz Rau and Jo\u0026atilde;o Corr\u0026ecirc;a streams) flow into the main river. Five samples were collected on the same day in April 2024.\u003c/p\u003e \u003cp\u003eGroundwater sampling sites included 12 tubular wells used for human consumption and other activities without prior treatment, one monitoring well, and two springs. The depths of the sampled wells ranged from 6 to 202 meters and included both unregulated wells and those officially licensed by the government. It should be emphasized that the vast majority of wells in Brazil are unregulated, constructed without technical expertise, maintenance, or prior chemical and microbiological analysis of water quality before consumption (Hirata et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Samples were taken directly from the well\u0026rsquo;s water outlet, without any treatment or alteration of the groundwater properties.\u003c/p\u003e \u003cp\u003eAnalytical method\u003c/p\u003e \u003cp\u003eThe analytical method used was Method 1633 \u0026ndash; Analysis of Per- and Polyfluoroalkyl Substances (PFAS) in Aqueous, Solid, Biosolids, and Tissue Samples by LC-MS/MS, with quantification by isotope dilution, as established by the United States Environmental Protection Agency (USEPA, 2021). The analyses were conducted by the commercial laboratory Eurofins Brazil (Rio Claro, S\u0026atilde;o Paulo, Brazil), accredited under ISO 17025. All samples were stored in 250 mL high-density polyethylene bottles, refrigerated at 4\u0026deg;C after collection, and analyzed within a maximum period of 9 days. Details of each analytical parameter and the compounds analyzed can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSystematic Literature Review Approach\u003c/p\u003e \u003cp\u003eThis study follows a systematic literature review methodology to assess the occurrence and environmental impact of per- and polyfluoroalkyl substances in groundwater and surface water in Brazil. The review was conducted to ensure the collection of data that is reproducible and comparable to the reporting in this study. A comprehensive search was performed in the ScienceDirect and PubMed using the keywords \u0026ldquo;PFAS,\u0026rdquo; \u0026ldquo;Brazil\u0026rdquo;, \u0026ldquo;water\u0026rdquo;. All studies published were considered.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePFAS analyzed and their parameters obtained in LC-MS/MS using the Method 1633 \u0026ndash; Analysis of Per- and Polyfluoroalkyl Substances (PFAS) in Aqueous, Solid, Biosolids, and Tissue Samples by LC-MS/MS (USEPA, 2021). The limit of quantification (LOQ) determined for all the analyzed chemical compounds was 2 ng/L\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAS\u003c/p\u003e \u003cp\u003eNUMBER\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUncertainty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOD\u003c/p\u003e \u003cp\u003e(ng/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorobutanoic acid (PFBA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e375-22-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluoropentanoic acid (PFPeA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e2706-90-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorohexanoic acid (PFHxA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e307-24-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluoroheptanoic acid (PFHpA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e375-85-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorooctanoic acid (PFOA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e335-67-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorononanoic acid (PFNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e375-95-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluoro(2-methyl-3-oxahexanoic) Acid (HFPO-DA) (Also known GenX)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e13252-13-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluoroundecanoic acid (PFUnA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e2058-94-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorododecanoic acid (PFDoA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e307-55-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorotridecanoic acid (PFTrDA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e72629-94-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorotetradecanoic acid (PFTreA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e376-06-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorohexadecanoic acid (PFHxDA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e67905-19-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorooctadecanoic acid (PFODA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e16517-11-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorooctanesulfonamide (PFOSA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e754-91-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-methylperfluoro-1-octanesulfonamidoacetic acid (NMeFOSAA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e2355-31-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-Ethyl-N-[(heptadecafluorooctyl)sulphonyl]glycine (NEtFOSAA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e2991-50-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1H,1H,2H,2H-Perfluorohexanesulphonic acid (4:2 FTS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e757124-72-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1H,1H,2H,2H-Tridecafluorooctane-1-sulphonic acid (6:2 FTS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e27619-97-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1H,1H,2H,2H-Perfluorodecanesulfonic acid (8:2 FTS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e39108-34-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1H,1H,2H,2H-Perfluorododecane sulfonic acid (10:2 FTS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e120226-60-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonafluorobutane-1-sulfonic acid (PFBS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e375-73-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorohexanesulfonic acid (PFHxS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e355-46-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorooctanesulfonic acid (PFOS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e1763-23-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluoropentanesulfonic acid (PFPeS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e2706-91-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluoroheptanesulfonic acid (PFHpS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e375-92-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorononanesulfonic acid (PFNS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e68259-12-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorodecane sulfonic acid (PFDS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e335-77-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfluorododecanesulfonic acid (PFDoS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e79780-39-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSEPA\u0026nbsp;1633\u0026nbsp;07/2050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eOf the 28 PFAS analyzed, 7 were detected in groundwater and 8 in surface waters (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The detection frequency for groundwater indicated a higher presence of PFOA (33.3%) and PFBA (20%), while in surface waters, 6:2 FTS (80%), PFOSA (80%) and PFOS (60%) were predominant (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePer and polyfluoroalkyl substances can be classified into different substance groups based on the functional groups attached to their perfluoroalkyl chains, with a carboxylate group for perfluoroalkyl carboxylic acids (PFCA) and a sulfonate group for perfluorosulfonic acids (PFSA) (Buck et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). There was a higher detection frequency of PFCA substances in groundwater from the Guarani Aquifer System and Permian Aquitards, whereas PFSA compounds and 6:2 FTS dominated in the Sinos River.\u003c/p\u003e \u003cp\u003ePFAS are also categorized as either short- or long-chain based on the length of their carbon chains, with seven or fewer carbons for PFCA and six or fewer for PFSA (Buck et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Long-chain PFAS (i.e PFOA and PFNA) are considered legacy pollutants due to the phasing out of their production in Western Europe, the United States, and Japan (Wang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, despite the lack of legislation or practical initiatives in Brazil to replace these compounds, PFOA was detected more frequently in groundwater (33.33%) than in surface water (20%).\u003c/p\u003e \u003cp\u003eBoth groundwater and surface water samples showed the presence of the same substances, except for 6:2 FTS, which was detected only in surface water. The 6:2 FTS compound is widely used in the production of paints and coatings and in the metallurgical and leather industries, functioning as a PFAS alternative. Under aerobic conditions, it can undergo significant biotransformation and degradation, mainly producing short-chain PFCA (Yan et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hamid et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003ea, b; Field and Seow, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePFOSA has been described as a degradation product of the bioactive pesticide sulfluramid, used for controlling ants and termites, and is widely applied in South America, with Brazil being one of the world's largest producers. Additionally, sulfluramid can contain PFOA impurities (Brazil, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zabaleta et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). PFOSA was detected more frequently in the Sinos River (80%) than in its underlying groundwater from the GAS and AP (6.67%). Ahrens et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), in an experimental study, found that PFOSA had up to 20% adsorption potential by sediments. Therefore, it can be inferred that PFOSA, like 6:2 FTS, undergoes a concentration reduction in the Guarani Aquifer System due to surface or shallow processes such as biodegradation and adsorption. Further investigation is needed to determine whether a connection exists between groundwater and surface water.\u003c/p\u003e \u003cp\u003eA new PFAS alternative, HFPO-DA (trade name GenX), has been used as a replacement for long-chain PFAS, more associated with health risks, particularly PFOA, and is now being detected in various countries in different water and soil matrices (Li et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Galloway et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study, HFPO-DA was not detected in either the Sinos River or in the groundwater from the Guarani Aquifer System and Permian Aquitards. Other PFAS compounds that were not detected include PFHpA, PFNA, PFUnA, PFDoA, PFTrDA, PFTreA, PFHxDA, PFODA, NMeFOSAA, NEtFOSAA, 4:2 FTS, 8:2 FTS, 10:2 FTS, PFHxS, PFPeS, PFHpS, PFNS, PFDS and PFDoS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcentration (ng/L) of PFAS in groundwater from the Sinos River Basin, with 14 samples from the Guarani Aquifer System and 1 sample from the Permian Aquitards, along with their respective detection frequencies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeep (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePFBA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePFPeA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePFHxA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePFOA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eΣPFCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePFOSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePFBS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePFOS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eΣPFSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eΣPFAS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e 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colname=\"c11\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd 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colname=\"c12\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.90*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetection Frequency (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csub\u003e* Concentrations between the limit of quantification (LOQ) (2 ng/L) and the limit of detection (LOD) (specified in Table 1 for each compound) should be interpreted as qualitative data\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003csub\u003e** Spring\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePFAS concentration (ng/L) in the Sinos River, along with its respective detection frequency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePFBA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePFPeA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePFHxA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePFOA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΣPFCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePFOSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePFBS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePFOS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eΣPFSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6:2FTS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eΣPFAS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.90*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.33*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e27.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.80*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.74*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e29.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.59*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e68.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e71.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetection Frequency (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;LOD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e68.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e71.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csub\u003e* Concentrations between the limit of quantification (LOQ) (2 ng/L) and the limit of detection (LOD) (specified in Table 1 for each compound) should be interpreted as qualitative data\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGroundwater\u003c/p\u003e \u003cp\u003eThe ΣPFAS concentration ranged from below the LOD to 16.77 ng/L, with four samples from the Guarani Aquifer System in surface or subsurface conditions and one from the Permian Aquitards. Among the samples collected from wells, PFAS was detected at depths ranging from 6 to 112 meters. Of the two springs sampled, PFAS was detected in only one (Sample P10), with a ΣPFAS concentration of 1.56 ng/L.\u003c/p\u003e \u003cp\u003eAll samples are in urbanized neighborhoods, generally with uncontrolled urbanization and limited sanitation services. In the recent urban expansion areas of the study region, located in the north and southeast, no PFAS were detected. Most of the samples in which PFAS were detected are in the older neighborhoods near the Sinos River.\u003c/p\u003e \u003cp\u003eIn all groundwater samples where PFAS were detected, at least two different compounds were identified, with PFOA always present, ranging from 0.66 to 1.41 ng/L (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). PFOA, widely used as a surfactant in the manufacture of teflon, leather, and pipe-sealing tapes, is frequently found in both surface and groundwater and has even been identified in human tissues (Li et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Post et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Steenland et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Its long residence time in soil and its persistence make it highly likely to infiltrate aquifers (Zareitalabad et al., 2023).\u003c/p\u003e \u003cp\u003eLong-chain PFAS exhibit higher adsorption in the presence of organic matter and are more likely to form aggregates, while short-chain PFAS tend to be more stable and pose a greater risk for aquifer contamination (Bai and Son, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gagliano et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e;). Among the PFAS analyzed in this study, four short-chain compounds (PFBA, PFPeA, PFHxA, PFBS) and three long-chain compounds (PFOA, PFOSA, PFOS) were found in samples from the Guarani Aquifer System and Permian Aquitards.\u003c/p\u003e \u003cp\u003eIn a study on the detection of PFAS in everyday products, Kotthoff et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported concentrations of up to 200 \u0026micro;g/kg for PFBA and 120 \u0026micro;g/kg for PFBS in the leather. Sample P5, collected from a very shallow well, 6 meters of depth, exhibited significant concentrations of PFBA and PFPeA, 7.06 and 6.31 ng/L, respectively. This may suggest a strong link to the leather and footwear industries, which dominate the Sinos River Basin. Moreover, PFBA and PFBS may also act as legacy compounds originating from tannery activities, particularly in decommissioned industries within the study area. These industries, however, have left behind numerous sites with environmental liabilities, primarily associated with heavy metal contamination, due to the absence of environmental damage mitigation practices.\u003c/p\u003e \u003cp\u003eStefano et al. (2022), in a study on PFAS occurrence in river waters, fractured and porous aquifers in Porto Alegre, the state capital and part of the metropolitan area that includes this study area, detected twelve PFAS in groundwater and nine in surface waters. In groundwater, ΣPFAS concentrations reached up to 718 ng/L, with PFHpA detected in approximately 70% of the samples. Porto Alegre, however, has a population ten times larger and a more intense history of urbanization, characterized by fractured aquifers and unconsolidated Cenozoic sedimentary aquifers, which generally exhibit a higher potential for contamination compared to the Guarani Aquifer System.\u003c/p\u003e \u003cp\u003ePFAS infiltration and sorption processes through sediments are complex, depending on local variables such as soil constituents (i.e., silty/clay ratio, organic matter, soil structure) and climate (Zhong et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wallis et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Further studies on PFAS migration through local soils are needed to better understand contamination processes in the GAS. However, a tendency to identify persistent compounds like PFOA and those related to specific regional activities, such as PFBA, can be observed. Another likely scenario is that PFAS infiltrates the GAS through leaks in sewer pipes, septic tanks, and cesspits, with the latter two being the predominant forms of wastewater management in the region (Plano Sinos, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSurface Water\u003c/p\u003e \u003cp\u003eThe ΣPFAS concentration in surface waters ranged from 0.97 to 71.09 ng/L, with the lowest concentration recorded before the Sinos River enters the urban area (Sample S16). This lower concentration may be due to reduced wastewater input, as PFAS presence is associated with local characteristics, the degree of urbanization, and the intensity of industrial activities (Liddie et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Viticoski et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the connection between the Luiz Rau Stream (Sample S17), there was a significant increase in ΣPFAS (27.58 ng/L), marked by the presence of PFOS, PFBA, and PFPeA. The Luiz Rau Stream shows severe water quality degradation due to industrial and domestic sewage discharge, with concentrations of total phosphorus, biochemical oxygen demand, and heavy metals (Cd and Pb) above the limits set by the Brazilian legislation (Petry et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Other sewage discharge indicators, such as Caffeine and Bisphenol A, were also found in concentrations up to 28.439 and 498.2 ng/L, respectively (Machado et al., 2018; Peteffi et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSample S18, collected near a public water supply watershed plant, had a ΣPFAS concentration of 1.81 ng/L. This sampling site is located near the largest natural urban wetland in southern Brazil. The understanding of PFAS bioaccumulation, degradation, and sorption capacity in wetlands remains limited and is dependent on the organisms, plants, and sediments present (Arslan and El-Din, 2021). Lott et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), in an experimental study in a constructed wetland, observed a reduction of PFAS precursors (6:3 FTCA, 7:3 FTCA, N-MeFOSAA, and N-EtFOSAA) associated with an increase in PFBA, PFNA, PFBS and PFOS.\u003c/p\u003e \u003cp\u003eThe highest ΣPFAS concentration, 71.09 ng/L, was found at the connection with the Jo\u0026atilde;o Corr\u0026ecirc;a Stream, a waterbody predominantly composed of untreated sewage, which intersects densely urbanized areas and receives industrial effluents, mainly from metallurgy and electroplating industries. The primary PFAS found in this sample was 6:2 FTS, possibly linked to metal coating industries common in the adjacent neighborhood (Yang et al., 2013).\u003c/p\u003e \u003cp\u003eMadeira et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), in a study of the background in surface waters in S\u0026atilde;o Paulo State, southeastern Brazil, an area with more intensive agricultural and industrial activities than the study area, detected eight PFAS. ΣPFAS ranged from 50 ng/L to below the limit of quantification, showing results similar to those obtained in the Sinos River Basin.\u003c/p\u003e \u003cp\u003eRisk Assessment\u003c/p\u003e \u003cp\u003eIn April 2024, the United States Environmental Protection Agency (USEPA) published the PFAS National Primary Drinking Water Regulation (NPDWR), establishing regulations, guidelines, monitoring, and Maximum Contaminant Levels (MCL) for PFOA, PFOS, PFHxS, PFNA, HFPO-DA (GenX), and mixtures of two or more of these, including PFHxS, PFNA, HFPO-DA and PFBS, in drinking water. The USEPA also set non-enforceable Maximum Contaminant Level Goals (MCLGs) for these PFAS, based on potential health risks from exposure, with a notification for public water systems that fail to reduce PFAS concentrations below the MCL by 2029 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) (USEPA, 2024).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaximum Contaminant Levels (MCL) and non-enforceable Maximum Contaminant Level Goals (MCLGs) for five PFAS defined in the National Primary Drinking Water Regulation of The United States (USEPA, 2024)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMCLG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 ng/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 ng/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFHxS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 ng/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 ng/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 ng/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 ng/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFPO-DA (GenX)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 ng/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 ng/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixtures containing two or more of PFHxS, PFNA, HFPO-DA, and PFBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (unitless)\u003c/p\u003e \u003cp\u003eHazard Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (unitless) Hazard Index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Europe, the recast of the Drinking Water Directive (2020) (DWD) sets limits for PFAS, though it is less stringent than the standards established by the NPDWR. The DWD regulates PFAS in drinking water under two criteria: Total PFAS and Sum of PFAS. Total PFAS refers to any substance classified within this group, with a maximum allowable limit of 4500 ng/L. The Sum of PFAS applies only to specific compounds, including PFBA, PFHxA, PFNA, and PFOS, with a cumulative limit of 100 ng/L (EU, 2020).\u003c/p\u003e \u003cp\u003eCurrently, Brazil lacks federal and state-level regulations for PFAS. The Brazilian Federal Legislation for drinking water regulates only heavy metals, some hydrocarbons, few pesticides, and disinfectant-related products (Brazil, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Environmental Agency of S\u0026atilde;o Paulo State (CETESB), responsible for environmental licensing and management of contaminated areas, has included some PFAS (PFOA, PFOS, PFBS, and related substances) in its toxicological information sheets. However, it does not enforce mandatory monitoring.\u003c/p\u003e \u003cp\u003eAll groundwater samples in this study are within the limits established by the NPDWR and DWD, indicating good water quality in the local Guarani Aquifer System, which is often used for drinking water, frequently without prior treatment and through informal means (Hirata et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e and \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nevertheless, in the long term, PFAS contamination in aquifers formed by fine-grained sediments such as clay and fine sand, which have low permeability, as in the study area, tends to increase the persistence of these contaminants and complicate removal processes (Sale et al., 2023; Abou-Khalil et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies on PFAS occurrence in Brazilian surface waters have already identified samples with concentrations exceeding the limits set by the USEPA for PFOA and/or PFOS. However, for the surface waters of the Sinos River Basin, none of the samples showed concentrations above the limits set by the European Union or U.S. regulations. It is important to highlight that the ΣPFAS concentration ranges observed in these other Brazilian studies are comparable to those reported in the present study (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) (Starling et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rodrigues et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Madeira et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Stefano et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Baabish et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nascimento et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schwanz et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Quinete et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt present, the local GAS groundwater and surface waters of Sinos River do not pose an immediate health risk due to PFAS exposure. However, when considering the detected concentrations in both groundwater and surface water, and future goals set by the USEPA, 5 out of 15 groundwater samples and 3 out of 5 surface water samples exhibit values above the Maximum Contaminant Level Goals. Additionally, the characteristics of the aquifer, along with inadequate domestic and industrial sewage treatment, may lead to the accumulation of PFAS over time, underscoring the need for monitoring measures to be implemented for these substances.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study represents the first recorded occurrence of PFAS in the transboundary Guarani Aquifer within an urban context, where this aquifer system is at its most vulnerable condition. A total of 28 PFAS were analyzed, with 7 compounds detected in wells and springs, ranging from shallow to deep well depths, located in older neighborhoods near the Sinos River. PFOA was predominant in the groundwater, likely due to its strong persistence and widespread use in consumer products, as well as PFBA, which is characteristic of the leather and footwear industries prevalent in the Sinos River Basin. In the surface waters of the Sinos River, 7 PFAS were detected, a high detection frequency for 6:2 FTS and PFOSA, associated with electroplating and pesticide use, respectively. Greater detection of FPSA in surface water than groundwater. These results may indicate attenuation by surface processes for PFSA such degradation and sedimental sorption. Although the detected PFAS concentrations do not currently raise immediate concerns for human health risks, there is a projected increase in these compounds due to poor sanitation, uncontrolled urbanization, and the characteristics of the GAS, which may retain PFAS and increase the cost of future removal efforts. PFAS studies conducted on surface and groundwater in Brazil have reported ΣPFAS concentration levels similar to those found in this study; however, data on groundwater remain extremely limited. A comprehensive understanding of PFAS infiltration and migration processes within the Guarani Aquifer System, including potential hydraulic linkages with surface waters, necessitates additional research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eThe first author thanks theNational Council for Scientific and Technological Development (CNPq) for the fellowship (grant CNPq 141143/2020-7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis study was financially supported by the National Institute of Advanced Analytical Sciences and Technologies (CNPq grant #465768/2014-8, and FAPESP grant #2014/50951-4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u0026nbsp;\u003c/strong\u003eMatheus Beretta Duarte: conceptualization, data collection, analysis and interpretation of results, writing; Ari Roisenberg: data collection, supervision, review of the manuscript; Cassiana Carolina Montagner: data analysis, supervision, review of the manuscript; Jos\u0026eacute; Andr\u0026eacute; Teixeira Azevedo and Vladimir Oliveira Elias: data analysis, resources, methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003eall authors have read, understood, and have complied as applicable with the statement on \u0026ldquo;Ethical responsibilities of Authors\u0026rdquo; as found in the Instructions for Authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbou-Khalil, C., Kewalramani, J., Zhang, Z., Sarkar, D., Abrams, S., \u0026amp; Boufadel, M.C. 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Occurrence, fate, and remediation for per-and polyfluoroalkyl substances (PFAS) in sewage sludge: A comprehensive review. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e, 466, 133637. https://doi.org/10.1016/j.jhazmat.2024.133637\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Guarani Aquifer System, PFAS, Sinos River, Southern Brazil","lastPublishedDoi":"10.21203/rs.3.rs-6261164/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6261164/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Guarani Aquifer System (GAS) is a transboundary sedimentary aquifer source of public water supply for millions of people in Brazil, Argentina, Uruguay, and Paraguay, known for the high quality of its groundwaters. However, under surface conditions in an urban environment with poor sanitation and intense industrial activity, water quality can degrade due to the input of a wide range of contaminants, including per and polyfluoroalkyl substances (PFAS). PFAS are anthropogenic substances widely used in industry for their unique properties, such as hydrophobicity and chemical stability. However, in the environment, they exhibit high persistence and potential health risks to humans and other organisms. In this study, 28 PFAS were investigated in the GAS within the context of the Sinos River Basin, located in southern Brazil, a region known for its lack of sewage treatment, uncontrolled urbanization, and strong leather, metallurgical and electroplating industries. Fifteen groundwater samples were collected from tubular wells and springs, and five samples were taken from the Sinos River. In five groundwater samples least two PFAS were detected, with the total PFAS (ΣPFAS) up to 16.78 ng/L, with PFOA and PFBA being the most frequent. In surface waters of the Sinos River, ΣPFAS ranged from 0.98 to 71.09 ng/L, with 6:2 FTS and PFOSA being the most frequently detected. Our study is the first one to identify the background of PFAS in the GAS and suggests the need for long-term monitoring of the aquifer, as its characteristics may promote the retention of these highly persistent compounds, likely associated with the use of pesticides and the leather and electroplating industries.\u003c/p\u003e","manuscriptTitle":"Occurrence of Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) in the Transboundary Guarani Aquifer System within a Highly Urbanized Context in the Sinos River Basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-18 07:52:44","doi":"10.21203/rs.3.rs-6261164/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"438f99ef-ebee-46ce-bc44-eafee71ef0cc","owner":[],"postedDate":"April 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-29T18:53:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-18 07:52:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6261164","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6261164","identity":"rs-6261164","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}