Simultaneous determination of multiresidues of pharmaceuticals and personal care products in surface water samples in the Czech Republic and Slovakia: a comparative study

Simultaneous determination of multiresidues of pharmaceuticals and personal care products in surface water samples in the Czech Republic and Slovakia: a comparative study | 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 Simultaneous determination of multiresidues of pharmaceuticals and personal care products in surface water samples in the Czech Republic and Slovakia: a comparative study Lucia Molnarova, Zuzana Bosakova This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6313065/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Aug, 2025 Read the published version in Monatshefte für Chemie - Chemical Monthly → Version 1 posted 4 You are reading this latest preprint version Abstract The UHPLC-MS/MS analytical method was used to assess the occurrence of 52 pharmaceutical substances in surface water samples from the Czech Republic and Slovakia. Of the 29 surface water samples analyzed, 28 contained at least one quantifiable analyte. This study represents a comprehensive comparative analysis of the occurrence of pharmaceuticals in surface waters of these neighbouring countries and reveals remarkable regional differences in the level of contamination and the occurrence of compounds, which depend mainly on population density, altitude and flow. Median concentrations of key analytes included 126.35 ng dm − 3 for caffeine, 50.81 ng dm − 3 for diclofenac, 134.22 ng dm − 3 for gabapentin, and 177.57 ng dm − 3 for iomeprol. The findings contribute to new knowledge on the transboundary environmental burden of pharmaceuticals and support efforts to assess and reduce potential environmental and health risks. Pollution Surface water contamination Pharmaceutical residues UHPLC-MS MS Direct injection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Pharmaceutical and personal care products (PPCPs) have become a growing concern in environmental science due to their widespread use and potential impact on aquatic ecosystems. The presence of pharmaceuticals in waters has been increasingly documented in recent years, raising questions about their long-term effects on both environmental and human health. These compounds enter the environment through various pathways, including improper disposal of medications, excretion of metabolized drugs, and inadequate removal by wastewater treatment plants [ 1 , 2 ]. As a result, a diverse array of pharmaceuticals, ranging from analgesics and antibiotics to hormones and psychiatric drugs, have been detected in surface waters worldwide [ 3 , 4 ]. Recent research has shown that PPCPs are ubiquitous in aquatic environments, with over 80% of monitored substances detected in surface waters [ 5 ]. For instance, a study conducted in the Czech Republic detected the presence of anticonvulsants such as carbamazepine, lamotrigine, and gabapentin in the Elbe River basin at concentrations ranging from tens to hundreds of ng dm − 3 [ 6 ]. The presence of these substances in the aquatic environment raises concerns about their potential long-term effects on ecosystems and human health. Even at low concentrations, some pharmaceuticals can have adverse effects on aquatic organisms, potentially disrupting ecosystem balance [ 7 , 8 ]. Aquatic organisms are highly vulnerable to the effects of PPCPs, even at low concentrations. Endocrine-disrupting chemicals, commonly found among PPCPs, can interfere with hormonal systems, causing reproductive abnormalities, skewed sex ratios, and population declines in fish and amphibians [ 9 ]. Psychoactive drugs, including antidepressants and sedatives, have been shown to alter the behavior and stress responses of fish, potentially affecting predator-prey dynamics and ecological balance [ 10 , 11 ]. Moreover, the presence of antibiotics in water bodies has been linked to the development of antibiotic-resistant bacteria, posing a serious threat to global public health [ 12 , 13 ]. Additionally, the presence of these compounds in water sources raises concerns about their potential impact on human health through exposure via drinking water or the food chain [ 14 ]. The environmental fate of PPCPs is complex, with many compounds showing persistence in aquatic ecosystems. Some pharmaceuticals, such as carbamazepine and diclofenac, have been found to be particularly resistant to degradation in conventional wastewater treatment processes [ 15 , 16 ]. This persistence contributes to their accumulation in the environment and potential long-term impacts on ecosystems. Recent monitoring efforts have employed advanced analytical techniques to detect and quantify PPCPs in surface waters. Ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) has emerged as a powerful tool for multi-residue analysis of pharmaceuticals in environmental samples [ 17 , 18 , 19 ]. These methods allow for the detection of a wide range of compounds at trace levels, typically in the ng dm − 3 range. By advancing our understanding of PPCPs occurrence, fate, and effects in aquatic ecosystems, we can develop more effective strategies for mitigating their environmental impact and protecting both ecosystem and human health. This study aims to contribute to this growing body of knowledge by assessing the prevalence and concentration levels of various PPCPs in surface waters of the Czech Republic and Slovakia, and evaluating their potential environmental and health risks. Results and Discussion The analysis of 29 surface water samples collected from various locations across the Czech Republic and Slovakia revealed a widespread occurrence of pharmaceuticals and personal care products (PPCPs), highlighting their persistence in aquatic environments. The study employed a robust UHPLC-MS/MS method capable of detecting 52 pharmaceutical compounds, of which 20 analytes were found above the quantification limit. Among the samples, 28 out of 29 contained at least one quantifiable pharmaceutical compound, demonstrating the extensive presence of these contaminants in surface waters. Occurrence in surface water of the Czech Republic A total of 23 surface water samples were analyzed from the Czech Republic, with 22 samples containing quantifiable levels of pharmaceuticals. The results regarding median concentrations are outlined in Table 1, whereas detailed information concerning specific sampling sites and their associated concentrations is provided in Table 2. The most frequently detected compound was caffeine, found in 20 samples with a median concentration of 121.83 ng dm⁻ 3 , reaching a maximum of 744.88 ng dm⁻ 3 . Gabapentin was also prevalent, appearing in 16 samples with a median concentration of 132.35 ng dm⁻ 3 and a peak value of 754.10 ng dm⁻ 3 . Among iodinated contrast media, iomeprol was detected in 13 samples, exhibiting a median concentration of 180.80 ng dm⁻ 3 and a maximum of 917.42 ng dm⁻ 3 , while iopromide was found in 6 samples with a median concentration of 77.63 ng dm⁻ 3 . Other frequently occurring compounds included carbamazepine (17 samples), metoprolol (16 samples), and tramadol (15 samples). Diclofenac was detected in 11 samples, with a median concentration of 46.66 ng dm⁻ 3 and a maximum of 223.56 ng dm⁻ 3 . The highest pharmaceutical concentrations were generally recorded in samples from densely populated areas such as Prague (CZ22 and CZ23), Hodonín (CZ09), and Brno (CZ08). A comparable study conducted in the Czech Republic revealed the widespread presence of pharmaceuticals in surface waters, particularly in densely populated areas. The research identified frequent occurrences of several pharmaceutical compounds, including carbamazepine, diclofenac, tramadol, and sulfamethoxazole [5]. The investigation identified substantial concentrations of five pharmaceutical residues within the surface waters of the Elbe River basin: sulfamethoxazole, ibuprofen, naproxen, diclofenac, and paracetamol (acetaminophen). These residues were detected across various samples, with ibuprofen exhibiting the highest recorded concentration at 1600 ng dm⁻³ [20]. The research concentrated on the surveillance of 112 pharmaceutical compounds within the Švihov Water Reservoir, uncovering elevated concentrations of various pharmaceutical and personal care products (PPCPs), including acesulfame, azithromycin, caffeine, gabapentin, hydrochlorothiazide, ibuprofen and its metabolites, oxypurinol, paraxanthine, and saccharin. These substances were identified in surface water samples between the years 2017 and 2019, with certain compounds achieving concentrations in the range of tens of thousands of ng dm⁻³ [21]. Table 1 Overview of concentration ranges, pharmaceutical groups, occurrence of positive analytes in the samples, maximum, minimum and median concentration of all studied compounds in the Czech Republic Compound Group Occurrence Maximum concentration [ng dm − 3 ] Minimum concentration [ng dm − 3 ] Median concentration [ng dm − 3 ] Atenolol beta-blocker 8 10.19 0.04 2.06 Caffeine central nervous system (CNS) stimulant 20 744.88 2.01 121.83 Carbamazepine anticonvulsant 17 142.08 0.25 12.67 Citalopram selective serotonin reuptake inhibitor (SSRI) 15 6.75 0.57 1.77 Diclofenac non-steroidal anti-inflammatory drug (NSAID) 11 223.56 3.58 46.66 Gabapentin anticonvulsant 16 754.10 14.29 132.35 Hydrochlorothiazide diuretic 15 125.34 2.73 23.12 Indomethacin non-steroidal anti-inflammatory drug (NSAID) 8 19.11 3.52 5.22 Iomeprol iodinated contrast media or dyes 13 917.42 9.09 180.80 Iopromide iodinated contrast media or dyes 6 269.22 27.94 77.63 Metoprolol beta-blocker 16 67.67 0.12 14.81 Naproxen non-steroidal anti-inflammatory drug (NSAID) 5 42.29 9.95 16.63 Paracetamol analgesic antipyretic drug 8 20.15 3.29 11.86 Sotalol antiarrhythmic drug 13 37.61 0.08 4.33 Sulfamethoxazole antibiotic 15 125.56 0.01 15.57 Tramadol opioid analgesic 15 141.62 0.49 27.08 Trimethoprim antibiotic 16 20.59 0.03 1.75 Valsartan angiotensin receptor blockers 16 34.25 2.07 5.68 Zolpidem sedative-hypnotic 13 10.49 0.03 0.15 Occurrence in surface water of Slovakia In Slovakia, 6 surface water samples were analyzed, also showing the presence of multiple pharmaceutical compounds. All 6 samples contained detectable levels of various PPCPs. The results regarding median concentrations are outlined in Table 3, whereas detailed information concerning specific sampling sites and their associated concentrations is provided in Table 4. Caffeine was detected in all 6 samples, with a median concentration of 137.60 ng dm⁻ 3 and a maximum of 825.65 ng dm⁻ 3 . Gabapentin was found in all samples, with a median of 145.82 ng dm⁻ 3 and a maximum of 427.95 ng dm⁻ 3 . Iodinated contrast media were also prevalent: Iomeprol was detected in 5 samples with a median of 174.34 ng dm⁻ 3 and a maximum of 942.58 ng dm⁻ 3 . Iopromide was found in all 6 samples with a median of 81.87 ng dm⁻ 3 and a maximum of 249.21 ng dm⁻ 3 . Diclofenac was detected in all samples with a median concentration of 71.13 ng dm⁻ 3 and a maximum of 112.59 ng dm⁻ 3 . Other compounds frequently detected included carbamazepine, tramadol, and valsartan, all found in all 6 samples. The highest concentrations were generally observed in samples from larger cities, such as Bratislava (SVK1), Žiar nad Hronom (SVK3), and Košice (SVK6). In two Slovak water samples, SVK1 (Bratislava) and SVK2 (Piešťany), one of the iodine-based contrast agents, iopamidol, was found, which was not detected in any of the remaining 27 samples. A corresponding investigation executed in the Elbe River basin disclosed comparable concentrations of gabapentin and carbamazepine relative to our empirical results. The research documented median concentrations of gabapentin fluctuating between 79 ng dm⁻³ and 353 ng dm⁻³ across diverse sampling sites, which closely aligns with our observed median concentrations of 134.22 ng dm⁻³ in the Czech Republic and 145.82 ng dm⁻³ in Slovakia. The concentrations of carbamazepine ranged from 12 ng dm⁻³ to 54.5 ng dm⁻³, exhibiting a strong correspondence with our median concentrations of 12.67 ng dm⁻³ in the Czech Republic and 21.19 ng dm⁻³ in Slovakia [6]. The comparison of pharmaceutical contamination in Slovakian water sources highlights significant differences between wastewater and surface water monitoring. The investigation conducted in 2016 concentrated on the effluent emanating from wastewater treatment plants in Slovakia, revealing significant levels of pharmaceutical compounds, notably valsartan (32.98 µg dm⁻³) and ciprofloxacin (25.66 µg dm⁻³); conversely, the present study discovered diminished concentrations in surface waters, with valsartan reaching only ~ 4.00 µg dm⁻³. This study establishes that urban regions display elevated contamination, with pharmaceuticals in wastewater showing significantly higher concentrations, implying considerable dilution and degradation prior to surface water entry. Antibiotics were prevalent in wastewater, yet their occurrence in surface waters was diminished, indicating partial elimination during treatment processes [22]. Another study analyzed wastewater from Slovakian WWTPs, identifying high concentrations of psychoactive pharmaceuticals, including tramadol (1560 ng dm⁻³) and venlafaxine (947 ng dm⁻³), particularly in urban areas and spa regions such as Piešťany. The present investigation identified markedly diminished pharmaceutical levels in surface waters, with valsartan and diclofenac, underscoring the influence of dilution and degradation [23]. These findings reinforce the role of WWTPs as primary sources of pharmaceutical pollution and underscore the necessity for improved treatment technologies to mitigate environmental contamination. Table 3 Overview of concentration ranges, pharmaceutical groups, occurrence of positive analytes in the samples, maximum, minimum and median concentration of all studied compounds in Slovakia Compound Group Occurrence Maximum concentration [ng dm − 3 ] Minimum concentration [ng dm − 3 ] Median concentration [ng dm − 3 ] Atenolol beta-blocker 3 3.21 0.68 1.46 Caffeine central nervous system (CNS) stimulant 6 825.65 46.88 137.60 Carbamazepine anticonvulsant 6 38.99 4.95 21.19 Citalopram selective serotonin reuptake inhibitor (SSRI) 5 3.09 1.39 2.53 Diclofenac non-steroidal anti-inflammatory drug (NSAID) 6 112.59 3.44 71.13 Gabapentin anticonvulsant 6 427.95 68.12 145.82 Hydrochlorothiazide diuretic 6 34.78 2.73 21.86 Indomethacin non-steroidal anti-inflammatory drug (NSAID) 4 10.31 4.58 6.24 Iomeprol iodinated contrast media or dyes 5 942.58 87.10 174.34 Iopamidol iodinated contrast media or dyes 2 122.25 82.39 102.32 Iopromide iodinated contrast media or dyes 6 249.21 21.49 81.87 Metoprolol beta-blocker 6 32.60 1.92 18.19 Naproxen non-steroidal anti-inflammatory drug (NSAID) 2 53.19 9.62 31.41 Paracetamol analgesic antipyretic drug 1 3.06 3.06 3.06 Sotalol antiarrhythmic drug 5 17.82 0.34 0.95 Sulfamethoxazole antibiotic 6 31.56 0.73 10.63 Tramadol opioid analgesic 6 48.57 3.22 24.03 Trimethoprim antibiotic 6 5.95 0.33 2.94 Valsartan angiotensin receptor blockers 6 121.72 4.20 29.52 Zolpidem sedative-hypnotic 6 3.29 0.04 0.24 Table 4 Overview of concentrations of determined drug analytes in surface water samples in the Slovakia Concentration [ng dm − 3 ] Sampling point SVK1 SVK2 SVK3 SVK4 SVK5 SVK6 Compound Atenolol 0.68 n.d. 1.46 n.d. n.d. 3.21 Caffeine 127.34 142.55 825.65 132.65 46.88 188.34 Carbamazepine 20.87 21.51 38.99 5.69 4.95 23.06 Citalopram 3.09 2.53 3.09 1.39 n.d. 2.22 Diclofenac 81.67 75.89 112.59 8.18 3.44 66.37 Gabapentin 132.38 159.26 427.95 75.75 68.12 193.80 Hydrochlorothiazide 34.78 14.50 34.45 4.84 2.73 29.21 Indomethacin n.d. 6.48 10.31 6.00 n.d. 4.58 Iomeprol 174.34 164.52 942.58 87.10 n.d. 229.76 Iopamidol 122.25 82.39 n.d. n.d. n.d. n.d. Iopromide 101.51 80.38 249.21 21.49 37.46 83.36 Metoprolol 23.65 12.72 32.60 3.23 1.92 26.85 Naproxen n.d. n.d. 53.19 n.d. n.d. 9.62 Paracetamol n.d. n.d. n.d. n.d. n.d. 3.06 Sotalol 6.92 0.95 0.34 n.d. 0.45 17.82 Sulfamethoxazole 21.22 16.31 4.43 4.95 0.73 31.56 Tramadol 25.01 23.05 48.57 5.36 3.22 46.94 Trimethoprim 3.22 2.65 5.08 1.04 0.33 5.95 Valsartan 78.06 40.46 121.72 18.58 4.20 11.76 Zolpidem 0.77 0.15 0.33 0.04 0.04 3.29 n.d.—not detected. Comparison of occurrence The comparison of pharmaceutical concentrations in surface water samples from the Czech Republic (CZ) and Slovakia (SK) revealed notable differences in the occurrence and distribution of specific compounds (Fig. 1). Statistical analysis via a T-test revealed significant differences for valsartan. This emphasizes a considerable difference in the concentration of this compound between the two countries, with increased levels observed in Slovak samples. Significance levels were determined using a GenEx™ Professional software that included multiparametric testing. Although caffeine, iopromide and iomeprol displayed large concentration differences between CZ and SK, substantial variability within each group limited the statistical significance of these findings. The identification of iopamidol solely in Slovak territories emphasizes the existence of regional disparities. Its exclusive occurrence in Slovak water bodies points to varying patterns of pharmaceutical application or the efficiency of wastewater treatment processes. The analysis also considered the impact of surrounding urban populations on pharmaceutical concentrations (Fig. 2). No statistically significant differences were found between PPCP concentrations and the size of nearby urban areas. However, a marked difference was observed among carbamazepine, citalopram, metoprolol, naproxen, sotalol, and tramadol. A 2022 study examined contamination sources and pharmaceutical presence in Elbe River surface waters. They identified mechanisms contributing to water pollution. Elevated concentrations were noted near urban and industrial areas due to waterway discharges. Wastewater treatment plants often fail to completely remove pharmaceuticals, allowing them to persist in treated water. Higher population density is associated with increased pharmaceutical usage, raising wastewater concentrations. Industrial practices may worsen pollution through the use or disposal of pharmaceuticals. Urban runoff introduces pollutants into waterways, while reduced natural filtration, like wetlands, increases pharmaceutical entry into ecosystems [6]. When conducting a comparative analysis of pharmaceutical contamination in surface waters across larger urban areas (exceeding 4,700 residents) in the Czech Republic and Slovakia, no statistically significant disparity was identified. Nevertheless, a clear differentiation was detected regarding valsartan in substantial urban centers, implying a possible localized impact in Slovak municipalities in relation to their Czech counterparts (Fig. 3). Additionally, the sampling locations in Slovakia were predominantly situated near large cities like Bratislava (SVK1) and Košice (SVK6), where higher pharmaceutical consumption and healthcare facility density would be expected. Altitude emerged as an additional variable of interest, with diminished concentrations of PPCPs generally observed at elevated altitudes (Fig. 4). A statistical examination employing a T-test uncovered significant disparities concerning the majority of the identified analytes. This underscores a substantial distinction in the concentrations of pharmaceutical compounds contingent upon altitude variability. A few studies indicated a clear trend: the further the sampling point was from the Elbe River's source, the higher the concentrations of the monitored pharmaceuticals. This implies that downstream regions experience heightened levels of contamination. Smaller tributaries exhibited significantly greater contamination levels compared to larger aquatic bodies, predominantly attributable to their reduced flow rates. This indicates that pollutants are more likely to accumulate in these smaller streams [6, 20]. A statistically significant inverse correlation was identified between the river flow rate and the concentrations of ibuprofen, naproxen, diclofenac, and paracetamol (acetaminophen), suggesting that diminished flow rates in smaller streams correspond to heightened concentrations of these pharmaceutical residues due to insufficient dilution [20]. These findings suggest that regional variations in pharmaceutical contamination are influenced by multiple environmental and anthropogenic factors, necessitating further research to fully understand their distribution and persistence. Figure 5 shows the relationships between concentrations of individual analytes from all sampling points (CZ and SVK) expressed using Spearman's correlation coefficient. The correlation matrix demonstrates the strength and direction of monotonic relationships between variables, where values range from − 1 to + 1. A correlation coefficient of + 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation between the analyzed pharmaceutical compounds. Notably, strong positive correlations were observed among tramadol, hydrochlorothiazide, citalopram, carbamazepine, gabapentin, and metoprolol. The Spearman's correlation analysis revealed several strong positive relationships between the concentrations of specific pharmaceuticals, highlighting shared environmental behaviors or common sources of pollution. For example, tramadol (an opioid analgesic) and hydrochlorothiazide (a diuretic) showed a strong correlation, suggesting their frequent co-occurrence in surface water samples. This could be attributed to their widespread use in medical treatments and the inefficiency of wastewater treatment plants in removing these compounds [24–27]. A similarly robust relationship was observed between citalopram (a selective serotonin reuptake inhibitor - SSRI), and carbamazepine (an anticonvulsant). Both substances are known for their high environmental persistence, which makes them resistant to conventional wastewater treatment processes, leading to their regular detection in surface waters [27–30]. Another notable correlation was found between gabapentin (an anticonvulsant) and metoprolol (a beta-blocker). This connection likely reflects their common prescription patterns and shared pathways into aquatic environments, including human excretion and improper pharmaceutical disposal [6, 31]. The relationship between tramadol and gabapentin further underscores their frequent co-prescription for conditions like chronic pain and neuropathy, which could explain their simultaneous presence in water samples [25]. Lastly, the correlation between carbamazepine and metoprolol suggests that these pharmaceuticals may exhibit similar environmental behaviors, such as resistance to degradation or comparable hydrophobic properties, which influence their persistence in aquatic ecosystem [27, 32, 33]. The visualization of these correlations highlights clusters of strongly related compounds, emphasizing patterns of pharmaceutical contamination and pointing to potential shared pollution sources. These findings reflect the intricate interplay of prescription trends, chemical characteristics, and environmental contamination processes. Understanding these relationships is crucial for designing effective strategies to reduce pharmaceutical pollution in surface waters. Conclusion This study provides a comprehensive assessment of pharmaceutical contamination in surface waters of the Czech Republic and Slovakia, highlighting widespread occurrence and significant concentration variations across regions. Employing a validated UHPLC–MS/MS method, we identified 20 analytes above the quantification limit across 29 sampling sites, with notable findings such as high median concentrations of caffeine, gabapentin, and iodinated contrast media. The results underscore the persistence of certain pharmaceuticals, such as carbamazepine and diclofenac, and their resistance to conventional wastewater treatment processes, which contribute to their accumulation in aquatic environments. Key differences between the two countries were observed, including the exclusive detection of iopamidol in Slovak samples and significant variations in valsartan concentrations. These findings highlight the importance of localized factors, such as urbanization, prescription practices, and wastewater treatment efficiencies, in shaping contamination profiles. The implications of this research extend beyond regional boundaries, as it emphasizes the need for enhanced wastewater treatment technologies, stricter regulation of pharmaceutical disposal, and continued monitoring to mitigate environmental and health risks associated with PPCPs. Future research should explore the long-term ecological effects of these contaminants, their bioaccumulation potential, and the efficacy of advanced removal methods, contributing to more sustainable water resource management in Central Europe and beyond. Experimental Chemicals and Reagents The analytical standards and isotopically labeled internal standards utilized in this study are detailed in Tables S1 and S2 of the Supplementary Materials. Acetonitrile and methanol, both of LC-MS grade, were sourced from Honeywell (Charlotte, NC, USA). Formic acid, MS grade, was acquired from Sigma-Aldrich® (Darmstadt, Germany). Ultrapure water (MQ) was obtained from a Milli-Q water purification system (Millipore®, Billerica, MA, USA). Sample collection, enrichment and preparation Surface water samples ( n = 29) were collected in January and February 2023 from locations across the Czech Republic and Slovakia. The sampling points were designated as CZ01 to CZ23 for the Czech Republic and SVK01 to SVK06 for Slovakia. Figures 6 and 7 provide maps indicating the sampling locations. Detailed characteristics of these sampling sites are presented in Table 5 . Table 5 Characteristics of sampling locations for surface water samples Sampling points Name of the water source Description of the sampling location Surrounding population CZ01 Teplá River flowing through the village of Teplička. 108 CZ02 Lake Laka Glacial lake Laka is the smallest and highest of the Šumava lakes in the Železnorud region. < 10 CZ03 Kořenský stream Stream flowing through the village of Strážný. < 10 CZ04 Lužnice River flowing through the town of Suchdol nad Lunžicí. 3 527 CZ05 Dyje River flowing through the village of Podhradí nad Dyjí. 45 CZ06 Jihlava River flowing through the city of Jihlava. 50 108 CZ07 Jihlava River flowing through the city of Třebíč. 34 415 CZ08 Svratka River flowing through the city of Brno. 379 466 CZ09 Morava River flowing through the city of Hodonín. 23 828 CZ10 Šťávnice River flowing through the Spa town of Luhačovice. 4 955 CZ11 Čeladenka Stream flowing near the Kocianka ski area. < 10 CZ12 Opava River flowing through the city of Opava. 54 840 CZ13 Morava River flowing through the city of Olomouc. 99 496 CZ14 Divoká Orlice River flowing through the town of Žamberk. 5 918 CZ15 Javorka River flowing through the town of Spa Bělohrad. 3 625 CZ16 Kamenice River flowing through the village of Plavy. 1 057 CZ17 Mumlava Mountain river flowing through the village of Harrachov. 1 301 CZ18 Jizera River flowing through the city of Mladá Boleslav. 41 868 CZ19 Labe River flowing through the city of Litoměřice. 22 950 CZ20 Flájský stream Mountain stream flowing into the Flajská water reservoir, which is a source of drinking water for Mostecko and Těplice. < 10 CZ21 Švihov The Švihov Reservoir is a water reservoir on the Želivka River, which serves as a source of drinking water for almost the entire Central Bohemian region, including Prague. 4 709 CZ22 Rokytka Small river flowing through the capital Prague. 1 275 406 CZ23 Vltava River flowing through the capital Prague. 1 275 406 SVK1 Dunaj River flowing through the capital Bratislava. 475 503 SVK2 Váh River flowing through the city of Piešťany. 27 307 SVK3 Hron River flowing through the city of Žiar nad Hronom. 17 246 SVK4 Váh River flowing through the city of Žilina. 82 656 SVK5 Hornád River flowing through the city of Spišská Nová Ves. 35 431 SVK6 Hornád River flowing through the city of Košice. 229 040 A total of 1 dm − 3 samples were collected from each of the 29 locations from a depth of one meter into polypropylene bottles that had been pre-rinsed with deionized water using sapling pole. After collection, the grab samples were transported under refrigeration at 3–5°C to the laboratory, where they were mixed, homogenized, and subsequently stored at − 20°C. The samples were immediately frozen upon arrival and analyzed without any addition of acidifying or fixing agents. Each sample was prepared in duplicate. Samples were then processed following the procedure previously mentioned in [ 23 ]. Briefly, for analysis, 10 cm − 3 of each water sample was transferred into 12 mL disposable plastic centrifuge tubes using a calibrated automatic pipette. To these samples, 100 µL of a 50 ng cm − 3 internal standard stock solution were added. The tubes were then capped and shaken gently by hand. Finally, the samples were filtered through 5 cm − 3 disposable plastic syringes using disc cellulose micro-filters (0.22 µm) into 2 cm − 3 dark glass crimp vials for further analysis. Stock Solutions, Calibration Standards, Quality Control Samples, Blank Samples, and Laboratory Control Samples The methodology for preparing individual stock solutions, calibration standards, and quality control (QC) samples is outlined in a prior study [ 23 ]. Blank samples and laboratory control samples were prepared following the same established procedures. Chromatographic Conditions The studies were conducted using a XEVO TQ-S MS analyzer (Waters, Milford, MA, USA) coupled with an I-class UPLC liquid chromatograph (Waters, USA). Chromatographic separations were performed using an Acquity UPLC BEH C18 column (1.7 µm, 2.1 mm × 100 mm) along with an Acquity UPLC BEH C18 pre-column (Waters, Milford, MA, USA). Gradient elution was achieved using a mobile phase consisting of 0.01% formic acid in Milli-Q water (A) and pure methanol (B), following this program: (min/%B) 0/2, 0.5/2, 5.0/95, 5.1/100, 7.0/100, 7.1/2, and 9.0/2. Both positive and negative electrospray ionization modes were employed for data acquisition using multiple reaction monitoring (MRM) mode for analysis. Two MRM transitions—one for quantification and another for qualification—were selected for each analyte. The MRM transitions along with cone voltages and collision energies are listed in Table S3. Method Validation Validation parameters have been characterized as described in previous study [ 23 ]. The UHPLC method has been validated concerning selectivity, limit of detection (LOD), limit of quantification (LOQ), linearity range, precision, and accuracy. Declarations Conflict of Interest The authors declare that they have no conflict of interest. Acknowledgements This research was funded by the Grant Agency of Charles University, project SVV. 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E3S Web of Conferences 550:01029 Trognon J, Albasi C, Choubert JM (2024) Sci Total Environ 912:169040 Zhu X, Luo T, Wang D, Zhao Y, Jin Y, Yang G (2023) Sci Total Environ 900:165732 Love D, Slovisky M, Costa KA, Megarani D, Mehdi Q, Colombo V, Ivantsova E, Subramaniam K, Bowden JA, Bisesi JH, Martyniuk CJ (2024) Environ Toxicol Chem 43(12):2530–2544 Mendes FS, Gonçalves ADA, Guiomar FIS, Martins RN, Ramalho JPP, Martins LFG (2024) Fluid Phase Equilib 580:114056 Meyer W, Reich M, Beier S, Behrendt J, Gulyas H, Otterpohl R (2016) Environ Monit Assess 188(8):1–16 Table 2 Table 2 is available in the Supplementary Files section. Supplementary Files SupplementaryMaterial.docx Table2.docx floatimage15.jpeg Graphical abstract Cite Share Download PDF Status: Published Journal Publication published 04 Aug, 2025 Read the published version in Monatshefte für Chemie - Chemical Monthly → Version 1 posted Reviewers agreed at journal 02 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Editor assigned by journal 01 Apr, 2025 First submitted to journal 30 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6313065","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436982261,"identity":"da540c30-90d8-440f-93d3-a7ec414efe4b","order_by":0,"name":"Lucia Molnarova","email":"","orcid":"","institution":"Univerzita Karlova Prirodovedecka fakulta","correspondingAuthor":false,"prefix":"","firstName":"Lucia","middleName":"","lastName":"Molnarova","suffix":""},{"id":436982262,"identity":"3981738b-3b67-44e0-81eb-516b785e2842","order_by":1,"name":"Zuzana Bosakova","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACAyBmbAAS/AgxMJ8ILZINcKXEajE4QEgpDJiztz/8OKPisLzx7ebnj3n3MMgb3G5uYPjxB7cWy54zxpIbzhw23HbnmGEzzzMGww13DjYw9vDgcdiNHAbJh21pjNtuJAC1HGBIMLiR2MDMIIFPS/rjn0At9ptnpH9E0mKAT0uCmeTGNpvEDRI5yLYk4PWLmeWMMzbJM27kFM6cc0DCcCZQy8GeA7i1AEPs8c2eCgnb/hnpGz68OWAjz3cj/eEDfCGGDiDexmPHKBgFo2AUjAJiAAAFp1gTMr1k0AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8483-5739","institution":"Univerzita Karlova Prirodovedecka fakulta","correspondingAuthor":true,"prefix":"","firstName":"Zuzana","middleName":"","lastName":"Bosakova","suffix":""}],"badges":[],"createdAt":"2025-03-26 13:59:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6313065/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6313065/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00706-025-03356-y","type":"published","date":"2025-08-04T15:57:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81141114,"identity":"6f848edb-a911-45ed-9e9e-399f30faf19a","added_by":"auto","created_at":"2025-04-22 16:50:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82690,"visible":true,"origin":"","legend":"\u003cp\u003eThe graph illustrating the comparison of pharmaceutical concentrations (with standard error of mean, SEM) between the Czech Republic (CZ) and Slovakia (SVK) surface water samples\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6313065/v1/5309e809af232c9729de9081.png"},{"id":81141978,"identity":"8b17af05-a252-49ad-8172-bc5040279c1f","added_by":"auto","created_at":"2025-04-22 16:58:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85681,"visible":true,"origin":"","legend":"\u003cp\u003eThe graph illustrating the comparison of pharmaceutical concentrations between A (sparsely populated areas with up to 4,700 inhabitants) and B (cities with over 4,700 inhabitants) across all sampling locations in the Czech Republic and Slovakia surface water samples\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6313065/v1/adf530ca20e211ff2ab04c7b.png"},{"id":81141116,"identity":"4e3671ef-312e-4b0e-97bd-64de6f264eb7","added_by":"auto","created_at":"2025-04-22 16:50:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":95878,"visible":true,"origin":"","legend":"\u003cp\u003eThe graph illustrating the comparison of pharmaceutical concentrations between A (Czech cities with over 4,700 inhabitants) and B (Slovak cities with over 4,700 inhabitants)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6313065/v1/889f3626f5b356038f858f8b.png"},{"id":81141120,"identity":"f0ee6cbe-722f-40fe-904d-3f22c1f7f5e7","added_by":"auto","created_at":"2025-04-22 16:50:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93115,"visible":true,"origin":"","legend":"\u003cp\u003eThe graph illustrating the comparison of pharmaceutical concentrations between A (sampling points at an altitude greater than 395 meters above sea level) and B (sampling points at an altitude lower than 395 meters above sea level)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6313065/v1/716278be5164a13ee37b36d3.png"},{"id":81141980,"identity":"c0298114-6d46-42f7-a982-dc6033e16146","added_by":"auto","created_at":"2025-04-22 16:58:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":715668,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of pharmaceutical concentrations in surface water samples expressed using Spearman's correlation coefficient\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6313065/v1/dada6f9bdfd8ebe841511ac3.png"},{"id":81141123,"identity":"50e927e8-9cb4-4664-ac60-fa7eb1e1a9a0","added_by":"auto","created_at":"2025-04-22 16:50:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":598203,"visible":true,"origin":"","legend":"\u003cp\u003eSampling map with sampling points in the Czech Republic\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6313065/v1/808e7a3a7581567b43ee99d7.png"},{"id":81141128,"identity":"40f6f1ab-9529-4254-adf8-5e716240e915","added_by":"auto","created_at":"2025-04-22 16:50:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":587588,"visible":true,"origin":"","legend":"\u003cp\u003eSampling map with sampling points in Slovakia\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6313065/v1/de348b6a35c4d2ef65faf6cd.png"},{"id":88814578,"identity":"2e1b870a-ad10-477f-85fa-084ce159727a","added_by":"auto","created_at":"2025-08-11 16:09:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3227938,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6313065/v1/0cdf0597-1d5e-4007-97d1-7d8706b25d68.pdf"},{"id":81141118,"identity":"722116f0-bf66-41a4-bb91-25866c2d2df7","added_by":"auto","created_at":"2025-04-22 16:50:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":151545,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6313065/v1/509a654370019ead74312d2f.docx"},{"id":81141117,"identity":"22a00227-6dd1-4376-8254-4d87b1da5f2a","added_by":"auto","created_at":"2025-04-22 16:50:27","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":145602,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6313065/v1/cf600cf19c93e3994ed1ec02.docx"},{"id":81141125,"identity":"bf0411aa-674d-481d-9f22-adc8f1b7aede","added_by":"auto","created_at":"2025-04-22 16:50:28","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":205059,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"floatimage15.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6313065/v1/d1c13a3b40ed57c06d0d987a.jpeg"}],"financialInterests":"","formattedTitle":"Simultaneous determination of multiresidues of pharmaceuticals and personal care products in surface water samples in the Czech Republic and Slovakia: a comparative study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePharmaceutical and personal care products (PPCPs) have become a growing concern in environmental science due to their widespread use and potential impact on aquatic ecosystems. The presence of pharmaceuticals in waters has been increasingly documented in recent years, raising questions about their long-term effects on both environmental and human health. These compounds enter the environment through various pathways, including improper disposal of medications, excretion of metabolized drugs, and inadequate removal by wastewater treatment plants [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs a result, a diverse array of pharmaceuticals, ranging from analgesics and antibiotics to hormones and psychiatric drugs, have been detected in surface waters worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recent research has shown that PPCPs are ubiquitous in aquatic environments, with over 80% of monitored substances detected in surface waters [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For instance, a study conducted in the Czech Republic detected the presence of anticonvulsants such as carbamazepine, lamotrigine, and gabapentin in the Elbe River basin at concentrations ranging from tens to hundreds of ng dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe presence of these substances in the aquatic environment raises concerns about their potential long-term effects on ecosystems and human health. Even at low concentrations, some pharmaceuticals can have adverse effects on aquatic organisms, potentially disrupting ecosystem balance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Aquatic organisms are highly vulnerable to the effects of PPCPs, even at low concentrations. Endocrine-disrupting chemicals, commonly found among PPCPs, can interfere with hormonal systems, causing reproductive abnormalities, skewed sex ratios, and population declines in fish and amphibians [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Psychoactive drugs, including antidepressants and sedatives, have been shown to alter the behavior and stress responses of fish, potentially affecting predator-prey dynamics and ecological balance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, the presence of antibiotics in water bodies has been linked to the development of antibiotic-resistant bacteria, posing a serious threat to global public health [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, the presence of these compounds in water sources raises concerns about their potential impact on human health through exposure via drinking water or the food chain [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe environmental fate of PPCPs is complex, with many compounds showing persistence in aquatic ecosystems. Some pharmaceuticals, such as carbamazepine and diclofenac, have been found to be particularly resistant to degradation in conventional wastewater treatment processes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This persistence contributes to their accumulation in the environment and potential long-term impacts on ecosystems.\u003c/p\u003e \u003cp\u003eRecent monitoring efforts have employed advanced analytical techniques to detect and quantify PPCPs in surface waters. Ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) has emerged as a powerful tool for multi-residue analysis of pharmaceuticals in environmental samples [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These methods allow for the detection of a wide range of compounds at trace levels, typically in the ng dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e range.\u003c/p\u003e \u003cp\u003eBy advancing our understanding of PPCPs occurrence, fate, and effects in aquatic ecosystems, we can develop more effective strategies for mitigating their environmental impact and protecting both ecosystem and human health. This study aims to contribute to this growing body of knowledge by assessing the prevalence and concentration levels of various PPCPs in surface waters of the Czech Republic and Slovakia, and evaluating their potential environmental and health risks.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThe analysis of 29 surface water samples collected from various locations across the Czech Republic and Slovakia revealed a widespread occurrence of pharmaceuticals and personal care products (PPCPs), highlighting their persistence in aquatic environments. The study employed a robust UHPLC-MS/MS method capable of detecting 52 pharmaceutical compounds, of which 20 analytes were found above the quantification limit. Among the samples, 28 out of 29 contained at least one quantifiable pharmaceutical compound, demonstrating the extensive presence of these contaminants in surface waters.\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eOccurrence in surface water of the Czech Republic\u003c/h2\u003e\n \u003cp\u003eA total of 23 surface water samples were analyzed from the Czech Republic, with 22 samples containing quantifiable levels of pharmaceuticals. The results regarding median concentrations are outlined in Table 1, whereas detailed information concerning specific sampling sites and their associated concentrations is provided in Table 2.\u003c/p\u003e\n \u003cp\u003eThe most frequently detected compound was caffeine, found in 20 samples with a median concentration of 121.83 ng dm⁻\u003csup\u003e3\u003c/sup\u003e, reaching a maximum of 744.88 ng dm⁻\u003csup\u003e3\u003c/sup\u003e. Gabapentin was also prevalent, appearing in 16 samples with a median concentration of 132.35 ng dm⁻\u003csup\u003e3\u003c/sup\u003e and a peak value of 754.10 ng dm⁻\u003csup\u003e3\u003c/sup\u003e. Among iodinated contrast media, iomeprol was detected in 13 samples, exhibiting a median concentration of 180.80 ng dm⁻\u003csup\u003e3\u003c/sup\u003e and a maximum of 917.42 ng dm⁻\u003csup\u003e3\u003c/sup\u003e, while iopromide was found in 6 samples with a median concentration of 77.63 ng dm⁻\u003csup\u003e3\u003c/sup\u003e. Other frequently occurring compounds included carbamazepine (17 samples), metoprolol (16 samples), and tramadol (15 samples). Diclofenac was detected in 11 samples, with a median concentration of 46.66 ng dm⁻\u003csup\u003e3\u003c/sup\u003e and a maximum of 223.56 ng dm⁻\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe highest pharmaceutical concentrations were generally recorded in samples from densely populated areas such as Prague (CZ22 and CZ23), Hodonín (CZ09), and Brno (CZ08).\u003c/p\u003e\n \u003cp\u003eA comparable study conducted in the Czech Republic revealed the widespread presence of pharmaceuticals in surface waters, particularly in densely populated areas. The research identified frequent occurrences of several pharmaceutical compounds, including carbamazepine, diclofenac, tramadol, and sulfamethoxazole [5].\u003c/p\u003e\n \u003cp\u003eThe investigation identified substantial concentrations of five pharmaceutical residues within the surface waters of the Elbe River basin: sulfamethoxazole, ibuprofen, naproxen, diclofenac, and paracetamol (acetaminophen). These residues were detected across various samples, with ibuprofen exhibiting the highest recorded concentration at 1600 ng dm⁻³ [20].\u003c/p\u003e\n \u003cp\u003eThe research concentrated on the surveillance of 112 pharmaceutical compounds within the Švihov Water Reservoir, uncovering elevated concentrations of various pharmaceutical and personal care products (PPCPs), including acesulfame, azithromycin, caffeine, gabapentin, hydrochlorothiazide, ibuprofen and its metabolites, oxypurinol, paraxanthine, and saccharin. These substances were identified in surface water samples between the years 2017 and 2019, with certain compounds achieving concentrations in the range of tens of thousands of ng dm⁻³ [21].\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eOverview of concentration ranges, pharmaceutical groups, occurrence of positive analytes in the samples, maximum, minimum and median concentration of all studied compounds in the Czech Republic\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOccurrence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum concentration\u003c/p\u003e\n \u003cp\u003e[ng dm\u003csup\u003e− 3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum concentration\u003c/p\u003e\n \u003cp\u003e[ng dm\u003csup\u003e− 3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian concentration\u003c/p\u003e\n \u003cp\u003e[ng dm\u003csup\u003e− 3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtenolol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebeta-blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaffeine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecentral nervous system (CNS) stimulant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e744.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbamazepine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eanticonvulsant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCitalopram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eselective serotonin reuptake inhibitor\u0026nbsp;(SSRI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiclofenac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-steroidal anti-inflammatory drug (NSAID)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e223.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGabapentin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eanticonvulsant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e754.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydrochlorothiazide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ediuretic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndomethacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-steroidal anti-inflammatory drug (NSAID)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIomeprol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiodinated contrast media or dyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e917.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIopromide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiodinated contrast media or dyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e269.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetoprolol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebeta-blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNaproxen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-steroidal anti-inflammatory drug (NSAID)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParacetamol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eanalgesic antipyretic drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSotalol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eantiarrhythmic drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSulfamethoxazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eantibiotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTramadol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eopioid analgesic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrimethoprim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eantibiotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValsartan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eangiotensin receptor blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZolpidem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esedative-hypnotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eOccurrence in surface water of Slovakia\u003c/h3\u003e\n\u003cp\u003eIn Slovakia, 6 surface water samples were analyzed, also showing the presence of multiple pharmaceutical compounds. All 6 samples contained detectable levels of various PPCPs. The results regarding median concentrations are outlined in Table 3, whereas detailed information concerning specific sampling sites and their associated concentrations is provided in Table 4.\u003c/p\u003e\n\u003cp\u003eCaffeine was detected in all 6 samples, with a median concentration of 137.60 ng dm⁻\u003csup\u003e3\u003c/sup\u003e and a maximum of 825.65 ng dm⁻\u003csup\u003e3\u003c/sup\u003e. Gabapentin was found in all samples, with a median of 145.82 ng dm⁻\u003csup\u003e3\u003c/sup\u003e and a maximum of 427.95 ng dm⁻\u003csup\u003e3\u003c/sup\u003e. Iodinated contrast media were also prevalent: Iomeprol was detected in 5 samples with a median of 174.34 ng dm⁻\u003csup\u003e3\u003c/sup\u003e and a maximum of 942.58 ng dm⁻\u003csup\u003e3\u003c/sup\u003e. Iopromide was found in all 6 samples with a median of 81.87 ng dm⁻\u003csup\u003e3\u003c/sup\u003e and a maximum of 249.21 ng dm⁻\u003csup\u003e3\u003c/sup\u003e. Diclofenac was detected in all samples with a median concentration of 71.13 ng dm⁻\u003csup\u003e3\u003c/sup\u003e and a maximum of 112.59 ng dm⁻\u003csup\u003e3\u003c/sup\u003e. Other compounds frequently detected included carbamazepine, tramadol, and valsartan, all found in all 6 samples.\u003c/p\u003e\n\u003cp\u003eThe highest concentrations were generally observed in samples from larger cities, such as Bratislava (SVK1), Žiar nad Hronom (SVK3), and Košice (SVK6). In two Slovak water samples, SVK1 (Bratislava) and SVK2 (Piešťany), one of the iodine-based contrast agents, iopamidol, was found, which was not detected in any of the remaining 27 samples.\u003c/p\u003e\n\u003cp\u003eA corresponding investigation executed in the Elbe River basin disclosed comparable concentrations of gabapentin and carbamazepine relative to our empirical results. The research documented median concentrations of gabapentin fluctuating between 79 ng dm⁻³ and 353 ng dm⁻³ across diverse sampling sites, which closely aligns with our observed median concentrations of 134.22 ng dm⁻³ in the Czech Republic and 145.82 ng dm⁻³ in Slovakia. The concentrations of carbamazepine ranged from 12 ng dm⁻³ to 54.5 ng dm⁻³, exhibiting a strong correspondence with our median concentrations of 12.67 ng dm⁻³ in the Czech Republic and 21.19 ng dm⁻³ in Slovakia [6].\u003c/p\u003e\n\u003cp\u003eThe comparison of pharmaceutical contamination in Slovakian water sources highlights significant differences between wastewater and surface water monitoring. The investigation conducted in 2016 concentrated on the effluent emanating from wastewater treatment plants in Slovakia, revealing significant levels of pharmaceutical compounds, notably valsartan (32.98 µg dm⁻³) and ciprofloxacin (25.66 µg dm⁻³); conversely, the present study discovered diminished concentrations in surface waters, with valsartan reaching only ~ 4.00 µg dm⁻³. This study establishes that urban regions display elevated contamination, with pharmaceuticals in wastewater showing significantly higher concentrations, implying considerable dilution and degradation prior to surface water entry. Antibiotics were prevalent in wastewater, yet their occurrence in surface waters was diminished, indicating partial elimination during treatment processes [22].\u003c/p\u003e\n\u003cp\u003eAnother study analyzed wastewater from Slovakian WWTPs, identifying high concentrations of psychoactive pharmaceuticals, including tramadol (1560 ng dm⁻³) and venlafaxine (947 ng dm⁻³), particularly in urban areas and spa regions such as Piešťany. The present investigation identified markedly diminished pharmaceutical levels in surface waters, with valsartan and diclofenac, underscoring the influence of dilution and degradation [23].\u003c/p\u003e\n\u003cp\u003eThese findings reinforce the role of WWTPs as primary sources of pharmaceutical pollution and underscore the necessity for improved treatment technologies to mitigate environmental contamination.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eOverview of concentration ranges, pharmaceutical groups, occurrence of positive analytes in the samples, maximum, minimum and median concentration of all studied compounds in Slovakia\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOccurrence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum concentration\u003c/p\u003e\n \u003cp\u003e[ng dm\u003csup\u003e− 3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum concentration\u003c/p\u003e\n \u003cp\u003e[ng dm\u003csup\u003e− 3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian concentration\u003c/p\u003e\n \u003cp\u003e[ng dm\u003csup\u003e− 3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtenolol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebeta-blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaffeine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecentral nervous system (CNS) stimulant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e825.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbamazepine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eanticonvulsant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCitalopram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eselective serotonin reuptake inhibitor\u0026nbsp;(SSRI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiclofenac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-steroidal anti-inflammatory drug (NSAID)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGabapentin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eanticonvulsant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e427.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydrochlorothiazide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ediuretic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndomethacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-steroidal anti-inflammatory drug (NSAID)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIomeprol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiodinated contrast media or dyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e942.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIopamidol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiodinated contrast media or dyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIopromide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiodinated contrast media or dyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e249.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetoprolol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebeta-blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNaproxen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-steroidal anti-inflammatory drug (NSAID)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParacetamol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eanalgesic antipyretic drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSotalol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eantiarrhythmic drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSulfamethoxazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eantibiotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTramadol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eopioid analgesic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrimethoprim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eantibiotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValsartan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eangiotensin receptor blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZolpidem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esedative-hypnotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eOverview of concentrations of determined drug analytes in surface water samples in the Slovakia\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eConcentration [ng dm\u003csup\u003e− 3\u003c/sup\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSampling point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSVK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSVK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSVK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSVK4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSVK5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSVK6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtenolol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaffeine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e825.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbamazepine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCitalopram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiclofenac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGabapentin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e427.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydrochlorothiazide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndomethacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIomeprol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e942.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIopamidol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIopromide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetoprolol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNaproxen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParacetamol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSotalol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSulfamethoxazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTramadol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrimethoprim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValsartan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZolpidem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003en.d.—not detected.\u003c/p\u003e\n\u003ch3\u003eComparison of occurrence\u003c/h3\u003e\n\u003cp\u003eThe comparison of pharmaceutical concentrations in surface water samples from the Czech Republic (CZ) and Slovakia (SK) revealed notable differences in the occurrence and distribution of specific compounds (Fig. 1). Statistical analysis via a T-test revealed significant differences for valsartan. This emphasizes a considerable difference in the concentration of this compound between the two countries, with increased levels observed in Slovak samples. Significance levels were determined using a GenEx™ Professional software that included multiparametric testing.\u003c/p\u003e\n\u003cp\u003eAlthough caffeine, iopromide and iomeprol displayed large concentration differences between CZ and SK, substantial variability within each group limited the statistical significance of these findings. The identification of iopamidol solely in Slovak territories emphasizes the existence of regional disparities. Its exclusive occurrence in Slovak water bodies points to varying patterns of pharmaceutical application or the efficiency of wastewater treatment processes.\u003c/p\u003e\n\u003cp\u003eThe analysis also considered the impact of surrounding urban populations on pharmaceutical concentrations (Fig. 2). No statistically significant differences were found between PPCP concentrations and the size of nearby urban areas. However, a marked difference was observed among carbamazepine, citalopram, metoprolol, naproxen, sotalol, and tramadol.\u003c/p\u003e\n\u003cp\u003eA 2022 study examined contamination sources and pharmaceutical presence in Elbe River surface waters. They identified mechanisms contributing to water pollution. Elevated concentrations were noted near urban and industrial areas due to waterway discharges. Wastewater treatment plants often fail to completely remove pharmaceuticals, allowing them to persist in treated water. Higher population density is associated with increased pharmaceutical usage, raising wastewater concentrations. Industrial practices may worsen pollution through the use or disposal of pharmaceuticals. Urban runoff introduces pollutants into waterways, while reduced natural filtration, like wetlands, increases pharmaceutical entry into ecosystems [6].\u003c/p\u003e\n\u003cp\u003eWhen conducting a comparative analysis of pharmaceutical contamination in surface waters across larger urban areas (exceeding 4,700 residents) in the Czech Republic and Slovakia, no statistically significant disparity was identified.\u003c/p\u003e\n\u003cp\u003eNevertheless, a clear differentiation was detected regarding valsartan in substantial urban centers, implying a possible localized impact in Slovak municipalities in relation to their Czech counterparts (Fig. 3). Additionally, the sampling locations in Slovakia were predominantly situated near large cities like Bratislava (SVK1) and Košice (SVK6), where higher pharmaceutical consumption and healthcare facility density would be expected.\u003c/p\u003e\n\u003cp\u003eAltitude emerged as an additional variable of interest, with diminished concentrations of PPCPs generally observed at elevated altitudes (Fig. 4). A statistical examination employing a T-test uncovered significant disparities concerning the majority of the identified analytes. This underscores a substantial distinction in the concentrations of pharmaceutical compounds contingent upon altitude variability.\u003c/p\u003e\n\u003cp\u003eA few studies indicated a clear trend: the further the sampling point was from the Elbe River's source, the higher the concentrations of the monitored pharmaceuticals. This implies that downstream regions experience heightened levels of contamination. Smaller tributaries exhibited significantly greater contamination levels compared to larger aquatic bodies, predominantly attributable to their reduced flow rates. This indicates that pollutants are more likely to accumulate in these smaller streams [6, 20].\u003c/p\u003e\n\u003cp\u003eA statistically significant inverse correlation was identified between the river flow rate and the concentrations of ibuprofen, naproxen, diclofenac, and paracetamol (acetaminophen), suggesting that diminished flow rates in smaller streams correspond to heightened concentrations of these pharmaceutical residues due to insufficient dilution [20].\u003c/p\u003e\n\u003cp\u003eThese findings suggest that regional variations in pharmaceutical contamination are influenced by multiple environmental and anthropogenic factors, necessitating further research to fully understand their distribution and persistence.\u003c/p\u003e\n\u003cp\u003eFigure 5 shows the relationships between concentrations of individual analytes from all sampling points (CZ and SVK) expressed using Spearman's correlation coefficient. The correlation matrix demonstrates the strength and direction of monotonic relationships between variables, where values range from − 1 to + 1. A correlation coefficient of + 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation between the analyzed pharmaceutical compounds.\u003c/p\u003e\n\u003cp\u003eNotably, strong positive correlations were observed among tramadol, hydrochlorothiazide, citalopram, carbamazepine, gabapentin, and metoprolol.\u003c/p\u003e\n\u003cp\u003eThe Spearman's correlation analysis revealed several strong positive relationships between the concentrations of specific pharmaceuticals, highlighting shared environmental behaviors or common sources of pollution. For example, tramadol (an opioid analgesic) and hydrochlorothiazide (a diuretic) showed a strong correlation, suggesting their frequent co-occurrence in surface water samples. This could be attributed to their widespread use in medical treatments and the inefficiency of wastewater treatment plants in removing these compounds [24–27].\u003c/p\u003e\n\u003cp\u003eA similarly robust relationship was observed between citalopram (a selective serotonin reuptake inhibitor - SSRI), and carbamazepine (an anticonvulsant). Both substances are known for their high environmental persistence, which makes them resistant to conventional wastewater treatment processes, leading to their regular detection in surface waters [27–30].\u003c/p\u003e\n\u003cp\u003eAnother notable correlation was found between gabapentin (an anticonvulsant) and metoprolol (a beta-blocker). This connection likely reflects their common prescription patterns and shared pathways into aquatic environments, including human excretion and improper pharmaceutical disposal [6, 31]. The relationship between tramadol and gabapentin further underscores their frequent co-prescription for conditions like chronic pain and neuropathy, which could explain their simultaneous presence in water samples [25].\u003c/p\u003e\n\u003cp\u003eLastly, the correlation between carbamazepine and metoprolol suggests that these pharmaceuticals may exhibit similar environmental behaviors, such as resistance to degradation or comparable hydrophobic properties, which influence their persistence in aquatic ecosystem [27, 32, 33].\u003c/p\u003e\n\u003cp\u003eThe visualization of these correlations highlights clusters of strongly related compounds, emphasizing patterns of pharmaceutical contamination and pointing to potential shared pollution sources. These findings reflect the intricate interplay of prescription trends, chemical characteristics, and environmental contamination processes. Understanding these relationships is crucial for designing effective strategies to reduce pharmaceutical pollution in surface waters.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a comprehensive assessment of pharmaceutical contamination in surface waters of the Czech Republic and Slovakia, highlighting widespread occurrence and significant concentration variations across regions. Employing a validated UHPLC\u0026ndash;MS/MS method, we identified 20 analytes above the quantification limit across 29 sampling sites, with notable findings such as high median concentrations of caffeine, gabapentin, and iodinated contrast media. The results underscore the persistence of certain pharmaceuticals, such as carbamazepine and diclofenac, and their resistance to conventional wastewater treatment processes, which contribute to their accumulation in aquatic environments.\u003c/p\u003e \u003cp\u003eKey differences between the two countries were observed, including the exclusive detection of iopamidol in Slovak samples and significant variations in valsartan concentrations. These findings highlight the importance of localized factors, such as urbanization, prescription practices, and wastewater treatment efficiencies, in shaping contamination profiles.\u003c/p\u003e \u003cp\u003eThe implications of this research extend beyond regional boundaries, as it emphasizes the need for enhanced wastewater treatment technologies, stricter regulation of pharmaceutical disposal, and continued monitoring to mitigate environmental and health risks associated with PPCPs. Future research should explore the long-term ecological effects of these contaminants, their bioaccumulation potential, and the efficacy of advanced removal methods, contributing to more sustainable water resource management in Central Europe and beyond.\u003c/p\u003e"},{"header":"Experimental","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eChemicals and Reagents\u003c/h2\u003e\n \u003cp\u003eThe analytical standards and isotopically labeled internal standards utilized in this study are detailed in Tables S1 and S2 of the Supplementary Materials. Acetonitrile and methanol, both of LC-MS grade, were sourced from Honeywell (Charlotte, NC, USA). Formic acid, MS grade, was acquired from Sigma-Aldrich\u0026reg; (Darmstadt, Germany). Ultrapure water (MQ) was obtained from a Milli-Q water purification system (Millipore\u0026reg;, Billerica, MA, USA).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSample collection, enrichment and preparation\u003c/h3\u003e\n\u003cp\u003eSurface water samples (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29) were collected in January and February 2023 from locations across the Czech Republic and Slovakia. The sampling points were designated as CZ01 to CZ23 for the Czech Republic and SVK01 to SVK06 for Slovakia. Figures \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e provide maps indicating the sampling locations. Detailed characteristics of these sampling sites are presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of sampling locations for surface water samples\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSampling points\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eName of the water source\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription of the sampling location\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSurrounding population\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTepl\u0026aacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the village of Teplička.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLake Laka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlacial lake Laka is the smallest and highest of the \u0026Scaron;umava lakes in the Železnorud region.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKořensk\u0026yacute; stream\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStream flowing through the village of Str\u0026aacute;žn\u0026yacute;.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLužnice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the town of Suchdol nad Lunžic\u0026iacute;.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyje\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the village of Podhrad\u0026iacute; nad Dyj\u0026iacute;.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJihlava\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Jihlava.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJihlava\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Třeb\u0026iacute;č.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSvratka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Brno.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e379 466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMorava\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Hodon\u0026iacute;n.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Scaron;ť\u0026aacute;vnice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the Spa town of Luhačovice.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eČeladenka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStream flowing near the Kocianka ski area.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOpava\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Opava.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMorava\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Olomouc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivok\u0026aacute; Orlice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the town of Žamberk.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJavorka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the town of Spa Bělohrad.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKamenice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the village of Plavy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMumlava\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMountain river flowing through the village of Harrachov.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJizera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Mlad\u0026aacute; Boleslav.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLabe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Litoměřice.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFl\u0026aacute;jsk\u0026yacute; stream\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMountain stream flowing into the Flajsk\u0026aacute; water reservoir, which is a source of drinking water for Mostecko and Těplice.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Scaron;vihov\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe \u0026Scaron;vihov Reservoir is a water reservoir on the Želivka River, which serves as a source of drinking water for almost the entire Central Bohemian region, including Prague.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRokytka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall river flowing through the capital Prague.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 275 406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZ23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVltava\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the capital Prague.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 275 406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDunaj\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the capital Bratislava.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e475 503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV\u0026aacute;h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Pie\u0026scaron;ťany.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Žiar nad Hronom.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVK4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV\u0026aacute;h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Žilina.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVK5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHorn\u0026aacute;d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Spi\u0026scaron;sk\u0026aacute; Nov\u0026aacute; Ves.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVK6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHorn\u0026aacute;d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver flowing through the city of Ko\u0026scaron;ice.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229 040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1 dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e samples were collected from each of the 29 locations from a depth of one meter into polypropylene bottles that had been pre-rinsed with deionized water using sapling pole. After collection, the grab samples were transported under refrigeration at 3\u0026ndash;5\u0026deg;C to the laboratory, where they were mixed, homogenized, and subsequently stored at \u0026minus;\u0026thinsp;20\u0026deg;C. The samples were immediately frozen upon arrival and analyzed without any addition of acidifying or fixing agents. Each sample was prepared in duplicate.\u003c/p\u003e\n\u003cp\u003eSamples were then processed following the procedure previously mentioned in [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Briefly, for analysis, 10 cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e of each water sample was transferred into 12 mL disposable plastic centrifuge tubes using a calibrated automatic pipette. To these samples, 100 \u0026micro;L of a 50 ng cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e internal standard stock solution were added. The tubes were then capped and shaken gently by hand. Finally, the samples were filtered through 5 cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e disposable plastic syringes using disc cellulose micro-filters (0.22 \u0026micro;m) into 2 cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e dark glass crimp vials for further analysis.\u003c/p\u003e\n\u003ch3\u003eStock Solutions, Calibration Standards, Quality Control Samples, Blank Samples, and Laboratory Control Samples\u003c/h3\u003e\n\u003cp\u003eThe methodology for preparing individual stock solutions, calibration standards, and quality control (QC) samples is outlined in a prior study [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Blank samples and laboratory control samples were prepared following the same established procedures.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eChromatographic Conditions\u003c/h2\u003e\n \u003cp\u003eThe studies were conducted using a XEVO TQ-S MS analyzer (Waters, Milford, MA, USA) coupled with an I-class UPLC liquid chromatograph (Waters, USA). Chromatographic separations were performed using an Acquity UPLC BEH C18 column (1.7 \u0026micro;m, 2.1 mm \u0026times; 100 mm) along with an Acquity UPLC BEH C18 pre-column (Waters, Milford, MA, USA). Gradient elution was achieved using a mobile phase consisting of 0.01% formic acid in Milli-Q water (A) and pure methanol (B), following this program: (min/%B) 0/2, 0.5/2, 5.0/95, 5.1/100, 7.0/100, 7.1/2, and 9.0/2. Both positive and negative electrospray ionization modes were employed for data acquisition using multiple reaction monitoring (MRM) mode for analysis. Two MRM transitions\u0026mdash;one for quantification and another for qualification\u0026mdash;were selected for each analyte. The MRM transitions along with cone voltages and collision energies are listed in Table S3.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eMethod Validation\u003c/h2\u003e\n \u003cp\u003eValidation parameters have been characterized as described in previous study [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. The UHPLC method has been validated concerning selectivity, limit of detection (LOD), limit of quantification (LOQ), linearity range, precision, and accuracy.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of Interest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e This research was funded by the Grant Agency of Charles University, project SVV.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArman N, Salmiati S, Aris A, Salim M, Nazifa T, Muhamad M, Marpongahtun M (2021) Water (Basel) DOI: 10.3390/w13223258\u003c/li\u003e\n\u003cli\u003eDu Y, Xu X, Liu Q, Bai L, Hang K, Wang D (2022) Sci Total Environ DOI:10.1016/j.scitotenv.2021.150691\u003c/li\u003e\n\u003cli\u003eCristina-Mihaela CN (2023) RAJAR 9(11):549\u0026ndash;562\u003c/li\u003e\n\u003cli\u003eGuettai N, Kadmi Y, Puri M, Kerkich K, Bouargane B (2024) J Clean Prod 466:142654\u003c/li\u003e\n\u003cli\u003eKode\u0026scaron; V, Ackermanov\u0026aacute; M (2024) DOI:10.59984/978-80-7653-063-8.32\u003c/li\u003e\n\u003cli\u003eFerencik M, Blahova J, Schovankova J, Siroka Z, Svobodova Z, Kodes V, Stepankova K, Lakdawala P (2022) Water (Switzerland) DOI: 10.3390/W14244122/S1\u003c/li\u003e\n\u003cli\u003eMezzelani M, Regoli F (2022) Ann Rev Mar Sci 14:105\u0026ndash;128\u003c/li\u003e\n\u003cli\u003eSrain HS, Beazley KF, Walker TR (2021) Environ Rev 29(2):142\u0026ndash;181\u003c/li\u003e\n\u003cli\u003eArslan P, \u0026Ouml;zeren SC, Yurdak\u0026ouml;k-Dikmen B (2021) Environ ResTechnol 4(2):145\u0026ndash;151\u003c/li\u003e\n\u003cli\u003eHuben\u0026aacute; P, Hork\u0026yacute; P, Grabic R, Grabicov\u0026aacute; K, Slav\u0026iacute;k O, Rand\u0026aacute;k T (2020) PeerJ 2020(7): e9356\u003c/li\u003e\n\u003cli\u003eMason RT, Martin JM, Tan H, Brand JA, Bertram MG, Tingley R, Todd-Weckmann A, Wong BBM (2021) Environ Sci Technol 55(19):13024\u0026ndash;13032\u003c/li\u003e\n\u003cli\u003eKulik K, Lenart-Boroń A, Wyrzykowska K (2023) Water (Basel) DOI: 10.3390/W15050975\u003c/li\u003e\n\u003cli\u003eTan Q, Li W, Zhang J, Zhou W, Chen J, Li Y, Ma J (2019) Front Environ Sci Eng DOI: 10.1007/S11783-019-1120-9\u003c/li\u003e\n\u003cli\u003eMolnarova L, Halesova T, Vaclavikova M, Bosakova Z (2023) Molecules DOI:10.3390/MOLECULES28155899\u003c/li\u003e\n\u003cli\u003eChaves M de JS, Kulzer J, Pujol de Lima P da R, Barbosa SC, Primel EG (2022) Environ Sci Process Impacts 24(11):1982\u0026ndash;2008\u003c/li\u003e\n\u003cli\u003eSanusi IO, Olutona GO, Wawata IG, Onohuean H (2023) Environ Sci Pollut Res Int 30(39):90595\u0026ndash;90614\u003c/li\u003e\n\u003cli\u003eGrobin A, Ro\u0026scaron;kar R, Trontelj J (2023) Anal Methods 15(21):2606\u0026ndash;2621\u003c/li\u003e\n\u003cli\u003eLeston S, Freitas A, Rosa J, Vila Pouca AS, Barbosa J, Reis-Santos P, Fonseca VF, Pardal MA, Ramos F (2023) Appl Sci DOI:10.3390/APP13105975\u003c/li\u003e\n\u003cli\u003eNinga E, Sapozhnikova Y, Lehotay SJ, Lightfield AR, Monteiro SH (2021) J Agric Food Chem 69(4):1169\u0026ndash;1174\u003c/li\u003e\n\u003cli\u003eSkocovska M, Ferencik M, Svoboda M, Svobodova Z (2021) Vet Med \u0026ndash; Czech 66(5):208-218\u003c/li\u003e\n\u003cli\u003eDatel J V., Hrabankova A (2020) Water 12(5):1387 \u003c/li\u003e\n\u003cli\u003eF\u0026aacute;berov\u0026aacute; M, Bod\u0026iacute;k I, Ivanov\u0026aacute; L, Grabic R, Mackuľak T (2017) Monatsh Chem 148(3):441\u0026ndash;448\u003c/li\u003e\n\u003cli\u003eMackuľak T, Biro\u0026scaron;ov\u0026aacute; L, G\u0026aacute;l M, Bod\u0026iacute;k I, Grabic R, Ryba J, \u0026Scaron;kub\u0026aacute;k J (2016) Environ Monit Assess 188(1):1\u0026ndash;12\u003c/li\u003e\n\u003cli\u003eBeltr\u0026aacute;n de Heredia I, Gonz\u0026aacute;lez-Gaya B, Zuloaga O, Garrido I, Acosta T, Etxebarria N, Ruiz-Romera E (2024) Sci Total Environ 946:174062\u003c/li\u003e\n\u003cli\u003eKlanovicz N, Pinto CA (2024) DOI: 10.21203/RS.3.RS-3877052/V1\u003c/li\u003e\n\u003cli\u003eLong BM, Harriage S, Schultz NL, Sherman CDH, Thomas M (2023) Environ Chem 19(6):375\u0026ndash;384\u003c/li\u003e\n\u003cli\u003eMolnarova L, Halesova T, Tomesova D, Vaclavikova M, Bosakova Z (2024) Molecules 29(7):1480\u003c/li\u003e\n\u003cli\u003eDrahoradova N, Ujhazy M, Kucerova R, Sezima T (2024) Use of physical pretreatment and biodegradation for the removal of antidepressants and psychiatrically active substances from wastewater. E3S Web of Conferences 550:01029\u003c/li\u003e\n\u003cli\u003eTrognon J, Albasi C, Choubert JM (2024) Sci Total Environ 912:169040\u003c/li\u003e\n\u003cli\u003eZhu X, Luo T, Wang D, Zhao Y, Jin Y, Yang G (2023) Sci Total Environ 900:165732\u003c/li\u003e\n\u003cli\u003eLove D, Slovisky M, Costa KA, Megarani D, Mehdi Q, Colombo V, Ivantsova E, Subramaniam K, Bowden JA, Bisesi JH, Martyniuk CJ (2024) Environ Toxicol Chem 43(12):2530\u0026ndash;2544\u003c/li\u003e\n\u003cli\u003eMendes FS, Gon\u0026ccedil;alves ADA, Guiomar FIS, Martins RN, Ramalho JPP, Martins LFG (2024) Fluid Phase Equilib 580:114056\u003c/li\u003e\n\u003cli\u003eMeyer W, Reich M, Beier S, Behrendt J, Gulyas H, Otterpohl R (2016) Environ Monit Assess 188(8):1\u0026ndash;16 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"monatshefte-fur-chemie-chemical-monthly","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mccm","sideBox":"Learn more about [Monatshefte für Chemie - Chemical Monthly](https://www.springer.com/journal/706)","snPcode":"706","submissionUrl":"https://www.editorialmanager.com/mccm/","title":"Monatshefte für Chemie - Chemical Monthly","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pollution, Surface water contamination, Pharmaceutical residues, UHPLC-MS, MS, Direct injection","lastPublishedDoi":"10.21203/rs.3.rs-6313065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6313065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe UHPLC-MS/MS analytical method was used to assess the occurrence of 52 pharmaceutical substances in surface water samples from the Czech Republic and Slovakia. Of the 29 surface water samples analyzed, 28 contained at least one quantifiable analyte. This study represents a comprehensive comparative analysis of the occurrence of pharmaceuticals in surface waters of these neighbouring countries and reveals remarkable regional differences in the level of contamination and the occurrence of compounds, which depend mainly on population density, altitude and flow. Median concentrations of key analytes included 126.35 ng dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for caffeine, 50.81 ng dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for diclofenac, 134.22 ng dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for gabapentin, and 177.57 ng dm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for iomeprol. The findings contribute to new knowledge on the transboundary environmental burden of pharmaceuticals and support efforts to assess and reduce potential environmental and health risks.\u003c/p\u003e","manuscriptTitle":"Simultaneous determination of multiresidues of pharmaceuticals and personal care products in surface water samples in the Czech Republic and Slovakia: a comparative study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 16:50:23","doi":"10.21203/rs.3.rs-6313065/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-02T05:20:19+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T15:28:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-01T12:42:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Monatshefte für Chemie - Chemical Monthly","date":"2025-03-31T03:54:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"monatshefte-fur-chemie-chemical-monthly","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mccm","sideBox":"Learn more about [Monatshefte für Chemie - Chemical Monthly](https://www.springer.com/journal/706)","snPcode":"706","submissionUrl":"https://www.editorialmanager.com/mccm/","title":"Monatshefte für Chemie - Chemical Monthly","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"55546a88-bcbf-48cb-9aef-8d4ae4a71233","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T16:08:22+00:00","versionOfRecord":{"articleIdentity":"rs-6313065","link":"https://doi.org/10.1007/s00706-025-03356-y","journal":{"identity":"monatshefte-fur-chemie-chemical-monthly","isVorOnly":false,"title":"Monatshefte für Chemie - Chemical Monthly"},"publishedOn":"2025-08-04 15:57:57","publishedOnDateReadable":"August 4th, 2025"},"versionCreatedAt":"2025-04-22 16:50:23","video":"","vorDoi":"10.1007/s00706-025-03356-y","vorDoiUrl":"https://doi.org/10.1007/s00706-025-03356-y","workflowStages":[]},"version":"v1","identity":"rs-6313065","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6313065","identity":"rs-6313065","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}