Occurrence and short-term variability of psychoactive substances in wastewater from a mixed urban catchment in central Chile

Occurrence and short-term variability of psychoactive substances in wastewater from a mixed urban catchment in central Chile | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Occurrence and short-term variability of psychoactive substances in wastewater from a mixed urban catchment in central Chile Wendy Calzadilla, Karla Montenegro, Andrés Yar, Sebastián Campos, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9236663/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Wastewater-based epidemiology (WBE) is increasingly used to assess community-level exposure to psychoactive substances; however, evidence from Latin America remains limited, and uncertainties related to population normalization and short-term variability persist.This study examines the occurrence and short-term variability of selected psychoactive substances in wastewater from a mixed urban catchment in central Chile, providing insight into the interpretation of WBE data under dynamic population conditions. Influent samples were collected during two monitoring campaigns in 2024 and 2025 and analyzed for amphetamine-type stimulants, cocaine and benzoylecgonine, ketamine, phentermine, and fentanyl-related compounds using LC–MS/MS. Population-normalized mass loads and consumption estimates were derived using dynamic normalization based on NH₃–N. Cocaine showed the highest levels among the monitored substances, while ketamine exhibited comparatively high mass loads with marked weekend-associated patterns. Among amphetamine-type stimulants, MDMA was consistently detected but decreased in 2025, whereas amphetamine increased slightly and methamphetamine remained low. Phentermine was detected at relatively high mass loads; however, dose-equivalent estimates suggested that these corresponded to a small fraction of the potentially treated population. Observed patterns were influenced by temporal changes in contributing population, with higher mass loads generally occurring during weekends. These findings underscore the importance of dynamic population normalization when interpreting short-term variability in WBE studies, particularly in mixed or relatively small catchments. While based on a limited dataset, this case study provides evidence of short-term variability patterns that may influence the interpretation of WBE-derived indicators and supports the use of WBE as a complementary approach for environmental monitoring of emerging contaminants. Wastewater-based epidemiology psychoactive substances population normalization urban catchment Chile Figures Figure 1 1. Introduction Traditional population surveys, clinical reports, and drug seizure statistics may not fully capture short-term variability in licit and illicit psychoactive substance use, particularly in dynamic urban settings where patterns may fluctuate within days. Wastewater-based epidemiology (WBE) has emerged as a complementary approach for assessing community-level exposure to these substances. In this context, WBE is used to monitor human metabolic residues in wastewater (WW), allowing the assessment of community-level occurrence and short-term variability of psychoactive substances. Over the past two decades, WBE has become a widely applied approach for monitoring temporal trends and supporting early warning systems in public health (Castiglioni et al., 2015 ; González-Mariño et al., 2018 ; Salgueiro-González et al., 2022 , 2021 ). Since early demonstrations that cocaine (COC) and its primary metabolite benzoylecgonine (BE) can be reliably quantified in municipal WW, WBE has been implemented to estimate population-normalized mass loads of widely consumed and emerging substances and to investigate temporal patterns across cities (Zuccato et al., 2005 ; Castiglioni et al., 2006). Coordinated international initiatives, including the SCORE (Sewage analysis CORe group Europe) network in Europe and monitoring programs led by the European Union Drugs Agency (EUDA), as well as national programs in Australia, Canada and other countries, now routinely apply WBE to monitor trends and support early warning systems for emerging substances (ACIC., 2024a, 2024b; Baz-Lomba et al., 2025 ; EUDA, 2025; Health Canada, 2025 ). In Latin America, published WBE studies are currently reported from a limited number of countries, primarily Brazil, Colombia and Mexico, where city-scale monitoring has quantified COC, ATS and other biomarkers (Bijlsma et al., 2016 ; Cruz-Cruz et al., 2021 ; Gomes et al., 2025 ; Sodré et al., 2018 ). Broader regional expansion remains limited due to variability in WW infrastructure, analytical capacity and uncertainties associated with population normalization (González-Mariño et al., 2019 ; Hahn et al., 2022 ; Sodré et al., 2018 ). In Chile, recent multi-year monitoring conducted in the Biobío Region demonstrated the feasibility of regional-scale wastewater-based drug surveillance (Reis et al., 2026 ). However, smaller mixed urban–industrial catchments, particularly within the Santiago Metropolitan Region (SMR), remain less characterized. In addition, there is limited evidence on how short-term variability (including weekday, weekend and holiday dynamics) and uncertainties associated with population normalization, which may influence WBE-derived estimates in mixed urban catchments. The SMR provides a suitable context for examining these aspects. National indicators in Chile continue to rely primarily on self-reported surveys and seizure statistics (SENDA, 2025), while the National Drug Strategy Action Plan 2024–2030 highlights the need to strengthen monitoring frameworks for emerging and synthetic drugs (SENDA, 2024 ). In this context, WBE provides complementary information for assessing short-term variability at the catchment level. The Santiago Poniente wastewater treatment plant (SP-WWTP) serves a mixed urban catchment characterized by residential, commercial and industrial WW inputs (Aguas Santiago Poniente, 2024 ). COC is consistently identified as a major illicit substance in Chile according to national surveys (Observatorio Chileno de Drogas, 2025 ), and is therefore considered a key target compound in wastewater-based studies, alongside other psychoactive substances. Analyzing its occurrence in WW, together with other psychoactive substances, allows examination of short-term variability at the catchment level under dynamic population conditions. In addition to their role as indicators of human consumption, these substances are also recognized as emerging contaminants in aquatic environments, as their continuous release through WW may result in chronic low-level exposure in receiving waters (Daughton & Ruhoy, 2009 ). Therefore, characterizing their occurrence and short-term variability in WW is also relevant from an environmental exposure perspective, particularly in regions where monitoring data remains scarce. In this study, population-normalized mass loads and consumption of ketamine (KET), COC, ATS and phentermine (PHT) were investigated in a mixed urban catchment of the SMR during two monitoring campaigns conducted in 2024 and 2025. Temporal variability across weekdays, weekends and a public holiday was evaluated using dynamic population normalization. The influence of short-term variability and population dynamics on the interpretation of WBE-derived indicators is examined in mixed urban catchments. While based on a limited dataset, this case study contributes to the understanding of variability patterns in mixed or relatively small catchments and highlights considerations relevant for the interpretation of short-term WBE monitoring campaigns. 2. Materials and methods 2.1 Chemical and materials Analytical reference standards and their isotopically labelled internal standards (ILIS) analogues of illicit and licit drugs were purchased from Cerilliant (Round Rock, TX, USA) and Merck (Darmstadt, Germany) as solutions in methanol or acetonitrile. Methanol, acetonitrile (HPLC-grade), and formic acid (LC-MS grade) were acquired from Scharlab (Scharlab, Spain). HPLC-grade water (resistivity > 18 MΩ cm) was obtained by purifying distilled water in a Thermo Scientific Smart2Pure system (Åtvidaberg, Sweden). See page S1 of the Supplementary Material for further details about chemicals and materials. 2.2 Characteristics of SP-WWTP The influent samples analyzed were collected at the SP-WWTP, located in the Enea industrial zone in the municipality of Pudahuel in the SRM, Chile, adjacent to Arturo Merino Benítez International Airport. According to data provided by SP-WWTP, in December 2024, there were 3681 customers connected to WWTP, equivalent to approximately 20000 inhabitants, of which 85% were residential customers, 8% were commercial customers, 1% were industrial customers, and 6% were other customers. The average daily flow of WW treated by the WWTP is 3503 m 3 ·day − 1 . 2.3 Sample collection A total of 14 composite 24-hour urban WW influent samples were collected from SP-WWTP using a Teledyne ISCO 6712 automatic sampler (Lincoln, NE, USA). Samples were collected in time-proportional sampling mode. Subsamples of 50 mL were taken every 15 minutes, with start and end times (from 9:00 a.m. to 9:00 a.m.). Because samples were collected from 9:00 a.m. to 9:00 a.m., consumption occurring during late-night hours may be reflected in the following day’s composite sample. In 2024, data were collected on four consecutive days, in April/May and July. It began on Monday, April 29, and continued until Thursday, May 3. Then, during July, it started on Monday, July 8, and continued until Thursday, July 11. In 2025, data were collected on seven consecutive days, from Tuesday, May 13 to Tuesday, May 20. Following the WBE framework, weekdays were defined as Tuesday–Thursday and the weekend period as Friday–Monday. A public holiday (1 May 2024) was considered separately due to its potential influence on wastewater composition. See pages S1 and S2 of the Supplementary Material for further details about sample collection and characterization. 2.4 Analytical procedure and instrumentation Preliminary tests showed that SPE preconcentration led to signal saturation for BE, COC, KET and PHT; therefore, direct injection was used to ensure quantification within the linear range of the method. In contrast, analytes present at lower concentrations, including AMP, METH, MDMA, fentanyl (FEN), and norfentanyl (NOR), required SPE preconcentration in accordance with our previously reported workflow (Herrera-Muñoz et al., 2024 ). See pages S4 and S5 of the Supplementary Material for further details about the analytical procedure and instrumentation. 2.5 Method validation and quality assurance Methodology performance was evaluated in terms of linearity, accuracy, precision, limits of detection and quantification, and matrix effects. See pages S8 and S9 of the Supplementary Material for further details about method validation and quality assurance. 2.6 Back-calculation of normalized mass loads and consumption of licit and illicit psychoactive substances Concentrations measured in influent WW were converted into population-normalized mass loads (mg·day⁻¹·1000 inhabitants⁻¹) using daily flow rates and an equivalent population (EP) estimated from NH₃–N. Daily consumption estimates were obtained by applying compound-specific correction factors (Cf) based on urinary excretion and molar-mass relationships (Castiglioni et al., 2014 ; Zuccato et al., 2005 , 2008 ). Full equations, assumptions, and Cf sources are provided in the Supplementary Material ( Eqs. S1–S3; Table S4 ). For PHT, mass loads were additionally expressed as therapeutic-equivalent doses to estimate the potentially treated fraction of the population (Eqs. S4–S5; Table S4 ). Mean daily mass loads and consumption values reported in this study correspond to the arithmetic mean of individual daily estimates within each monitoring campaign. Population normalization was performed using a per-capita NH₃–N load of 8 g·day⁻¹·inhabitant⁻¹. To evaluate the sensitivity of consumption estimates to this assumption, alternative values of 6 and 10 g·day⁻¹·inhabitant⁻¹ were tested, representing the lower and upper range commonly reported for mixed WW systems. 3. Results and discussion 3.1 Method validation and quality assurance Table 1 reports the performance parameters of the method. As shown, the calibration curves exhibited good linearity, with correlation coefficients greater than 0.99, and their working ranges adequately covered the concentration levels found in the samples. The comparison between calibration curves prepared in solvent and those prepared in matrix (WW) revealed a slope variability of less than 20% ( Table S5 , pages S11-S12 in Supplementary Material) for all analytes except METH (25.8%); therefore, the analyses were performed using the calibration curve in solvent. The recoveries obtained for most compounds were satisfactory at three fortified levels, with recoveries ranging from 70% to 120% and precision (RSD) below 20%. In the case of NOR, relatively high recoveries were obtained at medium and high levels (122 and 123%, respectively), but with good RSD (9 and 8%, respectively). Routine analysis was considered satisfactory when the relative recoveries of QC samples fell within the 60–140% range. Since the matrix used for the recovery tests contained detectable levels of some target analytes, the concentration measured in the “natural” (unspiked) samples was subtracted from that obtained in each spiked recovery test and QC. LODs and LOQs ranged from 0.3 to 4.6 and 1.2 to 26 ng L − 1 , respectively (Table 1 ). In the case of BE, COC, KET, and PHT, the LOQ was estimated from chromatograms of non-spiked WW samples, without adding a standard to the sample. Across all three spiking levels, the analytes exhibited either ionization suppression or ionization enhancement, with matrix effect values deviating from 100%. COC and BE showed the strongest ionization enhancement at the lowest spiking level, with signal increases of 21% and 42%, respectively. In contrast, AMP, METH, MDMA, FEN, and NOR exhibited ionization suppression, with signal reductions ranging from 21% to 61% at the medium and high spiking levels. All these analytes were quantified following SPE sample treatment. Analytes measured by direct injection after four-fold dilution exhibited lower matrix effects than those subjected to SPE (Table 1 ). This observation is consistent with previous reports indicating that while SPE effectively preconcentrates analytes, it does not necessarily minimize matrix effects, as previously reported (Botero-Coy et al., 2018 ). Finally, matrix effects were effectively minimized by applying ILIS for each compound ( Table S5 , pages S11-S12 in Supplementary Material). Table 1 Method performance parameters. Analyte LOD (ng L − 1 ) LOQ (ng L − 1 ) Linearity (solvent) Recovery % Precision (RSD) Matrix effects (%, RSD) 0.5 (µg L − 1 ) 2 (µg L − 1 ) 10 (µg L − 1 ) 0.5 (µg L − 1 ) 2 (µg L − 1 ) 10 (µg L − 1 ) 0.5 (µg L − 1 ) 2 (µg L − 1 ) 10 (µg L − 1 ) AMP 3.3 10. 9 0.99838 80 (14) 106(12) 117 (5) 14 12 5 75 (7) 39 (3) 62 (19) BE 4.6 15.4 0.99898 * * * 3 9 2 121 (7) 83 (12) 64 (1) COC 2.8 9.2 0.99721 * * * 7 4 3 142 (1) 110 (21) 87 (8) FEN 0.3 1.2 0.99784 119 (20) 70 (8) 95 (5) 20 8 5 61 (7) 63 (5) 54 (0) KET 7.8 26.0 0.99940 * * * 12 6 8 102 (2) 97 (2) 87 (3) MDMA 0.8 2.8 0.99777 95 (11) 99 (20) 104 (12) 11 20 12 82 (11) 43 (12) 44 (17) METH 4.6 15.2 0.99665 110 (17) 102 (6) 107 (16) 17 6 16 43 (13) 44 (8) 40 (19) NOR 1.2 4.1 0.99912 110 (14) 122 (9) 123 (8) 14 9 8 60 (5) 79 (2) 61 (2) PHT 1.6 5.4 0.99251 82 (12) 105(10) 98 (8) 12 10 8 93 (10) 83 (20) 79 (2) * Analytes determined by direct injection. Recovery (%) and precision (RSD) (n = 5). Matrix effects (%, RSD) (n = 3). 3.2 Occurrence of licit and illicit psychoactive substances in raw WW Table 2 reports the concentrations (ng·L − 1 ) of the target biomarkers measured in influent samples during the 2024 and 2025 campaigns. Daily presence of BE, COC, KET, MDMA, AMP and PHT was evident across the sampled days, indicating consistent occurrence of these substances in the studied catchment. The highest concentrations were observed for BE, with values exceeding 7000 and 10000 ng·L − 1 , followed by COC (2000 and 4000 ng·L − 1 ), KET (900 and 1100 ng· L − 1 ), PHT (800 and 500 ng· L − 1 ) and MDMA (130 and 30 ng· L − 1 ) in 2024 and 2025, respectively, suggesting differences in relative abundance among substance groups across campaigns. The presence of these compounds at ng L⁻¹ levels in influent WW indicates their continuous input into WW systems under the studied conditions. Although removal efficiencies were not evaluated in this study, their occurrence suggests potential release into receiving waters and possible environmental exposure. The high concentrations of BE, COC, KET and PHT meant that SPE preconcentration was not required for reliable quantification; these analytes were therefore quantified by direct injection, avoiding saturation effects observed when SPE was applied to high-level samples. In contrast, AMP and METH occurred at substantially lower concentrations and required SPE preconcentration, reflecting differences in concentration ranges among target compounds. This concentration profile, characterized by high BE and COC levels and comparatively low AMP and METH levels, is consistent with previous reports from Latin American settings (Bijlsma et al., 2016 ; Causanilles et al., 2017 ; Devault et al., 2014 ), supporting the regional relevance of the observed patterns. Neither FEN nor NOR was detected in influent samples during either campaign. This absence differs from reports in parts of North America and Mexico (Cruz-Cruz et al., 2021 ) and may reflect regional differences in substance use patterns or market availability, underscoring the importance of continued targeted monitoring, especially as fentanyl-related harms and markets evolve dynamically across regions. Confirmatory chromatograms and identity criteria based on retention time and q/Q ion ratios are provided in the Supplementary Material ( Fig. S1 ), following SANTE recommendations (SANTE, 2021 ). Table 2 Concentrations (ng·L − 1 , RSD) of the target biomarkers of licit and illicit psychoactive substances measured in untreated WW in the 2024 and 2025 (n = 2). Date Con AMP (ng L − 1 ) Con BE (ng L − 1 ) Con COC (ng L − 1 ) Con FEN (ng L − 1 ) Con KET (ng L − 1 ) Con MDMA (ng L − 1 ) Con METH (ng L − 1 ) Con NOR (ng L − 1 ) Con PHT (ng L − 1 ) Ratio COC/BE Mon 29-April-2024 21 (11) 5409 (1) 1490 (2) ND 914 (0) 111 (8) < LOQ* ND 714 (21) 0,28 Tue 30-April-2024 24 (11) 6580 (3) 2202 (1) ND 780 (3) 84 (11) < LOQ ND 775 (12) 0,30 Wed 01-May-2024 24 (4) 7264 (3) 1542 (3) ND 713 (1) 137 (3) < LOQ* ND 779 (11) 0,21 Thu 02-May-2024 23 (12) 6902 (3) 1779 (5) ND 778 (8) 103 (8) < LOQ* ND 812 (7) 0,26 Mon 08-July-2024 27 (2) 5072 (4) 1659 (4) ND 532 (7) 48 (7) < LOQ ND 686 (10) 0,33 Tue 09-July-2024 31 (15) 5354 (2) 1916 (10) ND 936 (4) 24 (7) < LOQ ND 764 (11) 0,36 Wed 10-July-2024 27 (4) 5184 (9) 1704 (12) ND 781 (12) 28 (5) ND ND 847 (13) 0,33 Tue 13-May-2025 21 (13) 2232 (6) 872 (8) ND 432 (9) 5 (17) < LOQ* ND 298 (3) 0,39 Wed 14-May-2025 61 (4) 8613 (16) 2192 (3) ND 1158 (8) 16 (3) 20 (15) ND 510 (8) 0,25 Thu 15-May-2025 54 (7) 8504 (2) 3324 (11) ND 1070 (2) 14 (21) 21 (0) ND 486 (0) 0,39 Fri 16-May-2025 41 (5) 10266 (7) 4108 (14) ND 1078 (1) 20 (2) 18 (9) ND 409 (8) 0,40 Sat 17-May-2025 31 (11) 8794 (3) 3752 (0) ND 1029 (17) 26 (14) 23 (4) ND 435 (0) 0,43 Sun 18-May-2025 14 (12) 6104 (1) 2638 (2) ND 922 (20) 22 (17) 11 (1) ND 287 (14) 0,43 Mon 19-May-2025 50 (1) 6078 (1) 2778 (0) ND 801 (3) 36 (11) 20 (11) ND 377 (3) 0,46 ND: not detected; * Values above the LOD but below the LOQ 3.3 Population normalization, mass loads and back-calculated consumption Population-normalized mass loads and estimated daily consumption (mg·day⁻¹·1000 inhabitants⁻¹) were calculated for all detected substances (Table 3 ). For concentrations detected above the LOD but below the LOQ, values were assigned as 0.5 × LOQ, a common approach in WBE studies for handling concentrations below the quantification limit (Bijlsma et al., 2024 ; Ryu et al., 2016 ), whereas concentrations below the LOD were set to zero. To normalize daily mass loads, the equivalent population (EP) served by the WWTP was estimated using NH₃–N ( Table S1 ). Although the WWTP operator reports a connected population of approximately 20,000 inhabitants, this fixed nominal value may not capture short-term variability associated with daily mobility and changes in non-residential WW contributions, potentially affecting the interpretation of population-normalized estimates. NH₃–N is widely used as an indirect anthropogenic marker to support short-term population estimates in WBE and is generally considered less influenced by non-human sources than conventional parameters such as COD, BOD₅, total nitrogen or total phosphorus (Been et al., 2014 ). For this reason, dynamic EP estimates based on NH₃–N were used to approximate day-to-day variability within the catchment, acknowledging that uncertainties associated with this approach may influence normalized values. Mean EP values were comparable across campaigns (17,806 ± 2,249 in 2024 and 17,490 ± 3,976 in 2025), but daily EP varied substantially within each period, ranging from 13,596 to 21,017 in 2024 and from 11,774 to 22,162 in 2025. The lowest EP in 2024 coincided with 1 May (public holiday), which may reflect reduced commuting and commercial–industrial activity. In 2025, lower EP values occurred on weekend days, which is consistent with typical mobility patterns. These observations indicate that short-term changes in contributing population may influence population-normalized estimates and support the use of dynamic normalization in this dataset, rather than relying on a fixed nominal population when interpreting short-term temporal variability in heterogeneous urban catchments. Complementary approaches (e.g., triangulation with mobility proxies) may help reduce Table 3 Normalized mass loads and estimated consumption of psychoactive substances (mg · day − 1 ·1000 inhabitants − 1 ) ± SD in untreated WW in 2024 and 2025. Date mg·day -1 ·1000 inhabitants -1 ± SD AMP COC KET 1 MDMA METH PHT Mon 29-April-2024 13 ± 1 4186 ± 40 197 ± 0 105 ± 9 2 ± 0 302 ± 63 Tue 30-April-2024 13 ± 1 4536 ± 144 150 ± 4 70 ± 8 0 ± 0 292 ± 35 Wed 01-May-2024 16 ± 1 6347 ± 204 174 ± 2 147 ± 5 3 ± 1 372 ± 40 Thu 02-May-2024 11 ± 2 4397 ± 111 138 ± 12 80 ± 6 1 ± 0 283 ± 19 Mon 08-July-2024 15 ± 0 3592 ± 128 105 ± 7 41 ± 3 0 ± 0 266 ± 27 Tue 09-July-2024 17 ± 2 3694 ± 74 180 ± 7 20 ± 1 0 ± 0 288 ± 32 Wed 10-July-2024 13 ± 0 3265 ± 280 137 ± 16 22 ± 5 0 ± 0 318 ± 40 Tue 13-May-2025 11 ± 1 1530 ± 7 82 ± 7 4 ± 1 2 ± 0 118 ± 67 Wed 14-May-2025 31 ± 1 5677 ± 899 213 ± 18 13 ± 0 9 ± 1 195 ± 43 Thu 15-May-2025 28 ± 2 5742 ± 92 201 ± 3 11 ± 2 10 ± 0 190 ± 2 Fri 16-May-2025 26 ± 1 8731 ± 581 255 ± 3 1 ± 0 10 ± 1 202 ± 31 Sat 17-May-2025 21 ± 2 7656 ± 234 249 ± 43 27 ± 4 13 ± 1 219 ± 2 Sun 18-May-2025 13 ± 2 7552 ± 49 318 ± 64 32 ± 5 9 ± 0 206 ± 41 Mon 19-May-2025 30 ± 0 4760 ± 39 175 ± 6 34 ± 4 10 ± 1 171 ± 10 1 Normalized mass loads are not corrected by a Cf. EP uncertainty (Baumgartner et al., 2025 ; Baz-Lomba et al., 2019 ; Castiglioni et al., 2013 ), but were not available in the present study. Consumption estimates were derived using established WBE approaches ( Supplementary Material, Eqs. S2–S3; Table S4 ). COC consumption was estimated using BE as the primary biomarker due to its higher urinary excretion and greater stability in WW compared with the parent compound (Castiglioni et al., 2013 ). For ATS (AMP, METH and MDMA), consumption was back-calculated using parent drugs as biomarkers, consistent with their predominantly unchanged excretion (Gracia-Lor et al., 2016 ) ( Table S4 ). For PHT, a Cf based on an unchanged excretion fraction representative of uncontrolled urinary pH conditions was applied (Baselt, 1989 ), and dose-equivalent metrics were used to provide context on the potentially treated population (Table 4 ; Table S4 ). For KET, normalized mass loads are reported without Cf correction because pharmacokinetic excretion profiles and WW observations remain inconsistent across studies, and KET mass loads are commonly reported uncorrected to facilitate international comparisons, reflecting methodological variability reported in the literature. Table 4 Therapeutic equivalent doses and estimated treated population for PHT. Year Mean PHT mass loads (mg·day⁻¹·1000 inh⁻¹) Equivalent doses (per 1000 inh·day) Estimated treated population (%) Prevalence of obesity (%) 2024 302.32 8.08 0.81 40.2 1 42.0 2 2025 186.17 4.96 0.50 1 (Ministerio de Salud Pública de Chile, 2018 ); 2 (World Obesity Federation, 2025 ) 3.4 Influence of population-normalization assumptions on consumption estimates Because Chile-specific per-capita NH₃–N excretion factors are not currently available, EP was estimated using a literature-derived value of 8 g·day⁻¹·inhabitant⁻¹ ( Table S2 ). To assess the sensitivity of the results to this assumption, alternative values of 6 and 10 g·day⁻¹·inhabitant⁻¹ were considered, representing commonly reported lower and upper bounds for mixed WW systems. This range falls within values reported for urban WW catchments worldwide ( Table S2 ), providing a plausible range to assess the sensitivity of population-normalized estimates to normalization assumptions. Across this range, absolute consumption estimates varied by approximately ± 25% relative to the baseline assumption, as expected from the proportional relationship between NH₃–N load and EP estimates. This variation reflects the expected proportional scaling associated with changes in the assumed per-capita NH₃–N load, these differences did not substantially alter the relative ranking of substances or the direction of observed differences between campaigns. Although absolute magnitudes depend on normalization assumptions, the comparative short-term patterns observed in this study remained consistent across plausible NH₃–N scenarios, supporting the interpretation of relative differences under varying population-normalization assumptions. 3.5. Temporal patterns and interpretation: weekdays, weekends and holidays Figure 1 summarizes the distribution of population-normalized mass loads and estimated consumption of the monitored substances across weekday, weekend, and holiday sampling events (mg·day⁻¹·1000 inhabitants⁻¹). Individual daily values are discussed in the text and reported in Table 3 . Given the limited temporal coverage, particularly in 2024, these patterns are interpreted as reflecting short-term variability rather than evidence of seasonality or stable annual cycles. These patterns underscore the influence of short-term population dynamics and sampling timing on WW measurements and should therefore be interpreted within the constraints of the sampling design. Regarding COC, BE-based consumption estimates showed the highest values among monitored substances. In 2024, the maximum value occurred on 1 May (public holiday), coinciding with reduced NH₃–N loads. This indicates that population size alone does not explain the elevated per-capita cocaine consumption estimate and may reflect higher per-capita contributions during the holiday or changes in contributing population composition. In 2025, the highest daily COC signal occurred during the Friday–Monday period, with Friday showing the maximum. Direct weekday–weekend comparisons between 2024 and 2025 are limited because the 2024 campaign did not systematically cover the full Friday–Monday window; nevertheless, mean daily COC consumption was higher in 2025 than in 2024 (Table 3 ), and this difference between campaigns was preserved under NH₃–N sensitivity scenarios (Section 3.4 ), supporting the robustness of this comparison under the evaluated assumptions. Boxplots represent the median and interquartile range, with whiskers indicating minimum and maximum values and points corresponding to individual sampling events Only one sampling event corresponds to a public holiday; therefore, this category is not treated as a distribution For KET, values correspond to population-normalized mass loads, whereas for the remaining substances values represent back-calculated consumption estimates. COC/BE ratios were used to evaluate potential contributions from unmetabolized COC. Human physiology suggests that COC/BE in WW is typically ~ 0.1 or lower (Castiglioni et al., 2013 ). Higher ratios are commonly interpreted as indicative of non-metabolic inputs (e.g., direct disposal, spillage during handling or transport, washing of utensils or hands) (Castiglioni et al., 2011 ; Lai et al., 2013 ). In this study, COC/BE ranged from 0.21 to 0.36 (mean 0.29) in 2024 and from 0.25 to 0.46 (mean 0.39) in 2025 (Table 2 ), suggesting contributions of unmetabolized COC in both campaigns, with a higher contribution in 2025. Similar ratios above 0.1 have been reported elsewhere (Castiglioni et al., 2013 ; Lai et al., 2013 ; van Nuijs et al., 2009 ). COC may undergo partial transformation within sewer systems through hydrolysis and biofilm-associated transformation prior to sampling (Pagsuyoin et al., 2022 ; Thai et al., 2014 ). While BE remains the most robust biomarker for estimating COC consumption in WBE, episodic direct inputs and sewer dynamics may influence absolute mass load magnitudes, may influence mass load estimates and should be considered when interpreting these results. KET, a dissociative anesthetic widely used in veterinary medicine and, to a lesser extent, in human medicine (International Narcotics Control Board, 2025 ), showed different temporal patterns between campaigns. In 2024, KET mass loads exhibited marked daily variability without a consistent weekday–weekend structure. In 2025, a clearer weekend-associated pattern was observed, with the maximum on Sunday 18 May (Table 3 ). Notably, this peak coincided with the lowest EP estimates of the campaign ( Table S1 ), suggesting that elevated KET mass loads are more likely associated with higher per-capita contributions rather than increased contributing population. Comparison between campaigns also indicated higher mean KET loads in 2025 than in 2024 (Table 3 ), a finding that remained consistent under NH₃–N sensitivity scaling (Section 3.4 ), suggesting that this pattern is not solely explained by population-normalization assumptions. MDMA was detected in all samples, indicating consistent presence in the catchment. In 2024, MDMA loads increased on 1 May, similarly to COC, suggesting that this increase may not be explained solely by population size. In 2025, MDMA displayed a weekend-associated signature, with higher loads on Friday and Saturday, consistent with widely reported recreational-use patterns in multi-city studies (Cruz-Cruz et al., 2021 ; European Union Drugs Agency (EUDA), 2025 ; Rice et al., 2020 ; Yargeau et al., 2014 ). AMP and METH increased in 2025 compared with 2024 but remained low relative to COC and KET across sampling days (Fig. 1 ; Table 3 ). In 2024, all METH concentrations were below the LOQ; therefore, the corresponding consumption estimates rely on concentrations detected between the LOD and LOQ and should be interpreted with caution, particularly when comparing across campaigns. Further considerations regarding ATS sources and analytical constraints are discussed in Section 3.7 . PHT showed mixed temporal behavior. In 2024, the highest value occurred on the public holiday, while in 2025 elevated values were observed during the weekend. As a prescription drug, short-term variation in PHT use may reflect resident-population dominance during weekends/holidays and catchment population dynamics, rather than direct evidence of misuse. Dose-equivalent interpretation supports this view, as inferred treated fractions remained low despite relatively high mass loads (Table 4 ), indicating limited contribution from treated populations. The lower mean PHT consumption observed in 2025 compared with 2024 may reflect changes in prescription dynamics; however, the limited temporal coverage does not allow causal attribution. Recent regulatory measures in Chile, including implementation of electronic prescription monitoring systems (Gobierno de Chile, 2025 ), may influence controlled stimulant dispensing, but dedicated pharmacoepidemiologic data would be required to evaluate such effects, and these observations should therefore be interpreted cautiously. Temporal profiles were influenced by population dynamics in this mixed catchment (Sections 3.3 – 3.4 ). Patterns such as elevated KET mass loads and persistent COC consumption were observed across normalization scenarios, suggesting that these short-term patterns can be consistently identified under the conditions evaluated in this study. 3.6. Comparison with international studies, surveys, and national seizures Comparisons were performed considering whether reported mass loads were corrected using Cf, depending on the methodological approach adopted in each study. For COC, mean daily consumption estimates for 2024 and 2025 (4,288 vs 5,949 mg·day⁻¹·1000 inhabitants⁻¹) correspond to BE mass loads of 1,194 and 1,657 mg·day⁻¹·1000 inhabitants⁻¹, respectively, when expressed without applying Cf = 3.59 (Table 3 ). These values are within the range of those reported in the European multi-city WW study coordinated by SCORE and EUDA (European Union Drugs Agency (EUDA), 2025 ). Within that framework, 2024 BE mass loads at SP-WWTP were of the same order of magnitude as high-consumption sites (e.g., Belo Horizonte, Natal, Antwerp), while 2025 loads were higher than some European sites (e.g., Tarragona, Brussels) but remained below the highest values reported for Brazil and Antwerp (European Union Drugs Agency (EUDA), 2025 ). Beyond the EUDA framework, international WBE studies report a broad range of BE-based COC loads and consumption estimates depending on normalization and Cf choices. Multi-city programs in Australia typically report mean COC consumption in the order of several hundred mg·day⁻¹·1000 inhabitants⁻¹, with substantial variability across catchments and time (ACIC., 2024a, 2024b). Canadian cities have reported comparatively low BE-based mass loads (Yargeau et al., 2014 ), whereas higher levels have been observed in specific settings in the Americas, including holiday-associated peaks (Croft et al., 2020 ; da Silva et al., 2018 ). Within Chile, recent WBE studies conducted in the Biobío Region have reported relatively high BE-derived COC consumption in several cities, including Concepción, Los Ángeles and Lebu (Reis et al., 2026 ). Reported values for these locations fall within international WW monitoring programs. For example, EUDA reports average BE-derived loads of approximately 1709, 2628 and 3110 mg·day⁻¹·1000 inhabitants⁻¹ for Concepción, Los Ángeles and Lebu, respectively (European Union Drugs Agency (EUDA), 2025 ). These values exceed those obtained in the present study, although the mass load reported for Concepción is comparable to our 2025 estimate (1657 mg·day⁻¹·1000 inhabitants⁻¹). These observations suggest that COC consumption in Chile falls within the range reported for several urban locations internationally. For KET, mean daily mass loads in this study (154 and 213 mg·day⁻¹·1000 inhabitants⁻¹ in 2024 and 2025) are within the upper range of values reported in many cities. EUDA lists the highest KET loads for Amsterdam, São Paulo, Eindhoven, Budapest and Antwerp Zuid (European Union Drugs Agency (EUDA), 2025 ). In this context, the SP-WWTP mean in 2024 exceeded Amsterdam’s reported value, and the 2025 mean was higher still. Weekend-associated KET peaks observed here are consistent with reports from other regions where KET exhibits recreational-use signatures (Castiglioni et al., 2015 ; Cruz-Cruz et al., 2021 ; Rice et al., 2020 ; Van Wichelen et al., 2025 ). National evidence supports increased KET availability in Chile in recent years. In particular, seizure statistics report a marked increase between 2020 and 2021 (from 133,285 kg to 276,820 kg of KET), alongside an increase in “fake 2C-B” cases later confirmed as KET, which may be relevant for interpreting KET loads observed in this study (Chicahual B et al., 2023 ; Unidad Especializada en Tráfico Ilícito de Estupefacientes y Sustancias Sicotrópicas, 2021 , 2022 ; United Nations Office on Drugs and Crime (UNODC), 2021 ). These levels fall within the range reported in other regions, where the occurrence of psychoactive substances in aquatic environments has been associated with potential ecological concerns, supporting their consideration as emerging contaminants of concern. ATS have long exhibited geographically structured profiles in WBE. European multi-city studies report pronounced weekend-associated MDMA patterns and region-specific AMP/METH configurations (European Union Drugs Agency (EUDA), 2025 ; Ort et al., 2014 ), whereas Australia often shows methamphetamine-dominated patterns with high MDMA in several capitals (ACIC., 2024b). In this framework, the SP-WWTP profile is characterized by lower ATS levels than COC and KET, with declining MDMA between 2024 and 2025 and modest increases in AMP/METH (Table 3 ). Interpretation should consider the analytical and source-related uncertainties discussed in Section 3.7 . For PHT, mean daily consumption in this study (303 and 186 mg·day⁻¹·1000 inhabitants⁻¹ in 2024 and 2025) exceeds values reported in South Korea (Kim & Oh, 2020 ) and several U.S. studies (Lemas et al., 2021 ; Oliveira et al., 2015 ). However, dose-equivalent estimates indicate that treated fractions remain low (Table 4 ), suggesting that high mass loads do not translate into large treated proportions at the catchment scale. This underscores the importance of interpreting prescription-drug patterns using complementary metrics (mass loads and dose-equivalents) and considering catchment population dynamics (Sections 3.3 – 3.5 ). Although FEN and NOR were not detected during the study period, their emergence in seizure statistics highlights the importance of continued targeted WW monitoring, particularly in the context of emerging substances with potential environmental relevance. 3.7 Sources of uncertainty and study limitations. Several uncertainties must be considered when interpreting the consumption estimates derived from this study. First, population normalization relies on NH₃–N as an indirect biomarker and on a per-capita NH₃–N load derived from international literature ( Table S2 ). Absolute values can vary depending on the assumptions used (Section 3.4 ), although relative patterns and substance ranking appeared consistent within the evaluated scenarios. Second, back-calculation depends on pharmacokinetic parameters and Cf values that are not fully harmonized across WBE studies, limiting strict comparability of absolute consumption estimates between regions and time periods. For PHT, excretion data remains limited and can depend on study design and urinary pH (Baselt, 1989 ). For KET, uncertainties in excretion profiles and the potential influence of transformation and/or direct disposal support reporting mass loads without correction. Third, alternative sources may bias ATS estimates. AMP mass loads may reflect recreational use but also metabolic interconversion from METH and contributions from pharmaceuticals such as fenproporex, selegiline or lisdexamfetamine (Gracia-Lor et al., 2016 ; International Narcotics Control Board, 2025 ; van Nuijs et al., 2011 ). Enantiomeric information was not available, limiting discrimination between licit and illicit ATS sources (Castrignanò et al., 2018 ; Kasprzyk-Hordern et al., 2010 ; van Nuijs et al., 2011 ). Nevertheless, INCB reports indicate that Chile did not import AMP for medical purposes in 2024 (International Narcotics Control Board, 2025 ), which suggests limited therapeutic AMP contributions in this context. Fourth, temporal coverage was constrained. The 2024 campaign did not include systematic sampling across the full Friday–Monday window, limiting direct cross-year weekday–weekend comparisons. Sampling covered discrete periods rather than continuous annual monitoring, so seasonal effects cannot be resolved, and formal hypothesis testing is not warranted. Finally, the catchment is relatively small (~ 20,000 inhabitants according to operator data) compared with the SMR population (Instituto Nacional de Estadística, 2024 ). Therefore, the results should not be interpreted as representative of metropolitan-scale conditions. Despite these limitations, this study provides an application of WBE for monitoring psychoactive substances in a heterogeneous urban–industrial catchment in central Chile. By combining dynamic population normalization with short-term sampling, the study generates baseline data and identifies detectable patterns under the conditions evaluated, particularly for COC and KET. These findings are consistent with recent WBE studies conducted in southern Chile (Reis et al., 2026 ) and may support future work focused on expanded spatial coverage, locally derived normalization parameters, increased temporal resolution, and systematic triangulation with prescribing and seizure statistics. Furthermore, the environmental fate, removal efficiency, and ecotoxicological effects of these compounds were not evaluated in this study, which limits direct assessment of ecological risk and should be addressed in future research. 4. Conclusions This study provides insight into short-term variability of psychoactive substances in WW and underscores the importance of sampling design and dynamic population normalization for interpreting WBE data in mixed urban systems. Monitoring campaigns conducted in 2024 and 2025 revealed daily variability in mass loads of COC, KET, ATS and PHT in a mixed urban catchment receiving residential, commercial and industrial WW inputs, supporting the application of WBE as a complementary approach for assessing short-term dynamics of chemical markers in WW systems. COC exhibited the highest consumption levels among the monitored analytes, with higher values in 2025 and elevated levels during the Friday–Monday period. KET mass loads were comparatively high relative to several international monitoring sites and showed weekend-associated increases consistent with patterns reported in other studies. In contrast, MDMA showed lower levels in 2025, while PHT mass loads indicated a limited but detectable therapeutic-equivalent use within the catchment population. Population-normalized estimates carry inherent uncertainty, primarily due to the absence of Chile-specific NH₃–N per-capita excretion factors and limited pharmacokinetic information for key substances such as KET and PHT. Nevertheless, the relative temporal patterns observed across weekdays, weekends and the public holiday remained consistent under plausible normalization scenarios, suggesting that the main pattern are robust under the evaluated assumptions despite uncertainties associated with population normalization and Cf. These results underscore the need for locally derived correction factors, updated pharmacokinetic data and improved population-normalization approaches to reduce uncertainty in future WBE studies. Beyond their role as indicators of consumption, the detected psychoactive substances represent a continuous input of bioactive compounds into aquatic systems. These findings highlight the importance of considering these substances as emerging contaminants and underscore the need for further research on their environmental fate, persistence, and potential ecological effects, particularly in regions where monitoring data remain limited. Declarations Acknowledgements The authors thank Santiago Poniente WWTP for their support during sample collection. Wendy Calzadilla and Ricardo Salazar-González acknowledge support from ANID-funded research projects. Ethical Approval Not applicable. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by ANID FONDECYT Postdoctoral 3230488, ANID/FONDAP/1523A0006 and FONDECYT Regular 1220077. Availability of data and materials The data supporting the findings of this study are included within the article and its Supplementary Information. Additional data is available from the corresponding author upon request. Authors’ contributions Karla Montenegro: Investigation, Methodology, Writing – Original draft preparation. Andrés Yar: Investigation, Software, Validation. Sebastián Campos: Data curation, Formal analysis, Writing – Original draft preparation. Ricardo Salazar-González: Resources, Funding acquisition, Writing – Review and Editing. Wendy Calzadilla: Conceptualization, Investigation, Project administration, Writing – Original draft preparation, Funding acquisition, Writing – Review and Editing. Use of generative AI During the preparation of this work, the authors used ChatGPT (OpenAI) to assist with language editing, text refinement, and content organization. After using this tool, the authors reviewed and edited the content and take full responsibility for the final version of the manuscript. References ACIC., A. C. I. C. (2024a, April). National Wastewater Drug Monitoring Program. 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Comparative measurement and quantitative risk assessment of alcohol consumption through wastewater-based epidemiology: An international study in 20 cities. Science of the Total Environment , 565 , 977–983. https://doi.org/10.1016/j.scitotenv.2016.04.138 Salgueiro-González, N., Rousis, N. I., Gracia-Lor, E., Borsotti, A., Zuccato, E., & Castiglioni, S. (2021). First comprehensive study of alcohol consumption in Italy using wastewater-based epidemiology. Science of the Total Environment , 776 , 145863. https://doi.org/10.1016/j.scitotenv.2021.145863 Salgueiro-González, N., Zuccato, E., & Castiglioni, S. (2022). Nationwide investigation on the use of new psychoactive substances in Italy through urban wastewater analysis. Science of the Total Environment , 843 . https://doi.org/10.1016/j.scitotenv.2022.156982 SANTE. (2021). Analytical quality control and method validation procedures for pesticide residues analysis in food and feed SANTE 11312/2021. In Document N° SANTE 11312/2021 . SENDA. (2024). Plan de Acción 2024-2030 de la Estrategia Nacional de Drogas . https://www.senda.gob.cl/plan-2024-2030/ SENDA. (2025, January 17). SENDA e ISP informan de seis nuevas drogas sintéticas detectadas en el país . senda.gob.cl/noticia/senda-e-isp-informan-de-seis-nuevas-drogas-sinteticas-detectadas-en-el-pais/ Sodré, F. F., Feitosa, R. S., Jardim, W. F., & Maldaner, A. O. (2018). Wastewater-based epidemiology of cocaine in the Brazilian Federal District: spatial distribution, weekly variation and sample preservation strategies. Journal of the Brazilian Chemical Society , 29 , 2287–2298. Thai, P. K., Jiang, G., Gernjak, W., Yuan, Z., Lai, F. Y., & Mueller, J. F. (2014). Effects of sewer conditions on the degradation of selected illicit drug residues in wastewater. Water Research , 48 (1), 538–547. https://doi.org/10.1016/j.watres.2013.10.019 Unidad Especializada en Tráfico Ilícito de Estupefacientes y Sustancias Sicotrópicas. (2021, September). Observatorio del Narcotráfico. Informe 2021 . https://www.fiscaliadechile.cl/sites/default/files/documentos/Informe_2021_Observatorio_Narcotrafico_Chile.pdf Unidad Especializada en Tráfico Ilícito de Estupefacientes y Sustancias Sicotrópicas. (2022, December). Observatorio del Narcotráfico. VII Informe Anual 2022 . https://www.fiscaliadechile.cl/sites/default/files/documentos/Informe_2022_Observatorio_Narcotrafico_Chile.pdf United Nations Office on Drugs and Crime (UNODC). (2021, September). Synthetic drugs and new psychoactive substances in Latin America and the Caribbean 2021 . https://www.unodc.org/unodc/en/scientists/2021-synthetic-drugs-and-new-psychoactive-substances-in-latin-america-and-the-caribbean.html van Nuijs, A. L. N., Castiglioni, S., Tarcomnicu, I., Postigo, C., de Alda, M. L., Neels, H., Zuccato, E., Barcelo, D., & Covaci, A. (2011). Illicit drug consumption estimations derived from wastewater analysis: A critical review. In Science of the Total Environment (Vol. 409, Number 19, pp. 3564–3577). https://doi.org/10.1016/j.scitotenv.2010.05.030 van Nuijs, A. L. N., Pecceu, B., Theunis, L., Dubois, N., Charlier, C., Jorens, P. G., Bervoets, L., Blust, R., Neels, H., & Covaci, A. (2009). Spatial and temporal variations in the occurrence of cocaine and benzoylecgonine in waste- and surface water from Belgium and removal during wastewater treatment. Water Research , 43 (5), 1341–1349. https://doi.org/10.1016/j.watres.2008.12.020 Van Wichelen, N., Boogaerts, T., Quireyns, M., Dermitzaki, R., Delputte, P., Hudda, N. U., De Roeck, N., Verhaegen, B., Van Hoorde, K., Maloux, H., Hutse, V., Gys, C., Covaci, A., & van Nuijs, A. L. N. (2025). Ketamine, a new (or old) kid on the block: A comprehensive three-year spatio-temporal study in Belgium through wastewater-based epidemiology. Water Research , 276 . https://doi.org/10.1016/j.watres.2025.123269 World Obesity Federation. (2025, March). World Obesity Atlas . https://data.worldobesity.org/publications/?cat=23 Yargeau, V., Taylor, B., Li, H., Rodayan, A., & Metcalfe, C. D. (2014). Analysis of drugs of abuse in wastewater from two Canadian cities. Science of the Total Environment , 487 (1), 722–730. https://doi.org/10.1016/j.scitotenv.2013.11.094 Zuccato, E., Chiabrando, C., Castiglioni, S., Bagnati, R., & Fanelli, R. (2008). Estimating community drug abuse by wastewater analysis. Environmental Health Perspectives , 116 (8), 1027–1032. https://doi.org/10.1289/ehp.11022 Zuccato, E., Chiabrando, C., Castiglioni, S., Bagnati, R., Schiarea, S., & Fanelli, R. (2005). Cocaine in surface waters: a new evidence-based tool to monitor community drug abuse. Environmental Health: A Global Access Science Source , 4 , 1–7. https://doi.org/10.1186/ 1476-069X-4-14 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9236663","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629203207,"identity":"347c83d5-ee31-48c2-bd5c-f378354b1968","order_by":0,"name":"Wendy Calzadilla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYJACCRDBB2IkFFgw8EMELQhrYQNrMZBgkGxACBKhhQGoxeAAAS0Gx3sP3uZhsJNn4z/88MYDAwl54xvpTzcw1ODRcuZcsjUPQ7JhG8MxYwugwwy33cgxu8FwDLcWM6ACaR6GA4xtjA1mIL8wArWw3WBswKPl/huwFvs2ZvZvIC32m2ekP8Ov5QYPWEtiGxsP2JbEDRIJZni12J/JMbacY5Cc3MbDUwzyS/KMM2/MbiTg8Ytk+xnDG28q7Gz7+Y9vvPmjwsa2vx3osA81Nji1gAATjwG6UAJeDQwMjD8IKBgFo2AUjIIRDgDNkUxwbu6h3AAAAABJRU5ErkJggg==","orcid":"","institution":"Federico Santa María Technical University","correspondingAuthor":true,"prefix":"","firstName":"Wendy","middleName":"","lastName":"Calzadilla","suffix":""},{"id":629203211,"identity":"52ae6e47-7c74-4c09-9abb-1644a1aaec91","order_by":1,"name":"Karla Montenegro","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Karla","middleName":"","lastName":"Montenegro","suffix":""},{"id":629203218,"identity":"2c198e70-4ccc-4edb-9fa5-f3350c1f7311","order_by":2,"name":"Andrés Yar","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Andrés","middleName":"","lastName":"Yar","suffix":""},{"id":629203231,"identity":"b886e7db-be1a-4d41-8df1-285ff1662182","order_by":3,"name":"Sebastián Campos","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Sebastián","middleName":"","lastName":"Campos","suffix":""},{"id":629203236,"identity":"49841d47-4147-4bf9-a782-821e7bb8d960","order_by":4,"name":"Ricardo Salazar-González","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Salazar-González","suffix":""}],"badges":[],"createdAt":"2026-03-26 16:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9236663/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9236663/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107897304,"identity":"52eec01e-4c50-4ed4-8e36-b05632192ea2","added_by":"auto","created_at":"2026-04-27 10:57:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91653,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of population-normalized mass loads and back-calculated consumption of monitored substances across weekday, weekend and holiday sampling events (mg·day⁻¹·1000 inhabitants⁻¹) (a) COC (b) KET and PHT (c) AMP, METH and MDMA Boxplots represent the median and interquartile range, with whiskers indicating minimum and maximum values and points corresponding to individual sampling events Only one sampling event corresponds to a public holiday; therefore, this category is not treated as a distribution\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9236663/v1/cc4990b19bc04a1c7f32594f.png"},{"id":107897501,"identity":"7c91e27e-c7f3-451d-a438-c55293460f0f","added_by":"auto","created_at":"2026-04-27 10:57:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":731761,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9236663/v1/d3c9aee8-5006-4a60-869e-63973fcf72a2.pdf"},{"id":107897416,"identity":"6e2d8576-f97a-4a71-8db4-aa19acdcedfe","added_by":"auto","created_at":"2026-04-27 10:57:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":385822,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9236663/v1/577593c05588836659bbeeb2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Occurrence and short-term variability of psychoactive substances in wastewater from a mixed urban catchment in central Chile","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTraditional population surveys, clinical reports, and drug seizure statistics may not fully capture short-term variability in licit and illicit psychoactive substance use, particularly in dynamic urban settings where patterns may fluctuate within days. Wastewater-based epidemiology (WBE) has emerged as a complementary approach for assessing community-level exposure to these substances. In this context, WBE is used to monitor human metabolic residues in wastewater (WW), allowing the assessment of community-level occurrence and short-term variability of psychoactive substances. Over the past two decades, WBE has become a widely applied approach for monitoring temporal trends and supporting early warning systems in public health (Castiglioni et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gonz\u0026aacute;lez-Mari\u0026ntilde;o et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Salgueiro-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince early demonstrations that cocaine (COC) and its primary metabolite benzoylecgonine (BE) can be reliably quantified in municipal WW, WBE has been implemented to estimate population-normalized mass loads of widely consumed and emerging substances and to investigate temporal patterns across cities (Zuccato et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Castiglioni et al., 2006). Coordinated international initiatives, including the SCORE (Sewage analysis CORe group Europe) network in Europe and monitoring programs led by the European Union Drugs Agency (EUDA), as well as national programs in Australia, Canada and other countries, now routinely apply WBE to monitor trends and support early warning systems for emerging substances (ACIC., 2024a, 2024b; Baz-Lomba et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; EUDA, 2025; Health Canada, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Latin America, published WBE studies are currently reported from a limited number of countries, primarily Brazil, Colombia and Mexico, where city-scale monitoring has quantified COC, ATS and other biomarkers (Bijlsma et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cruz-Cruz et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gomes et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sodr\u0026eacute; et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Broader regional expansion remains limited due to variability in WW infrastructure, analytical capacity and uncertainties associated with population normalization (Gonz\u0026aacute;lez-Mari\u0026ntilde;o et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hahn et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sodr\u0026eacute; et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In Chile, recent multi-year monitoring conducted in the Biob\u0026iacute;o Region demonstrated the feasibility of regional-scale wastewater-based drug surveillance (Reis et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). However, smaller mixed urban\u0026ndash;industrial catchments, particularly within the Santiago Metropolitan Region (SMR), remain less characterized. In addition, there is limited evidence on how short-term variability (including weekday, weekend and holiday dynamics) and uncertainties associated with population normalization, which may influence WBE-derived estimates in mixed urban catchments.\u003c/p\u003e \u003cp\u003eThe SMR provides a suitable context for examining these aspects. National indicators in Chile continue to rely primarily on self-reported surveys and seizure statistics (SENDA, 2025), while the National Drug Strategy Action Plan 2024\u0026ndash;2030 highlights the need to strengthen monitoring frameworks for emerging and synthetic drugs (SENDA, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this context, WBE provides complementary information for assessing short-term variability at the catchment level.\u003c/p\u003e \u003cp\u003eThe Santiago Poniente wastewater treatment plant (SP-WWTP) serves a mixed urban catchment characterized by residential, commercial and industrial WW inputs (Aguas Santiago Poniente, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). COC is consistently identified as a major illicit substance in Chile according to national surveys (Observatorio Chileno de Drogas, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and is therefore considered a key target compound in wastewater-based studies, alongside other psychoactive substances. Analyzing its occurrence in WW, together with other psychoactive substances, allows examination of short-term variability at the catchment level under dynamic population conditions. In addition to their role as indicators of human consumption, these substances are also recognized as emerging contaminants in aquatic environments, as their continuous release through WW may result in chronic low-level exposure in receiving waters (Daughton \u0026amp; Ruhoy, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, characterizing their occurrence and short-term variability in WW is also relevant from an environmental exposure perspective, particularly in regions where monitoring data remains scarce.\u003c/p\u003e \u003cp\u003eIn this study, population-normalized mass loads and consumption of ketamine (KET), COC, ATS and phentermine (PHT) were investigated in a mixed urban catchment of the SMR during two monitoring campaigns conducted in 2024 and 2025. Temporal variability across weekdays, weekends and a public holiday was evaluated using dynamic population normalization. The influence of short-term variability and population dynamics on the interpretation of WBE-derived indicators is examined in mixed urban catchments. While based on a limited dataset, this case study contributes to the understanding of variability patterns in mixed or relatively small catchments and highlights considerations relevant for the interpretation of short-term WBE monitoring campaigns.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Chemical and materials\u003c/h2\u003e \u003cp\u003eAnalytical reference standards and their isotopically labelled internal standards (ILIS) analogues of illicit and licit drugs were purchased from Cerilliant (Round Rock, TX, USA) and Merck (Darmstadt, Germany) as solutions in methanol or acetonitrile. Methanol, acetonitrile (HPLC-grade), and formic acid (LC-MS grade) were acquired from Scharlab (Scharlab, Spain). HPLC-grade water (resistivity\u0026thinsp;\u0026gt;\u0026thinsp;18 MΩ cm) was obtained by purifying distilled water in a Thermo Scientific Smart2Pure system (\u0026Aring;tvidaberg, Sweden). See page S1 of the Supplementary Material for further details about chemicals and materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Characteristics of SP-WWTP\u003c/h2\u003e \u003cp\u003eThe influent samples analyzed were collected at the SP-WWTP, located in the Enea industrial zone in the municipality of Pudahuel in the SRM, Chile, adjacent to Arturo Merino Ben\u0026iacute;tez International Airport. According to data provided by SP-WWTP, in December 2024, there were 3681 customers connected to WWTP, equivalent to approximately 20000 inhabitants, of which 85% were residential customers, 8% were commercial customers, 1% were industrial customers, and 6% were other customers. The average daily flow of WW treated by the WWTP is 3503 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sample collection\u003c/h2\u003e \u003cp\u003eA total of 14 composite 24-hour urban WW influent samples were collected from SP-WWTP using a Teledyne ISCO 6712 automatic sampler (Lincoln, NE, USA). Samples were collected in time-proportional sampling mode. Subsamples of 50 mL were taken every 15 minutes, with start and end times (from 9:00 a.m. to 9:00 a.m.). Because samples were collected from 9:00 a.m. to 9:00 a.m., consumption occurring during late-night hours may be reflected in the following day\u0026rsquo;s composite sample. In 2024, data were collected on four consecutive days, in April/May and July. It began on Monday, April 29, and continued until Thursday, May 3. Then, during July, it started on Monday, July 8, and continued until Thursday, July 11. In 2025, data were collected on seven consecutive days, from Tuesday, May 13 to Tuesday, May 20. Following the WBE framework, weekdays were defined as Tuesday\u0026ndash;Thursday and the weekend period as Friday\u0026ndash;Monday. A public holiday (1 May 2024) was considered separately due to its potential influence on wastewater composition. See pages S1 and S2 of the Supplementary Material for further details about sample collection and characterization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analytical procedure and instrumentation\u003c/h2\u003e \u003cp\u003ePreliminary tests showed that SPE preconcentration led to signal saturation for BE, COC, KET and PHT; therefore, direct injection was used to ensure quantification within the linear range of the method. In contrast, analytes present at lower concentrations, including AMP, METH, MDMA, fentanyl (FEN), and norfentanyl (NOR), required SPE preconcentration in accordance with our previously reported workflow (Herrera-Mu\u0026ntilde;oz et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). See pages S4 and S5 of the Supplementary Material for further details about the analytical procedure and instrumentation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Method validation and quality assurance\u003c/h2\u003e \u003cp\u003eMethodology performance was evaluated in terms of linearity, accuracy, precision, limits of detection and quantification, and matrix effects. See pages S8 and S9 of the Supplementary Material for further details about method validation and quality assurance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Back-calculation of normalized mass loads and consumption of licit and illicit psychoactive substances\u003c/h2\u003e \u003cp\u003eConcentrations measured in influent WW were converted into population-normalized mass loads (mg\u0026middot;day⁻\u0026sup1;\u0026middot;1000 inhabitants⁻\u0026sup1;) using daily flow rates and an equivalent population (EP) estimated from NH₃\u0026ndash;N. Daily consumption estimates were obtained by applying compound-specific correction factors (Cf) based on urinary excretion and molar-mass relationships (Castiglioni et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zuccato et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Full equations, assumptions, and Cf sources are provided in the Supplementary Material (\u003cb\u003eEqs. S1\u0026ndash;S3; Table S4\u003c/b\u003e). For PHT, mass loads were additionally expressed as therapeutic-equivalent doses to estimate the potentially treated fraction of the population \u003cb\u003e(Eqs. S4\u0026ndash;S5; Table S4\u003c/b\u003e). Mean daily mass loads and consumption values reported in this study correspond to the arithmetic mean of individual daily estimates within each monitoring campaign.\u003c/p\u003e \u003cp\u003ePopulation normalization was performed using a per-capita NH₃\u0026ndash;N load of 8 g\u0026middot;day⁻\u0026sup1;\u0026middot;inhabitant⁻\u0026sup1;. To evaluate the sensitivity of consumption estimates to this assumption, alternative values of 6 and 10 g\u0026middot;day⁻\u0026sup1;\u0026middot;inhabitant⁻\u0026sup1; were tested, representing the lower and upper range commonly reported for mixed WW systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Method validation and quality assurance\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reports the performance parameters of the method. As shown, the calibration curves exhibited good linearity, with correlation coefficients greater than 0.99, and their working ranges adequately covered the concentration levels found in the samples. The comparison between calibration curves prepared in solvent and those prepared in matrix (WW) revealed a slope variability of less than 20% (\u003cb\u003eTable S5\u003c/b\u003e, pages S11-S12 in Supplementary Material) for all analytes except METH (25.8%); therefore, the analyses were performed using the calibration curve in solvent.\u003c/p\u003e \u003cp\u003eThe recoveries obtained for most compounds were satisfactory at three fortified levels, with recoveries ranging from 70% to 120% and precision (RSD) below 20%. In the case of NOR, relatively high recoveries were obtained at medium and high levels (122 and 123%, respectively), but with good RSD (9 and 8%, respectively).\u003c/p\u003e \u003cp\u003eRoutine analysis was considered satisfactory when the relative recoveries of QC samples fell within the 60\u0026ndash;140% range. Since the matrix used for the recovery tests contained detectable levels of some target analytes, the concentration measured in the \u0026ldquo;natural\u0026rdquo; (unspiked) samples was subtracted from that obtained in each spiked recovery test and QC.\u003c/p\u003e \u003cp\u003eLODs and LOQs ranged from 0.3 to 4.6 and 1.2 to 26 ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the case of BE, COC, KET, and PHT, the LOQ was estimated from chromatograms of non-spiked WW samples, without adding a standard to the sample.\u003c/p\u003e \u003cp\u003eAcross all three spiking levels, the analytes exhibited either ionization suppression or ionization enhancement, with matrix effect values deviating from 100%. COC and BE showed the strongest ionization enhancement at the lowest spiking level, with signal increases of 21% and 42%, respectively. In contrast, AMP, METH, MDMA, FEN, and NOR exhibited ionization suppression, with signal reductions ranging from 21% to 61% at the medium and high spiking levels. All these analytes were quantified following SPE sample treatment. Analytes measured by direct injection after four-fold dilution exhibited lower matrix effects than those subjected to SPE (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This observation is consistent with previous reports indicating that while SPE effectively preconcentrates analytes, it does not necessarily minimize matrix effects, as previously reported (Botero-Coy et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Finally, matrix effects were effectively minimized by applying ILIS for each compound (\u003cb\u003eTable S5\u003c/b\u003e, pages S11-S12 in Supplementary Material).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMethod performance parameters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnalyte\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLOD\u003c/p\u003e \u003cp\u003e(ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLOQ\u003c/p\u003e \u003cp\u003e(ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLinearity (solvent)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRecovery %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ePrecision (RSD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eMatrix effects (%, RSD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.5\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(\u0026micro;g L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(\u0026micro;g L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(\u0026micro;g L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.5\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(\u0026micro;g L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(\u0026micro;g L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(\u0026micro;g L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.5\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(\u0026micro;g L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(\u0026micro;g L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(\u0026micro;g L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10. 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e117 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e75 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e39 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e62 (19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e121 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e83 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e64 (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e142 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e110 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e87 (8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e61 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e63 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e54 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e102 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e97 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e87 (3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e104 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e82 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e43 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e44 (17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e107 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e43 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e44 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e40 (19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e122 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e123 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e60 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e79 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e61 (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e105(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e93 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e83 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e79 (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e*\u003c/b\u003eAnalytes determined by direct injection. Recovery (%) and precision (RSD) (n\u0026thinsp;=\u0026thinsp;5). Matrix effects (%, RSD) (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Occurrence of licit and illicit psychoactive substances in raw WW\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the concentrations (ng\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of the target biomarkers measured in influent samples during the 2024 and 2025 campaigns. Daily presence of BE, COC, KET, MDMA, AMP and PHT was evident across the sampled days, indicating consistent occurrence of these substances in the studied catchment.\u003c/p\u003e \u003cp\u003eThe highest concentrations were observed for BE, with values exceeding 7000 and 10000 ng\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, followed by COC (2000 and 4000 ng\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), KET (900 and 1100 ng\u0026middot; L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), PHT (800 and 500 ng\u0026middot; L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and MDMA (130 and 30 ng\u0026middot; L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in 2024 and 2025, respectively, suggesting differences in relative abundance among substance groups across campaigns.\u003c/p\u003e \u003cp\u003eThe presence of these compounds at ng L⁻\u0026sup1; levels in influent WW indicates their continuous input into WW systems under the studied conditions. Although removal efficiencies were not evaluated in this study, their occurrence suggests potential release into receiving waters and possible environmental exposure.\u003c/p\u003e \u003cp\u003eThe high concentrations of BE, COC, KET and PHT meant that SPE preconcentration was not required for reliable quantification; these analytes were therefore quantified by direct injection, avoiding saturation effects observed when SPE was applied to high-level samples. In contrast, AMP and METH occurred at substantially lower concentrations and required SPE preconcentration, reflecting differences in concentration ranges among target compounds.\u003c/p\u003e \u003cp\u003eThis concentration profile, characterized by high BE and COC levels and comparatively low AMP and METH levels, is consistent with previous reports from Latin American settings (Bijlsma et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Causanilles et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Devault et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), supporting the regional relevance of the observed patterns.\u003c/p\u003e \u003cp\u003eNeither FEN nor NOR was detected in influent samples during either campaign. This absence differs from reports in parts of North America and Mexico (Cruz-Cruz et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and may reflect regional differences in substance use patterns or market availability, underscoring the importance of continued targeted monitoring, especially as fentanyl-related harms and markets evolve dynamically across regions.\u003c/p\u003e \u003cp\u003eConfirmatory chromatograms and identity criteria based on retention time and q/Q ion ratios are provided in the Supplementary Material (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), following SANTE recommendations (SANTE, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcentrations (ng\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, RSD) of the target biomarkers of licit and illicit psychoactive substances measured in untreated WW in the 2024 and 2025 (n\u0026thinsp;=\u0026thinsp;2).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCon AMP (ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCon BE\u003c/p\u003e \u003cp\u003e(ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCon COC (ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCon FEN (ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCon KET (ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCon MDMA (ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCon METH (ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCon NOR (ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCon PHT (ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRatio COC/BE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMon 29-April-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5409 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1490 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e914 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e111 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt; LOQ*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e714 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTue 30-April-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6580 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2202 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e780 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt; LOQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e775 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWed 01-May-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7264 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1542 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e713 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e137 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt; LOQ*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e779 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThu 02-May-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6902 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1779 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e778 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e103 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt; LOQ*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e812 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMon 08-July-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5072 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1659 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e532 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt; LOQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e686 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTue 09-July-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5354 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1916 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e936 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt; LOQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e764 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWed 10-July-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5184 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1704 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e781 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e847 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTue 13-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2232 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e872 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e432 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt; LOQ*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e298 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWed 14-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8613 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2192 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1158 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e510 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThu 15-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8504 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3324 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1070 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e486 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFri 16-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10266 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4108 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1078 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e409 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSat 17-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8794 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3752 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1029 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e435 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSun 18-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6104 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2638 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e922 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e287 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMon 19-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6078 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2778 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e801 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e377 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eND: not detected; * Values above the LOD but below the LOQ\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Population normalization, mass loads and back-calculated consumption\u003c/h2\u003e \u003cp\u003ePopulation-normalized mass loads and estimated daily consumption (mg\u0026middot;day⁻\u0026sup1;\u0026middot;1000 inhabitants⁻\u0026sup1;) were calculated for all detected substances (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For concentrations detected above the LOD but below the LOQ, values were assigned as 0.5 \u0026times; LOQ, a common approach in WBE studies for handling concentrations below the quantification limit (Bijlsma et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ryu et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), whereas concentrations below the LOD were set to zero.\u003c/p\u003e \u003cp\u003eTo normalize daily mass loads, the equivalent population (EP) served by the WWTP was estimated using NH₃\u0026ndash;N (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Although the WWTP operator reports a connected population of approximately 20,000 inhabitants, this fixed nominal value may not capture short-term variability associated with daily mobility and changes in non-residential WW contributions, potentially affecting the interpretation of population-normalized estimates.\u003c/p\u003e \u003cp\u003eNH₃\u0026ndash;N is widely used as an indirect anthropogenic marker to support short-term population estimates in WBE and is generally considered less influenced by non-human sources than conventional parameters such as COD, BOD₅, total nitrogen or total phosphorus (Been et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For this reason, dynamic EP estimates based on NH₃\u0026ndash;N were used to approximate day-to-day variability within the catchment, acknowledging that uncertainties associated with this approach may influence normalized values.\u003c/p\u003e \u003cp\u003eMean EP values were comparable across campaigns (17,806\u0026thinsp;\u0026plusmn;\u0026thinsp;2,249 in 2024 and 17,490\u0026thinsp;\u0026plusmn;\u0026thinsp;3,976 in 2025), but daily EP varied substantially within each period, ranging from 13,596 to 21,017 in 2024 and from 11,774 to 22,162 in 2025. The lowest EP in 2024 coincided with 1 May (public holiday), which may reflect reduced commuting and commercial\u0026ndash;industrial activity. In 2025, lower EP values occurred on weekend days, which is consistent with typical mobility patterns. These observations indicate that short-term changes in contributing population may influence population-normalized estimates and support the use of dynamic normalization in this dataset, rather than relying on a fixed nominal population when interpreting short-term temporal variability in heterogeneous urban catchments. Complementary approaches (e.g., triangulation with mobility proxies) may help reduce\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNormalized mass loads and estimated consumption of psychoactive substances (mg \u0026middot; day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026middot;1000 inhabitants\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u0026thinsp;\u0026plusmn;\u0026thinsp;SD in untreated WW in 2024 and 2025.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003emg\u0026middot;day\u003csup\u003e-1\u003c/sup\u003e\u0026middot;1000 inhabitants\u003csup\u003e-1\u003c/sup\u003e \u0026plusmn; SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAMP\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCOC\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eKET\u003c/b\u003e \u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMDMA\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eMETH\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ePHT\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMon 29-April-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4186\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e197\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e105\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e302\u0026thinsp;\u0026plusmn;\u0026thinsp;63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTue 30-April-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4536\u0026thinsp;\u0026plusmn;\u0026thinsp;144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e150\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e70\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e292\u0026thinsp;\u0026plusmn;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWed 01-May-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6347\u0026thinsp;\u0026plusmn;\u0026thinsp;204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e174\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e147\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e372\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThu 02-May-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4397\u0026thinsp;\u0026plusmn;\u0026thinsp;111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e138\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e80\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e283\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMon 08-July-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3592\u0026thinsp;\u0026plusmn;\u0026thinsp;128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e105\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e41\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e266\u0026thinsp;\u0026plusmn;\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTue 09-July-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3694\u0026thinsp;\u0026plusmn;\u0026thinsp;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e180\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e288\u0026thinsp;\u0026plusmn;\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWed 10-July-2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3265\u0026thinsp;\u0026plusmn;\u0026thinsp;280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e137\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e22\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e318\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTue 13-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1530\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e82\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e118\u0026thinsp;\u0026plusmn;\u0026thinsp;67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWed 14-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5677\u0026thinsp;\u0026plusmn;\u0026thinsp;899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e213\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e9\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e195\u0026thinsp;\u0026plusmn;\u0026thinsp;43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThu 15-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e28\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5742\u0026thinsp;\u0026plusmn;\u0026thinsp;92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e201\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e11\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e190\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFri 16-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e26\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8731\u0026thinsp;\u0026plusmn;\u0026thinsp;581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e255\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e202\u0026thinsp;\u0026plusmn;\u0026thinsp;31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSat 17-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7656\u0026thinsp;\u0026plusmn;\u0026thinsp;234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e249\u0026thinsp;\u0026plusmn;\u0026thinsp;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e27\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e219\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSun 18-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7552\u0026thinsp;\u0026plusmn;\u0026thinsp;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e318\u0026thinsp;\u0026plusmn;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e9\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e206\u0026thinsp;\u0026plusmn;\u0026thinsp;41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMon 19-May-2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e30\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4760\u0026thinsp;\u0026plusmn;\u0026thinsp;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e175\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e34\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e171\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003csup\u003e \u003cb\u003e1\u003c/b\u003e \u003c/sup\u003e Normalized mass loads are not corrected by a Cf.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eEP uncertainty (Baumgartner et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Baz-Lomba et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Castiglioni et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), but were not available in the present study.\u003c/p\u003e \u003cp\u003eConsumption estimates were derived using established WBE approaches (\u003cb\u003eSupplementary Material, Eqs. S2\u0026ndash;S3; Table S4\u003c/b\u003e). COC consumption was estimated using BE as the primary biomarker due to its higher urinary excretion and greater stability in WW compared with the parent compound (Castiglioni et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For ATS (AMP, METH and MDMA), consumption was back-calculated using parent drugs as biomarkers, consistent with their predominantly unchanged excretion (Gracia-Lor et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (\u003cb\u003eTable S4\u003c/b\u003e). For PHT, a Cf based on an unchanged excretion fraction representative of uncontrolled urinary pH conditions was applied (Baselt, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), and dose-equivalent metrics were used to provide context on the potentially treated population (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; \u003cb\u003eTable S4\u003c/b\u003e). For KET, normalized mass loads are reported without Cf correction because pharmacokinetic excretion profiles and WW observations remain inconsistent across studies, and KET mass loads are commonly reported uncorrected to facilitate international comparisons, reflecting methodological variability reported in the literature.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTherapeutic equivalent doses and estimated treated population for PHT.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean PHT mass loads (mg\u0026middot;day⁻\u0026sup1;\u0026middot;1000 inh⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEquivalent doses (per 1000 inh\u0026middot;day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimated treated population (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrevalence of obesity (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e302.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e40.2 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e42.0 \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e1\u003c/sup\u003e (Ministerio de Salud P\u0026uacute;blica de Chile, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); \u003csup\u003e2\u003c/sup\u003e (World Obesity Federation, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Influence of population-normalization assumptions on consumption estimates\u003c/h2\u003e \u003cp\u003eBecause Chile-specific per-capita NH₃\u0026ndash;N excretion factors are not currently available, EP was estimated using a literature-derived value of 8 g\u0026middot;day⁻\u0026sup1;\u0026middot;inhabitant⁻\u0026sup1; (\u003cb\u003eTable S2\u003c/b\u003e). To assess the sensitivity of the results to this assumption, alternative values of 6 and 10 g\u0026middot;day⁻\u0026sup1;\u0026middot;inhabitant⁻\u0026sup1; were considered, representing commonly reported lower and upper bounds for mixed WW systems. This range falls within values reported for urban WW catchments worldwide (\u003cb\u003eTable S2\u003c/b\u003e), providing a plausible range to assess the sensitivity of population-normalized estimates to normalization assumptions.\u003c/p\u003e \u003cp\u003eAcross this range, absolute consumption estimates varied by approximately\u0026thinsp;\u0026plusmn;\u0026thinsp;25% relative to the baseline assumption, as expected from the proportional relationship between NH₃\u0026ndash;N load and EP estimates. This variation reflects the expected proportional scaling associated with changes in the assumed per-capita NH₃\u0026ndash;N load, these differences did not substantially alter the relative ranking of substances or the direction of observed differences between campaigns. Although absolute magnitudes depend on normalization assumptions, the comparative short-term patterns observed in this study remained consistent across plausible NH₃\u0026ndash;N scenarios, supporting the interpretation of relative differences under varying population-normalization assumptions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Temporal patterns and interpretation: weekdays, weekends and holidays\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the distribution of population-normalized mass loads and estimated consumption of the monitored substances across weekday, weekend, and holiday sampling events (mg\u0026middot;day⁻\u0026sup1;\u0026middot;1000 inhabitants⁻\u0026sup1;). Individual daily values are discussed in the text and reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eGiven the limited temporal coverage, particularly in 2024, these patterns are interpreted as reflecting short-term variability rather than evidence of seasonality or stable annual cycles. These patterns underscore the influence of short-term population dynamics and sampling timing on WW measurements and should therefore be interpreted within the constraints of the sampling design.\u003c/p\u003e \u003cp\u003eRegarding COC, BE-based consumption estimates showed the highest values among monitored substances. In 2024, the maximum value occurred on 1 May (public holiday), coinciding with reduced NH₃\u0026ndash;N loads. This indicates that population size alone does not explain the elevated per-capita cocaine consumption estimate and may reflect higher per-capita contributions during the holiday or changes in contributing population composition. In 2025, the highest daily COC signal occurred during the Friday\u0026ndash;Monday period, with Friday showing the maximum. Direct weekday\u0026ndash;weekend comparisons between 2024 and 2025 are limited because the 2024 campaign did not systematically cover the full Friday\u0026ndash;Monday window; nevertheless, mean daily COC consumption was higher in 2025 than in 2024 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and this difference between campaigns was preserved under NH₃\u0026ndash;N sensitivity scenarios (Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e), supporting the robustness of this comparison under the evaluated assumptions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBoxplots represent the median and interquartile range, with whiskers indicating minimum and maximum values and points corresponding to individual sampling events\u003c/p\u003e \u003cp\u003eOnly one sampling event corresponds to a public holiday; therefore, this category is not treated as a distribution\u003c/p\u003e \u003cp\u003eFor KET, values correspond to population-normalized mass loads, whereas for the remaining substances values represent back-calculated consumption estimates.\u003c/p\u003e \u003cp\u003eCOC/BE ratios were used to evaluate potential contributions from unmetabolized COC. Human physiology suggests that COC/BE in WW is typically\u0026thinsp;~\u0026thinsp;0.1 or lower (Castiglioni et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Higher ratios are commonly interpreted as indicative of non-metabolic inputs (e.g., direct disposal, spillage during handling or transport, washing of utensils or hands) (Castiglioni et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lai et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this study, COC/BE ranged from 0.21 to 0.36 (mean 0.29) in 2024 and from 0.25 to 0.46 (mean 0.39) in 2025 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting contributions of unmetabolized COC in both campaigns, with a higher contribution in 2025. Similar ratios above 0.1 have been reported elsewhere (Castiglioni et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lai et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; van Nuijs et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). COC may undergo partial transformation within sewer systems through hydrolysis and biofilm-associated transformation prior to sampling (Pagsuyoin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Thai et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). While BE remains the most robust biomarker for estimating COC consumption in WBE, episodic direct inputs and sewer dynamics may influence absolute mass load magnitudes, may influence mass load estimates and should be considered when interpreting these results.\u003c/p\u003e \u003cp\u003eKET, a dissociative anesthetic widely used in veterinary medicine and, to a lesser extent, in human medicine (International Narcotics Control Board, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), showed different temporal patterns between campaigns. In 2024, KET mass loads exhibited marked daily variability without a consistent weekday\u0026ndash;weekend structure. In 2025, a clearer weekend-associated pattern was observed, with the maximum on Sunday 18 May (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, this peak coincided with the lowest EP estimates of the campaign (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), suggesting that elevated KET mass loads are more likely associated with higher per-capita contributions rather than increased contributing population. Comparison between campaigns also indicated higher mean KET loads in 2025 than in 2024 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), a finding that remained consistent under NH₃\u0026ndash;N sensitivity scaling (Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e), suggesting that this pattern is not solely explained by population-normalization assumptions.\u003c/p\u003e \u003cp\u003eMDMA was detected in all samples, indicating consistent presence in the catchment. In 2024, MDMA loads increased on 1 May, similarly to COC, suggesting that this increase may not be explained solely by population size. In 2025, MDMA displayed a weekend-associated signature, with higher loads on Friday and Saturday, consistent with widely reported recreational-use patterns in multi-city studies (Cruz-Cruz et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; European Union Drugs Agency (EUDA), \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Rice et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yargeau et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAMP and METH increased in 2025 compared with 2024 but remained low relative to COC and KET across sampling days (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In 2024, all METH concentrations were below the LOQ; therefore, the corresponding consumption estimates rely on concentrations detected between the LOD and LOQ and should be interpreted with caution, particularly when comparing across campaigns. Further considerations regarding ATS sources and analytical constraints are discussed in Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e3.7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003ePHT showed mixed temporal behavior. In 2024, the highest value occurred on the public holiday, while in 2025 elevated values were observed during the weekend. As a prescription drug, short-term variation in PHT use may reflect resident-population dominance during weekends/holidays and catchment population dynamics, rather than direct evidence of misuse. Dose-equivalent interpretation supports this view, as inferred treated fractions remained low despite relatively high mass loads (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), indicating limited contribution from treated populations.\u003c/p\u003e \u003cp\u003eThe lower mean PHT consumption observed in 2025 compared with 2024 may reflect changes in prescription dynamics; however, the limited temporal coverage does not allow causal attribution. Recent regulatory measures in Chile, including implementation of electronic prescription monitoring systems (Gobierno de Chile, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), may influence controlled stimulant dispensing, but dedicated pharmacoepidemiologic data would be required to evaluate such effects, and these observations should therefore be interpreted cautiously.\u003c/p\u003e \u003cp\u003eTemporal profiles were influenced by population dynamics in this mixed catchment (Sections \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e). Patterns such as elevated KET mass loads and persistent COC consumption were observed across normalization scenarios, suggesting that these short-term patterns can be consistently identified under the conditions evaluated in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Comparison with international studies, surveys, and national seizures\u003c/h2\u003e \u003cp\u003eComparisons were performed considering whether reported mass loads were corrected using Cf, depending on the methodological approach adopted in each study. For COC, mean daily consumption estimates for 2024 and 2025 (4,288 vs 5,949 mg\u0026middot;day⁻\u0026sup1;\u0026middot;1000 inhabitants⁻\u0026sup1;) correspond to BE mass loads of 1,194 and 1,657 mg\u0026middot;day⁻\u0026sup1;\u0026middot;1000 inhabitants⁻\u0026sup1;, respectively, when expressed without applying Cf\u0026thinsp;=\u0026thinsp;3.59 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These values are within the range of those reported in the European multi-city WW study coordinated by SCORE and EUDA (European Union Drugs Agency (EUDA), \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Within that framework, 2024 BE mass loads at SP-WWTP were of the same order of magnitude as high-consumption sites (e.g., Belo Horizonte, Natal, Antwerp), while 2025 loads were higher than some European sites (e.g., Tarragona, Brussels) but remained below the highest values reported for Brazil and Antwerp (European Union Drugs Agency (EUDA), \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond the EUDA framework, international WBE studies report a broad range of BE-based COC loads and consumption estimates depending on normalization and Cf choices. Multi-city programs in Australia typically report mean COC consumption in the order of several hundred mg\u0026middot;day⁻\u0026sup1;\u0026middot;1000 inhabitants⁻\u0026sup1;, with substantial variability across catchments and time (ACIC., 2024a, 2024b). Canadian cities have reported comparatively low BE-based mass loads (Yargeau et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), whereas higher levels have been observed in specific settings in the Americas, including holiday-associated peaks (Croft et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; da Silva et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Within Chile, recent WBE studies conducted in the Biob\u0026iacute;o Region have reported relatively high BE-derived COC consumption in several cities, including Concepci\u0026oacute;n, Los \u0026Aacute;ngeles and Lebu (Reis et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Reported values for these locations fall within international WW monitoring programs. For example, EUDA reports average BE-derived loads of approximately 1709, 2628 and 3110 mg\u0026middot;day⁻\u0026sup1;\u0026middot;1000 inhabitants⁻\u0026sup1; for Concepci\u0026oacute;n, Los \u0026Aacute;ngeles and Lebu, respectively (European Union Drugs Agency (EUDA), \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These values exceed those obtained in the present study, although the mass load reported for Concepci\u0026oacute;n is comparable to our 2025 estimate (1657 mg\u0026middot;day⁻\u0026sup1;\u0026middot;1000 inhabitants⁻\u0026sup1;). These observations suggest that COC consumption in Chile falls within the range reported for several urban locations internationally.\u003c/p\u003e \u003cp\u003eFor KET, mean daily mass loads in this study (154 and 213 mg\u0026middot;day⁻\u0026sup1;\u0026middot;1000 inhabitants⁻\u0026sup1; in 2024 and 2025) are within the upper range of values reported in many cities. EUDA lists the highest KET loads for Amsterdam, S\u0026atilde;o Paulo, Eindhoven, Budapest and Antwerp Zuid (European Union Drugs Agency (EUDA), \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this context, the SP-WWTP mean in 2024 exceeded Amsterdam\u0026rsquo;s reported value, and the 2025 mean was higher still. Weekend-associated KET peaks observed here are consistent with reports from other regions where KET exhibits recreational-use signatures (Castiglioni et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cruz-Cruz et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rice et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Van Wichelen et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). National evidence supports increased KET availability in Chile in recent years. In particular, seizure statistics report a marked increase between 2020 and 2021 (from 133,285 kg to 276,820 kg of KET), alongside an increase in \u0026ldquo;fake 2C-B\u0026rdquo; cases later confirmed as KET, which may be relevant for interpreting KET loads observed in this study (Chicahual B et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Unidad Especializada en Tr\u0026aacute;fico Il\u0026iacute;cito de Estupefacientes y Sustancias Sicotr\u0026oacute;picas, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; United Nations Office on Drugs and Crime (UNODC), \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These levels fall within the range reported in other regions, where the occurrence of psychoactive substances in aquatic environments has been associated with potential ecological concerns, supporting their consideration as emerging contaminants of concern.\u003c/p\u003e \u003cp\u003eATS have long exhibited geographically structured profiles in WBE. European multi-city studies report pronounced weekend-associated MDMA patterns and region-specific AMP/METH configurations (European Union Drugs Agency (EUDA), \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ort et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), whereas Australia often shows methamphetamine-dominated patterns with high MDMA in several capitals (ACIC., 2024b). In this framework, the SP-WWTP profile is characterized by lower ATS levels than COC and KET, with declining MDMA between 2024 and 2025 and modest increases in AMP/METH (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Interpretation should consider the analytical and source-related uncertainties discussed in Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e3.7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor PHT, mean daily consumption in this study (303 and 186 mg\u0026middot;day⁻\u0026sup1;\u0026middot;1000 inhabitants⁻\u0026sup1; in 2024 and 2025) exceeds values reported in South Korea (Kim \u0026amp; Oh, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and several U.S. studies (Lemas et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oliveira et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, dose-equivalent estimates indicate that treated fractions remain low (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting that high mass loads do not translate into large treated proportions at the catchment scale. This underscores the importance of interpreting prescription-drug patterns using complementary metrics (mass loads and dose-equivalents) and considering catchment population dynamics (Sections \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e3.5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough FEN and NOR were not detected during the study period, their emergence in seizure statistics highlights the importance of continued targeted WW monitoring, particularly in the context of emerging substances with potential environmental relevance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Sources of uncertainty and study limitations.\u003c/h2\u003e \u003cp\u003eSeveral uncertainties must be considered when interpreting the consumption estimates derived from this study. First, population normalization relies on NH₃\u0026ndash;N as an indirect biomarker and on a per-capita NH₃\u0026ndash;N load derived from international literature (\u003cb\u003eTable S2\u003c/b\u003e). Absolute values can vary depending on the assumptions used (Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e), although relative patterns and substance ranking appeared consistent within the evaluated scenarios.\u003c/p\u003e \u003cp\u003eSecond, back-calculation depends on pharmacokinetic parameters and Cf values that are not fully harmonized across WBE studies, limiting strict comparability of absolute consumption estimates between regions and time periods. For PHT, excretion data remains limited and can depend on study design and urinary pH (Baselt, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). For KET, uncertainties in excretion profiles and the potential influence of transformation and/or direct disposal support reporting mass loads without correction.\u003c/p\u003e \u003cp\u003eThird, alternative sources may bias ATS estimates. AMP mass loads may reflect recreational use but also metabolic interconversion from METH and contributions from pharmaceuticals such as fenproporex, selegiline or lisdexamfetamine (Gracia-Lor et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; International Narcotics Control Board, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; van Nuijs et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Enantiomeric information was not available, limiting discrimination between licit and illicit ATS sources (Castrignan\u0026ograve; et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kasprzyk-Hordern et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; van Nuijs et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Nevertheless, INCB reports indicate that Chile did not import AMP for medical purposes in 2024 (International Narcotics Control Board, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which suggests limited therapeutic AMP contributions in this context.\u003c/p\u003e \u003cp\u003eFourth, temporal coverage was constrained. The 2024 campaign did not include systematic sampling across the full Friday\u0026ndash;Monday window, limiting direct cross-year weekday\u0026ndash;weekend comparisons. Sampling covered discrete periods rather than continuous annual monitoring, so seasonal effects cannot be resolved, and formal hypothesis testing is not warranted.\u003c/p\u003e \u003cp\u003eFinally, the catchment is relatively small (~\u0026thinsp;20,000 inhabitants according to operator data) compared with the SMR population (Instituto Nacional de Estad\u0026iacute;stica, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, the results should not be interpreted as representative of metropolitan-scale conditions.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study provides an application of WBE for monitoring psychoactive substances in a heterogeneous urban\u0026ndash;industrial catchment in central Chile. By combining dynamic population normalization with short-term sampling, the study generates baseline data and identifies detectable patterns under the conditions evaluated, particularly for COC and KET. These findings are consistent with recent WBE studies conducted in southern Chile (Reis et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) and may support future work focused on expanded spatial coverage, locally derived normalization parameters, increased temporal resolution, and systematic triangulation with prescribing and seizure statistics. Furthermore, the environmental fate, removal efficiency, and ecotoxicological effects of these compounds were not evaluated in this study, which limits direct assessment of ecological risk and should be addressed in future research.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study provides insight into short-term variability of psychoactive substances in WW and underscores the importance of sampling design and dynamic population normalization for interpreting WBE data in mixed urban systems.\u003c/p\u003e \u003cp\u003eMonitoring campaigns conducted in 2024 and 2025 revealed daily variability in mass loads of COC, KET, ATS and PHT in a mixed urban catchment receiving residential, commercial and industrial WW inputs, supporting the application of WBE as a complementary approach for assessing short-term dynamics of chemical markers in WW systems.\u003c/p\u003e \u003cp\u003eCOC exhibited the highest consumption levels among the monitored analytes, with higher values in 2025 and elevated levels during the Friday\u0026ndash;Monday period. KET mass loads were comparatively high relative to several international monitoring sites and showed weekend-associated increases consistent with patterns reported in other studies. In contrast, MDMA showed lower levels in 2025, while PHT mass loads indicated a limited but detectable therapeutic-equivalent use within the catchment population.\u003c/p\u003e \u003cp\u003ePopulation-normalized estimates carry inherent uncertainty, primarily due to the absence of Chile-specific NH₃\u0026ndash;N per-capita excretion factors and limited pharmacokinetic information for key substances such as KET and PHT. Nevertheless, the relative temporal patterns observed across weekdays, weekends and the public holiday remained consistent under plausible normalization scenarios, suggesting that the main pattern are robust under the evaluated assumptions despite uncertainties associated with population normalization and Cf. These results underscore the need for locally derived correction factors, updated pharmacokinetic data and improved population-normalization approaches to reduce uncertainty in future WBE studies.\u003c/p\u003e \u003cp\u003eBeyond their role as indicators of consumption, the detected psychoactive substances represent a continuous input of bioactive compounds into aquatic systems. These findings highlight the importance of considering these substances as emerging contaminants and underscore the need for further research on their environmental fate, persistence, and potential ecological effects, particularly in regions where monitoring data remain limited.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Santiago Poniente WWTP for their support during sample collection. Wendy Calzadilla and Ricardo Salazar-Gonz\u0026aacute;lez acknowledge support from ANID-funded research projects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by ANID FONDECYT Postdoctoral 3230488, ANID/FONDAP/1523A0006 and FONDECYT Regular 1220077.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are included within the article and its Supplementary Information. Additional data is available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKarla Montenegro: Investigation, Methodology, Writing \u0026ndash; Original draft preparation. Andr\u0026eacute;s Yar: Investigation, Software, Validation. Sebasti\u0026aacute;n Campos: Data curation, Formal analysis, Writing \u0026ndash; Original draft preparation. Ricardo Salazar-Gonz\u0026aacute;lez: Resources, Funding acquisition, Writing \u0026ndash; Review and Editing. Wendy Calzadilla: Conceptualization, Investigation, Project administration, Writing \u0026ndash; Original draft preparation, Funding acquisition, Writing \u0026ndash; Review and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse of generative AI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the authors used ChatGPT (OpenAI) to assist with language editing, text refinement, and content organization. After using this tool, the authors reviewed and edited the content and take full responsibility for the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eACIC., A. C. I. C. (2024a, April). \u003cem\u003eNational Wastewater Drug Monitoring Program. Report 23\u003c/em\u003e. https://www.acic.gov.au/publications/national-wastewater-drug-monitoring-program-reports/report-23-national-wastewater-drug-monitoring-program\u003c/li\u003e\n \u003cli\u003eACIC., A. C. I. C. 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Ketamine, a new (or old) kid on the block: A comprehensive three-year spatio-temporal study in Belgium through wastewater-based epidemiology. \u003cem\u003eWater Research\u003c/em\u003e, \u003cem\u003e276\u003c/em\u003e. https://doi.org/10.1016/j.watres.2025.123269\u003c/li\u003e\n \u003cli\u003eWorld Obesity Federation. (2025, March). \u003cem\u003eWorld Obesity Atlas\u003c/em\u003e. https://data.worldobesity.org/publications/?cat=23\u003c/li\u003e\n \u003cli\u003eYargeau, V., Taylor, B., Li, H., Rodayan, A., \u0026amp; Metcalfe, C. D. (2014). Analysis of drugs of abuse in wastewater from two Canadian cities. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e487\u003c/em\u003e(1), 722\u0026ndash;730. https://doi.org/10.1016/j.scitotenv.2013.11.094\u003c/li\u003e\n \u003cli\u003eZuccato, E., Chiabrando, C., Castiglioni, S., Bagnati, R., \u0026amp; Fanelli, R. (2008). Estimating community drug abuse by wastewater analysis. \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e, \u003cem\u003e116\u003c/em\u003e(8), 1027\u0026ndash;1032. https://doi.org/10.1289/ehp.11022\u003c/li\u003e\n \u003cli\u003eZuccato, E., Chiabrando, C., Castiglioni, S., Bagnati, R., Schiarea, S., \u0026amp; Fanelli, R. (2005). Cocaine in surface waters: a new evidence-based tool to monitor community drug abuse. \u003cem\u003eEnvironmental Health: A Global Access Science Source\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 1\u0026ndash;7. https://doi.org/10.1186/ 1476-069X-4-14\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wastewater-based epidemiology, psychoactive substances, population normalization, urban catchment, Chile","lastPublishedDoi":"10.21203/rs.3.rs-9236663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9236663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWastewater-based epidemiology (WBE) is increasingly used to assess community-level exposure to psychoactive substances; however, evidence from Latin America remains limited, and uncertainties related to population normalization and short-term variability persist.This study examines the occurrence and short-term variability of selected psychoactive substances in wastewater from a mixed urban catchment in central Chile, providing insight into the interpretation of WBE data under dynamic population conditions. Influent samples were collected during two monitoring campaigns in 2024 and 2025 and analyzed for amphetamine-type stimulants, cocaine and benzoylecgonine, ketamine, phentermine, and fentanyl-related compounds using LC\u0026ndash;MS/MS. Population-normalized mass loads and consumption estimates were derived using dynamic normalization based on NH₃\u0026ndash;N. Cocaine showed the highest levels among the monitored substances, while ketamine exhibited comparatively high mass loads with marked weekend-associated patterns. Among amphetamine-type stimulants, MDMA was consistently detected but decreased in 2025, whereas amphetamine increased slightly and methamphetamine remained low. Phentermine was detected at relatively high mass loads; however, dose-equivalent estimates suggested that these corresponded to a small fraction of the potentially treated population. Observed patterns were influenced by temporal changes in contributing population, with higher mass loads generally occurring during weekends. These findings underscore the importance of dynamic population normalization when interpreting short-term variability in WBE studies, particularly in mixed or relatively small catchments. While based on a limited dataset, this case study provides evidence of short-term variability patterns that may influence the interpretation of WBE-derived indicators and supports the use of WBE as a complementary approach for environmental monitoring of emerging contaminants.\u003c/p\u003e","manuscriptTitle":"Occurrence and short-term variability of psychoactive substances in wastewater from a mixed urban catchment in central Chile","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 10:55:57","doi":"10.21203/rs.3.rs-9236663/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f7ec0c3c-4930-450d-b327-37053a97e4ea","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-09T20:42:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T03:08:08+00:00","index":40,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T04:06:06+00:00","index":39,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T22:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 10:55:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9236663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9236663","identity":"rs-9236663","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}