Another emerging contaminant in the sinking city: The first evidence of metformin detected in Jakarta waters

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Hazell, Riyana Subandi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3374407/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 Pharmaceutically active compounds have been considered contaminants of emerging concern, in response to evidence that these substances may adversely affect non target organisms. The pharmaceutical metformin is the most commonly prescribed anti-diabetes medicine throughout the world. Metformin has been detected in numerous freshwater systems as well as in seawater at a number of sites around the world over the last few years, but has never been reported in the Indonesian capital city Jakarta. Several recent studies have highlighted various ecotoxicological effects of this medicine on aquatic organisms. Here we report the first evidence of metformin’s presence in Jakarta waters. Samples from the Angke river, one of the main rivers in Jakarta, were collected from six sites. Metformin was detected at three sites in concentrations ranging from 27 ng/L to 414 ng/L. Metformin is one of the most detected APIs (active pharmaceutical ingredients) in aquatic environments worldwide, and there is increasing concern regarding its impact on the health of wildlife and humans. However, this is the first report of metformin contamination in Jakarta waters, adding to the evidence of potentially increased pollution with pharmaceuticals, as noted in our previous studies. With no natural degradation processes, these chemical compounds can be easily reintroduced to the food chain and impact human health. emerging contaminants metformin pharmaceuticals Jakarta water pollution Figures Figure 1 Figure 2 Figure 3 Introduction Water pollution caused by emerging contaminants such as pharmaceuticals is of growing concern worldwide (Morin-Crini et al., 2021 ). The presence of these contaminants in water sources has been shown to have adverse effects on aquatic life and human health (López-Pacheco et al., 2019 ). Indonesia, the world's fourth most populous country, has faced its share of significant environmental challenges, including water pollution (Comte et al., 2012 ). Indeed, recent studies have shown the presence of pharmaceuticals in surface waters in Indonesia, including rivers and lakes (Koagouw, Arifin, et al., 2021 ; Wilkinson et al., 2022 ; Nozaki et al., 2023 ). Jakarta, the capital city of Indonesia, is one of the most populated cities in the world, with a population of over 10 million people (Martinez & Masron, 2020 ). Like many other cities in developing countries, Jakarta is facing a number of environmental challenges, with water contamination among the most pressing. The Angke River is one of Jakarta’s major water courses, and receives numerous sources of pollution, including untreated domestic sewage, industrial wastewater, and solid waste (Siregar et al., 2016 ), all of which may contain a variety of pharmaceuticals and their active metabolites. Metformin is an antidiabetic drug that has been widely prescribed for the treatment of type 2 diabetes, with its use increasing rapidly worldwide (Zhou et al., 2018 ). In 2030, the World Health Organization estimates that almost 400 million people will be diagnosed with diabetes, and that consequently metformin will see a spike in production. Unlike many pharmaceutical drugs, metformin is not metabolized by humans but passes through the body unchanged. Entering aquatic compartments, it can be bacterially transformed into its metabolite, guanylurea (Trautwein et al., 2014 ). As a result of its widespread use, metformin has been detected in various environmental matrices in recent years (Ambrosio-Albuquerque et al., 2021 ), including surface waters (Kong et al., 2015 ), sediments (Huber et al., 2016 ), and soils (Briones et al., 2016 ), raising concerns about its potential environmental impact. In Europe, metformin has been recorded at concentrations as high as 325 ug/L - the level detected in wastewater influent in Portugal (de Jesus Gaffney et al., 2017 ). In Asia, the highest recorded concentration is 53.6 ug/L, detected by Yan et al. ( 2019 ) in wastewater treatment plant influent in China. In North America, a concentration of 107 ug/L was recorded in Mexican groundwater by Lesser et al. ( 2018 ). Meanwhile, Archer et al. ( 2017 ) recorded the highest level of metformin detected so far in Africa, with a concentration of 9.3 ug/L in South African wastewater influent. Despite this growing global body of evidence for its occurrence, metformin has never been reported in the environment in Jakarta, Indonesia. In aquatic animals, several significant negative impacts of metformin exposure have been reported, such as severe gonadal tissue pathologies, destabilization of lysosomal membranes in hemocytes, and altered mRNA expression in the mussel Mytilus edulis after short exposure (Koagouw & Ciocan, 2018 ; Koagouw, Hazell, et al., 2021). Ussery et al. ( 2018 ) showed that exposure of medaka fish Oryzias latipes to metformin at early stages of development led to negative effects on growth metrics, metabolomes, and transcriptomes, including an increased production of some steroid hormones in adult female medaka. Interestingly, a recent study by Nielsen et al. ( 2022 ) suggested that wild embryos of fathead minnow ( Pimephales sp.) are more sensitive to environmentally relevant concentrations of metformin, than their lab spawned counterparts. This study reports for the first time the presence of metformin in Angke River in Jakarta, providing the first evidence of the occurrence of this pharmaceutical in Jakarta waters. The findings of this study will contribute to a better understanding of the extent of pharmaceutical contamination of Jakarta's rivers, and their bioavailability for the aquatic biota, therefore supporting the development of new strategies to tackle water pollution. Materials and Methods 1. Water sample collection Surface water samples were collected in June 2022 from six different sites along the Angke River in Jakarta, Indonesia, as depicted in Fig. 1 . The comprehensive descriptions of these sampling sites, including the location names, coordinates, and environmental conditions at the time of sample collection, are provided in Table 1 . Prior to the sampling process, all containers and equipment were cleaned and treated with methanol to ensure sterility and prevent contamination. Aluminum foil and other consumables were discarded immediately after use, and each container was used for one sample only. During the sampling process, a 2-liter bucket was used to collect the water, which was then transferred into 25 ml sample bottles and 2-liter water containers. To maintain consistency and accuracy in the results obtained, duplicate samples were collected from each site using grab sampling techniques as recommended by Wang et al. ( 2020 ). The plastic bucket was thoroughly cleaned before and after each use. To preserve the integrity of the samples, they were placed in pre-cooled cool boxes with ice, minimizing any potential biological or chemical alterations that could occur during transportation. The samples were promptly transported to the laboratory for comprehensive analysis. The collection, storage, and preparation of samples were conducted in accordance with good laboratory practice standards, following guidelines provided by the United States Environmental Protection Agency (US-EPA) (Decker & Simmons, 2013 ) and the American Public Health Association (APHA) (Baird et al., 2017 ). Adhering to these guidelines ensured that the data gathered would provide valuable insights into the water quality of the Angke River and identify any potential environmental issues that may need to be addressed. Table 1 Description of sampling sites along Angke River, Jakarta, Indonesia ID Location Name Coordinates Description 1 Duri Kosambi, West Jakarta -6.187045, 106.720453 Near Kembangan power plant; characterized by swift, turbid brown water; accessed through an area with tall grass and numerous large trees; rapid flow conditions and potential influence from the power plant. 2 Rawa Buaya, West Jakarta -6.165606, 106.748331 Located beside Kembangan Baru road; presence of a fish feed vendor; calm, light brown water with significant amounts of trash visible; likely impacted by nearby human activity and waste disposal. 3 Kedaung Kali Angke, West Jakarta -6.158201, 106.7599355 Situated beside Daan Mogot road; active bridge construction site; calm, shallow water conditions; possible construction-related impacts on water quality. 4 Wijaya Kusuma, West Jakarta -6.145298, 106.775080 Densely populated area; calm water conditions; the river splits into two channels here, potentially affecting water flow and dispersion of pollutants. 5 Kapuk Muara, Penjaringan, North Jakarta -6.123370, 106.774228 Located beside a highway bridge; moderately swift water flow with substantial amounts of trash; presence of people and goods ferries, possibly influencing water quality due to increased human activity and resuspension of sediments. 6 Pluit, Penjaringan, North Jakarta -6.110076, 106.774421 Positioned at the Angke dock under a bridge; calm, turbid water conditions; significant amounts of trash and a line of moored boats; potential impacts from boat-related activities and waste disposal. 2. Sample processing and preparation Upon arrival at the laboratory, the surface water samples were processed immediately to ensure the integrity of the analytes. The analyses of the physicochemical parameters were conducted following the procedures in the method references column in Table 3 , which includes Standard Methods for Examination of Water and Wastewater published by the American Public Health Association (Baird et al., 2017 ), Indonesian National Standards (SNI), and the standardized protocols from the Jakarta Regional Health Laboratory. All analyses were conducted at the Jakarta Regional Health Laboratory, a testing laboratory accredited by ISO/IEC 17025:2017, ISO 45001:2018, and ISO 15189:2012, indicating its compliance with recognized international standards. For the metformin analysis, the sample preparation process started with the filtration of a 15 mL aliquot using a filter disc (Sartorius, Grade: 390, diameter 125 mm) and silanized glassware, eliminating potential interferences due to particulate matter. From the filtered sample, a 3 mL volume was taken and supplemented with 10 µL of a 10 ppm internal standard (ISTD) mefruside for quantitation purposes. This sample was subsequently loaded onto a Sep-Pak C18 solid-phase extraction (SPE) cartridge (Nexus, 200 mg, 6 mL), which had been preconditioned with 3 mL of methanol and 3 mL of water to ensure proper retention of the analytes. Samples were loaded at a flow rate of 3 mL min − 1, followed by a washing step with two sequential 1 mL aliquots of water to remove any matrix interferences. The SPE sorbent was dried under vacuum at room temperature, and the analytes were subsequently eluted from the column using 3 x 1 mL aliquots of methanol at a flow rate of 1 mL min − 1, ensuring optimal recovery of the compounds of interest. The methanol eluate was evaporated to dryness at 40°C using a Turbovap under a gentle stream of nitrogen. The dried residue was then reconstituted in 150 µL of a methanol:water (1:1) solution and subsequently injected into the LC-MS/MS system for analysis. 3. Metformin quantification and quality control Chromatographic analysis was performed on an Agilent 6470 series Triple Quad LC-MS/MS system coupled with a 1260 Infinity II HPLC (Agilent Technologies, USA) using positive electrospray ionization. Analytes were separated using an Infinity C18 column (150 × 2.1 mm; 2.7 µm particle size) with a 0.2 µm pre-filter. The mobile phase consisted of 20% acetonitrile (solvent A) and 80% of 0.1% formic acid in water (solvent B). A flow rate of 0.2 mL/min was applied, and the column temperature was maintained at 40°C. The injection volume was 10 µL, with a stop time of 18 minutes and a post-time of 2 minutes. The gradient used in this procedure, consisting of six steps with varying proportions of solvents A and B, ensures optimal separation and peak resolution of the target analytes, as presented in Table 2 . Table 2 Gradient mobile phase program Step Time Solvent A (Acetonitrile) Solvent B (0.1% Formic Acid in Water) Flow Rate 1 1 min 40% 60% 0.2 mL/min 2 4 min 90% 10% 0.2 mL/min 3 5 min 40% 60% 0.2 mL/min 4 7 min 50% 50% 0.2 mL/min 5 9 min 20% 80% 0.2 mL/min 6 13 min 20% 80% 0.2 mL/min The system was set up with specific source and ionization parameters to ensure optimal sensitivity, selectivity, and accurate quantification. The source parameters included a capillary voltage of 3500 V, desolvation temperature of 350°C, gas temperature of 300°C, nitrogen gas flow rate of 11 L/min, nebulizing pressure of 45 psi, sheath gas temperature of 350°C, and sheath gas flow of 11 L/min. Nitrogen gas was employed as the nebulizing, desolvation, and collision gas. Two multiple reaction monitoring (MRM) transitions were monitored for each analyte, allowing for efficient ionization of the analytes and maximizing signal intensity. Additional quality criteria, such as pre-determined ion ratio and retention time tolerances, were employed to ensure the reliability of the data. Supplementary Table S1 provides more details on the instrumental parameters employed in the ESI-MS analysis, and the analytical parameters for each analyte. To ensure the accuracy and reliability of the analysis, analytical reference standards of metformin (99.60% purity, MT20231219, Pharma Metric Labs) and mefruside (CAS No. 7195-27-9, TRC-M205150-50MG, LGC Standards) as an internal standard were employed. The standards were prepared at concentrations of 0.1 and 1.0 mg/mL in methanol and stored at − 20°C in darkness. HPLC grade methanol and acetonitrile were obtained from Merck, while ultrapure water with a quality of 18.2 MΩ cm − 1 was used. To maintain the reliability of the metformin analysis, quality controls were integrated within the method development and during the analysis of river water samples. This included the calibration linearity range for each compound, method detection limits, and analysis of reagent blanks. Reproducibility was verified by processing duplicates of each sample, and the method accuracy was validated through spike recoveries in reagent water and river water samples, aiming for a standard recovery rate of 90–110%. Moreover, at least one method blank (ultrapure water), one duplicate, and one spiked sample were processed with each batch of river water samples analyzed, ensuring the precision and accuracy of the LC-MS/MS detection process. A 10 L river water sample was employed for the development and validation of the analytical method within the sample matrix. Results The physicochemical properties of the water samples. Table 3 presents water quality data from all sampling sites along the Angke river, with measurements for parameters in Attachment 1 Chapter II Point A of the Regulation of the Minister of Health of the Republic of Indonesia No. 32 of 2017 regarding Standard Quality of Environmental Health and Health Requirements for Hygiene, Sanitation, Swimming Pools, Per Aqua Solutions, and Public Baths. Water samples were analyzed using various methods and standard references as shown in Table 3 . In general, the water samples demonstrated varying degrees of compliance with maximum allowable concentrations. Site 6 shows particularly concerning results, with several parameters exceeding their respective maximum allowable concentrations. Dissolved solids (2602 mg/L), hardness (as CaCO3, 5320.5 mg/L), fluoride (1.6 mg/L), and sulfate (2226.57 mg/L) levels all surpass the limits. Additionally, the organic matter (KMnO4) levels at this site (39.39 mg/L) are significantly higher than the maximum allowable concentration (10 mg/L). Turbidity at Site 5 also exceeds the limit, with a measured value of 126.5 NTU. The remaining sites display parameter levels mostly within acceptable ranges, with some sites showing elevated nitrite as N (Site 2), manganese (Site 3), and organic matter (KMnO4) levels (Sites 2, 3, 4, and 5). The water samples were generally odorless, tasteless, and within acceptable temperature ranges. Table 3 The water quality parameters in selected sampling sites, compared with the Indonesian government regulation limits +++ No. Parameter Unit Site Maximum Allowable Concentration +++ Method Reference 1 2 3 4 5 6 1 Turbidity NTU Scale 41.15 31.7 26.8 16.4 126.5 7.245 25 IK02/PP16.5-Air-17025/Labkesda 2 Color TCU Scale 412.5 325.5 272.5 275 439 473.5 50 SNI 6989.80:2011 3 Dissolved Solids mg/L 123 164 202 167 99.5 2602 1000 IK.01/PP16.5-Air-17025/Labkesda 4 Temperature (Ex situ) ++ °C 26.85 26.95 26.7 26.45 26.6 26.45 - IK.03/PP16.5-Air-17025/Labkesda' 6 Odor - Odorless Odorless Odorless Odorless Odorless Odorless Odorless SNI 3554:2015 7 DH (Ex situ) ++ - 6.965 7.055 7.045 6.99 6.86 7.195 - SNI 6989.11:2019 8 Iron mg/L 0.02905 0.0145 0.04875 0.01655 0.0234 0.0021 1 Std Met.APHA 3120B/23/2017;3030B/23/2017 9 Fluoride mg/L 0.075 0.2 0.165 0.24 0.075 1.6 1.5 SNI 06-6989.29-2005 10 Hardness (as CaCO3) mg/L 68.135 108.935 95.055 82.02 61.83 5320.5 500 SNI 06-6989.12-2004 11 Manganese mg/L 0.0567 0.0027 0.2514 0.08245 0.0077 0.0027 0.5 Std Met.APHA 3120B/23/2017;3030B/23/2017 12 Nitrate as N mg/L 1.03 0.905 0.016 0.45 1.36 0.016 10 Std Met. APHA 4110C/23/2017 13 Nitrite as N mg/L 0.478 4.075 0.016 0.145 0.075 0.016 1 Std Met. APHA 4110C/23/2017 14 Cyanide + mg/L 0.0015 0.0015 0.0015 0.0015 0.0015 0.0015 0.1 IK.15/PP16.5-Air-17025/Labkesda 17 Mercury + mg/L 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.001 IK.21/PP16.5-Air-17025/Labkesda 18 Arsenic mg/L 0.0085 0.0085 0.0085 0.0085 0.0085 0.0085 0.05 Std Met.APHA 3120B/23/2017;3030B/23/2017 19 Cadmium mg/L 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.005 Std Met.APHA 3120B/23/2017;3030B/23/2017 20 Chromium Hexavalent + mg/L 0.005 0.005 0.005 0.005 0.005 0.005 0.05 Std Met.APHA 3500-Cr.B/23/2017 21 Selenium mg/L 0.002 0.002 0.002 0.002 0.002 0.002 0.01 Std Met.APHA 3120B/23/2017;3030B/23/2017 22 Zinc mg/L 0.0099 0.0099 0.0099 0.0099 0.0099 0.0099 15 Std Met.APHA 3120B/23/2017;3030B/23/2017 23 Sulfate mg/L 14.47 26.58 21.71 15.14 12.94 2226.57 400 Std Met.APHA 4110C/23/2017 24 Lead mg/L 0.0017 0.0017 0.0017 0.0017 0.0017 0.0017 0.05 Std Met.APHA 3120B/23/2017;3030B/23/2017 26 Organic Matter (KMnO4) mg/L 7.225 12.22 11.405 10.365 15.265 39.39 10 SNI 06-6989.22-2004 + Parameter is not yet accredited. ++ The examination of Ex Situ temperature and pH cannot be compared to the Maximum Quality Standard. +++ In accordance with Attachment 1 Chapter II Point A of the Regulation of the Minister of Health of the Republic of Indonesia No. 32 of 2017 regarding Standard Quality of Environmental Health and Health Requirements for Hygiene, Sanitation, Swimming Pools, Per Aqua Solutions, and Public Baths. 2. The concentration of metformin in the water samples. The results show that metformin was present at 3 sites, with concentrations ranging from 27 to 414 ng/L (Fig. 2 ). To the best of our knowledge, this is the first time metformin has been detected in Jakarta waters. The concentration of metformin at site 3 is considerably higher than at site 2. The range of values between replicates is larger for site 3 than for site 2, suggesting greater variability in the metformin concentration at site 3. Figure 3 shows the metformin levels detected in this study, relative to other reported concentrations from around the world. Our lowest concentration detected at Site 2 (27 ng/L) falls within the 5th percentile. This indicates that the metformin concentration at Site 2 is relatively low compared to the global range of metformin concentrations in surface water. The highest concentration in this study (414 ng/L) falls within the 40th percentile. This indicates that the metformin concentration at Site 3 is higher than 40% of the global range of metformin concentrations in surface water but still lower than the remaining 60%. More detail on the concentration ranges detected in these studies can be found in Supplementary Table S2 . Discussion 1. The Comparative Analysis of Metformin Concentrations in Indonesia's Angke River with Global Data Our study reports data on the presence of metformin, a common antidiabetic drug, in the Angke River, a significant waterway in Jakarta, Indonesia. We detected metformin concentrations ranging from 27 ng/L to 414 ng/L. These findings, while considered relatively moderate in a global context, represent an important step in understanding pharmaceutical pollution in Indonesia's aquatic environments. Our highest recorded concentration of 414 ng/L is comparable to ranges observed in German surface waters, which vary between 35 ng/L and 643 ng/L (Trautwein et al., 2014 ). Turkey recorded lower metformin concentrations in surface water, ranging from 0.14 ng/L to 14.1 ng/L (Guzel et al., 2019 ). In contrast, Canada reported significantly higher concentrations, with a range of 145 ng/L to 10,100 ng/L (de Solla et al., 2016 ). Other countries with notable metformin concentrations in surface water include Vietnam (Chau et al., 2018 ) with a range of 10 ng/L to 8,247 ng/L, Saudi Arabia (Ali et al., 2017 ) with a concentration of 4,801 ng/L in the Red Sea, and South Africa (Archer et al., 2017 ) with a range of 65 ng/L to 316 ng/L. The prevalence of diabetes in Indonesia's adult population stands at 6.2%, a figure significantly lower than the global average of 9.3% as recorded in the International Diabetes Federation (IDF) Atlas (2019). This may partially account for the relatively reduced traces of metformin observed in our study. However, current global trends suggest an accelerating adoption of metformin due to its proven efficacy, advantageous safety profile, and economic affordability as supported by Ahmad et al. ( 2020 ). Given these contributing factors and the projected escalation in diabetes prevalence in Indonesia - the adult diabetic population is anticipated to rise from 10.7 million in 2019 to an estimated 16.6 million by 2045 (IDF Atlas, 2019) - it is reasonable to predict a subsequent surge in metformin levels within the country's aquatic environments in the forthcoming years. The Angke River, already facing significant pollution from sources such as domestic sewage discharge and industrial effluents, could be increasingly impacted by this trend. While the precise contribution of these sources to metformin levels is unclear in the absence of direct data, the combined influence of industrial and domestic waste is likely substantial. Our work underscores the necessity for more comprehensive research on the occurrence and impacts of pharmaceuticals in Indonesian water bodies. As metformin use is likely to rise rapidly, proactive measures to minimize pharmaceutical pollution are essential. Comprehensive understanding requires consideration of factors such as evolving drug prescription practices, changing population health trends, waste management systems, and specific environmental conditions. Future studies should focus on the potential impacts on aquatic ecosystems and public health, with the goal of informing pollution management strategies and contributing to the global understanding of pharmaceutical pollution. 2. Associations between Physicochemical Parameters and Metformin Concentrations in the Angke River, Jakarta: A Comparative Analysis of Polluted Sites Based on the water quality parameters presented in Table 3 , we can analyze the potential links and connections between the physicochemical properties and the metformin concentrations detected at the various sampling sites. The presence of metformin in the Angke River, particularly at Sites 2, 3, and 4, raises questions about the potential factors influencing its concentrations in the water. A comprehensive analysis of the physicochemical parameters of the water quality needs to be taken into consideration when discussing the transport and behavior of metformin in the river. In this context, it is essential to consider the local context, land use, and nearby pollution sources when interpreting these findings. Turbidity, color, and organic matter content appear to be the most likely parameters related to metformin concentrations in the Angke River. High turbidity levels at Site 2 (31.7 NTU) and Site 3 (26.8 NTU), both exceeding the maximum allowable concentration (25 NTU), indicate the presence of suspended particles, which can contribute to the adsorption and transport of metformin in the water (Boyd, 2000 ). Additionally, high color values at all three sites (Site 2: 325.5 TCU, Site 3: 272.5 TCU, Site 4: 275 TCU) suggest an elevated organic matter content or the presence of other pollutants, which could affect metformin concentrations. Boyd ( 2000 ) elucidates that water turbidity, color, and organic matter significantly affect the transport, adsorption, and degradation of pharmaceuticals in water, with turbidity enhancing surface area for sorption and sedimentation, color impacting solubility and photodegradation, and organic matter competing for sorption sites or aiding in biodegradation. Additionally, the organic matter, which can be derived from both natural and anthropogenic sources, plays a critical role in determining water's biological, chemical, and physical properties such as oxygen demand, pH, and conductivity. The manganese concentration at Site 3 was observed to be significantly high at 0.2514 mg/L, notably surpassing the values recorded at Site 2 (0.0027 mg/L) and Site 4 (0.08245 mg/L). Intriguingly, this pattern mirrors the distribution of metformin levels across these sites. Elevated manganese concentrations may serve as a potential marker for pollution, often resulting from processes such as erosion of geological substrates, discharge of industrial waste, or leaching from landfills (U.S. Agency for Toxic Substances and Disease Registry (ATSDR), 2012). These findings suggest the possible presence of industrial waste and/or landfill leaching activities in the vicinity of Site 3, thereby warranting a more detailed investigation into potential sources of contamination in this area. The pH and temperature of the water samples from the three sites do not show significant variations that could account for the differences in metformin concentrations. The pH values at Site 2 (7.055), Site 3 (7.045), and Site 4 (6.99) are close to neutral, which should not significantly impact the metformin concentrations, as metformin is more stable and soluble at neutral pH levels (da Trindade et al., 2018 ; Desai et al., 2015 ). Similarly, there are no significant differences in temperature among the sampling sites, making it unlikely that temperature variations contribute to the observed metformin concentrations, as suggested by Sharma et al. ( 2010 ). Although metformin levels were undetectable, site 6 presented the most concerning water quality profile, with multiple parameters exceeding the defined limits, suggestive of potential water contamination. Notably, dissolved solids exhibited an extreme deviation at 2602 mg/L, more than double the allowable limit of 1000 mg/L, indicative of potential pollution. Moreover, fluoride and hardness (expressed as CaCO3) levels were over the set limits, suggesting the presence of industrial contaminants and high levels of dissolved minerals (Ali et al., 2016 ), respectively. The sulfate concentration at site 6 was markedly high, further pointing to possible runoff of industrial or household waste, particularly detergent and cleaning products (de F. Araújo et al., 2018 ; World Health Organization, 2004 ). Additionally, organic matter, measured using KMnO4, was considerably over the limit, indicative of a potential influx of organic waste (ISO 8467, 1993). These alarming findings underscore the need for a thorough investigation into the source of these exceedances to devise appropriate remedial measures, in consideration of the intended water usage in this area. 3. Implications of the study The detection of metformin in the Angke River in Jakarta, Indonesia, brings attention to an often overlooked aspect of water pollution—pharmaceuticals. Despite concentrations in this study falling within the lower 40% of the global range, the potential ecological and human health impacts must not be underestimated, given the projected expansion of the number of diabetes diagnoses, both in Indonesia and globally. In addition, metformin’s use as an ovulation induction agent for polycystic ovarian syndrome (Johnson, 2014 ) suggests its global use could further increase with increasing demand for fertility treatments. Metformin's adverse effects on aquatic organisms have been documented, even at low concentrations. In invertebrates, metformin has been shown to potentially alter reproduction and morphological traits, cause immobilization, and induce oxidative stress (Cleuvers, 2003 ; Godoy et al., 2018 ; Koagouw & Ciocan, 2018 ; Koagouw, Hazell, et al., 2021). These factors may, over time, affect population dynamics and alter the balance within these ecosystems. In fish, exposure to metformin has been found to induce endocrine disruption and intersex in males, affect embryogenesis, and reduce overall growth (Elizalde-Velázquez et al., 2021 ; Lee et al., 2019 ; Niemuth & Klaper, 2015 ). These factors may potentially lead to reduced fitness and survivability, contributing to population declines and, over extended periods, could lead to local extinctions. The potential ecological ramifications are profound, as they can ripple through food webs and alter biodiversity across trophic levels, with potential impacts on ecosystem functions and services. The presence of metformin in surface waters, moreover, poses potential risks to human health, as these waters serve various purposes such as irrigation, recreation, and drinking water sources. With this study acting as a key stepping stone in understanding pharmaceutical pollution in Indonesian waters, it underscores the urgency for enhanced wastewater treatment and management practices, alongside increased monitoring and assessment of pharmaceutical pollutants. This includes the need for well-established regulatory frameworks and public awareness campaigns promoting responsible drug disposal practices. Pharmaceutical pollution poses a global threat to environmental and human health (Nassiri Koopaei & Abdollahi, 2017 ; Ortúzar et al., 2022 ), as well as to the delivery of the United Nations Sustainable Development Goals, especially SDGs 3 (Good health and well-being), 6 (Clean water and sanitation), 12 (Responsible consumption and production) and 14 (Life below water). Further research is required to comprehensively evaluate the extent of contamination and the potential risks associated with exposure to metformin and other emerging contaminants in Indonesian waters. This would enable the development of informed strategies to address this urgent concern. Conclusion This study represents the first detection of metformin in the waters of Jakarta, and thus highlights the intersection of pharmaceutical consumption, particularly due to growing diabetes prevalence, and environmental health in Indonesia. In addition to illuminating the extent of this issue, the study provides valuable insights into the parameters influencing metformin's prevalence in water bodies, including turbidity, color, organic matter content, and manganese levels. The environmental and public health concerns presented here support the growing need for extensive monitoring and assessment of pharmaceutical pollutants, improved wastewater treatment methods, and enhanced public awareness of responsible drug disposal practices. The development and implementation of appropriate regulatory frameworks will be vital in safeguarding both public health and the environment. Finally, this research underscores the need for a more thorough understanding of the potential risks associated with exposure to metformin and other pharmaceutical pollutants. This knowledge is crucial for developing effective strategies to address this issue, which not only impacts Indonesia's aquatic ecosystems but also has far-reaching implications for global environmental health and the achievement of the United Nations Sustainable Development Goals. Declarations Ethical Approval Ethical approval was not necessary for this study as it did not involve human or animal subjects. Consent to Participate Not applicable, as the study does not involve human or animal participants, but all procedures were conducted ethically and responsibly. Consent to Publish All authors listed have reviewed the final version of the manuscript and unanimously consent to its publication. Author contribution statement Wulan Koagouw : Conceptualization, Methodology, Formal analysis, Investigation, Resources, Writing - Original Draft (main contributor) Erna Simanjuntak : Methodology, Writing - Original Draft Richard J. Hazell : Formal analysis, Investigation, Resources, Writing - Original Draft Riyana Subandi : Methodology, Writing - Original Draft Corina Ciocan : Resources, Writing - Original Draft, Supervision All authors read and approved the final manuscript. Funding This work was supported by the University of Brighton, United Kingdom through the School of Applied Sciences - Research Investment Fund (SASRIF) and the Centre for Aquatic Environments research fund, both awarded to Wulan Koagouw. The funding bodies have no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. 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. Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. 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Biomedicine & Pharmacotherapy , 106 , 1227-1235. https://doi.org/https://doi.org/10.1016/j.biopha.2018.07.085 Supplementary Files SupplementaryTableS1.docx SupplementaryTableS2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3374407","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":240199068,"identity":"3cf060ca-f7b6-4973-9cb6-aac5aeea6ada","order_by":0,"name":"Wulan Koagouw","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYFAC5gYgkQDlVBxgYIOwJBgbcGphRNZyhmQtjG0HUMWxAYMbiY2fCxjS5Pj7Fx+T5p13J59PuoH5xQcGC1k8WpqlZzDkGEvceJYmzbvtmWWbzAE2yxkMEsa4tJjdSGyQ5mGoSGy4ccZMOnfbYQM2iQQ2Yx4GiUQ8Wpp/g7TMB2uZQ5yWNqAtOYkbzvcAtTSAtTA/xqfF/szDNmsegzRjwxtsydZ/jj0DaklsY5xhgNsvku3Jh2/zVCTLyZ0/fPDmjJo7BvIzkg9/+FBRhzPEIMAAiCUSYDzGNgmwCEHAfwDOZP5AjIZRMApGwSgYMQAAls1XSP6VxZYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6387-5232","institution":"National Research and Innovation Agency - Republic of Indonesia (BRIN)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Wulan","middleName":"","lastName":"Koagouw","suffix":""},{"id":240199069,"identity":"48913cff-c346-472f-99d8-4134bcb5e08d","order_by":1,"name":"Erna Simanjuntak","email":"","orcid":"","institution":"Jakarta Regional Health Laboratory","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Erna","middleName":"","lastName":"Simanjuntak","suffix":""},{"id":240199070,"identity":"1fca8375-fad0-40ec-b7e3-1724af2dc73b","order_by":2,"name":"Richard J. 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Inset map shows the location of Jakarta within Indonesia.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3374407/v1/6c1d237d4b03b8a4c61afeaa.png"},{"id":44812729,"identity":"b1bafdcf-29d2-4346-835c-d11554130aa2","added_by":"auto","created_at":"2023-10-17 22:12:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32512,"visible":true,"origin":"","legend":"\u003cp\u003eMean metformin concentrations detected in Angke river. Error bars depict maximum and minimum values (two samples were collected per site). The red line is the limit of detection (LOD) at 100 ng/L.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3374407/v1/8657075d5e2f431bb6a3bb27.png"},{"id":44812730,"identity":"38b28759-f815-4c5a-997e-a3430fb15114","added_by":"auto","created_at":"2023-10-17 22:12:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31803,"visible":true,"origin":"","legend":"\u003cp\u003eThe concentrations of metformin reported in surface waters around the world. Bars depict the range of concentrations detected in each study. Studies that only provided a single concentration value are represented by a horizontal line. The red bar shows the data from this study.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3374407/v1/3f113a9d4b5f8b12b6c53494.png"},{"id":48165343,"identity":"2a9f9c0f-89a2-4f22-875e-c0db395b5e24","added_by":"auto","created_at":"2023-12-14 01:08:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1885811,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3374407/v1/8b8dc0e1-0870-4745-9405-1f84161d43fe.pdf"},{"id":44812732,"identity":"99d4054d-bb8f-4492-bf47-9b3624933c73","added_by":"auto","created_at":"2023-10-17 22:12:15","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":19140,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3374407/v1/1eb8aa76c8864dac2f3cba3e.docx"},{"id":44812733,"identity":"801a5010-a73b-439c-838f-c9ec9c4a0ca6","added_by":"auto","created_at":"2023-10-17 22:12:15","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":25130,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3374407/v1/e16f4a87ed7adede5c1532e7.docx"}],"financialInterests":"","formattedTitle":"Another emerging contaminant in the sinking city: The first evidence of metformin detected in Jakarta waters","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater pollution caused by emerging contaminants such as pharmaceuticals is of growing concern worldwide (Morin-Crini et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The presence of these contaminants in water sources has been shown to have adverse effects on aquatic life and human health (L\u0026oacute;pez-Pacheco et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Indonesia, the world's fourth most populous country, has faced its share of significant environmental challenges, including water pollution (Comte et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Indeed, recent studies have shown the presence of pharmaceuticals in surface waters in Indonesia, including rivers and lakes (Koagouw, Arifin, et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wilkinson et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nozaki et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eJakarta, the capital city of Indonesia, is one of the most populated cities in the world, with a population of over 10\u0026nbsp;million people (Martinez \u0026amp; Masron, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Like many other cities in developing countries, Jakarta is facing a number of environmental challenges, with water contamination among the most pressing. The Angke River is one of Jakarta\u0026rsquo;s major water courses, and receives numerous sources of pollution, including untreated domestic sewage, industrial wastewater, and solid waste (Siregar et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), all of which may contain a variety of pharmaceuticals and their active metabolites.\u003c/p\u003e \u003cp\u003eMetformin is an antidiabetic drug that has been widely prescribed for the treatment of type 2 diabetes, with its use increasing rapidly worldwide (Zhou et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In 2030, the World Health Organization estimates that almost 400\u0026nbsp;million people will be diagnosed with diabetes, and that consequently metformin will see a spike in production. Unlike many pharmaceutical drugs, metformin is not metabolized by humans but passes through the body unchanged. Entering aquatic compartments, it can be bacterially transformed into its metabolite, guanylurea (Trautwein et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As a result of its widespread use, metformin has been detected in various environmental matrices in recent years (Ambrosio-Albuquerque et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), including surface waters (Kong et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), sediments (Huber et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and soils (Briones et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), raising concerns about its potential environmental impact.\u003c/p\u003e \u003cp\u003eIn Europe, metformin has been recorded at concentrations as high as 325 ug/L - the level detected in wastewater influent in Portugal (de Jesus Gaffney et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In Asia, the highest recorded concentration is 53.6 ug/L, detected by Yan et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in wastewater treatment plant influent in China. In North America, a concentration of 107 ug/L was recorded in Mexican groundwater by Lesser et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Meanwhile, Archer et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) recorded the highest level of metformin detected so far in Africa, with a concentration of 9.3 ug/L in South African wastewater influent. Despite this growing global body of evidence for its occurrence, metformin has never been reported in the environment in Jakarta, Indonesia.\u003c/p\u003e \u003cp\u003eIn aquatic animals, several significant negative impacts of metformin exposure have been reported, such as severe gonadal tissue pathologies, destabilization of lysosomal membranes in hemocytes, and altered mRNA expression in the mussel \u003cem\u003eMytilus edulis\u003c/em\u003e after short exposure (Koagouw \u0026amp; Ciocan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Koagouw, Hazell, et al., 2021). Ussery et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) showed that exposure of medaka fish \u003cem\u003eOryzias latipes\u003c/em\u003e to metformin at early stages of development led to negative effects on growth metrics, metabolomes, and transcriptomes, including an increased production of some steroid hormones in adult female medaka. Interestingly, a recent study by Nielsen et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) suggested that wild embryos of fathead minnow (\u003cem\u003ePimephales\u003c/em\u003e sp.) are more sensitive to environmentally relevant concentrations of metformin, than their lab spawned counterparts.\u003c/p\u003e \u003cp\u003eThis study reports for the first time the presence of metformin in Angke River in Jakarta, providing the first evidence of the occurrence of this pharmaceutical in Jakarta waters. The findings of this study will contribute to a better understanding of the extent of pharmaceutical contamination of Jakarta's rivers, and their bioavailability for the aquatic biota, therefore supporting the development of new strategies to tackle water pollution.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1. Water sample collection\u003c/h2\u003e \u003cp\u003eSurface water samples were collected in June 2022 from six different sites along the Angke River in Jakarta, Indonesia, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The comprehensive descriptions of these sampling sites, including the location names, coordinates, and environmental conditions at the time of sample collection, are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Prior to the sampling process, all containers and equipment were cleaned and treated with methanol to ensure sterility and prevent contamination. Aluminum foil and other consumables were discarded immediately after use, and each container was used for one sample only.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the sampling process, a 2-liter bucket was used to collect the water, which was then transferred into 25 ml sample bottles and 2-liter water containers. To maintain consistency and accuracy in the results obtained, duplicate samples were collected from each site using grab sampling techniques as recommended by Wang et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The plastic bucket was thoroughly cleaned before and after each use. To preserve the integrity of the samples, they were placed in pre-cooled cool boxes with ice, minimizing any potential biological or chemical alterations that could occur during transportation.\u003c/p\u003e \u003cp\u003eThe samples were promptly transported to the laboratory for comprehensive analysis. The collection, storage, and preparation of samples were conducted in accordance with good laboratory practice standards, following guidelines provided by the United States Environmental Protection Agency (US-EPA) (Decker \u0026amp; Simmons, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and the American Public Health Association (APHA) (Baird et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Adhering to these guidelines ensured that the data gathered would provide valuable insights into the water quality of the Angke River and identify any potential environmental issues that may need to be addressed.\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\u003eDescription of sampling sites along Angke River, Jakarta, Indonesia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocation Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuri Kosambi, West Jakarta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.187045, 106.720453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNear Kembangan power plant; characterized by swift, turbid brown water; accessed through an area with tall grass and numerous large trees; rapid flow conditions and potential influence from the power plant.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRawa Buaya, West Jakarta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.165606, 106.748331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocated beside Kembangan Baru road; presence of a fish feed vendor; calm, light brown water with significant amounts of trash visible; likely impacted by nearby human activity and waste disposal.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKedaung Kali Angke, West Jakarta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.158201, 106.7599355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSituated beside Daan Mogot road; active bridge construction site; calm, shallow water conditions; possible construction-related impacts on water quality.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWijaya Kusuma, West Jakarta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.145298, 106.775080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDensely populated area; calm water conditions; the river splits into two channels here, potentially affecting water flow and dispersion of pollutants.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKapuk Muara, Penjaringan, North Jakarta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.123370, 106.774228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocated beside a highway bridge; moderately swift water flow with substantial amounts of trash; presence of people and goods ferries, possibly influencing water quality due to increased human activity and resuspension of sediments.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePluit, Penjaringan, North Jakarta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.110076, 106.774421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositioned at the Angke dock under a bridge; calm, turbid water conditions; significant amounts of trash and a line of moored boats; potential impacts from boat-related activities and waste disposal.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2. Sample processing and preparation\u003c/h2\u003e \u003cp\u003eUpon arrival at the laboratory, the surface water samples were processed immediately to ensure the integrity of the analytes. The analyses of the physicochemical parameters were conducted following the procedures in the method references column in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which includes Standard Methods for Examination of Water and Wastewater published by the American Public Health Association (Baird et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Indonesian National Standards (SNI), and the standardized protocols from the Jakarta Regional Health Laboratory. All analyses were conducted at the Jakarta Regional Health Laboratory, a testing laboratory accredited by ISO/IEC 17025:2017, ISO 45001:2018, and ISO 15189:2012, indicating its compliance with recognized international standards.\u003c/p\u003e \u003cp\u003eFor the metformin analysis, the sample preparation process started with the filtration of a 15 mL aliquot using a filter disc (Sartorius, Grade: 390, diameter 125 mm) and silanized glassware, eliminating potential interferences due to particulate matter. From the filtered sample, a 3 mL volume was taken and supplemented with 10 \u0026micro;L of a 10 ppm internal standard (ISTD) mefruside for quantitation purposes. This sample was subsequently loaded onto a Sep-Pak C18 solid-phase extraction (SPE) cartridge (Nexus, 200 mg, 6 mL), which had been preconditioned with 3 mL of methanol and 3 mL of water to ensure proper retention of the analytes. Samples were loaded at a flow rate of 3 mL min\u0026thinsp;\u0026minus;\u0026thinsp;1, followed by a washing step with two sequential 1 mL aliquots of water to remove any matrix interferences. The SPE sorbent was dried under vacuum at room temperature, and the analytes were subsequently eluted from the column using 3 x 1 mL aliquots of methanol at a flow rate of 1 mL min\u0026thinsp;\u0026minus;\u0026thinsp;1, ensuring optimal recovery of the compounds of interest. The methanol eluate was evaporated to dryness at 40\u0026deg;C using a Turbovap under a gentle stream of nitrogen. The dried residue was then reconstituted in 150 \u0026micro;L of a methanol:water (1:1) solution and subsequently injected into the LC-MS/MS system for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3. Metformin quantification and quality control\u003c/h2\u003e \u003cp\u003eChromatographic analysis was performed on an Agilent 6470 series Triple Quad LC-MS/MS system coupled with a 1260 Infinity II HPLC (Agilent Technologies, USA) using positive electrospray ionization. Analytes were separated using an Infinity C18 column (150 \u0026times; 2.1 mm; 2.7 \u0026micro;m particle size) with a 0.2 \u0026micro;m pre-filter. The mobile phase consisted of 20% acetonitrile (solvent A) and 80% of 0.1% formic acid in water (solvent B). A flow rate of 0.2 mL/min was applied, and the column temperature was maintained at 40\u0026deg;C. The injection volume was 10 \u0026micro;L, with a stop time of 18 minutes and a post-time of 2 minutes. The gradient used in this procedure, consisting of six steps with varying proportions of solvents A and B, ensures optimal separation and peak resolution of the target analytes, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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\u003eGradient mobile phase program\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolvent A (Acetonitrile)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSolvent B (0.1% Formic Acid in Water)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFlow Rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 mL/min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 mL/min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 mL/min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 mL/min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 mL/min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 mL/min\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\u003eThe system was set up with specific source and ionization parameters to ensure optimal sensitivity, selectivity, and accurate quantification. The source parameters included a capillary voltage of 3500 V, desolvation temperature of 350\u0026deg;C, gas temperature of 300\u0026deg;C, nitrogen gas flow rate of 11 L/min, nebulizing pressure of 45 psi, sheath gas temperature of 350\u0026deg;C, and sheath gas flow of 11 L/min. Nitrogen gas was employed as the nebulizing, desolvation, and collision gas. Two multiple reaction monitoring (MRM) transitions were monitored for each analyte, allowing for efficient ionization of the analytes and maximizing signal intensity. Additional quality criteria, such as pre-determined ion ratio and retention time tolerances, were employed to ensure the reliability of the data. Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e provides more details on the instrumental parameters employed in the ESI-MS analysis, and the analytical parameters for each analyte.\u003c/p\u003e \u003cp\u003eTo ensure the accuracy and reliability of the analysis, analytical reference standards of metformin (99.60% purity, MT20231219, Pharma Metric Labs) and mefruside (CAS No. 7195-27-9, TRC-M205150-50MG, LGC Standards) as an internal standard were employed. The standards were prepared at concentrations of 0.1 and 1.0 mg/mL in methanol and stored at \u0026minus;\u0026thinsp;20\u0026deg;C in darkness. HPLC grade methanol and acetonitrile were obtained from Merck, while ultrapure water with a quality of 18.2 MΩ cm\u0026thinsp;\u0026minus;\u0026thinsp;1 was used.\u003c/p\u003e \u003cp\u003eTo maintain the reliability of the metformin analysis, quality controls were integrated within the method development and during the analysis of river water samples. This included the calibration linearity range for each compound, method detection limits, and analysis of reagent blanks. Reproducibility was verified by processing duplicates of each sample, and the method accuracy was validated through spike recoveries in reagent water and river water samples, aiming for a standard recovery rate of 90\u0026ndash;110%. Moreover, at least one method blank (ultrapure water), one duplicate, and one spiked sample were processed with each batch of river water samples analyzed, ensuring the precision and accuracy of the LC-MS/MS detection process. A 10 L river water sample was employed for the development and validation of the analytical method within the sample matrix.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe physicochemical properties of the water samples.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents water quality data from all sampling sites along the Angke river, with measurements for parameters in Attachment 1 Chapter II Point A of the Regulation of the Minister of Health of the Republic of Indonesia No. 32 of 2017 regarding Standard Quality of Environmental Health and Health Requirements for Hygiene, Sanitation, Swimming Pools, Per Aqua Solutions, and Public Baths. Water samples were analyzed using various methods and standard references as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In general, the water samples demonstrated varying degrees of compliance with maximum allowable concentrations.\u003c/p\u003e \u003cp\u003eSite 6 shows particularly concerning results, with several parameters exceeding their respective maximum allowable concentrations. Dissolved solids (2602 mg/L), hardness (as CaCO3, 5320.5 mg/L), fluoride (1.6 mg/L), and sulfate (2226.57 mg/L) levels all surpass the limits. Additionally, the organic matter (KMnO4) levels at this site (39.39 mg/L) are significantly higher than the maximum allowable concentration (10 mg/L). Turbidity at Site 5 also exceeds the limit, with a measured value of 126.5 NTU. The remaining sites display parameter levels mostly within acceptable ranges, with some sites showing elevated nitrite as N (Site 2), manganese (Site 3), and organic matter (KMnO4) levels (Sites 2, 3, 4, and 5). The water samples were generally odorless, tasteless, and within acceptable temperature ranges.\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\u003eThe water quality parameters in selected sampling sites, compared with the Indonesian government regulation limits\u003csup\u003e+++\u003c/sup\u003e\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=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMaximum Allowable Concentration\u003csup\u003e+++\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMethod Reference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNTU Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e126.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIK02/PP16.5-Air-17025/Labkesda\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCU Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e412.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e325.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e272.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e473.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSNI 6989.80:2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDissolved Solids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIK.01/PP16.5-Air-17025/Labkesda\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature (Ex situ)\u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIK.03/PP16.5-Air-17025/Labkesda'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdorless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdorless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdorless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdorless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOdorless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOdorless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOdorless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSNI 3554:2015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDH (Ex situ)\u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSNI 6989.11:2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIron\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStd Met.APHA 3120B/23/2017;3030B/23/2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFluoride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSNI 06-6989.29-2005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHardness (as CaCO3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5320.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSNI 06-6989.12-2004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManganese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStd Met.APHA 3120B/23/2017;3030B/23/2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrate as N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStd Met. APHA 4110C/23/2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrite as N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStd Met. APHA 4110C/23/2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyanide\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIK.15/PP16.5-Air-17025/Labkesda\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMercury\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIK.21/PP16.5-Air-17025/Labkesda\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArsenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStd Met.APHA 3120B/23/2017;3030B/23/2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCadmium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStd Met.APHA 3120B/23/2017;3030B/23/2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromium Hexavalent\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStd Met.APHA 3500-Cr.B/23/2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelenium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStd Met.APHA 3120B/23/2017;3030B/23/2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZinc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStd Met.APHA 3120B/23/2017;3030B/23/2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2226.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStd Met.APHA 4110C/23/2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStd Met.APHA 3120B/23/2017;3030B/23/2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganic Matter (KMnO4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSNI 06-6989.22-2004\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 \u003csup\u003e+\u003c/sup\u003e Parameter is not yet accredited.\u003c/p\u003e \u003cp\u003e \u003csup\u003e++\u003c/sup\u003e The examination of Ex Situ temperature and pH cannot be compared to the Maximum Quality Standard.\u003c/p\u003e \u003cp\u003e \u003csup\u003e+++\u003c/sup\u003e In accordance with Attachment 1 Chapter II Point A of the Regulation of the Minister of Health of the Republic of Indonesia No. 32 of 2017 regarding Standard Quality of Environmental Health and Health Requirements for Hygiene, Sanitation, Swimming Pools, Per Aqua Solutions, and Public Baths.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003e2. The concentration of metformin in the water samples.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e The results show that metformin was present at 3 sites, with concentrations ranging from 27 to 414 ng/L (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). To the best of our knowledge, this is the first time metformin has been detected in Jakarta waters. The concentration of metformin at site 3 is considerably higher than at site 2. The range of values between replicates is larger for site 3 than for site 2, suggesting greater variability in the metformin concentration at site 3.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the metformin levels detected in this study, relative to other reported concentrations from around the world. Our lowest concentration detected at Site 2 (27 ng/L) falls within the 5th percentile. This indicates that the metformin concentration at Site 2 is relatively low compared to the global range of metformin concentrations in surface water. The highest concentration in this study (414 ng/L) falls within the 40th percentile. This indicates that the metformin concentration at Site 3 is higher than 40% of the global range of metformin concentrations in surface water but still lower than the remaining 60%. More detail on the concentration ranges detected in these studies can be found in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e1. The Comparative Analysis of Metformin Concentrations in Indonesia\u0026apos;s Angke River with Global Data\u003c/h2\u003e\n \u003cp\u003eOur study reports data on the presence of metformin, a common antidiabetic drug, in the Angke River, a significant waterway in Jakarta, Indonesia. We detected metformin concentrations ranging from 27 ng/L to 414 ng/L.\u003c/p\u003e\n \u003cp\u003eThese findings, while considered relatively moderate in a global context, represent an important step in understanding pharmaceutical pollution in Indonesia\u0026apos;s aquatic environments. Our highest recorded concentration of 414 ng/L is comparable to ranges observed in German surface waters, which vary between 35 ng/L and 643 ng/L (Trautwein et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Turkey recorded lower metformin concentrations in surface water, ranging from 0.14 ng/L to 14.1 ng/L (Guzel et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, Canada reported significantly higher concentrations, with a range of 145 ng/L to 10,100 ng/L (de Solla et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eOther countries with notable metformin concentrations in surface water include Vietnam (Chau et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) with a range of 10 ng/L to 8,247 ng/L, Saudi Arabia (Ali et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) with a concentration of 4,801 ng/L in the Red Sea, and South Africa (Archer et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) with a range of 65 ng/L to 316 ng/L.\u003c/p\u003e\n \u003cp\u003eThe prevalence of diabetes in Indonesia\u0026apos;s adult population stands at 6.2%, a figure significantly lower than the global average of 9.3% as recorded in the International Diabetes Federation (IDF) Atlas (2019). This may partially account for the relatively reduced traces of metformin observed in our study. However, current global trends suggest an accelerating adoption of metformin due to its proven efficacy, advantageous safety profile, and economic affordability as supported by Ahmad et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Given these contributing factors and the projected escalation in diabetes prevalence in Indonesia - the adult diabetic population is anticipated to rise from 10.7\u0026nbsp;million in 2019 to an estimated 16.6\u0026nbsp;million by 2045 (IDF Atlas, 2019) - it is reasonable to predict a subsequent surge in metformin levels within the country\u0026apos;s aquatic environments in the forthcoming years.\u003c/p\u003e\n \u003cp\u003eThe Angke River, already facing significant pollution from sources such as domestic sewage discharge and industrial effluents, could be increasingly impacted by this trend. While the precise contribution of these sources to metformin levels is unclear in the absence of direct data, the combined influence of industrial and domestic waste is likely substantial.\u003c/p\u003e\n \u003cp\u003eOur work underscores the necessity for more comprehensive research on the occurrence and impacts of pharmaceuticals in Indonesian water bodies. As metformin use is likely to rise rapidly, proactive measures to minimize pharmaceutical pollution are essential. Comprehensive understanding requires consideration of factors such as evolving drug prescription practices, changing population health trends, waste management systems, and specific environmental conditions. Future studies should focus on the potential impacts on aquatic ecosystems and public health, with the goal of informing pollution management strategies and contributing to the global understanding of pharmaceutical pollution.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2. Associations between Physicochemical Parameters and Metformin Concentrations in the Angke River, Jakarta: A Comparative Analysis of Polluted Sites\u003c/strong\u003e\u003c/p\u003eBased on the water quality parameters presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, we can analyze the potential links and connections between the physicochemical properties and the metformin concentrations detected at the various sampling sites. The presence of metformin in the Angke River, particularly at Sites 2, 3, and 4, raises questions about the potential factors influencing its concentrations in the water. A comprehensive analysis of the physicochemical parameters of the water quality needs to be taken into consideration when discussing the transport and behavior of metformin in the river. In this context, it is essential to consider the local context, land use, and nearby pollution sources when interpreting these findings.\u003cp\u003eTurbidity, color, and organic matter content appear to be the most likely parameters related to metformin concentrations in the Angke River. High turbidity levels at Site 2 (31.7 NTU) and Site 3 (26.8 NTU), both exceeding the maximum allowable concentration (25 NTU), indicate the presence of suspended particles, which can contribute to the adsorption and transport of metformin in the water (Boyd, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). Additionally, high color values at all three sites (Site 2: 325.5 TCU, Site 3: 272.5 TCU, Site 4: 275 TCU) suggest an elevated organic matter content or the presence of other pollutants, which could affect metformin concentrations. Boyd (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e) elucidates that water turbidity, color, and organic matter significantly affect the transport, adsorption, and degradation of pharmaceuticals in water, with turbidity enhancing surface area for sorption and sedimentation, color impacting solubility and photodegradation, and organic matter competing for sorption sites or aiding in biodegradation. Additionally, the organic matter, which can be derived from both natural and anthropogenic sources, plays a critical role in determining water\u0026apos;s biological, chemical, and physical properties such as oxygen demand, pH, and conductivity.\u003c/p\u003e\n \u003cp\u003eThe manganese concentration at Site 3 was observed to be significantly high at 0.2514 mg/L, notably surpassing the values recorded at Site 2 (0.0027 mg/L) and Site 4 (0.08245 mg/L). Intriguingly, this pattern mirrors the distribution of metformin levels across these sites. Elevated manganese concentrations may serve as a potential marker for pollution, often resulting from processes such as erosion of geological substrates, discharge of industrial waste, or leaching from landfills (U.S. Agency for Toxic Substances and Disease Registry (ATSDR), 2012). These findings suggest the possible presence of industrial waste and/or landfill leaching activities in the vicinity of Site 3, thereby warranting a more detailed investigation into potential sources of contamination in this area.\u003c/p\u003e\n \u003cp\u003eThe pH and temperature of the water samples from the three sites do not show significant variations that could account for the differences in metformin concentrations. The pH values at Site 2 (7.055), Site 3 (7.045), and Site 4 (6.99) are close to neutral, which should not significantly impact the metformin concentrations, as metformin is more stable and soluble at neutral pH levels (da Trindade et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Desai et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, there are no significant differences in temperature among the sampling sites, making it unlikely that temperature variations contribute to the observed metformin concentrations, as suggested by Sharma et al. (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAlthough metformin levels were undetectable, site 6 presented the most concerning water quality profile, with multiple parameters exceeding the defined limits, suggestive of potential water contamination. Notably, dissolved solids exhibited an extreme deviation at 2602 mg/L, more than double the allowable limit of 1000 mg/L, indicative of potential pollution. Moreover, fluoride and hardness (expressed as CaCO3) levels were over the set limits, suggesting the presence of industrial contaminants and high levels of dissolved minerals (Ali et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), respectively. The sulfate concentration at site 6 was markedly high, further pointing to possible runoff of industrial or household waste, particularly detergent and cleaning products (de F. Ara\u0026uacute;jo et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; World Health Organization, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). Additionally, organic matter, measured using KMnO4, was considerably over the limit, indicative of a potential influx of organic waste (ISO 8467, 1993). These alarming findings underscore the need for a thorough investigation into the source of these exceedances to devise appropriate remedial measures, in consideration of the intended water usage in this area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3. Implications of the study\u003c/h2\u003e\n \u003cp\u003eThe detection of metformin in the Angke River in Jakarta, Indonesia, brings attention to an often overlooked aspect of water pollution\u0026mdash;pharmaceuticals. Despite concentrations in this study falling within the lower 40% of the global range, the potential ecological and human health impacts must not be underestimated, given the projected expansion of the number of diabetes diagnoses, both in Indonesia and globally. In addition, metformin\u0026rsquo;s use as an ovulation induction agent for polycystic ovarian syndrome (Johnson, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) suggests its global use could further increase with increasing demand for fertility treatments. Metformin\u0026apos;s adverse effects on aquatic organisms have been documented, even at low concentrations. In invertebrates, metformin has been shown to potentially alter reproduction and morphological traits, cause immobilization, and induce oxidative stress (Cleuvers, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Godoy et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Koagouw \u0026amp; Ciocan, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Koagouw, Hazell, et al., 2021). These factors may, over time, affect population dynamics and alter the balance within these ecosystems.\u003c/p\u003e\n \u003cp\u003eIn fish, exposure to metformin has been found to induce endocrine disruption and intersex in males, affect embryogenesis, and reduce overall growth (Elizalde-Vel\u0026aacute;zquez et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lee et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Niemuth \u0026amp; Klaper, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). These factors may potentially lead to reduced fitness and survivability, contributing to population declines and, over extended periods, could lead to local extinctions. The potential ecological ramifications are profound, as they can ripple through food webs and alter biodiversity across trophic levels, with potential impacts on ecosystem functions and services.\u003c/p\u003e\n \u003cp\u003eThe presence of metformin in surface waters, moreover, poses potential risks to human health, as these waters serve various purposes such as irrigation, recreation, and drinking water sources. With this study acting as a key stepping stone in understanding pharmaceutical pollution in Indonesian waters, it underscores the urgency for enhanced wastewater treatment and management practices, alongside increased monitoring and assessment of pharmaceutical pollutants. This includes the need for well-established regulatory frameworks and public awareness campaigns promoting responsible drug disposal practices.\u003c/p\u003e\n \u003cp\u003ePharmaceutical pollution poses a global threat to environmental and human health (Nassiri Koopaei \u0026amp; Abdollahi, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ort\u0026uacute;zar et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), as well as to the delivery of the United Nations Sustainable Development Goals, especially SDGs 3 (Good health and well-being), 6 (Clean water and sanitation), 12 (Responsible consumption and production) and 14 (Life below water). Further research is required to comprehensively evaluate the extent of contamination and the potential risks associated with exposure to metformin and other emerging contaminants in Indonesian waters. This would enable the development of informed strategies to address this urgent concern.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study represents the first detection of metformin in the waters of Jakarta, and thus highlights the intersection of pharmaceutical consumption, particularly due to growing diabetes prevalence, and environmental health in Indonesia. In addition to illuminating the extent of this issue, the study provides valuable insights into the parameters influencing metformin's prevalence in water bodies, including turbidity, color, organic matter content, and manganese levels. The environmental and public health concerns presented here support the growing need for extensive monitoring and assessment of pharmaceutical pollutants, improved wastewater treatment methods, and enhanced public awareness of responsible drug disposal practices. The development and implementation of appropriate regulatory frameworks will be vital in safeguarding both public health and the environment. Finally, this research underscores the need for a more thorough understanding of the potential risks associated with exposure to metformin and other pharmaceutical pollutants. This knowledge is crucial for developing effective strategies to address this issue, which not only impacts Indonesia's aquatic ecosystems but also has far-reaching implications for global environmental health and the achievement of the United Nations Sustainable Development Goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not necessary for this study as it did not involve human or animal subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, as the study does not involve human or animal participants, but all procedures were conducted ethically and responsibly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors listed have reviewed the final version of the manuscript and unanimously consent to its publication.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWulan Koagouw : Conceptualization, Methodology, Formal analysis, Investigation, Resources, Writing - Original Draft (main contributor)\u003c/p\u003e\n\u003cp\u003eErna Simanjuntak : Methodology, Writing - Original Draft\u003c/p\u003e\n\u003cp\u003eRichard J. Hazell\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; : Formal analysis, Investigation, Resources, Writing - Original Draft\u003c/p\u003e\n\u003cp\u003eRiyana Subandi : Methodology, Writing - Original Draft\u003c/p\u003e\n\u003cp\u003eCorina Ciocan : Resources, Writing - Original Draft, Supervision\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the University of Brighton, United Kingdom through the School of Applied Sciences - Research Investment Fund (SASRIF) and the Centre for Aquatic Environments research fund, both awarded to Wulan Koagouw. The funding bodies have no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\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\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003ePreprint availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have not submitted the manuscript to a preprint server before submitting it to Environmental Science and Pollution Research.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad, E., Sargeant, J. 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A preclinical overview of metformin for the treatment of type 2 diabetes. \u003cem\u003eBiomedicine \u0026amp; Pharmacotherapy\u003c/em\u003e,\u003cem\u003e 106\u003c/em\u003e, 1227-1235. https://doi.org/https://doi.org/10.1016/j.biopha.2018.07.085 \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":"emerging contaminants, metformin, pharmaceuticals, Jakarta, water pollution","lastPublishedDoi":"10.21203/rs.3.rs-3374407/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3374407/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePharmaceutically active compounds have been considered contaminants of emerging concern, in response to evidence that these substances may adversely affect non target organisms. The pharmaceutical metformin is the most commonly prescribed anti-diabetes medicine throughout the world. Metformin has been detected in numerous freshwater systems as well as in seawater at a number of sites around the world over the last few years, but has never been reported in the Indonesian capital city Jakarta. Several recent studies have highlighted various ecotoxicological effects of this medicine on aquatic organisms. Here we report the first evidence of metformin\u0026rsquo;s presence in Jakarta waters. Samples from the Angke river, one of the main rivers in Jakarta, were collected from six sites. Metformin was detected at three sites in concentrations ranging from 27 ng/L to 414 ng/L. Metformin is one of the most detected APIs (active pharmaceutical ingredients) in aquatic environments worldwide, and there is increasing concern regarding its impact on the health of wildlife and humans. However, this is the first report of metformin contamination in Jakarta waters, adding to the evidence of potentially increased pollution with pharmaceuticals, as noted in our previous studies. With no natural degradation processes, these chemical compounds can be easily reintroduced to the food chain and impact human health.\u003c/p\u003e","manuscriptTitle":"Another emerging contaminant in the sinking city: The first evidence of metformin detected in Jakarta waters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-17 22:12:10","doi":"10.21203/rs.3.rs-3374407/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":"96eabeb9-5b3a-4459-aabf-f633f7a3d881","owner":[],"postedDate":"October 17th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-12-14T01:00:14+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-17 22:12:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3374407","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3374407","identity":"rs-3374407","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}