Multi-region assessment of pharmaceutical exposures and predicted effects in USA wadeable urban-gradient streams

Paul M. Bradley; Celeste A. Journey; Daniel T. Button; Daren M. Carlisle; Bradley J. Huffman; Sharon L. Qi; Kristin M. Romanok; Peter C. Van Metre

doi:10.1371/journal.pone.0228214

Abstract

Human-use pharmaceuticals in urban streams link aquatic-ecosystem health to human health. Pharmaceutical mixtures have been widely reported in larger streams due to historical emphasis on wastewater-treatment plant (WWTP) sources, with limited investigation of pharmaceutical exposures and potential effects in smaller headwater streams. In 2014–2017, the United States Geological Survey measured 111 pharmaceutical compounds in 308 headwater streams (261 urban-gradient sites sampled 3–5 times, 47 putative low-impact sites sampled once) in 4 regions across the US. Simultaneous exposures to multiple pharmaceutical compounds (pharmaceutical mixtures) were observed in 91% of streams (248 urban-gradient, 32 low-impact), with 88 analytes detected across all sites and cumulative maximum concentrations up to 36,142 ng/L per site. Cumulative detections and concentrations correlated to urban land use and presence/absence of permitted WWTP discharges, but pharmaceutical mixtures also were common in the 75% of sampled streams without WWTP. Cumulative exposure-activity ratios (EAR) indicated widespread transient exposures with high probability of molecular effects to vertebrates. Considering the potential individual and interactive effects of the detected pharmaceuticals and the recognized analytical underestimation of the pharmaceutical-contaminant (unassessed parent compounds, metabolites, degradates) space, these results demonstrate a nation-wide environmental concern and the need for watershed-scale mitigation of in-stream pharmaceutical contamination.

Citation: Bradley PM, Journey CA, Button DT, Carlisle DM, Huffman BJ, Qi SL, et al. (2020) Multi-region assessment of pharmaceutical exposures and predicted effects in USA wadeable urban-gradient streams. PLoS ONE 15(1): e0228214. https://doi.org/10.1371/journal.pone.0228214

Editor: Miguel Cañedo-Argüelles Iglesias, Universitat de Barcelona, SPAIN

Received: July 26, 2019; Accepted: January 9, 2020; Published: January 30, 2020

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: All relevant data are within the manuscript and its Supporting Information files. All data are also available from the USGS National Water Information System at http://doi.org/10.5066/F7P55KJN and from USGS ScienceBase at https://doi.org/10.5066/P97MW2T8.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Human-use-pharmaceuticals in surface-water systems directly link human health with surface-water food-web structure and function (aquatic-ecosystem health) [1–8]. Pharmaceutical contaminants are intrinsic concerns in urban surface waters due to multiple wastewater point and non-point sources [2, 7, 9], aqueous mobility [10, 11], pH-variable activity [12], occurrence as complex mixtures [13], and designed biological activity [10, 11]; the latter often targeted at highly-conserved biological endpoints [5, 14–18], increasing the potential for adverse outcomes in multiple non-target organisms [1–4, 6, 18–24].

Wastewater treatment plants (WWTP) are generally cited as primary sources of pharmaceuticals to stream environments, in the United States (US) [7, 25] and globally [2, 10, 18, 26, 27]. Because WWTP surface-water outfalls are typically positioned to maximize dilution of effluent, most investigations of pharmaceutical occurrence and potential effects in stream ecosystems have focused on higher-order stream reaches [2, 10, 26, 27], despite the fact that headwater streams are fluvial capillaries [28] that dominate total stream length [29–33] and landscape-scale hydrologic connectivity [29–33] and provide critical habitat variability [30, 34, 35]. The US Geological Survey (USGS) National Water Quality Assessment (NAWQA) investigated pharmaceutical-contaminant concentrations in 59 wadeable headwater streams within the Piedmont ecoregion southern “megalopolis” [36, 37] during 2014 (Southeastern Stream Quality Assessment, SESQA [7, 38, 39]). The results illustrated that, while WWTP were indeed important point sources, extensive and diverse pharmaceutical-contaminant mixtures also were widely observed in urban stream settings with no National Pollution Discharge Elimination System (NPDES) permitted WWTP discharges, demonstrating the importance of non-WWTP sources and broad-scale mitigation approaches [7]. Notable SESQA results, such as the near-ubiquity of the anti-diabetic metformin, highlighted the fundamental biochemical link between global human-health crises, like Type II diabetes, and aquatic-ecosystem health and argued for assessment of headwater stream pharmaceutical exposures and potential effects in other regions across the US [7].

Herein, we expand the assessment of stream-ecosystem pharmaceutical risk (exposure and hazard) [40–42] to four US regions (including SESQA) sampled during 2014–2017 to test the hypothesis that pharmaceutical contamination is common across the US in urban-gradient headwater streams, including in those with no NPDES-permitted WWTP discharges. The potential for cumulative-contaminant effects (hazard) to in-stream biota was assessed based on 1) occurrence and cumulative concentrations of pharmaceutical mixtures, and 2) cumulative Exposure Activity Ratios (∑_EAR) [43–45] based on high-throughput screening data in Toxicity Forecaster [ToxCast^™, 46], as described in [44].

Material and methods

Site description and analytical method

Filtered water samples (10 mL) were collected by the USGS NAWQA Regional Stream Quality Assessments (RSQA) from perennial, wadeable (less than 10 m width and 1 m depth at base-flow) headwater stream sites in watersheds with varying degrees of urban and agricultural land use as part of four regional assessments (Pacific Northwest, PNSQA [47, 48]; California, CaSQA [49]; Northeast, NESQA [50, 51]; and SESQA [38, 39]) (Fig 1, S1 Table). During each regional water-quality assessment period (spring to summer), 3–5 water samples were collected from sites representing region-specific gradients in urban and agricultural development and a limited number of single samples were collected from nominal low-impact (low-development) watershed sites. Site selection and sampling methodologies are as described [38, 48, 50]. Samples were syringe filtered (0.7 μm pore size glass-fiber) into baked (500 C) amber glass vials and shipped on ice for analysis at the USGS National Water Quality Laboratory (NWQL) in Denver CO. Direct aqueous injection (100 μL), isotope dilution, high performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) was used to quantify an environmentally-relevant, representative subset consisting of 111 human-use pharmaceutical and pharmaceutical degradate compounds [52]; among these, gabapentin, guanylurea, and hexamethylenetetramine were added to the method in 2017 and analyzed only in CaSQA samples. Typically interpreted as a pesticide environmental contaminant, piperonyl butoxide also is medically indicated for treatment of lice [53, 54] and, thus, retained herein. Two additional analytes (atrazine, herbicide; methyl-1H-benzotriazole, solvent/deicing agent) included in the NWQL pharmaceutical method are not recognized pharmaceutical agents and are not included herein. Analytes, with Chemical Abstracts Services numbers and laboratory reporting limits (RL), are listed in the supporting information (S2 Table).

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Fig 1. Cumulative maximum concentrations (ng/L) and numbers of pharmaceuticals detected at least once during the 2014–2017 synoptic samplings of water from wadeable streams in the Pacific Northwest (PNSQA, 2015; upper left), Northeast (NESQA, 2016; upper right), California (CaSQA, 2017; lower left), and Southeast (SESQA, 2014; lower right) regions as part of the USGS Regional Stream Quality Assessment (RSQA).

For site details see S1 and S6 Tables. North is at top. Base-map image is from the USGS National Map [55].

https://doi.org/10.1371/journal.pone.0228214.g001

Quality assurance quality control (QAQC)

HPLC-MS/MS pharmaceutical analysis included addition of 19 surrogate standards (400 ng/L nominal final concentration) to field-filtered samples to evaluate whole-method recovery (median = 102%, interquartile range [IQR] = 95–110%, range 2–320%). A total of 44 field blanks were prepared across all regions by processing pesticide-grade blank water through all field collection equipment and analyzing as above [52] for environmental samples. Only 9 of the 88 pharmaceutical analytes detected at least once in environmental samples in this study were also detected in field blanks. Among these, 6 (metformin, methadone, metoprolol, nevirapine, norethindrone, and omeprazole/esomeprazole) were only detected in a single field blank; corresponding environmental data were not blank adjusted, but interpretation of results below blank detection levels warrants caution. Lidocaine, nicotine, and caffeine were detected in 5 (11%), 5 (11%), and 2 (5%) field blanks, respectively; environmental concentrations less than corresponding 90^th percentile field blank concentrations were blank corrected to non-detect (nd).

Data handling, statistics, and ∑_EAR analysis

The reporting limits for pharmaceutical analytes were determined using DQCALC² software, a spreadsheet-based tool for graphically modeling relative standard deviation versus concentration and assigning a data precision statement in water analytical methods [56](RLDQC; S2 Table). Laboratory-estimated water concentrations below the RLDQC (positive detections with reduced quantitative certainty) were used as is (S3A–S5 Tables). Pharmaceutical results were aggregated to estimate maximum- and median-concentration exposures relative to the 108 pharmaceutical analytes (111 for CaSQA). S3A and S3B Table include the sample and detection counts, respectively, by compound and site for all pharmaceuticals detected at least once in the study. S4 Table includes the maximum concentrations of each pharmaceutical detected in this study by compound and site. S5 Table contains median concentrations (all samples) by compound and site and only includes those pharmaceuticals that were detected in at least half of the samples at one or more sites. S6 Table contains land-use/land-cover (LULC), specific conductivity, and major ion (maximum and median concentrations of Ca, Mg, Na, Cl, SO₄, K) data.

As a first step, multivariate pharmaceutical detection or maximum/median concentration data matrices were log-transformed and normalized, converted to respective Euclidean-distance dissimilarity (similarity) matrices, and explored in three dimensions using non-metric multi-dimensional scaling (NMDS; Plymouth Routines in Multivariate Ecological Research, PRIMER v7; PRIMER-E Ltd., Plymouth, UK)[57]. Based on suggestive NDMS patterns, permutation-based one-way analysis of similarity (ANOSIM; permutation N = 999) was employed to test the null hypothesis (H₀) of no difference between groups (i.e., that between-group dissimilarity was equivalent to or less than within-group dissimilarity), for a priori site groupings including study regions (CaSQA, NESQA, PNSQA, SESQA), urban centers (cities), and broad land-use categories (high urban, low urban, low-impact reference)(S7 Table). Cophenetic correlations between detection/concentration dissimilarity matrices and site-specific major ion or LULC Euclidean-distance dissimilarity matrices were assessed using the RELATE (H₀: rho (ρ) = 0; permutation N = 999) routine (PRIMER v7)[57](S7 Table). Subsets of pharmaceutical, major ion, and LULC metrics that best explained the patterns in detection/concentration dissimilarity matrices were then identified using the permutation-based stepwise BEST(BVSTEP) (H₀: ρ = 0; permutation N = 999) routine (PRIMER v7) with stop criteria of ρ > 0.95 or delta ρ < 0.001 (S7 Table). Finally, bivariate correlations (H₀: rho (ρ) = 0; permutation N = 9999) between site-specific cumulative (sum of detected analytes) contaminant metrics (median detections and concentrations) and individual pharmaceutical, major ion, and LULC metrics identified above were assessed by nonparametric Spearman’s Rank-Order Correlation (Paleontological Statistics, PAST v3.25)(S8 and S9 Tables)[58]. The permuted (permutation N = 9999) probability that the centroids and dispersions of regional and WWTP-related site groupings were the same (H₀: no difference between groups) was assessed using nonparametric One-way PERMANOVA on Euclidean distance [58–60].

Integrated effects of pharmaceutical contaminants were estimated, as described [44], using the toxEval package [61] of the open source statistical software R [62] to sum (presumptive additive effects [63–68]) individual EAR (ratio of detected concentration to activity concentration at cutoff (ACC) from Toxicity ForeCaster [ToxCast^™; 46] high-throughput screening data [69, 70]) to provide site-specific cumulative EAR (∑_EAR)[43–45, 71–73]. EAR ≥ 1 indicate exposures reported to modulate molecular targets in vitro, whereas EAR ≤ 1 suggest proportionately lower probability of biological activity. A recent comparison of EAR and benchmark-based Toxicity Quotients for select surface-water contaminants, for which both ToxCast and aquatic-toxicity benchmark data exist, indicated agreement between the commonly-employed 0.1 benchmark-based Toxicity Quotient threshold of concern and EAR = 0.001 [45]. Non-specific-endpoint, baseline, and unreliable response-curve assays were excluded [as described in 43, 44, 45, 71–73](S10 Table). ∑_EAR results are summarized in S11–S14 Tables.

No permits were required for this work. All data are also available from the USGS National Water Information System (NWIS) [74] and from USGS ScienceBase [75].

Results and discussion

Pharmaceutical mixtures were common in all regions

Among the 308 wadeable streams sampled across all regions during the 2014–2017 RSQA studies, multiple pharmaceuticals (pharmaceutical mixtures) were detected at least once (maximum exposure dataset) in 95% (248) of the 261 multiple-sample, urban-gradient sites and in 68% (32) of the 47 single-sample, non-urban, presumptive low-impact, sites (Fig 1, S3B and S4 Tables). Importantly, pharmaceutical mixtures were detected in at least half of the samples (median exposure dataset) from 81% (212) of the multiple-sample, urban-gradient sites (S1 Fig, S5 Table). Cumulative (sum of detected pharmaceuticals) maximum and median detections ranged 0–60 per site (median: 4; IQR: 2–8) and 0–43 per site (median: 2; IQR: 1–4), respectively, with the centroid (mean) of cumulative pharmaceutical detections per site estimated (PERMANOVA) to be greater (permutation N = 9999 probability of being the same = 0.0001) in eastern (NESQA, SESQA) than in western (PNSQA, CaSQA) study streams (Fig 2A and 2B, S4 and S5 Tables). Site-specific cumulative maximum and median concentrations ranged nd-36,142 ng/L (median: 88 ng/L; IQR: 29–306) and nd-8,756 ng/L (median: 19 ng/L; IQR: 3–75 ng/L), respectively (Fig 2C and 2D, S4 and S5 Tables). The cumulative maximum concentrations were not clearly different (PERMANOVA; probability of being the same, p = 0.0812) between regions. There is some evidence to suggest the centroid of cumulative median concentrations was greater (permutation N = 9999 probability of being the same = 0.0154) in the eastern region (NESQA, SESQA) streams than in PNSQA streams, with CaSQA streams intermediate, but extensive overlap of the data distributions was apparent in all cases. The apparently greater detections and, possibly, median concentrations of pharmaceuticals between eastern and western regions are consistent with reported differences in cumulative 2016 prescription drug sales (87 vs 50 million US dollars [76]) and populations (79 vs 50 million people [77]) between corresponding eastern and western region states, respectively.

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Fig 2.

Cumulative maximum (left) and median (right) concentrations (ng/L; bottom) and numbers of pharmaceuticals detected in water samples from wadeable streams in each region (total sites in parentheses) as part of the USGS Regional Stream Quality Assessment (RSQA). Circles indicate maximum (left) or median (right) data for sites sampled multiple times (n = 3–5). Open triangles indicate data from single-sample sites and do not differ between maximum and median plots. Boxes, centerlines, and whiskers indicate interquartile range, median, and 5^th and 95^th percentiles, respectively, for multiple-sample sites (circles), only. For each plot, p is the permuted probability that the centroids and dispersions across all groups are the same and different letters indicate groups with pairwise probabilities that are less than 0.05 (PERMANOVA) for multi-sample sites only. Numbers above X-axes (bottom plots) indicate numbers of sites in each region with no detections (nd) under maximum and median exposure conditions.

https://doi.org/10.1371/journal.pone.0228214.g002

Eighty-eight of the 108 pharmaceutical analytes (111 in CaSQA) were detected at least once (Fig 3). Nicotine and its metabolite, cotinine, were detected at 70% and 47% of sites, respectively. Consistent with the SESQA findings reported earlier [7], the type-II diabetes medicine, metformin, was common (68% of sites) across all sites and regions. Caffeine-related compounds, caffeine and 1,7-dimethylxanthine, were detected at least once at 42% and 10% of sites, respectively. Other frequently detected (detected at more than 50 sites or 15% of sites) compounds included lidocaine (intravenous/topical analgesic, 42%), carbamazepine (anti-seizure medication, 41%), acetaminophen (oral analgesic, 26%), fexofenadine (anti-histamine, 21%), and tramadol (opioid analgesic, 17%); These pharmaceuticals also exhibited the highest maximum and median concentrations observed in this study (Fig 3 and S2 Fig). The numbers of frequently detected (detected at more than 15% of sites in the region) pharmaceuticals were higher in eastern regions (NESQA: 14; SESQA: 29) than in western regions (PNSQA: 8; CaSQA: 3), consistent with previously noted regional differences in cumulative 2016 prescription drug sales [76] and population estimates [77].

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Fig 3. Maximum concentrations (ng/L) of 88 pharmaceuticals detected at least once (in order of decreasing number of detections across all regions and samples) in water samples from wadeable streams in each region as part of the USGS Regional Stream Quality Assessment (RSQA).

Circles are data for individual sites. Boxes, centerlines, and whiskers indicate interquartile range, median, and 5^th and 95^th percentiles, respectively. Numbers to the right of each plot indicate the percentage of sites within the region at which the compound was detected at least once. Gabapentin, guanylurea, and hexamethylenetetramine were analyzed only in CaSQA samples.

https://doi.org/10.1371/journal.pone.0228214.g003

Likewise, the spatial detection frequencies (the percentage of study sites at which analytes were detected) of several individual pharmaceuticals and pharmaceutical groups differed markedly between regions (Fig 3 and S2 Fig). For example, the antidepressants, venlafaxine and desvenlafaxine, were detected at a substantially higher (approximately four times higher) percentage of eastern region sites than western region sites. The percentages of eastern region study sites, at which pharmaceuticals associated with seasonal and perennial allergies (fexofenadine, pseudoephedrine/ephedrine, diphenhydramine, loratadine) were detected, were generally double those observed in the two western study regions (Fig 3), despite similar reported percentages of hay fever and respiratory allergies for children [78] and adults [79] in the eastern and western US in 2018. Correlations between co-detected analytes are presented in S8 Table.

Land-use/land-cover and major ion predictors of pharmaceuticals

Frequent occurrence, multiple detections per site (median: 4 compounds per site across all sites including in pre-selected low-impact watersheds), and elevated cumulative concentrations (up to >36 μg/L per site) emphasize the need for identification, monitoring, and mitigation of pharmaceutical sources in wadeable-stream ecosystems. ANOSIM analysis of site-specific pharmaceutical and LULC data matrices indicated marginal (Global R range: 0.043–0.239) differences between RSQA study unit, urban-center, and ecoregion groupings (S7 Table). Likewise, weak correlations (RELATE; ρ range: 0.029–0.116) were found between median pharmaceutical similarity matrices (detection/concentrations under median conditions) and watershed LULC similarity matrices, with multiple individual urban development metrics (e.g., percentage of developed industrial/military [Dev_IndusMilitary2012] and semi-developed anthropogenic other [SemiDev_AnthroOth2012] land cover from the National Wall-to-Wall Anthropogenic Land Use Trends (NWALT) 1974–2012 database [80]; estimated road density [RoadDensity2016] from the 2016 U.S. Census data [81]) and wastewater metrics (e.g., number of NPDES-permitted wastewater facilities [NumFacilities2012] in 2012 [82]) consistently the strongest among the identified correlates (BEST routine; S7 Table).

Based on these suggestive multi-variate results, bivariate correlations between site-specific summary metrics (cumulative detections/concentrations) under the estimated median exposure conditions and readily available LULC metrics identified by BEST were further explored using Spearman Rank-Order Correlation (S9 Table). Several watershed urbanization metrics (e.g., percentage land cover as high-density urban development [DevelopedHigh2011] from the 2011 National Land Cover Database [83]) correlated well with cumulative detections (ρ range: 0.475–0.543) and concentrations (ρ range: 0.424–0.525) under median exposure conditions (S9, S15 and S16 Tables). Weaker correlations (ρ range: 0.294–0.395) were observed between various wastewater discharge metrics (e.g., number of NPDES-permitted wastewater facilities [NumFacilities2012] in 2012 [82]) and cumulative median pharmaceutical detections or concentrations (S9, S15 and S16 Tables). Differences (permutation N = 9999 probability of being the same = 0.0001) in the centroids of cumulative median detections and concentrations were observed between sites with (detections median: 3, IQR: 2–9; cumulative concentrations median: 62 ng/L; IQR: 20–405 ng/L) and without permitted wastewater discharges (detections median: 1, IQR: 0–3; cumulative concentrations median: 11 ng/L; IQR: 0–55 ng/L). These results are consistent with the documented importance of WWTP discharges as pharmaceutical-contaminant sources [84, 85] and the substantial pharmaceutical-contaminant reductions in urban-area wadeable streams following WWTP-treatment upgrades [84, 85] or WWTP closures [86, 87].

The pharmaceutical contaminants observed at the 75% of RSQA sites without NPDES-permitted discharges, however, confirm previous conclusions that WWTP outfalls are not the only important pathways of pharmaceutical contaminants to urban/suburban streams [1, 7, 88, 89]. Other potential urban-gradient sources of pharmaceuticals to streams include aging sewer infrastructure [90, 91], combined (sanitary/stormwater) sewer overflows [92–96], private septic and on-site waste-handling systems [97–99], gray-water systems [100–102], green space and golf course wastewater reuse [103], and animal waste runoff [104–106]. Notably, a recent national reconnaissance demonstrated that untreated stormwater can be an important episodic source of mixed pharmaceuticals to surface waters, at levels comparable to and often exceeding those in treated WWTP effluent [107]. Thus, these results reiterate the need for whole-watershed, contaminant-mitigation approaches, including improved pharmaceutical disposal practices, wastewater treatment and transfer systems, and stormwater controls.

More, the results argue for research and implementation of new high-frequency or continuous sensor technologies for direct or indirect monitoring of pharmaceutical contaminants in next generation water observing systems [e.g., 108, 109] in urban settings. Surface-water ions (and related conductivity measures) have been suggested as potentially useful surrogates for indirect monitoring of pharmaceutical contamination in streams [e.g., 110, 111], because physiological ions (electrolytes) and pharmaceuticals are both primarily excreted in urine [112–115] and are frequently reported together in wastewater-impacted streams at locally-elevated concentrations [e.g., 92, 93]. The most useful monitoring approaches are expected to be fixed-station or single watershed applications [111], due to the potential confounding effects of site-to-site variability in non-wastewater sources of ions in urban-gradient streams [116, 117], including geologic minerals [118], fertilizer runoff [104], road salt [119, 120], and concrete infrastructure [121–123]. Nevertheless, the current multi-region dataset provides a unique opportunity to test the broad geospatial validity of the approach by assessing the probability (p) of no correlation (H₀: rho (ρ) = 0; permutation N = 9999; Spearman Rank-Order Correlation) between in-stream pharmaceutical and ion concentrations (S9 Table).

The results support the potential utility of surface-water ions as surrogates for wastewater-associated contaminants like pharmaceuticals [110, 111]. Although no relation (p ≥ 0.2528) was observed between specific conductivity and cumulative pharmaceutical metrics under median exposure conditions, correlations were observed between pharmaceuticals and sodium or chloride (p ≤ 0.0182), with the strongest correlations observed for potassium (p ≤ 0.0001). Comparison of the Spearman correlation coefficients provided additional insight into the importance of the yellow-water/wastewater pathway relative to potential confounders (i.e., non-wastewater sources [104, 116–123]). Correlations between median pharmaceutical metrics and median concentrations of sodium and chloride were weak (ρ ≤ 0.202), consistent with numerous confounding non-wastewater sources of these ions in urban settings [116, 119, 120]. However, promising correlations (ρ range: 0.314–0.328) were observed between median concentrations of potassium and cumulative median detections and concentrations of pharmaceuticals, indicating the potential for potassium as an indicator of in-stream pharmaceutical contamination in fixed-place or single watershed applications and consistent with the strong correlation (R² > 0.89) reported between instream concentrations of potassium and pharmaceuticals in the Leine River watershed in Germany [111]. The stronger broad regional correlation in this study between potassium and pharmaceuticals may reflect comparatively less variability in non-wastewater potassium sources as well as the usage of potassium salts (e.g., ferrate, ferrocyanate) as floculants/coagulants in wastewater treatment [124]. Emerging sensor technologies that hold promise for next generation monitoring of potassium (and other ions) include recently described nanorod-based potassium ion sensors [125] and multi-parameter potentiometric microanalyzers (lab-on-a-chip platforms) developed for space travel [126–128] and environmental water quality monitoring [129, 130].

Potential for mixed-pharmaceutical biological effects

The 111-pharmaceutical analytical space assessed in this study is a fractional indicator of the presumptive pharmaceutical-contaminant universe, with more than 4000 active ingredients (parent compounds) [2, 131] in current use and an unknown chemical-space [132] of metabolites and environmental degradates [13]. Given the breadth of species, life stages, biomasses, and concomitant vulnerabilities present in urban-gradient aquatic food webs [133–136] and the designed bioactivity of commercial pharmaceuticals [1–6, 14–17, 19–24, 137], their detection in RSQA headwater streams is prima facie evidence of the potential for molecular toxicity and sub-lethal effects in non-target, organisms in urban-gradient headwater streams across the US [138–141].

The in vitro ToxCast-based EAR approach provides an additional line of evidence for sub-lethal effects at a reported concentration [43, 73], supports estimation of cumulative effects (∑_EAR) of mixed-contaminant exposures using the CA-model methodology [63–68], and predicts probable effects consistent with traditional in vivo water-quality benchmark-based toxicity quotient (TQ) approaches (EAR = 0.001 comparable to commonly-employed TQ = 0.1 effects threshold) [45]. ToxCast [46] includes exposure-response metrics for 9000+ organic chemicals and approximately 1000 standardized, predominantly-vertebrate, molecular bioassay endpoints (e.g., nuclear receptor, DNA binding) [70, 142, 143]. ToxCast EAR results for estimated maximum and median pharmaceutical exposure conditions are summarized in Fig 4 and S11–S14 Tables. Given the diversity of organisms and concomitant range of vulnerabilities in surface-water food webs [133–136], we employed the recently suggested effects-screening threshold of 0.001 [45], as described [44]. Of the 88 pharmaceuticals detected at least once in this study, only 43% (38) had acceptable ToxCast data at the time of access. Under maximum exposure conditions, 63% (194) of study sites had one or more compounds with individual EAR greater than the 0.001 effects-screening threshold, 65% (201) had cumulative EAR (ΣEAR_max) ≥ 0.001, and 3 sites had ΣEAR_max ≥ 1 (Fig 4). These results indicate transient exposures with a probability of vertebrate molecular effects were common in urban-gradient headwater streams across the US. Of the 62 pharmaceuticals in the estimated median exposure dataset, 61% (38) had exposure-effects data in ToxCast. Approximately 25% of the study sites had individual or cumulative EAR_med ≥ 0.001 under the estimated median exposure conditions (Fig 4), indicating that sites with persistent exposures with a probability of molecular effects were common in urban headwater streams across the US. Zebra fish (ZF; Danio rario) embryo metrics in ToxCast inform organism-level as well as vulnerable, early-life-cycle effects in fish [144, 145]. Thus, the results also indicate the potential for pharmaceutical effects to fish at the organism level in at least some headwater streams, because 7 and 5 sites, respectively, had ΣEAR_{max ZF} and ΣEAR_{med ZF} ≥ 0.001 across all ZF endpoints.

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Fig 4.

Cumulative maximum (left) and median (right) Exposure-Activity Ratios (Σ_EAR) of ToxCast all (primarily molecular, top) and zebra-fish only (bottom) endpoints for pharmaceuticals detected in water samples from wadeable streams in each region (total sites in parentheses) as part of the USGS Regional Stream Quality Assessment (RSQA). Boxes, centerlines, and whiskers indicate interquartile range, median, and 5^th and 95^th percentiles, respectively, for multiple-sample sites (circles), only. For each plot, p is the permuted probability that the centroids and dispersions across all groups are the same and different letters indicate groups with pairwise probabilities that are less than 0.05 (PERMANOVA) for multi-sample sites only. Numbers above X-axes in each plot indicate number of sites with no significant activity (Σ_EAR < 0.00001).

https://doi.org/10.1371/journal.pone.0228214.g004

Implications for stream ecosystem health and remediation

The results indicate substantial pharmaceutical-contaminant concerns in wadeable, urban-gradient, headwater streams not only in SESQA [7], but in other regions across the US [1, 4, 6, 19], irrespective of WWTP discharge. Crucially, the pharmaceutical-analyte space [52] assessed herein is an order(s) of magnitude underestimate of the presumptive pharmaceutical-contaminant universe, with 4000+ parent compounds in current use [2] and unquantified numbers of environmental metabolites/degradates [13, 132]. Considering only those pharmaceuticals assessed in this study, individual concentrations up to μg/L levels and multiple detections per site (median = 4 across all sites including pre-selected, low-impact watersheds) at cumulative concentrations ranging more than 36 μg/L are notable concerns, given documented adverse impacts of individual pharmaceuticals at low ng/L concentrations [20] and the widespread cumulative exceedances of in vitro molecular effects thresholds (∑_EAR) observed in these urban-impacted headwater streams.

Notably, these pharmaceutical results confirm earlier concerns about the linkage between human- and aquatic-health [7, 27, 146, 147]. Echoing earlier SESQA-only results [7], the Type II diabetes medicine, metformin again was the most consistently detected pharmaceutical across all sites and samples (more than half of the samples at 57% of sites; detected at least once at 68% of sites) at site-specific maximum concentrations ranging nd-3,077 ng/L (median: 11 ng/L; IQR: nd-46 ng/L), despite the fact that NPDES discharges occurred in only 25% of the study watersheds. In 2017 when the metabolite, guanylurea, was added to the analytical portfolio, metformin and guanylurea were detected at least once in 54% (46/85) and 5% (4/85) of CaSQA sites, respectively. The anti-diabetic pharmaceuticals, sitagliptin and glyburide, also were detected in 8% (23/308) and 1% (3/308) of the sites in this study, respectively. Importantly, a number of other potentially diabetes-related pharmaceuticals were widely detected in this study, including several analgesics (e.g., tramadol [17% of sites], desvenlafaxine [14%], venlafaxine [10%], gabapentin [2%], amitriptyline [2%]), prescribed for treatment of peripheral neuropathic pain, a common symptom of progressive Type II diabetes [148–152].

Metformin was the second most frequently detected pharmaceutical across all sites in this study and the fourth most prescribed pharmaceutical in the US, with an estimated 81 million prescriptions in 2016 alone [153, 154]. US and global metformin usage is expected to increase, as a first-line diabetes therapy [155–157] and treatment candidate for polycystic ovarian syndrome [158] and cancers [159]. Metformin is excreted essentially unchanged in human urine [160], poorly removed by wastewater treatment technologies [161], considered environmentally recalcitrant [161, 162], and increasingly reported in environmental samples [7, 9, 163]. Environmentally-relevant [161, 164, 165] metformin exposures in the μg/L range have recently been shown to induce biological responses in fish [166–169], including up-regulation of vitellogenin mRNA [170, 171] and other gene targets [169, 171, 172], male intersex in fathead minnow (Pimephales) [170], and behavioral modifications in Siamese fighting fish (Betta splendens) [167]. Guanylurea, metformin’s only currently recognized persistent environmental degradate, is often observed in surface waters at higher concentrations than metformin [155, 163–165] and, importantly, has been recently reported to cause growth effects in Japanese medaka (Oryzias latipes) similar to metformin but at low (<10) ng/L concentrations [166].

Fish and fish-embryos are widely-used animal models in the pharmaceutical development pipeline [173, 174], including for anti-diabetics [175, 176]; from this perspective, fish are arguably pharmaceutical target organisms with unintended environmental exposures. Consistent with this use, individual and simple mixtures of pharmaceuticals have been shown to cause unintended effects to the health and behavior of laboratory and wild fish at environmentally-relevant concentrations [24, 177–179] and the potential biological impacts of characteristically complex environmental pharmaceutical cocktails are global concerns [64, 68, 180, 181]. In light of the documented potential for pharmaceutical bioconcentration [182–184] and trophic transfer [182] within aquatic food webs and for trophic transfer of pharmaceuticals from aquatic to riparian food webs [185], measured water concentrations may substantially underestimate the ecological exposures and effects from in-stream pharmaceutical contaminants [182, 185]. Thus, considering potential individual and interactive effects of the 88 pharmaceuticals detected in headwater streams herein and the recognized orders-of-magnitude analytical underestimation of the presumptive pharmaceutical-contaminant (parent compounds, metabolites, degradates) space, the results of the present study demonstrate a nation-wide need for watershed-scale pharmaceutical-contaminant mitigation approaches that extend the current emphasis on WWTP-effluent sources to include more broadly distributed inputs such as septic systems, leaking wastewater transfer systems, and urban stormwater runoff.

Supporting information

S1 Fig. Cumulative median concentrations and detections of pharmaceuticals (ng/L) detected in at least half of the water samples at one or more wadeable stream sites during the 2014–2017 synoptic samplings in the Pacific Northwest (PNSQA, 2015; upper left), Northeast (NESQA, 2016; upper right), California (CaSQA, 2017; lower left), and Southeast (SESQA, 2014; lower right) regions as part of the USGS Regional Stream Quality Assessment (RSQA).

For site details see S1 and S6 Tables. North is at top. Base-map image is from the USGS National Map [55].

https://doi.org/10.1371/journal.pone.0228214.s001

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S2 Fig. Median concentrations ng/L of pharmaceuticals detected in at least half of the water samples (in order of decreasing number of detections across all regions and samples) at one or more wadeable stream sites in the (left to right) Pacific Northwest (PNSQA, 2015), Northeast (NESQA, 2016), California (CaSQA, 2017), and Southeast (SESQA, 2014) regions as part of the USGS Regional Stream Quality Assessment (RSQA).

Circles are data for individual sites. Boxes, centerlines, and whiskers indicate interquartile range, median, and 5th and 95th percentiles, respectively. Numbers to the right of each plot indicate the percentage of sites at which the compound was detected at least once. Gabapentin, guanylurea, and hexamethylenetetramine were analyzed only in CaSQA samples.

https://doi.org/10.1371/journal.pone.0228214.s002

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S3 Fig. Spearman rank-order correlation plot for cumulative median concentrations and detections as well as median individual concentrations of pharmaceuticals (ng/L) detected in at least half of the water samples at one or more wadeable stream sites during the 2014–2017 synoptic samplings in the Pacific Northwest (PNSQA, 2015), Northeast (NESQA, 2016), California (CaSQA, 2017), and Southeast (SESQA, 2014) regions as part of the USGS Regional Stream Quality Assessment (RSQA).

https://doi.org/10.1371/journal.pone.0228214.s003

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S4 Fig. Spearman rank-order correlation plot for cumulative median concentrations and detections of pharmaceuticals (ng/L), median concentrations of major ions, and GIS LULC metrics at one wadeable stream sites during the 2014–2017 synoptic samplings in the Pacific Northwest (PNSQA, 2015; upper left), Northeast (NESQA, 2016; upper right), California (CaSQA, 2017; lower left), and Southeast (SESQA, 2014; lower right) regions as part of the USGS Regional Stream Quality Assessment (RSQA).

https://doi.org/10.1371/journal.pone.0228214.s004

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S1 Table. Site information and select summary analytical results (detections and concentrations in nanograms per liter, ng/L) for wadeable streams sampled during 2014–2017 as part of the USGS Regional Stream Quality Assessment (RSQA) synoptic samplings in the Pacific Northwest (PNSQA, 2015), Northeast (NESQA, 2016), California (CaSQA, 2017), and Southeast (SESQA, 2014) regions.

https://doi.org/10.1371/journal.pone.0228214.s005

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S2 Table. Pharamceutical compound information for analyses performed by the USGS National Water Quality Laboratory (NWQL) as part of the USGS Regional Stream Quality Assessment (RSQA) synoptic samplings in the Pacific Northwest (PNSQA, 2015), Northeast (NESQA, 2016), California (CaSQA, 2017), and Southeast (SESQA, 2014) regions.

Gabapentin, guanylurea, and hexamethylenetetramine were analyzed only in CaSQA samples.

https://doi.org/10.1371/journal.pone.0228214.s006

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S3 Table.

a. Number of pharmaceutical water samples collected during USGS Regional Stream Quality Assessment (RSQA) synoptic samplings in the Pacific Northwest (PNSQA, 2015), Northeast (NESQA, 2016), California (CaSQA, 2017), and Southeast (SESQA, 2014) regions. b. Number of detections for pharmaceuticals detected at least once during the USGS Regional Stream Quality Assessment (RSQA) synoptic samplings in the Pacific Northwest (PNSQA, 2015), Northeast (NESQA, 2016), California (CaSQA, 2017), and Southeast (SESQA, 2014) regions.

https://doi.org/10.1371/journal.pone.0228214.s007

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S4 Table. Maximum detected concentration (nanograms per liter, ng/L) of pharmaceuticals detected at least once during the USGS Regional Stream Quality Assessment (RSQA) synoptic samplings in the Pacific Northwest (PNSQA, 2015), Northeast (NESQA, 2016), California (CaSQA, 2017), and Southeast (SESQA, 2014) regions.

https://doi.org/10.1371/journal.pone.0228214.s008

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S5 Table. Median detected concentration (nanograms per liter, ng/L) of pharmaceuticals detected in at least half of the water samples during the USGS Regional Stream Quality Assessment (RSQA) synoptic samplings in the Pacific Northwest (PNSQA, 2015), Northeast (NESQA, 2016), California (CaSQA, 2017), and Southeast (SESQA, 2014) regions.

https://doi.org/10.1371/journal.pone.0228214.s009

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S6 Table. Watershed-specific GIS metrics (see S15 Table for GIS data dictionary) and median and maximum major ion concentrations (milligrams per liter, mg L-1) for wadeable streams in the USGS Regional Stream Quality Assessment (RSQA) synoptic samplings in the Pacific Northwest (PNSQA, 2015), Northeast (NESQA, 2016), California (CaSQA, 2017), and Southeast (SESQA, 2014) regions.

https://doi.org/10.1371/journal.pone.0228214.s010

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S7 Table. Summary statistics for multivariate relations between pharmaceutical cumulative detection/concentration (log-transformed and normalized) data matrices (Euclidean distance) and site-specific LULC or major ion data matrices (Euclidean distance) assessed by non-metric multi-dimensional scaling (NMDS), one-way analysis of similarity (ANOSIM), and permutation-based (permutations = 999) cophenetic correlation (RELATE) routines.

https://doi.org/10.1371/journal.pone.0228214.s011

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S8 Table. Spearman rho (r) rank-order correlation coefficients (r; lower triangle) and 2-tail probability (permutation N = 9999) that no correlation exists (upper triangle) for site-specific cumulative pharmaceutical detection and concentrations and individual pharmaceutical concentrations (nanograms per liter, ng/L) under estimated maximum (max) or median (med) exposure conditions.

https://doi.org/10.1371/journal.pone.0228214.s012

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S9 Table. Spearman rho (r) rank-order correlation coefficients (r; lower triangle) and 2-tail probability (permutation N = 9999) that no correlation exists (upper triangle) for site-specific cumulative median pharmaceutical detections or concentrations (nanograms per liter, ng/L), major ions, and select GIS metrics identified by multi-variate RELATE and BEST analyses.

https://doi.org/10.1371/journal.pone.0228214.s013

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S10 Table. Compound:Endpoint combinations excluded from ToxCast evaluation due to unreliable concentration-response relationship and resulting lack of confidence in activity concentration at cutoff (ACC).

https://doi.org/10.1371/journal.pone.0228214.s014

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S11 Table. Site-specific Exposure Activity Ratios (EAR) under Maximum exposure conditions for those compounds with exact Chemical Abstract Service (CAS) number matches and with reliable concentration-response relationship and ACC data in ToxCast.

https://doi.org/10.1371/journal.pone.0228214.s015

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S12 Table. Site-specific Exposure Activity Ratios (EAR) under Maximum exposure conditions for all bioassay endpoints within each class shown.

Data are for those compounds with exact Chemical Abstract Service (CAS) number matches and with reliable concentration-response relationship and ACC data in ToxCast.

https://doi.org/10.1371/journal.pone.0228214.s016

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S13 Table. Site-specific Exposure Activity Ratios (EAR) under Median exposure conditions for those compounds with exact Chemical Abstract Service (CAS) number matches and with reliable concentration-response relationship and ACC data in ToxCast.

https://doi.org/10.1371/journal.pone.0228214.s017

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S14 Table. Site-specific Exposure Activity Ratios (EAR) under Median exposure conditions for all bioassay endpoints within each class shown.

Data are for those compounds with exact Chemical Abstract Service (CAS) number matches and with reliable concentration-response relationship and ACC data in ToxCast.

https://doi.org/10.1371/journal.pone.0228214.s018

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S15 Table. Data dictionary describing metrics in S9 Table.

Additional citation details provided in S16 Table.

https://doi.org/10.1371/journal.pone.0228214.s019

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S16 Table. Additional citation information for metrics in S9 and S15 Tables.

https://doi.org/10.1371/journal.pone.0228214.s020

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Acknowledgments

This research was conducted by the USGS National Water Quality Program’s Regional Stream Quality Assessment, in collaboration with the USGS Toxic Substances Hydrology Program. We thank J.M. Clark, USGS, and three anonymous referees for their reviews. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

References

1. Rosi-Marshall EJ, Royer TV. Pharmaceutical compounds and ecosystem function: an emerging research challenge for aquatic ecologists. Ecosystems. 2012;15(6):867–80. https://doi.org/10.1007/s10021-012-9553-z.
- View Article
- Google Scholar
2. Monteiro SC, Boxall AB. Occurrence and fate of human pharmaceuticals in the environment. Reviews of Environmental Contamination and Toxicology: Springer; 2010. p. 53–154. https://doi.org/10.1007/978-1-4419-1157-5_2 pmid:19898761
3. Van Donk E, Peacor S, Grosser K, Domis LNDS, Lürling M. Pharmaceuticals may disrupt natural chemical information flows and species interactions in aquatic systems: ideas and perspectives on a hidden global change. In: Whitacre D, editor. Reviews of Environmental Contamination and Toxicology. 235. New York: Springer International Publishing; 2015. p. 1–15. https://doi.org/10.1007/978-3-319-10861-2_1
4. Hughes SR, Kay P, Brown LE. Global synthesis and critical evaluation of pharmaceutical data sets collected from river systems. Environ Sci Technol. 2013;47(2):661–77. pmid:23227929
- View Article
- PubMed/NCBI
- Google Scholar
5. Brown AR, Gunnarsson L, Kristiansson E, Tyler CR. Assessing variation in the potential susceptibility of fish to pharmaceuticals, considering evolutionary differences in their physiology and ecology. Philos Trans R Soc, B. 2014;369(1656):20130576. https://doi.org/10.1098/rstb.2013.0576.
- View Article
- Google Scholar
6. Corcoran J, Winter MJ, Tyler CR. Pharmaceuticals in the aquatic environment: a critical review of the evidence for health effects in fish. Crit Rev Toxicol. 2010;40(4):287–304. pmid:20225984
- View Article
- PubMed/NCBI
- Google Scholar
7. Bradley PM, Journey CA, Button DT, Carlisle DM, Clark JM, Mahler BJ, et al. Metformin and Other Pharmaceuticals Widespread in Wadeable Streams of the Southeastern United States. Environ Sci Technol Lett. 2016;3(6):243–9. https://doi.org/10.1021/acs.estlett.6b00170.
- View Article
- Google Scholar
8. Fatta-Kassinos D, Vasquez M, Kümmerer K. Transformation products of pharmaceuticals in surface waters and wastewater formed during photolysis and advanced oxidation processes–degradation, elucidation of byproducts and assessment of their biological potency. Chemosphere. 2011;85(5):693–709. pmid:21835425
- View Article
- PubMed/NCBI
- Google Scholar
9. Bradley P, Journey C, Romanok K, Barber L, Buxton HT, Foreman WT, et al. Expanded Target-Chemical Analysis Reveals Extensive Mixed-Organic-Contaminant Exposure in USA Streams. Environ Sci Technol. 2017;51(9):4792–802. pmid:28401767
- View Article
- PubMed/NCBI
- Google Scholar
10. Daughton C, Ternes T. Pharmaceuticals and personal care products in the environment: Agents of subtle change? Environ Health Perspect. 1999;107(6):907–38. https://doi.org/10.1289/ehp.99107s6907.
- View Article
- Google Scholar
11. Daughton C, Brooks B. Active pharmaceutical ingredients and aquatic organisms. In: Beyer W, Meador J, editors. Environmental Contaminants in Biota: Interpreting Tissue Concentrations. Boca Raton, FL: CRC Press; 2011. p. 288–348. https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1248&context=usepapapers.
12. Boström ML, Berglund O. Influence of pH-dependent aquatic toxicity of ionizable pharmaceuticals on risk assessments over environmental pH ranges. Water Res. 2015;72:154–61. pmid:25262444
- View Article
- PubMed/NCBI
- Google Scholar
13. Vasquez M, Lambrianides A, Schneider M, Kümmerer K, Fatta-Kassinos D. Environmental side effects of pharmaceutical cocktails: What we know and what we should know. J Hazard Mater. 2014;279:169–89. pmid:25061892
- View Article
- PubMed/NCBI
- Google Scholar
14. Gunnarsson L, Jauhiainen A, Kristiansson E, Nerman O, Larsson DGJ. Evolutionary Conservation of Human Drug Targets in Organisms used for Environmental Risk Assessments. Environ Sci Technol. 2008;42(15):5807–13. pmid:18754513
- View Article
- PubMed/NCBI
- Google Scholar
15. Gunnarsson L, Kristiansson E, Larsson DJ. Environmental comparative pharmacology: theory and application. Human Pharmaceuticals in the Environment: Springer; 2012. p. 85–108. https://doi.org/10.1007/978-1-4614-3473-3_5.
16. McRobb FM, Sahaguún V, Kufareva I, Abagyan R. In silico analysis of the conservation of human toxicity and endocrine disruption targets in aquatic species. Environ Sci Technol. 2014;48(3):1964–72. pmid:24392850
- View Article
- PubMed/NCBI
- Google Scholar
17. Carter LJ, Williams M, Böttcher C, Kookana RS. Uptake of pharmaceuticals influences plant development and affects nutrient and hormone homeostases. Environ Sci Technol. 2015;49(20):12509–18. pmid:26418514
- View Article
- PubMed/NCBI
- Google Scholar
18. Rand-Weaver M, Margiotta-Casaluci L, Patel A, Panter GH, Owen SF, Sumpter JP. The Read-Across Hypothesis and Environmental Risk Assessment of Pharmaceuticals. Environ Sci Technol. 2013;47(20):11384–95. pmid:24006913
- View Article
- PubMed/NCBI
- Google Scholar
19. Li WC. Occurrence, sources, and fate of pharmaceuticals in aquatic environment and soil. Environ Pollut. 2014;187:193–201. pmid:24521932
- View Article
- PubMed/NCBI
- Google Scholar
20. Kidd KA, Paterson MJ, Rennie MD, Podemski CL, Findlay DL, Blanchfield PJ, et al. Direct and indirect responses of a freshwater food web to a potent synthetic oestrogen. Philos Trans R Soc, B. 2014;369(1656):20130578. https://dx.doi.org/10.1098/rstb.2013.0578.
- View Article
- Google Scholar
21. Brönmark C, Hansson L-A. Aquatic chemical ecology: new directions and challenges for the future. Chemical Ecology in Aquatic Systems Oxford University Press, Oxford. 2012:272–8. https://doi.org/10.1093/acprof:osobl/9780199583096.003.0019.
22. Schultz MM, Painter MM, Bartell SE, Logue A, Furlong ET, Werner SL, et al. Selective uptake and biological consequences of environmentally relevant antidepressant pharmaceutical exposures on male fathead minnows. Aquat Toxicol. 2011;104(1):38–47. https://doi.org/10.1016/j.aquatox.2011.03.011.
- View Article
- Google Scholar
23. Giacomini ACV, Abreu MS, Giacomini LV, Siebel AM, Zimerman FF, Rambo CL, et al. Fluoxetine and diazepam acutely modulate stress induced-behavior. Behavioural Brain Research. 2016;296:301–10. pmid:26403161
- View Article
- PubMed/NCBI
- Google Scholar
24. Brodin T, Fick J, Jonsson M, Klaminder J. Dilute concentrations of a psychiatric drug alter behavior of fish from natural populations. Science. 2013;339(6121):814–5. pmid:23413353
- View Article
- PubMed/NCBI
- Google Scholar
25. Batt AL, Kincaid TM, Kostich MS, Lazorchak JM, Olsen AR. Evaluating the extent of pharmaceuticals in surface waters of the United States using a National‐scale Rivers and Streams Assessment survey. Environ Toxicol Chem. 2016;35(4):874–81. pmid:26562077
- View Article
- PubMed/NCBI
- Google Scholar
26. Luo Y, Guo W, Ngo HH, Nghiem LD, Hai FI, Zhang J, et al. A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment. Sci Total Environ. 2014;473–474:619–41. pmid:24394371
- View Article
- PubMed/NCBI
- Google Scholar
27. Michael I, Rizzo L, McArdell C, Manaia C, Merlin C, Schwartz T, et al. Urban wastewater treatment plants as hotspots for the release of antibiotics in the environment: a review. Water Res. 2013;47(3):957–95. pmid:23266388
- View Article
- PubMed/NCBI
- Google Scholar
28. Lowe WH, Likens GE. Moving headwater streams to the head of the class. BioScience. 2005;55(3):196–7. https://doi.org/10.1641/0006-3568(2005)055[0196:MHSTTH]2.0.CO;2.
- View Article
- Google Scholar
29. Leopold LB. Rivers. American Scientist. 1962;50(4):511–37. https://www.jstor.org/stable/27838523.
- View Article
- Google Scholar
30. Downing JA, Cole JJ, Duarte C, Middelburg JJ, Melack JM, Prairie YT, et al. Global abundance and size distribution of streams and rivers. Inland waters. 2012;2(4):229–36. https://doi.org/10.5268/IW-2.4.502.
- View Article
- Google Scholar
31. Bishop K, Buffam I, Erlandsson M, Fölster J, Laudon H, Seibert J, et al. Aqua Incognita: the unknown headwaters. Hydrological Processes. 2008;22(8):1239–42. https://doi.org/10.1002/hyp.7049.
- View Article
- Google Scholar
32. Clarke A, Mac Nally R, Bond N, Lake P. Macroinvertebrate diversity in headwater streams: a review. Freshwater Biol. 2008;53(9):1707–21. https://doi.org/10.1111/j.1365-2427.2008.02041.x.
- View Article
- Google Scholar
33. Meyer JL, Strayer DL, Wallace JB, Eggert SL, Helfman GS, Leonard NE. The contribution of headwater streams to biodiversity in river networks. JAWRA Journal of the American Water Resources Association. 2007;43(1):86–103. https://doi.org/10.1111/j.1752-1688.2007.00008.x.
- View Article
- Google Scholar
34. Temnerud J, Fölster J, Buffam I, Laudon H, Erlandsson M, Bishop K. Can the distribution of headwater stream chemistry be predicted from downstream observations? Hydrological Processes. 2010;24(16):2269–76. https://doi.org/10.1002/hyp.7615.
- View Article
- Google Scholar
35. Freeman MC, Pringle CM, Jackson CR. Hydrologic connectivity and the contribution of stream headwaters to ecological integrity at regional scales. JAWRA Journal of the American Water Resources Association. 2007;43(1):5–14. https://doi.org/10.1111/j.1752-1688.2007.00002.x.
- View Article
- Google Scholar
36. Terando AJ, Costanza J, Belyea C, Dunn RR, McKerrow A, Collazo JA. The southern megalopolis: using the past to predict the future of urban sprawl in the Southeast US. PLoS One. 2014;9(7):e102261. pmid:25054329
- View Article
- PubMed/NCBI
- Google Scholar
37. Van Metre PC, Waite IR, Qi S, Mahler B, Terando A, Wieczorek M, et al. Projected urban growth in the southeastern USA puts small streams at risk. PLOS ONE. 2019;14(10):e0222714. pmid:31618213
- View Article
- PubMed/NCBI
- Google Scholar
38. Journey C, Van Metre P, Bell A, Garrett J, Button D, Nakagaki N, et al. Design and Methods of the Southeast Stream Quality Assessment (SESQA), 2014. U.S. Geological Survey, 2015 U.S. Geological Survey Open File Report 2015–109 Contract No.: Open File Report 2015–109. http://doi.org/10.3133/ofr20151095.
39. Van Metre PC, Journey CA. The Southeast Stream Quality Assessment. Report. Reston, VA: 2014 2014–3023. https://doi.org/10.3133/fs20143023.
40. Moretto A, Bachman A, Boobis A, Solomon KR, Pastoor TP, Wilks MF, et al. A framework for cumulative risk assessment in the 21st century. Crit Rev Toxicol. 2017;47(2):85–97. pmid:27685779
- View Article
- PubMed/NCBI
- Google Scholar
41. Norton SB, Rodier DJ, van der Schalie WH, Wood WP, Slimak MW, Gentile JH. A framework for ecological risk assessment at the EPA. Environ Toxicol Chem. 1992;11(12):1663–72. https://doi.org/10.1002/etc.5620111202.
- View Article
- Google Scholar
42. Rodier D, Norton S. Framework for ecological risk assessment. Environmental Protection Agency, Washington, DC (United States). Risk Assessment Forum, 1992 Environmental Protection Agency, EPA-630/R-92/001. https://www.epa.gov/sites/production/files/2014-11/documents/framework_eco_assessment.pdf.
43. Blackwell BR, Ankley GT, Corsi SR, De Cicco LA, Houck KA, Judson RS, et al. An" EAR" on environmental surveillance and monitoring: A case study on the use of exposure-activity ratios (EARs) to prioritize sites, chemicals, and bioactivities of concern in Great Lakes waters. Environ Sci Technol. 2017;51(15):8713–24. pmid:28671818
- View Article
- PubMed/NCBI
- Google Scholar
44. Bradley PM, Journey CA, Berninger JP, Button DT, Clark JM, Corsi SR, et al. Mixed-chemical exposure and predicted effects potential in wadeable southeastern USA streams. Sci Total Environ. 2019;655:70–83. pmid:30469070
- View Article
- PubMed/NCBI
- Google Scholar
45. Corsi SR, De Cicco LA, Villeneuve DL, Blackwell BR, Fay KA, Ankley GT, et al. Prioritizing chemicals of ecological concern in Great Lakes tributaries using high-throughput screening data and adverse outcome pathways. Sci Total Environ. 2019;686:995–1009. pmid:31412529
- View Article
- PubMed/NCBI
- Google Scholar
46. U.S. Environmental Protection Agency. ToxCast & Tox21 Summary Files from invitrodb_v3 2019 [July 5, 2019]. Available from: https://www.epa.gov/chemical-research/toxicity-forecaster-toxcasttm-data.
47. Van Metre PC, Morace JL, Sheibley RW. The Pacific northwest stream quality assessment. Report. Reston, VA: 2015 U. S. Geological Survey Fact Sheet 2015–3020. https://doi.org/10.3133/fs20153020.
48. Sheibley RW, Morace JL, Journey CA, Van Metre PC, Bell AH, Nakagaki N, et al. Design and methods of the Pacific Northwest Stream Quality Assessment (PNSQA), 2015. Report. Reston, VA: 2017 U. S. Geological Survey Open-File Report 2017–1103. https://doi.org/10.3133/ofr20171103.
49. Van Metre PC, Egler AL, May JT. The California stream quality assessment. Report. Reston, VA: 2017 U. S. Geological Survey Fact Sheet 2017–3014. https://doi.org/10.3133/fs20173014.
50. Coles JF, Riva-Murray K, Van Metre PC, Button DT, Bell AH, Qi SL, et al. Design and methods of the U.S. Geological Survey Northeast Stream Quality Assessment (NESQA), 2016. Report. Reston, VA: 2019 U. S. Geological Survey Open-File Report 2018–1183. https://doi.org/10.3133/ofr20181183.
51. Van Metre PC, Riva-Murray K, Coles JF. The Northeast Stream Quality Assessment. Report. Reston, VA: 2016 U. S. Geological Survey Fact Sheet 2016–3021. https://doi.org/10.3133/fs20163021.
52. Furlong E, Noriega M, Kanagy C, Kanagy L, Coffey L, Burkhardt M. Methods of the National Water Quality Laboratory. Chapter B10. Determination of human-use pharmaceuticals in filtered water by direct aqueous injection–high-performance liquid chromatography/tandem mass spectrometry. 2014 U.S. Geological Survey Techniques and Methods. Book 5. Laboratory Analysis. Chap. B10. http://dx.doi.org/10.3133/tm5B10.
53. Cummings C, Finlay JC, MacDonald NE. Head lice infestations: A clinical update. Paediatrics & Child Health. 2018;23(1):e18–e24. https://doi.org/10.1093/pch/pxx165.
- View Article
- Google Scholar
54. Klootwijk T, Le Cleach L, Do G, Vander Stichele R, Knuistingh Neven A, Eekhof JAH, et al. Interventions for treating head lice. The Cochrane Database of Systematic Reviews. 2018;2018(5):CD009321. https://doi.org/10.1002/14651858.CD009321.pub2. PMC6494540.
- View Article
- Google Scholar
55. U.S. Geological Survey. The National Map 2019 [cited 2019 October 16]. Available from: https://viewer.nationalmap.gov/advanced-viewer/.
56. ASTM International. ASTM D7729-12(2018)e1, Standard Practice for Determining and Expressing Precision of Measurement Results, in the Analysis of Water, as Relative Standard Deviation, Utilizing DQCALC Software. West Conshohocken, PA: 2018 ASTM D7729-12(2018)e1. https://doi.org/10.1520/D7729-12R18E01.
57. Clarke K, Gorley R, Somerfield P, Warwick R. Change in marine communities: an approach to statistical analysis and interpretation, V7. 3rd ed. Plymouth, UK: Primer-E, Ltd.; 2014. 260 p.
58. Hammer Ø, Harper DA, Ryan PD. PAST: paleontological statistics software package for education and data analysis. Palaeontologia electronica. 2001;4(1):9. https://palaeo-electronica.org/2001_1/past/issue1_01.htm.
- View Article
- Google Scholar
59. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecology. 2001;26(1):32–46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x.
- View Article
- Google Scholar
60. Anderson MJ, Walsh DCI. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? Ecological Monographs. 2013;83(4):557–74. https://doi.org/10.1890/12-2010.1.
- View Article
- Google Scholar
61. De Cicco L, Corsi SR, Villeneuve D, Blackwell BR, Ankley GT. toxEval: Evaluation of measured concentration data using the ToxCast high-throughput screening database or a user-defined set of concentration benchmarks. R package version 1.0.0 2018 [May 1, 2018]. Available from: https://owi.usgs.gov/R/gran.html.
- View Article
- Google Scholar
62. R Development Core Team. R: A Language and Environment for Statistical Computing. Version 3.5.2. Vienna Austria: R Foundation for Statistical Computing, 2019.
63. Ermler S, Scholze M, Kortenkamp A. The suitability of concentration addition for predicting the effects of multi-component mixtures of up to 17 anti-androgens with varied structural features in an in vitro AR antagonist assay. Toxicol Appl Pharmacol. 2011;257(2):189–97. pmid:21945941
- View Article
- PubMed/NCBI
- Google Scholar
64. Cedergreen N, Christensen AM, Kamper A, Kudsk P, Mathiassen SK, Streibig JC, et al. A review of independent action compared to concentration addition as reference models for mixtures of compounds with different molecular target sites. Environ Toxicol Chem. 2008;27(7):1621–32. pmid:18271647
- View Article
- PubMed/NCBI
- Google Scholar
65. Thrupp TJ, Runnalls TJ, Scholze M, Kugathas S, Kortenkamp A, Sumpter JP. The consequences of exposure to mixtures of chemicals: Something from ‘nothing’ and ‘a lot from a little’ when fish are exposed to steroid hormones. Sci Total Environ. 2018;619–620:1482–92. pmid:29734624
- View Article
- PubMed/NCBI
- Google Scholar
66. Altenburger R, Scholz S, Schmitt-Jansen M, Busch W, Escher BI. Mixture toxicity revisited from a toxicogenomic perspective. Environ Sci Technol. 2012;46(5):2508–22. pmid:22283441
- View Article
- PubMed/NCBI
- Google Scholar
67. Kortenkamp A, Backhaus T, Faust M. State of the art report on mixture toxicity. 2009 Contract 70307/2007485103. https://ec.europa.eu/environment/chemicals/effects/pdf/report_mixture_toxicity.pdf.
- View Article
- Google Scholar
68. Altenburger R, Backhaus T, Boedeker W, Faust M, Scholze M. Simplifying complexity: Mixture toxicity assessment in the last 20 years. Environ Toxicol Chem. 2013;32(8):1685–7. pmid:23843317
- View Article
- PubMed/NCBI
- Google Scholar
69. iCSS ToxCast Dashboard [Internet]. 2018 [cited March 22, 2018]. Available from: https://actor.epa.gov/dashboard/.
70. U.S. Environmental Protection Agency. ACToR 2018 [cited 2018 March 22, 2018]. Available from: https://actor.epa.gov/actor/home.xhtml.
71. Li S, Villeneuve DL, Berninger JP, Blackwell BR, Cavallin JE, Hughes MN, et al. An integrated approach for identifying priority contaminant in the Great Lakes Basin–Investigations in the Lower Green Bay/Fox River and Milwaukee Estuary areas of concern. Sci Total Environ. 2017;579:825–37. pmid:27866739
- View Article
- PubMed/NCBI
- Google Scholar
72. Schroeder AL, Ankley GT, Houck KA, Villeneuve DL. Environmental surveillance and monitoring—The next frontiers for high‐throughput toxicology. Environ Toxicol Chem. 2016;35(3):513–25. pmid:26923854
- View Article
- PubMed/NCBI
- Google Scholar
73. Becker RA, Friedman KP, Simon TW, Marty MS, Patlewicz G, Rowlands JC. An exposure: activity profiling method for interpreting high-throughput screening data for estrogenic activity—Proof of concept. Regul Toxicol Pharm. 2015;71(3):398–408. https://doi.org/10.1016/j.yrtph.2015.01.008.
- View Article
- Google Scholar
74. National Water Information System (NWIS)-Web interface [Internet]. 2019 [cited January 12, 2019]. Available from: http://waterdata.usgs.gov/nwis.
75. Romanok KM, Bradley PM, Journey CA. Concentrations of Pharmaceutical Contaminants and Their Predicted Effects from a Multi-Regional Assessment of Wadeable USA Streams, 2014–2017. 2019 U.S. Geological Survey data release https://doi.org/10.5066/P97MW2T8.
- View Article
- Google Scholar
76. Kaiser Family Foundation’s State Health Facts INPAD. IQVIA 2018 Retail Sales for Prescription Drugs Filled at Pharmacies by Payer 2018 [cited 2019 October 15]. Available from: https://www.kff.org/health-costs/state-indicator/total-sales-for-retail-rx-drugs/.
77. U.S. Census Bureau. Annual Estimates of the Resident Population: April 1, 2010 to July 1, 2018 2019 [cited 2019 October 16]. Available from: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=PEP_2018_PEPANNRES&src=pt.
78. Black L, Benson V. U.S. Centers for Disease Control and Prevention: 2018 National Health Interview Survey Tables of Summary Health Statistics for U.S. Children: U.S. Centers for Disease Control and Prevention,; 2019 [cited 2020 January 6]. Available from: https://www.cdc.gov/nchs/nhis/SHS/tables.htm.
79. Villarroel M, Blackwell D, Jen A. U.S. Centers for Disease Control and Prevention: 2018 National Health Interview Survey Tables of Summary Health Statistics for U.S. Adults: U.S. Centers for Disease Control and Prevention,; 2019 [cited 2020 January 6]. Available from: https://www.cdc.gov/nchs/nhis/SHS/tables.htm.
80. Falcone JA. U.S. conterminous wall-to-wall anthropogenic land use trends (NWALT), 1974–2012. Reston, VA: 2015 U.S. Geological Survey Data Series Report 948. https://doi.org/10.3133/ds948.
81. U.S. Census Bureau. TIGER/Line Shapefiles. 2016.
82. U.S. Environmental Protection Agency. NPDES Permits Around the Nation. 2013.
83. U.S. Geological Survey. National Land Cover Database (NLCD) 2011 Edition. 2014.
84. Barber LB, Vajda AM, Douville C, Norris DO, Writer JH. Fish endocrine disruption responses to a major wastewater treatment facility upgrade. Environ Sci Technol. 2012;46(4):2121–31. pmid:22300164
- View Article
- PubMed/NCBI
- Google Scholar
85. Barber LB, Hladik ML, Vajda AM, Fitzgerald KC, Douville C. Impact of wastewater infrastructure upgrades on the urban water cycle: Reduction in halogenated reaction byproducts following conversion from chlorine gas to ultraviolet light disinfection. Sci Total Environ. 2015;529:264–74. pmid:26025637
- View Article
- PubMed/NCBI
- Google Scholar
86. Bradley PM, Barber LB, Clark JM, Duris JW, Foreman WT, Furlong ET, et al. Pre/post-closure assessment of groundwater pharmaceutical fate in a wastewater-facility-impacted stream reach. Sci Total Environ. 2016;568:916–25. pmid:27350092
- View Article
- PubMed/NCBI
- Google Scholar
87. Bradley PM, Journey CA, Clark J. Effect of Wastewater Treatment Facility Closure on Endocrine Disrupting Chemicals in a Coastal Plain Stream. Remediation Journal. 2016;26(2):9–24. https://doi.org/10.1002/rem.21455.
- View Article
- Google Scholar
88. Warner W, Licha T, Nödler K. Qualitative and quantitative use of micropollutants as source and process indicators. A review. Sci Total Environ. 2019;686:75–89. pmid:31176825
- View Article
- PubMed/NCBI
- Google Scholar
89. Arnold KE, Brown AR, Ankley GT, Sumpter JP. Medicating the environment: assessing risks of pharmaceuticals to wildlife and ecosystems. Philos Trans R Soc, B. 2014;369(1656):20130569. https://doi.org/10.1098/rstb.2013.0569.
- View Article
- Google Scholar
90. Rutsch M, Rieckermann J, Cullmann J, Ellis JB, Vollertsen J, Krebs P. Towards a better understanding of sewer exfiltration. Water Res. 2008;42(10):2385–94. https://doi.org/10.1016/j.watres.2008.01.019.
- View Article
- Google Scholar
91. Wolf L, Zwiener C, Zemann M. Tracking artificial sweeteners and pharmaceuticals introduced into urban groundwater by leaking sewer networks. Sci Total Environ. 2012;430:8–19. pmid:22609959
- View Article
- PubMed/NCBI
- Google Scholar
92. Ferrell GM, Yearout MS, Grimes BH, Graves AK, Fitzgerald SA, Meyer MT. Water-quality characteristics indicative of wastewater in selected streams in the upper Neuse River Basin, Durham and Orange Counties, North Carolina, from 2004 to 2013. Report. Reston, VA: 2014 2014–1215. https://doi.org/10.3133/ofr20141215.
93. Peters NE. Effects of urbanization on stream water quality in the city of Atlanta, Georgia, USA. Hydrological Processes. 2009;23(20):2860–78. https://doi.org/10.1002/hyp.7373.
- View Article
- Google Scholar
94. Phillips P, Chalmers A. Wastewater Effluent, Combined Sewer Overflows, and Other Sources of Organic Compounds to Lake Champlain1. JAWRA Journal of the American Water Resources Association. 2009;45(1):45–57. https://doi.org/10.1111/j.1752-1688.2008.00288.x.
- View Article
- Google Scholar
95. Weyrauch P, Matzinger A, Pawlowsky-Reusing E, Plume S, von Seggern D, Heinzmann B, et al. Contribution of combined sewer overflows to trace contaminant loads in urban streams. Water Res. 2010;44(15):4451–62. pmid:20599243
- View Article
- PubMed/NCBI
- Google Scholar
96. Phillips P, Chalmers A, Gray J, Kolpin D, Foreman W, Wall G. Combined sewer overflows: An environmental source of hormones and wastewater micropollutants. Environ Sci Technol. 2012;46:5336–43. pmid:22540536
- View Article
- PubMed/NCBI
- Google Scholar
97. Conn KE, Lowe KS, Drewes JE, Hoppe-Jones C, Tucholke MB. Occurrence of pharmaceuticals and consumer product chemicals in raw wastewater and Septic tank effluent from single-family homes. Environmental Engineering Science. 2010;27(4):347–56. https://doi.org/10.1089/ees.2009.0364.
- View Article
- Google Scholar
98. Carrara C, Ptacek CJ, Robertson WD, Blowes DW, Moncur MC, Sverko E, et al. Fate of pharmaceutical and trace organic compounds in three septic system plumes, Ontario, Canada. Environ Sci Technol. 2008;42(8):2805–11. pmid:18497127
- View Article
- PubMed/NCBI
- Google Scholar
99. Humphrey C, Finley A, O'Driscoll M, Manda A, Iverson G. Groundwater and stream E. coli concentrations in coastal plain watersheds served by onsite wastewater and a municipal sewer treatment system. Water Sci Technol. 2015;72(10):1851–60. pmid:26540548
- View Article
- PubMed/NCBI
- Google Scholar
100. Yu ZL, Rahardianto A, DeShazo J, Stenstrom MK, Cohen Y. Critical review: regulatory incentives and impediments for onsite graywater reuse in the United States. Water Environ Res. 2013;85(7):650–62. pmid:23944147
- View Article
- PubMed/NCBI
- Google Scholar
101. Eriksson E, Auffarth K, Eilersen AM, Henze M, Ledin A. Household chemicals and personal care products as sources for xenobiotic organic compounds in grey wastewater. Water Sa. 2003;29(2):135–46. http://dx.doi.org/10.4314/wsa.v29i2.4848.
- View Article
- Google Scholar
102. Oteng-Peprah M, Acheampong MA, deVries NK. Greywater Characteristics, Treatment Systems, Reuse Strategies and User Perception—a Review. Water, Air, Soil Pollut. 2018;229(8). https://doi.org/10.1007/s11270-018-3909-8.
- View Article
- Google Scholar
103. Fatta-Kassinos D, Kalavrouziotis IK, Koukoulakis PH, Vasquez MI. The risks associated with wastewater reuse and xenobiotics in the agroecological environment. Sci Total Environ. 2011;409(19):3555–63. pmid:20435343
- View Article
- PubMed/NCBI
- Google Scholar
104. Fissore C, Hobbie SE, King JY, McFadden JP, Nelson KC, Baker LA. The residential landscape: fluxes of elements and the role of household decisions. Urban Ecosystems. 2012;15(1):1–18. https://doi.org/10.1007/s11252-011-0189-0.
- View Article
- Google Scholar
105. Margalida A, Bogliani G, Bowden CGR, Donázar JA, Genero F, Gilbert M, et al. One Health approach to use of veterinary pharmaceuticals. Science. 2014;346(6215):1296–8. pmid:25477214
- View Article
- PubMed/NCBI
- Google Scholar
106. Boxall ABA. Veterinary Medicines and the Environment. In: Cunningham F, Elliott J, Lees P, editors. Comparative and Veterinary Pharmacology. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. p. 291–314. https://doi.org/10.1007/978-3-642-10324-7_12.
107. Masoner JR, Kolpin DW, Cozzarelli IM, Barber LB, Burden DS, Foreman WT, et al. Urban Stormwater: An Overlooked Pathway of Extensive Mixed Contaminants to Surface and Groundwaters in the United States. Environ Sci Technol. 2019;53(17):10070–81. pmid:31432661
- View Article
- PubMed/NCBI
- Google Scholar
108. U.S. Geological Survey. Next Generation Water Observing System: Delaware River Basin 2020 [cited 2020 December 30]. Available from: https://www.usgs.gov/mission-areas/water-resources/science/next-generation-water-observing-system-delaware-river-basin?qt-science_center_objects=0#qt-science_center_objects.
109. U.S. Geological Survey. USGS Next Generation Water Observing System (NGWOS) 2020 [cited 2020 December 30]. Available from: https://www.usgs.gov/mission-areas/water-resources/science/usgs-next-generation-water-observing-system-ngwos?qt-science_center_objects=0#qt-science_center_objects.
110. Bai H, Zeng SY, Dong X, Chen JN. Research of potassium flow and circulation based on substance flow analysis. Huanjing Kexue/Environmental Science. 2013;34(6):2493–6.
- View Article
- Google Scholar
111. Nödler K, Licha T, Fischer S, Wagner B, Sauter M. A case study on the correlation of micro-contaminants and potassium in the Leine River (Germany). Appl Geochem. 2011;26(12):2172–80. https://doi.org/10.1016/j.apgeochem.2011.08.001.
- View Article
- Google Scholar
112. Winker M, Vinnerås B, Muskolus A, Arnold U, Clemens J. Fertiliser products from new sanitation systems: Their potential values and risks. Bioresour Technol. 2009;100(18):4090–6. pmid:19375910
- View Article
- PubMed/NCBI
- Google Scholar
113. Winker M, Faika D, Gulyas H, Otterpohl R. A comparison of human pharmaceutical concentrations in raw municipal wastewater and yellowwater. Sci Total Environ. 2008;399(1):96–104. https://doi.org/10.1016/j.scitotenv.2008.03.027.
- View Article
- Google Scholar
114. Jönsson H, Stenström T-A, Svensson J, Sundin A. Source separated urine-nutrient and heavy metal content, water saving and faecal contamination. Water Sci Technol. 1997;35(9):145–52. https://doi.org/10.2166/wst.1997.0338.
- View Article
- Google Scholar
115. Lienert J, Bürki T, Escher BI. Reducing micropollutants with source control: substance flow analysis of 212 pharmaceuticals in faeces and urine. Water Sci Technol. 2007;56(5):87–96. pmid:17881841
- View Article
- PubMed/NCBI
- Google Scholar
116. Kaushal SS, McDowell WH, Wollheim WM. Tracking evolution of urban biogeochemical cycles: past, present, and future. Biogeochemistry. 2014;121(1):1–21. https://doi.org/10.1007/s10533-014-0014-y.
- View Article
- Google Scholar
117. Kaushal SS, Likens GE, Pace ML, Utz RM, Haq S, Gorman J, et al. Freshwater salinization syndrome on a continental scale. Proc Natl Acad Sci. 2018:201711234. https://doi.org/10.1073/PNAS.1711234115.
- View Article
- Google Scholar
118. Meybeck M. Global chemical weathering of surficial rocks estimated from river dissolved loads. American journal of science. 1987;287(5):401–28. https://doi.org/10.2475/ajs.287.5.401.
- View Article
- Google Scholar
119. Corsi SR, Graczyk DJ, Geis SW, Booth NL, Richards KD. A fresh look at road salt: aquatic toxicity and water-quality impacts on local, regional, and national scales. Environ Sci Technol. 2010;44(19):7376–82. pmid:20806974
- View Article
- PubMed/NCBI
- Google Scholar
120. Corsi SR, De Cicco LA, Lutz MA, Hirsch RM. River chloride trends in snow-affected urban watersheds: increasing concentrations outpace urban growth rate and are common among all seasons. Sci Total Environ. 2015;508:488–97. pmid:25514764
- View Article
- PubMed/NCBI
- Google Scholar
121. Davies PJ, Wright IA, Jonasson OJ, Findlay SJ. Impact of concrete and PVC pipes on urban water chemistry. Urban Water Journal. 2010;7(4):233–41. https://doi.org/10.1080/1573062X.2010.484502.
- View Article
- Google Scholar
122. Kaushal SS, Duan S, Doody TR, Haq S, Smith RM, Newcomer Johnson TA, et al. Human-accelerated weathering increases salinization, major ions, and alkalinization in fresh water across land use. Appl Geochem. 2017;83:121–35. pmid:30220785
- View Article
- PubMed/NCBI
- Google Scholar
123. Connor NP, Sarraino S, Frantz DE, Bushaw-Newton K, MacAvoy SE. Geochemical characteristics of an urban river: Influences of an anthropogenic landscape. Appl Geochem. 2014;47:209–16. https://doi.org/10.1016/j.apgeochem.2014.06.012.
- View Article
- Google Scholar
124. Jiang J-Q, Wang S, Panagoulopoulos A. The exploration of potassium ferrate(VI) as a disinfectant/coagulant in water and wastewater treatment. Chemosphere. 2006;63(2):212–9. pmid:16213564
- View Article
- PubMed/NCBI
- Google Scholar
125. Ahn M-S, Ahmad R, Yoo J-Y, Hahn Y-B. Synthesis of manganese oxide nanorods and its application for potassium ion sensing in water. J Colloid Interface Sci. 2018;516:364–70. pmid:29408124
- View Article
- PubMed/NCBI
- Google Scholar
126. Calvo-López A, Puyol M, Casalta JM, Alonso-Chamarro J. Multi-parametric polymer-based potentiometric analytical microsystem for future manned space missions. Anal Chim Acta. 2017;995:77–84. pmid:29126483
- View Article
- PubMed/NCBI
- Google Scholar
127. Calvo-López A, Arasa-Puig E, Puyol M, Casalta JM, Alonso-Chamarro J. Biparametric potentiometric analytical microsystem for nitrate and potassium monitoring in water recycling processes for manned space missions. Anal Chim Acta. 2013;804:190–6. pmid:24267081
- View Article
- PubMed/NCBI
- Google Scholar
128. Artemyeva AA. Analytical chemistry in water quality monitoring during manned space missions. Acta Astronautica. 2016;126:11–7. https://doi.org/10.1016/j.actaastro.2016.04.016.
- View Article
- Google Scholar
129. Pol R, Céspedes F, Gabriel D, Baeza M. Microfluidic lab-on-a-chip platforms for environmental monitoring. TrAC—Trends in Analytical Chemistry. 2017;95:62–8. https://doi.org/10.1016/j.trac.2017.08.001.
- View Article
- Google Scholar
130. Jaywant SA, Mahmood Arif K. A comprehensive review of micro fluidic water quality monitoring sensors. Sensors (Switzerland). 2019;19(21). https://doi.org/10.3390/s19214781.
- View Article
- Google Scholar
131. U.S. Food and Drug Administration. Approved Drug Products with Therapeutic Equivalence Evaluations (Orange Book) 2018 [June 29, 2018]. Available from: https://www.fda.gov/drugs/drug-approvals-and-databases/approved-drug-products-therapeutic-equivalence-evaluations-orange-book.
132. Dobson CM. Chemical space and biology. Nature. 2004;432(7019):824–8. pmid:15602547
- View Article
- PubMed/NCBI
- Google Scholar
133. McKinney ML. Urbanization as a major cause of biotic homogenization. Biological Conservation. 2006;127(3):247–60. https://doi.org/10.1016/j.biocon.2005.09.005.
- View Article
- Google Scholar
134. Lydeard C, Mayden RL. A diverse and endangered aquatic ecosystem of the southeast United States. Conservation Biology. 1995;9(4):800–5. https://doi.org/10.1046/j.1523-1739.1995.09040800.x.
- View Article
- Google Scholar
135. Warren ML Jr, Burr BM, Walsh SJ, Bart HL Jr, Cashner RC, Etnier DA, et al. Diversity, distribution, and conservation status of the native freshwater fishes of the southern United States. Fisheries. 2000;25(10):7–31. https://doi.org/10.1577/1548-8446(2000)025<0007:DDACSO>2.0.CO;2.
- View Article
- Google Scholar
136. Scott MC. Winners and losers among stream fishes in relation to land use legacies and urban development in the southeastern US. Biological Conservation. 2006;127(3):301–9. https://doi.org/10.1016/j.biocon.2005.07.020.
- View Article
- Google Scholar
137. Painter M, Buerkley M, Julius M, Vajda A, Norris D, Barber L, et al. Antidepressants at environmentally relevant concentrations affect predator avoidance behavior of larval fathead minnows (Pimephales promelas). Environ Toxicol Chem. 2009;28(12):2677–84. pmid:19405782
- View Article
- PubMed/NCBI
- Google Scholar
138. Russom CL, Bradbury SP, Broderius SJ, Hammermeister DE, Drummond RA. Predicting modes of toxic action from chemical structure: acute toxicity in the fathead minnow (Pimephales promelas). Environ Toxicol Chem. 1997;16(5):948–67. https://doi.org/10.1002/etc.5620160514.
- View Article
- Google Scholar
139. Veith GD, Call DJ, Brooke L. Structure-toxicity relationships for the fathead minnow, Pimephales promelas: Narcotic industrial chemicals. Can J Fish Aquat Sci. 1983;40(6):743–8. https://doi.org/10.1139/f83-096.
- View Article
- Google Scholar
140. Bradbury SP. Predicting modes of toxic action from chemical structure: an overview. SAR QSAR Environ Res. 1994;2(1–2):89–104. pmid:8790641
- View Article
- PubMed/NCBI
- Google Scholar
141. Könemann H. Quantitative structure-activity relationships in fish toxicity studies Part 1: Relationship for 50 industrial pollutants. Toxicology. 1981;19(3):209–21. pmid:7233445
- View Article
- PubMed/NCBI
- Google Scholar
142. Kavlock R, Chandler K, Houck K, Hunter S, Judson R, Kleinstreuer N, et al. Update on EPA’s ToxCast program: providing high throughput decision support tools for chemical risk management. Chem Res Toxicol. 2012;25(7):1287–302. pmid:22519603
- View Article
- PubMed/NCBI
- Google Scholar
143. Richard AM, Judson RS, Houck KA, Grulke CM, Volarath P, Thillainadarajah I, et al. ToxCast chemical landscape: paving the road to 21st century toxicology. Chem Res Toxicol. 2016;29(8):1225–51. pmid:27367298
- View Article
- PubMed/NCBI
- Google Scholar
144. Padilla S, Corum D, Padnos B, Hunter DL, Beam A, Houck KA, et al. Zebrafish developmental screening of the ToxCast^™ Phase I chemical library. Reproductive Toxicology. 2012;33(2):174–87. pmid:22182468
- View Article
- PubMed/NCBI
- Google Scholar
145. Truong L, Reif DM, St Mary L, Geier MC, Truong HD, Tanguay RL. Multidimensional in vivo hazard assessment using zebrafish. Toxicol Sci. 2013;137(1):212–33. pmid:24136191
- View Article
- PubMed/NCBI
- Google Scholar
146. Rosi-Marshall EJ, Kincaid DW, Bechtold HA, Royer TV, Rojas M, Kelly JJ. Pharmaceuticals suppress algal growth and microbial respiration and alter bacterial communities in stream biofilms. Ecol Appl. 2013;23(3):583–93. pmid:23734487
- View Article
- PubMed/NCBI
- Google Scholar
147. Quinlan EL, Nietch CT, Blocksom K, Lazorchak JM, Batt AL, Griffiths R, et al. Temporal dynamics of periphyton exposed to tetracycline in stream mesocosms. Environ Sci Technol. 2011;45(24):10684–90. pmid:22050031
- View Article
- PubMed/NCBI
- Google Scholar
148. Gallagher HC, Gallagher RM, Butler M, Buggy DJ, Henman MC. Venlafaxine for neuropathic pain in adults. Cochrane Database of Systematic Reviews. 2015;(8). https://doi.org/10.1002/14651858.CD011091.pub2.
- View Article
- Google Scholar
149. Hossain SM, Hussain SM, Ekram ARMS. Duloxetine in Painful Diabetic Neuropathy. The Clinical Journal of Pain. 2016;32(11):1005–10. pmid:26710221
- View Article
- PubMed/NCBI
- Google Scholar
150. Moore RA, Derry S, Aldington D, Cole P, Wiffen PJ. Amitriptyline for neuropathic pain in adults. Cochrane Database of Systematic Reviews. 2015;(7). https://doi.org/10.1002/14651858.CD008242.pub3.
- View Article
- Google Scholar
151. Pop-Busui R, Boulton AJM, Feldman EL, Bril V, Freeman R, Malik RA, et al. Diabetic Neuropathy: A Position Statement by the American Diabetes Association. Diabetes Care. 2017;40(1):136–54. pmid:27999003
- View Article
- PubMed/NCBI
- Google Scholar
152. Wiffen PJ, Derry S, Bell RF, Rice ASC, Tölle TR, Phillips T, et al. Gabapentin for chronic neuropathic pain in adults. Cochrane Database of Systematic Reviews. 2017;(6). https://doi.org/10.1002/14651858.CD007938.pub4.
- View Article
- Google Scholar
153. Kane SP. ClinCalc DrugStats Database, Version 19.1: ClinCalc.com; 2019 [updated November 24, 2018; cited 2019 October 16]. Available from: https://clincalc.com/DrugStats.
154. Fuentes AV, Pineda MD, Venkata KCN. Comprehension of Top 200 Prescribed Drugs in the US as a Resource for Pharmacy Teaching, Training and Practice. Pharmacy (Basel). 2018;6(2):43. https://doi.org/10.3390/pharmacy6020043.
- View Article
- Google Scholar
155. Oosterhuis M, Sacher F, ter Laak TL. Prediction of concentration levels of metformin and other high consumption pharmaceuticals in wastewater and regional surface water based on sales data. Sci Total Environ. 2013;442:380–8. pmid:23183121
- View Article
- PubMed/NCBI
- Google Scholar
156. NCD Risk Factor Collaboration. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. The Lancet. 2016:1–18. https://doi.org/10.1016/S0140-6736(16)00618-8.
- View Article
- Google Scholar
157. World Health Organization. Global report on diabetes. France2016. 88 p.
158. Tang T, Lord JM, Norman RJ, Yasmin E, Balen AH. Insulin-sensitising drugs (metformin, rosiglitazone, pioglitazone, D-chiro-inositol) for women with polycystic ovary syndrome, oligo amenorrhoea and subfertility. Cochrane Database Syst Rev. 2012;5. https://doi.org/10.1002/14651858.CD003053.pub5.
- View Article
- Google Scholar
159. Sahra IB, Le Marchand-Brustel Y, Tanti J-F, Bost F. Metformin in cancer therapy: a new perspective for an old antidiabetic drug? Molecular cancer therapeutics. 2010;9(5):1092–9. pmid:20442309
- View Article
- PubMed/NCBI
- Google Scholar
160. Gong L, Goswami S, Giacomini KM, Altman RB, Klein TE. Metformin pathways: pharmacokinetics and pharmacodynamics. Pharmacogenet Genomics. 2012;22(11):820–7. pmid:22722338
- View Article
- PubMed/NCBI
- Google Scholar
161. Trautwein C, Berset J-D, Wolschke H, Kümmerer K. Occurrence of the antidiabetic drug Metformin and its ultimate transformation product Guanylurea in several compartments of the aquatic cycle. Environ Int. 2014;70:203–12. pmid:24954924
- View Article
- PubMed/NCBI
- Google Scholar
162. Trautwein C, Kümmerer K. Incomplete aerobic degradation of the antidiabetic drug Metformin and identification of the bacterial dead-end transformation product Guanylurea. Chemosphere. 2011;85(5):765–73. pmid:21752423
- View Article
- PubMed/NCBI
- Google Scholar
163. Blair BD, Crago JP, Hedman CJ, Klaper RD. Pharmaceuticals and personal care products found in the Great Lakes above concentrations of environmental concern. Chemosphere. 2013;93(9):2116–23. pmid:23973285
- View Article
- PubMed/NCBI
- Google Scholar
164. Scheurer M, Michel A, Brauch H-J, Ruck W, Sacher F. Occurrence and fate of the antidiabetic drug metformin and its metabolite guanylurea in the environment and during drinking water treatment. Water Res. 2012;46(15):4790–802. pmid:22770965
- View Article
- PubMed/NCBI
- Google Scholar
165. Scheurer M, Sacher F, Brauch H-J. Occurrence of the antidiabetic drug metformin in sewage and surface waters in Germany. J Environ Monit. 2009;11(9):1608–13. pmid:19724829
- View Article
- PubMed/NCBI
- Google Scholar
166. Ussery E, Bridges KN, Pandelides Z, Kirkwood AE, Guchardi J, Holdway D. Developmental and Full-Life Cycle Exposures to Guanylurea and Guanylurea–Metformin Mixtures Results in Adverse Effects on Japanese Medaka (Oryzias latipes). Environ Toxicol Chem. 2019;38(5):1023–8. pmid:30835871
- View Article
- PubMed/NCBI
- Google Scholar
167. MacLaren RD, Wisniewski K, MacLaren C. Environmental concentrations of metformin exposure affect aggressive behavior in the Siamese fighting fish, Betta splendens. PLOS ONE. 2018;13(5):e0197259. pmid:29763426
- View Article
- PubMed/NCBI
- Google Scholar
168. Ussery E, Bridges KN, Pandelides Z, Kirkwood AE, Bonetta D, Venables BJ, et al. Effects of environmentally relevant metformin exposure on Japanese medaka (Oryzias latipes). Aquat Toxicol. 2018;205:58–65. pmid:30336378
- View Article
- PubMed/NCBI
- Google Scholar
169. Niemuth NJ, Klaper RD. Low-dose metformin exposure causes changes in expression of endocrine disruption-associated genes. Aquat Toxicol. 2018;195:33–40. pmid:29248761
- View Article
- PubMed/NCBI
- Google Scholar
170. Niemuth NJ, Jordan R, Crago J, Blanksma C, Johnson R, Klaper RD. Metformin exposure at environmentally relevant concentrations causes potential endocrine disruption in adult male fish. Environ Toxicol Chem. 2015;34(2):291–6. pmid:25358780
- View Article
- PubMed/NCBI
- Google Scholar
171. Crago J, Bui C, Grewal S, Schlenk D. Age-dependent effects in fathead minnows from the anti-diabetic drug metformin. General and comparative endocrinology. 2016;232:185–90. pmid:26752244
- View Article
- PubMed/NCBI
- Google Scholar
172. Crago J, Klaper R. Place-based screening of mixtures of dominant emerging contaminants measured in Lake Michigan using zebrafish embryo gene expression assay. Chemosphere. 2018;193:1226–34. pmid:29874752
- View Article
- PubMed/NCBI
- Google Scholar
173. Raldúa D, Piña B. In vivo zebrafish assays for analyzing drug toxicity. Expert opinion on drug metabolism & toxicology. 2014;10(5):685–97. https://doi.org/10.1517/17425255.2014.896339.
- View Article
- Google Scholar
174. McGrath P, Li C-Q. Zebrafish: a predictive model for assessing drug-induced toxicity. Drug Discovery Today. 2008;13(9):394–401. https://doi.org/10.1016/j.drudis.2008.03.002.
- View Article
- Google Scholar
175. Elo B, Villano C, Govorko D, White L. Larval zebrafish as a model for glucose metabolism: expression of phosphoenolpyruvate carboxykinase as a marker for exposure to anti-diabetic compounds. J Mol Endocrinol. 2007;38(4):433–40. pmid:17446233
- View Article
- PubMed/NCBI
- Google Scholar
176. Capiotti KM, Antonioli Junior R, Kist LW, Bogo MR, Bonan CD, Da Silva RS. Persistent impaired glucose metabolism in a zebrafish hyperglycemia model. Comp Biochem Physiol B: Biochem Mol Biol. 2014;171:58–65. http://dx.doi.org/10.1016/j.cbpb.2014.03.005.
- View Article
- Google Scholar
177. Weinberger J, Klaper R. Environmental concentrations of the selective serotonin reuptake inhibitor fluoxetine impact specific behaviors involved in reproduction, feeding and predator avoidance in the fish Pimephales promelas (fathead minnow). Aquat Toxicol. 2014;151:77–83. pmid:24210950
- View Article
- PubMed/NCBI
- Google Scholar
178. Brooks BW. Fish on Prozac (and Zoloft): ten years later. Aquat Toxicol. 2014;151:61–7. pmid:24503458
- View Article
- PubMed/NCBI
- Google Scholar
179. Sanchez W, Sremski W, Piccini B, Palluel O, Maillot-Marechal E, Betoulle S, et al. Adverse effects in wild fish living downstream from pharmaceutical manufacture discharges. Environ Int. 2011;37(8):1342–8. pmid:21722962
- View Article
- PubMed/NCBI
- Google Scholar
180. Cedergreen N. Quantifying synergy: a systematic review of mixture toxicity studies within environmental toxicology. PloS one. 2014;9(5):e96580. pmid:24794244
- View Article
- PubMed/NCBI
- Google Scholar
181. Kortenkamp A. Ten Years of Mixing Cocktails: A Review of Combination Effects of Endocrine-Disrupting Chemicals. Environ Health Perspect. 2007;115(Suppl 1):98–105. https://doi.org/10.1289/ehp.9357.
- View Article
- Google Scholar
182. Brodin T, Piovano S, Fick J, Klaminder J, Heynen M, Jonsson M. Ecological effects of pharmaceuticals in aquatic systems—impacts through behavioural alterations. Phil Trans R Soc B. 2014;369(1656):20130580. pmid:25405968
- View Article
- PubMed/NCBI
- Google Scholar
183. Jonsson M, Fick J, Klaminder J, Brodin T. Antihistamines and aquatic insects: bioconcentration and impacts on behavior in damselfly larvae (Zygoptera). Sci Total Environ. 2014;472:108–11. pmid:24291135
- View Article
- PubMed/NCBI
- Google Scholar
184. Fick J, Lindberg RH, Parkkonen J, Arvidsson B, Tysklind M, Larsson DGJ. Therapeutic Levels of Levonorgestrel Detected in Blood Plasma of Fish: Results from Screening Rainbow Trout Exposed to Treated Sewage Effluents. Environ Sci Technol. 2010;44(7):2661–6. pmid:20222725
- View Article
- PubMed/NCBI
- Google Scholar
185. Richmond EK, Rosi EJ, Walters DM, Fick J, Hamilton SK, Brodin T, et al. A diverse suite of pharmaceuticals contaminates stream and riparian food webs. Nature Communications. 2018;9(1):4491. pmid:30401828
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Rosi-Marshall EJ, Royer TV. Pharmaceutical compounds and ecosystem function: an emerging research challenge for aquatic ecologists. Ecosystems. 2012;15(6):867–80. https://doi.org/10.1007/s10021-012-9553-z.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Monteiro SC, Boxall AB. Occurrence and fate of human pharmaceuticals in the environment. Reviews of Environmental Contamination and Toxicology: Springer; 2010. p. 53–154. https://doi.org/10.1007/978-1-4419-1157-5_2 pmid:19898761

[ref3] 3. Van Donk E, Peacor S, Grosser K, Domis LNDS, Lürling M. Pharmaceuticals may disrupt natural chemical information flows and species interactions in aquatic systems: ideas and perspectives on a hidden global change. In: Whitacre D, editor. Reviews of Environmental Contamination and Toxicology. 235. New York: Springer International Publishing; 2015. p. 1–15. https://doi.org/10.1007/978-3-319-10861-2_1

[ref4] 4. Hughes SR, Kay P, Brown LE. Global synthesis and critical evaluation of pharmaceutical data sets collected from river systems. Environ Sci Technol. 2013;47(2):661–77. pmid:23227929
View Article
PubMed/NCBI
Google Scholar

[7] View Article

[8] PubMed/NCBI

[9] Google Scholar

[ref5] 5. Brown AR, Gunnarsson L, Kristiansson E, Tyler CR. Assessing variation in the potential susceptibility of fish to pharmaceuticals, considering evolutionary differences in their physiology and ecology. Philos Trans R Soc, B. 2014;369(1656):20130576. https://doi.org/10.1098/rstb.2013.0576.
View Article
Google Scholar

[11] View Article

[12] Google Scholar

[ref6] 6. Corcoran J, Winter MJ, Tyler CR. Pharmaceuticals in the aquatic environment: a critical review of the evidence for health effects in fish. Crit Rev Toxicol. 2010;40(4):287–304. pmid:20225984
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref7] 7. Bradley PM, Journey CA, Button DT, Carlisle DM, Clark JM, Mahler BJ, et al. Metformin and Other Pharmaceuticals Widespread in Wadeable Streams of the Southeastern United States. Environ Sci Technol Lett. 2016;3(6):243–9. https://doi.org/10.1021/acs.estlett.6b00170.
View Article
Google Scholar

[18] View Article

[19] Google Scholar

[ref8] 8. Fatta-Kassinos D, Vasquez M, Kümmerer K. Transformation products of pharmaceuticals in surface waters and wastewater formed during photolysis and advanced oxidation processes–degradation, elucidation of byproducts and assessment of their biological potency. Chemosphere. 2011;85(5):693–709. pmid:21835425
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref9] 9. Bradley P, Journey C, Romanok K, Barber L, Buxton HT, Foreman WT, et al. Expanded Target-Chemical Analysis Reveals Extensive Mixed-Organic-Contaminant Exposure in USA Streams. Environ Sci Technol. 2017;51(9):4792–802. pmid:28401767
View Article
PubMed/NCBI
Google Scholar

[25] View Article

[26] PubMed/NCBI

[27] Google Scholar

[ref10] 10. Daughton C, Ternes T. Pharmaceuticals and personal care products in the environment: Agents of subtle change? Environ Health Perspect. 1999;107(6):907–38. https://doi.org/10.1289/ehp.99107s6907.
View Article
Google Scholar

[29] View Article

[30] Google Scholar

[ref11] 11. Daughton C, Brooks B. Active pharmaceutical ingredients and aquatic organisms. In: Beyer W, Meador J, editors. Environmental Contaminants in Biota: Interpreting Tissue Concentrations. Boca Raton, FL: CRC Press; 2011. p. 288–348. https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1248&context=usepapapers.

[ref12] 12. Boström ML, Berglund O. Influence of pH-dependent aquatic toxicity of ionizable pharmaceuticals on risk assessments over environmental pH ranges. Water Res. 2015;72:154–61. pmid:25262444
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref13] 13. Vasquez M, Lambrianides A, Schneider M, Kümmerer K, Fatta-Kassinos D. Environmental side effects of pharmaceutical cocktails: What we know and what we should know. J Hazard Mater. 2014;279:169–89. pmid:25061892
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref14] 14. Gunnarsson L, Jauhiainen A, Kristiansson E, Nerman O, Larsson DGJ. Evolutionary Conservation of Human Drug Targets in Organisms used for Environmental Risk Assessments. Environ Sci Technol. 2008;42(15):5807–13. pmid:18754513
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref15] 15. Gunnarsson L, Kristiansson E, Larsson DJ. Environmental comparative pharmacology: theory and application. Human Pharmaceuticals in the Environment: Springer; 2012. p. 85–108. https://doi.org/10.1007/978-1-4614-3473-3_5.

[ref16] 16. McRobb FM, Sahaguún V, Kufareva I, Abagyan R. In silico analysis of the conservation of human toxicity and endocrine disruption targets in aquatic species. Environ Sci Technol. 2014;48(3):1964–72. pmid:24392850
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref17] 17. Carter LJ, Williams M, Böttcher C, Kookana RS. Uptake of pharmaceuticals influences plant development and affects nutrient and hormone homeostases. Environ Sci Technol. 2015;49(20):12509–18. pmid:26418514
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref18] 18. Rand-Weaver M, Margiotta-Casaluci L, Patel A, Panter GH, Owen SF, Sumpter JP. The Read-Across Hypothesis and Environmental Risk Assessment of Pharmaceuticals. Environ Sci Technol. 2013;47(20):11384–95. pmid:24006913
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref19] 19. Li WC. Occurrence, sources, and fate of pharmaceuticals in aquatic environment and soil. Environ Pollut. 2014;187:193–201. pmid:24521932
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref20] 20. Kidd KA, Paterson MJ, Rennie MD, Podemski CL, Findlay DL, Blanchfield PJ, et al. Direct and indirect responses of a freshwater food web to a potent synthetic oestrogen. Philos Trans R Soc, B. 2014;369(1656):20130578. https://dx.doi.org/10.1098/rstb.2013.0578.
View Article
Google Scholar

[62] View Article

[63] Google Scholar

[ref21] 21. Brönmark C, Hansson L-A. Aquatic chemical ecology: new directions and challenges for the future. Chemical Ecology in Aquatic Systems Oxford University Press, Oxford. 2012:272–8. https://doi.org/10.1093/acprof:osobl/9780199583096.003.0019.

[ref22] 22. Schultz MM, Painter MM, Bartell SE, Logue A, Furlong ET, Werner SL, et al. Selective uptake and biological consequences of environmentally relevant antidepressant pharmaceutical exposures on male fathead minnows. Aquat Toxicol. 2011;104(1):38–47. https://doi.org/10.1016/j.aquatox.2011.03.011.
View Article
Google Scholar

[66] View Article

[67] Google Scholar

[ref23] 23. Giacomini ACV, Abreu MS, Giacomini LV, Siebel AM, Zimerman FF, Rambo CL, et al. Fluoxetine and diazepam acutely modulate stress induced-behavior. Behavioural Brain Research. 2016;296:301–10. pmid:26403161
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref24] 24. Brodin T, Fick J, Jonsson M, Klaminder J. Dilute concentrations of a psychiatric drug alter behavior of fish from natural populations. Science. 2013;339(6121):814–5. pmid:23413353
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref25] 25. Batt AL, Kincaid TM, Kostich MS, Lazorchak JM, Olsen AR. Evaluating the extent of pharmaceuticals in surface waters of the United States using a National‐scale Rivers and Streams Assessment survey. Environ Toxicol Chem. 2016;35(4):874–81. pmid:26562077
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref26] 26. Luo Y, Guo W, Ngo HH, Nghiem LD, Hai FI, Zhang J, et al. A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment. Sci Total Environ. 2014;473–474:619–41. pmid:24394371
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref27] 27. Michael I, Rizzo L, McArdell C, Manaia C, Merlin C, Schwartz T, et al. Urban wastewater treatment plants as hotspots for the release of antibiotics in the environment: a review. Water Res. 2013;47(3):957–95. pmid:23266388
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref28] 28. Lowe WH, Likens GE. Moving headwater streams to the head of the class. BioScience. 2005;55(3):196–7. https://doi.org/10.1641/0006-3568(2005)055[0196:MHSTTH]2.0.CO;2.
View Article
Google Scholar

[89] View Article

[90] Google Scholar

[ref29] 29. Leopold LB. Rivers. American Scientist. 1962;50(4):511–37. https://www.jstor.org/stable/27838523.
View Article
Google Scholar

[92] View Article

[93] Google Scholar

[ref30] 30. Downing JA, Cole JJ, Duarte C, Middelburg JJ, Melack JM, Prairie YT, et al. Global abundance and size distribution of streams and rivers. Inland waters. 2012;2(4):229–36. https://doi.org/10.5268/IW-2.4.502.
View Article
Google Scholar

[95] View Article

[96] Google Scholar

[ref31] 31. Bishop K, Buffam I, Erlandsson M, Fölster J, Laudon H, Seibert J, et al. Aqua Incognita: the unknown headwaters. Hydrological Processes. 2008;22(8):1239–42. https://doi.org/10.1002/hyp.7049.
View Article
Google Scholar

[98] View Article

[99] Google Scholar

[ref32] 32. Clarke A, Mac Nally R, Bond N, Lake P. Macroinvertebrate diversity in headwater streams: a review. Freshwater Biol. 2008;53(9):1707–21. https://doi.org/10.1111/j.1365-2427.2008.02041.x.
View Article
Google Scholar

[101] View Article

[102] Google Scholar

[ref33] 33. Meyer JL, Strayer DL, Wallace JB, Eggert SL, Helfman GS, Leonard NE. The contribution of headwater streams to biodiversity in river networks. JAWRA Journal of the American Water Resources Association. 2007;43(1):86–103. https://doi.org/10.1111/j.1752-1688.2007.00008.x.
View Article
Google Scholar

[104] View Article

[105] Google Scholar

[ref34] 34. Temnerud J, Fölster J, Buffam I, Laudon H, Erlandsson M, Bishop K. Can the distribution of headwater stream chemistry be predicted from downstream observations? Hydrological Processes. 2010;24(16):2269–76. https://doi.org/10.1002/hyp.7615.
View Article
Google Scholar

[107] View Article

[108] Google Scholar

[ref35] 35. Freeman MC, Pringle CM, Jackson CR. Hydrologic connectivity and the contribution of stream headwaters to ecological integrity at regional scales. JAWRA Journal of the American Water Resources Association. 2007;43(1):5–14. https://doi.org/10.1111/j.1752-1688.2007.00002.x.
View Article
Google Scholar

[110] View Article

[111] Google Scholar

[ref36] 36. Terando AJ, Costanza J, Belyea C, Dunn RR, McKerrow A, Collazo JA. The southern megalopolis: using the past to predict the future of urban sprawl in the Southeast US. PLoS One. 2014;9(7):e102261. pmid:25054329
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref37] 37. Van Metre PC, Waite IR, Qi S, Mahler B, Terando A, Wieczorek M, et al. Projected urban growth in the southeastern USA puts small streams at risk. PLOS ONE. 2019;14(10):e0222714. pmid:31618213
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref38] 38. Journey C, Van Metre P, Bell A, Garrett J, Button D, Nakagaki N, et al. Design and Methods of the Southeast Stream Quality Assessment (SESQA), 2014. U.S. Geological Survey, 2015 U.S. Geological Survey Open File Report 2015–109 Contract No.: Open File Report 2015–109. http://doi.org/10.3133/ofr20151095.

[ref39] 39. Van Metre PC, Journey CA. The Southeast Stream Quality Assessment. Report. Reston, VA: 2014 2014–3023. https://doi.org/10.3133/fs20143023.

[ref40] 40. Moretto A, Bachman A, Boobis A, Solomon KR, Pastoor TP, Wilks MF, et al. A framework for cumulative risk assessment in the 21st century. Crit Rev Toxicol. 2017;47(2):85–97. pmid:27685779
View Article
PubMed/NCBI
Google Scholar

[123] View Article

[124] PubMed/NCBI

[125] Google Scholar

[ref41] 41. Norton SB, Rodier DJ, van der Schalie WH, Wood WP, Slimak MW, Gentile JH. A framework for ecological risk assessment at the EPA. Environ Toxicol Chem. 1992;11(12):1663–72. https://doi.org/10.1002/etc.5620111202.
View Article
Google Scholar

[127] View Article

[128] Google Scholar

[ref42] 42. Rodier D, Norton S. Framework for ecological risk assessment. Environmental Protection Agency, Washington, DC (United States). Risk Assessment Forum, 1992 Environmental Protection Agency, EPA-630/R-92/001. https://www.epa.gov/sites/production/files/2014-11/documents/framework_eco_assessment.pdf.

[ref43] 43. Blackwell BR, Ankley GT, Corsi SR, De Cicco LA, Houck KA, Judson RS, et al. An" EAR" on environmental surveillance and monitoring: A case study on the use of exposure-activity ratios (EARs) to prioritize sites, chemicals, and bioactivities of concern in Great Lakes waters. Environ Sci Technol. 2017;51(15):8713–24. pmid:28671818
View Article
PubMed/NCBI
Google Scholar

[131] View Article

[132] PubMed/NCBI

[133] Google Scholar

[ref44] 44. Bradley PM, Journey CA, Berninger JP, Button DT, Clark JM, Corsi SR, et al. Mixed-chemical exposure and predicted effects potential in wadeable southeastern USA streams. Sci Total Environ. 2019;655:70–83. pmid:30469070
View Article
PubMed/NCBI
Google Scholar

[135] View Article

[136] PubMed/NCBI

[137] Google Scholar

[ref45] 45. Corsi SR, De Cicco LA, Villeneuve DL, Blackwell BR, Fay KA, Ankley GT, et al. Prioritizing chemicals of ecological concern in Great Lakes tributaries using high-throughput screening data and adverse outcome pathways. Sci Total Environ. 2019;686:995–1009. pmid:31412529
View Article
PubMed/NCBI
Google Scholar

[139] View Article

[140] PubMed/NCBI

[141] Google Scholar

[ref46] 46. U.S. Environmental Protection Agency. ToxCast & Tox21 Summary Files from invitrodb_v3 2019 [July 5, 2019]. Available from: https://www.epa.gov/chemical-research/toxicity-forecaster-toxcasttm-data.

[ref47] 47. Van Metre PC, Morace JL, Sheibley RW. The Pacific northwest stream quality assessment. Report. Reston, VA: 2015 U. S. Geological Survey Fact Sheet 2015–3020. https://doi.org/10.3133/fs20153020.

[ref48] 48. Sheibley RW, Morace JL, Journey CA, Van Metre PC, Bell AH, Nakagaki N, et al. Design and methods of the Pacific Northwest Stream Quality Assessment (PNSQA), 2015. Report. Reston, VA: 2017 U. S. Geological Survey Open-File Report 2017–1103. https://doi.org/10.3133/ofr20171103.

[ref49] 49. Van Metre PC, Egler AL, May JT. The California stream quality assessment. Report. Reston, VA: 2017 U. S. Geological Survey Fact Sheet 2017–3014. https://doi.org/10.3133/fs20173014.

[ref50] 50. Coles JF, Riva-Murray K, Van Metre PC, Button DT, Bell AH, Qi SL, et al. Design and methods of the U.S. Geological Survey Northeast Stream Quality Assessment (NESQA), 2016. Report. Reston, VA: 2019 U. S. Geological Survey Open-File Report 2018–1183. https://doi.org/10.3133/ofr20181183.

[ref51] 51. Van Metre PC, Riva-Murray K, Coles JF. The Northeast Stream Quality Assessment. Report. Reston, VA: 2016 U. S. Geological Survey Fact Sheet 2016–3021. https://doi.org/10.3133/fs20163021.

[ref52] 52. Furlong E, Noriega M, Kanagy C, Kanagy L, Coffey L, Burkhardt M. Methods of the National Water Quality Laboratory. Chapter B10. Determination of human-use pharmaceuticals in filtered water by direct aqueous injection–high-performance liquid chromatography/tandem mass spectrometry. 2014 U.S. Geological Survey Techniques and Methods. Book 5. Laboratory Analysis. Chap. B10. http://dx.doi.org/10.3133/tm5B10.

[ref53] 53. Cummings C, Finlay JC, MacDonald NE. Head lice infestations: A clinical update. Paediatrics & Child Health. 2018;23(1):e18–e24. https://doi.org/10.1093/pch/pxx165.
View Article
Google Scholar

[150] View Article

[151] Google Scholar

[ref54] 54. Klootwijk T, Le Cleach L, Do G, Vander Stichele R, Knuistingh Neven A, Eekhof JAH, et al. Interventions for treating head lice. The Cochrane Database of Systematic Reviews. 2018;2018(5):CD009321. https://doi.org/10.1002/14651858.CD009321.pub2. PMC6494540.
View Article
Google Scholar

[153] View Article

[154] Google Scholar

[ref55] 55. U.S. Geological Survey. The National Map 2019 [cited 2019 October 16]. Available from: https://viewer.nationalmap.gov/advanced-viewer/.

[ref56] 56. ASTM International. ASTM D7729-12(2018)e1, Standard Practice for Determining and Expressing Precision of Measurement Results, in the Analysis of Water, as Relative Standard Deviation, Utilizing DQCALC Software. West Conshohocken, PA: 2018 ASTM D7729-12(2018)e1. https://doi.org/10.1520/D7729-12R18E01.

[ref57] 57. Clarke K, Gorley R, Somerfield P, Warwick R. Change in marine communities: an approach to statistical analysis and interpretation, V7. 3rd ed. Plymouth, UK: Primer-E, Ltd.; 2014. 260 p.

[ref58] 58. Hammer Ø, Harper DA, Ryan PD. PAST: paleontological statistics software package for education and data analysis. Palaeontologia electronica. 2001;4(1):9. https://palaeo-electronica.org/2001_1/past/issue1_01.htm.
View Article
Google Scholar

[159] View Article

[160] Google Scholar

[ref59] 59. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecology. 2001;26(1):32–46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x.
View Article
Google Scholar

[162] View Article

[163] Google Scholar

[ref60] 60. Anderson MJ, Walsh DCI. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? Ecological Monographs. 2013;83(4):557–74. https://doi.org/10.1890/12-2010.1.
View Article
Google Scholar

[165] View Article

[166] Google Scholar

[ref61] 61. De Cicco L, Corsi SR, Villeneuve D, Blackwell BR, Ankley GT. toxEval: Evaluation of measured concentration data using the ToxCast high-throughput screening database or a user-defined set of concentration benchmarks. R package version 1.0.0 2018 [May 1, 2018]. Available from: https://owi.usgs.gov/R/gran.html.
View Article
Google Scholar

[168] View Article

[169] Google Scholar

[ref62] 62. R Development Core Team. R: A Language and Environment for Statistical Computing. Version 3.5.2. Vienna Austria: R Foundation for Statistical Computing, 2019.

[ref63] 63. Ermler S, Scholze M, Kortenkamp A. The suitability of concentration addition for predicting the effects of multi-component mixtures of up to 17 anti-androgens with varied structural features in an in vitro AR antagonist assay. Toxicol Appl Pharmacol. 2011;257(2):189–97. pmid:21945941
View Article
PubMed/NCBI
Google Scholar

[172] View Article

[173] PubMed/NCBI

[174] Google Scholar

[ref64] 64. Cedergreen N, Christensen AM, Kamper A, Kudsk P, Mathiassen SK, Streibig JC, et al. A review of independent action compared to concentration addition as reference models for mixtures of compounds with different molecular target sites. Environ Toxicol Chem. 2008;27(7):1621–32. pmid:18271647
View Article
PubMed/NCBI
Google Scholar

[176] View Article

[177] PubMed/NCBI

[178] Google Scholar

[ref65] 65. Thrupp TJ, Runnalls TJ, Scholze M, Kugathas S, Kortenkamp A, Sumpter JP. The consequences of exposure to mixtures of chemicals: Something from ‘nothing’ and ‘a lot from a little’ when fish are exposed to steroid hormones. Sci Total Environ. 2018;619–620:1482–92. pmid:29734624
View Article
PubMed/NCBI
Google Scholar

[180] View Article

[181] PubMed/NCBI

[182] Google Scholar

[ref66] 66. Altenburger R, Scholz S, Schmitt-Jansen M, Busch W, Escher BI. Mixture toxicity revisited from a toxicogenomic perspective. Environ Sci Technol. 2012;46(5):2508–22. pmid:22283441
View Article
PubMed/NCBI
Google Scholar

[184] View Article

[185] PubMed/NCBI

[186] Google Scholar

[ref67] 67. Kortenkamp A, Backhaus T, Faust M. State of the art report on mixture toxicity. 2009 Contract 70307/2007485103. https://ec.europa.eu/environment/chemicals/effects/pdf/report_mixture_toxicity.pdf.
View Article
Google Scholar

[188] View Article

[189] Google Scholar

[ref68] 68. Altenburger R, Backhaus T, Boedeker W, Faust M, Scholze M. Simplifying complexity: Mixture toxicity assessment in the last 20 years. Environ Toxicol Chem. 2013;32(8):1685–7. pmid:23843317
View Article
PubMed/NCBI
Google Scholar

[191] View Article

[192] PubMed/NCBI

[193] Google Scholar

[ref69] 69. iCSS ToxCast Dashboard [Internet]. 2018 [cited March 22, 2018]. Available from: https://actor.epa.gov/dashboard/.

[ref70] 70. U.S. Environmental Protection Agency. ACToR 2018 [cited 2018 March 22, 2018]. Available from: https://actor.epa.gov/actor/home.xhtml.

[ref71] 71. Li S, Villeneuve DL, Berninger JP, Blackwell BR, Cavallin JE, Hughes MN, et al. An integrated approach for identifying priority contaminant in the Great Lakes Basin–Investigations in the Lower Green Bay/Fox River and Milwaukee Estuary areas of concern. Sci Total Environ. 2017;579:825–37. pmid:27866739
View Article
PubMed/NCBI
Google Scholar

[197] View Article

[198] PubMed/NCBI

[199] Google Scholar

[ref72] 72. Schroeder AL, Ankley GT, Houck KA, Villeneuve DL. Environmental surveillance and monitoring—The next frontiers for high‐throughput toxicology. Environ Toxicol Chem. 2016;35(3):513–25. pmid:26923854
View Article
PubMed/NCBI
Google Scholar

[201] View Article

[202] PubMed/NCBI

[203] Google Scholar

[ref73] 73. Becker RA, Friedman KP, Simon TW, Marty MS, Patlewicz G, Rowlands JC. An exposure: activity profiling method for interpreting high-throughput screening data for estrogenic activity—Proof of concept. Regul Toxicol Pharm. 2015;71(3):398–408. https://doi.org/10.1016/j.yrtph.2015.01.008.
View Article
Google Scholar

[205] View Article

[206] Google Scholar

[ref74] 74. National Water Information System (NWIS)-Web interface [Internet]. 2019 [cited January 12, 2019]. Available from: http://waterdata.usgs.gov/nwis.

[ref75] 75. Romanok KM, Bradley PM, Journey CA. Concentrations of Pharmaceutical Contaminants and Their Predicted Effects from a Multi-Regional Assessment of Wadeable USA Streams, 2014–2017. 2019 U.S. Geological Survey data release https://doi.org/10.5066/P97MW2T8.
View Article
Google Scholar

[209] View Article

[210] Google Scholar

[ref76] 76. Kaiser Family Foundation’s State Health Facts INPAD. IQVIA 2018 Retail Sales for Prescription Drugs Filled at Pharmacies by Payer 2018 [cited 2019 October 15]. Available from: https://www.kff.org/health-costs/state-indicator/total-sales-for-retail-rx-drugs/.

[ref77] 77. U.S. Census Bureau. Annual Estimates of the Resident Population: April 1, 2010 to July 1, 2018 2019 [cited 2019 October 16]. Available from: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=PEP_2018_PEPANNRES&src=pt.

[ref78] 78. Black L, Benson V. U.S. Centers for Disease Control and Prevention: 2018 National Health Interview Survey Tables of Summary Health Statistics for U.S. Children: U.S. Centers for Disease Control and Prevention,; 2019 [cited 2020 January 6]. Available from: https://www.cdc.gov/nchs/nhis/SHS/tables.htm.

[ref79] 79. Villarroel M, Blackwell D, Jen A. U.S. Centers for Disease Control and Prevention: 2018 National Health Interview Survey Tables of Summary Health Statistics for U.S. Adults: U.S. Centers for Disease Control and Prevention,; 2019 [cited 2020 January 6]. Available from: https://www.cdc.gov/nchs/nhis/SHS/tables.htm.

[ref80] 80. Falcone JA. U.S. conterminous wall-to-wall anthropogenic land use trends (NWALT), 1974–2012. Reston, VA: 2015 U.S. Geological Survey Data Series Report 948. https://doi.org/10.3133/ds948.

[ref81] 81. U.S. Census Bureau. TIGER/Line Shapefiles. 2016.

[ref82] 82. U.S. Environmental Protection Agency. NPDES Permits Around the Nation. 2013.

[ref83] 83. U.S. Geological Survey. National Land Cover Database (NLCD) 2011 Edition. 2014.

[ref84] 84. Barber LB, Vajda AM, Douville C, Norris DO, Writer JH. Fish endocrine disruption responses to a major wastewater treatment facility upgrade. Environ Sci Technol. 2012;46(4):2121–31. pmid:22300164
View Article
PubMed/NCBI
Google Scholar

[220] View Article

[221] PubMed/NCBI

[222] Google Scholar

[ref85] 85. Barber LB, Hladik ML, Vajda AM, Fitzgerald KC, Douville C. Impact of wastewater infrastructure upgrades on the urban water cycle: Reduction in halogenated reaction byproducts following conversion from chlorine gas to ultraviolet light disinfection. Sci Total Environ. 2015;529:264–74. pmid:26025637
View Article
PubMed/NCBI
Google Scholar

[224] View Article

[225] PubMed/NCBI

[226] Google Scholar

[ref86] 86. Bradley PM, Barber LB, Clark JM, Duris JW, Foreman WT, Furlong ET, et al. Pre/post-closure assessment of groundwater pharmaceutical fate in a wastewater-facility-impacted stream reach. Sci Total Environ. 2016;568:916–25. pmid:27350092
View Article
PubMed/NCBI
Google Scholar

[228] View Article

[229] PubMed/NCBI

[230] Google Scholar

[ref87] 87. Bradley PM, Journey CA, Clark J. Effect of Wastewater Treatment Facility Closure on Endocrine Disrupting Chemicals in a Coastal Plain Stream. Remediation Journal. 2016;26(2):9–24. https://doi.org/10.1002/rem.21455.
View Article
Google Scholar

[232] View Article

[233] Google Scholar

[ref88] 88. Warner W, Licha T, Nödler K. Qualitative and quantitative use of micropollutants as source and process indicators. A review. Sci Total Environ. 2019;686:75–89. pmid:31176825
View Article
PubMed/NCBI
Google Scholar

[235] View Article

[236] PubMed/NCBI

[237] Google Scholar

[ref89] 89. Arnold KE, Brown AR, Ankley GT, Sumpter JP. Medicating the environment: assessing risks of pharmaceuticals to wildlife and ecosystems. Philos Trans R Soc, B. 2014;369(1656):20130569. https://doi.org/10.1098/rstb.2013.0569.
View Article
Google Scholar

[239] View Article

[240] Google Scholar

[ref90] 90. Rutsch M, Rieckermann J, Cullmann J, Ellis JB, Vollertsen J, Krebs P. Towards a better understanding of sewer exfiltration. Water Res. 2008;42(10):2385–94. https://doi.org/10.1016/j.watres.2008.01.019.
View Article
Google Scholar

[242] View Article

[243] Google Scholar

[ref91] 91. Wolf L, Zwiener C, Zemann M. Tracking artificial sweeteners and pharmaceuticals introduced into urban groundwater by leaking sewer networks. Sci Total Environ. 2012;430:8–19. pmid:22609959
View Article
PubMed/NCBI
Google Scholar

[245] View Article

[246] PubMed/NCBI

[247] Google Scholar

[ref92] 92. Ferrell GM, Yearout MS, Grimes BH, Graves AK, Fitzgerald SA, Meyer MT. Water-quality characteristics indicative of wastewater in selected streams in the upper Neuse River Basin, Durham and Orange Counties, North Carolina, from 2004 to 2013. Report. Reston, VA: 2014 2014–1215. https://doi.org/10.3133/ofr20141215.

[ref93] 93. Peters NE. Effects of urbanization on stream water quality in the city of Atlanta, Georgia, USA. Hydrological Processes. 2009;23(20):2860–78. https://doi.org/10.1002/hyp.7373.
View Article
Google Scholar

[250] View Article

[251] Google Scholar

[ref94] 94. Phillips P, Chalmers A. Wastewater Effluent, Combined Sewer Overflows, and Other Sources of Organic Compounds to Lake Champlain1. JAWRA Journal of the American Water Resources Association. 2009;45(1):45–57. https://doi.org/10.1111/j.1752-1688.2008.00288.x.
View Article
Google Scholar

[253] View Article

[254] Google Scholar

[ref95] 95. Weyrauch P, Matzinger A, Pawlowsky-Reusing E, Plume S, von Seggern D, Heinzmann B, et al. Contribution of combined sewer overflows to trace contaminant loads in urban streams. Water Res. 2010;44(15):4451–62. pmid:20599243
View Article
PubMed/NCBI
Google Scholar

[256] View Article

[257] PubMed/NCBI

[258] Google Scholar

[ref96] 96. Phillips P, Chalmers A, Gray J, Kolpin D, Foreman W, Wall G. Combined sewer overflows: An environmental source of hormones and wastewater micropollutants. Environ Sci Technol. 2012;46:5336–43. pmid:22540536
View Article
PubMed/NCBI
Google Scholar

[260] View Article

[261] PubMed/NCBI

[262] Google Scholar

[ref97] 97. Conn KE, Lowe KS, Drewes JE, Hoppe-Jones C, Tucholke MB. Occurrence of pharmaceuticals and consumer product chemicals in raw wastewater and Septic tank effluent from single-family homes. Environmental Engineering Science. 2010;27(4):347–56. https://doi.org/10.1089/ees.2009.0364.
View Article
Google Scholar

[264] View Article

[265] Google Scholar

[ref98] 98. Carrara C, Ptacek CJ, Robertson WD, Blowes DW, Moncur MC, Sverko E, et al. Fate of pharmaceutical and trace organic compounds in three septic system plumes, Ontario, Canada. Environ Sci Technol. 2008;42(8):2805–11. pmid:18497127
View Article
PubMed/NCBI
Google Scholar

[267] View Article

[268] PubMed/NCBI

[269] Google Scholar

[ref99] 99. Humphrey C, Finley A, O'Driscoll M, Manda A, Iverson G. Groundwater and stream E. coli concentrations in coastal plain watersheds served by onsite wastewater and a municipal sewer treatment system. Water Sci Technol. 2015;72(10):1851–60. pmid:26540548
View Article
PubMed/NCBI
Google Scholar

[271] View Article

[272] PubMed/NCBI

[273] Google Scholar

[ref100] 100. Yu ZL, Rahardianto A, DeShazo J, Stenstrom MK, Cohen Y. Critical review: regulatory incentives and impediments for onsite graywater reuse in the United States. Water Environ Res. 2013;85(7):650–62. pmid:23944147
View Article
PubMed/NCBI
Google Scholar

[275] View Article

[276] PubMed/NCBI

[277] Google Scholar

[ref101] 101. Eriksson E, Auffarth K, Eilersen AM, Henze M, Ledin A. Household chemicals and personal care products as sources for xenobiotic organic compounds in grey wastewater. Water Sa. 2003;29(2):135–46. http://dx.doi.org/10.4314/wsa.v29i2.4848.
View Article
Google Scholar

[279] View Article

[280] Google Scholar

[ref102] 102. Oteng-Peprah M, Acheampong MA, deVries NK. Greywater Characteristics, Treatment Systems, Reuse Strategies and User Perception—a Review. Water, Air, Soil Pollut. 2018;229(8). https://doi.org/10.1007/s11270-018-3909-8.
View Article
Google Scholar

[282] View Article

[283] Google Scholar

[ref103] 103. Fatta-Kassinos D, Kalavrouziotis IK, Koukoulakis PH, Vasquez MI. The risks associated with wastewater reuse and xenobiotics in the agroecological environment. Sci Total Environ. 2011;409(19):3555–63. pmid:20435343
View Article
PubMed/NCBI
Google Scholar

[285] View Article

[286] PubMed/NCBI

[287] Google Scholar

[ref104] 104. Fissore C, Hobbie SE, King JY, McFadden JP, Nelson KC, Baker LA. The residential landscape: fluxes of elements and the role of household decisions. Urban Ecosystems. 2012;15(1):1–18. https://doi.org/10.1007/s11252-011-0189-0.
View Article
Google Scholar

[289] View Article

[290] Google Scholar

[ref105] 105. Margalida A, Bogliani G, Bowden CGR, Donázar JA, Genero F, Gilbert M, et al. One Health approach to use of veterinary pharmaceuticals. Science. 2014;346(6215):1296–8. pmid:25477214
View Article
PubMed/NCBI
Google Scholar

[292] View Article

[293] PubMed/NCBI

[294] Google Scholar

[ref106] 106. Boxall ABA. Veterinary Medicines and the Environment. In: Cunningham F, Elliott J, Lees P, editors. Comparative and Veterinary Pharmacology. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. p. 291–314. https://doi.org/10.1007/978-3-642-10324-7_12.

[ref107] 107. Masoner JR, Kolpin DW, Cozzarelli IM, Barber LB, Burden DS, Foreman WT, et al. Urban Stormwater: An Overlooked Pathway of Extensive Mixed Contaminants to Surface and Groundwaters in the United States. Environ Sci Technol. 2019;53(17):10070–81. pmid:31432661
View Article
PubMed/NCBI
Google Scholar

[297] View Article

[298] PubMed/NCBI

[299] Google Scholar

[ref108] 108. U.S. Geological Survey. Next Generation Water Observing System: Delaware River Basin 2020 [cited 2020 December 30]. Available from: https://www.usgs.gov/mission-areas/water-resources/science/next-generation-water-observing-system-delaware-river-basin?qt-science_center_objects=0#qt-science_center_objects.

[ref109] 109. U.S. Geological Survey. USGS Next Generation Water Observing System (NGWOS) 2020 [cited 2020 December 30]. Available from: https://www.usgs.gov/mission-areas/water-resources/science/usgs-next-generation-water-observing-system-ngwos?qt-science_center_objects=0#qt-science_center_objects.

[ref110] 110. Bai H, Zeng SY, Dong X, Chen JN. Research of potassium flow and circulation based on substance flow analysis. Huanjing Kexue/Environmental Science. 2013;34(6):2493–6.
View Article
Google Scholar

[303] View Article

[304] Google Scholar

[ref111] 111. Nödler K, Licha T, Fischer S, Wagner B, Sauter M. A case study on the correlation of micro-contaminants and potassium in the Leine River (Germany). Appl Geochem. 2011;26(12):2172–80. https://doi.org/10.1016/j.apgeochem.2011.08.001.
View Article
Google Scholar

[306] View Article

[307] Google Scholar

[ref112] 112. Winker M, Vinnerås B, Muskolus A, Arnold U, Clemens J. Fertiliser products from new sanitation systems: Their potential values and risks. Bioresour Technol. 2009;100(18):4090–6. pmid:19375910
View Article
PubMed/NCBI
Google Scholar

[309] View Article

[310] PubMed/NCBI

[311] Google Scholar

[ref113] 113. Winker M, Faika D, Gulyas H, Otterpohl R. A comparison of human pharmaceutical concentrations in raw municipal wastewater and yellowwater. Sci Total Environ. 2008;399(1):96–104. https://doi.org/10.1016/j.scitotenv.2008.03.027.
View Article
Google Scholar

[313] View Article

[314] Google Scholar

[ref114] 114. Jönsson H, Stenström T-A, Svensson J, Sundin A. Source separated urine-nutrient and heavy metal content, water saving and faecal contamination. Water Sci Technol. 1997;35(9):145–52. https://doi.org/10.2166/wst.1997.0338.
View Article
Google Scholar

[316] View Article

[317] Google Scholar

[ref115] 115. Lienert J, Bürki T, Escher BI. Reducing micropollutants with source control: substance flow analysis of 212 pharmaceuticals in faeces and urine. Water Sci Technol. 2007;56(5):87–96. pmid:17881841
View Article
PubMed/NCBI
Google Scholar

[319] View Article

[320] PubMed/NCBI

[321] Google Scholar

[ref116] 116. Kaushal SS, McDowell WH, Wollheim WM. Tracking evolution of urban biogeochemical cycles: past, present, and future. Biogeochemistry. 2014;121(1):1–21. https://doi.org/10.1007/s10533-014-0014-y.
View Article
Google Scholar

[323] View Article

[324] Google Scholar

[ref117] 117. Kaushal SS, Likens GE, Pace ML, Utz RM, Haq S, Gorman J, et al. Freshwater salinization syndrome on a continental scale. Proc Natl Acad Sci. 2018:201711234. https://doi.org/10.1073/PNAS.1711234115.
View Article
Google Scholar

[326] View Article

[327] Google Scholar

[ref118] 118. Meybeck M. Global chemical weathering of surficial rocks estimated from river dissolved loads. American journal of science. 1987;287(5):401–28. https://doi.org/10.2475/ajs.287.5.401.
View Article
Google Scholar

[329] View Article

[330] Google Scholar

[ref119] 119. Corsi SR, Graczyk DJ, Geis SW, Booth NL, Richards KD. A fresh look at road salt: aquatic toxicity and water-quality impacts on local, regional, and national scales. Environ Sci Technol. 2010;44(19):7376–82. pmid:20806974
View Article
PubMed/NCBI
Google Scholar

[332] View Article

[333] PubMed/NCBI

[334] Google Scholar

[ref120] 120. Corsi SR, De Cicco LA, Lutz MA, Hirsch RM. River chloride trends in snow-affected urban watersheds: increasing concentrations outpace urban growth rate and are common among all seasons. Sci Total Environ. 2015;508:488–97. pmid:25514764
View Article
PubMed/NCBI
Google Scholar

[336] View Article

[337] PubMed/NCBI

[338] Google Scholar

[ref121] 121. Davies PJ, Wright IA, Jonasson OJ, Findlay SJ. Impact of concrete and PVC pipes on urban water chemistry. Urban Water Journal. 2010;7(4):233–41. https://doi.org/10.1080/1573062X.2010.484502.
View Article
Google Scholar

[340] View Article

[341] Google Scholar

[ref122] 122. Kaushal SS, Duan S, Doody TR, Haq S, Smith RM, Newcomer Johnson TA, et al. Human-accelerated weathering increases salinization, major ions, and alkalinization in fresh water across land use. Appl Geochem. 2017;83:121–35. pmid:30220785
View Article
PubMed/NCBI
Google Scholar

[343] View Article

[344] PubMed/NCBI

[345] Google Scholar

[ref123] 123. Connor NP, Sarraino S, Frantz DE, Bushaw-Newton K, MacAvoy SE. Geochemical characteristics of an urban river: Influences of an anthropogenic landscape. Appl Geochem. 2014;47:209–16. https://doi.org/10.1016/j.apgeochem.2014.06.012.
View Article
Google Scholar

[347] View Article

[348] Google Scholar

[ref124] 124. Jiang J-Q, Wang S, Panagoulopoulos A. The exploration of potassium ferrate(VI) as a disinfectant/coagulant in water and wastewater treatment. Chemosphere. 2006;63(2):212–9. pmid:16213564
View Article
PubMed/NCBI
Google Scholar

[350] View Article

[351] PubMed/NCBI

[352] Google Scholar

[ref125] 125. Ahn M-S, Ahmad R, Yoo J-Y, Hahn Y-B. Synthesis of manganese oxide nanorods and its application for potassium ion sensing in water. J Colloid Interface Sci. 2018;516:364–70. pmid:29408124
View Article
PubMed/NCBI
Google Scholar

[354] View Article

[355] PubMed/NCBI

[356] Google Scholar

[ref126] 126. Calvo-López A, Puyol M, Casalta JM, Alonso-Chamarro J. Multi-parametric polymer-based potentiometric analytical microsystem for future manned space missions. Anal Chim Acta. 2017;995:77–84. pmid:29126483
View Article
PubMed/NCBI
Google Scholar

[358] View Article

[359] PubMed/NCBI

[360] Google Scholar

[ref127] 127. Calvo-López A, Arasa-Puig E, Puyol M, Casalta JM, Alonso-Chamarro J. Biparametric potentiometric analytical microsystem for nitrate and potassium monitoring in water recycling processes for manned space missions. Anal Chim Acta. 2013;804:190–6. pmid:24267081
View Article
PubMed/NCBI
Google Scholar

[362] View Article

[363] PubMed/NCBI

[364] Google Scholar

[ref128] 128. Artemyeva AA. Analytical chemistry in water quality monitoring during manned space missions. Acta Astronautica. 2016;126:11–7. https://doi.org/10.1016/j.actaastro.2016.04.016.
View Article
Google Scholar

[366] View Article

[367] Google Scholar

[ref129] 129. Pol R, Céspedes F, Gabriel D, Baeza M. Microfluidic lab-on-a-chip platforms for environmental monitoring. TrAC—Trends in Analytical Chemistry. 2017;95:62–8. https://doi.org/10.1016/j.trac.2017.08.001.
View Article
Google Scholar

[369] View Article

[370] Google Scholar

[ref130] 130. Jaywant SA, Mahmood Arif K. A comprehensive review of micro fluidic water quality monitoring sensors. Sensors (Switzerland). 2019;19(21). https://doi.org/10.3390/s19214781.
View Article
Google Scholar

[372] View Article

[373] Google Scholar

[ref131] 131. U.S. Food and Drug Administration. Approved Drug Products with Therapeutic Equivalence Evaluations (Orange Book) 2018 [June 29, 2018]. Available from: https://www.fda.gov/drugs/drug-approvals-and-databases/approved-drug-products-therapeutic-equivalence-evaluations-orange-book.

[ref132] 132. Dobson CM. Chemical space and biology. Nature. 2004;432(7019):824–8. pmid:15602547
View Article
PubMed/NCBI
Google Scholar

[376] View Article

[377] PubMed/NCBI

[378] Google Scholar

[ref133] 133. McKinney ML. Urbanization as a major cause of biotic homogenization. Biological Conservation. 2006;127(3):247–60. https://doi.org/10.1016/j.biocon.2005.09.005.
View Article
Google Scholar

[380] View Article

[381] Google Scholar

[ref134] 134. Lydeard C, Mayden RL. A diverse and endangered aquatic ecosystem of the southeast United States. Conservation Biology. 1995;9(4):800–5. https://doi.org/10.1046/j.1523-1739.1995.09040800.x.
View Article
Google Scholar

[383] View Article

[384] Google Scholar

[ref135] 135. Warren ML Jr, Burr BM, Walsh SJ, Bart HL Jr, Cashner RC, Etnier DA, et al. Diversity, distribution, and conservation status of the native freshwater fishes of the southern United States. Fisheries. 2000;25(10):7–31. https://doi.org/10.1577/1548-8446(2000)025<0007:DDACSO>2.0.CO;2.
View Article
Google Scholar

[386] View Article

[387] Google Scholar

[ref136] 136. Scott MC. Winners and losers among stream fishes in relation to land use legacies and urban development in the southeastern US. Biological Conservation. 2006;127(3):301–9. https://doi.org/10.1016/j.biocon.2005.07.020.
View Article
Google Scholar

[389] View Article

[390] Google Scholar

[ref137] 137. Painter M, Buerkley M, Julius M, Vajda A, Norris D, Barber L, et al. Antidepressants at environmentally relevant concentrations affect predator avoidance behavior of larval fathead minnows (Pimephales promelas). Environ Toxicol Chem. 2009;28(12):2677–84. pmid:19405782
View Article
PubMed/NCBI
Google Scholar

[392] View Article

[393] PubMed/NCBI

[394] Google Scholar

[ref138] 138. Russom CL, Bradbury SP, Broderius SJ, Hammermeister DE, Drummond RA. Predicting modes of toxic action from chemical structure: acute toxicity in the fathead minnow (Pimephales promelas). Environ Toxicol Chem. 1997;16(5):948–67. https://doi.org/10.1002/etc.5620160514.
View Article
Google Scholar

[396] View Article

[397] Google Scholar

[ref139] 139. Veith GD, Call DJ, Brooke L. Structure-toxicity relationships for the fathead minnow, Pimephales promelas: Narcotic industrial chemicals. Can J Fish Aquat Sci. 1983;40(6):743–8. https://doi.org/10.1139/f83-096.
View Article
Google Scholar

[399] View Article

[400] Google Scholar

[ref140] 140. Bradbury SP. Predicting modes of toxic action from chemical structure: an overview. SAR QSAR Environ Res. 1994;2(1–2):89–104. pmid:8790641
View Article
PubMed/NCBI
Google Scholar

[402] View Article

[403] PubMed/NCBI

[404] Google Scholar

[ref141] 141. Könemann H. Quantitative structure-activity relationships in fish toxicity studies Part 1: Relationship for 50 industrial pollutants. Toxicology. 1981;19(3):209–21. pmid:7233445
View Article
PubMed/NCBI
Google Scholar

[406] View Article

[407] PubMed/NCBI

[408] Google Scholar

[ref142] 142. Kavlock R, Chandler K, Houck K, Hunter S, Judson R, Kleinstreuer N, et al. Update on EPA’s ToxCast program: providing high throughput decision support tools for chemical risk management. Chem Res Toxicol. 2012;25(7):1287–302. pmid:22519603
View Article
PubMed/NCBI
Google Scholar

[410] View Article

[411] PubMed/NCBI

[412] Google Scholar

[ref143] 143. Richard AM, Judson RS, Houck KA, Grulke CM, Volarath P, Thillainadarajah I, et al. ToxCast chemical landscape: paving the road to 21st century toxicology. Chem Res Toxicol. 2016;29(8):1225–51. pmid:27367298
View Article
PubMed/NCBI
Google Scholar

[414] View Article

[415] PubMed/NCBI

[416] Google Scholar

[ref144] 144. Padilla S, Corum D, Padnos B, Hunter DL, Beam A, Houck KA, et al. Zebrafish developmental screening of the ToxCast^™ Phase I chemical library. Reproductive Toxicology. 2012;33(2):174–87. pmid:22182468
View Article
PubMed/NCBI
Google Scholar

[418] View Article

[419] PubMed/NCBI

[420] Google Scholar

[ref145] 145. Truong L, Reif DM, St Mary L, Geier MC, Truong HD, Tanguay RL. Multidimensional in vivo hazard assessment using zebrafish. Toxicol Sci. 2013;137(1):212–33. pmid:24136191
View Article
PubMed/NCBI
Google Scholar

[422] View Article

[423] PubMed/NCBI

[424] Google Scholar

[ref146] 146. Rosi-Marshall EJ, Kincaid DW, Bechtold HA, Royer TV, Rojas M, Kelly JJ. Pharmaceuticals suppress algal growth and microbial respiration and alter bacterial communities in stream biofilms. Ecol Appl. 2013;23(3):583–93. pmid:23734487
View Article
PubMed/NCBI
Google Scholar

[426] View Article

[427] PubMed/NCBI

[428] Google Scholar

[ref147] 147. Quinlan EL, Nietch CT, Blocksom K, Lazorchak JM, Batt AL, Griffiths R, et al. Temporal dynamics of periphyton exposed to tetracycline in stream mesocosms. Environ Sci Technol. 2011;45(24):10684–90. pmid:22050031
View Article
PubMed/NCBI
Google Scholar

[430] View Article

[431] PubMed/NCBI

[432] Google Scholar

[ref148] 148. Gallagher HC, Gallagher RM, Butler M, Buggy DJ, Henman MC. Venlafaxine for neuropathic pain in adults. Cochrane Database of Systematic Reviews. 2015;(8). https://doi.org/10.1002/14651858.CD011091.pub2.
View Article
Google Scholar

[434] View Article

[435] Google Scholar

[ref149] 149. Hossain SM, Hussain SM, Ekram ARMS. Duloxetine in Painful Diabetic Neuropathy. The Clinical Journal of Pain. 2016;32(11):1005–10. pmid:26710221
View Article
PubMed/NCBI
Google Scholar

[437] View Article

[438] PubMed/NCBI

[439] Google Scholar

[ref150] 150. Moore RA, Derry S, Aldington D, Cole P, Wiffen PJ. Amitriptyline for neuropathic pain in adults. Cochrane Database of Systematic Reviews. 2015;(7). https://doi.org/10.1002/14651858.CD008242.pub3.
View Article
Google Scholar

[441] View Article

[442] Google Scholar

[ref151] 151. Pop-Busui R, Boulton AJM, Feldman EL, Bril V, Freeman R, Malik RA, et al. Diabetic Neuropathy: A Position Statement by the American Diabetes Association. Diabetes Care. 2017;40(1):136–54. pmid:27999003
View Article
PubMed/NCBI
Google Scholar

[444] View Article

[445] PubMed/NCBI

[446] Google Scholar

[ref152] 152. Wiffen PJ, Derry S, Bell RF, Rice ASC, Tölle TR, Phillips T, et al. Gabapentin for chronic neuropathic pain in adults. Cochrane Database of Systematic Reviews. 2017;(6). https://doi.org/10.1002/14651858.CD007938.pub4.
View Article
Google Scholar

[448] View Article

[449] Google Scholar

[ref153] 153. Kane SP. ClinCalc DrugStats Database, Version 19.1: ClinCalc.com; 2019 [updated November 24, 2018; cited 2019 October 16]. Available from: https://clincalc.com/DrugStats.

[ref154] 154. Fuentes AV, Pineda MD, Venkata KCN. Comprehension of Top 200 Prescribed Drugs in the US as a Resource for Pharmacy Teaching, Training and Practice. Pharmacy (Basel). 2018;6(2):43. https://doi.org/10.3390/pharmacy6020043.
View Article
Google Scholar

[452] View Article

[453] Google Scholar

[ref155] 155. Oosterhuis M, Sacher F, ter Laak TL. Prediction of concentration levels of metformin and other high consumption pharmaceuticals in wastewater and regional surface water based on sales data. Sci Total Environ. 2013;442:380–8. pmid:23183121
View Article
PubMed/NCBI
Google Scholar

[455] View Article

[456] PubMed/NCBI

[457] Google Scholar

[ref156] 156. NCD Risk Factor Collaboration. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. The Lancet. 2016:1–18. https://doi.org/10.1016/S0140-6736(16)00618-8.
View Article
Google Scholar

[459] View Article

[460] Google Scholar

[ref157] 157. World Health Organization. Global report on diabetes. France2016. 88 p.

[ref158] 158. Tang T, Lord JM, Norman RJ, Yasmin E, Balen AH. Insulin-sensitising drugs (metformin, rosiglitazone, pioglitazone, D-chiro-inositol) for women with polycystic ovary syndrome, oligo amenorrhoea and subfertility. Cochrane Database Syst Rev. 2012;5. https://doi.org/10.1002/14651858.CD003053.pub5.
View Article
Google Scholar

[463] View Article

[464] Google Scholar

[ref159] 159. Sahra IB, Le Marchand-Brustel Y, Tanti J-F, Bost F. Metformin in cancer therapy: a new perspective for an old antidiabetic drug? Molecular cancer therapeutics. 2010;9(5):1092–9. pmid:20442309
View Article
PubMed/NCBI
Google Scholar

[466] View Article

[467] PubMed/NCBI

[468] Google Scholar

[ref160] 160. Gong L, Goswami S, Giacomini KM, Altman RB, Klein TE. Metformin pathways: pharmacokinetics and pharmacodynamics. Pharmacogenet Genomics. 2012;22(11):820–7. pmid:22722338
View Article
PubMed/NCBI
Google Scholar

[470] View Article

[471] PubMed/NCBI

[472] Google Scholar

[ref161] 161. Trautwein C, Berset J-D, Wolschke H, Kümmerer K. Occurrence of the antidiabetic drug Metformin and its ultimate transformation product Guanylurea in several compartments of the aquatic cycle. Environ Int. 2014;70:203–12. pmid:24954924
View Article
PubMed/NCBI
Google Scholar

[474] View Article

[475] PubMed/NCBI

[476] Google Scholar

[ref162] 162. Trautwein C, Kümmerer K. Incomplete aerobic degradation of the antidiabetic drug Metformin and identification of the bacterial dead-end transformation product Guanylurea. Chemosphere. 2011;85(5):765–73. pmid:21752423
View Article
PubMed/NCBI
Google Scholar

[478] View Article

[479] PubMed/NCBI

[480] Google Scholar

[ref163] 163. Blair BD, Crago JP, Hedman CJ, Klaper RD. Pharmaceuticals and personal care products found in the Great Lakes above concentrations of environmental concern. Chemosphere. 2013;93(9):2116–23. pmid:23973285
View Article
PubMed/NCBI
Google Scholar

[482] View Article

[483] PubMed/NCBI

[484] Google Scholar

[ref164] 164. Scheurer M, Michel A, Brauch H-J, Ruck W, Sacher F. Occurrence and fate of the antidiabetic drug metformin and its metabolite guanylurea in the environment and during drinking water treatment. Water Res. 2012;46(15):4790–802. pmid:22770965
View Article
PubMed/NCBI
Google Scholar

[486] View Article

[487] PubMed/NCBI

[488] Google Scholar

[ref165] 165. Scheurer M, Sacher F, Brauch H-J. Occurrence of the antidiabetic drug metformin in sewage and surface waters in Germany. J Environ Monit. 2009;11(9):1608–13. pmid:19724829
View Article
PubMed/NCBI
Google Scholar

[490] View Article

[491] PubMed/NCBI

[492] Google Scholar

[ref166] 166. Ussery E, Bridges KN, Pandelides Z, Kirkwood AE, Guchardi J, Holdway D. Developmental and Full-Life Cycle Exposures to Guanylurea and Guanylurea–Metformin Mixtures Results in Adverse Effects on Japanese Medaka (Oryzias latipes). Environ Toxicol Chem. 2019;38(5):1023–8. pmid:30835871
View Article
PubMed/NCBI
Google Scholar

[494] View Article

[495] PubMed/NCBI

[496] Google Scholar

[ref167] 167. MacLaren RD, Wisniewski K, MacLaren C. Environmental concentrations of metformin exposure affect aggressive behavior in the Siamese fighting fish, Betta splendens. PLOS ONE. 2018;13(5):e0197259. pmid:29763426
View Article
PubMed/NCBI
Google Scholar

[498] View Article

[499] PubMed/NCBI

[500] Google Scholar

[ref168] 168. Ussery E, Bridges KN, Pandelides Z, Kirkwood AE, Bonetta D, Venables BJ, et al. Effects of environmentally relevant metformin exposure on Japanese medaka (Oryzias latipes). Aquat Toxicol. 2018;205:58–65. pmid:30336378
View Article
PubMed/NCBI
Google Scholar

Figures

Abstract

Introduction

Material and methods

Site description and analytical method

Quality assurance quality control (QAQC)

Data handling, statistics, and ∑EAR analysis

Results and discussion

Pharmaceutical mixtures were common in all regions

Land-use/land-cover and major ion predictors of pharmaceuticals

Potential for mixed-pharmaceutical biological effects

Implications for stream ecosystem health and remediation

Supporting information

S3 Table.

S10 Table. Compound:Endpoint combinations excluded from ToxCast evaluation due to unreliable concentration-response relationship and resulting lack of confidence in activity concentration at cutoff (ACC).

S11 Table. Site-specific Exposure Activity Ratios (EAR) under Maximum exposure conditions for those compounds with exact Chemical Abstract Service (CAS) number matches and with reliable concentration-response relationship and ACC data in ToxCast.

S12 Table. Site-specific Exposure Activity Ratios (EAR) under Maximum exposure conditions for all bioassay endpoints within each class shown.

S13 Table. Site-specific Exposure Activity Ratios (EAR) under Median exposure conditions for those compounds with exact Chemical Abstract Service (CAS) number matches and with reliable concentration-response relationship and ACC data in ToxCast.

S14 Table. Site-specific Exposure Activity Ratios (EAR) under Median exposure conditions for all bioassay endpoints within each class shown.

S15 Table. Data dictionary describing metrics in S9 Table.

S16 Table. Additional citation information for metrics in S9 and S15 Tables.

Acknowledgments

References

Data handling, statistics, and ∑_EAR analysis