Pharmaceutical bioaccumulation by periphyton and snails in an effluent-dependent stream during an extreme drought

Chemosphere 119 (2015) 927–934

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Pharmaceutical bioaccumulation by periphyton and snails in an effluent-dependent stream during an extreme drought Bowen Du a,b,⇑, Samuel P. Haddad a,b, W. Casan Scott a,b, C. Kevin Chambliss a,b,c, Bryan W. Brooks a,b a

Department of Environmental Science, Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA The Institute of Ecological, Earth and Environmental Sciences, Baylor University, Waco, TX 76798, USA c Department of Chemistry and Biochemistry, Baylor University, Waco, TX 76798, USA b

h i g h l i g h t s  Effluent-dependent streams result from population growth and climate change.  Such systems may represent worst case scenarios for drug and other chemical exposure.  An effluent-dependent stream was selected for study during an extreme drought.  Accumulation of contaminants of emerging concern occurred in snails and periphyton.  Pharmaceutical exposure in water was more important than dietary exposure for snails.

a r t i c l e

i n f o

Article history: Received 3 May 2014 Received in revised form 10 August 2014 Accepted 13 August 2014 Available online 28 September 2014 Handling Editor: Klaus Kümmerer Keywords: Pharmaceuticals Invertebrates Periphyton Bioaccumulation Wadeable stream Ionization Instream flow

a b s t r a c t Increasing evidence indicates that pharmaceutical bioaccumulate in fish collected downstream from municipal wastewater effluent discharges. However, studies of pharmaceutical bioaccumulation by other aquatic organisms, including primary producers (e.g., periphyton) and grazers (e.g., snails), are lacking in wadeable streams. Here, we examined environmental occurrence and bioaccumulation of a range of pharmaceuticals and other contaminants of emerging concern in surface water, a common snail (Planorbid sp.) and periphyton from an effluent-dependent stream in central Texas, USA, during a historic drought, because such limited dilution and instream flows may represent worst-case exposure scenarios for aquatic life to pharmaceuticals. Water and tissue samples were liquid–liquid extracted and analyzed by liquid chromatography–tandem mass spectrometry (LC-MS/MS) with electrospray ionization. Target analytes included 21 pharmaceuticals across multiple drug classes and 2 pharmacologically active metabolites. Several pharmaceuticals were detected at up to 4.7 lg kg1 in periphyton and up to 42 lg kg1 in Planorbid sp. We then identified limitations of several bioconcentration factor and bioaccumulation factor models, developed for other invertebrates, to assist interpretation of such field results. Observations from the present study suggest that waterborne exposure to pharmaceuticals may be more important than dietary exposure for snails. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Effluent-dominated or -dependent streams potentially represent worst-case scenarios for studying accumulation and associated effects of pharmaceuticals in aquatic ecosystems of developed countries (Brooks et al., 2006). In these waterbodies, pharmaceuticals can be considered to be ‘‘pseudo-persistent’’ ⇑ Corresponding author at: The Institute of Ecological, Earth and Environmental Sciences, Baylor University, Waco, TX 76798, USA. Tel.: +1 2547104478; fax: +1 2547103409. E-mail address: [email protected] (B. Du). http://dx.doi.org/10.1016/j.chemosphere.2014.08.044 0045-6535/Ó 2014 Elsevier Ltd. All rights reserved.

because their half-lives are longer relative to loading rates from effluent discharges (Daughton, 2002). Higher exposure concentrations of pharmaceuticals and other consumer chemicals in streams and longer effective exposure durations are expected under low flow conditions in regions experiencing frequent drought (Ankley et al., 2007). Pharmaceuticals are designed to produce specific therapeutic effects in humans or animals at particular administered doses. However, exposure to pharmaceuticals in the environment may also result in bioaccumulation and subsequent toxic responses to non-target organisms. Pharmaceuticals have been shown to accumulate in fish tissues (Brooks et al., 2005; Ramirez et al., 2007,

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2009; Fick et al., 2010; Metcalfe et al., 2010; Schultz et al., 2010; Du et al., 2012) and other aquatic organisms, such as algae (Vannini et al., 2011), mussels (Bringolf et al., 2010; Contardo-Jara et al., 2011; Maruya et al., 2014) and sharks (Gelsleichter and Szabo, 2013) when these organisms are exposed to pharmaceuticals in laboratory settings or to wastewater effluent and surface waters receiving effluent discharges. Studies regarding the mechanisms of uptake and elimination of pharmaceuticals by fish and invertebrates are limited (Nakamura et al., 2008; Paterson and Metcalfe, 2008; Rendal et al., 2011; Meredith-Williams et al., 2012). Unlike other traditional pollutants, more than 70% of pharmaceuticals are ionizable across environmentally relevant pH gradients (Manallack, 2007). The neutral fraction, which is more lipophilic, is more bioavailable for uptake by aquatic life than its ionized counterpart (Valenti et al., 2012). Thus, uptake of ionizable pharmaceuticals can be pH dependent (Nakamura et al., 2008; Meredith-Williams et al., 2012; Valenti et al., 2012). Subsequent adverse outcomes also depend on the ionization state of a pharmaceutical and site-specific conditions (e.g., surface water pH), which can be influenced by climatological changes (Brooks et al., 2011; Valenti et al., 2011). To date, understanding the factors influencing partitioning of pharmaceuticals to various aquatic compartments, such as water, sediment, biosolids and organisms is still less developed relative to nonionizable organic chemicals (Brooks et al., 2009). Further, data regarding bioaccumulation in invertebrates and dietary exposure of aquatic organisms to pharmaceuticals from food sources is largely lacking and represents a major research need (Boxall et al., 2012). A better understanding of whether various pharmaceuticals accumulate in aquatic invertebrates and the mechanisms of uptake and biotransformation in invertebrates will support future bioaccumulation studies and risk assessments of pharmaceuticals at lower trophic positions. Thus, in the present study, the invertebrate Planorbid sp. (primary consumer) and periphyton (primary producer) were sampled from the North Bosque River, an effluent-dependent wadeable stream in central Texas, USA, during an extreme drought in 2011. The primary objective of the present study was to identify whether select pharmaceuticals, active pharmaceutical metabolites and other emerging contaminants accumulated in Planorbid sp. and periphyton communities. We further compared the utility of employing previously developed bioconcentration and bioaccumulation models (Arnot and Gobas, 2006; Meredith-Williams et al., 2012) to interpret observed tissue bioaccumulation factors (BAFs) for Planorbid sp. in the field. Finally, based on field observations, we estimated dietary exposure of fish consuming snails and compare this dosage to human therapeutic usage of the antidepressant fluoxetine.

2. Materials and methods 2.1. Study site and sample collection The North Bosque River is located in central Texas, USA. Perennial flow of the river begins downstream from a major effluent discharge from Stephenville, TX; instream flows downstream are comprised of effluent discharge from 6 wastewater treatment plants along the river, and it can be effluent-dependent during non-storm conditions, particularly during summer months. Flows upstream are ephemeral with some perennial isolated pools. A sampling site downstream from the Stephenville, TX, Wastewater Treatment Plant (WWTP) was selected because this is the primary wastewater effluent discharge to the North Bosque River and the only major discharge permitted to discharge approximately 1 million gallons of reclaimed water per day.

Field sampling occurred in August 2011 when Texas experienced a historical drought. No stream flow was observed in the river upstream of the Stephenville WWTP, and regional streams without effluent discharges were ephemeral (e.g., Neils Creek). Thus, the sampling site downstream of the Stephenville WWTP was effluent dependent during the present study. Invertebrate samples were collected, stored on ice and transported back to the laboratory. Periphyton samples were collected from quantified areas on flat rocks using a razor blade, stored in whirlpack bags and kept on ice during transport to the laboratory. All invertebrate and periphyton samples were stored at 80 °C until further analyses. Grab water samples were collected during snails and periphyton collection in 4-L amber glass bottles that were pre-rinsed with acetone and nanopure water. Water samples were kept on ice, transported to the laboratory, and stored at 4 °C. Routine water quality parameters (e.g., DO, pH, temperature) were collected from these field samples, which were then prefiltered and extracted within 48 h of collection (Du et al., 2014a). 2.2. Chemicals and regents A Thermo Barnstead Nanopure (Dubuque, IA, USA) Diamond UV water purification system was used to provide 18 MX water to prepare all aqueous solutions throughout the study. Based on previously reported occurrences in environmental and biological compartments receiving effluent discharges (Kolpin et al., 2002; Ramirez et al., 2007, 2009; Gheorghe et al., 2008; Loos et al., 2009; Radjenovic´ et al., 2009; Letzel et al., 2010; MacLeod and Wong, 2010; Du et al., 2012), 24 target analytes were selected for the present study. All chemical standards and labeled analogs were purchased with the purity P97% and used as received (Supplementary Table 1). HPLC grade methanol, formic acid, and acetic acid were obtained from various vendors, which are provided elsewhere (Du et al., 2012). Isotopically-labeled analogs were used as the internal standard for each corresponding compound with the exception of ivermectin, for which abamectin was used as an internal standard due to the lack of commercially available isotopicallylabeled ivermectin. Ivermectin was used because the North Bosque River watershed has historically been severely impacted by dairy concentrated animal feeding operations and thus may represent an ideal study site to assess veterinary medicines in aquatic systems (Brooks et al., 2008). 2.3. Sample preparation and analysis Snail samples were grouped in composites (n = 3) to reach a suitable wet weight for analysis. Soft tissues of snails and periphyton was rinsed with DI water and gently towel dried before weighing in an effort to remove contaminants not incorporated with tissues (Brooks et al., 2004). All samples were homogenized using a Tissuemiser (Fisher Scientific, Fair Lawn, NJ) at 30 000 rpm. Tissues were extracted according to previously reported methodologies (Ramirez et al., 2007, 2009; Du et al., 2012). For each sample, 1.0 g of periphyton or snail composite wet weights were extracted with 8 mL of a 1:1 mixture of 0.1 M aqueous acetic acid and methanol in a 20-mL borosilicate glass vial. A mixture of 24 internal standards was added to each sample prior to extraction. The remaining extraction protocol is identical to the previously reported method along with instrumentation parameters, separation strategy, and detection of target analytes (Du et al., 2012, 2014a). Water samples were prefiltered according to previously published methods (Du et al., 2014a). Extraction of selective serotonin reuptake inhibitor (SSRI) antidepressants and metabolites, including

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fluoxetine, norfluoxetine, sertraline, desmethylsertraline, and paroxetine, followed another procedure (Lajeunesse et al., 2008). 500 mL samples were sequentially spiked with internal standards, 5.0 mL of methanol, and 100 lL of phosphoric acid (85%) prior to extraction using strong cation-exchange cartridges (Strata-SCX, Phenomenex, Torrance, CA, USA). For the remaining analytes, extraction generally followed a previously reported protocol (Vanderford and Snyder, 2006). Briefly, 500 mL of each sample was spiked with an internal standards mixture without adjusting pH prior to loading on a preconditioned HLB cartridge (Waters Corp., Milford, MA, USA). The eluate from two separate extractions was evaporated to dryness under a stream of nitrogen and reconstituted in 1 mL of chromatographic mobile phase before instrumental analysis. Samples were analyzed using isotope dilution liquid chromatography–tandem mass spectrometry (LC-MS/MS). Details pertaining to the ionization mode (ESI+ or ESI) and precursor-to-product ion transitions were previously described (Du et al., 2012, 2014a). Chromatographic separation was achieved by binary gradient. A Varian model 1200 L triple-quadrupole mass analyzer equipped with electrospray ionization (ESI) was used to monitor eluted analytes. The detection was operated in multiple reaction monitoring (MRM) mode throughout the analysis. Quantitation was performed using an isotope dilution calibration method with the minimum of a six-point calibration curve ranging in concentration from below each analyte’s method detection limit (MDL) to 500 ng mL1 with a weighted (1/x2) linear regression, resulting in r2 P 0.99. Calibration standards, containing mixture of 24 internal standards and variable concentrations of target compounds, were prepared in 95:5 0.1% (v v1) aqueous formic acid–methanol. Limit of detection (LOD) and limit of quantitation (LOQ) were determined by extracting and analyzing reference samples fortified with internal standards only (Table 1). LOD was defined as the concentrations that had the response equal to a signal-to-noise ratio of 3. LOQ was calculated as the concentration that yielded 10 times signal-to-noise ratio. The determination

protocol of MDLs (Table 1) was reported in a previous study (Du et al., 2012). A MDL represented the lowest concentration of analyte that was reported in the present study. Half the MDL values were utilized for calculation of observed BAF if select target analytes were detected, but below corresponding MDL value. One blank (i.e., reference tissue spiked with internal standards only) and duplicate matrix spikes were included in each analytical sample batch (Du et al., 2012).

2.4. Calculation of observed and predicted BAFs and BCFs As noted above, observations of pharmaceutical bioaccumulation or bioconcentration by invertebrates are limited in laboratory or field studies. In the present study, an observed BAF was defined as the ratio of a pharmaceutical identified in periphyton or Planorbid sp. and associated surface water concentration. Predicted BAFs were generated by using five previously developed linear regression models (Arnot and Gobas, 2006; Meredith-Williams et al., 2012). If pharmaceuticals were detected below their corresponding MDLs or not detected in Planorbid sp. tissue or water samples, half MDLs were used to calculate observed log BAFs. Arnot and Gobas (2006) provided an empirical relationship (Eq. (1); R2 = 0.55) for estimating bioaccumulation of organic chemicals in invertebrates; however, this linear regression model did not account for influences of pH on bioaccumulation of ionizable compounds. Thus, in the present study, we modified Eqs. (1)–(3) by substituting octanol–water distribution coefficient (Dow) and liposome-water distribution coefficient (Dlipw) at measured pH (8.0) from the field for octanol–water partition coefficient (Kow), respectively.

log BAF ¼ 0:82 log K ow þ 0:09

ð1Þ

log BAF ¼ 0:82 log Dow ðpH ¼ 8:0Þ þ 0:09

ð2Þ

log BAF ¼ 0:82 log Dlipw ðpH ¼ 8:0Þ þ 0:09

ð3Þ

Table 1 Method detection limit (MDL), limit of detection (LOD), limit of quantitation (LOQ) of all analytes, and occurrence of analytes in Planorbid sp. and periphyton, along with field bioaccumulation factors (BAF). In Planorbid sp. and the occurrence of analytes in water from the North Bosque River, Texas, USA. Planorbid sp. (lg kg1)

Analytes

Analgesic Antihypertensive

Stimulant Anti-seizure Benzodiazepine Antihistamine Antidepressant

Antidepressant metabolite Antilipemic Antibiotic

Anti-inflammatory Anticoagulant Artificial sweetener Antiparasitic a b

ND – not detected. NA – not applicable.

Acetaminophen Codeine Atenolol Propranolol Diltiazem Caffeine Methylphenidate Carbamazepine Diazepam Diphenhydramine Fluoxetine Paroxetine Sertraline Norfluoxetine Desmethylsertraline Gemfibrozil Sulfamethoxazole Trimethoprim Erythromycin Diclofenac Celecoxib Warfarin Sucralose Ivermectin

Periphyton (lg kg1)

Water (ng L1)

MDL

LOD

LOQ

Detected (±SD, n = 3)

BAF (L kg1)

MDL

LOD

LOQ

Detected (±SD, n = 5)

MDL

Detected

4.7 4.7 1.5 1.9 0.21 5.1 0.42 0.61 4.2 0.18 4.8 2.8 3.3 4.3 5.3 3.1 3.5 1.9 2.5 2.6 8.5 0.68 50 51

0.34 0.54 0.21 0.54 0.06 0.34 0.03 0.15 0.51 0.06 0.78 0.97 1.0 0.35 1.7 0.40 0.33 0.43 0.96 0.45 2.0 0.12 11 6.8

1.0 1.6 0.63 1.6 0.19 1.0 0.10 0.45 1.6 0.17 2.4 2.9 3.1 1.1 5.2 1.2 1.0 1.3 2.9 1.4 6.2 0.38 33 21

NDa ND ND 1.0 ± 0.28
NAb NA NA 1000 48 200 NA 3.6 NA 67 3000 NA 990 510 16 000 NA NA NA 290 NA 170 NA NA NA

6.6 5.7 2.1 1.9 0.58 6.7 0.88 0.62 4.7 0.16 4.8 2.6 3.9 4.1 5.8 3.8 4.7 2.9 2.1 3.1 7.8 0.73 52 48

0.45 0.45 0.57 0.22 0.20 0.82 0.11 0.06 1.5 0.05 1.1 0.87 1.2 1.1 2.0 0.53 0.34 0.72 0.78 0.41 1.4 0.19 11 17

1.4 1.4 1.7 0.65 0.61 2.5 0.34 0.19 4.6 0.16 3.3 2.6 3.5 3.3 6.0 1.6 1.0 2.2 2.4 1.3 4.2 0.57 32 53

ND ND ND ND ND ND ND 0.90 ± 0.07 ND 4.7 ± 0.39 ND ND ND ND ND ND ND ND
2.9 8.3 4.3 1.8 0.24 4.5 0.30 0.53 4.6 0.22 8.4 11 6.1 8.5 5.4 2.1 1.3 1.3 8.6 2.8 11 0.78 36 93

7.6 ND 26 ND 2.2 37 0.68 340 ND 15 ND ND ND ND ND 5.6 270
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Meredith-Williams et al. recently developed four empirical relationships (Eqs. (4)–(7)). The relationships between log bioconcentration factor (BCF) and log Dlipw were developed for estimating BCF of pharmaceuticals in the aquatic invertebrates Gammarus pulex (Eq. (4); R2 = 0.89) and Notonecta glauca (Eq. (5); R2 = 0.83). BCF values in Gammarus pulex and Notonecta glauca were also estimated by linear models (Eq. (6) (R2 = 0.5) and 7 (R2 = 0.7), respectively) between log BCF and log volume of distribution (log VD). It is important to recognize that these linear regression models may represent the first empirical relationships that were exclusively developed for ionizable pharmaceuticals in aquatic invertebrates. Further, these models account for pH influences on speciation of ionizable pharmaceuticals and thus use Dlipw as the prediction descriptor.

log BCF ¼ 0:71 log Dlipw  0:23

ð4Þ

log BCF ¼ 0:27 log Dlipw  0:93

ð5Þ

log BCF ¼ 1:3 log V D þ 1:2

ð6Þ

log BCF ¼ 0:5 log V D þ 0:4

ð7Þ

2.5. Statistical analysis Regression analyses of observed log BAF in the field and predicted log BAF or predicted log BCF from previous studies were conducted using SigmaPlot (Systat Software, San Jose, CA, USA) using a = 0.05. 3. Results and discussion 3.1. Occurrence of pharmaceuticals in Planorbid sp. and periphyton

3.2. Comparison of observed BAFs and predicted BAFs and BCFs in invertebrates

A

5

B

5

4

Observed log BAF

As introduced above, many ionizable organic compounds (IOCs) may exhibit various ionization states at environmentally relevant pHs; thus, ionization may play an important role in governing transport of IOCs across biological membranes and subsequent toxicity to aquatic organisms (Erickson et al., 2006; Valenti et al., 2009, 2010, 2012; Berninger et al., 2011). Robust, empiricallybased models for predicting bioconcentration or bioaccumulation of pharmaceuticals and other IOCs in aquatic invertebrates are largely lacking (Meredith-Williams et al., 2012), which represents a major research need (Boxall et al., 2012). Recent empirical and modeling studies (Valenti et al., 2009, 2011, 2012; Rendal et al., 2011; Armitage et al., 2013) have suggested that aquatic assessment of ionizable industrial compounds, particularly pharmaceuticals, should account pH influences on bioaccumulation. Significant (p < 0.05) relationships were observed between field observed BAFs for pharmaceuticals in Planorbid sp. and log Kow, log Dow or log Dlipw (Fig. 1A–C), based on pH of water samples from the field, though R2 values for each of these observations were fairly low (ranging from 0.187 to 0.212). We further examined the relationship between field observed BAF values and VD, because VD represents a human pharmacology parameter for therapeutics that may be useful for predicting environmental partitioning, including bioaccumulation potential (Brooks et al., 2012). For example Williams et al. (2009), previously observed significant

Observed log BAF

No target analytes were detected in method blank samples; however, various pharmaceuticals were detected in surface waters, and Planorbid sp. and periphyton (Table 1). Eleven target analytes were detected in Planorbid sp., though 4 of these 11 were detected below corresponding MDLs. Detected analytes included 7 pharma-

ceutical categories, including antihypertensive, antibiotic, antiinflammatory, stimulant, antihistamine, anti-seizure, and SSRI (and metabolites) classes. Typically, the SSRIs were detected at highest levels (up to 42 lg kg1 wet weight) in Planorbid sp., which compares to previous observations (Brooks et al., unpublished data) by our group from Pecan Creek in north Texas (Brooks et al., 2005). Diphenhydramine, carbamazepine, and erythromycin were the only common analytes detected in both Planorbid sp. and periphyton from the North Bosque River. For example, diphenhydramine and carbamazepine were observed in periphyton above MDLs, but erythromycin was detected below the MDL.

4

3 2 1

R2=0.212 p<0.001

0

3 2 1

R2=0.187 p<0.001

0 -2

0

2

4

6

-2

0

5 4

2

4

6

log Dow

3 2 1

R2=0.187 p<0.001

0

D

5

Observed log BAF

C Observed log BAF

log Kow

4 3 2 1

R2=0.164 p=0.003

0 -2

0

2

log Dlipw

4

6

-2

0

2

4

6

log VD (L)

Fig. 1. Relationships between observed log bioaccumulation factors (log BAFs) and log Kow (A), log Dow (B), log Dlipw (C), and log VD (D) for pharmaceuticals detected in either water or Planorbid sp. from the North Bosque River, an effluent-dependent stream in central Texas, USA.

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relationships between VD and Kd values for a range of pharmaceuticals partitioning to soils and sediments. Similar to observations for partition coefficients, field BAF values in Planorbid sp. were significantly related to VD (Fig. 1D), but here again the relationship was low (R2 = 0.164). A similar significant (p < 0.05), but stronger (R2 = 0.7), relationship was observed between BCF and VD in laboratory studies with several pharmaceuticals, Gammarus pulex and Notonecta glauca (Meredith-Williams et al., 2012). We also examined the utility of previous models developed for invertebrate BAF or BCF values (Arnot and Gobas, 2006; MeredithWilliams et al., 2012) to predict field BAFs of pharmaceuticals in Planorbid sp. in the North Bosque River. These field observed log BAFs were then compared with the log BAFs predicted using Eq. (1) (Fig. 2A), Eq. (2) (Fig. 2B), and Eq. (3) (Fig. 2C). Here again, similar to observations in Fig. 1, significant (p < 0.05) relationships, though with relatively low R2 values, were observed between predicted and observed BAF values (Fig. 2) for these three models. Relatively poor predictions of field BAFs may have resulted from different chemical properties (e.g., log Kow) between chemicals (majority of log Kow 4–8) examined by Arnot and Gobas (2006) and chemicals (majority with log Kow ranging from 0 to 4) detected in the present study. We further compared BAFs for Planorbid sp. from the North Bosque River with predicted log BCFs using Eq. (4). (Fig. 3A) and Eq. (5) (Fig. 3B) developed from empirical studies of uptake and depuration of select pharmaceuticals in G. pulex and N. glauca (MeredithWilliams et al., 2012), which employed log Dlipw to account for speciation of the parent compounds at study pH. Similar to

5

B

C

4

Predicted log BAF

Predicted log BAF

4

5

3 2 1 0

R2=0.212 p<0.001

-1

3 2 1 0

R2=0.187 p<0.001

-1

-2 -1

0

1

2

3

4

5

3 2 1 0

R2=0.187 p<0.001

-1 -2

-2 -2

5 4

Predicted log BAF

A

evaluations of Eqs. (1)–(3), significant (p < 0.05) relationships, also with relatively low R2 values, were observed between predicted BCF and observed BAF values (Fig. 3A and B). We then examined the potential use of VD to interpret field BAFs using Eqs. (6) and (7), which were also derived by Meredith-Williams et al. (2012) for the amphipod G. pulex, and the insect N. glauca, respectively. Here again, significant (p < 0.05) relationships with low R2 values were observed (Fig. 4). Differences between observations reported by MeredithWilliams et al. (2012) and the present study likely related to different food sources for Planorbid sp., N. glauca and G. pulex, in addition to different exposure scenarios between laboratory experiments focusing on bioconcentration (Meredith-Williams et al., 2012) and the field studies of bioaccumulation reported here. As one example of differences in exposure, the present study employed grab samples to characterized water concentrations of target analytes. Clearly, employing even the most relevant laboratory observations to understand pharmaceutical bioaccumulation dynamics in the field presents inherent challenges. Further, laboratory BCF studies with pharmaceutical and snails are largely lacking. For example, Meredith-Williams et al. (2012) also examined BCF of the betablocker carvedilol by the freshwater snail Planobarius corneus, which may represent the only other report of pharmaceutical bioaccumulation by freshwater snails. Recognizing that Planorbid sp. were co-exposed to compounds discharged to the North Bosque River, it remains also possible that other compounds may have enhanced or decreased biotransformation and thus bioaccumulation of pharmaceuticals by Planorbid sp. in the present study.

-2

-1

0

Observed log BAF

1

2

3

4

5

-2

-1

0

Observed log BAF

1

2

3

4

5

Observed log BAF

Fig. 2. Comparisons between log bioaccumulation factors (log BAFs), predicted by bioaccumulation models based on log Kow (A), log Dow (B), log Dlipw (C) (Arnot and Gobas, 2006) and observed log BAFs from the North Bosque River, an effluent-dependent stream in central Texas, USA.

A5

B

4

Predicted log BCF

4

Predicted log BCF

5

3 2 1 0

R2=0.187 p<0.001

-1

3 2 1 0

R2=0.187 p<0.001

-1 -2

-2 -2

-1

0

1

2

3

Observed log BAF

4

5

-2

-1

0

1

2

3

4

5

Observed log BAF

Fig. 3. Comparisons between log bioconcentration factors (log BCFs), predicted by empirical linear bioconcentration models (for G. pulex (A) and N. glauca (B)) based on Dlipw (Meredith-Williams et al., 2012) and observed log bioaccumulation factors (log BAFs) from the North Bosque River, an effluent-dependent stream in central Texas, USA.

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A

5

B

4

Predicted log BCF

Predicted log BCF

4

5

3 2 1 0 -1

3 2 1 0 -1

2

R =0.164 p=0.003

-2

R2=0.164 p=0.003

-2 -2

-1

0

1

2

3

4

5

Observed log BAF

-2

-1

0

1

2

3

4

5

Observed log BAF

Fig. 4. Comparisons between log bioconcentration factors (log BCFs), predicted by empirical linear bioconcentration models (for G. pulex (A) and N. glauca (B)) based on VD (Meredith-Williams et al., 2012) and observed log bioaccumulation factors (log BAFs) from the North Bosque River, an effluent-dependent stream in central Texas, USA.

3.3. Pharmaceutical bioaccumulation by periphyton and invertebrates, and fish exposure through diet Whereas we observed select pharmaceuticals and their metabolites to accumulate in an invertebrate residing in an effluent-dependent stream during an extreme drought (Table 1), biotransformation of pharmaceuticals by aquatic organisms is not well understood, particularly in invertebrates. As demonstrated recently by our research group (Connors et al., 2013), biotransformation of many drugs, many of which were selected as target compounds in current field study, appears potentially limited in fish, which generally possess higher similarity for pharmaceutical targets (e.g., receptors, enzymes) to humans than invertebrates (Gunnarsson et al., 2008). For example, diltiazem, carbamazepine, celecoxib, sulfamethoxazole, warfarin, methylphenidate, paroxetine, acetaminophen, diphenhydramine, and fluoxetine exhibited no significant metabolism in naive rainbow trout liver S9 fractions (Connors et al., 2013). However, coexpoure to other substances could alter pharmaceutical metabolism in aquatic life. For example, (Smith et al. (2010) demonstrated that hepatic metabolism of fluoxetine in trout was limited unless induced by pre-exposure to carbamazepine. In fact, limited information exists on invertebrate metabolism of organic chemicals (Katagi, 2010); however, biotransformation by the invertebrate G. pulex has been to shown to alter internal concentrations of xenobiotics (Ashauer et al., 2012). Clearly, an advanced understanding of biotransformation of pharmaceuticals in fish, invertebrate, plant and periphytic communities is needed to improve bioaccumulation assessments (Boxall et al., 2012). Periphytic communities include a complex assemblage of algae and heterotrophs (bacteria and fungi) (Rier and Stevenson, 2002; Tarkowska-Kukuryk and Mieczan, 2012), and represent important components of inland waters for environmental assessments (Vera et al., 2010). Invertebrates and organisms at higher trophic positions graze on periphyton, thereby potentially exposing them to chemicals via dietary uptake (Jones and Sayer, 2003). Planorbidae are grazers (Tarkowska-Kukuryk and Mieczan, 2012) that are found on all continents except Antartica and, depending on the species, reside in a range of water bodies including lakes, streams, swamps, ponds, ephemeral pools, and ditches (Scott et al., 2009; Tarkowska-Kukuryk and Mieczan, 2012). Though observations of the occurrence of pharmaceuticals in periphyton and snails in the present study are novel, further research is needed to understand pharmaceutical disposition in primary producers and consumers of wadeable streams. In the present study, seven target compounds were detected in snails, but not periphyton, at levels greater than corresponding MDLs. Gill uptake is the primary exposure route of pharmaceuti-

cals and other contaminants of emerging concern (CECs) for aquatic organisms, especially fish (Mimeault et al., 2005; Erickson et al., 2006). To date, dietary exposure of fish to pharmaceuticals has received limited study (Daughton and Brooks, 2011; Boxall et al., 2012). As noted above, a BCF value was previously reported for the beta-blocker carvedilol in the freshwater snail P. corneus during a laboratory uptake study (Meredith-Williams et al., 2012). Because uptake of carvedilol to P. corneus is thought to be through pulmonary respiration (Jones, 1964; Meredith-Williams et al., 2012) suggested that aquatic species that use pulmonary respiration may be less exposed to contaminants in ambient water compared to those utilizing gills for respiration. Limited observations during the present study of pharmaceuticals in periphyton may have been influenced by analytical sensitivity and MDLs, due to the limited number of compounds detected in periphyton, compared to water and snail samples (Table 1). In the present study, fluoxetine was observed to accumulate to highest levels in snails, reaching a mean (N = 3) concentration of 13 (±4.8) lg kg1 and maximum concentrations of 42 lg kg1 (Table 1). A bioenergetics model (Weininger, 1978) can be used to estimate fish feeding rate (FD, g food d1) as a function of fish body weight (VF, kg) and surface water temperature (T, °C):

F D ¼ 0:022  V 0:85  expð0:06  TÞ F

ð8Þ

Surface water temperature of the North Bosque River during field sampling was approximately 30 °C. The North Bosque River includes a number of common taxa contributing to fish community structure, including several different Lepomis sp. for the purposes of this exercise, moderately sized Lepomis sp. of 25 g were selected based on subsequent field sampling (Du et al., 2014b), to estimate that fish would consume 215.4 ng fluoxetine d1, if their diet was solely comprised of snails (as can be the case for the redear sunfish, Lepomis microlophus). This scenario would result in fluoxetine daily dosage to Lepomis sp. of 8.62 lg kg1, which is approximately 33-fold lower than fluoxetine daily dosage of 285.7 lg kg1 for humans (25 mg 70 kg1). Research by our group (Berninger et al., 2011; Valenti et al., 2012; Du et al., 2014a) and others (Brodin et al., 2013) demonstrates that pharmaceutical residues in effluent discharges may present adverse outcomes to fish, invertebrates, and other aquatic organisms, resulting from therapeutic mechanisms/modes of action when functions of target receptors in fish are conserved with humans (Gunnarsson et al., 2008). In the case of fluoxetine, Brooks (Brooks, 2014) recently predicted that waterborne exposure at the environmental relevant level of 41 ng L1 would result in therapeutic hazards to fish, because this water concentration would be expected to accumulate in fish plasma to a level equaling

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a human therapeutic dose (Cmax) of fluoxetine. In the present study, fluoxetine, norfluoxetine, sertraline and desmethylsertraline were detected in snail tissues, but not detected in water, potentially because SSRIs appear to sorb strongly to suspended particulates in surface waters (Baker and Kasprzyk-Hordern, 2011), which would have been removed during a prefiltering step prior to extraction and analysis methods in the present study. Thus, based on observations of the present study and more recent observations across trophic positions (Du et al., 2014b), water exposure may be more important for fish, but perhaps not filter feeding invertebrates (e.g., bivalves) (Franzellitti et al., 2014; Hazelton et al., 2014), than diet. Though the current study was not designed to study other trophic positions or relevant trophic transfer of target pharmaceuticals in wadeable streams, the occurrence of select pharmaceuticals in periphyton and Planorbid sp. suggests that further investigations are needed to determine the relevance of bioaccumulation of pharmaceuticals in aquatic organisms (Daughton and Brooks, 2011) and examine potential transfer of pharmaceuticals through food chains (Boxall et al., 2012). Our ongoing research (Du et al., 2014b) is employing isotope-ratio mass spectrometry to support an understanding of trophic magnification or trophic dilution of pharmaceuticals in effluent-dominated and dependent streams. Acknowledgements The present study was supported by the City of Waco Water Utilities, the United States Department of Agriculture and the Baylor University Department of Environmental Science and Center for Reservoir and Aquatic Systems Research. We also gratefully acknowledged Kristin A. Connors, Gavin Saari, Lauren A. Kristofco, Christopher Breed, and Christopher Rash for assistance with useful comments, sample collection, and sample analysis. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.chemosphere. 2014.08.044. References Ankley, G.T., Brooks, B.W., Huggett, D.B., Sumpter, J.P., 2007. Repeating history: pharmaceuticals in the environment. Environ. Sci. Technol. 41, 8211–8217. Armitage, J.M., Arnot, J.A., Wania, F., Mackay, D., 2013. Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish. Environ. Toxicol. Chem. 32, 115–128. Arnot, J.A., Gobas, F., 2006. A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms. Environ. Rev. 14, 257–297. Ashauer, R., Hintermeister, A., O’Connor, I., Elumelu, M., Hollender, J., Escher, B.I., 2012. Significance of xenobiotic metabolism for bioaccumulation kinetics of organic chemicals in Gammarus pulex. Environ. Sci. Technol. 46, 3498–3508. Baker, D.R., Kasprzyk-Hordern, B., 2011. Multi-residue determination of the sorption of illicit drugs and pharmaceuticals to wastewater suspended particulate matter using pressurised liquid extraction, solid phase extraction and liquid chromatography coupled with tandem mass spectrometry. J. Chromatogr. A 1218, 7901–7913. Berninger, J.P., Du, B., Connors, K.A., Eytcheson, S.A., Kolkmeier, M.A., Prosser, K.N., Valenti, T.W., Chambliss, C.K., Brooks, B.W., 2011. Effects of the antihistamine diphenhydramine on selected aquatic organisms. Environ. Toxicol. Chem. 30, 2065–2072. Boxall, A.B., Rudd, M.A., Brooks, B.W., Caldwell, D.J., Choi, K., Hickmann, S., Innes, E., Ostapyk, K., Staveley, J.P., Verslycke, T., Ankley, G.T., Beazley, K.F., Belanger, S.E., Berninger, J.P., Carriquiriborde, P., Coors, A., Deleo, P.C., Dyer, S.D., Ericson, J.F., Gagne, F., Giesy, J.P., Gouin, T., Hallstrom, L., Karlsson, M.V., Larsson, D.G., Lazorchak, J.M., Mastrocco, F., McLaughlin, A., McMaster, M.E., Meyerhoff, R.D., Moore, R., Parrott, J.L., Snape, J.R., Murray-Smith, R., Servos, M.R., Sibley, P.K., Straub, J.O., Szabo, N.D., Topp, E., Tetreault, G.R., Trudeau, V.L., Van Der Kraak, G., 2012. Pharmaceuticals and personal care products in the environment: what are the big questions? Environ. Health Perspect. 120, 1221–1229.

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