This worksheet will you to summarize the most important information from a peer-reviewed journal article. The article will be provided to you by your instructor. You should summarize the information and put it into your own words – do not use quotations or copy text directly from the article. It is recommend that you complete the worksheet in the order it is written: bibliography, abstract, introduction, discussion, methods and results. THIS IS THE ARCTICLE Spatial patterns of pharmaceuticals and wastewater tracers in the Hudson River Estuary Mark G. Cantwell a, *, David R. Katz a, Julia C. Sullivan b, Daniel Shapley c, John Lipscomb c, Jennifer Epstein c, Andrew R. Juhl d, Carol Knudson d, Gregory D. O’Mullan e a U.S. Environmental Protection Agency, Office of Research and Development, 27 Tarzwell Drive, Narragansett, RI 02882, USA b Oak Ridge Associated Universities, Narragansett, RI 02882, USA c Riverkeeper Inc., 20 Secor Road, Ossining, NY 10562, USA d Lamont Doherty Earth Observatory of Columbia University, 61 Route 9W, Palisades, NY 10964, USA e School of Earth and Environmental Sciences, Queens College, City University of New York, 65-30 Kissena Blvd., Flushing, NY 11367, USA articleinfo abstract Article history: Received 5 September 2017 Received in revised form 7 December 2017 Accepted 19 December 2017 Available online 22 December 2017 Keywords: Pharmaceuticals Wastewater tracers Hudson river Emerging contaminants The widespread use of pharmaceuticals by human populations results in their sustained discharge to surface waters via wastewater treatment plants (WWTPs). In this study, 16 highly prescribed pharma- ceuticals were quantified along a 250 km transect of the Hudson River Estuary and New York Harbor to describe their sources and spatial patterns. Sampling was conducted over two dry weather periods in May and July 2016, at 72 sites which included mid-channel and nearshore sites, as well as locations influenced by tributaries and WWTP outfalls. The detection frequency of the study pharmaceuticals was almost identical between the May and July sampling periods at 55% and 52%, respectively. Six phar- maceuticals were measurable at 92% or more of the sites during both sampling periods, illustrating their ubiquitous presence throughout the study area. Individual pharmaceutical concentrations were highly variable spatially, ranging from non-detect to 3810ng/L during the study. Major factors controlling concentrations were proximity and magnitude of WWTP discharges, inputs from tributaries and tidal mixing. Two compounds, sucralose and caffeine, were evaluated as tracers to identify wastewater sources and assess pharmaceutical behavior. Sucralose was useful in identifying wastewater inputs to the river and concentrations showed excellent correlations with numerous pharmaceuticals in the study. Caffeine-sucralose ratios showed potential in identifying discharges of untreated wastewater occurring during a combined sewage overflow event. Many of the study pharmaceuticals were present throughout the Hudson River Estuary as a consequence of sustained wastewater discharge. Whereas some con- centrations were above published effects levels, a more complete risk assessment is needed to under- stand the potential for ecological impacts due to pharmaceuticals in the Hudson River Estuary. 1. Introduction Pharmaceuticals comprise a large and growing class of chemical compounds present at elevated levels in water bodies of developed nations, primarily entering the environment following human use via wastewater treatment plant (WWTP) discharges (Gaw et al., 2016). Pharmaceutical compounds including prescription, nonprescription and illegal drugs may number in the hundreds in WWTP effluents. Many pharmaceuticals are highly prescribed and * Corresponding author. E-mail address: (M.G. Cantwell). https://doi.org/10.1016/j.watres.2017.12.044 0043-1354/Published by Elsevier Ltd. Published by Elsevier Ltd. as a result enter the waste stream at high concentrations. Removal efficiency of pharmaceuticals during wastewater treatment is var- iable and often poor, resulting in their continuous release into the aquatic environment (Kolpin et al., 2002; Verlicchi et al., 2012). Under certain conditions, such as when combined sewage overflow (CSO) events occur, treatment systems are bypassed, resulting in the release of untreated sewage, further increasing the levels of some wastewater contaminants present (Kay et al., 2017). Conse- quently, many pharmaceuticals in receiving waters may be present in the ng/L to mg/L range (Roig and D’Aco, 2016). In rivers, estuaries and coastal ecosystems that are urbanized or near densely-populated cities, the high volume and continuous discharge of WWTP effluents is a significant concern. In many such 336 M.G. Cantwell et al. / Water Research 137 (2018) 335e343 locations episodic releases of untreated wastewater via CSOs and undocumented discharges are also a factor in water quality degradation (Launay et al., 2016). It is thought that most pharma- ceutical compounds remain biologically active in aquatic systems with the potential to exert adverse effects on aquatic life if present at levels above known effects thresholds (Seiler, 2002). The sus- tained discharge of pharmaceuticals may result in receiving waters with areas of pseudo-persistence (Daughton, 2001), resulting in chronic exposure and possible ecological effects. Pharmaceuticals are a class of pollutants that have been identified as “contaminants of emerging concern” (CECs). In the United States, there are currently no regulatory standards associated with them and there is limited information on their occurrence and potential to impart adverse effects (USEPA, 2017). Most CECs, including pharmaceuti- cals, are not included in current monitoring protocols, but may be candidates for future regulation based on their toxicity and other adverse effects. To ascertain the risk of CECs such as pharmaceu- ticals, information on contaminant sources (e.g., domestic waste- water (WW) discharges), individual CEC loadings, and their potential for adverse effects is needed. This information can be used to inform recently developed monitoring criteria that employs a risk based framework which focuses on whether concentrations of CECs measured in the environment exceed already established thresholds for biological effects (Sengupta et al., 2014). Further, these risk based methods enable a tiered approach to monitoring and could potentially provide support for future regulation of CECs (Maruya et al., 2014). The Hudson River Estuary (HRE) is an estuary of vital ecological and economic importance that has been understudied with regard to WW derived CECs, particularly pharmaceuticals. The HRE sup- ports many activities, providing critical services to >15 million residents, as well as millions of visitors annually and others who indirectly benefit from economic activity within the watershed. Major uses include transportation, commerce, industrial, and as a drinking water source. The entire length of the HRE is a receiving water for numerous WWTP discharges, along with CSO releases, of untreated WW. New York City alone discharges over 4.9 106 m3/ d of treated WW (NYCDEP, 2012), and over 7 107 m3 of CSO dis- charges annually (NYCDEP, 2016). The large-scale, sustained discharge of WW results in numerous sewage-related contami- nants being released to the HRE, including pharmaceuticals. Bac- terial fecal indicators in the HRE show high spatial and temporal variability, though with recognizable patterns related to untreated sewage inputs and precipitation (Young et al., 2013). Although long-term trends in most water quality indicators show consider- able improvement in the HRE in recent decades (Steinberg et al., 2004; Brosnan et al., 2006), ongoing discharges combined with legacy pollutants (e.g., PCBs, PAHs) continue to present widespread water quality issues with potential impacts on human health, ecosystem function and economic activity. In this study, the behavior and fate of 16 high-volume-use pharmaceutical compounds, caffeine and the artificial sweetener sucralose were investigated. These pharmaceuticals were selected using a conceptual approach which prioritized highly prescribed drugs based on their potential to cause biological effects in wastewater (Batt et al., 2016; Kostich et al., 2014). This approach is similar to others used to identify CECs for monitoring and further investigation (Maruya et al., 2014). The compounds were measured during dry weather along a 250-km (155-mile) transect of the HRE. Sites within a heavily CSO impacted New York Harbor (NYH) embayment were also sampled during both wet and dry weather conditions to begin to assess urban CSO influence at the mouth of the river. The objectives were to: (1) measure the study pharma- ceuticals in the water column at high spatial resolution to develop an understanding of the factors controlling their occurrence and spatial patterns during dry weather; and (2) evaluate two potential tracers, caffeine (Benotti and Brownawell, 2009) and sucralose (Buerge et al., 2003; Oppenheimer et al., 2012), for tracking WW impacts in tidal rivers and estuaries such as the Hudson River. 2. Materials and methods 2.1. Study location The morphology of the HRE is best described as a drowned river valley with little vertical rise (0.006 m/km) over a 250 km distance between the Battery (NYH) and the dam at Troy, NY and drains a watershed area of 13,750 km2 (USGS, 2017). The path of the HRE main channel runs in a relatively straight line from New York City to Albany (Fig. 1). It is ~1.3 km in width at river kilometer (RK) 0 and widens, reaching its widest point of ~5.6kmat RK 63. Further north, widths taper to and remain at approximately 0.5 km from RK 188 to RK 241. River depths are highly variable, with navigable channel depths averaging 12 m and a maximum depth of 61 m. The HRE is classified as a partially mixed estuary with a moderate salinity gradient and vertical stratification (Geyer and Chant, 2006). The river is tidally influenced up to the Federal Dam at Troy (RK 245) with a tidal magnitude of approximately 1.5 m. Tidal cycles are semidiurnal, with an average tidal current of 0.7 m/s, and play an important role in salinity gradients and stratification within the river, as does the volume of fresh water (Geyer and Chant, 2006). Approximately 80% of the fresh water entering the HRE at Troy annually originates from the upper Hudson and the Mohawk Rivers, with the balance entering from tributaries (Cooper et al., 1988) (Fig. 1, Table S1). Within the HRE, the position of the Fig. 1. Map of the study area (sites identified by circles). salinity front can be highly variable over time, with the volume of fresh water being the primary regulator (Geyer and Chant, 2006). Information on the residence time of water within the HRE is very limited, with estimates of 1e4 days for the haline part (Howarth et al., 2006), and from 25 to 100 days for the freshwater section (Cooper et al., 1988), varying with freshwater flows and tidal cycles. The locations of sampling sites along the river transect are re- ported in RKs, starting at the New York City Battery where the Hudson enters NYH (RK 0) continuing up to RK 250. There were 65 sites along the transect, 63 of which were in the tidal estuary (Fig. 1, Table S2). There were two sites at the mouths of the Mohawk and upper Hudson Rivers, just above the Troy Dam, which flow into the HRE and account for >99% of the drainage above the dam (Wall et al., 2008). Finally, seven sites in the interconnected waterways of upper NYH were sampled, as were CSO discharges during a wet weather event. 2.2. Sampling Water samples were collected May 19e23 and July 12e16, 2016, off the Riverkeeper vessel R. Ian Fletcher. Sampling of the transect started at RK 0 and progressed to RK 249.6. Over a period of 5 days, a single grab sample was collected from 0.25 m below the water surface at each site (Table S2). Samples were kept on ice until returned to the laboratory, and stored in the dark at 4C until processed. Extraction and analysis of samples was performed within 7 days of sample collection. Surface water conditions (e.g., salinity, temperature) were also recorded at each station during sampling with a Hydrolab data sonde. Samples from Flushing Bay within the East River were also collected from July 29 to August 6, 2016 to begin assessing urban CSO impacts on NYH. 2.3. Water extractions Before extraction, 250 mL of water was passed through a 0.7 mm glass fiber filter (man GFF) and stored in amber glass bottles. Extraction protocols followed EPA Method 1694 with slight modi- fications using Oasis HLB solid phase extraction (SPE) cartridges (6 cc, 500 mg, Waters Corporation). For the extractions, 250-mL samples were adjusted to pH 2 using hydrochloric acid (6 N) and spiked with 100 ng of isotopically labeled internal standards (IS) (Table S3). Cartridges were conditioned with 6 mL of methanol, followed by 6 mL of pH 2 Milli-Q water, and 6 mL of pH 2 filtered artificial seawater. Samples were loaded onto SPEs using a vacuum manifold at a rate of 5e10 mL/min. After loading, the SPEs were rinsed with 12 mL of pH 2 Milli-Q water, dried for 15 min under vacuum and eluted with 12 mL of methanol. Extracts were then evaporated to dryness, reconstituted with 1mL mobile phase (Milli-Q:methanol, 80:20), vortexed, transferred to vials and stored at 4C until analysis. Each set of extractions included a blank, for- tified blank, duplicate, and matrix evaluation. 2.4. Analysis The 16 pharmaceuticals in the present study were antihyper- tensives (acebutolol (ACB), atenolol (ATE), diltiazem (DIL), labetalol (LAB), losartan (LOS), metoprolol (MET), propranolol (PRO), val- sartan (VAL), and verapamil (VER)); antibiotics (sulfamethoxazole (SUL) and trimethoprim (TRI)); an analgesic (acetaminophen (ACE)); an anticonvulsant (carbamazepine (CAR)); a diuretic (furosemide (FUR)); an antilipemic (gemfibrozil (GEM)); and an antiulcerative (ranitidine (RAN)). Caffeine (CAF) and sucralose (SUC) were measured because of their potential as WW tracers. The compounds were quantified using high purity standards (Sigma Aldrich) with isotopically enriched surrogates (deuterated and/or 13C) as an IS (CDN Isotope) (Table S4). Analysis was performed on a Waters Acquity UPLC using a Waters Xevo TQD MS/MS operated in electrospray ionization (ESI) mode. Compounds were detected by MS/MS with ionization conditions of the capillary set to 0.5 kV in ESIþ and 3.5 kV in ESI- (Table S5). Compound specific settings were also used for quantification and confirmation multiple reaction monitoring (MRM) transitions (Table S3). Compounds were cali- brated using a 10-point curve ranging from 0.25 ng/mL to 300 ng/ mL. Calibration curves consistently had an r2 1⁄4 0.99 or better for all compounds. Calibration verification standards were also analyzed every 10 samples to confirm instrumental performance over the course of the analytical run. Recoveries for each compound were generally within 10% of reference values. Study compounds were not detected in the blanks (n 1⁄4 17), with the exception of CAF. One blank had a value of 3ng/L, with all others near or below the detection limit of 0.3 ng/L. Since the minimum and mean concen- trations of CAF during this study were approximately 22 ng/L and 109 ng/L, respectively, this was not regarded as a substantial issue and a blank correction was not performed. The method detection limits (MDLs) for the study compounds ranged from a high of 10 ng/ L to a low of 0.01 ng/L. Because of the potential for bias in the fre- quency of detection based on the range of individual compound MDLs, we statistically examined all data using histogram frequency distribution analysis. No patterns indicating MDL bias were found for any of the study compounds. Method detection limits were determined for each of the compounds using instrument detection limits defined as a signal to noise ratio >10 and are reported in Supplemental Data, Table S6, along with further information on quality assurance. 3. Results and discussion 3.1. River conditions During the May and July sampling periods, average daily freshwater flows entering the HRE above the dam at Troy were 1.9 107 m3/d and 1.4 107 m3/d, respectively (USGS, 2017) (Table S1), with a 26% decline in freshwater flow to the river in July. These levels are lower than 5-year monthly flow averages of 4.8 107 m3/d and 2.9 107 m3/d for May and July, respectively, reflecting the dry conditions during this study. Currently, at least 90 municipal WWTPs discharge effluent directly or into tributaries entering the HRE (Table S7). Estimates of daily discharge indicate approximately 1.7 106 m3/d of effluent entering the HRE from locations above NYH (USEPA, 2016). This is approximately 7.5 and 11% of the fresh water input from the Upper Hudson and major tributaries during the May and July sample periods, respectively (Table S1). Surface water temperatures ranged considerably between sampling periods (Fig. S1). In May temperatures ranged from 12.7 to 19.4 C while July temperatures ranged from 22.7 to 28.3 C. Temperatures during both periods were coolest at the mouth of the river and rose steadily up the transect, which is mostly explained by cooler, seawater entering the river during incoming tides. Surface salinities were highest at the river mouth (RK 0), registering values of 14.6 and 20.8 for May and July, respectively, declining with distance upriver (Fig. S2). Measurable surface salinity (0.3 psu) extended as far north as RK 74 in May and RK 98 in July, with decreased freshwater flow explaining the salinity front extension in July. Strong horizontal salinity gradients have previ- ously been reported between RK 40 and 66, with salinity fronts as far north as Poughkeepsie (RK 124). Overall, salinity and temper- ature observations are consistent with historical seasonal trends, which are largely driven by the variability of freshwater flow (Geyer and Chant, 2006). M.G. Cantwell et al. / Water Research 137 (2018) 335e343 337 338 M.G. Cantwell et al. / Water Research 137 (2018) 335e343 3.2. Pharmaceutical occurrence and distribution The frequency of occurrence and spatial patterns of the study pharmaceuticals were determined to provide information on their sources, distribution and behavior (Fig. 2). The frequency of occurrence (expressed as %) across the whole study area were almost identical in the two months, with an average of 55% of pharmaceuticals occurring at each site during the May sampling and 52% in July. Results are presented by sites within the river and those within NYH. The absence of significant precipitation throughout the watershed resulted in low freshwater flow volumes during both sampling events and the expectation for little to no CSO input. 3.2.1. River transect The occurrence frequencies of pharmaceuticals were somewhat variable over the length of the river, with slightly lower frequencies observed in July (Fig. 2). The largest increases in occurrence were associated with sites at WWTP discharges, especially at RKs 28.2, 41.8, and 148.2 where the number of compounds present exceeded 90%. Above the Troy Dam, fresh water enters the HRE originating from the Mohawk and Upper Hudson River watersheds. Here, the percent of study pharmaceuticals present averaged between 56 and 63%, reflecting their widespread presence in these major tributaries as a result of 4.0105m3/d of WW effluents discharged daily (Table S7). The occurrence frequencies at sites just below the dam (e.g., RKs 245.4e197.1), influenced by the cities of Troy and Albany, were similar to those above the dam ranging between 50% and 81%. The percentage of compounds present declined from RKs 188.3 through 156.1, dropping to 44e56%, due in part to dilution from major tributaries (e.g., Stockport/Kinderhook, Esopus, Catskill) entering this reach of the river. Because of the low population densities in this region, these tributaries receive smaller volumes of WW discharges (8.1 103 m3/d) than those above the dam (Table S7). Combined, all of these tributaries provide significant quantities of freshwater based on recent flow data (Table S1). From RKs 141.6 through 45.1, the occurrence of pharmaceuticals ranged between 38 and 56%. One exception is at RK 84.5 (located by the WWTP outfall at the military academy at West Point), where the frequency of occurrence dropped from 81% during the May sam- pling to 44% in July, which likely reflects the population drop be- tween academic sessions. Below RK 45.1, the percentage of study pharmaceuticals present increased, with well-defined spikes at RKs 41.8 and 28.2, sites with major WW inputs. The trend from RKs 43.5 Fig. 2. Frequency of occurrence (in percent) of pharmaceuticals along the river transect. through 0 is complex and suggests a number of factors influenced the percentage of pharmaceuticals present. The proximity of New York and New Jersey urban areas, with an estimated population of >12M, along with numerous large-volume WWTP discharges clearly exerted their influence, with an average of 58% of pharma- ceuticals measurable for both sampling periods. The sustained, high volume of effluent entering the river, combined with harbor water reentering the river on incoming tides, resulted in conditions with numerous pharmaceuticals present. During the May sampling, 7 of the 16 pharmaceuticals (ATE, CAR, LOS, MET, SUL, TRI, and VAL) were present at !98% of the 65 river sites. This compared closely to July, where the same com- pounds (excepting TRI at 77%) were present at !92% of the river sites (Fig. 2, Table 1). The similarity in trends between compounds along the transect and between sampling periods indicates the ubiquitous nature of these compounds under similar environ- mental conditions (e.g., precipitation, river flow). Concentrations of individual pharmaceuticals varied along the river transect, with many trending in a similar manner from the start of the estuary (RK 245.4) to the Battery (RK 0) (Fig. 3, Table 1, Table S8). Four pharmaceuticals present throughout the river were all antihypertensive medications and can be credited for some of the highest concentrations recorded in this study. Although median concentrations for these compounds were fairly consistent be- tween sampling periods, the maximum concentrations recorded were much higher in May, with values as high as 1070 ng/L for ATE, 1700ng/L for LOS, 2020ng/L for MET, and 3810ng/L for VAL. It should be noted that for most compounds, the maximum concen- trations reported in this study were recorded at RK 148.2da site which is in direct proximity to a WW outfall. The other three frequently detected-compoundsdCAR, SUL, and TRIdfollowed the same pattern, with higher maximum concentrations in May and nearly identical median values between sampling periods. Three other pharmaceuticals (ACE, DIL, and GEM) were present at less than 50% of the sites along the transect, but were generally present at sites near WWTPs. In particular, ACE and GEM were more abundant in May and exhibited greater variability between sampling periods. The occurrence of ACE dropped from 49% in May to 11% in July, and GEM experienced a similar magnitude in decline, occurring at 37% of the sites in May and 18% in July. Aside from a few prominent peaks, concentrations generally remained below 18 ng/L for both compounds. DIL was present near WWTP outfalls along with a few sites in the lower and upper reaches of the river at low levels. Finally, ACB, FUR, LAB, PRO, RAN and VER were present 25% of the time during both sampling periods (Table 1). These com- pounds were present almost exclusively by WWTP outfalls. LAB and PRO were present at 6 and 8% of the sites during May, occurring slightly more frequently in July at 11 and 18%, respectively. VER was present at 6% of sites in May, compared to 22% in July. RAN was found at 8% of the sites in May and 5% in July when it was present exclusively near large WWTP outfalls. ACB was detected at 6% of sites in May and 5% in July. Concentrations of these compounds were generally higher in May than in July. Spatial patterns identified major tributaries and WWTPs along the transect as key factors influencing pharmaceutical concentra- tions. Trends between sampling periods provided insight into behavior of individual pharmaceuticals. Decreased river flow dur- ing July likely increased residence time to an undetermined extent as evidenced by the salinity profiles. However, only two com- pounds, CAR and SUL, were generally higher along the transect in July (Fig. 3). Conversely, GEM, TRI and VAL were slightly lower in July. A number of processes may explain the behavior of some of the pharmaceuticals in the river. The sorption potential of individual pharmaceuticals gives an indication of their likelihood to be Compound May Min. Med. River Transect Acebutolol (ACB) n.d. 8.2 Acetaminophen (ACE) n.d. 8.0 Atenolol (ATE) 1.5 8.1 Caffeine (CAF) 23.5 70.3 Carbamazepine (CAR) 0.9 3.9 Diltiazem (DIL) n.d. 0.7 Furosemide (FUR) n.d. 130.0 Gemfibrozil (GEM) n.d. 19.9 Labetalol (LAB) n.d. 122.7 Losartan (LOS) 4.2 14.8 Metoprolol (MET) 8.0 16.2 Propranolol (PRO) n.d. 8.9 Ranitidine (RAN) n.d. 30.1 Sucralose (SUC) 588.4 870.2 Sulfamethoxazole (SUL) n.d. 12.3 Trimethoprim (TRI) n.d. 2.7 Valsartan (VAL) 11.4 28.1 Verapamil (VER) n.d. 8.7 New York Harbora Acebutolol (ACB) n.d. 0.6 Acetaminophen (ACE) 4.9 13.0 Atenolol (ATE) 14.7 18.2 Caffeine (CAF) 111.9 141.7 Carbamazepine (CAR) 3.6 8.3 Diltiazem (DIL) n.d. n.d. Furosemide (FUR) n.d. 8.8 Gemfibrozil (GEM) n.d. 26.9 Labetalol (LAB) n.d. 2.4 Losartan (LOS) 23.2 33.0 Metoprolol (MET) 24.4 27.6 Propranolol (PRO) n.d. 0.5 Ranitidine (RAN) n.d. 1.8 Sucralose (SUC) 708.3 887.0 Sulfamethoxazole (SUL) 15.6 22.3 Trimethoprim (TRI) 4.3 7.7 Valsartan (VAL) 60.2 77.9 Verapamil (VER) n.d. 2.0 July Max. Freq. Min. Med. 22.0 6 n.d. 5.1 327.7 49 n.d. 17.5 1074.3 100 n.d. 7.6 2056.7 100 22.2 49.1 542.6 100 2.6 5.6 73.5 20 n.d. 1.2 1234.8 8 n.d. 137.4 1440.4 37 n.d. 17.4 304.8 6 n.d. 4.7 1699.8 100 8.3 16.9 2020.6 100 7.7 14.1 134.1 8 n.d. 0.8 1002.1 9 n.d. 29.1 16,203.0 100 498.2 1181.2 616.6 98 n.d. 19.1 350.0 98 n.d. 2.7 3811.9 100 2.7 21.9 51.4 6 n.d. 0.8 0.8 43 n.d. n.d. 138.3 100 n.d. 92.3 31.8 100 16.5 24.5 589.5 100 78.0 142.6 25.1 100 4.3 6.5 n.d. 0 2.1 2.4 8.8 14 n.d. n.d. 43.1 86 n.d. 20.5 2.4 14 n.d. 2.2 48.6 100 34.2 48.2 47.6 100 31.1 40.4 1.2 43 n.d. 0.4 1.8 14 n.d. n.d. 1251.9 100 1204.2 1386.0 32.7 100 n.d. 50.0 10.4 100 7.1 10.5 117.4 100 82.4 94.9 2.4 57 n.d. 0.5 Max. Freq. 7.7 5 170.6 11 326.7 92 2265.1 100 105.7 100 77.0 46 291.2 5 457.4 18 136.7 11 584.6 100 612.2 100 30.3 18 202.0 5 10,107.9 100 336.8 98 230.9 77 1852.2 100 18.8 22 n.d. 0 161.7 43 30.9 100 520.2 100 12.4 100 5.6 100 n.d. 0 43.6 86 4.1 57 65.9 100 66.8 100 0.6 71 n.d. 0 1472.8 100 69.0 29 13.7 100 110.7 100 0.6 71 M.G. Cantwell et al. / Water Research 137 (2018) 335e343 339 Table 1 Minimum (Min), median (Med) and maximum (Max) concentrations of study compounds (ng/L) along with their frequency of occurrence in percent (Freq). a NY Harbor sites are sites that are not located on the main Hudson River transect: East River (2), Harlem River (2), Newtown Creek (2) and Gowanus Canal. removed from the water column. The Log Kows of the pharmaceu- ticals in this study are low, with five having Log Kows less than 1 and only four above 3.0, indicating little potential for solid phase partitioning (Table S9). Examination of the data based on the compounds’ respective Kows did not reveal any consistent patterns of behavior. Similarly, distribution coefficients (Kds) provide direct evidence of partitioning behavior in the water column. Cantwell et al. (2016a) determined field-derived Kds for eight of the com- pounds (Table S9), with four other compounds exhibiting insuffi- cient solid-phase concentrations to determine Kds (e.g., ACE, GEM, SUL, and VAL). Median Kd values for six of the eight pharmaceuti- cals were below 2.5, with the other two below 4.0. Ternes et al. (2004) observed that compounds with Log Kd values of 2.7 or less were shown to have minimal removal from the dissolved phase (<10%) by sorption processes. The acid dissociation constant (or pKa) is an important factor controlling the therapeutic behavior of pharmaceuticals as the degree of ionization is strongly influenced by pH, which can also have implications when pharmaceutical residues are present in aquatic systems (Cunningham, 2008). The study compounds have a broad range of pKa values, from 4.8 to 17.3 (Table S9). The pH of the receiving water could affect the degree of ionization of indi- vidual pharmaceuticals to some extent, as ionized compounds will be more soluble in contrast to their respective neutral species. This would make them less likely to partition to solid phases and potentially affect their distribution in the water column. While pH was not measured in this study, long-term values in the Hudson range from 6.4 to 8.2, with most above 7.0 (Cooper et al., 1988), which could potentially affect the behavior of some of the phar- maceuticals. Recent work, however, has not shown a relationship between pKa and solubility with a similar suite of compounds in estuarine conditions (Zhao et al., 2015). Overall, sorption does not appear to be an important mechanism of removal for most of the compounds examined during this study, suggesting that many of the declines observed may be due to degradation by abiotic and biotic processes. A decrease in abun- dance and concentrations of some compounds in July suggests that degradation may have been a factor for more labile pharmaceuti- cals. Reduced freshwater inputs (Table S1) to the HRE (which would increase residence time) and elevated water temperatures (Fig. S1) in July may create enhanced conditions supporting degradation. 3.2.2. New York harbor The New York Harbor sites are located in East River, Harlem River, Newtown Creek and Gowanus Canal. The occurrence of pharmaceuticals present in NYH was relatively high, ranging from 56 to 83% and usually at slightly higher concentrations in Newtown Creek and East River. Six compoundsdATE, CAR, LOS, MET, TRI, and VALdwere present at all seven sites during both sampling periods. Additionally, ACE and SUL were present at all sites in May, while DIL 340 M.G. Cantwell et al. / Water Research 137 (2018) 335e343 Fig. 3. Concentrations (ng/L) of frequently detected pharmaceuticals along the river transect. had 100% occurrence in July but was not detected at all in May. PRO and VER occurred at 43% and 57%, respectively, of sites in May, while each had occurrence rates of 71% in July. LAB occurred at 14% of sites in May and 57% of sites in July. ACB was only detected in May, with an occurrence rate of 43%. RAN was detected only once in May, and FUR was not detected during either sampling period. Median concentrations of ACE, ATE, GEM, LOS, MET, SUL, TRI, and VAL in NYH were mostly higher than in the transect. These compounds, with the exception of GEM, were higher during July, with median values ranging 7.7e78 ng/L in May and 10e95 ng/L in July. Median values remained below 9 ng/L for CAR and did not exceed 2.4 ng/L for ACB, DIL, LAB, PRO and VER. New York Harbor has numerous large WWTPs in both the Hudson and East River tributaries that contribute approximately 3.8 106 m3/d of effluent to this area (Table S7). The large volume of water entering from both the Hudson and East Rivers, already elevated in pharmaceuticals, is subjected to the Harbor's complex hydrodynamics and additional WW inputs. Here, successive tidal cycles advect large volumes of water from the harbor up the river. However, no decrease in percent occurrence of pharmaceuticals was observed. Tidal cycling in the lower river and harbor here can cause equivalent flow in both directions. The complex hydrody- namics and dynamic mixing of water combined with the location and volume of WW discharged daily into the harbor explain the spatial patterns of pharmaceuticals observed in this area. These findings highlight the importance of hydrodynamics along with input levels and source locations in regulating contaminant con- centrations in coastal rivers and embayments. 3.3. Environmental perspective Comparing pharmaceutical responses in this study to other river systems provides some context to the levels observed. Recently, Batt et al. (2016) conducted a national survey of phar- maceuticals in 182 US rivers and streams that included 13 of the 16 compounds (except ACB, LAB, and LOS) examined in this study. Between the two studies, the mean frequency of detection across our sites was greater in this study (Table S10). Comparison of concentrations from both studies also revealed that with the exception of VER, numerous compounds in this study (e.g., ACE, ATE, DIL, FUR, GEM, MET, RAN, TRI, and VAL) were higher and the others (CAR, PRO, and SUL) were nearly equal. Similar trends were found in the Garonne River estuary of France with mean con- centrations of CAR and PRO nearly equal to those in this study, but with lower mean levels of ATE, GEM, LOS, MET, and RAN (Aminot et al., 2016). Combined, the high frequency of occurrence and elevated concentrations of many of the study pharmaceuticals illustrates the impact WWTP discharges have on the HRE relative to other rivers (Table S10), which raises questions regarding the possibility of ecological effects. Pharmaceutical compounds are frequently detected in fresh- water and marine environments, though they are rarely found at levels high enough to cause acute toxicity (Brausch et al., 2012). However, since many pharmaceuticals (particularly highly pre- scribed ones) are constantly entering the environment, there is interest regarding the potential for chronic effects. At some sites in this study, particularly those situated by WWTP outfalls, several pharmaceuticals were measured at concentrations reported to cause chronic effects to aquatic organisms: SUL (Yu et al., 2011), CAR (De Lange et al., 2006), PRO (Franzellitti et al., 2011), and ACE (Parolini et al., 2013). At RK 148.2, which is situated at a WWTP outfall and at low tide is essentially undiluted effluent, five other compounds were measured at concentrations reported to cause chronic effects: TRI (Parolini et al., 2013), RAN (Rocco et al., 2010), FUR (Rocco et al., 2010), GEM (Rocco et al., 2012), and MET (Dietrich et al., 2010). Although these compounds were not found at levels this high throughout the entirety of HRE, their high concentrations at several sites indicate that minimum effect concentrations for a number of pharmaceuticals may be exceeded near WW point sources (e.g., WWTP outfalls, CSOs). 3.4. Tracer evaluation Two compounds, CAF and SUC, were evaluated to assess their efficacy as tracers of sanitary wastewater in the HRE and NYH. Previously, CAF has been used to identify WW in surface waters (Buerge et al., 2003), and track CSO and undocumented sanitary discharges to estuarine waters (Buerge et al., 2006; Cantwell et al., 2016b). Caffeine is efficiently removed (>95%) by most sanitary WWTP processes (Buerge et al., 2003) making it well suited to identify untreated WW sources (e.g., CSOs) (Benotti and Brownawell, 2009). Sucralose is used extensively as a food and beverage sweetener and has also been evaluated as a WW tracer in aquatic systems. (Oppenheimer et al., 2011, 2012). As opposed to CAF, SUC is highly resistant to degradation as it is mostly inert to metabolic and environmental processes (Soh et al., 2011), resulting in negligible removal by WWTPs (Yang et al., 2017). The differential behavior of SUC and CAF along with their elevated levels in receiving waters indicates that combined, they may discriminate between sources of treated and untreated sanitary wastewater (e.g., WWTP effluents and CSOs). Both SUC and CAF were present at all sites and sampling periods at high concentrations, reflecting their extensive use in foods and beverages as well as excipient ingredients in pharmaceutical for- mulations. Along the transect, SUC concentrations ranged from 498 to 16,200 ng/L, with median values of 876 and 1180 ng/L for the May and July sampling periods, respectively. This increase is likely due to the 26% decline in freshwater flow during July, which increased the proportion of WW effluent in the river. Compared to SUC, CAF was an order of magnitude lower along the transect, ranging from 22 to 2260 ng/L with median values of 70 and 49 ng/L for May and July, respectively. For perspective, SUC and CAF concentrations measured by Bernot et al. (2016) in rivers and streams throughout the US were lower than in this study, with sucralose ranging from nondetect to 12,000 ng/L and caffeine ranging from nondetect to 420 ng/L. Along the transect, SUC showed similar trends during both sampling periods with several discrete differences. SUC concen- trations entering the HRE at RK 249.6 were 700 and 498 ng/L in May and July, respectively (Fig. 4). Concentrations spike slightly at RK 249.4 due to its close proximity to a WWTP. In May from RKs Fig. 4. Caffeine and sucralose concentrations (ng/L) along the river transect. 245 through 86.9, concentrations stayed within the range of 700e1200 ng/L, excepting one large peak near a WWTP. Below RK 86.9 in May, concentrations only rose over 950 ng/L at discrete lo- cations along the transect. In July from RKs 245.4 through RK 148.2, concentrations rarely fell below 1300 ng/L. At the sites below that point, values generally remain in the range of 800e1300 ng/L, again with the exception of a few discrete peaks. Generally, large spikes in SUC concentrations coincided with high volume WWTP discharges (e.g., RKs 148.2 and 41.8). In May there were several prominent SUC peaks at RKs 19.3e12.7 that were absent in July. The sources of these peaks are unknown, but may be from episodic, undocu- mented WW discharges. Maximum levels of CAF for both sampling periods occurred at RK 41.8, which is near two major WWTP discharges (Table S7). Spatial trends for CAF were also similar between sampling periods with exception of RKs 28.2e0.2 during May. In May, CAF is twice the July levels from RKs 28.2 through 0.2, a generalized increase that suggests discharge of untreated WW. In May below RK 19.3, there were several well-defined peaks of SUC present, suggesting too that there may be unidentified WW discharge in the lower segment of the river. The enhanced responses of SUC throughout the river at locations with known WW outfalls combined with its inert behavior supports its potential as a WW tracer in large systems such as the HRE. Another objective was to examine whether tracers can explain the behavior and fate of WW associated contaminants. Concen- trations of SUC were compared against the study compounds from the river transect. Concentrations of pharmaceuticals present >75% of the time were regressed against SUC and CAF to examine their relationships (Table S11). Coefficients of determination (r2) for SUC were uniformly higher, with r2 values ranging 0.77e0.97 for both May and July, exhibiting strong linear relationships. In contrast, r2s for CAF were much lower, ranging from 0.01 to 0.59. CAF also showed greater variability between sampling periods with a lower r2 in May. The weak relationship between CAF and the study compounds likely reflects CAF’s non-conservative behavior (lability) in the water column (Benotti and Brownawell, 2009). SUC showed less variability between sampling periods and slightly higher r2s for July. With SUC’s well documented resistance to degradation (Soh et al., 2011), the strong linear relationships with these pharmaceuticals (i.e., conservative behavior) further in- dicates that degradation or sorption processes are not a significant factor controlling their fate in the HRE during our sampling period, but may vary over longer time scales. Consequently, the concen- trations of these compounds are controlled primarily by the vol- ume of effluent and dilution from tributaries and tidal processes. The strong spatial correlation also demonstrates the potential of SUC as a tracer for recalcitrant contaminants in receiving waters emanating from WWTPs. Finally, the differential behavior of SUC and CAF was examined as a potential tool for discriminating between WW sources in surface waters using the ratio of CAF to SUC (C/S) concentrations. For example, a high C/S ratio would indicate that the relative amount of untreated WW was elevated relative to treated WW, while a lower ratio would indicate a lower proportion or absence of untreated WW. To test this concept, sampling was conducted in Flushing Bay, a CSO impacted urbanized tidal embayment on the East River of NYH during wet and dry weather conditions in JulyeAugust 2016. Water samples were collected from sites in close proximity to CSOs during a release event triggered by heavy pre- cipitation and 5 days later under dry conditions. Samples collected during the CSO event all showed C/S ratios >1 (1.1e3.0), indicating a high proportion of untreated WW (Fig. 5). The samples collected during dry weather had C/S ratios between 0.12 and 0.2. The de- clines in CAF between wet and dry conditions were as much as 2 M.G. Cantwell et al. / Water Research 137 (2018) 335e343 341 342 M.G. Cantwell et al. / Water Research 137 (2018) 335e343 sustained discharge of pharmaceutical residues associated with Fig. 5. Caffeine-sucralose (C/S) ratios in Flushing Bay of NYH under wet and dry conditions. orders of magnitude and clearly showed the impact of CSO dis- charges. Ratios were also calculated for the river transect to examine how C/S ratios responded in the river. Ratios along the transect ranged from a high of 0.31 (RK 41.8) to a low of 0.0033 at RK 148.1, indicating an absence of untreated WW discharges during both river sampling events (Fig. S3), which is supported by the lack of significant precipitation during both sampling events and no weather triggered CSO events in the HRE. 4. Conclusions In this study we investigated the occurrence and fate of sixteen highly prescribed pharmaceuticals and two potential wastewater tracers in the Hudson River, a large urbanized estuary. Conducting sampling at high spatial resolution permitted evaluation of the variables controlling pharmaceutical behavior in the study area. The main conclusions were: WW discharges into effluent dominated estuaries. Acknowledgments The authors Drs. Abigail Joyce, James Lake, and Mr. Steven Rego for their technical reviews. This is NHEERL Contribution ORD- 022066. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency. Any mention of trade names, products, or services does not imply an endorsement by the U.S. Government or the U.S. Environmental Protection Agency. The EPA does not endorse any commercial products, services, or enterprises. Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.watres.2017.12.044. References Aminot, Y., Le Menach, K., Pardon, P., Etcheber, H., Budzinski, H., 2016. Inputs and seasonal removal of pharmaceuticals in the estuarine Garonne River. Mar. Chem. 185, 3e11. Batt, A.L., Kincaid, T.M., Kostich, M.S., Lazorchak, J.M., Olsen, A.R., 2016. Evaluating the extent of pharmaceuticals in surface waters of the United States using a national-scale rivers and streams assessment survey. Environ. Toxicol. Chem. 35 (4), 874e881. Benotti, M.J., Brownawell, B.J., 2009. Microbial degradation of pharmaceuticals in estuarine and coastal seawater. Environ. Pollut. 157 (3), 994e1002. Bernot, M.J., Becker, J.C., Doll, J., Lauer, T.E., 2016. A national reconnaissance of trace organic compounds (TOCs) in United States lotic ecosystems. Sci. Total Environ. 572, 422e433. Brausch, J.M., Connors, K.A., Brooks, B.W., Rand, G.M., 2012. Human pharmaceuticals in the aquatic environment: a review of recent toxicological studies and con- siderations for toxicity testing. In: Whitacre, D.M. (Ed.), Reviews of Environ- mental Contamination and Toxicology, vol. 218. Springer, New York, pp. 1e99. Brosnan, T.M., Stoddard, A., Hetling, L.J., 2006. Hudson River sewage inputs and impacts: past and present. In: Levinton, J.S., Waldman, J.R. (Eds.), The Hudson River Estuary. Cambridge University Press, New York, pp. 335e348. Buerge, I.J., Poiger, T., Müller, M.D., Buser, H.R., 2003. Caffeine, an anthropogenic marker for wastewater contamination of surface waters. Environ. Sci. Technol. 37 (4), 691e700. Buerge, I.J., Poiger, T., Müller, M.D., Buser, H.R., 2006. Combined sewer overflows to surface waters detected by the anthropogenic marker caffeine. Environ. Sci. Technol. 40 (13), 4096e4102. Cantwell, M.G., Katz, D.R., Sullivan, J.C., Ho, K., Burgess, R.M., Cashman, M., 2016a. Selected pharmaceuticals entering an estuary: concentrations, temporal trends, partitioning, and fluxes. Environ. Toxicol. Chem. 35 (11), 2665e2673. Cantwell, M.G., Katz, D.R., Sullivan, J.C., Borci, T., Chen, R.F., 2016b. Caffeine in Boston Harbor past and present, assessing its utility as a tracer of wastewater contamination in an urban estuary. Mar. Pollut. Bull. 108 (1), 321e324. Cooper, J.C., Cantelmo, F.R., Newton, C.E., 1988. Overview of the Hudson River es- tuary. In: Barnthouse, L.W., Klauda, R.J., Vaughan, D.S., Kendall, R.L. (Eds.), Sci- ence, Law and Hudson River Power Plants: a Case Study in Environmental Impact Assessment. American Fisheries Society, Bethesda, Maryland, pp. 11e24. Cunningham, V.L., 2008. Special characteristics of pharmaceuticals related to environmental fate. In: Kummerer, K. (Ed.), Pharmaceuticals in the Environ- ment: Sources, Fate and Risk. Springer-Verlag Berlin Heidelberg, pp. 23e34. Daughton, C.G., 2001. Pharmaceuticals and care products in the environment: overarching issues and overview. In: Daughton, C.G., Jones-Lepp, T.L. (Eds.), ACS Symposium Series, vol. 791. American Chemical Society, pp. 2e38. De Lange, H.J., Noordoven, W., Murk, A.J., Lürling, M., Peeters, E.T.H.M., 2006. Behavioural responses of Gammarus pulex (Crustacea, Amphipoda) to low concentrations of pharmaceuticals. Aquat. Toxicol. 78, 209e216. Dietrich, S., Ploessl, F., Bracher, F., Laforsch, C., 2010. Single and combined toxicity of pharmaceuticals at environmentally relevant concentrations in Daphnia magna e a multigenerational study. Chemosphere 79 (1), 60e66. Franzellitti, S., Buratti, S., Valbonesi, P., Capuzzo, A., Fabbri, E., 2011. The b-blocker propranolol affects cAMP-dependent signaling and induces the stress response in Mediterranean mussels, Mytilus galloprovincialis. Aquat. Toxicol. 101, 299e308. Gaw, S., Thomas, K., Hutchinson, T.H., 2016. Pharmaceuticals in the marine envi- ronment. Issues Environ. Sci. Technol. 41, 70e91. Geyer, W.R., Chant, R., 2006. The physical oceanography processes in the Hudson River estuary. In: Levinton, J.S., Waldman, J.R. (Eds.), The Hudson River Estuary. Cambridge University Press, New York, pp. 24e38. Howarth, R.W., Marino, R., Swaney, D.P., Boyer, E.W., 2006. Wastewater and watershed influences on primary productivity and oxygen dynamics in the lower Hudson River estuary. In: Levinton, J.S., Waldman, J.R. (Eds.), The Hudson River Estuary. Cambridge University Press, New York, pp. 121e139. Kay, P., Hughes, S.R., Ault, J.R., Ashcroft, A.E., Brown, L.E., 2017. Widespread, routine occurrence of pharmaceuticals in sewage effluent, combined sewer overflows and receiving waters. Environ. Pollut. 220, 1447e1455. Kolpin, D.W., Furlong, E.T., Meyer, M.T., Thurman, E.M., Zaugg, S.D., Barber, L.B., Buxton, H.T., 2002. Pharmaceuticals, hormones, and other organic wastewater contaminants in US streams, 1999-2000: a national reconnaissance. Environ. Sci. Technol. 36 (6), 1202e1211. Kostich, M.S., Batt, A.L., Lazorchak, J.M., 2014. Concentrations of prioritized phar- maceuticals in effluents from 50 large wastewater treatment plants in the US and implications for risk estimation. Environ. Pollut. 184, 354e359. https://doi. org/10.1016/j.envpol.2013.09.013. Launay, M.A., Dittmer, U., Steinmetz, H., 2016. Organic micropollutants discharged by combined sewer overflows e characterisation of pollutant sources and stormwater-related processes. Water Res. 104, 82e92. Maruya, K.A., Schlenk, D., Anderson, P.D., Denslow, N.D., Drewes, J.E., Olivieri, A.W., Snyder, S.A., et al., 2014. An adaptive, comprehensive monitoring strategy for chemicals of emerging concern (CECs) in California’s aquatic ecosystems. In- tegrated Environ. Assess. Manag. 10 (1), 69e77. NYCDEP, 2012. The state of the harbor. Retrieved from. http://www.nyc.gov/html/ dep/pdf/hwqs2012.pdf. NYCDEP, 2016. Green infrastructure performance metrics report. Retrieved from. http://www.nyc.gov/html/dep/pdf/green_infrastructure/gi-performance- metrics-report-2016.pdf. Oppenheimer, J.A., Badruzzaman, M., Jacangelo, J.G., 2012. Differentiating sources of anthropogenic loading to impaired water bodies utilizing ratios of sucralose and other microconstituents. Water Res. 46 (18), 5904e5916. Oppenheimer, J., Eaton, A., Badruzzaman, M., Haghani, A.W., Jacangelo, J.G., 2011. Occurrence and suitability of sucralose as an indicator compound of wastewater loading to surface waters in urbanized regions. Water Res. 45 (13), 4019e4027. Parolini, M., Pedriali, A., Binelli, A., 2013. Application of a biomarker response index for ranking the toxicity of five pharmaceutical and personal care products (PPCPs) to the bivalve Dreissena polymorpha. Arch. Environ. Contam. Toxicol. 64 (3), 439e447. Rocco, L., Frenzilli, G., Fusco, D., Peluso, C., Stingo, V., 2010. Evaluation of zebrafish DNA integrity after exposure to pharmacological agents present in aquatic environments. Ecotoxicol. Environ. Saf. 73 (7), 1530e1536. Rocco, L., Frenzilli, G., Zito, G., Archimandritis, A., Peluso, C., Stingo, V., 2012. Gen- otoxic effects in fish induced by pharmacological agents present in the sewage of some Italian water-treatment plants. Environ. Toxicol. 27 (1), 18e25. Roig, B., D’Aco, V., 2016. Distribution of pharmaceutical residues in the environment. Issues Environ. Sci. Technol. 41, 34e69. Seiler, J.P., 2002. Pharmacodynamic activity of drugs and ecotoxicologydcan the two be connected? Toxicol. Lett. 131 (1), 105e115. Sengupta, A., Lyons, J.M., Smith, D.J., Drewes, J.E., Snyder, S.A., Heil, A., Maruya, K.A., 2014. The occurrence and fate of chemicals of emerging concern in coastal urban rivers receiving discharge of treated municipal wastewater effluent. Environ. Toxicol. Chem. 33 (2), 350e358. Soh, L., Connors, K.A., Brooks, B.W., Zimmerman, J., 2011. Fate of sucralose through environmental and water treatment processes and impact on plant indicator species. Environ. Sci. Technol. 45 (4), 1363e1369. Steinberg, N., Suszkowski, D., Clark, L., Way, J., 2004. Health of the Harbor Report. Hudson River Foundation, p. 81. Ternes, T.A., Herrmann, N., Bonerz, M., Knacker, T., Siegrist, H., Joss, A., 2004. A rapid method to measure the solidewater distribution coefficient (Kd) for pharma- ceuticals and musk fragrances in sewage sludge. Water Res. 38 (19), 4075e4084. USEPA, 2016. Enforcement and compliance history online. https://echo.epa.gov/ (Accessed 2 December 2016). USEPA, 2017. Contaminants of emerging concern including pharmaceuticals and personal care products. https://www.epa.gov/wqc/contaminants-emerging- concern-including-pharmaceuticals-and-personal-care-products (Accessed 30 June 2017). USGS, 2017. National water information system. http://waterdata.usgs.gov/nwis (Accessed 30 June 2017). Verlicchi, P., Al Aukidy, M., Zambello, E., 2012. Occurrence of pharmaceutical compounds in urban wastewater: removal, mass load and environmental risk after a secondary treatmentda review. Sci. Total Environ. 429, 123e155. Wall, G.R., Nystrom, E.A., Litten, S., 2008. Suspended sediment transport in the freshwater reach of the Hudson River estuary in eastern New York. Estuar. Coast 31 (3), 542e553. Yang, Y.Y., Liu, W.R., Liu, Y.S., Zhao, J.L., Zhang, Q.Q., Zhang, M., Zhang, J.N., Jiang, Y.X., Zhang, L.J., Ying, G.G., 2017. Suitability of pharmaceuticals and personal care products (PPCPs) and artificial sweeteners (ASs) as wastewater indicators in the Pearl River Delta. South China. Sci. Tot. Environ 590, 611e619. Young, S., Juhl, A., O’Mullan, G.D., 2013. Antibiotic-resistant bacteria in the Hudson River Estuary linked to wet weather sewage contamination. J. Water Health 11 (2), 297e310. Yu, Z., Jiang, L., Yin, D., 2011. Behavior toxicity to Caenorhabditis elegans transferred to the progeny after exposure to sulfamethoxazole at environmentally relevant concentrations. J. Environ. Sci. 23, 294e300. Zhao, H., Zhou, J.L., Zhang, J., 2015. Tidal impact on the dynamic behavior of dis- solved pharmaceuticals in the Yangtze Estuary, China. Sci. Total Environ. 536, 946e954.
https://writemyresearchpaper.us/wp-content/uploads/2021/08/whatsapp-logo-300x115.jpeg 0 0 Write my research paper https://writemyresearchpaper.us/wp-content/uploads/2021/08/whatsapp-logo-300x115.jpeg Write my research paper2020-01-17 10:20:062020-01-17 10:20:06This worksheet will you to summarize the most important information from a peer-reviewed journal article. The article will be provided to you by your instructor. You should summarize the information