Next Article in Journal
Impact of Nano–Sized Polyethylene Terephthalate on Microalgal–Bacterial Granular Sludge in Non–Aerated Wastewater Treatment
Next Article in Special Issue
Accumulation and Origin of Phosphorus and Heavy Metals in Citrus Orchard Soils in Jeju Island, South Korea: Potential Ecological Risks and Bioavailability
Previous Article in Journal
A Review of Drought Disturbance on Socioeconomic Development
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 15
Issue 22
10.3390/w15223913
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Environmental Monitoring and Risk Assessment of Pharmaceutical Residues Discharged from Large Livestock Complex in the Geum River Basin, South Korea

by
Hyeri Lee
1,2,
Minhee Chae
1 and
Seokwon Lee
1,*
1
Geum River Environment Research Center, National Institute of Environmental Research, 182-18 Jiyong-ro, Okcheon-gun 29027, Chungbuk, Republic of Korea
2
Environmental Measurement and Analysis Center, National Institute of Environmental Research, 42 Hwangyeong-ro, Seo-gu, Incheon 22689, Republic of Korea
*
Author to whom correspondence should be addressed.
Water 2023, 15(22), 3913; https://doi.org/10.3390/w15223913
Submission received: 12 October 2023 / Revised: 31 October 2023 / Accepted: 31 October 2023 / Published: 9 November 2023
(This article belongs to the Special Issue Transport of Pollutants in Agricultural Watersheds)
Browse Figures
Versions Notes

Abstract

:
This study aims to collect water samples from two tributaries within the Geum River basin in South Korea, where large-scale livestock complexes are located, to quantify the measured environmental concentration (MEC) of pharmaceutical residues using a multiresidue analytical method developed with liquid chromatography–tandem mass spectrometry (LC-MS/MS), and to evaluate the environmental risks posed by the detected pharmaceuticals to aquatic organisms. The water samples were collected at a total of 17 points, including up-, middle-, and downstream of the Seoksong and Nonsan-Gangkyoung streams connected to the Geum River, from October 2018 to March 2019. A multiresidue analytical method using LC-MS/MS was developed to quantify 49 pharmaceuticals with hydrophilic lipophilic balance using solid phase extraction. The recovery rates varied between 67.23% and 136.98%, while the limits of quantification were from 3.99 to 46.32 ng/L. Ecotoxicological information on acute and chronic effect endpoints (e.g., EC50, NOEC, etc.) was obtained from the U.S. EPA ECOTOX Knowledgebase. Considering the worst-case scenario, the lowest observed effect endpoint (mainly NOEC) of the most sensitive species was selected, and predicted no effect concentration (PNEC) values were calculated by dividing the endpoint by an assessment factor (AF). The mean, minimum, and maximum MECs of pharmaceuticals were divided by PNECs to calculate risk quotient (RQ). Caffeine was detected in all sampling sites with a detection frequency of 100%. High levels of pharmaceuticals (9.212 μg/L of sulfathiazole, 8.479 μg/L of acetaminophen, and 5.885 μg/L of florfenicol) were detected. The RQ values exceeded 1 and reached up to 84.79 (high risk category) for acetaminophen, and were between 0.11 and 0.83 (moderate risk) for carbamazepine, etc. The RQs for the rest of the 15 substances were below 1 (low risk). In the future, further studies should be conducted to monitor other micropollutants, including industrial chemicals, pesticides, etc., at different locations of the Geum River basin, including livestock farms, pharmaceutical manufacturing facilities, wastewater treatment plants, and other facilities, for long-term period.

1. Introduction

In recent years, trace levels of various groups of chemicals, typically ranging from ng/L to μg/L, have been found to be persistent in aquatic environments for long extended periods, adversely affecting the water quality and aquatic ecosystems, and have been named “micropollutants” [1,2,3]. These micropollutants include various substances, such as pharmaceuticals, personal care products (PCPs), pesticides, flame retardants, and perfluorinated compounds (PFCs) [4,5,6]. Pharmaceutical compounds are mainly discharged into aquatic environments through a variety of sources, such as municipal wastewater, industrial wastewater, livestock wastewater, and manure application [7]. They are mainly used in humans, animals, and agriculture for the treatment and prevention of diseases. These low concentrations of chemical compounds are directly released into the aquatic environment through various channels, including public sewage treatment plants, hospitals, industrial complexes, and wastewater treatment facilities. Several studies have reported that certain highly persistent micropollutants are not completely broken down and frequently detected at high concentration levels in aquatic environments [7,8,9,10].
In previous studies, Jaffrézic et al. [11] reported that animal-specific pharmaceuticals were detected at higher levels of concentrations than those of human-specific pharmaceuticals. Sulfonamides of twelve antibiotics were detected at the maximum concentrations (24–385 ng/L) with maximum detection frequencies (76–100%) [12]. Iopromide, atenolol, TCPP (tris(chloroisopropyl) phosphate), TECP (tris(2-chloroethyl) phosphate), musk ketone, naproxen, DEET (N,N-diethyl-meta-toluamide), carbamazepine, caffeine, and benzophenone are frequently detected in both river and creek samples from the Han River, Republic of Korea [13]. Carbamazepine was detected in the overall water system at representative sites and at the Geum River tributary, whereas tetracycline pharmaceuticals and epimer isomers were detected around livestock farm areas [14]. At a livestock wastewater disposal plant, the highest level of lincomycin detected was 477 μg/L in the Nakdong River [15].
Pesticides and pharmaceutical substances, including atrazine, carbamazepine, and metformin, were detected in the Han River, Nakdong River, and Yeongsan River, which are among the four major river basins in South Korea. The concentration range of these micropollutants was found to be 0.1–58 μg/L. Furthermore, 13 types of perfluorinated compounds, including PFOA, PFOS, PFBS, and PFCAs, were also detected at concentrations ranging from 0.01 to 0.5 μg/L. Consequently, it was observed that the exposure risks of both human health and aquatic environments were relatively high, considering the measured concentrations of these micropollutants [10]. Most pharmaceuticals are polar and non-volatile and are usually analyzed by liquid chromatography–(tandem) mass spectrometry (LC-MS or LC-MS/MS). Various types of MS have been applied, for example triple quadrupole (QqQ) MS [11,16,17], LC-Q-IT (iontrap) MS [8], LC-IT-TOF (time of flight) MS [18], and other techniques. Pharmaceuticals have been extracted using solid phase extraction (SPE) cartridges from water samples, including hydrophilic–lipophilic balance (HLB) [11,16,19], strong anion exchange (SAX) + HLB [12], mixed-mode cation exchange (MCX) [18], and on-line SPE [11].
However, few studies, such as via regular environmental monitoring and measurements of organic substances, including pharmaceutical residues, have investigated detection patterns and the primary pollution routes in the surrounding rivers and streams of the Geum River basin in South Korea. This area is characterized by a high density of livestock farms, organic fertilizer plants, and agricultural industrial complexes. Furthermore, an accurate quantitative analysis method has not been established to measure and analyze various types of pharmaceutical substances. As a result, the concentration levels of these substances, their spatio-temporal patterns, and specific locations of point sources have not been fully identified in major rivers within the Geum River watershed. Consequently, robust legal regulations and environmental pollution management processes have been insufficient due to the lack of environmental monitoring data, toxicological information on their acute and chronic effects, and information on potential exposure risks for humans and the aquatic environment.
The objective of this study is to develop a multiresidue analytical methodology using LC-MS/MS with HLB cartridge pretreatment. This method will enable the simultaneous analysis of surface water samples collected from large livestock and agricultural complexes in two tributaries of the Geum River basin. The present study also aims to determine the measured concentration levels, identify detection patterns of pharmaceutical residues, and finally evaluate the environmental risk posed by the detected substances to aquatic organisms.

2. Materials and Methods

2.1. Selection of Study Sites and Environmental Sampling

After reviewing the data obtained from the 2018 Water Emission Management System [20], field surveys were conducted at five large livestock complexes with substantial emissions related to livestock manure treatment facilities, situated alongside rivers and tributaries in the Geum River basin. Out of these five livestock complexes, the Seokseong and Nonsan-Gangkyoung streams, located near Nonsan city, Chungcheongnam-do, were chosen as they have the highest livestock populations, including pigs and chickens. This is due to their high operational percentages and the direct discharge of significant amounts of livestock excrement into the main stream of the Geum River through livestock manure treatment facilities. Sampling sites (n = 15) in the tributary rivers were selected, covering the upstream, middle, and downstream segments of the Seokseong (S1 to S6) and Nonsan-Gangkyoung (N1 to N9) streams (Figure 1).
In October 2018, surface water samples were collected for various water specifications, including water temperature, pH, conductivity, and dissolved oxygen concentrations, using a multiparameter water quality meter (Pro DSS, YSI Inc., Yellow Springs, OH, USA), and the river flow was assessed with a flow meter (Model 002, Valeport Ltd., Devon, UK). For all sites, a total of 1 L was collected as a grab sample using pre-cleaned amber glass containers. Samples were kept on ice during transportation to the laboratory and stored at 4 °C until extraction. All samples were extracted and analyzed within fourteen days from collection. The basic water quality parameters (e.g., BOD, COD, TOC, SS, TN, DTN, NO3-N, and NH3-N) were also analyzed using the Ministry of Environment’s water quality testing method.

2.2. Quantitative Analysis and Method Validation

In this study, all pharmaceutical substances were identified through qualitative analysis, and the general information is summarized in Table 1. For the quantitative analysis of these pharmaceutical substances, HPLC-grade high-purity standard substances (>90%) were used (Table 2). These compounds were purchased from Sigma–Aldrich (St. Louis, MO, USA), Acros Organics (Geel, Belgium), and Dr. Ehrenstorfer (Teddinton, UK). Atrazine-d5, used as an internal standard, was purchased from Sigma–Aldrich, and 13C3-trimethoprim, 13C6-sulfamethazine, 13C6-sulfamethoxazole, and thiabendazole (ring-13C6) used as surrogate standards were purchased from Cambridge Isotope Laboratories, Inc. (Tewksbury, MA, USA). According to Table 2, individual stock standard, isotopically labeled internal standard, and surrogate standard solutions were prepared at the concentrations of 50, 100, 200, 500, and 1000 mg/L with appropriate solvents. After completing preparation, all standards were stored at −20 °C. The working standard solutions, containing all antibiotics, were prepared in methanol.
The cartridges used for SPE were Oasis HLB (200 mg, 3 mL) from Waters Corporation (Milford, MA, USA). GF/C filter papers were purchased from Whatman (UK). HPLC-grade methanol, acetonitrile, and water were supplied by J.T. Baker (Darmstadt, Germany). Ammonium hydroxide, hydrochloric acid (37%), ethylenediaminetetraacetic acid disodium salt solution (Na2EDTA), and formic acid (98%) were from Sigma–Aldrich. The water samples (500 mL) were spiked with 0.2 mL of surrogate standard solutions (100 μg/L) and filtered through GF/C filters, and the pH was adjusted to 2 with 1 M hydrochloric acid solution. Divalent cations were complexed by the addition of 500 mg of Na2EDTA to extend the extraction efficiency. Oasis HLB cartridges were employed to clean up and concentrate the samples. The cartridges were pre-conditioned sequentially with 5 mL methanol and 5 mL deionized water. Samples were loaded through the cartridges and afterwards, the target compounds were eluted with methanol (4 mL × 2). Eluates were concentrated with a gentle nitrogen stream at 40 °C and reconstituted with methanol (1 mL) after adding the internal standard solution (100 ng).
LC-MS/MS analysis was performed on a 6470 Triple Quad LC/MS coupled to 1290 Infinity II UHPLC (Agilent Technologies, Inc., Santa Clara, CA, USA) operated in electrospray ionization mode. The analytical column was an HSS T3 column (100 × 2.1 mm i.d., 2.6 μm, Waters Corporation, Milford, MA, USA), and the oven temperature of the column was 40 °C. The injection volume was 5 μL and the mobile phases were eluted at 0.3 mL min−1. Mobile phases were 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). For gradient elution, the initial combination was 95:5 (A:B, v/v), and after 2 min the B solution was increased to 95% for 5 min, and held for 2 min. To establish the scheduled multiple reaction monitoring (MRM) condition on the 6470 Triple Quad LC/MS, precursor ions, product ions, fragmentor voltages, and collision voltages were optimized through the flow injection of each compound standard solution (1 μg/mL) (Table 2). To calculate the method of detection limits (MDLs) and limits of quantification (LOQs), seven replicated samples were prepared by adding fortifying compounds (14 ng/L) to deionized water and analyzed with the established method. For the recovery test, the deionized water sample (500 mL) was placed into a 1000 mL glass bottle and fortified with the standard mixture solution at 100 ng/L levels, except for acetaminophen, clinafloxacin, and nifedipine at 200 ng/L. Subsequently, the sample was treated via the above sample preparation method and quantitatively analyzed by LC–MS/MS.

2.3. Environmental Risk Assessment

Ecotoxicological data and information on 49 pharmaceutical substances were collected from the US EPA ECOTOX Knowledgebase (https://cfpub.epa.gov/ecotox/) (accessed on 23 July 2023), which is one of the largest databases to have been validated by previous studies [21,22] regarding the accuracy and reliability of the test methods, species, and results. The ecotoxicological effects of each pharmaceutical were assessed based on toxicity values, such as EC50, LC50, LOEC, and NOEC, which covered acute and chronic endpoints (e.g., survival, growth, behavior, reproduction, etc.). These tests were conducted on standard test species in freshwater, including algae, crustaceans, and fish, with high accuracy and reliability. Considering for the worst-case scenario, the lowest toxicity values were selected from the most sensitive test species. Assessment factors (AF) were also chosen in the range of 10 to 1000, taking into consideration the different nutrition stages of each species (US EPA, http://www.epa.gov/risk_assessment/glossary.htm) (accessed on 1 August 2023), as guided by reference guidelines [23] and the European Commission [24]. Subsequently, we calculated the predicted no-effect concentration (PNEC) values by dividing the acute or chronic toxicity value by the selected AF, following Equation (1).
P N E C = L o w e s t   N O E C   o r   E C 50 A F
Risk quotient (RQ) was determined by dividing the mean, minimum, and maximum values of the measured environmental concentration (MEC) for pharmaceuticals in the surface water samples collected from the upstream to the downstream of the Seokseong and Nonsan-Gangkyoung streams in the Geum River basin area by PNEC at a screening level.
R i s k   Q u o t i e n t   ( R Q ) = M E C P N E C
Based on the criteria suggested by several previous studies [3,25,26,27,28], the risk category was determined by classifying the calculated RQ into three categories. The three risk categories are defined as follows: RQ < 0.1 (low risk), 0.1 ≤ RQ < 1 (moderate risk), and RQ ≥ 1 (high risk), with RQ exceeding 1 indicating a high level of risk. All statistical analyses were conducted using IBM SPSS Statistics for Windows version 23.0 (IBM Corp., Armonk, NY, USA) and R statistical software version 4.2.2 with Rstudio version 2023.03.1+446 (Rstudio Inc., Boston, MA, USA). A p-value less than 0.05 in a two-sided test was considered statistically significant.

3. Results and Discussion

The performance of the analytical method was evaluated through the estimation of the linearity, recoveries, MDLs, and LOQs. Quantification was based on linear regression calibration curves. The calibration curves provided good fits (r2 > 0.99) over the established concentrations, ranging from 0.5, 1, 2, 5, 10, 20, 25, 50, 75, and 100 ng/L, depending on the compounds (Table 3). The concentrations of surrogate and internal standards were set at 50 ng/L and 100 ng/L, respectively. MDLs were calculated based on the standard deviations of seven surface water samples spiked with target analytes at concentrations of 100 ng/L, except for acetaminophen, clinafloxacin, and nifedipine, at concentrations of 200 ng/L. The MDLs of target compounds were within the range of 2.39 ng/L to 14.54 ng/L, while LOQs ranged from 7.60 ng/L to 46.32 ng/L (Table 3).
The recovery rates were calculated to verify the accuracy and precision of the measurements. The recoveries of water samples ranged from 67.2% to 137.0% and the relative standard deviations (%RSD) were satisfactory, ranging from 3.2% to 17.6%. Acetaminophen, clinafloxacin, and nifedipine, with lower sensitivities than the others, were fortified at 200 ng/L (Figure 2). The water quality parameters (e.g., water temperature, pH, biochemical oxygen demand, chemical oxygen demand, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, electrical conductivity, and suspended solids) of samples were measured in the field using a water quality multiprobe. The values of electrical conductivities at the upstream points, such as S1 and N1, were 1896 μS/cm and 950 μS/cm, respectively, which are higher than those taken at the rest of the sampling sites (Figure 3).
The validated methodology developed in this study was applied to all water samples collected from the Seokseong and Nonsan-Gangkyoung streams of the Geum River watershed. The summary statistics for the measured environmental concentrations of 49 pharmaceutical substances are presented in Table 4. The overall arithmetic mean (AM) and standard deviation (SD) were calculated as 0.017 ± 0.74 μg/L, with concentrations ranging from 0.001 to 9.212 μg/L. The six highest concentrations detected were as follows: 9.212 μg/L (sulfathiazole at S1), 8.479 μg/L (acetaminophen at S1), 8.036 μg/L (marbofloxacin at N1), 5.885 μg/L (florfenicol at N1), 1.591 μg/L (4-epichlortetracycline at N1), and 1.487 μg/L (chlortetracycline at N1) (Figure 4). Out of the 49 pharmaceuticals, the overall detection frequency (%) was 25.5%, with the highest detection frequency being 100% for caffeine. Some pharmaceuticals, including acetaminophen, azithromycin, carbamazepine, florfenicol, sulfamerazine, sulfamethazine, sulfamethoxazole, and sulfathiazole, were also detected at a frequency of ≥50%. While higher concentrations and a greater variety of pharmaceutical compounds were observed in the Seokseong stream (AM ± SD: 0.20 ± 0.89 μg/L) compared to the Nonsan-Gangkyoung stream (AM ± SD: 0.17 ± 0.69 μg/L), there was no significant difference between the two streams (Table 4).
Nevertheless, we observed a similar pattern in the concentration levels of pharmaceuticals measured in the Seokseong and Nonsan-Gangkyoung streams, as well as the Geum River, with the highest MEC at the upstream locations, which values gradually decreased into the downstream areas (Figure 5). The concentrations of six pharmaceuticals, including azithromycin, carbamazepine, clarithromycin, florfenicol, roxithromycin, and trimethoprim, were higher in the middle points of both streams (S4 and S5 of Seokseong stream, N5 of Nonsan-Gangkyoung stream) compared to the upper and downstream locations. For example, carbamazepine, which is used for human diseases such as seizure disorders and neuropathic pain [29], was highly detected, with concentrations of 0.065 μg/L (at S4) and 0.055 μg/L (at N5) in the middle points of both streams. This suggests that the elevated concentration levels at these middle points might be attributed to the significant amounts of human activities and sewage treatment plants, rather than the upstream livestock farms.
Carbamazepine was also detected in five rivers in the Busan area, including the Nakdong and Maekdo Rivers, at concentrations ranging from 0.012 to 0.095 μg/L, with an average concentration of 0.037 ± 0.030 μg/L. In rivers in the Ulsan watershed, such as the Taehwagang and Dongcheongang Rivers, concentrations of carbamazepine were also detected, with the concentrations reaching up to 0.146 μg/L [30]. Within the Nakdong River watershed, the maximum observed level of carbamazepine was 0.089 μg/L in the middle reaches and 0.177 μg/L in the lower reaches, while the mean concentration was relatively lower, at 0.0016 μg/L in the upper reaches [31]. Moreover, in the surface water collected from rivers near a large pharmaceutical industrial complex area, the concentrations of acetaminophen were found to be high, reaching levels of 341 μg/L in the middle and 127 μg/L in the lower reaches [32]. Most of the detected pharmaceuticals, such as acetaminophen, anhydrotetracycline, caffeine, and chlortetracycline, were detected at higher concentrations in the upstream areas compared to the downstream areas. This could be attributed to the proximity of these upstream sites to livestock farming complexes. A group of tetracyclines, including anhydrotetracycline and chlortetracycline, and a group of sulfonamides, including sulfamerazine, sulfamethazine, sulfamethoxazole, and sulfathiazole, were also detected in the study area. This finding aligns with those of Lim et al. [33], who reported that antimicrobials of tetracycline, penicillin and sulfonamide have higher rates of sales than others.
Kim et al. [14] reported 2.91 μg/L, 3.52 μg/L, 0.73 μg/L, and 1.23 μg/L as the maximum concentrations for tetracycline, 4-epitetracycline, anhydrotetracycline, and 4-epianhydrotetracycline, respectively, in river samples from the livestock complex area. Lee et al. [31] also reported that clarithromycin was detected at 0.0316 μg/L, with mean concentrations of 20–65%, in the Nakdong River Basin, whereas it was detected at 10.07 to 45.12 ng/L in the Nonsan-Gangkyoung stream in this study. The highest detection frequency was observed for sulfamethazine (75%) at 0.03 to 211 μg/L, followed by oxytetracycline (64%) ranging from 0.07 to 72.9 μg/L in animal wastewater and surface water around farms in Jiangsu Province, China (Wei et al. 2011) [17]. In this study, sulfamethazine and oxytetracycline were detected at levels ranging from 5.76 to 385.47 ng/L and 14.22 to 21.22 ng/L, respectively.
Table 5 gives detailed information on the test species, type, duration, toxicological effects, endpoints, lowest concentrations, AF, and PNEC values for each pharmaceutical. Among the 49 pharmaceuticals, only 30, including acetaminophen, carbamazepine, florfenicol, and others, had acute or chronic aquatic toxicity data available in the US EPA ECOTOX Knowledgebase (https://cfpub.epa.gov/ecotox/) (accessed on 23 July 2023). The most sensitive endpoints in these toxicity data were mainly chronic effects (LOEC or NOEC) on the population, and the growth and mortality of algae, fish, and crustaceans. The lowest PNEC was estimated as 0.01 μg/L for fluoxetine and diphenhydramine, and the highest one was 1000 μg/L for sulfadimethoxine.
Table 6 presents the results of the risk assessment, including risk quotients (RQ) and three risk categories (low, medium, and high). The RQ values are categorized as follows: The values of 6.33 (Min–Max: 0.04–58.85) for florfenicol, 5.27 (0.13–84.79) for acetaminophen, 2.22 for fluoxetine, 1.15 (0.11–6.20) for chlortetracycline were classified into the high-risk category. RQ values of 0.83 (0.08–7.92) for diphenhydramine, 0.32 (0.11–0.85) for ampicillin, 0.24 (0.02–0.71) for carbamazepine, 0.20 (0.02–1.56) for caffeine, 0.15 (0.001–0.79) for clarithromycin, 0.11 (0.01–0.66) for ofloxacin, and 0.10 (0.001–0.41) for thiabendazole were classified into the moderate-risk category. The remaining pharmaceuticals, including roxithromycin (RQ = 0.08), azithromycin and marbofloxacin (0.04), oxytetracycline (0.03), and the rest of the substances, had lower RQ values (below 0.1), and these were categorized as low-risk (Figure 6). In a previous study, Wang et al. [59] found that chlortetracycline’s (137.59 mg/L at 48 h-EC50) toxicity against Daphnia magna was significantly higher than that of tetracycline (617.2 mg/L at 48 h-EC50). In another study, Kim et al. [52] reported that the hazard quotients calculated for carbamazepine and trimethoprim were 0.0044 and 0.0017, respectively. However, the hazard quotients for sulfamethoxazole and acetaminophen were 6.3 and 1.8, respectively. The hazard quotient (HQ) values reported by Lee et al. [31] for carbamazepine, clarithromycin, sulfathiazole, and trimethoprim in the Nakdong River watershed, with values of 0.001, 0.14, 0.00003, and 0.00004, respectively, are quite similar to those found in our study.
It is worth noting that the RQ values for acetaminophen, carbamazepine, and sulfathiazole were significantly lower than 0.1, indicating a low risk. On the other hand, two pharmaceuticals, clarithromycin and sulfamethazine, were found to pose higher potential risks to the aquatic environment, with RQ values exceeding 1 during the spring, summer, and autumn seasons in a study that identified temporal–spatial variations and environmental risks [27]. In another study conducted by Kim et al. [10], the RQ values for pharmaceuticals and personal care products (PPCPs) detected in the surface water of the four major rivers (Han River, Nakdong River, Geum River, and Yeongsan River) in Korea were high in several pharmaceutical substances, with values of 17.34 for clotrimazole, 2.54 for azithromycin, 1.66 for Imidacloprid, 1.61 for dichlorovos, and 1.00 for lincomycin. However, the RQ values of 19 PPCPs, including clarithromycin, albendazole, and sulfapyridine, were observed to be lower than 0.1. Similar to previous study results, we found that most of the detected pharmaceutical substances had low risks of less than 0.1, but the risks of several pharmaceuticals, such as azithromycin and clarithromycin, exceeded 1.
On the other hand, high concentrations of pharmaceuticals, including carbamazepine (ranging from 0.4 to 35.0 ng/L), sulfamethoxazole (ranging from 0.1 to 4.2 ng/L), ketoprofen (ranging from 55.4 to 888.4 ng/L), gemfibrozil (ranging from 16.16 to 17.1 ng/L), and ibuprofen (ranging from 22.6 to 8330.9 ng/L), were detected in surface waters directly discharged from wastewater treatment plants in the Gwangju area of South Korea. The RQ values for these substances exceeded 1, indicating high potential risks to aquatic environments [26]. In another study conducted within the metropolitan area of South Korea, high concentrations of pharmaceuticals were also detected in surface water samples collected from two large concentrated animal feeding operations (CAFO) facilities. These pharmaceuticals included acetaminophen (ranging from 0.53 to 38.8 μg/L), chlortetracycline (from 0.28 to 3.33 μg/L), oxytetracycline (from 0.10 to 16.9 μg/L), sulfachlorpyridazine (from 0.003 to 6.13 μg/L), sulfamethazine (from 0.20 to 21.30 μg/L), and sulfamethoxazole (from 0.11 to 3.91 μg/L). These pharmaceuticals were analyzed using LC-MS/MS, and the calculated RQ values ranged from 1 to a maximum level of 3,880 [60].
In 2020, high concentrations of pharmaceuticals, including acetaminophen (341 μg/L), clarithromycin (4.97 μg/L), diclofenac (34.5 μg/L), ibuprofen (86 μg/L), and mefenamic acid (44.2 μg/L), were found in the surface water collected from the effluents of industrial complexes with pharmaceutical manufacturing facilities producing various pharmaceuticals and sanitary products. These complexes were located in the Korean metropolitan area, and the waterways were directly connected to discharge ports of wastewater treatment plants. For most of the pharmaceuticals, the RQ values exceeded 1, ranging from 0.01 to 221.0. Notably, the RQ values were relatively high at the upstream points, and significantly decreased toward the downstream. This suggests a potential environmental risk associated with the discharge of these pharmaceuticals into the water systems connected to the wastewater treatment plants [32].
In a study conducted by Park et al. [61], a similar pattern to that seen in several previous studies was observed [26,32,60,61]. In the upstream regions of Nakdong River, pharmaceuticals, pesticides, and industrial chemical complexes were detected at high concentrations in the effluents, leading to the increase in environmental risks. However, as these substances flowed toward the downstream area from the discharge points, there were significant reductions in both measured concentration levels and associated environmental risks. Similarly, in the present study, we observed that in the upper reaches of the Seokseong and Nonsan-Gangkyoung streams, the measured concentrations and RQ values for pharmaceuticals were also high. As the water moved downstream, both pharmaceutical concentrations and RQ values significantly decreased. These recent studies, which collected indicator water samples from pollution sources such as wastewater treatment facilities, pharmaceutical and hygiene product manufacturing facilities, and livestock facilities, detected high concentrations of pharmaceuticals like acetaminophen. This encourages further studies, with the additional monitoring and evaluation of environmental risks and investigations, in the future.
To the best of our knowledge, this is the first study performing environmental monitoring, field surveys, risk assessments, and literature reviews for large-scale livestock complexes where pharmaceutical residues are being generated in the Geum River basin. The study subjects were selected considering various environmental factors, including livestock manure production, the operation status and final discharge routes of wastewater treatment facilities, the confluence with the Geum River, and the feasibility of collecting surface water samples on site. Subsequently, the water samples were collected from the Seokseong and Nonsan-Gangkyoung stream areas located in the western part of the Geum River basin, where the target subjects were identified. After the pre-treatment of the HLB (hydrophilic-lipophilic balance), the diluted standard solutions were prepared using solvents, such as methanol. Furthermore, we established an effective analytical method using LC-MS/MS under multiple reaction monitoring (MRM) conditions. That is, we established optimal analytical conditions by systematically considering sensitivity and selectivity through full and product ion scans. We also validated the analytical conditions through a robust QA/QC process, including recovery rates, accuracy, and precision calculations. Using this validated method, we conducted quantitative analyses of the 49 pharmaceuticals present in the collected water samples.
Next, the PNEC values were used to evaluate the environmental risks of pharmaceuticals using chronic toxicity data, mostly NOEC, collected from the latest reliable ecological toxicity database (i.e., US EPA ECOTOX Knowledgebase). Several studies have reported various types of pharmaceuticals in the four major river basins of South Korea, including the Han River basin, Nakdong River basin, and Yeongsan River basin. The environmental risks of these detected pharmaceutical substances have been quantitatively evaluated in different river basins and time periods. The authors found that some RQ values exceeded 1, while others fell below it. In the present study, the concentration levels of detected pharmaceuticals and their environmental risks were consistent with the previous study results. Therefore, our findings suggest that the measured concentrations of pharmaceuticals shown in this study can be used as baseline information and standards for future environmental monitoring and risk assessments related to various pharmaceuticals or other types of micropollutants in other rivers or tributaries within the Geum River basin in the future.
However, there are limitations in this study. Since the water quality samples were collected in autumn and winter (some in early spring) from two tributaries in the Geum River basin, the sample size was relatively small and thus unable to yield results related to seasonal and spatial variations, and the study results are insufficient to represent environmental concentrations and risks for all pharmaceutical substances in the entire Geum River watershed. Similarly to a study conducted by Im et al. [27], which investigated the seasonal variation of pharmaceuticals in the Han River basin from spring to autumn in the mid-2010s, it is necessary to conduct additional environmental monitoring over the course of a year or even longer, in the same study area, in order to accurately analyze and infer a temporal trend or tendency. Furthermore, expanding the environmental monitoring and risk assessment of pharmaceuticals to other rivers, streams, or tributaries with large livestock complexes within the Geum River basin, which may have different point sources of water pollution from industrial, pharmaceutical, and agricultural manufacturing complexes, including main rivers and tributaries (e.g., the Miho River and Gapcheon stream, etc.), is also needed.
Therefore, further studies should include long-term monitoring for various pharmaceuticals considering environmental factors, such as season, location, and pollution sources. This will allow us to build large-scale monitoring datasets of pharmaceuticals of interest, identify temporal–spatial patterns and variations, and comprehensively assess human health and environmental risks in aquatic environments. Based on these efforts, it is anticipated that the characteristics and trends of micropollutants (i.e., pharmaceuticals, etc.) discharged from point sources within the Geum River basin can be fully understood and evaluated in the future.

4. Conclusions

In summary, this study involved the environmental monitoring and analysis of surface water samples collected from both the Seokseong and Nonsan-Gangkyoung streams near large-scale livestock complexes in the Geum River basin in order to assess the measured concentration levels and environmental risks of 49 pharmaceutical residues. We established a multiresidue analytical method using the LC-MS/MS instrument after pretreatment with HLB cartridges. Using the established method, we successfully quantified the concentration levels of 49 pharmaceuticals, and the maximum concentrations of individual pharmaceuticals were detected as 9.212 μg/L of sulfathiazole, 8.479 μg/L of acetaminophen, 8.036 μg/L of marbofloxacin, and 5.885 μg/L of florfenicol in the aquatic environment. Moreover, the RQ values were calculated to be in the range of 1.15–84.79 (high risk) for four pharmaceuticals including acetaminophen, 0.11–0.83 (moderate risk) for seven substances including carbamazepine, and below 0.1 (low risk) for the rest of the substances. Our study findings emphasize that there may be high exposure potential and environmental risks associated with pharmaceuticals that impact human health, aquatic environments, and various species of organisms in the Geum River basin. In the future, further longitudinal studies should be conducted for the long-term monitoring of various pharmaceuticals in the Geum River basin. It is also important to build a large-scale monitoring database based not only on the Seokseong and Nonsan-Gangkyoung streams, but also on other main rivers and tributaries within the Geum River basin. This will help us characterize spatial and temporal patterns of detected pharmaceuticals and identify exact point sources of pollution; such investigations will be beneficial to evaluations of human health and environmental risks.

Author Contributions

Conceptualization, H.L., S.L. and M.C.; methodology, H.L. and S.L.; software, H.L. and S.L.; validation, H.L., S.L. and M.C.; formal analysis, H.L. and S.L.; investigation, H.L.; resources, S.L.; data curation, H.L. and S.L.; writing—original draft preparation, H.L. and S.L.; writing—review and editing, H.L., S.L. and M.C.; visualization, H.L. and S.L.; supervision, M.C.; project administration, H.L. and S.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (ME) of Republic of Korea (no. NIER-2018-01-01-076).

Data Availability Statement

The data presented in this study are available on request from the corresponding author. Requests should be sent to [email protected] and are subject to approval by all named authors participating in this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Luo, Y.; Guo, W.; Ngo, H.H.; Nghiem, L.D.; Hai, F.I.; Zhang, J.; Liang, S.; Wang, X.C. 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, 619–641. [Google Scholar] [CrossRef] [PubMed]
  2. Ebele, A.J.; Abou-Elwafa Abdallah, M.; Harrad, S. Pharmaceuticals and personal care products (PPCPs) in the freshwater aquatic environment. Emerg. Contam. 2017, 3, 1–16. [Google Scholar] [CrossRef]
  3. Yang, Y.; Zhang, X.; Jiang, J.; Han, J.; Li, W.; Li, X.; Yee Leung, K.M.; Snyder, S.A.; Alvarez, P.J.J. Which Micropollutants in Water Environments Deserve More Attention Globally? Environ. Sci. Technol. 2022, 56, 13–29. [Google Scholar] [CrossRef]
  4. Nikolaou, A.; Meric, S.; Fatta, D. Occurrence patterns of pharmaceuticals in water and wastewater environments. Anal. Bioanal. Chem. 2007, 387, 1225–1234. [Google Scholar] [CrossRef] [PubMed]
  5. Daughton, C.G. Chapter 1. Pharmaceuticals in the environment: Sources and their management. In Comprehensive Analytical Chemistry; Petrović, M., Barceló, D., Eds.; Elsevier: Amsterdam, The Netherlands, 2007; Volume 50, pp. 1–58. [Google Scholar]
  6. Venkatesan, A.K.; Halden, R.U. Wastewater treatment plants as chemical observatories to forecast ecological and human health risks of manmade chemicals. Sci. Rep. 2014, 4, 3731. [Google Scholar] [CrossRef]
  7. Andersson, D.I.; Hughes, D. Microbiological effects of sublethal levels of antibiotics. Nat. Rev. Microbiol. 2014, 12, 465–478. [Google Scholar] [CrossRef] [PubMed]
  8. Gros, M.; Rodríguez-Mozaz, S.; Barceló, D. Rapid analysis of multiclass antibiotic residues and some of their metabolites in hospital, urban wastewater and river water by ultra-high-performance liquid chromatography coupled to quadrupole-linear ion trap tandem mass spectrometry. J. Chromatogr. A 2013, 1292, 173–188. [Google Scholar] [CrossRef]
  9. Jeong, D.-H.; Ham, S.-Y.; Lee, W.; Chung, H.; Kim, H. Study on occurrence and management of organic micropollutants in sewer systems. J. Korean Soc. Water Wastewater 2017, 31, 551–566. [Google Scholar] [CrossRef]
  10. Kim, J.Y.; Jeon, J.; Kim, S.D. Prioritization of pharmaceuticals and personal care products in the surface waters of Korea: Application of an optimized risk-based methods. Ecotoxicol. Environ. Saf. 2023, 259, 115024. [Google Scholar] [CrossRef] [PubMed]
  11. Jaffrézic, A.; Jardé, E.; Soulier, A.; Carrera, L.; Marengue, E.; Cailleau, A.; Le Bot, B. Veterinary pharmaceutical contamination in mixed land use watersheds: From agricultural headwater to water monitoring watershed. Sci. Total Environ. 2017, 609, 992–1000. [Google Scholar] [CrossRef]
  12. Luo, Y.; Xu, L.; Rysz, M.; Wang, Y.; Zhang, H.; Alvarez, P.J. Occurrence and transport of tetracycline, sulfonamide, quinolone, and macrolide antibiotics in the Haihe River Basin, China. Environ. Sci. Technol. 2011, 45, 1827–1833. [Google Scholar] [CrossRef] [PubMed]
  13. Yoon, Y.; Ryu, J.; Oh, J.; Choi, B.-G.; Snyder, S.A. Occurrence of endocrine disrupting compounds, pharmaceuticals, and personal care products in the Han River (Seoul, South Korea). Sci. Total Environ. 2010, 408, 636–643. [Google Scholar] [CrossRef] [PubMed]
  14. Kim, I.; Park, Y.; Kim, S.; Sim, K.; Chung, I.; Suk, K.; Hwang, S. Analysis of tetracycline antibiotics and their metabolites samples from streams near concentrated livestock operations using LC/MS/MS. J. Korean Soc. Environ. Anal. 2016, 19, 199–208. [Google Scholar]
  15. Lee, S.-H.; Jung, H.-W.; Jung, J.-Y.; Min, H.-J.; Kim, B.-R.; Park, C.-G.; Oh, J.-E.; Onoda, Y.; Satou, N. Characteristics of occurrence of pharmaceuticals in the Nakdong River. J. Korean Soc. Environ. Eng. 2013, 35, 45–56. [Google Scholar] [CrossRef]
  16. Vanderford, B.J.; Snyder, S.A. Analysis of pharmaceuticals in water by isotope dilution liquid chromatography/tandem mass spectrometry. Environ. Sci. Technol. 2006, 40, 7312–7320. [Google Scholar] [CrossRef] [PubMed]
  17. Wei, R.; Ge, F.; Huang, S.; Chen, M.; Wang, R. Occurrence of veterinary antibiotics in animal wastewater and surface water around farms in Jiangsu Province, China. Chemosphere 2011, 82, 1408–1414. [Google Scholar] [CrossRef] [PubMed]
  18. Kim, H.; Hong, Y.; Park, J.-e.; Sharma, V.K.; Cho, S.-i. Sulfonamides and tetracyclines in livestock wastewater. Chemosphere 2013, 91, 888–894. [Google Scholar] [CrossRef] [PubMed]
  19. US Environmental Protection Agency (EPA). EPA Method: 1694, Pharmaceuticals and Personal Care Products in Water, Soil, Sediment and Biosolids by HPLC/M/M; EPA-821-R-08-002; USEPA: Washington, DC, USA, 2007.
  20. NIER. Water Emission Management System. Available online: https//wems.nier.go.kr (accessed on 17 October 2018).
  21. Moermond, C.T.; Kase, R.; Korkaric, M.; Ågerstrand, M. CRED: Criteria for reporting and evaluating ecotoxicity data. Environ. Toxicol. Chem. 2016, 35, 1297–1309. [Google Scholar] [CrossRef]
  22. Olker, J.H.; Elonen, C.M.; Pilli, A.; Anderson, A.; Kinziger, B.; Erickson, S.; Skopinski, M.; Pomplun, A.; LaLone, C.A.; Russom, C.L.; et al. The ECOTOXicology Knowledgebase: A Curated Database of Ecologically Relevant Toxicity Tests to Support Environmental Research and Risk Assessment. Environ. Toxicol. Chem. 2022, 41, 1520–1539. [Google Scholar] [CrossRef] [PubMed]
  23. NIER. Regulations on Specific Methods of Chemical Risk Assessment; NIER: Incheon, Republic of Korea, 2021. [Google Scholar]
  24. EC. Revised Technical Guidance for Deriving Environmental Quality Standards. Common Implementation Strategy for the Water Framework Directive Guidance Document No. 27; European Commission: Luxembourg, 2018. [Google Scholar]
  25. Yan, S.; Chen, R.; Wang, M.; Zha, J. Carbamazepine at environmentally relevant concentrations caused DNA damage and apoptosis in the liver of Chinese rare minnows (Gobiocypris rarus) by the Ras/Raf/ERK/p53 signaling pathway. Environ. Pollut. 2021, 270, 116245. [Google Scholar] [CrossRef] [PubMed]
  26. Offiong, N.; Lema, E.L.; Kang, S.; Inam, E.I.; Kang, S.; Kim, K. Risk evaluation of pharmaceutical residues in waste water from selected treatment plants in Gwangju, South Korea. J. Chem. Soc. Niger. 2019, 44, 504–514. [Google Scholar]
  27. Im, J.K.; Hwang, M.Y.; Lee, E.H.; Noh, H.R.; Yu, S.J. Pharmaceutical compounds in tributaries of the Han River watershed, South Korea. Environ. Res. 2020, 188, 109758. [Google Scholar] [CrossRef] [PubMed]
  28. Anagnostopoulpou, K.; Nannou, C.; Aschonitis, V.G.; Lambropoulou, D.A. Screening of pesticides and emerging contaminants in eighteen Greek lakes by using target and non-target HRMS approaches: Occurrence and ecological risk assessment. Sci. Total Environ. 2022, 849, 157887. [Google Scholar] [CrossRef]
  29. Gianturco, S.L.; Pavlech, L.L.; Storm, K.D.; Yoon, S.; Yuen, M.V.; Mattingly, A.N. Carbamazepine: Summary Report; University of Maryland: Baltimore, MD, USA, 2020. [Google Scholar]
  30. Kwon, H.; Sim, W.; Kim, H.; Oh, J.; Choi, S. Distribution of pharmaceuticals and personal care products (PPCPs) in main rivers of Ulsan. Korea. J. Korea Soc. Environ. Anal. 2011, 14, 158–164. [Google Scholar]
  31. Lee, H.-J.; Kim, H.-Y.; Kim, K.Y.; Yang, D.-S.; Lee, I.; Lim, Y.-K.; Kim, J.-H.; Oh, J.-E. Characteristic occurrence and distributions of pharmaceuticals in the Nakdong River. J. Korean Soc. Environ. Eng. 2017, 39, 403–411. [Google Scholar] [CrossRef]
  32. Park, S.; Kang, H.; Shin, H.; Ryoo, I.; Choi, K.; Kho, Y.; Park, K.; Kim, K.; Ji, K. Ecological Risk Assessment of Pharmaceuticals in the Surface Water Near a Pharmaceutical Manufacturing Complex in Korea. J. Environ. Health Sci. 2020, 46, 45–64. [Google Scholar]
  33. Lim, S.-K.; Lee, J.-E.; Lee, H.-S.; Nam, H.-M.; Moon, D.-C.; Jang, G.-C.; Park, Y.-J.; Jung, Y.-G.; Jung, S.-C.; Wee, S.-H. Trends in antimicrobial sales for livestock and fisheries in Korea during 2003–2012. Korean J. Vet. Res. 2014, 54, 81–86. [Google Scholar] [CrossRef]
  34. David, A.; Pancharatna, K. Effects of acetaminophen (paracetamol) in the embryonic development of zebrafish, Danio rerio. J. Appl. Toxicol. 2009, 29, 597–602. [Google Scholar] [CrossRef]
  35. Qian, H.; Pan, X.; Chen, J.; Zhou, D.; Chen, Z.; Zhang, L.; Fu, Z. Analyses of gene expression and physiological changes in Microcystis aeruginosa reveal the phytotoxicities of three environmental pollutants. Ecotoxicology 2012, 21, 847–859. [Google Scholar] [CrossRef]
  36. Li, Y.; Ma, Y.; Yang, L.; Duan, S.; Zhou, F.; Chen, J.; Liu, Y.; Zhang, B. Effects of azithromycin on feeding behavior and nutrition accumulation of Daphnia magna under the different exposure pathways. Ecotoxicol. Environ. Saf. 2020, 197, 110573. [Google Scholar] [CrossRef]
  37. Lawrence, J.R.; Zhu, B.; Swerhone, G.D.; Roy, J.; Tumber, V.; Waiser, M.J.; Topp, E.; Korber, D.R. Molecular and microscopic assessment of the effects of caffeine, acetaminophen, diclofenac, and their mixtures on river biofilm communities. Environ. Toxicol. Chem. 2012, 31, 508–517. [Google Scholar] [CrossRef] [PubMed]
  38. Koeypudsa, W.; Yakupitiyage, A.; Tangtrongpiros, J. The fate of chlortetracycline residues in a simulated chicken–fish integrated farming systems. Aquac. Res. 2005, 36, 570–577. [Google Scholar] [CrossRef]
  39. Watanabe, H.; Tamura, I.; Abe, R.; Takanobu, H.; Nakamura, A.; Suzuki, T.; Hirose, A.; Nishimura, T.; Tatarazako, N. Chronic toxicity of an environmentally relevant mixture of pharmaceuticals to three aquatic organisms (alga, daphnid, and fish). Environ. Toxicol. Chem. 2016, 35, 996–1006. [Google Scholar] [CrossRef] [PubMed]
  40. Meinertz, J.R.; Schreier, T.M.; Bernardy, J.A.; Franz, J.L. Chronic toxicity of diphenhydramine hydrochloride and erythromycin thiocyanate to daphnia, Daphnia magna, in a continuous exposure test system. Bull. Environ. Contam. Toxicol. 2010, 85, 447–451. [Google Scholar] [CrossRef] [PubMed]
  41. Zhu, Y.; Yang, D.; Duan, X.; Zhang, Y.; Chen, D.; Gong, Z.; Liu, C. Perfluorooctane sulfonate promotes doxycycline-induced liver tumor progression in male Krasv12 transgenic zebrafish. Environ. Res. 2021, 196, 110962. [Google Scholar] [CrossRef]
  42. Robinson, A.A.; Belden, J.B.; Lydy, M.J. Toxicity of fluoroquinolone antibiotics to aquatic organisms. Environ. Toxicol. Chem. Int. J. 2005, 24, 423–430. [Google Scholar] [CrossRef]
  43. Zhang, Y.; Zhang, X.; Guo, R.; Zhang, Q.; Cao, X.; Suranjana, M.; Liu, Y. Effects of florfenicol on growth, photosynthesis and antioxidant system of the non-target organism Isochrysis galbana. Comp. Biochem. Physiol. Part. C Toxicol. Pharmacol. 2020, 233, 108764. [Google Scholar] [CrossRef] [PubMed]
  44. Lützhøft, H.-C.H.; Halling-Sørensen, B.; Jørgensen, S. Algal toxicity of antibacterial agents applied in Danish fish farming. Arch. Environ. Contam. Toxicol. 1999, 36, 1–6. [Google Scholar] [CrossRef]
  45. Chai, T.; Cui, F.; Di, S.; Wu, S.; Zhang, Y.; Wang, X. New insights into cardiotoxicity induced by chiral fluoxetine at environmental-level: Enantioselective arrhythmia in developmental zebrafish (Danio rerio). Environ. Pollut. 2021, 270, 116182. [Google Scholar] [CrossRef]
  46. Kergaravat, S.V.; Hernandez, S.R.; Gagneten, A.M. Second-, third-and fourth-generation quinolones: Ecotoxicity effects on Daphnia and Ceriodaphnia species. Chemosphere 2021, 262, 127823. [Google Scholar] [CrossRef]
  47. Meng, H.; Liang, J.; Zheng, X.; Zhang, K.; Zhao, Y. Using a high-throughput zebrafish embryo screening approach to support environmental hazard ranking for cardiovascular agents. Sci. Total Environ. 2020, 702, 134703. [Google Scholar] [CrossRef] [PubMed]
  48. Garcia, R.J.; Kane, A.S.; Petullo, D.; Reimschuessel, R. Localization of Oxytetracycline in Chlamydomonas Reinhardtii (Chlorophyceae)1. J. Phycol. 2008, 44, 1282–1289. [Google Scholar] [CrossRef] [PubMed]
  49. Guo, J.; Bai, Y.; Chen, Z.; Mo, J.; Li, Q.; Sun, H.; Zhang, Q. Transcriptomic analysis suggests the inhibition of DNA damage repair in green alga Raphidocelis subcapitata exposed to roxithromycin. Ecotoxicol. Environ. Saf. 2020, 201, 110737. [Google Scholar] [CrossRef] [PubMed]
  50. Białk-Bielińska, A.; Stolte, S.; Arning, J.; Uebers, U.; Böschen, A.; Stepnowski, P.; Matzke, M. Ecotoxicity evaluation of selected sulfonamides. Chemosphere 2011, 85, 928–933. [Google Scholar] [CrossRef] [PubMed]
  51. Bundschuh, M.; Hahn, T.; Ehrlich, B.; Höltge, S.; Kreuzig, R.; Schulz, R. Acute toxicity and environmental risks of five veterinary pharmaceuticals for aquatic macroinvertebrates. Bull. Environ. Contam. Toxicol. 2016, 96, 139–143. [Google Scholar] [CrossRef]
  52. Kim, Y.; Choi, K.; Jung, J.; Park, S.; Kim, P.-G.; Park, J. Aquatic toxicity of acetaminophen, carbamazepine, cimetidine, diltiazem and six major sulfonamides, and their potential ecological risks in Korea. Environ. Int. 2007, 33, 370–375. [Google Scholar] [CrossRef] [PubMed]
  53. Lu, G.; Li, Z.; Liu, J. Effects of selected pharmaceuticals on growth, reproduction and feeding of Daphnia Magna. Fresenius Environ. Bull. 2013, 22, 2588–2594. [Google Scholar]
  54. De Liguoro, M.; Fioretto, B.; Poltronieri, C.; Gallina, G. The toxicity of sulfamethazine to Daphnia magna and its additivity to other veterinary sulfonamides and trimethoprim. Chemosphere 2009, 75, 1519–1524. [Google Scholar] [CrossRef] [PubMed]
  55. Park, S.; Choi, K. Hazard assessment of commonly used agricultural antibiotics on aquatic ecosystems. Ecotoxicology 2008, 17, 526–538. [Google Scholar] [CrossRef]
  56. Yang, W.; Tang, Z.; Zhou, F.; Zhang, W.; Song, L. Toxicity studies of tetracycline on Microcystis aeruginosa and Selenastrum capricornutum. Environ. Toxicol. Pharmacol. 2013, 35, 320–324. [Google Scholar] [CrossRef]
  57. US Environmental Protection Agency (EPA). Framework for Ecological Risk Assessment; EPA/630/R-92/001; US Environmental Protection Agency (EPA): Washington, DC, USA, 1992.
  58. Madureira, T.V.; Rocha, M.J.; Cruzeiro, C.; Rodrigues, I.; Monteiro, R.A.; Rocha, E. The toxicity potential of pharmaceuticals found in the Douro River estuary (Portugal): Evaluation of impacts on fish liver, by histopathology, stereology, vitellogenin and CYP1A immunohistochemistry, after sub-acute exposures of the zebrafish model. Environ. Toxicol. Pharmacol. 2012, 34, 34–45. [Google Scholar] [CrossRef] [PubMed]
  59. Wang, H.; Luo, Y.; Xu, W.; Zhou, Q.; Tang, B.; Wang, Y. Ecotoxic effects of tetracycline and chlortetracycline on aquatic organisms. J. Agro-Environ. Sci. 2008, 27, 1536–1539. [Google Scholar]
  60. Kim, B.; Ji, K.; Kim, C.; Kang, H.; Lee, S.; Kwon, B.; Kho, Y.; Park, K.; Kim, K.; Choi, K. Pharmaceutical residues in streams near concentrated animal feeding operations of Korea—Occurrences and associated ecological risks. Sci. Total Environ. 2019, 655, 408–413. [Google Scholar] [CrossRef] [PubMed]
  61. Park, N.; Kang, D.; Jeon, J. Occurrence and Concentration of Micropollutants in the Middle-and Down-stream of Nakdong River. J. Environ. Anal. Health Toxicol. 2021, 24, 1–12. [Google Scholar] [CrossRef]
Water 15 03913 g001
Figure 1. Map of sampling sites used in the study (Seokseong and Nonsan-Gangkyoung streams of the Geum River basin, South Korea).
Figure 1. Map of sampling sites used in the study (Seokseong and Nonsan-Gangkyoung streams of the Geum River basin, South Korea).
Water 15 03913 g001
Water 15 03913 g002
Figure 2. Recovery rates of 49 pharmaceuticals selected in this study.
Figure 2. Recovery rates of 49 pharmaceuticals selected in this study.
Water 15 03913 g002
Water 15 03913 g003
Figure 3. Water quality parameters measured in Seokseong (a–c) and Nonsan-Gangkyoung (d–f) streams of the Geum River basin (abbreviations: BOD, biochemical oxygen demand; COD, chemical oxygen demand; DO, dissolved oxygen; Flow, discharge flow (m3/s); Temp, water temperature (°C); TOC, total organic carbon; TN, total nitrogen; TP, total phosphorus; pH, hydrogen ion concentration; EC, electrical conductivity; SS, suspended solids).
Figure 3. Water quality parameters measured in Seokseong (a–c) and Nonsan-Gangkyoung (d–f) streams of the Geum River basin (abbreviations: BOD, biochemical oxygen demand; COD, chemical oxygen demand; DO, dissolved oxygen; Flow, discharge flow (m3/s); Temp, water temperature (°C); TOC, total organic carbon; TN, total nitrogen; TP, total phosphorus; pH, hydrogen ion concentration; EC, electrical conductivity; SS, suspended solids).
Water 15 03913 g003
Water 15 03913 g004
Figure 4. The log-transformed measured environment concentrations (MECs) of 49 pharmaceuticals analyzed using LC-MS/MS.
Figure 4. The log-transformed measured environment concentrations (MECs) of 49 pharmaceuticals analyzed using LC-MS/MS.
Water 15 03913 g004
Water 15 03913 g005
Figure 5. The mean concentration levels of 49 pharmaceutical residues collected from October 2018 to March 2019 at each site: (a) Seokseong stream, (b) Nonsan-Gangkyoung stream, (c) Geum River.
Figure 5. The mean concentration levels of 49 pharmaceutical residues collected from October 2018 to March 2019 at each site: (a) Seokseong stream, (b) Nonsan-Gangkyoung stream, (c) Geum River.
Water 15 03913 g005
Water 15 03913 g006
Figure 6. Risk ranking of pharmaceuticals based on the mean effect levels of acute or chronic aquatic toxicity for the most sensitive species (algae, crustaceans, or fish). Risk quotients were calculated and classified into one of three categories (red: high risk; orange: moderate risk; green: low risk).
Figure 6. Risk ranking of pharmaceuticals based on the mean effect levels of acute or chronic aquatic toxicity for the most sensitive species (algae, crustaceans, or fish). Risk quotients were calculated and classified into one of three categories (red: high risk; orange: moderate risk; green: low risk).
Water 15 03913 g006
Table 1. The information on the usage and chemical properties of the 49 pharmaceuticals selected in this study.
Table 1. The information on the usage and chemical properties of the 49 pharmaceuticals selected in this study.
GroupPharmaceuticalsCAS No.UsageChemical FormulaMW *logKow
Tetracyclines4-Epichlortetracycline14297-93-9AntibacterialC22H23ClN2O8478.90-
Tetracyclines4-epi-Oxytetracycline14206-58-7AntibioticC22H24N2O9460.40-
Tetracyclines4-Epianhydrotetracycline7518-17-4AntibioticC22H22N2O7426.40-
AnilinesAcetaminophen103-90-2Anti-inflammatoryC8H9NO2151.160.46
PhenicillinesAmpicillin69-53-4AntibacterialC16H19N3O4S349.111.35
TetracyclinesAnhydrotetracycline1665-56-1AntibioticC22H22N2O7426.40-
MacrolidesAzithromycin83905-01-5AntibacterialC38H72N2O12748.514.02
MethylxanthinesCaffeine58-08-2Neuropsychiatric agentC8H10N4O2194.19−0.07
CarboxamidesCarbamazepine298-46-4Neuropsychiatric agentC15H12N2O236.272.45
TetracyclinesChlortetracycline57-62-5AntibacterialC22H23ClN2O8478.90-
MacrolidesClarithromycin81103-11-9AntibacterialC38H69NO13747.483.16
FluoroquinolonesClinafloxacin105956-97-6AntibacterialC17H17ClFN3O3365.80-
DihydropyridnesDehydronifedipine67035-22-7Nifedipine metaboliteC17H16N2O6344.32-
Digitalis glycosidesDigoxigenin1672-46-4Digoxin metaboliteC23H34O5390.501.10
DiphenhydraminesDiphenhydramine58-73-1Anti-allergic agentC17H21NO255.163.27
TetracyclinesDoxycycline564-25-0AntibacterialC22H24N2O8444.40−0.02
FluoroquinolonesEnrofloxacin93106-60-6AntibacterialC19H22FN3O3359.40-
AmphenicolsFlorfenicol73231-34-2AntibacterialC12H14Cl2FNO4S358.20-
QuinolonesFlumequine42835-25-6AntibacterialC14H12FNO3261.081.6
OthersFluoxetine54910-89-3Neuropsychiatric agentC17H18F3NO309.334.05
FluoroquinolonesLomefloxacin98079-51-7AntibacterialC17H19F2N3O3351.142.8
FluoroquinolonesMarbofloxacin115550-35-1AntibacterialC17H19FN4O4362.14-
QuinolonesNalidixic acid389-08-2AntibacterialC12H12N2O3232.091.41
DihydropyridnesNifedipine21829-25-4Cardiovascular agentC17H18N2O6346.122.2
ProgesteronesNorgestimate35189-28-7ProgesteroneC23H31NO3369.234.98
FluoroquinolonesOfloxacin82419-36-1AntibacterialC18H20FN3O4361.40−0.39
OthersOrmetoprim6981-18-6AntibacterialC14H18N4O2274.14-
TetracyclinesOxytetracycline79-57-2AntibacterialC22H24N2O9460.15−0.9
MacrolidesRoxithromycin80214-83-1AntibacterialC41H76N2O15837.001.7
SulfonamidesSulfachloropyridazine80-32-0AntibacterialC10H9ClN4O2S284.72-
SulfonamidesSulfaclozine102-65-8AntibacterialC10H9ClN4O2S284.72-
SulfonamidesSulfadiazine68-35-9AntibacterialC10H10N4O2S250.28−0.09
SulfonamidesSulfadimethoxine122-11-2AntibacterialC12H14N4O4S310.331.63
SulfonamidesSulfadoxine2447-57-6AntibacterialC12H14N4O4S310.070.7
SulfonamidesSulfaethoxypyridazine963-14-4AntibacterialC12H14N4O3S294.33-
SulfonamidesSulfamerazine127-79-7AntibacterialC11H12N4O2S264.310.14
SulfonamidesSulfamethazine57-68-1AntibacterialC12H14N4O2S278.330.14
SulfonamidesSulfamethizole144-82-1AntibacterialC9H10N4O2S2270.030.54
SulfonamidesSulfamethoxazole723-46-6AntibacterialC10H11N3O3S253.280.89
SulfonamidesSulfamethoxypyridazine80-35-3AntibacterialC11H12N4O3S280.06-
SulfonamidesSulfamonomethoxine1220-83-3AntibacterialC11H12N4O3S280.06−0.037
SulfonamidesSulfaquinoxaline59-40-5AntibacterialC14H12N4O2S300.341.68
SulfonamidesSulfathiazole72-14-0AntibacterialC9H9N3O2S2255.300.05
SulfonamidesSulfisoxazole127-69-5AntibacterialC11H13N3O3S267.071.01
TetracyclinesTetracycline60-54-8AntibioticC22H24N2O8444.40−1.37
BenzimidazolesThiabendazole148-79-8AntibioticC10H7N3S201.252.47
OthersTrimethoprim738-70-5AntibacterialC14H18N4O3290.320.91
OthersVirginiamycin M121411-53-0AntibioticC28H35N3O7525.6-
OthersVirginiamycin S123152-29-6AntibioticC43H49N7O10823.9-
Note: * MW: molecular weight.
Table 2. The concentrations and solvent types of stock solutions and mass spectrometer characteristics of target compounds.
Table 2. The concentrations and solvent types of stock solutions and mass spectrometer characteristics of target compounds.
No.NameConcentration of Stock Solution (mg/L)Solvent for Stock SolutionRetention Time (min)Precursor Ion (Fragmentor, V)Product Ion (CE, V)
14-Epianhydrotetracycline (HCl)1000Methanol5.167427.2 (126) 410.1 (17), 98.1 (45)
24-Epichlortetracycline1000Methanol4.875479.1 (134) 444.1 (21), 462.1 (17), 98.1 (41)
34-Epioxytetracycline1000Methanol4.586461.2 (132) 426.1 (21), 444.1 (17), 201 (45)
4Acetaminophen1000Methanol3.954152.1 (112) 110.1 (17), 93.1 (25), 65.1 (33)
5Ampicillin1000Methanol4.417350.1 (120) 106.1 (21), 160 (13), 114 (33)
6Anhydrotetracycline (HCl)1000Methanol5.423427.2 (122) 410.1 (17), 97.9 (49), 154 (21)
7Azithromycin1000Methanol4.81749.5 (165) 591.4 (29), 158.1 (45), 116.1 (45)
8Caffeine100020% Methanol4.472195.1 (130) 138 (21), 110.1 (25), 83.1 (33)
9Carbamazepine1000Methanol5.974237.1 (132) 194 (21), 193 (41), 165 (57)
10Chlortetracycline (HCl)1000Methanol5.052479 (85) 444.1 (20), 426 (25), 154 (30)
11Clarithromycin1000Methanol5.803748.5 (167) 158.1 (29), 590.4 (17), 83.2 (77)
12Clinafloxacin20050% Methanol4.653366.1 (173) 322.1 (17), 279 (25)
13Dehydronifedipine200Methanol6.576345.1 (175) 283.8 (29), 151.9 (80), 267.8 (33)
14Digoxigenin1000Methanol5.184391.3 (134) 355.2 (13), 105.1 (57), 91.1 (77)
15Diphenhydramine (HCl)1000Methanol5.448256.2 (81) 167 (17), 165 (49), 152 (45)
16Doxycycline (HCl)1000Methanol5.134445 (130) 428.1 (20), 321.1 (29), 267 (35)
17Enrofloxacin1000Methanol4.671360.2 (83) 342.2 (25), 316.2 (17), 245 (29)
18Florfenicol1000Methanol5.418355.9 (150) 336 (7), 185 (19)
19Flumequine200Methanol6.147262.1 (120) 244 (17), 202 (37), 174 (48)
20Fluoxetine500Methanol5.809310.1 (79) 148.1 (5), 91.1 (80), 117.1 (65)
21Lomefloxacin (HCl)1000Methanol4.608352.2 (122) 265 (25), 334.1 (21), 308.1 (17)
22Marbofloxacin1000Dimethyl sulfoxide4.477362.8 (140) 72 (25), 344.9 (21), 319.8 (15)
23Nalidixic acid200Methanol:acetone (1:1)6.087232.8 (110) 214.8 (12), 158.9 (36), 186.8 (27)
24Nifedipine1000Methanol6.603347.1 (79) 314.8 (5), 253.8 (17), 167.1 (65)
25Norgestimate1000Methanol7.644370.2 (179) 124 (37), 77.1 (77), 91.1 (61)
26Ofloxacin200Methanol4.526362.2 (134) 318.1 (21), 261 (29), 205 (45)
27Ormetoprim1000Methanol4.596275.2 (169) 259.1 (29), 123 (25), 81.1 (53)
28Oxytetracycline (HCl)1000Methanol4.57461 (130) 426 (20), 321.1 (29), 267 (35)
29Roxithromycin1000Methanol5.817837.5 (155) 679.5 (21), 116 (41), 158.2 (37)
30Sulfachloropyridazine1000Methanol5.267285 (110) 156 (13), 92.1 (33), 108 (29)
31Sulfaclozine sodium1000Dimethyl sulfoxide5.668285 (120) 92.1 (33), 108 (25), 156 (17)
32Sulfadiazine200Methanol4.389251.1 (118) 156 (15), 65.1 (53), 92.1 (29)
33Sulfadimethoxine1000Methanol5.697311.1 (126) 156 (21), 92.1 (41), 65.1 (61)
34Sulfadoxine1000Methanol5.377310.8 (140) 156 (18), 107.9 (30), 92 (36)
35Sulfaethoxypyridazine1000Methanol5.378294.8 (140) 155.8 (17), 139.9 (19), 107.9 (30)
36Sulfamerazine1000Methanol4.727265.1 (122) 92.1 (33), 65.1 (61), 156 (17)
37Sulfamethazine1000Methanol4.95279.1 (128) 186 (17), 92.1 (33), 156 (19)
38Sulfamethizole1000Methanol4.904271 (79) 156 (13), 92.1 (29), 65.1 (57)
39Sulfamethoxazole1000Methanol5.385254.1 (110) 156 (15), 65.1 (53), 92.1 (29)
40Sulfamethoxypyridazine1000Dimethyl sulfoxide4.949281 (130) 156 (17), 108 (27), 92.1 (31)
41Sulfamonomethoxine1000Methanol5.141280.8 (80) 156 (19), 107.9 (28), 92 (31)
42Sulfaquinoxaline1000Acetone5.684300.8 (80) 155.8 (17), 107.9 (25), 91.9 (31)
43Sulfathiazole1000Methanol4.501256 (112) 155.9 (13), 92.1 (25), 65.1 (53)
44Sulfisoxazole1000Methanol5.478267.8 (70) 155.8 (11), 112.9 (15), 92 (29)
45Tetracycline (HCl)1000Methanol4.701445 (95) 410 (15), 154 (30)
46Thiabendazole1000Methanol4.423202 (167) 175 (29), 131 (37), 65.1 (53)
47Trimethoprim1000Methanol4.498291.2 (169) 230 (25), 261 (29), 123 (29)
48Virginiamycin M11000Methanol6.247526.3 (116) 354.9 (15), 507.8 (11), 108.9 (37)
49Virginiamycin S1500Methanol6.82823.8 (230) 204.9 (54), 289.9 (36), 565.7 (32)
-13C3-Trimethoprim50Methanol4.497293.8 (170) 125.9 (27), 232.8 (29), 263.9 (29)
-13C6 Sulfamethazine100Acetonitrile4.948285.1 (132) 185.8 (16), 161.8 (18), 113.9 (29)
-13C6-Sulfamethoxazole100Acetonitrile5.383260.1 (122) 98 (29), 113.9 (25), 161.8 (14)
-Thiabendazole (ring-13C6)100Acetonitrile4.422208 (171) 180.8 (29), 70 (53), 136.9 (41)
-Atrazine-d51000Methanol-221.1 (124) 179 (21), 69.1 (45), 101.1 (29)
Table 3. Regression equations, coefficients of determination (r2), linear ranges, MDLs, and LOQs of 49 pharmaceuticals analyzed by LC-MS/MS.
Table 3. Regression equations, coefficients of determination (r2), linear ranges, MDLs, and LOQs of 49 pharmaceuticals analyzed by LC-MS/MS.
No.NameRegression Equationr2Linear Range
(ng/mL)		MDL
(ng/L)		LOQ
(ng/L)
14-Epianhydrotetracycliney = 0.001121x − 0.0015260.99702–506.2519.90
24-Epichlortetracycliney = 1.558725−4x − 2.440391−40.99895–509.7731.12
34-Epioxytetracycliney = 0.001002x − 0.0014250.99982–759.7230.95
4Acetaminopheny = 1.749150−4x + 1.243825−40.99845–1004.7815.21
5Ampicilliny = 2.354800−4x − 0.0011630.99455–755.2916.85
6Anhydrotetracycliney = 0.002668x − 0.0014530.99265–5010.4533.29
7Azithromyciny = 0.002338x − 0.0071400.99885–753.6811.71
8Caffeiney = 4.009872−4x + 4.980610−40.99805–1004.8515.44
9Carbamazepiney = 0.002668x − 3.984711−40.99910.5–107.7624.71
10Chlortetracycliney = 3.231145−4x − 0.0016580.99195–505.6017.82
11Clarithromyciny = 0.018081x − 5.018711−40.99950.5–103.9612.61
12Clinafloxaciny = 6.727147−4x − 0.0107090.999520–10014.5346.27
13Dehydro nifedipiney = 0.006114x + 9.052315−50.99970.5–253.8512.27
14Digoxigeniny = 2.547734−4x + 2.013892−40.99825–755.0616.10
15Diphenhydraminey = 0.062808x − 0.0070920.99930.5–103.1610.05
16Doxycycliney = 6.306953−4x − 0.0019760.99185–508.9528.52
17Enrofloxaciny = 8.330491−4x − 0.0013090.99752–507.6924.47
18Florfenicoly = 3.093383−4x − 1.114011−40.99192–207.1922.89
19Flumequiney = 0.002215x + 4.140469−50.99980.5–502.728.67
20Fluoxetiney = 0.001533x + 3.075431−40.99710.5–105.5717.73
21Lomefloxaciny = 0.001862x − 0.0077390.99655–5014.3445.66
22Marbofloxaciny = 7.051740−4x − 0.0027320.99315–7514.5446.32
23Nalidixic acidy = 0.002525x − 1.533324−40.99790.5–202.678.49
24Nifedipiney = 7.860068−4x − 4.214378−60.99870.5–207.8825.11
25Norgestimatey = 0.001300x − 9.673252−50.99960.5–203.4410.94
26Ofloxaciny = 0.001558x − 0.0061640.99765–5011.6637.14
27Ormetoprimy = 0.012046x + 0.0014970.99690.5–104.7615.17
28Oxytetracycliney = 4.831707−4x − 0.0010260.99625–508.1926.07
29Roxithromyciny = 0.005459x − 1.874623−40.99910.5–102.698.55
30Sulfachloropyridaziney = 0.002094x + 9.687999−40.99950.5–1006.4420.52
31Sulfacloziney = 3.860528−4x − 8.547179−60.99281–254.7115.00
32Sulfadiaziney = 6.807058−4x − 6.161956−50.99941–1005.0616.11
33Sulfadimethoxiney = 0.002052x + 7.023815−40.99890.5–505.4517.37
34Sulfadoxiney = 0.001233x + 1.072618−40.99881–205.2616.76
35Sulfaethoxypyridaziney = 0.001003x − 6.420953−50.99921–505.0716.16
36Sulfameraziney = 7.798603−4x + 5.484095−50.99890.5–256.0519.28
37Sulfamethaziney = 0.001340x − 2.742459−60.99950.5–206.0819.35
38Sulfamethizoley = 0.001298x − 1.781222−40.99960.5–103.6311.56
39Sulfamethoxazoley = 8.795675−4x − 1.933642−40.99771–203.9012.42
40Sulfamethoxypyridaziney = 0.001079x + 3.132912−40.99962–504.4114.04
41Sulfamonomethoxiney = 4.505160−4x − 9.963152−50.99632–755.6417.95
42Sulfaquinoxaliney = 6.615775−4x + 1.406531−40.99820.5–503.4010.82
43Sulfathiazoley = 9.418226−4x + 6.183180−50.99970.5–204.2313.46
44Sulfisoxazoley = 7.569998−4x − 9.792676−50.99831–204.6414.77
45Tetracycliney = 2.871229−4x − 0.0012640.99235–506.3820.31
46Thiabendazoley = 0.005822x + 4.603184−40.99670.5–102.397.60
47Trimethoprimy = 0.005269x + 0.0013990.99880.5–105.6918.12
48Virginiamycin M1y = 1.333264−4x + 5.226565−50.99982–7510.1432.29
49Virginiamycin S1y = 1.754436−5x − 2.779775−50.993710–753.6811.72
Table 4. Summary statistics of the measured concentration levels of 49 pharmaceuticals in the Seokseong and Nonsan-Gangkyoung streams of the Geum River basin.
Table 4. Summary statistics of the measured concentration levels of 49 pharmaceuticals in the Seokseong and Nonsan-Gangkyoung streams of the Geum River basin.
GroupPharmaceuticalsCAS no.N
(Total)		N
(Detected)		Detection Frequency (%)AM *
(μg/L)		SD *Median
(μg/L)		Min
(μg/L)		Max
(μg/L)
Total231559125.50.0170.7380.0200.0019.212
Tetracyclines4-Epichlortetracycline14297-93-9511223.50.2710.4530.0410.0231.591
Tetracyclines4-epi-Oxytetracycline14206-58-748510.40.1400.2750.0120.0050.632
Tetracyclines4-Epianhydrotetracycline7518-17-44836.30.1060.1410.0290.0210.269
AnilinesAcetaminophen103-90-2494489.80.5271.3350.0980.0138.479
PhenicillinesAmpicillin69-53-44748.50.0320.0350.0170.0110.085
TetracyclinesAnhydrotetracycline1665-56-147612.80.0290.0230.0240.0100.073
MacrolidesAzithromycin83905-01-5482960.40.0400.0530.0160.0010.188
MethylxanthinesCaffeine58-08-25151100.00.1000.1310.0600.0100.781
CarboxamidesCarbamazepine298-46-4473676.60.0220.0190.0130.0020.065
TetracyclinesChlortetracycline57-62-5511427.50.2750.4090.0480.0271.487
MacrolidesClarithromycin81103-11-9473166.00.0370.0530.0140.0010.193
FluoroquinolonesClinafloxacin105956-97-647510.60.0420.0210.0320.0260.078
DihydropyridnesDehydronifedipine67035-22-74736.40.0010.0010.0010.0010.002
Digitalis glycosidesDigoxigenin1672-46-44836.30.0780.0460.0580.0450.131
DiphenhydraminesDiphenhydramine58-73-1472144.70.0100.0210.0030.0010.095
TetracyclinesDoxycycline564-25-04736.40.0490.0380.0290.0260.093
FluoroquinolonesEnrofloxacin93106-60-6491734.70.0860.1370.0220.0050.478
AmphenicolsFlorfenicol73231-34-2504182.00.6331.3110.1020.0045.885
QuinolonesFlumequine42835-25-651815.70.0250.0440.0080.0020.131
OthersFluoxetine54910-89-34712.10.021----
FluoroquinolonesLomefloxacin98079-51-74700.0-----
FluoroquinolonesMarbofloxacin115550-35-149816.31.0822.8110.0570.0108.036
QuinolonesNalidixic acid389-08-24700.0-----
DihydropyridnesNifedipine21829-25-44700.0-----
ProgesteronesNorgestimate35189-28-74700.0-----
FluoroquinolonesOfloxacin82419-36-1481531.30.0450.0780.0130.0030.277
OthersOrmetoprim6981-18-64700.0-----
TetracyclinesOxytetracycline79-57-248918.80.0620.1390.0160.0040.431
MacrolidesRoxithromycin80214-83-1482347.90.0110.0140.0030.0010.052
SulfonamidesSulfachloropyridazine80-32-04700.0-----
SulfonamidesSulfaclozine102-65-84724.30.0140.0080.0140.0080.019
SulfonamidesSulfadiazine68-35-94712.10.011----
SulfonamidesSulfadimethoxine122-11-24700.0-----
SulfonamidesSulfadoxine2447-57-64700.0-----
SulfonamidesSulfaethoxypyridazine963-14-44700.0-----
SulfonamidesSulfamerazine127-79-7473166.00.0150.0280.0050.0010.133
SulfonamidesSulfamethazine57-68-1493877.60.0530.0950.0130.0010.385
SulfonamidesSulfamethizole144-82-14712.10.008----
SulfonamidesSulfamethoxazole723-46-6473268.10.0230.0400.0120.0020.167
SulfonamidesSulfamethoxypyridazine80-35-34712.10.005----
SulfonamidesSulfamonomethoxine1220-83-34712.10.013----
SulfonamidesSulfaquinoxaline59-40-54724.30.0070.0050.0070.0040.011
SulfonamidesSulfathiazole72-14-0503060.00.4761.6830.0240.0029.212
SulfonamidesSulfisoxazole127-69-54700.0-----
TetracyclinesTetracycline60-54-850918.00.0710.0950.0280.0050.310
BenzimidazolesThiabendazole148-79-8492244.90.0230.0180.0220.0010.098
OthersTrimethoprim738-70-5472859.60.0370.1110.0100.0010.593
PeptidesVirginiamycin M121411-53-03200.0-----
PeptidesVirginiamycin S123152-29-63213.10.005----
Note: * AM—arithmetic mean, SD—standard deviation.
Table 5. The information on test species (algae, crustaceans, fish), test type, duration, toxicological effects, endpoints, concentrations, assessment factors (AF), and predicted no effect concentrations (PNEC) of pharmaceuticals in the aquatic environment (collected from the US EPA ECOTOX Knowledgebase).
Table 5. The information on test species (algae, crustaceans, fish), test type, duration, toxicological effects, endpoints, concentrations, assessment factors (AF), and predicted no effect concentrations (PNEC) of pharmaceuticals in the aquatic environment (collected from the US EPA ECOTOX Knowledgebase).
PharmaceuticalsSpeciesClassEffectTest TypeDuration (Days)EndpointConcentration(μg/L)AFPNEC
(μg/L)		Reference
AcetaminophenDanio rerioFishMortalityChronic7NOEC1.00100.10David and Pancharatna [34]
AmpicillinMicrocystis aeruginosaAlgaeGeneticsChronic4NOEC10.001000.10Qian et al. [35]
AzithromycinDaphnia magnaCrustaceansBehaviorChronic4LOEC48.00500.96Li et al. [36]
CaffeineRaphidocelis subcapitataAlgaePopulationChronic56LOEC5.00100.50Lawrence and Zhu [37]
CarbamazepineGobiocypris rarusFishBiochemistryChronic28NOEC0.91100.09Yan et al. [25]
ChlortetracyclineOreochromis niloticusFishGrowthChronic48NOEC12.00500.24Koeypudsa et al. [38]
ClarithromycinPseudokirchneriella subcapitataAlgaeGrowthChronic3NOEC2.45100.25Watanabe et al. [39]
DiphenhydramineCeriodaphnia dubiaCrustaceansReproductionChronic21NOEC0.12100.01Meinertz et al. [40]
DoxycyclineDanio rerioFishGeneticsChronic10NOEC20000.0100200.0Zhu et al. [41]
EnrofloxacinMicrocystis aeruginosaAlgaePopulationChronic5NOEC49.00104.90Robinson et al. [42]
FlorfenicolIsochrysis galbanaAlgaeBiochemistryChronic3NOEC1.00100.10Zhang et al. [43]
FlumequineMicrocystis aeruginosaAlgaePopulationAcute7EC50159.01015.90Lützhøft et al. [44]
FluoxetineDanio rerioFishGeneticsChronic9LOEC0.09100.01Chai et al. [45]
LomefloxacinMicrocystis aeruginosaAlgaePopulationAcute7EC50186.0503.72Robinson et al. [42]
MarbofloxacinCeriodaphnia dubiaCrustaceansMortalityChronic21NOEC2500.010025.00Kergaravat et al. [46]
NifedipineDanio rerioFishPhysiologyChronic2NOEC346.31003.46Meng et al. [47]
OfloxacinMicrocystis aeruginosaAlgaePopulationAcute5EC5021.00500.42Robinson et al. [42]
OxytetracyclineChlamydomonas reinhardtiiAlgaePopulationChronic7NOEC100.00502.00Garcia et al. [48]
RoxithromycinRaphidocelis subcapitataAlgaePopulationChronic7NOEC6.60500.13Guo et al. [49]
SulfachloropyridazineChlorella fusca var. vacuolataAlgaePopulationAcute1EC5032250.0100322.5Bialk-Bielinska et al. [50]
SulfadiazineDaphnia magnaCrustaceansMortalityChronic4NOEC50.00501.00Bundschuh et al. [51]
SulfadimethoxineOryzias latipesFishMortalityAcute4LC50100000.01001000.0Kim et al. [52]
SulfamerazineChlorella fusca var. vacuolataAlgaePopulationAcute2EC5011900.0100119.0Bialk-Bielinska et al. [50]
SulfamethazineGammarus pulexCrustaceansMortalityChronic4NOEC100.01010.00Bundschuh et al. [51]
SulfamethoxazoleDaphnia magnaCrustaceansGrowthChronic21NOEC120.01012.00Lu et al. [53]
SulfaquinoxalineDaphnia magnaCrustaceansIntoxicationAcute2EC50131000.01000131.0De Liguoro et al. [54]
SulfathiazoleDaphnia magnaCrustaceansReproductionChronic21NOEC11000.0100110.0Park and Choi [55]
TetracyclineMicrocystis aeruginosaAlgaePopulationChronic7NOEC50.00105.00Yang et al. [56]
ThiabendazoleOncorhynchus mykissFishGrowthChronic21NOEC12.00500.24U.S. EPA [57]
TrimethoprimDanio rerioFishMortalityChronic21NOEC157.01001.57Madureira et al. [58]
Table 6. Results of risk assessment using the measured concentrations of pharmaceuticals and three risk categories classified as low, moderate, and high.
Table 6. Results of risk assessment using the measured concentrations of pharmaceuticals and three risk categories classified as low, moderate, and high.
GroupPharmaceuticalsCAS No.RQ *Risk Category
AMMinMax
AnilinesAcetaminophen103-90-25.270.1384.79High
PhenicillinesAmpicillin69-53-40.320.110.85Moderate
MacrolidesAzithromycin83905-01-50.040.000.20Low
MethylxanthinesCaffeine58-08-20.200.021.56Moderate
CarboxamidesCarbamazepine298-46-40.240.020.71Moderate
TetracyclinesChlortetracycline57-62-51.150.116.20High
MacrolidesClarithromycin81103-11-90.150.000.79Moderate
DiphenhydraminesDiphenhydramine58-73-10.830.087.92Moderate
TetracyclinesDoxycycline564-25-00.000.000.00Low
FluoroquinolonesEnrofloxacin93106-60-60.020.000.10Low
AmphenicolsFlorfenicol73231-34-26.330.0458.85High
QuinolonesFlumequine42835-25-60.000.000.01Low
OthersFluoxetine54910-89-32.22 **--High
FluoroquinolonesMarbofloxacin115550-35-10.040.000.32Low
FluoroquinolonesOfloxacin82419-36-10.110.010.66Moderate
TetracyclinesOxytetracycline79-57-20.030.000.22Low
MacrolidesRoxithromycin80214-83-10.080.010.39Low
SulfonamidesSulfadiazine68-35-90.01 **--Low
SulfonamidesSulfamerazine127-79-70.000.000.00Low
SulfonamidesSulfamethazine57-68-10.010.000.04Low
SulfonamidesSulfamethoxazole723-46-60.000.000.01Low
SulfonamidesSulfaquinoxaline59-40-50.000.000.00Low
SulfonamidesSulfathiazole72-14-00.000.000.08Low
TetracyclinesTetracycline60-54-80.010.000.06Low
BenzimidazolesThiabendazole148-79-80.100.000.41Moderate
OthersTrimethoprim738-70-50.020.000.38Low
Notes: * Risk quotients (RQ) were calculated to divide the arithmetic mean, minimum and maximum levels of measured environmental concentrations (MEC) of pharmaceuticals by PNECs. ** Only one sample of pharmaceuticals was detected, thus no other values exist.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lee, H.; Chae, M.; Lee, S. Environmental Monitoring and Risk Assessment of Pharmaceutical Residues Discharged from Large Livestock Complex in the Geum River Basin, South Korea. Water 2023, 15, 3913. https://doi.org/10.3390/w15223913

AMA Style

Lee H, Chae M, Lee S. Environmental Monitoring and Risk Assessment of Pharmaceutical Residues Discharged from Large Livestock Complex in the Geum River Basin, South Korea. Water. 2023; 15(22):3913. https://doi.org/10.3390/w15223913

Chicago/Turabian Style

Lee, Hyeri, Minhee Chae, and Seokwon Lee. 2023. "Environmental Monitoring and Risk Assessment of Pharmaceutical Residues Discharged from Large Livestock Complex in the Geum River Basin, South Korea" Water 15, no. 22: 3913. https://doi.org/10.3390/w15223913

APA Style

Lee, H., Chae, M., & Lee, S. (2023). Environmental Monitoring and Risk Assessment of Pharmaceutical Residues Discharged from Large Livestock Complex in the Geum River Basin, South Korea. Water, 15(22), 3913. https://doi.org/10.3390/w15223913

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop