Simultaneous Determination of Pesticides and Veterinary Pharmaceuticals in Environmental Water Samples by UHPLC–Quadrupole-Orbitrap HRMS Combined with On-Line Solid-Phase Extraction

Abstract

Pesticides and veterinary pharmaceuticals are used for effective crop production and prevention of livestock diseases; these chemicals are released into the environment via various pathways. Although the chemicals are typically present in trace amounts post-release, they could disturb aquatic ecosystems and public health through resistance development toward drugs or diseases, e.g., reproductive disorders. Thus, the residues of pesticides and veterinary pharmaceuticals in the environment must be managed and monitored. To that end, we developed a simultaneous analysis method for 41 target chemicals in environmental water samples using ultra-high-performance liquid chromatography (UHPLC)–quadrupole-orbitrap high-resolution mass spectrometry (HRMS) coupled with an on-line solid-phase extraction system. Calibration curves for determining linearity were constructed for 10–750 ng∙L⁻¹, and the coefficient of determination for each chemical exceeded 0.99. The method’s detection and quantitation limits were 0.32–1.72 ng∙L⁻¹ and 1.02–5.47 ng∙L⁻¹, respectively. The on-line solid-phase extraction system exhibited excellent method reproducibility and reduced experimental error. As the proposed method is applicable to the monitoring of pesticides and veterinary pharmaceuticals in surface water and groundwater samples acquired near agricultural areas, it allows for the management of chemicals released into the environment.

1. Introduction

Pesticides and veterinary pharmaceuticals are commonly used in the agriculture sector (crop production and livestock breeding) to promote productivity and to prevent and treat diseases [1,2,3,4,5,6,7,8]. Many studies reported that pesticides and veterinary pharmaceuticals are globally found in environmental water [9,10,11,12]. Because their release rates are higher than their degradation rates, the chemicals from these sources are persistent in the environment [8,13,14,15]. Pesticides and veterinary pharmaceuticals can be released into the surface water and groundwater through various pathways (e.g., runoff from agricultural lands, direct discharge of effluents from wastewater treatment plants, and direct discharge of animal wastewater from livestock farms) [8,16,17,18,19].

The presence of pesticide and veterinary pharmaceutical residues in the environment is of concern because these residues adversely affect ecosystems and human health [19,20,21,22,23]. For example, some organochlorine pesticides are known to cause side effects by mimicking or antagonizing human hormones. In addition, long-term, low-dose exposure is associated with adverse impacts on human health such as immuno-suppression, hormone disruption, and reproductive abnormalities [1,24,25]. Moreover, antibiotics in surface water or groundwater can promote the emergence of antibiotic-resistant bacteria in spite of their low concentrations [8,14,18,25].

Therefore, monitoring the pesticide and veterinary pharmaceutical residues in environmental water of the agricultural watershed is necessary because surface water and groundwater are commonly used for drinking and irrigation. According to a U.S. Food and Drug Administration (U.S. FDA) report, the sales of veterinary antibiotics were approximately 28.8 million pounds in 2009, which is estimated to be four times higher than those of human antibiotics (approximately 7.3 million pounds in 2009) [26,27]. Many studies focused on the monitoring of pesticides and veterinary pharmaceuticals in environmental water, samples obtained from surface water, and wastewater treatment plants located near urban watersheds [9,10,11,12]. The monitoring of pesticides and veterinary pharmaceuticals in agricultural watersheds was not extensively investigated [28,29,30].

Recently, use of high-performance liquid chromatography coupled with high-resolution mass spectrometry (HRMS) was widely explored for the analysis of trace residues, such as pharmaceuticals, pesticides, metabolites, and hormones, in the environment [31,32,33,34,35]. Quadrupole-orbitrap (q-orbitrap) HRMS is advantageous because many chemicals can be simultaneously analyzed. In addition, q-orbitrap HRMS can determine preselected ion transitions corresponding to specific compounds and provide full scan data [33,34,35,36,37,38]. Moreover, q-orbitrap HRMS is known to provide accurate mass measurements that facilitate selectivity for the detection of analytes even at low concentrations in complex matrices such as environmental water [38].

Moreover, residual chemicals such as pharmaceuticals, pesticides, metabolites, and hormones exist in trace amounts in the environment; thus, their extraction and concentration are required for analyses [39,40]. Furthermore, compared to conventional methods such as off-line solid-phase extraction (SPE), an on-line SPE system has many advantages including cost and labor efficiency; therefore, it was recently applied in combination with liquid chromatography–mass spectrometry (LC–MS) to analyze trace residue chemicals in environmental water [41,42,43]. However, only a few studies applied the on-line SPE method for the analysis of trace residue chemicals in environmental water samples from agricultural watersheds [44,45]. In particular, to the best of our knowledge, a simultaneous analysis of pesticides and veterinary pharmaceuticals using an on-line SPE system combined with LC-q-orbitrap HRMS was not previously conducted.

This study aimed to develop an analytical method for the simultaneous determination of pesticides and veterinary pharmaceuticals belonging to different therapeutic classes in environmental water samples using ultra-high-performance liquid chromatography (UHPLC)–q-orbitrap HRMS combined with an on-line SPE system. The proposed method was validated based on its linearity, precision, and accuracy. Moreover, the validated method was successfully applied to analyze the concentrations of multiclass target chemicals in environmental water samples from agricultural watersheds.

2. Materials and Methods

2.1. Chemicals

Forty-one pesticides and veterinary pharmaceuticals were chosen for identification in environmental water samples, and 12 stable isotope-labeled chemicals were selected in this study as internal standards (ISTDs). The target chemicals comprised eight pesticides and 33 veterinary pharmaceuticals. The pesticides included fungicides, insecticides, and herbicides, while the veterinary pharmaceuticals were classified as antiprotozoal, anthelmintic, sedative, anti-inflammatory, antibiotic, synthetic progesterone, and digestion-stimulating agents. The classes of the target chemicals are listed in

Table 1. Analytical data of high-resolution mass spectrometry (HRMS) for target chemicals in this study.

Chemical Name	Therapeutic Class	Adduct	Precursor Ion (m/z)	Product Ion (m/z)		Retention Time (Min)	Internal Standard
Ampicillin	Veterinary antibiotics	[M + H]+	350.12	160.04	106.06	9.33	Ampicillin-d5
Ceftiofur	Veterinary antibiotics	[M + H]+	524.04	126.01	210.04	10.55
Penicillin-G	Veterinary antibiotics	[M + H]+	335.11	160.04	176.07	10.99
Clanobutin	Veterinary digestion simulating agent	[M + H]+	348.10	192.10	174.09	11.66	Fenbendazole-d3
Dexamethasone	veterinary anti-inflammatory	[M + H]+	393.21	147.08	237.13	11.08
Fenbendazole	Veterinary antibiotics	[M + H]+	300.08	268.05	159.04	11.45
Flumequine	Veterinary antibiotics	[M + H]+	262.09	238.05	234.08	11.20
Meloxicam	veterinary anti-inflammatory	[M + H]+	352.04	115.03	141.01	11.86
Methabenzthiazuron	herbicide	[M + H]+	222.07	165.05	124.02	11.36
Tiamulin	Veterinary antibiotics	[M + H]+	494.33	192.11	119.02	10.54
Virginiamycin	Veterinary antibiotics	[M + Na]+	548.24	287.06	548.24	11.4
Carbendazim	Fungicide	[M + H]+	192.08	160.05	132.06	8.93	Lincomycin-d3
Lincomycin	Veterinary antibiotics	[M + H]+	407.22	126.13	359.22	8.92
Azaperone	Veterinary sedative	[M + H]+	328.18	165.07	121.08	9.25	Ofloxacin-d3
Marbofloxacin	Veterinary antibiotics	[M + H]+	363.15	277.06	205.04	9.16
Orbifloxacin	Veterinary antibiotics	[M + H]+	396.15	295.11	352.16	9.44
Sulfadiazine	Veterinary antibiotics	[M + H]+	251.06	156.01	96.06	9.44	Sulfadiazine-13C6
Colchicine	Antigout, anti-inflammatory	[M + H]+	400.18	310.12	358.16	10.47	Sulfadimethoxine-d6
Sulfadimethoxine	Veterinary antibiotics	[M + H]+	311.08	245.10	245.10	10.71
Sulfaquinoxaline	Veterinary antibiotics	[M + H]+	301.08	156.01	146.07	10.75
Chlortetracycline	Veterinary antibiotics	[M + H]+	479.12	154.05	444.08	9.77	Sulfamethazine-13C6
Oxytetracycline	Veterinary antibiotics	[M + H]+	161.16	337.07	381.06	9.28
Sulfamethazine	Veterinary antibiotics	[M + H]+	279.09	204.04	124.09	9.84
Dimethoate	Insecticide	[M + H]+	230.01	170.97	170.97	10.38	Sulfamethoxazole-d4
Doxycycline	Veterinary antibiotics	[M + H]+	445.16	428.13	410.12	9.93
Sulfachloropyridazine	Veterinary antibiotics	[M + H]+	285.02	156.01	108.04	10.42
Sulfamethoxazole	Veterinary antibiotics	[M + H]+	254.06	156.01	147.08	10.54
Sulfathiazole	Veterinary antibiotics	[M + H]+	256.02	156.01	101.02	9.60	Sulfathiazole-13C6
Altrenogest	Veterinary Synthetic progestin	[M + H]+	311.20	227.14	225.13	12.48	Terbuthylazine-(ethyl-d5)
Azoxystrobin	Fungicide	[M + H]+	404.12	344.10	329.08	12.28
Boscalid	Fungicide	[M + H]+	343.04	307.06	272.09	12.36
Flunixin	Veterinary anti-inflammatory	[M + H]+	297.08	279.07	277.08	12.22
Decoquinate	Veterinary antiprotozoal	[M + H]+	418.26	372.22	250.07	14.33	Triclocarban-d4
Imiprothrin	Fungicide	[M + H]+	319.17	123.12	135.12	12.54
Mepanipyrim	Fungicide	[M + H]+	224.12	209.09	167.07	12.54
Methoxyfenozide	Insecticide	[M + H]+	313.15	149.06	133.06	12.50
Moxidectin	Veterinary endectocide	[M + H]+	640.38	199.11	145.06	16.77
Narasin	Veterinary antibiotics	[M + H]+	787.50	407.24	255.16	19.19
Clopidol	Veterinary antibiotics	[M + H]+	192.00	192.00	174.03	9.45	Trimethoprim-d9
Ormetoprim	Veterinary antibiotics	[M + H]+	275.15	123.07	259.12	9.20
Trimethoprim	Veterinary antibiotics	[M + H]+	291.15	230.12	123.07	9.08

. The ISTDs used for the quantification of the target chemical were selected from compounds that were structurally like the target chemical or had a retention time similar to that of the target chemical as per the UHPLC–MS analysis (

Table 2. Analytical data of HRMS for the internal standards in this study.

Chemical Name	Ionization Form	Precursor Ion (m/z)	Retention Time (Min)
Ampicillin-d5	[M + H]+	355.15	9.32
Fenbendazole-d3	[M + H]+	303.10	11.47
Lincomycin-d3	[M + H]+	410.24	8.91
Ofloxacin-d3	[M + H]+	365.17	9.24
Sulfadiazine-13C6	[M + H]+	257.08	9.40
Sulfadimethoxine-d6	[M + H]+	317.12	10.68
Sulfamethazine-13C6	[M + H]+	285.11	9.82
Sulfamethoxazole-d4	[M + H]+	258.08	10.52
Sulfathiazole-13C6	[M + H]+	262.04	9.57
Terbuthylazine-(ethyl-d5)	[M + H]+	235.15	12.08
Triclocarban-d4	[M + H]+	319.01	13.34
Trimethoprim-d9	[M + H]+	282.07	11.79

).

Ampicillin-d5, tiamulin-d10, clanobutin, and ormethoprim were obtained from Toronto Research Chemicals Inc. (North York, ON, Canada). Triclocarban-d4, and virginiamycin were purchased from CDN isotopes (Gwangju, Korea) and LKT Laboratories Inc. (St. Louis, MO, USA), respectively, while the 38 pesticides and veterinary pharmaceuticals (altrenogest, ampicillin, azaperone, azoxystrobin, boscalid, carbendazime, ceftiofur, chlortetracycline, clopidol, colchicine, decoquinate, dexamethaxone, dimethoate, doxycycline, fenbendazole, flumequine, flunixin, imiprothrin, lincomycin, marbofloxacin, meloxicam, mepanipyrim, methabenzthiazuron, methoxyfenozide, moxidectin, narasin, orbifloxacin, oxytetracycline, penicillin-G, sulfachloropyridazine, sulfadiazine, sulfadimethoxine, sulfamethazine, sulfamethoxazole, sulfaquinoxaline, sulfathiazole, tiamulin, and trimethoprim) and 11 ISTDs (carbendazime-d3, fenbendazole-d3, meloxicam-d 3, ofloxacin-d3, sulfadiamethoxine-d6, sulfadiazine-13C6, sulfamethazine-13C6, terbythylazine-(ethyl-d5), and trimethoprim-d9) were purchased from Sigma-Aldrich (St. Louis, MO, USA), except for lincomycin-d3 and sulfamethoxazole-d4, which were obtained from LGC standards (Teddington, UK).

LC–MS grade organic solvents (methanol and acetonitrile (ACN)) and distilled water (DW) used during LC–HRMS operation were obtained from Fisher Scientific (Fairlawn, NJ, USA) and Merck Millipore (Billerica, MA, USA), respectively. Citric acid and trisodium citrate dehydrate for the preparation of 1.5 M citrate buffer solution (pH 6.5) and ethylenediaminetetraacetic acid disodium salt dehydrate for the preparation of the 5% EDTA solution were bought from Sigma-Aldrich (St. Louis, MO, USA).

2.2. Sampling and Sample Preparation

All groundwater and river water samples were collected using polypropylene bottles at a point in the midstream basin of the Cheongmi River from an intensive livestock farming area in Gyeonggi-do in March 2019. The collected samples were stored at <4 °C in darkness to prevent changes in the sample; the samples were extracted within 24 h. The water samples were centrifuged at 4 °C and 24,600× g for 5 min to eliminate suspended matter, and the supernatant was filtered through a 0.2-µm polyvinylidene fluoride syringe filter. The pH of the water samples was considered as an important factor during sample preparation. To prevent the degradation of target chemicals during the experiment, we adjusted the pH to 6.5 using 1.5 M citrate buffer (0.5 mL per 50 mL sample), followed by the addition of 0.5 mL of a 5% EDTA solution to the 50 mL samples. For the ISTD method, the ISTD solutions (25 µL, 500 ng∙L⁻¹) were added to 50-mL samples. Moreover, DW as a blank was subjected to pretreatment using the same procedure as that for the environmental water samples. The prepared samples were stored in 10-mL vials; they were extracted and analyzed using UHPLC–q-orbitrap HRMS coupled with an on-line SPE system within 7 days.

2.3. UHPLC–q-Orbitrap HRMS Instrumentation and Conditions

Compared to traditional methods such as off-line SPE, an on-line SPE system has many advantages, including cost and labor efficiency. However, the pretreatment process, including sample extraction and concentration, is challenging to optimize for satisfactory accuracy and sensitivity because the target compounds have different physicochemical properties. In addition, several factors affect SPE performance, including the type of SPE cartridge, sample pH, sample column, and the solvent used for elution, among others [25,42,43,46,47,48,49,50].

The operating parameters used in the method described by Kim et al. [7] were used for the on-line SPE and UHPLC–q-orbitrap HRMS, with optimization. We used the same type of column because our samples also had a pH of 6.5; however, we combined two Hypersil columns to extract larger amounts of the target chemicals. To determine the appropriate column for separating the target chemicals in this study, the performance of the XBridge C₁₈ column (50 mm × 2.1 mm inner diameter (i.d.), 2.5 µm, Waters, Milford, MA, USA) used by Kim et al. [7] was compared with the CORTECS C₁₈ column in a preliminary experiment. In this preliminary experiment, samples were analyzed by injecting standard compounds at a concentration of 500 ng∙L⁻¹ in DW, and the retention times, peak shapes, and peak intensities were compared. The CORTECS C₁₈ column produced cleaner baselines and peak shapes than the XBridge C₁₈ column; hence, we concluded that the CORTECS C₁₈ column was suitable for further studies. The detailed analysis conditions for selected target chemicals are described in

Table 3. Analysis condition for selected target chemicals. UHPLC—ultra-high-performance liquid chromatography; SPE—solid-phase extraction; DW—distilled water.

Equipment	UHPLC	Thermo Scientific Dionex ultimate 3000 (Thermo Scientific Fisher, Bremen, Germany)
	On-line SPE	EQuan Max Plus (Thermo Scientific, San Jose, CA, USA)
Column	UHPLC	Waters CORTECS UPLC C18 reverse-phase column (2.1 × 50 mm i.d. and 1.6 µm particle size, Waters Corp, Milford, MA, USA)
	On-line SPE	Two Hypersil GOLD aQ Columns (2.1 × 30 mm i.d. and 1.9 mµm particle size, Thermo Scientific Fisher, Bremen, Germany)
Temperature (°C)	Column	35
	Autosampler	4
Mobile phase	0.1% formic acid in DW (A)
	Acetonitrile (B)
Injection volume (mL)	5
Chromatography condition	UHPLC	Time (min)	0	5.1	10	18.5	26	26.1	30
		Flow rate (mL∙min−1)	0.2
		A (%)	98	98	35	35	2	98	98
		B (%)	2	2	65	65	98	2	2
	On-line SPE	Time (min)	0	5.1	5.2	27.3	27.4	30
		Flow rate (mL∙min−1)	1	1	0.1	0.1	1	1
		A (%)	98
		B (%)	2

.

A Q ExactiveTM Plus Hybrid Quadrupol-OrbitrapTM mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) fitted to the UHPLC system was used to detect the pesticides and veterinary pharmaceuticals in water. The mass spectrometer employed a heated electrospray ionization source (HESI-II) in positive mode with the following conditions: ion spray voltage, capillary temperature, flow rate of sheath gas, sweep gas, auxiliary gas, and auxiliary temperature of 3.5 kV, 250 °C, 45 arb, 2 arb, 10 arb, and 400 °C, respectively. The resolving power, automatic gain control (AGC), target, and maximum injection time (IT) were set at 17,500 full width at half maximum (FWHM. m/z 200), 5 × 10⁵, and 100 ms, respectively. The precursor ions were fragmented in the high-energy collisional dissociation (HCD) collision cell with a normalized collision energy (NCE) of 30.

Xcalibur 4.0 (Thermo Fisher Scientific, San Joes, CA, USA) software was used for instrument control and data acquisition. Positive ionization mode was selected to qualify and quantify the pesticides and veterinary pharmaceuticals. The positive ions of the target chemicals were all in the [M + H]⁺ form, except for virginiamycin which was in the [M + Na]⁺ form (Table 1). To confirm the identity of each analyte, the m/z values of the monitored adduct (mass error < 5 ppm), two fragment ions, and the relevant retention time were verified (Table 1).

2.4. Method Validation

To validate the method developed in this study, a calibration curve was constructed for each chemical using the ISTD method; the method detection limit (MDL), method quantitation limit (MQL), precision, and relative accuracy were determined for each target chemical. Standard solutions used to construct the calibration curves were prepared in DW and pretreated in the same manner as the environmental water samples. Solutions at seven concentrations in the 10–750 ng∙L⁻¹ range containing 250 ng∙L⁻¹ of the ISTD mixture were used to construct the calibration curves, and the coefficients of determination (R²) were evaluated. The values of MDL and MQL for each chemical were obtained by multiplying the standard deviation, which was obtained by analyzing the 10 ng∙L⁻¹ standard solution seven times, by 3.14 and 10, respectively [51,52,53]. The recovery and relative standard deviation (RSD, %) for determining the precision data were derived by analyzing seven replicate DW samples spiked with a mixed target chemical solution (10 ng∙L⁻¹, 50 ng∙L⁻¹, and 500 ng∙L⁻¹). The relative recovery for determining the accuracy data was achieved by analyzing the groundwater and river water samples in triplicate containing the target chemicals (50 ng∙L⁻¹ and 500 ng∙L⁻¹); all samples also contained 250 ng∙L⁻¹ of the mixed ISTD solution. The value of the accuracy was calculated using Equation (1) and expressed as relative recovery (%).

$$\mathrm{Relative}\mathrm{recovery}(\%)=\left[\frac{C_M-C_C}{C_S}\right]\times 100$$

(1)

where C_M and C_C are the measured preside and veterinary pharmaceutical concentrations of the spiked and un-spiked samples, respectively, and C_S is the expected concentration of the spiked sample.

3. Results and Discussion

3.1. Method Validation

3.1.1. Linearity

The ISTD method was used to create a calibration curve for each chemical to minimize potential errors that may occur during analysis. Each of the 12 stable isotope-labeled chemicals was used at a concentration of 250 ng∙L⁻¹. The ISTD list of target chemicals is summarized in Table 2 according to retention time and chemical structure. The calibration curve for each chemical was constructed by plotting the area ratios of the ISTD and target chemical peaks against the concentration of the target chemical (10–750 ng∙L⁻¹), and R² was determined as a measure of linearity. R² values ranged from 0.9917 (tiamulin) to 0.9999 (clanobutin). Details of R² values, calibration curves, and peak-analysis results for each chemical are listed in

Table 4. Method validation data of the developed method with on-line SPE for target chemicals. MDL—method detection limit; MQL—method quantitation limit.

Chemical Name	Linearity (R2)	This Study		Literature
		MDL (ng∙L−1)	MQL (ng∙L−1)	MDL (ng∙L−1)
Altrenogest	0.9959	0.66	2.09
Ampicillin	0.9964	0.72	2.29	0.50 a
Azaperone	0.996	0.95	3.03
Azoxystrobin	0.9968	1.54	4.92
Boscalid	0.9984	0.76	2.41	0.50 b
Carbendazim	0.9939	0.94	3.01	2.70 c
Cetiofur	0.9974	0.57	1.82	0.20 d
Chlortetracycline	0.9964	0.32	1.02
Clanobutin	0.9999	0.59	1.88
Clopidol	0.9997	0.56	1.80
Colchicine	0.9937	0.44	1.40
Decoquinate	0.9958	1.35	4.31
Dexamethasone	0.9989	0.74	2.35
Dimethoate	0.9957	0.95	3.04
Doxycycline	0.9956	1.03	3.27	8.80 c
Fenbendazole	0.9990	0.70	2.23
Flumequine	0.9989	0.49	1.55	0.80 a, 3.20 c
Flunixin	0.9954	0.59	1.87
Imiprothrin	0.9992	1.09	3.46
Lincomycin	0.9986	0.44	1.40	1.81 e
Marbofloxacin	0.9939	0.51	1.62	0.80 a
Meloxicam	0.9991	0.81	2.57
Mepanipyrim	0.9933	1.30	4.15
Methabenzthiazuron	0.9992	0.92	2.93
Methoxyfenozide	0.9968	1.13	3.60
Moxidectin	0.9983	1.72	5.49
Narasin	0.9967	0.90	2.86
Orbifloxacin	0.9963	1.30	4.13
Ormetoprim	0.9921	0.39	1.23
Oxytetracycline	0.9929	0.51	1.63	8.00 b
Penicillin-G	0.9967	0.83	2.64	1.10 a
Sulfachloropyridazine	0.9987	0.36	1.16	5.40 e
Sulfadiazine	0.9946	1.59	5.07	7.91 e, 2.3 b
Sulfadimethoxine	0.9976	0.48	1.53	3.01 e, 3.4 b
Sulfamethazine	0.9978	0.52	1.66	1.32 e, 1.0 b
Sulfamethoxazole	0.9983	0.89	2.82	4.60 e
Sulfaquinoxaline	0.9968	0.67	2.15	3.4 b
Sulfathiazole	0.9975	1.07	3.41	6.06 e
Tiamulin	0.9917	0.47	1.49	1.6 b
Trimethoprim	0.9954	0.44	1.41	1.58 e, 6.5 b
Virginiamycin	0.9991	0.72	2.30

^a Pozo et al., 2006 [20]; ^b Gulkowska et al., 2014 [54]; ^c Chitescu et al., 2015 [55]; ^d Puig et al., 2008 [56]; ^e Panditi et al., 2013 [47].

and displayed in Figure 1 and Figure 2, and Figures S1–S3 (Supplementary Materials).

3.1.2. MDLs and MQLs

A solution of mixed target chemicals (10 ng∙L⁻¹) in DW was analyzed seven times to determine MDL and MQL values. The highest MDL value was observed for moxidectin (1.72 ng∙L⁻¹), while chlorotetracycline exhibited the lowest (0.32 ng∙L⁻¹), with MQL values between 1.02 and 5.47 ng∙L⁻¹ (Table 3). International organizations like the European Union (EU) regulate the residual allowable concentration in surface water for 33 types of pesticides [54]. They also set the residual allowable concentration in drinking water for all pesticides at 0.1 µg∙L⁻¹ [54]. Comparing the residual allowable concentration with MDL values obtained in this study, the MDL value was found to be lower than the residual criteria. This suggests that the MDL and MQL values in this study are sufficiently low for environmental monitoring.

The literature MDL values were obtained via LC–MS/MS coupled with an on-line SPE system equivalent to that used in this study. Most of the values in the current study were found to be several times lower than the values reported in the literature [20,47,54,55,56]. Panditi et al. [47] developed a method for the analysis of 31 antibiotics (sulfonamide and macrolide) in several environmental water samples (groundwater, river water, and reclaimed water). Eight of these 31 chemicals (lincomycin, sulfachloropyridazine, sulfadiazine, sulfadimethoxine, sulfamethazine, sulfamethoxazole, sulfathiazole, and trimethoprim) reported overlap with this study, and the MDLs were found to be 2.52–14.82 times higher than those in our study. The MDL value of 5.4 ng∙L⁻¹ for sulfachloropyridazine reported by Panditi et al. [47] differs the most from that in our study (0.36). Based on these results, we conclude that the method developed in this study has satisfactorily low MDL and MQL values for the analysis of target chemicals in environmental water samples.

3.1.3. Precision and Accuracy

The values of recovery and RSD for each chemical were determined in order to obtain a measure of precision. The recovery and RSD values were calculated using the averages and standard deviations of the analysis results for samples spiked with DW with 10, 50, and 500 ng∙L⁻¹ of a mixed solution of 41 pesticides and veterinary pharmaceuticals; all recovery and RSD values were found to be over 90% and lower than 15%, respectively (

Table 5. Precisions including recovery and relative standard deviation (RSD) of the developed method with on-line SPE for target chemicals.

Chemical Name	Precision (n = 7) a
	At 10 ng∙L−1		At 50 ng∙L−1		At 500 ng∙L−1
	Recovery (%)	RSD (%)	Recovery (%)	RSD (%)	Recovery (%)	RSD (%)
Altrenogest	102 ± 2.09	2.06	91.2 ± 3.42	3.75	86.66 ± 2.85	3.29
Ampicillin	117 ± 2.3	1.96	82.86 ± 0.52	0.63	97.6 ± 1.02	1.05
Azaperone	85.2 ± 3.03	3.56	104 ± 2.84	2.73	84.8 ± 2.13	2.51
Azoxystrobin	89.77 ± 4.92	5.48	98.2 ± 1.96	2.00	102 ± 2.97	2.92
Boscalid	102 ± 2.41	2.38	102 ± 1.38	1.35	97.3 ± 2.53	2.60
Carbendazim	95.0 ± 3.01	3.17	82.8 ± 1.04	1.25	95.8 ± 4.62	4.82
Cetiofur	100 ± 1.82	1.82	101 ± 1.76	1.74	101 ± 1.65	1.65
Chlortetracycline	116 ± 1.02	0.88	92.6 ± 0.50	0.54	100 ± 0.85	0.84
Clanobutin	96.3 ± 1.88	1.95	86.9 ± 2.42	2.79	103 ± 1.17	1.14
Clopidol	95.8 ± 1.80	1.88	102 ± 2.41	2.36	99.0 ± 3.20	3.23
Colchicine	97.1 ± 1.40	1.44	95.9 ± 2.71	2.83	117 ± 3.58	3.06
Decoquinate	97.0 ± 4.31	4.44	101 ± 2.44	2.42	81.9 ± 1.97	2.40
Dexamethasone	88.5 ± 2.35	2.66	81.6 ± 1.16	1.42	98.2 ± 1.23	1.25
Dimethoate	112 ± 3.04	2.73	97.6 ± 2.61	2.67	117 ± 2.66	2.28
Doxycycline	95.4 ± 3.27	3.42	101 ± 0.76	0.75	100 ± 1.86	1.86
Fenbendazole	89.4 ± 2.23	2.50	100 ± 1.00	1.00	109 ± 0.70	0.65
Flumequine	95.7 ± 1.54	1.61	93.9 ± 2.75	2.93	112 ± 2.37	2.11
Flunixin	96.7 ± 1.87	1.94	109 ± 3.51	3.23	102 ± 3.94	3.86
Imiprothrin	99.3 ± 3.46	3.49	100 ± 1.61	1.61	99.7 ± 2.14	2.15
Lincomycin	93.4 ± 1.40	1.49	96.5 ± 0.39	0.40	96.8 ± 0.75	0.78
Marbofloxacin	80.46 ± 1.62	2.01	92.47 ± 1.95	2.11	99.2 ± 1.75	1.76
Meloxicam	96.8 ± 2.57	2.66	94.7 ± 1.70	1.79	107 ± 1.84	1.73
Mepanipyrim	95.33 ± 4.15	4.36	94.8 ± 1.65	1.74	89.2 ± 2.65	2.97
Methabenzthiazuron	92.6 ± 2.93	3.16	96.7 ± 3.72	3.85	102 ± 1.95	1.91
Methoxyfenozide	93.8 ± 3.60	3.83	98.0 ± 2.15	2.19	103 ± 1.81	1.75
Moxidectin	97.4 ± 5.49	5.64	101 ± 2.16	2.14	99.39 ± 2.87	2.89
Narasin	94.2 ± 2.86	3.04	84.2 ± 1.51	1.80	103 ± 3.90	3.77
Orbifloxacin	91.6 ± 4.13	4.50	101 ± 0.66	0.65	101 ± 0.60	0.60
Ormetoprim	99.1 ± 1.23	1.24	95.8 ± 0.85	0.88	92.7 ± 1.98	2.14
Oxytetracycline	106 ± 1.63	1.54	89.1 ± 0.55	0.62	91.9 ± 0.81	0.88
Penicillin-G	101 ± 2.64	2.60	95.5 ± 2.89	3.03	97.7 ± 3.08	3.15
Sulfachloropyridazine	93.3 ± 1.16	1.24	103 ± 1.04	1.01	99.4 ± 1.20	1.20
Sulfadiazine	93.6 ± 0.51	5.42	98.86 ± 2.37	2.40	100.6 ± 1.58	1.57
Sulfadimethoxine	91.8 ± 1.53	1.67	94.36 ± 1.40	1.49	110 ± 1.33	1.21
Sulfamethazine	94.9 ± 1.66	1.77	97.8 ± 1.17	1.19	101 ± 1.09	1.08
Sulfamethoxazole	93.7 ± 2.82	3.01	99.2 ± 2.72	2.74	109 ± 2.54	2.33
Sulfaquinoxaline	97.5 ± 2.15	2.20	101 ± 1.77	1.75	101 ± 1.44	1.42
Sulfathiazole	103 ± 3.41	3.31	101 ± 0.59	0.59	99.9 ± 2.20	2.20
Tiamulin	110 ± 1.49	1.35	96.8 ± 1.18	1.22	104 ± 0.52	0.50
Trimethoprim	109 ± 1.41	1.29	95.1 ± 0.54	0.57	86.6 ± 0.63	0.73
Virginiamycin	82.9 ± 2.30	2.77	108 ± 1.37	1.27	103 ± 2.66	2.58

^a The values of recovery are expressed as means ± standard deviation.

).

Environmental water samples (groundwater and river water) containing 50 and 500 ng∙L⁻¹ of the target chemicals were analyzed three times, while the relative recoveries and coefficients of variation (CVs, %) determined the accuracy of the developed method. The relative recoveries and CVs of the 41 pesticides and veterinary pharmaceuticals were 70–120% and 0.172–10.83%, respectively. The data for each chemical are listed in

Table 6. Accuracy including relative recovery and coefficient of variation (CV) data of the developed method with on-line SPE for target agrochemicals.

Chemical Name	Accuracy (n = 3) a
	Groundwater (at 50 ng∙L−1)		Groundwater (at 500 ng∙L−1)		Surface Water (at 50 ng∙L−1)		River Water (at 500 ng L−1)
	Relative Recovery (%)	CV (%)	Relative Recovery (%)	CV (%)	Relative Recovery (%)	CV(%)	Relative Recovery (%)	CV (%)
Altrenogest	97 ± 6.7	7.0	82± 4.3	5.2	95 ± 5.3	5.5	90 ± 1.6	1.8
Ampicillin	95 ± 0.37	0.38	110 ± 3.5	3.2	89 ± 1.7	1.9	94 ± 4.5	4.7
Azaperone	96 ± 7.9	8.2	98 ± 7.7	7.9	86 ± 4.4	5.2	90 ± 1.7	1.9
Azoxystrobin	100 ± 4.5	4.5	110 ± 2.5	2.2	100 ± 2.9	2.9	100 ± 5.8	5.6
Boscalid	110 ± 1.2	1.1	89 ± 2.2	2.4	86 ± 2.1	3.1	110 ± 7.5	6.9
Carbendazim	110 ± 3.9	3.7	110 ± 2.4	2.1	91 ± 3.2	3.5	86 ± 3.8	4.4
Cetiofur	98 ± 5.2	5.3	100 ± 1.8	1.7	87 ± 7.5	8.6	89 ± 8.7	9.8
Chlortetracycline	94 ± 6.1	6.5	97 ± 6.5	6.7	97 ± 5.2	5.3	95 ± 1.7	1.8
Clanobutin	100 ± 3.0	3.0	100 ± 1.3	1.3	99 ± 6.0	6.1	94 ± 4.6	4.9
Clopidol	99 ± 3.0	3.0	100 ± 6.4	6.2	100 ± 4.2	4.1	95 ± 4.4	4.7
Colchicine	93 ± 0.64	0.68	96 ± 6.3	6.6	90 ± 5.1	5.7	91 ± 6.8	7.5
Decoquinate	92 ± 0.21	0.23	100 ± 5.5	5.3	94 ± 3.3	3.5	86 ± 7.4	8.6
Dexamethasone	88 ± 4.7	5.4	100 ± 5.7	5.6	110 ± 4.3	4.1	80 ± 2.9	3.6
Dimethoate	100 ± 6.9	6.9	93± 2.0	2.2	91 ± 9.1	10	92 ± 5.3	5.7
Doxycycline	94 ± 4.9	5.2	97 ± 5.5	5.6	99 ± 5.4	5.5	91 ± 3.8	4.1
Fenbendazole	100 ± 1.6	1.6	99 ± 1.1	1.1	100 ± 4.6	4.5	100 ± 2.2	2.0
Flumequine	99 ± 1.1	1.1	99 ± 2.9	2.9	110 ± 6.4	5.8	93 ± 3.1	3.3
Flunixin	110 ± 2.5	2.3	89 ± 3.3	3.7	93 ± 3.1	3.3	91 ± 7.0	7.7
Imiprothrin	100 ± 1.3	1.2	86 ± 4.7	5.4	85 ± 6.1	7.1	98 ± 8.8	8.8
Lincomycin	92 ± 1.2	1.3	99 ± 0.77	0.8	91 ± 1.8	2.0	100 ± 1.3	1.3
Marbofloxacin	95 ± 5.7	6.0	120 ± 2.1	1.7	70 ± 3.7	5.2	88 ± 4.1	4.7
Meloxicam	88 ± 6.7	7.6	91 ± 2.3	2.6	97 ± 10.5	11	100 ± 3.9	3.8
Mepanipyrim	99 ± 6.1	6.2	93 ± 3.2	3.5	97 ± 2.5	2.6	100 ± 8.8	8.8
Methabenzthiazuron	110 ± 6.7	5.9	110 ± 2.0	1.7	98 ± 4.2	4.3	80 ± 3.8	4.8
Methoxyfenozide	91 ± 2.5	2.8	79 ± 3.1	3.9	88 ± 5.2	5.9	83 ± 2.5	3.1
Moxidectin	94 ± 1.2	1.3	95 ± 2.3	2.4	100 ± 0.7	0.7	93 ± 4.0	4.3
Narasin	110 ± 2.8	2.6	110 ± 4.5	4.3	88 ± 1.6	1.8	85 ± 3.2	3.7
Orbifloxacin	100 ± 8.5	8.2	110 ± 1.7	1.5	84 ± 2.7	3.2	84 ± 1.5	1.8
Ormetoprim	91 ± 2.4	2.7	100 ± 4.2	4.2	87 ± 5.0	5.8	97 ± 4.0	4.1
Oxytetracycline	110 ± 3.1	3.0	99 ± 2.1	2.1	92 ± 1.3	1.4	99 ± 2.3	2.3
Penicillin-G	100 ± 2.9	2.9	98 ± 8.1	8.3	83 ± 4.7	5.7	93 ± 4.3	4.7
Sulfachloropyridazine	96 ± 7.0	7.2	97 ± 1.1	1.2	93 ± 5.7	6.2	86 ± 5.3	6.1
Sulfadiazine	89 ± 3.9	4.4	110 ± 3.0	2.8	94 ± 4.7	4.9	95 ± 3.2	3.3
Sulfadimethoxine	100 ± 9.5	9.5	100 ± 1.7	1.7	91 ± 4.6	5.1	92 ± 3.1	3.4
Sulfamethazine	89 ± 1.7	1.9	93 ± 1.7	1.8	88 ± 7.7	8.7	100 ± 1.1	1.1
Sulfamethoxazole	91 ± 2.7	3.0	93 ± 2.3	2.5	93 ± 5.9	6.4	93 ± 3.8	4.1
sulfaquinoxaline	99 ± 9.0	9.1	95 ± 1.7	1.7	87 ± 4.5	5.2	87 ± 2.8	3.2
Sulfathiazole	100 ± 0.17	0.17	97 ± 3.5	3.6	99 ± 2.6	2.6	110 ± 7.8	6.9
Tiamulin	95 ± 5.0	5.3	85 ± 6.2	7.3	94 ± 6.0	6.4	110 ± 5.3	4.8
Trimethoprim	120 ± 2.5	2.1	110 ± 1.1	1.0	90 ± 2.3	2.5	80 ± 0.9	1.1
Virginiamycin	100 ± 6.6	6.4	98 ± 2.5	2.5	100 ± 5.7	5.6	99 ± 0.52	0.5

^a The values of relative recovery are expressed as means ± standard deviation.

.

Even though the ISTD method using stable isotope-labeled chemicals was applied to correct the matrix effect, ionization was enhanced or suppressed during the LC–MS/MS analyses of the target chemicals [57]. This shows that extraction and ionization performances are affected by the complexity of the matrix. According to widely applied guidelines for pesticide residue analysis, mean recoveries must be within the range of 70–120%, with RSD values ≤20% for all analytes [58]. The result of method validation satisfied the criteria of the guidelines [58]. Therefore, the method developed in this study has excellent precision and accuracy for the analysis of residual pesticides and veterinary pharmaceuticals.

3.1.4. Method Application

The concentrations of the target chemicals in environmental water samples were analyzed using the developed method. Six chemicals (boscalid, ceftiofur, dexamethasone, methabenzthiazuron, sulfadiazine, and virginiamycin) were not detected in groundwater and river water, while 19 and 31 target chemicals were identified in groundwater and river water, respectively. In addition, eight of the 19 chemicals in the groundwater samples, and 12 of the 31 chemicals in the river water samples were found to be below their MQL values, which means that these chemicals were present in these samples but were not quantifiable. Furthermore, five chemicals (altrenogest, decoquinate, doxycycline, flunixin, and trimethoprim) were found at levels above their MQLs in all samples, with decoquinate (71.46 ng∙L⁻¹ in groundwater) and doxycycline (42.98 ng∙L⁻¹ in river water) detected at the highest levels in the environmental water samples. In contrast, trimethoprim and altrenogest were analyzed at levels of 10.33 ng∙L⁻¹ and 7.84 ng∙L⁻¹ in groundwater and river water, respectively, which were the lowest concentrations found in this study (

Table 7. Analysis results using the developed method with on-line SPE for target agrochemicals in environmental water samples (groundwater and surface water). ND—not detected.

Chemical Name	Mean ± Standard Deviation, n = 3 (ng∙L−1)
	Groundwater	River Water
Altrenogest	12.92 ± 0.49	7.84 ± 0.20
Ampicillin	ND	11.03 ± 1.13
Azaperone	7.20 ± 9.69	<MQL
Azoxystrobin	<MQL	<MQL
Carbendazim	<MQL	<MQL
Chlortetracycline	8.32 ± 4.27	ND
Clanobutin	ND	8.50 ± 0.34
Clopidol	ND	30.41 ± 1.79
Colchicine	<MQL	<MQL
Decoquinate	71.46 ± 28.77	22.96 ± 0.74
Dimethoate	ND	<MQL
Doxycycline	34.01 ± 12.55	42.98 ± 3.56
Fenbendazole	0.37 ± 0.17	0.59 ± 0.56
Flumequine	<MQL	<MQL
Flunixin	12.66 ± 0.75	19.08 ± 1.40
Imiprothrin	5.34 ± 7.37	ND
Lincomycin	ND	17.63 ± 0.42
Marbofloxacin	<MQL	59.95 ± 4.15
Meloxicam	ND	<MQL
Mepanipyrim	<MQL	ND
Methabenzthiazuron	ND	ND
Methoxyfenozide	<MQL	<MQL
Moxidectin	49.87 ± 27.15	ND
Narasin	<MQL	<MQL
Orbifloxacin	ND	<MQL
Ormetoprim	<MQL	<MQL
Oxytetracycline	ND	34.25 ± 5.93
Penicillin-G	ND	13.45 ± 0.75
Sulfachloropyridazine	ND	49.85 ± 2.13
Sulfadimethoxine	ND	<MQL
Sulfamethazine	ND	24.77 ± 1.07
Sulfamethoxazole	ND	33.07 ± 0.70
sulfaquinoxaline	ND	2.25 ± 1.15
Sulfathiazole	ND	103.07 ± 4.37
Tiamulin	ND	12.52 ± 0.28
Trimethoprim	10.33 ± 0.20	34.51 ± 0.83

).

Among the 41 target chemicals, sulfathiazole was identified only in the river water samples, and was present at the highest concentration (103.07 ng∙L⁻¹), while decoquinate was found to be present at the highest concentration (71.46 ng∙L⁻¹) in groundwater. While this chemical was also detected in river water, it was 3.11 times lower than that in groundwater.

Most of the 31 veterinary pharmaceuticals selected in this study were identified in river water or groundwater. In contrast, the pesticides were detected at very low concentrations, or not at all. Because large numbers of livestock are bred in the sampling area, the use of veterinary pharmaceuticals is high; therefore, various veterinary pharmaceuticals were expected to be detected in river water samples.

4. Conclusions

In this study, we developed a method for the simultaneous analysis of 41 pesticides and veterinary pharmaceuticals in environmental water samples by using on-line SPE combined with UHPLC–MS/MS. The developed method was verified by measuring the linearities, MDLs, MQLs, and relative recoveries in water samples containing mixtures of the pesticides and veterinary chemicals. The developed method was successfully applied for determining the concentration of the target chemicals in groundwater and river water samples collected from near the agricultural watersheds. Thirty-five of the 41 chemicals were detected in concentrations that ranged from 0.35 to 103.07 ng∙L⁻¹ from groundwater and river water samples.

Pesticides and veterinary pharmaceuticals have adverse effects on health and the environment, including antibiotic resistance of the ecosystem, thereby necessitating monitoring. The on-line SPE method developed in this study reduces the consumption of organic solvents, time, cost, and labor required during the extraction experiment while delivering reliable results. HRMS might not be used as widely as LC–MS because of the high initial installation cost. However, as shown in this study, when using HRMS, low contaminant concentrations in environmental water can be accurately analyzed, which is very useful for the evaluation of the contamination levels in environmental water. Analytical methods using UHPLC–q-orbitrap HRMS combined with the on-line SPE system were previously developed for the detection of contaminants in environmental water samples. Although various therapeutic classes of chemicals coexist in environmental water, analytical methods were developed to separately analyze the different classes of chemicals such as pesticides and pharmaceuticals. In contrast, the developed method in this study analyzes various therapeutic classes of chemicals. Moreover, the developed method has the advantage of simultaneously analyzing multiple classes of chemicals in environmental water. This study shows novelty in that the developed method with a very fast system using on-line SPE can be applied with high performance for pesticide and veterinary pharmaceutical monitoring of environmental water in agricultural watersheds, as well as other environmental water sources such as urban watersheds. Because of this widescale applicability, the developed method will facilitate the management of chemical use and control the amounts released into the environment. The developed method could not be adequately applied to the identification of some pharmaceuticals such as aminoglycoside and macrolide antibiotics. Therefore, a method with on-line SPE for the analysis of amoxicillin, aminoglycoside, and macrolide antibiotics needs to be developed in the near future.