Evaluation of the Health Risk and Distribution Characteristics of Pesticides in Shallow Groundwater, South Korea

Abstract

In this work, a method of simultaneously analyzing pesticide concentration and assessing its risks was developed. Assessments were conducted to evaluate the distribution characteristics and risks to human health of pesticides in shallow groundwater in agricultural areas. We developed multi-residue analytical methods using liquid chromatography (LC-MS/MS) and gas chromatography–tandem mass spectrometry (GC-MS/MS) to analyze 57 pesticides in groundwater. In addition, risk assessments were performed by setting scenarios considering the routes of pesticide infiltration into groundwater. For the simultaneous analysis of 57 pesticides, the liquid–liquid extraction method was applied twice using dichloromethane under acidic and alkaline conditions. The extract was concentrated and analyzed using LC-MS/MS (41 pesticides) and GC-MS/MS (16 pesticides). The precision and accuracy ranges of the analytical methods were 0.1~12.9% (within ±15%) and 80.3~113.6% (within ±20%), respectively. The limit of quantification was found to range from 0.0004 to 0.0677 μg/L. In total, 57 pesticides were monitored in 200 groundwater wells from 2019 to 2020. Twenty-six pesticides, including metolachlor and imidacloprid, were detected, with an average concentration of 0.0008 μg/L in groundwater. The pesticide types and detection levels differed depending on the survey period and surrounding land. When the risks associated with alachlor, metolachlor, and carbofuran were assessed, their health risks when found in groundwater were evaluated to be negligible (non-carcinogenic risk: less than 10⁻³, carcinogenic risk: less than 10⁻⁶).

1. Introduction

In South Korea, groundwater has been developed and used mostly for residential, industrial, and agricultural purposes. It has high utilization potential in the future as only approximately 22% of its development capacity has been used: 2.98 billion m³ of groundwater has been used per year from the 1.69 million groundwater utilization facilities. The results of groundwater quality evaluations over the last ten years (2011~2020) show that the quality is relatively good; the non-conformity rate is 2.8% (1.1~5.1%) on average. However, concerns regarding the contamination of shallow groundwater due to agricultural activities (pesticides and livestock manure) have been raised. Since two organophosphorus pesticides (diazinon 20 μg/L and parathion 60 μg/L) have been set and managed as non-drinking groundwater reference items in South Korea, groundwater quality monitoring for more diverse pesticides in groundwater is needed (Ministry of Environment (ME), 2022) [1].

The European Environment Agency (EEA) has set an upper limit of 0.1 μg/L for a single pesticide and 0.5 μg/L as the limit for total pesticide concentrations in groundwater [2]. Recently, the EEA published pesticide status survey results for surface water (29 countries) and groundwater (22 countries) for 30 EU member countries for samples taken between 2013 and 2020 [3]. Imidacloprid and metazachlor were mainly detected in surface water, whereas atrazine and its metabolites were mainly found in groundwater. Among all survey points, the proportion of points at which 0.1 μg/L was exceeded for at least one pesticide was reported as 4~11% for groundwater. In addition, according to ETC/ICM technical paper data [4], groundwater monitoring for 228 pesticides was conducted at 13,863 points for 8 years.

In California, USA, pesticide monitoring and risk assessment for groundwater wells are performed every year by the California EPA Department of Pesticide Regulation, the State Water Resources Control Board, and the United States Geological Survey [5]. The state recently tested for 196 pesticides in a total of 5195 groundwater wells [6]. One or more pesticides were detected in 476 wells, and a total of 39 pesticides were detected. The detection level was different by pesticide, but the concentration range observed was between 1 and 10 μg/L. The detected pesticides included dibromochloropropane, ethylene dichloride, and simazine, as well as atrazine and its decomposition products (2,4-diamino-6-chloro-s-triazine). In South Korea, the status of commonly used pesticides must also be identified through groundwater quality monitoring for the active management of shallow groundwater in rural areas.

In this study, we selected 57 pesticides based on pesticide usage in South Korea and studied analysis methods that could be used to analyze them simultaneously as much as possible. Next, 57 pesticides were investigated in the groundwater monitoring network of the MOE to study the detection characteristics of pesticides in shallow groundwater. In addition, a human risk assessment was conducted considering the routes of exposure to pesticides in shallow groundwater.

2. Materials and Methods

2.1. Water Quality Survey Point Selection and Survey Cycles

Two hundred wells in the nationwide non-drinking groundwater monitoring network were tested twice a year from 2019 to 2020. Target points were selected to create a uniform distribution of survey points by region and surrounding pollution sources (e.g., industrial activities, urban areas, and background points). For agricultural areas, wells to be surveyed were selected based on their proximity to rice paddies, fields, and cultivation facilities.

2.2. Groundwater Sampling and Field Measurement Item Analysis

Groundwater sampling was performed using the sample collection and preservation method (ES 04130.1e) in the water pollution test standard, and the water temperature, pH, electrical conductivity (EC), oxidation–reduction potential (ORP), and dissolved oxygen (DO) were measured in the field [7]. Separate sampling was carried out for the analysis of cations and anions in 125 mL polyethylene bottles after filtration using a membrane filter (0.45 µm, mixed cellulose ester, Advantec, Tokyo, Japan). Cations were stored by adjusting the pH to 2 or lower through the addition of HNO₃. Pesticide samples were collected in 1 L brown glass bottles, and the bottles were sealed to prevent contact with the atmosphere. All samples were stored and transported in a 4 °C icebox.

2.3. Analysis of Major Cations and Anions

A total of seven items in groundwater, including four major cations (Ca²⁺, Mg²⁺, Na⁺, and K⁺) and three anions (HCO₃⁻, SO₄²⁻, and Cl⁻), were analyzed. The cations were analyzed using the standard method 3120 through the use of an ICP-OES analyzer (Optima 8300&7300DV, PerkinElmer, Shelton, CT, USA; 720-ES, Varian Inc., Palo Alto, CA, USA) [8]. Among the anions, SO₄²⁻ and Cl⁻ were analyzed using the anion–ion chromatography method in the water quality test standard using an IC analyzer (850 Professional, Metrohm, Herisau, Switzerland; ICS-5000+, Dionex Thermo Fisher Scientific, Waltham, MA, USA). HCO₃⁻ was analyzed through 0.05 N HCl titration in the field. The cation/anion analysis results were used for the classification of groundwater types using the Piper diagram. The Ca²⁺-HCO₃⁻ type represents shallow groundwater and the Ca²⁺-(Cl⁻ + NO₃⁻) type represents artificial contamination. The Na⁺-HCO₃⁻ type indicates the reaction between shallow groundwater and underground media, and the Na⁺-(Cl⁻ + NO₃⁻) type indicates the influence of seawater [9,10,11,12].

2.4. Methods to Analyze Pesticides in Groundwater (Liquid Chromatography and Gas Chromatography–Tandem Mass Spectrometry)

We filtered 1 L of groundwater through a 0.45 µm PTFE filter (Advantec, Chiyoda-ku, Tokyo, Japan), and 500 mL was accurately obtained in a separating funnel. Then, 1 + 1 HCl was added to adjust the pH to a range of 3~4, 20 g of NaCl was added, and the sample was mixed. Next, 50 mL of DCM was added twice to perform the first extraction. After re-adjusting the pH to 10 by adding 5 M NaOH to the water layer, 50 mL of DCM was applied twice for the secondary extraction of residual pesticides from the sample. Then, 200 mL of DCM extract was concentrated into approximately 5 mL using a rotary evaporator (N-1300, EYELA, Koishikawa Bunkyo-ku, Tokyo, Japan). The sample was transferred into a concentration vial and completely dried using a nitrogen concentrator (MGS-3100, EYELA). After complete drying, 0.5 mL of ACN was added for dissolution and 0.25 mL of the sample was taken. Then, 0.25 mL of 100 mM ammonium formate buffer was added to make a volume of 0.5 mL, and this solution was analyzed using LC-MS/MS (Agilent 6495 TQ, Santa Clara, CA, USA).

Table 1. LC-MS/MS conditions for 41 pesticides.

No.	Compound	Chemical Class	RT	Prec Ion	Prod Ion1	Prod Ion2
1	Chlorpyrifos	organophosphate	18.84	350	198	97
2	Oxadiazon	oxadiazole	8.76	345.1	303	219.9
3	Carbaryl	carbamate	12.27	202.1	145	127
4	Phenthoate	organophosphate	13.02	321	135	91
5	Tebuconazole	triazole	12.93	308.2	125	70
6	Isoprothiolane	dithiolane	16.81	291.1	231	188.9
7	Hexaconazole	conazole	10.94	314.1	158.9	70
8	Imidacloprid	neonicotinoid	10.53	256.1	209	175.1
9	Tricyclazole	triazole	17.76	190	162.9	135.9
10	Boscalid	carboxamide	13.62	343	307	139.9
11	Fluquinconazole	conazole	12.57	376	307.1	108.1
12	Flubendiamide	diamide	15.12	408	274	147
13	Benzobicyclon	carbobicyclic	20.16	447	257	229
14	Diazinon	organophosphate	14.72	305.1	169	153
15	Pyrimethanil	anilinopyrimidine	14.12	200.1	107	82
16	Carbofuran	carbamate	13.08	222.1	165.1	123
17	Etofenprox	pyrethroid	17.62	394.3	177	107
18	Novaluron	benzoylurea	13.18	493	158.2	141.2
19	Sulfoxaflor	sulfoximine	13.80	278	174.2	154.2
20	Pencycuron	phenylurea	13.03	329.1	218	124.9
21	Acetamiprid	neonicotinoid	14.07	223.1	125.9	56
22	Linuron	phenylurea	15.27	249	160	133
23	Pyraclostrobin	methoxycarbamate	8.42	388.1	194	163
24	Fluopicolide	benzamide	14.48	383	172.9	108.9
25	Azoxystrobin	methoxyacrylate	13.69	404.1	372.1	344.1
26	Chromafenozide	diacylhydrazine	13.29	395.2	339	175
27	Fludioxonil	phenylpyrrole	14.72	266	229	158
28	Flutolanil	carboxamide	13.08	324.1	262	242
29	Kresoxim-methyl	strobilurin	15.67	314.1	222.1	116
30	Quinoclamine	-	17.07	208	105	77.2
31	Thiobencarb	thiocarbamate	15.52	258.1	125	89.1
32	Buprofezin	thiadiazinanes	14.57	306.2	116	106
33	Flufenoxuron	benzoylurea	10.05	489.1	158.1	141.2
34	Iprovalicarb	carbamate	15.16	321.2	119	116
35	Iprobenfos	phosphorothiolate	13.12	289.1	205	91
36	Probenazole	benzothiazole	10.47	224	41	39
37	Abamectin B1	avermectin	17.08	890.5	567.3	305.2
38	Clomazone	isoxazolidinone	8.97	240	124.9	89
39	Fentrazamide	tetrazolinone	14.83	350.1	194	83
40	Sethoxydim	cyclohexene oxime	15.69	328.2	282.2	178.1
41	Halosulfuron-methyl	sulfonylurea	9.56	435	189	139

shows the LC-MS/MS analysis conditions to simultaneously analyze 41 pesticides. We added 0.25 mL of ACN into the remaining solution of 0.25 mL to make 0.5 mL, and this solution was analyzed using GC-MS/MS (Agilent 7000C TQ, Santa Clara, CA, USA).

Table 2. GC-MS/MS conditions for 16 pesticides.

No.	Compound	Chemical Class	RT	Prec Ion	Prod Ion1	Prod Ion2
1	Pendimethalin	dinitroaniline	18.09	252	162	208
2	Alachlor	chloroacetamide	16.26	188	160	131
3	Fenitrothion	organophosphate	16.86	277	109	260
4	Butachlor	chloroacetamide	19.01	188	160	130
5	Difenoconazole	triazole	27.61	323	265	202
6	Procymidone	dicarboximide	18.57	283	96	67
7	Chlorothalonil	chloronitrile	15.02	266	133	231
8	Deltamethrin	pyrethroid	28.01	253	93	172
9	Metolachlor	chloroacetamide	17.20	238	162	134
10	Phorate	organophosphate	13.62	260	231	129
11	Bifenthrin	pyrethroid	22.63	181	166	164
12	Cyfluthrin	pyrethroid	25.66	163	127	129
13	Etridiazole	heteroaromatic	10.18	211	183	108
14	Terbufos	organophosphate	14.73	231	129	175
15	Thifluzamide	carboxamide	19.69	194	125	166
16	Cyprodinil	anilinopyrimidine	18.10	224	208	118

shows the GC-MS/MS analysis conditions to simultaneously analyze 16 items.

2.5. Health Risk Assessment Considering Groundwater Exposure Routes

The Setting of Risk Assessment Scenarios and Formulas Considering Groundwater Exposure Routes

As shown in Figure 1, health risks were assessed by setting the exposure scenarios for drinking (ingestion) and non-drinking water (dermal contact and inhalation) [13,14]. The exposure scenarios of dermal contact and inhalation routes were set for showers and indoor/outdoor agricultural activities. The reference concentration for the risk assessment was set to the 95th percentile concentration, a high-end risk, which is known to affect approximately 68% of the receptors within the total distribution through exposure [15,16]. The 95th percentile was derived using crystal ball ver. 11.0 (Oracle, Redwood Shores, CA, USA) based on the precision survey results, and the physicochemical properties and toxicity reference values for each item were investigated (

Table 3. Physical and chemical properties of the three pesticides.

No.	Substance	Physical and Chemical Properties
		Molecular Mass (g/mol)	Water–Octanol Partition Coefficient (Log Kow)	Diffusion Coefficient in Water (m2/s)	Diffusion Coefficient in Air (m2/s)	Henry’s Constant (Pa·L/mol)
1	Alachlor	269.77	3.52	6.68 × 10−10	1.33 × 10−5	1.39
2	Carbofuran	221.25	2.32	6.81 × 10−10	1.34 × 10−5	0.11
3	Metolachlor	283.79	3.13	6.64 × 10−10	1.32 × 10−5	0.16

Note: Data source: https://pubchem.ncbi.nlm.nih.gov/ (accessed on 31 December 2020).

and

Table 4. Human toxicity reference dose and carcinogenic potential of the three pesticides.

Compound	Institution	Toxicity Endpoint	Point of Departure (POD)	Uncertainty Factor (UF)	Reference Dose (RfD) and Cancer Slope Factor (CSF)
Alachlor	US EPA (1993)	Hepatotoxicity	NOAEL 1 mg/kg-day	100	RfD: 10 μg/kg-day
Carbofuran	US EPA (1987)	Neuro and reproductive toxicity	NOAEL 0.5 mg/kg-day	100	RfD: 5 μg/kg-day
Metolachlor	US OPP (2014)	Liver cancer (carcinogenicity)			CSF: 0.0035 per mg/kg-day
	US OPP (2014)	Systemic toxicity (weight loss)	NOAEL 9.7 mg/kg-day	100	RfD: 100 μg/kg-day
	US EPA (1990)	Reproductive toxicity	NOEL 15 mg/kg-day	100	RfD: 150 μg/kg-day

).

Table 5. Risk assessment formula considering ingestion, dermal contact, and inhalation.

Drinking (O/X)	Exposure Pathway	Equation	Reference
O	Ingestion	${LADD}_{Inges.}={IR}_{D_{water}}\times \frac{EF\times ED}{AT}\times C_{G_{water}}$	[17]
X	Dermal contact	${LADD}_{Dermal,i}=\frac{{SA}_i\times SP}{BW}\times \frac{{ET}_i\times {EF}_i\times {ED}_i}{AT}\times C_{G_{water}}$ [i: Water use (Washing, Shower, Farm, etc.)]	[18]
	Inhalation	${LADD}_{inhal.}=\frac{{BR}_i}{BW}\times \frac{{ET}_i\times {EF}_i\times {ED}_i}{AT}\times VF\times C_{G_{water}}$ [i: Water use (Washing, Shower, Farm, etc.)]	[19,20]
Total non-cancer risk		$HQ=\frac{{LADD}_{Inges.}+{LADD}_{Dermal.}+{LADD}_{Inhal.}}{RfD}$	[17]

lists the contents of the formulas and the related factors for each route obtained during risk assessment.

Table 6. Exposure coefficients and representative values related to the groundwater multi-pathway for the human exposure algorithm.

Exposure Parameter	Representative
	Average	Standard Deviation	Reference
Weight (kg)	64.5	12.65	[22]
Body surface: full body (cm2)	17,352	1979
Body surface: forearm (cm2)	1041	69
Body surface: foot and calf (cm2)	3488	226
Water intake per unit body weight (mL/kg/day)	15.93	10.92
Respiration rate per hour during light exercise (m3/h)	1.0	0.16
Domestic water usage hours for housework (h/day)	0.88	1.18
Domestic water usage hours for shower (h/day)	0.12	0.53
Kitchen volume (m3)	24.5	-	[23]
Bathroom volume (m3)	9.3	-
Kitchen ventilation rate (m3/h)	61.25	-
Bathroom ventilation rate (m3/h)	18.6	-
Domestic water usage for housework (L/h)	471.4	565.7	[24]
Domestic water usage for showers (L/h)	381.3	495.7
House of indoor farming volume (m3)	3360	-	[21,25]
House of indoor farming surface (m2)	700	-
Outdoor agricultural cropland area (m2)	3000	-
Indoor farming labor hours (h/1000 m2/year)	211.7	83.4
Outdoor farming labor hours (h/1000 m2/year)	48.7	33.1
Indoor watering hours (h/1000 m2/year)	5.41	4.11
Time duration in contact with outdoor agricultural water (h/1000 m2/year)	1.13	2.42
House ventilation rate (m3/m2/h)	1	-
Indoor agricultural sprinkler flow rate (L/h)	146.3	-

presents the physiological exposure coefficients and representative values applied in this study. In the case of agricultural activities, the reference data for the usage, usage hours, and usage frequency of agricultural canals as well as the skin exposure area (body surface area) were established considering 10 activities with the longest indoor/outdoor labor hours [21]. When the indoor agricultural labor VF was calculated, the standard value of the agricultural house size in Table 6 was considered. When outdoor agricultural labor VF was calculated, the body exposure of the pollutants was calculated using a box model, as shown in Figure 2.

3. Results and Discussion

3.1. Quality Control Results by Pesticide Item

In this study (2019–2020), we established methods to analyze 57 residual pesticides in groundwater using GC-MS/MS (16 pesticides) and LC-MS/MS (41 pesticides). As methods to verify the effectiveness, the linearity of the calibration curve, the limit of quantification (LOQ), accuracy, precision, and method blank (MB) were analyzed.

The calibration curve of the 16 GC-MS/MS residual pesticides was prepared from 0.0001 to 0.1 μg/L, and its linearity was confirmed because the R² value was 0.995 or higher. The relative standard deviation was calculated through seven repeated analyses with 0.01 μg/L of pesticide using the established analytical methods. It was multiplied by 3.14 to calculate the detection limit and by 10 to calculate the LOQ. Consequently, the LOQ ranged from 0.0014 to 0.0677 μg/L for the entire research period. Due to improved analytical methods, the LOD and LOQ were relatively lower in 2020 (

Table 7. Annual calibration curve linearity, limit of detection, and limit of quantification (GC-MS/MS, 16 pesticides).

No.	Compound	Linearity (R2)		LOD (μg/L, n = 7)		LOQ (μg/L, n = 7)
		2019	2020	2019	2020	2019	2020
1	Pendimethalin	0.9992	0.9980	0.0016	0.0010	0.0050	0.0030
2	Alachlor	0.9988	0.9970	0.0022	0.0009	0.0070	0.0028
3	Fenitrothion	0.9985	0.9976	0.0011	0.0011	0.0035	0.0035
4	Butachlor	0.9997	0.9982	0.0213	0.0006	0.0677	0.0019
5	Difenoconazole	0.9992	0.9970	0.0020	0.0008	0.0063	0.0026
6	Procymidone	0.9986	0.9990	0.0012	0.0005	0.0037	0.0016
7	Chlorothalonil	0.9987	0.9986	0.0009	0.0009	0.0030	0.0027
8	Deltamethrin	0.9986	0.9982	0.0175	0.0011	0.0558	0.0036
9	Metolachlor	0.9995	0.9973	0.0015	0.0008	0.0048	0.0024
10	Phorate	0.9999	0.9971	0.0163	0.0007	0.0518	0.0022
11	Bifenthrin	0.9979	0.9980	0.0030	0.0004	0.0094	0.0014
12	Cyfluthrin	0.9993	0.9987	0.0200	0.0005	0.0637	0.0017
13	Etridiazole	0.9992	0.9981	0.0020	0.0008	0.0065	0.0027
14	Terbufos	0.9993	0.9970	0.0010	0.0011	0.0031	0.0036
15	Thifluzamide	0.9991	0.9998	0.0024	0.0006	0.0077	0.0018
16	Cyprodinil	0.9995	0.9994	0.0015	0.0009	0.0048	0.0028

). Accuracy was repeatedly analyzed by preparing four samples for each concentration in the same manner. Accuracy ranged from 87.6 to 101.9% (less than ±20%). Precision was also analyzed in the same way as accuracy and was found to range from 0.5 to 5.1% (less than ±10%) for the research period (

Table 8. Annual accuracy and precision (GC-MS/MS, 16 pesticides).

No.	Compound	Accuracy (%)				Precision (%)
		2019		2020		2019		2020
		Low (n = 4)	High (n = 4)	Low (n = 4)	High (n = 4)	Low (n = 4)	High (n = 4)	Low (n = 4)	High (n = 4)
1	Pendimethalin	96.7	97.2	89.1	88.8	2.9	2.4	1.1	3.0
2	Alachlor	96.9	97.5	100.0	99.5	3.0	2.5	2.4	2.1
3	Fenitrothion	97.0	93.5	93.6	93.6	2.1	2.9	4.5	4.5
4	Butachlor	101.9	97.6	87.9	89.2	1.0	1.5	3.2	2.2
5	Difenoconazole	95.7	96.3	91.1	90.5	3.2	5.1	1.8	2.9
6	Procymidone	92.8	98.0	94.4	94.3	1.3	3.6	2.2	3.2
7	Chlorothalonil	97.2	98.7	99.8	99.8	1.7	0.8	3.4	3.0
8	Deltamethrin	98.0	100.1	97.5	99.2	1.1	1.1	3.5	4.6
9	Metolachlor	98.1	95.7	98.9	98.3	2.3	1.5	3.0	4.3
10	Phorate	99.1	97.1	100.5	101.0	2.2	1.3	1.6	2.4
11	Bifenthrin	100.4	99.9	90.5	90.4	4.9	2.4	0.5	0.5
12	Cyfluthrin	96.9	97.6	95.7	93.9	1.0	1.8	2.5	3.1
13	Etridiazole	100.8	98.6	91.7	90.2	2.0	1.4	1.6	2.2
14	Terbufos	100.0	96.5	98.8	96.9	3.0	0.5	3.9	2.9
15	Thifluzamide	97.0	97.1	87.6	88.7	2.0	1.3	0.8	3.1
16	Cyprodinil	97.3	98.2	91.7	90.6	3.5	1.7	1.4	2.8

). During the monitoring period, the field blank sample was confirmed as non-detectable, and the average RFD (%) of the double sample was confirmed to be 7.1%.

The calibration curve of the 41 LC-MS/MS residual pesticides was also prepared from 0.0001 to 0.1 μg/L in the same way as GC-MS/MS, and its linearity was confirmed because the R² value was 0.995 or higher. The LOQ, accuracy, and precision were also analyzed using the same method as those for GC-MS/MS to verify effectiveness. The LOQ ranged from 0.0005 to 0.0545 μg/L. Acetamiprid showed the lowest LOQ, while fludioxonil exhibited the highest LOQ (

Table 9. Annual calibration curve linearity, limit of detection, and limit of quantification (LC-MS/MS, 41 pesticides).

No.	Compound	Linearity (R2)		LOD (μg/L, n = 7)		LOQ (μg/L, n = 7)
		2019	2020	2019	2020	2019	2020
1	Chlorpyrifos	0.9985	0.9998	0.0016	0.0060	0.0050	0.0190
2	Oxadiazon	0.9968	0.9999	0.0003	0.0014	0.0008	0.0044
3	Carbaryl	0.9992	0.9980	0.0002	0.0013	0.0006	0.0043
4	Phenthoate	0.9978	0.9990	0.0005	0.0021	0.0017	0.0068
5	Tebuconazole	0.9999	0.9995	0.0012	0.0026	0.0037	0.0083
6	Isoprothiolane	0.9984	0.9992	0.0003	0.0010	0.0008	0.0033
7	Hexaconazole	0.9998	0.9988	0.0007	0.0030	0.0023	0.0095
8	Imidacloprid	0.9979	0.9991	0.0004	0.0009	0.0013	0.0029
9	Tricyclazole	0.9998	0.9999	0.0001	0.0010	0.0004	0.0031
10	Boscalid	0.9998	0.9974	0.0005	0.0015	0.0015	0.0047
11	Fluquinconazole	0.9995	0.9969	0.0043	0.0117	0.0136	0.0372
12	Flubendiamide	0.9979	0.9996	0.0019	0.0082	0.0060	0.0260
13	Benzobicyclon	0.9998	0.9980	0.0004	0.0013	0.0013	0.0041
14	Diazinon	0.9991	0.9997	0.0003	0.0009	0.0008	0.0029
15	Pyrimethanil	0.9996	0.9980	0.0021	0.0073	0.0067	0.0232
16	Carbofuran	0.9995	0.9998	0.0002	0.0006	0.0006	0.0019
17	Etofenprox	0.9994	0.9997	0.0002	0.0005	0.0005	0.0016
18	Novaluron	0.9979	0.9995	0.0027	0.0088	0.0085	0.0279
19	Sulfoxaflor	0.9992	0.9998	0.0013	0.0113	0.0043	0.0360
20	Pencycuron	0.9995	0.9993	0.0002	0.0009	0.0006	0.0028
21	Acetamiprid	0.9999	0.9998	0.0001	0.0005	0.0005	0.0015
22	Linuron	0.9987	0.9997	0.0027	0.0098	0.0085	0.0311
23	Pyraclostrobin	0.9996	0.9997	0.0002	0.0011	0.0006	0.0035
24	Fluopicolide	0.9980	0.9997	0.0004	0.0012	0.0014	0.0038
25	Azoxystrobin	0.9998	0.9998	0.0003	0.0005	0.0010	0.0017
26	Chromafenozide	0.9995	0.9987	0.0002	0.0013	0.0007	0.0043
27	Fludioxonil	0.9931	0.9999	0.0045	0.0171	0.0143	0.0545
28	Flutolanil	0.9998	0.9966	0.0003	0.0009	0.0008	0.0029
29	Kresoxim-methyl	0.9986	0.9996	0.0003	0.0012	0.0010	0.0037
30	Quinoclamine	0.9987	0.9996	0.0023	0.0071	0.0073	0.0227
31	Thiobencarb	0.9963	0.9994	0.0003	0.0013	0.0009	0.0042
32	Buprofezin	0.9970	0.9994	0.0001	0.0011	0.0005	0.0036
33	Flufenoxuron	0.9984	0.9999	0.0002	0.0007	0.0006	0.0022
34	Iprovalicarb	0.9995	0.9984	0.0001	0.0007	0.0004	0.0023
35	Iprobenfos	0.9970	0.9993	0.0002	0.0008	0.0006	0.0027
36	Probenazole	0.9992	0.9998	0.0014	0.0120	0.0043	0.0382
37	Abamectin B1	0.9974	0.9970	0.0016	0.0108	0.0051	0.0344
38	Clomazone	0.9998	0.9973	0.0002	0.0007	0.0008	0.0024
39	Fentrazamide	0.9992	0.9992	0.0003	0.0082	0.0010	0.0036
40	Sethoxydim	0.9979	0.9983	0.0002	0.0011	0.0005	0.0035
41	Halosulfuron -methyl	0.9993	0.9995	0.0043	0.0125	0.0138	0.0398

).

The accuracy ranged from 80.3% to 113.6% and satisfied the requirement of less than ±20%; however, the range was somewhat wider than that observed for GC-MS/MS. Precision ranged from 0.1% to 12.9% and satisfied the requirement for less than ±15% for all concentrations; however, the range was also wider than that observed for GC-MS/MS, similar to that for accuracy. In 2019, novaluron showed higher precision (0.34~12.9%) than the other components at low concentrations and benzobicyclon exhibited the highest precision (0.51~7.8%) at high concentrations, indicating differences depending on the concentration. In 2020, precision was lower than that in 2019, with 0.2~5.8% at low concentrations and 0.4~2.9% at high concentrations (

Table 10. Annual accuracy and precision (LC-MS/MS, 41 pesticides).

No.	Compound	Accuracy (%)				Precision (%)
		2019		2020		2019		2020
		Low (n = 4)	High (n = 4)	Low (n = 4)	High (n = 4)	Low (n = 4)	High (n = 4)	Low (n = 4)	High (n = 4)
1	Chlorpyrifos	95.5	89.9	107.8	106.6	2.7	1.3	1.1	2.8
2	Oxadiazon	95.4	108.6	93.0	95.5	6.7	7.2	2.0	1.2
3	Carbaryl	94.1	96.0	94.3	99.0	4.2	3.8	3.1	0.9
4	Phenthoate	96.7	92.6	100.2	102.4	6.0	2.5	5.8	1.9
5	Tebuconazole	98.3	92.1	107.0	105.9	6.1	1.1	1.0	0.5
6	Isoprothiolane	108.6	101.8	105.7	105.9	1.5	4.2	1.1	1.9
7	Hexaconazole	101.8	92.0	104.1	108.5	6.8	0.5	1.3	0.9
8	Imidacloprid	92.5	89.7	100.7	105.0	1.1	3.9	1.9	0.4
9	Tricyclazole	94.9	89.6	94.0	97.8	3.1	0.6	1.5	0.8
10	Boscalid	93.5	89.8	95.1	99.8	5.7	5.5	5.1	2.7
11	Fluquinconazole	94.4	97.9	101.8	105.2	3.8	5.0	2.7	1.1
12	Flubendiamide	108.7	99.0	104.9	103.4	7.2	1.1	0.2	1.3
13	Benzobicyclon	99.1	89.6	97.3	103.9	5.1	7.8	5.8	2.9
14	Diazinon	94.0	90.0	100.7	103.9	7.3	0.7	1.2	1.2
15	Pyrimethanil	96.4	94.7	96.3	102.1	5.4	2.3	3.7	2.1
16	Carbofuran	97.3	96.1	106.0	113.6	4.3	1.3	1.8	2.1
17	Etofenprox	101.3	113.5	111.4	112.8	4.4	2.3	1.3	1.2
18	Novaluron	96.1	80.3	98.2	104.1	12.9	1.3	1.8	2.5
19	Sulfoxaflor	96.8	96.7	100.0	106.4	2.8	2.3	1.2	1.2
20	Pencycuron	100.5	94.1	103.9	109.6	3.2	2.1	1.0	1.7
21	Acetamiprid	98.0	94.9	101.7	106.3	4.1	0.6	0.9	1.5
22	Linuron	106.5	102.1	106.9	102.7	0.6	0.5	1.0	1.3
23	Pyraclostrobin	96.5	86.3	101.6	109.5	4.7	1.0	1.9	1.2
24	Fluopicolide	108.7	97.2	104.6	100.1	3.2	4.3	2.7	2.6
25	Azoxystrobin	97.2	91.5	103.0	107.7	4.8	1.8	1.6	1.0
26	Chromafenozide	99.6	94.0	103.3	111.0	4.0	2.9	3.3	0.5
27	Fludioxonil	106.3	97.4	108.9	99.6	2.8	2.7	2.6	1.8
28	Flutolanil	100.3	92.7	104.6	106.1	0.3	3.5	3.2	1.2
29	Kresoxim-methyl	91.2	90.4	104.3	110.9	0.5	4.8	4.5	1.4
30	Quinoclamine	101.5	99.3	104.4	109.7	3.1	2.2	1.7	1.3
31	Thiobencarb	98.2	94.0	110.0	110.6	4.1	1.8	2.9	1.0
32	Buprofezin	93.7	89.9	104.9	109.4	4.7	1.0	3.2	1.4
33	Flufenoxuron	95.7	93.9	89.9	95.9	8.8	4.9	1.6	1.2
34	Iprovalicarb	97.7	93.6	103.2	103.9	7.5	3.5	2.1	1.6
35	Iprobenfos	102.0	99.1	106.2	107.3	4.5	1.8	1.8	1.3
36	Probenazole	99.9	100.8	89.9	90.6	4.3	1.4	1.0	0.9
37	Abamectin B1	93.1	96.8	102.4	102.6	2.5	2.8	2.7	1.8
38	Clomazone	105.3	98.4	108.8	109.4	4.2	1.3	1.4	0.7
39	Fentrazamide	99.9	89.5	101.4	104.1	3.1	2.6	2.6	2.4
40	Sethoxydim	92.2	97.2	92.1	97.9	3.6	2.5	1.4	1.3
41	Halosulfuron -methyl	83.7	94.8	97.8	103.3	1.3	1.9	2.0	2.0

). MB was analyzed using purified water that is used for glass cleaning, and the 57 residual pesticides were not detected during the research period.

3.2. Pesticide Status Survey Results

3.2.1. Field Measurement Items and Major Cation/Anion Analysis

The field measurement items (minimum to maximum values) included the temperature (12.3~20.1 °C), pH (4.8~8.5), EC (55.0~795.0 µS/cm), ORP (−59.8~648.0 mV), and DO (0.1~11.7 mg/L). In the major cation/anion analysis results, the concentration ranges (minimum to maximum values) were Cl⁻ (0.4~302.1 mg/L), NO₃⁻ (N.D~834.6 mg-N/L), SO₄²⁻ (N.D~340.0 mg/L), HCO₃⁻ (1.0~610.0 mg/L), K⁺ (N.D~51.6 mg/L), Na⁺ (1.7~250.7 mg/L), Ca²⁺ (1.0~179.6 mg/L), and Mg²⁺ (0.4~57.2 mg/L), which were similar to previous research results. When groundwater types were analyzed based on the major cation/anion analysis results, the Ca-HCO₃ type indicating uncontaminated shallow groundwater was found to represent 39%, the Ca-(Cl+NO₃⁻) type indicating the influence of artificial pollution sources was found to represent 39%, the Na-HCO₃ type indicating groundwater-media reactions was found to represent 15%, and the Na-(Cl+NO₃⁻) type indicating the effects of seawater was found to represent 7% (Figure 3) [9,10,11].

3.2.2. Pesticide Status Survey Results

• Pesticide status survey at the monitoring network points

Figure 4 shows the results of a survey on the distribution status of residual pesticides at 200 points in the nationwide groundwater quality monitoring network for 2 years (twice/year).

In the survey results, 26 pesticides, including metolachlor, imidacloprid, and alachlor, were detected as being above the LOQ. Metolachlor exhibited the highest detection frequency (10.13%) followed by imidacloprid (8.50%), alachlor (8.13%), tricyclazole (5.88%), isoprothiolane (5.50%), and carbofuran (3.13%). Metolachlor exhibited the highest average concentration (0.0123 μg/L) followed by alachlor (0.0077 μg/L) and imidacloprid (0.0065 μg/L). The distribution status by pesticide class in groundwater is shown in Figure 5. A total of 11 pesticide classes were detected, including the amide class (e.g., alachlor and metolachlor), the azole class (e.g., tricyclazole and oxadiazon), and the neonicotinoid class (e.g., imidacloprid). When the pesticide detection status by measurement network type was compared, relatively higher detection levels were confirmed in the contamination and agricultural areas compared to the background measurement network.

• Groundwater quality survey results according to agricultural area cultivation purpose

The results of the survey conducted twice a year at 50 points in the agricultural monitoring network (12 rice paddies, 19 fields, and 19 facility cultivation sites, for cultivation purposes) indicated that 18 pesticide items were detected (Figure 6). In the agricultural area, imidacloprid exhibited the highest detection frequency (18%) followed by metolachlor (13%), isoprothiolane (11%), alachlor (10%), and tricyclazole (10%).

In the pesticide detection frequency investigation results according to the cultivation purpose, imidacloprid, which is a representative neonicotinoid pesticide, showed relatively higher detection frequencies in rice paddies, fields, and facility cultivation. It kills pests by blocking neurotransmitter receptors and has been widely used in rice paddies [26]. Imidacloprid is less likely to be leached into groundwater because it has high soil absorptivity, with its Freundlich adsorption constant (Kf) ranging from 1.7 to 2.6 and its soil organic carbon adsorption coefficient (Koc) ranging from 228 to 249 [27].

Etridiazole and isoprothiolane showed relatively higher detection frequencies in groundwater around fields. Etridiazole is a sterilizing agent that can inhibit the oxidative decomposition of lipids, and it is known as a mobile compound with moderate persistence and high volatility [28]. It is less likely to be released into groundwater but can remain in a water environment over an extended period because of its stable characteristics against hydrolysis and aquatic photolysis. Etridiazole was detected only at two points, as suggested above, but a maximum of 1.185 μg/L was detected at one point, indicating that continuous monitoring is required at this point. Isoprothiolane, a sterilizing agent that inhibits phospholipid biosynthesis and methyltransferase, is used in large quantities for rice cultivation. It has a high water solubility of 48 mg/L, and thus, it is highly likely to be introduced into the water environment, including groundwater [29]. When pesticides were investigated in major rivers in South Korea in 2011, isoprothiolane exhibited a relatively high detection frequency compared to other pesticides because it was sprayed in the form of granules with large input per unit area and was released into rivers during rainfall or drainage while staying in rice paddies [30].

Alachlor, metolachlor, and tricyclazole showed relatively high detection frequencies in groundwater around facility cultivation sites. Alachlor and metolachlor are herbicides that have a mechanism of inhibiting the synthesis of long-chain fatty acids. Alachlor is the second most commonly used herbicide in the USA and is mainly used for corn cultivation. It has high mobility in water environments and exhibits high water solubility of 170.3 mg/L and moderate environmental persistence. Therefore, alachlor and its decomposition products are highly likely to be detected in groundwater [31]. In South Korea, alachlor is mainly used to remove weeds during the cultivation of potatoes and beans, and it was detected at a concentration of 0.62 μg/L in the Yeongsan River water system (river water) and at up to 4.2 μg/L in a rice paddy district (paddy water) [32]. Metolachlor has been classified as a human carcinogen by the US EPA, and its average half-life in water is known to be 200 days [33]. It is highly likely to be released into groundwater when used as a pesticide because its chemical properties are similar to those of alachlor. Tricyclazole, a pesticide used as a sterilizing agent in rice cultivation, is known to be stable against hydrolysis under liquid conditions. Its half-life in soil ranges from 58 to 795 days, and its toxicity to living organisms in the environment is considered low. It is less likely to be released into groundwater due to its high organic carbon adsorption coefficient of 533~1199 [34]. In the results of the survey on the status of pesticides in domestic rivers conducted by Hwang et al. (2019), however, tricyclazole exhibited a higher detection frequency than other pesticides. They stated that this was because the pesticides scattered during spray and those left in rice paddies were introduced into rivers by rainfall [35]. A detailed investigation indicated that tricyclazole also exhibited a higher detection frequency than other pesticides as in the river water survey results presumably due to the same reason.

3.3. Evaluation Results for the Health Risks of Pesticides

Table 11. Risk and exposure assessment results of pesticides in groundwater monitored from 2019 to 2020.

Pesticide	Cancer	95th Percentile Human Exposure (ng/kg-day)				Non-Carcinogenic Risk by Exposure Route (HQ)
		Drink	Non-Drink		Total	Drink	Non-Drink		Total
			Dermal	Inhalation			Dermal	Inhalation
Alachlor	Non-Carcinogenic	2.54	1.79 × 10−5	7.8 × 10−6	2.54	<0.001	<0.001	<0.001	<0.001
Metolachlor	Carcinogenic	6.81	1.71 × 10−4	3.06 × 10−5	6.81	<0.001	<0.001	<0.001	<0.001
Carbofuran	Non-Carcinogenic	1.07	1.51 × 10−6	3.08 × 10−6	1.07	<0.001	<0.001	<0.001	<0.001

shows the results of the risk assessment by human exposure and exposure route based on the 95th percentile concentration for each item derived from the pesticide background survey results.

The risk assessment results showed that the non-carcinogenic risk was less than 0.001 for all items. The carcinogenic risk of metolachlor, which is known to be carcinogenic, was also evaluated to be less than 10⁻⁶ in all exposure routes. The health risks caused by pesticide exposure in groundwater were found to be negligible (non-carcinogenic risk: less than 10⁻³; carcinogenic risk: less than 10⁻⁶). The background survey target points, however, are mostly less likely to be directly exposed to pesticides. Therefore, it will be desirable to continuously perform detailed investigations for groundwater around farmland where pesticides are used, perform risk assessment after the accumulation of survey results over a certain level, evaluate the results, and present management measures.

4. Conclusions

In this study, we identified the distribution status and background concentrations of pesticides in South Korea by developing and using multi-residue analytical methods for pesticides in groundwater. In addition, the detection characteristics according to land use as well as the behavioral characteristics and health risks of the items mainly detected were evaluated to contribute to the preparation of management measures for pesticides in groundwater in South Korea. The following conclusions were drawn:

• Multi-residue analytical methods (LC-MS/MS 41 items and GC-MS/MS 16 items) were developed for 57 pesticides. The precision and accuracy ranges of the analytical methods were 0.2~12.9% (within ±15%) and 80.3~113.6% (within ±20%), respectively, and the LOQ was found to be in the range from 0.0004 to 0.0677 μg/L.
• The pesticide distribution status survey results for groundwater showed that the detected concentrations were less than the minimum concentration (diazinon, 20 μg/L) in the domestic groundwater pesticide standard at all points for all items. Some items (GUS > 2.8) that are highly likely to be released into groundwater (e.g., alachlor and metolachlor), however, tended to be detected regardless of land use, confirming that it is necessary to develop countermeasures for them.
• When the health risks of the items mainly detected were assessed, carcinogenic and non-carcinogenic risks were found to be less than 10⁻⁶ and 10⁻¹ for all items, confirming that their health risks are negligible.