固相萃取-液相色谱-串联质谱法测定污水中8种新烟碱类农药

doi:10.3724/SP.J.1123.2023.11010

摘要/Abstract

摘要：

新烟碱类农药作为一类新型农药,由于其对非靶标生物造成生态风险而引起广泛关注。为了实现污水中痕量新烟碱类农药的快速、准确定量,本研究建立了同时检测污水中8种新烟碱类农药(呋虫胺、E-烯啶虫胺、噻虫嗪、噻虫胺、吡虫啉、氯噻啉、啶虫脒和噻虫啉)的固相萃取-液相色谱-串联质谱法。确定选择色谱流动相类型和质谱参数后,采用单因素法确定固相萃取(SPE)的条件:萃取柱类型为HLB (500 mg/6 mL),上样体积为500 mL,上样速度为10 mL/min,样品pH为6~8。通过优化色谱梯度洗脱程序、样品的稀释倍数并采用同位素内标定量法降低污水样品的基质效应,确定污水稀释5倍进行前处理,采用ZORBAX Eclipse Plus C18色谱柱(100 mm×2.1 mm, 1.8 μm),以含0.1%甲酸的2 mmol/L乙酸铵水溶液和甲醇为流动相进行梯度洗脱,在正离子多反应监测模式下分析10 min,用吡虫啉-d4作为同位素内标进行定量。通过响应曲面法进一步优化SPE的淋洗液及洗脱液类型和用量,确定用10%甲醇水溶液淋洗,7 mL甲醇-乙腈(1∶1, v/v)混合溶液洗脱。8种新烟碱类化合物在相应范围内线性关系良好(线性相关系数(r)均大于0.9990),方法检出限(MDL)为0.2~1.2 ng/L,方法定量限(MQL)为0.8~4.8 ng/L,在低、中、高3个加标水平下的加标回收率为82.6%~94.2%, RSD为3.9%~9.4%。该方法成功用于4个城镇污水处理厂进水水样的分析,8种新烟碱类农药的检出含量为ND~256 ng/L。与类似方法相比,该方法检出限低,准确度高,适用于污水中8种新烟碱类农药的痕量检测。

关键词: 液相色谱-串联质谱, 固相萃取, 新烟碱类农药, 污水, 基质效应, 响应曲面法

Abstract:

Neonicotinoid pesticides are a relatively new class of pesticides that have garnered significant attention owing to their potential ecological risks to nontarget organisms. A method combining solid phase extraction with liquid chromatography-tandem mass spectrometry (SPE-LC-MS/MS) was developed for the rapid and accurate detection of eight neonicotinoid pesticides (dinotefuran, E-nitenpyram, thiamethoxam, clothianidin, imidacloprid, imidaclothiz, acetamiprid, and thiacloprid) in wastewater. The chromatographic mobile phase and MS parameters were selected, and a single-factor method was used to determine the optimal column type, extraction volume, sample loading speed, and pH for SPE. The optimal parameters were as follows: column type, HLB column (500 mg/6 mL); sample extraction volume, 500 mL; sample loading speed, 10 mL/min; and sample pH, 6-8. The matrix effects of the wastewater samples were reduced by optimizing the chromatographic gradient-elution program, examining the dilution factor of the samples, and using the isotope internal standard calibration method. Prior to analysis, the wastewater samples were diluted 5-fold with ultrapure water for pretreatment. Subsequently, 2 mmol/L ammonium acetate aqueous solution containing 0.1% (v/v) formic acid and methanol was used as mobile phases for gradient elution on a ZORBAX Eclipse Plus C18 column (100 mm×2.1 mm, 1.8 μm). The samples were quantified using positive-ion multiple reaction monitoring (MRM) mode for 10 min. Imidacloprid-d4 was used as the isotope internal standard. The SPE process was further optimized by applying response surface methodology to select the type and mass of rinsing and elution solvents. The optimal pretreatment of the SPE column included rinsing with 10% methanol aqueous solution and elution with methanol-acetonitrile (1∶1, v/v) mixture (7 mL). The eight neonicotinoid pesticides showed satisfactory linearity within the relevant range, with linear correlation coefficients (r) all greater than 0.9990. The method detection limits (MDLs) ranged from 0.2 to 1.2 ng/L, and the method quantification limits (MQLs) ranged from 0.8 to 4.8 ng/L. The average recoveries of the eight neonicotinoid pesticides were in the range of 82.6%-94.2% at three spiked levels, with relative standard deviations (RSDs) ranging from 3.9% to 9.4%. Finally, the optimized method was successfully applied to analyze wastewater samples collected from four sewage treatment plants. The results indicated that the eight neonicotinoid pesticides could be generally detected at concentrations ranging from not detected (ND) to 256 ng/L. The developed method has a low MDL and high accuracy, rendering it a suitable choice for the trace detection of the eight neonicotinoid pesticides in wastewater when compared with other similar methods. The proposed method can be utilized to monitor the environmental impact and assess the potential risks of neonicotinoid pesticides in wastewater, thus promoting the protection of nontarget organisms and the sustainable use of these pesticides in agriculture.

Key words: liquid chromatography-tandem mass spectrometry (LC-MS/MS), solid phase extraction (SPE), neonicotinoid pesticides, wastewater, matrix effect, response surface methodology (RSM)

中图分类号:

O658

王海棠, 李含音, 陆启伟, 何士龙. 固相萃取-液相色谱-串联质谱法测定污水中8种新烟碱类农药[J]. 色谱, 2024, 42(9): 856-865.

WANG Haitang, LI Hanyin, LU Qiwei, HE Shilong. Determination of eight neonicotinoid pesticides in wastewater by solid phase extraction combined with liquid chromatography-tandem mass spectrometry[J]. Chinese Journal of Chromatography, 2024, 42(9): 856-865.

图/表 13

表1 目标化合物的保留时间和质谱参数

Table 1 Retention times and MS parameters of the target compounds

Compound	Retention time/min	Precursor ion (m/z)	Product ion (m/z)	Fragmentor/V	Collision energy/eV
Dinotefuran	3.35	203.2	129.1^*	80	12
			87.1	80	14
E-Nitenpyram	3.87	271.1	99.1	95	14
			56.2^*	95	23
Thiamethoxam	5.07	292.1	211.0^*	80	10
			131.8	80	18
Clothianidin	5.37	250.1	168.9^*	85	14
			131.8	85	16
Imidacloprid-d4	6.09	260.1	213.1^*	100	14
			179.1	100	20
Imidacloprid	6.11	256.1	209.1^*	85	12
			175.1	85	18
Imidaclothiz	6.26	262.1	181.0^*	95	16
			122.0	95	35
Acetamiprid	6.72	223.1	126.0^*	110	20
			56.1	110	16
Thiacloprid	7.45	253.1	125.9^*	110	20
			89.9	110	45

图1 不同流动相下目标化合物的响应值

Fig. 1 Response values of the target compounds using different mobile phases

图2 不同因素对目标化合物回收率的影响(n=3)

Fig. 2 Effect of different factors on the recoveries of the target compounds (n=3) a. extraction column; b. extraction volume; c. sample loading speed; d. pH.

图3 目标化合物在不同梯度洗脱程序下的色谱图

Fig. 3 Chromatograms of the target compounds under different gradient elution programs Gradient elution program: a. 0-3 min, 10%B-45%B; 3-7 min, 45%B-80%B; 7-7.1 min, 80%B-10%B; 7.1-8.1 min, 10%B. b. 0-5 min, 10%B-45%B; 5-9 min, 45%B-80%B; 9-9.1 min, 80%B-10%B; 9.1-10.1 min, 10%B. Peak identifications: 1. dinotefuran; 2. E-nitenpyram; 3. thiamethoxam; 4. clothianidin; 5. imidacloprid; 6. imidaclothiz; 7. acetamiprid; 8. thiacloprid.

图4 不同稀释倍数与定量方法对目标化合物基质效应的影响

Fig. 4 Effects of different dilution folds and quantitative methods on the matrix effects (MEs) of the target compounds

表2 响应曲面的三因素与三水平

Table 2 Three factors and three levels of response surface methodology test

Factor	Code	Levels
Factor	Code	-1	0	1
Volume fraction of methanol	X₁	0	5	15
in water/%
Volume fraction of acetonitrile	X₂	0	50	100
in methanol/%
Volume of eluent/mL	X₃	3	6	9

表3 回归模型方差分析

Table 3 Analysis of variance (ANOVA) of regression model

Source	Sum of square	Degree of freedom	Mean square	F- value	P- value
Model	288.37	9	32.04	26.06	0.0001^*
X₁	3.19	1	3.19	2.59	0.1514
X₂	10.24	1	10.24	8.33	0.0235^*
X₃	112.5	1	112.5	91.5	<0.0001^*
X₁X₂	0.0025	1	0.0025	0.002	0.9653
X₁X₃	0.0006	1	0.0006	0.0005	0.9826
X₂X₃	0.0306	1	0.0306	0.0249	0.879
$X 1 2$	0.005	1	0.005	0.0041	0.9509
$X 2 2$	32.79	1	32.79	26.67	0.0013^*
$X 3 2$	121.78	1	121.78	99.05	<0.0001^*
Residual	8.61	7	1.23
Lack of fit	0.2144	3	0.0715	0.0341	0.9903
Pure error	8.39	4	2.1
Cor total	296.98	16

表3 回归模型方差分析

Table 3 Analysis of variance (ANOVA) of regression model

Source	Sum of square	Degree of freedom	Mean square	F- value	P- value
Model	288.37	9	32.04	26.06	0.0001^*
X₁	3.19	1	3.19	2.59	0.1514
X₂	10.24	1	10.24	8.33	0.0235^*
X₃	112.5	1	112.5	91.5	<0.0001^*
X₁X₂	0.0025	1	0.0025	0.002	0.9653
X₁X₃	0.0006	1	0.0006	0.0005	0.9826
X₂X₃	0.0306	1	0.0306	0.0249	0.879
$X 1 2$	0.005	1	0.005	0.0041	0.9509
$X 2 2$	32.79	1	32.79	26.67	0.0013^*
$X 3 2$	121.78	1	121.78	99.05	<0.0001^*
Residual	8.61	7	1.23
Lack of fit	0.2144	3	0.0715	0.0341	0.9903
Pure error	8.39	4	2.1
Cor total	296.98	16

图5 淋洗液、洗脱液和洗脱体积对噻虫啉回收率影响的响应曲面图

Fig. 5 Response surface methodology plots of effect of rinsing, elution solvent and elution volume on the recoveries of thiacloprid

表4 8种新烟碱类农药通过响应曲面法获得的最优参数

Table 4 Optimal parameters of the eight neonicotinoid pesticides obtained by response surface methodology

Compound	X₁/%	X₂/%	X₃/mL	Recovery/%
Dinotefuran	10.49	50	7.056	96.17
E-Nitenpyram	13.32	80	6.561	96.38
Thiamethoxam	9.32	50	6.444	96.63
Clothianidin	4.24	80	7.137	93.81
Imidacloprid	3.92	60	6.711	95.16
Imidaclothiz	6.08	30	6.678	96.01
Acetamiprid	6.66	60	7.026	98.53
Thiacloprid	12.10	70	6.714	96.47

表5 8种新烟碱类化合物的线性方程、相关系数、线性范围、方法检出限及方法定量限

Table 5 Linear regression equations, correlation coefficients (r), linear ranges, method detection limits (MDLs) and method quantification limits (MQLs) of the eight neonicotinoid compounds

Compound	Regression equation	r	Linear range/(μg/L)	MDL/(ng/L)	MQL/(ng/L)
Dinotefuran	y=1.723x+0.0374	0.9996	0.5-100	0.8	3.2
E-Nitenpyram	y=1.545x+0.0376	0.9995	0.2-100	0.4	1.6
Thiamethoxam	y=3.734x+0.0654	0.9996	0.2-100	0.5	2.0
Clothianidin	y=0.499x+0.013	0.9991	0.2-100	0.6	2.4
Imidacloprid	y=1.345x+0.0458	0.9995	0.5-100	1.2	4.8
Imidaclothiz	y=1.302x+0.0256	0.9993	0.2-100	0.4	1.6
Acetamiprid	y=7.443x+0.201	0.9994	0.1-100	0.2	0.8
Thiacloprid	y=7.342x+0.091	0.9995	0.1-100	0.3	1.2

表6 8种新烟碱类化合物在3个水平下的加标回收率和RSD (n=3)

Table 6 Recoveries and RSDs of the eight neonicotinoid compounds at three spiked levels (n=3)

Compound	Added/(ng/L)	Recovery/%	RSD/%	Compound	Added/(ng/L)	Recovery/%	RSD/%
Dinotefuran	10	86.4	8.7	Imidacloprid	10	87.1	9.4
	200	93.8	6.5		200	91.8	5.9
	1000	94.2	7.9		1000	87.5	5.5
E-Nitenpyram	10	91.8	7.9	Imidaclothiz	10	85.4	8.6
	200	93.5	6.5		200	92.5	7.8
	1000	93.8	5.8		1000	91.7	6.9
Thiamethoxam	10	89.4	9.4	Acetamiprid	10	82.6	6.8
	200	92.1	8.4		200	86.8	4.7
	1000	94.1	5.2		1000	92.7	6.2
Clothianidin	10	87.2	9.3	Thiacloprid	10	85.8	6.4
	200	92.1	5.8		200	91.1	4.5
	1000	93.3	6.7		1000	93.4	3.9

表7 与其他文献的比较

Table 7 Comparison with other literatures

Analytes	Sample type	Sample volume/ (mL)	MDL/ (ng/L)		Linear range/ (μg/L)		RSD/%		RE/%	Ref.
Dinotefuran, E-nitenpyram, thiamethoxam, clothianidin, imidacloprid, imidaclothiz, acetamiprid, thiacloprid	wastewater	100	0.2-	1.2	0.1-	100^*	3.9-	9.4	82.6-94.2	this study
Acetamiprid, imidacloprid, thiacloprid, thiamethoxam, clothianidin, dinotefuran	wastewater	500	1.8-	6.8	1-	500	5.26-	11.5	33.7-116	[17]
Dinotefuran, thiamethoxam, clothianidin, imidacloprid, imidaclothiz, acetamiprid, thiacloprid	drinking water	500	0.01-	0.2	0.01-	200	1.6-	7.3	74.0-123	[9]
Thiamethoxam, clothianidin, imidacloprid, acetamiprid,	sea water	1000	0.1-	7.8	0.05-	100	3-	18	72.0-117	[24]
thiacloprid	river water		0.1-	1.0

表8 实际废水样品中8种新烟碱类化合物的含量和基质效应

Table 8 Contents and MEs of the eight neonicotinoid compounds in real wastewater samples

Compound	Contents/(ng/L)				ME/%
Compound	S1	S2	S3	S4	ME/%
Dinotefuran	ND	5.49	6.69	23.1	84.3-98.7
E-Nitenpyram	ND	4.83	5.39	13.2	80.9-96.2
Thiamethoxam	11.6	18.1	9.46	15.2	90.4-103.5
Clothianidin	8.91	13.4	10.7	17.9	89.7-108.1
Imidacloprid	31.1	256	178	189	97.2-113.7
Imidaclothiz	ND	14.5	14.2	12.3	93.2-104.8
Acetamiprid	8.45	17.9	16.6	57.2	96.9-112.7
Thiacloprid	135	1.98	2.47	1.89	87.6-105.3

参考文献

[1]	Zhang J W, Yang R Q, Zhou X Y, et al. Chinese Journal of Analysis Laboratory, 2023, 42(7): 974
[1]	张婧文, 杨瑞琴, 周新洋, 等. 分析试验室, 2023, 42(7): 974
[2]	Chen Y C, Zang L, Shen G F, et al. Environ Sci Technol, 2019, 53: 2539
[3]	Yi X H, Zhang C, Liu H B, et al. Environ Pollut, 2019, 251: 892
[4]	Krishnan N, Jurenka R A, Bradbury S P. Sci Rep, 2021, 11(1): 15787
[5]	Li Y, Miao R, Khanna M. Nat Sustain, 2020, 3(12): 1027
[6]	Hallmann C A, Foppen R P B, van Turnhout C A M, et al. Nature, 2014, 511(7509): 341
[7]	Klarich K L, Pflug N C, DeWald E M, et al. Environ Sci Technol Lett, 2017, 4(5): 168
[8]	Li X, Chen J, He X, et al. Chemosphere, 2019, 234: 224
[9]	Shah V P, Midha K K, Findlay J W, et al. Pharmaceutical Research, 2000, 17(12): 1551
[10]	Tisler S, Pattison D, Christensen J H. Anal Chem, 2021, 93(24): 8432
[11]	Avery M J. Rapid Commun Mass Spectrom, 2003, 17(3): 197
[12]	Ferrer C, Lozano A, Agueera A, et al. J Chromatogr A, 2011, 1218(42): 7634
[13]	Kloepfer A, Quintana J B, Reemtsma T. J Chromatogr A, 2005, 1067(1): 153
[14]	McManus M M, Oates R P, Subbiah S, et al. J Chromatogr A, 2019, 1602: 246
[15]	Dankyi E, Carboo D, Gordon C, et al. J Food Compost Anal, 2015, 44: 149
[16]	Song W, Liu K Y, Chen L J, et al. Chinese Journal of Chromatography, 2024, 42(1): 52
[16]	宋伟, 刘开永, 陈莉君, 等. 色谱, 2024, 42(1): 52
[17]	Zhang J, Wei Y, Li H, et al. Talanta, 2017, 170: 392
[18]	Yang B, Qi F, Li X, et al. Int J Environ Anal Chem, 2015, 95(12): 1112
[19]	Porto R S, Rodrigues-Silva C, Schneider J, et al. J Environ Manage, 2019, 232: 729
[20]	Zaidona S Z, Ho Y B, Hamsana H, et al. Microchem J, 2019, 145: 614
[21]	Wu G G, Ma J P, Wei C X, et al. Int J Environ Res Public Health, 2023, 20: 715
[22]	Hernandez F, Sancho J V, Pozo O J. Anal Bioanal Chem, 2005, 382(4): 934
[23]	Van Eeckhaut A, Lanckmans K, Sarre S, et al. J Chromatogr B, 2009, 877(23): 2198
[24]	Matuszewski B K, Constanzer M L, Chavez-Eng C M. Anal Chem, 2003, 75(13): 3019
[25]	Yang J Q, He X M, Shi M F, et al. Environmental Monitoring in China, 2019, 35(3): 19
[25]	杨金泉, 贺小敏, 施敏芳, 等. 中国环境监测, 2019, 35(3): 19
[26]	Zhang J, Liu J, Wang Y, et al. J Chromatogr B, 2023, 1222: 123689
[27]	Naumann T, Bento C P M, Wittmann A, et al. Water Res, 2022, 209: 117912
[28]	Pua A, Yeam C W, Huang Y, et al. Talanta, 2022, 241: 123234
[29]	Lü M, Chen L X. Chinese Journal of Chromatography, 2020, 38(1): 95
[29]	吕敏, 陈令新. 色谱, 2020, 38(1): 95
[30]	Mottaleb M A, Abedin M Z, Islam M S. Anal Sci, 2003, 19(10): 1365
[31]	Wang Z, Chen J, Zhan T, et al. Ecotoxicol Environ Saf, 2020, 189: 110002
[32]	Chi Y, Deng Y, Pu S, et al. RSC Adv, 2021, 11(8): 4547
[33]	Abril C, Santos J L, Malvar J L, et al. J Chromatogr A, 2018, 1576: 34
[34]	Gao J, Xu H, Li Q, et al. Bioresour Technol, 2010, 101(18): 7076
[35]	Qian Z, Li Z, Ma J, et al. Talanta, 2017, 173: 101
[36]	Niju S, Rabia R, Sumithra Devi K, et al. Waste Biomass Valorization, 2020, 11(3): 793
[37]	Yan Y, Yu C, Chen J, et al. Carbohydr Polym, 2011, 83(1): 217