Occurrence of Nine Pesticide Residues in Table Grapes and Exposure Assessment in Shandong Province, China

doi:10.21203/rs.3.rs-2631055/v1

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Occurrence of Nine Pesticide Residues in Table Grapes and Exposure Assessment in Shandong Province, China

https://doi.org/10.21203/rs.3.rs-2631055/v1

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Shandong is a significant region for grape production in China, but the presence of pesticide residues in table grapes represents a major concern for consumer health. This study aimed to investigate pesticide residues and exposure in table grapes from Shandong Province. Of the 86 survey samples, pesticides were detected in 80 (93.0%), all above the limit of quantification (LOQ) of residues. The most commonly detected pesticide was Dimethomorph, which was found in 65 (75.6%) of the samples in a concentration range from 0.007 to 4.27 mg/kg. The results were evaluated according to the maximum residue levels (MRLs) set by Chinese regulation. Among the contaminated samples, 6.98% exceeded the MRLs and more than one pesticide contamination was found in 65 (75.6%) of the samples. The short-term and long-term risks associated with consuming these grapes were both below 10%, indicating no significant threat to human health. Nevertheless, strict regulation of pesticide residues in grapes is necessary.

pesticide residues

table grape

exposure assessment

Grape is a common fruit with a long history of planting, and it is also an important economic crop. China is the world's leading producer of edible grapes, with 14.195 million tons produced in 2019. Fungal diseases occur during viticulture; the most common are grey mould, powdery mildew, and downy mildew (González-Rodríguez et al. 2021). These fungal diseases can be controlled by pesticides. Pesticides on grapes are used to prevent rot during the different cultivation and storage stages of grapes after harvest (Juan-Garc et al. 2004). The high occurrence rate of fungal diseases in vineyards demands a formulated use of pesticides, in order to control pests, guarantee the absence of residues in grapes, reduce economic losses. Pesticides used in an excessive or improper manner can harm persons and the environment. Therefore, maximum residue limits (MRLs) have been legislated in China and other countries to control pesticide residues in foods within a safe range to minimize consumer exposure to harmful pesticides. The MRLs refer to the maximum allowable pesticide residues in food (Zhang et al. 2021; Bakirci et al. 2012; Banerjee et al. 2013; Baša-Česnik et al. 2015; Lagunas-Allue et al. 2012). However, MRLs are not a toxicological limit value, but a toxicologically acceptable standard (Knežević et al. 2009). Although monitoring data (detection rate and percentage of samples exceeding the MRL) can indicate violations of agricultural regulations, they lack information that correctly interprets food safety. To assess consumer health risks from pesticide residues, it is It is essential to evaluate pesticide exposure and compare it with toxicological limits (Campillo et al. 2013; Claeys et al. 2011).

Pesticide residues are commonly analysed using a variety of technologies for analysing pesticides in fruits and vegetables (Esparza et al. 2013; Kovalczuk et al. 2008; Shida et al. 2015; Tran et al. 2012; Wang et al. 2011). High resolution quadrupole time-of-flight (Q-TOF) mass spectrometry (MS) has enabled new possibilities for pesticide analysis, recently (Liu et al. 2021; Wang et al. 2016). Time-of-flight MS (TOF-MS) has been able to produce excellent accuracy without restricting the number of target chemicals that can be detected at the same time. Each scan is of excellent quality, with high resolution, allowing for a higher decrease in chemical noise and hence improved selectivity (García-Reyes et al. 2017). Sample handling will be decreased even further due to the increased precision of this high-resolution mass spectrum (HRMS) acquisition, resulting in a cost-effective analysis of the largest number of pesticides with acceptable precision. This type of analysis, therefore, is becoming a favourable tool for pesticide evaluation in fruit samples (Gomez-Ramos et al. 2013; Oellig et al. 2014; Pico et al. 2007; Saito-Shida et al. 2015; Sivaperumal et al. 2015).

The QuEChERS (quick, easy, cheap, effective, rugged, and safe) approach was developed by analysing a large number of pesticides in several food matrices at the same time (Varela-Martínez et al. 2019; Costa et al. 2014; Koesukwiwat et al. 2021; Martinez-Dominguez et al. 2015; Rizzetti et al. 2016). This proven method has gained global recognition due to its simplicity and high recovery rate. This method ensures that the laboratory can process more samples within a certain period, as opposed to previous approaches. The purification process can remove lipids, sugars, organic acids, proteins, sterols, pigments, and excess water from QuEChERS, which was designed as a pesticide extraction method in fruits and vegetables.

Shandong Province is a coastline province in eastern China that is situated in a warm-temperate monsoon climate zone, forming a table grape growing area. According to the China Statistical Yearbook (National Bureau of Statistics China 2020), grape production in Shandong Province ranked third in China in 2019, with 1.125 million tons, accounting for 7.92% of the total grape production in China in the same period. The long growth cycle of grapes and the relative stability of the vineyard environment have led to many pests and diseases that seriously affect the yield and quality of grapes. Pesticides are now the primary management technique for grape pests and diseases in Shandong Province. There are some studies on pesticide residues in grapes in other countries, but no survey studies on pesticides in grapes in Shandong Province have been seen. There is a great need to investigate the occurrence of pesticides in grapes in Shandong Province to ensure the quality of grapes and the food safety of consumers. Therefore, this study is conducted in the following aspects: (1) to study the occurrence of commonly used pesticides on grapes in Shandong Province, China. Ultra-High Performance Liquid Chromatography/quadrupole time-of-flight (UPLC/Q-TOF) was used to quantify the concentration of the pesticides; (2) to determine whether these concentrations comply with the concentration of pesticides allowed for use on grapes as stipulated in existing Chinese regulations; and (3) to evaluate the health risk to humans through dietary exposure assessment.

2.1. Reagents

Pesticide standards (purity > 98%) were procured from Dr. Ehrenstorfer. Methanol, acetonitrile, and formic acid were HPLC grade and procured from Merck (Darmstadt, Germany). Deionised water (resistivity 18.2 MΩ/cm) was manufactured by a Milli-Q water purification system (Millipore Co., Bedford, MA, USA) and used throughout this study to maintain quantifiable results. Approved solutions were made at 1000 mg/L by dissolving 10 mg of the compounds into 10 mL solvent. For the individual approved solutions, a multi-compound standard solution was adapted with a concentration of 10 mg/L. The standard solutions used to generate a matrix mixing calibration curve were within a range of 0.005 to 0.5 mg/kg.

2.2. Sample Collection and Preparation

Between July and October 2019, 86 table grape specimens were picked from local markets and supermarkets in seven cities in Shandong Province, China. Grape samples were in refrigerated containers and kept at 4°C. From here, each sample takes 1 kg of grapes, they was diced and homogenized in a high-speed commercial blender. Then, within 24 hours of your arrival, conduct a pesticide residue analysis.

After that, A QuEChERS-based approach was used to extract the materials (Lopez et al. 2014; Rajski et al. 2014). In 50-mL polypropylene centrifuge tubes, 10 mL acetonitrile was added to 10g homogenized material. A mechanical horizontal shaker was used to shake the samples for 5 minutes. Then, the QuEChERS extract salt packet (Agela Technologies, Tianjin, China, MS-NMS5050, EN 15662 method) was added into each tube. To keep the salt compounds from adhering together, vigorously shake the test tube by hand. A mechanical shaker was then used to shake the mixture for another 5 minutes. The top layer was transferred to a 2 mL dispersive Solid-Phase Extraction (dSPE) tube containing 150 mg MgSO4 and 50 mg primary secondary amine (PSA, Agela Technologies, Tianjin, China, MS-PA0251) after centrifugation at 8000 rpm for 3 minutes. The dSPE tube was shaken for 30 seconds before being centrifuged for 1 minute at 8000 rpm. The dSPE supernatant was then solvent swapped, with 1 mL being blown to near dryness under mild nitrogen flow and the residue being re-dissolved in 1 mL of acetonitrile: water. (v/v, 1:3). After filtering the solution using a 0.22 µm membrane filter, it was transferred to a vial for UHPLC/Q-TOF MS analysis. The resulting approach is a balance between avoiding pesticide precipitation and obtaining sufficient chromatographic peak morphologies, which is especially important for early eluting pesticides with higher polarity. Each sample was injected three times and the relative standard deviation (RSD) was used to calculate the measurement uncertainty.

2.3. Chromatographic and Mass Spectrometer Conditions

MS analysis was using liquid chromatography system (ACQUITY™, Waters, Manchester, UK)) and mass spectrometer (SYNAPT G2-Si Q-TOF, Waters, Manchester, UK). The chromatography separation was achieved with a Waters ACQUITY BEH C18 column (100 mm × 2.1 mm, 1.7 µm). The mobile phase consisted of 0.1% formic acid in water (solvent A) and acetonitrile (solvent B), with a gradient elution of 0–0.5 min, 5% B; 0.5–9.0 min, 5–95% B; 9.0–9.1 min, 95–5% B; 9.1–13 min, 5% B was used for the equilibration of the column. The flow rate was 0.5 mL/min, and the injection volume was 5 µL. The eluent was placed in a Q-TOFMS equipped with an electrospray ionization (ESI) source (Waters, Manchester, UK) and operated in positive ion mode. The external reference of LockSprayTM was leucine–enkephalin infused at a steady flow of 5 L/min, and data were obtained during acquisition. Data was collected and processed using the MassLynx 4.1 software. The pesticide contaminants in the samples were qualitatively analyzed and validated using QuanLynx software.

2.4. Method Validation and Quality Control

Linearity, recovery, precision, limit of detection (LOD), and limit of quantification were all used to verify the analytical technique (LOQ). Linearity is expressed as the correlation coefficient (r); LOD and LOQ were defined using a signal-to-noise ratio of 3 and 10 times, respectively. Recovery experiments using two different concentrations of standard solutions in a fungus-free sample (blank sample) were used to determine accuracy and precision.

2.5. Health Risk Assessment

By comparing the results of the dietary exposure assessment with the health guidance values of the corresponding food chemicals, the risk level of the hazard can be determined (Authority E. F. S. et al. 2021). For accuracy, the evaluation was classified into adults (68.21 kg) and children (43.51 kg) (The weight data come from the survey of total diet and health status of residents in Shandong Province, 2014). If the hazard quotient (HQ) is greater than 100%, the risk is unacceptable. The formulas are as follows (Bhandari et al. 2019):

ESTI = Maximum residue × consumption of food/body weight	(1)
aHQ = %ARfD = ESTI/ARfD × 100%	(2)
EDI = mean residue level × consumption of food/body weight	(3)
cHQ = %ADI = EDI/ADI × 100%	(4)

where ESTI is estimated short-term intake, aHQ is acute/short-term consumer health risk, ARfD is acute reference dose, cHQ is chronic/long-term consumer health risk, EDI is estimated daily intake, and ADI is acceptable daily intake.

Information of ARfD and ADI were got from the Joint FAO/WHO Meeting on Pesticide Residues (JMPR) (Joint FAO/WHO Meeting on Pesticide Residues 2022).

3.1. Validation Results

High resolution MS, UPLC-TOF-MS can achieve a more definite mass of protonated molecules; the high accuracy of this test means less than 3 ppm accuracy was obtained. Mass accuracy of all cases is shown in Table 1. The accuracy of the testing was in agreement with the China National Standard GB/T 27404 − 2008. Evaluation was tested in six replicates at two levels for the grape matrices. Table 2 outlines the correlation coefficient, recovery, and repeatability at and above the LOQ level. The LODs were ranged from 0.001 to 0.005 mg/kg, whereas the LOQs ranged from 0.003 to 0.015 mg/kg. These values are much lower than the MRLs set by the China for grapes. The relative standard deviation was used to assess the accuracy of the analytical testing (RSD). The nine analytes had recoveries and RSDs in the concentration ranges of 85.4–93.8 percent and 8.2–15.8%, respectively. It can be concluded from the results achieved that the analytical method was viable for determining the pesticide contaminants in the grapes.

Table 1

UPLC/Q-TOF accurate mass measurements.
Compound	RT (min)	Ion	Elemental Compositions	Theoretical m/z	Experimental m/z	Error
Compound	RT (min)	Ion	Elemental Compositions	Theoretical m/z	Experimental m/z	mDa	ppm
Carbendazim	4.28	[M + H]⁺	C₉H₁₀N₃O₂	192.0773	192.0769	−0.4	−2.08
		Fragment 1	C₈H₆N₃O	160.0511	160.0507	−0.4	−2.50
		Fragment2	C₇H₆N₃	132.0562	132.0559	−0.3	−2.27
Pyrimethanil	3.95	[M + H]⁺	C₁₂H₁₄N₃	200.1188	200.1185	−0.3	−1.50
		Fragment 1	C₁₁H₁₁N₂	171.0922	171.0917	−0.5	−2.92
Metalaxyl	4.92	[M + Na]⁺	C₁₅H₂₁NO₄Na	302.1368	302.1365	−0.3	−0.99
		[M + H]⁺	C₁₅H₂₂NO₄	280.1549	280.1543	−0.6	−2.14
		Fragment 1	C₁₄H₁₈NO₃	248.1287	248.1284	−0.3	−1.21
		Fragment2	C₁₃H₁₈NO₂	220.1338	220.1334	−0.4	−1.82
		Fragment3	C₁₂H₁₈NO	192.1388	192.1384	−0.4	−2.08
Dimethomorph (Z + E isomers)	5.46, 5.63	[M + Na]⁺	C₂₁H₂₂NO₄ClNa	410.1135	410.1126	−0.9	−2.19
		[M + H]⁺	C₂₁H₂₃NO₄Cl	388.1316	388.1306	−1.0	−2.58
		Fragment 1	C₁₇H₁₃O₃Cl	300.0553	300.0559	0.6	2.00
Prochloraz	5.72	[M + H]⁺	C₁₅H₁₇N₃O₂Cl₃	376.0386	376.0377	−0.9	−2.39
		Fragment 1	C₁₂H₁₃NO₂Cl₃	308.0012	308.0006	−0.6	−1.95
		Fragment2	C₉H₇NO₂Cl₃	265.9542	265.9538	−0.4	−1.50
Difenoconazole	6.84	[M + H]⁺	C₁₉H₁₈N₃O₃Cl₂	406.0725	406.0718	−0.7	−1.72
		Fragment 1	C₁₇H₁₅O₃Cl₂	337.0398	337.0395	−0.3	−0.89
		Fragment2	C₁₃H₉OCl₂	251.0030	251.0029	−0.1	−0.40
Procymidone	5.71	[M + Na]⁺	C₁₃H₁₁NO₂Cl₂Na	306.0065	306.0061	−0.4	−1.31
		[M + H]⁺	C₁₃H₁₁NO₂Cl₂	284.0245	284.0240	−0.5	−1.76
		Fragment 1	C₁₂H₁₁Cl₂NO	255.0218	255.0215	−0.3	−1.18
Thiophanatemethyl	4.28	[M + H]⁺	C₁₂H₁₄N₄O₄S₂	343.0535	343.0531	−0.4	−1.17
		Fragment 1	C7H4N2S	148.0095	148.0091	−0.4	−2.70
Triadimefon	5.93	[M + H]⁺	C₁₄H₁₆N₃O₂Cl	294.1009	294.1001	−0.8	−2.72
		Fragment 1	C₁₂H₁₄O₂Cl	225.0682	225.0687	0.5	2.22

Table 2

Validation data of pesticides in grapes matrix by the QuEChERS-UPLC/Q-TOF method.
Pesticide	Linearity			LOD (mg/kg)	LOQ (mg/kg)	Recovery (%), n = 6		Repeatability, %RSD, n = 6
Pesticide	Range (mg/kg)	Equation	R²	LOD (mg/kg)	LOQ (mg/kg)	0.01 mg/kg	0.5 mg/kg	0.01 mg/kg	0.5 mg/kg
Carbendazim	0.005–0.5	y = 609.792x + 3862.51	0.9901	0.001	0.003	90.4	92.2	15.8	9.70
Pyrimethanil	0.005–0.5	y = 3222.41x + 5182.30	0.9971	0.001	0.003	91.5	91.4	10.7	11.3
Metalaxyl	0.005–0.5	y = 388.050x + 1630.93	0.9928	0.001	0.003	85.4	89.4	9.70	8.40
Dimethomorph (Z + E isomers)	0.005–0.5	y = 68.6812x + 16.4972	0.9992	0.001	0.003	86.2	88.6	14.0	9.80
Prochloraz	0.005–0.5	y = 130.361x + 486.562	0.9931	0.001	0.003	87.9	89.3	11.7	9.81
Difenoconazole	0.005–0.5	y = 297.264x + 575.931	0.9983	0.001	0.003	84.3	91.6	13.2	14.1
Procymidone	0.005–0.5	y = 874.972x − 855.541	0.9966	0.001	0.003	92.5	93.8	9.40	8.20
ThiophanateMethyl	0.01–0.5	y = 279.621x + 587.909	0.9948	0.003	0.010	82.6	85.9	10.7	12.3
Triadimefon	0.01–0.5	y = 386.123x + 468.212	0.9991	0.005	0.015	87.6	86.4	11.6	10.9

3.2. Detection of Nine Pesticide Pesticides in Grape Specimens

Among the 86 analysed samples, pesticides were detected in 80 (93.0%), all above the LOQ residues. Contamination percentages, concentration range, and prevalence of pesticides are outlined in Table 3. The most detected pesticides were Dimethomorph, followed by Pyrimethanil, Difenoconazole, Carbendazim, Prochloraz, Procymidone, Thiophanate, Metalaxyl, and Triadimefon. Dimethomorph was detected in 65 (75.6%) of the samples and occurred in 0.007 to 4.27 mg/kg. In China, Dimethomorph is a common pesticide contained in many registered mixed preparations. (Yang et al. 2020). Pyrimethanil was found in 56 (65.1%) samples at levels from 0.003 to 8.7 mg/kg. Difenoconazole was found in 50 (58.1%) samples at levels from 0.001 to 0.890 mg/kg.

Table 3

The pesticide residues detected in the grape samples.
Pesticide	Mean ^a (mg/kg)	Median ^a (mg/kg)	Minimum (mg/kg)	Maximum (mg/kg)	Detected Samples		MRLs (mg/kg)	Violative Compound		Violative Samples
Pesticide	Mean ^a (mg/kg)	Median ^a (mg/kg)	Minimum (mg/kg)	Maximum (mg/kg)	n	%	MRLs (mg/kg)	n	%	n	%
Carbendazim	0.053	0.027	0.013	0.156	26	30.2	3	—	—	6	6.98
Pyrimethanil	0.471	0.091	0.003	8.70	56	65.1	4	1	1.16
Metalaxyl	0.359	0.226	0.092	0.729	5	5.81	1	—	—
Dimethomorph (Z + E isomers)	0.524	0.142	0.007	4.27	65	75.6	5	—	—
Prochloraz	0.710	0.218	0.015	3.30	12	14.0	2	1	1.16
Difenoconazole	0.134	0.027	0.001	0.890	50	58.1	0.5	4	4.7
ThiophanateMethyl	0.283	0.099	0.005	1.18	8	9.30	3	—	—
Triadimefon	0.005	0.005	0.005	0.005	1	1.16	0.3	—	—
Procymidone	0.225	0.150	0.010	1.09	12	14.0	5	—	—
^a Mean of samples where residues were detected. MRL: Maximum Residue Limit was obtained from GB 2763 − 2019, China.

The research on pesticide residues in grapes from other groups had similar detection rates to this article. In one recent study (Si et al. 2021), the percentage of positive samples in grapes from farms of Shanghai of China was 97.5% (only one grape sample without any pesticide residues in 40 samples). While in another Chinese study was 100% (Zhang et al. 2021). Dibenzotriazole was the most identified pesticide in grapes, with the ranging from 0.011 to 0.408 mg/kg. Another study (Bouagga et al. 2019) also showed that all samples contained multiple residues in 64 grapes in Tunisia from 2015 to 2017. Among them, the detection rates of Carbendazim and Thiophanate Methyl were more than 50%. In the 2019 European Union report on pesticide residues in food (Authority E. F. S. et al. 2021), the number of grape samples without quantified residues was 377, while the number of grape samples with one or more residues was 1516. The calculated detection rate was 80.08%. One previous study (Bakirci et al. 2014) of pesticides in fruits from the Aegean region of Turkey showed that the detection rate of grape was 83%, while the most detected pesticide mentioned in this article in grapes was Pyrimethanil (19 of 83 samples), followed by Metalxyl (10 of 83 samples), then Dimethomorph and Carbendazim (both 9 of 83 samples). In Egypt, the detection rate of multi-class pesticides was 81.25% of the green grapes selected from the local market in 2011 (Eissa et al. 2013). The detection rates of the pesticides studied related this article were Carbendazim (40.63%), Difenoconazole (3.13%), and Metalaxyl (9.37%). In research from Slovenia (Baša-Česnik et al. 2008), the overall detection rate of pesticide residues was 97.9%, only one grape in 47 wine grape samples (2.1%) contained no residues. The pesticides related to our article were Metalaxyl (0.05–0.18 mg/kg) and Pyrimethanil (0.01–0.53 mg/kg), and the detection rates were 10.6% and 14.9%, respectively. However, the detection rates of pesticide residues in grapes in some other studies were lower than our experimental results. In a recent study, the percentage of positive samples in grapes from the mid-western region of China was 70.00% (Qin et al. 2021). Among them, the detection rates of the pesticides studied related to this article were Carbendazim (22.31%, ND-0.90 mg/kg), Difenoconazole (16.15%, ND-0.12 mg/kg), and thiophanate methyl (10.77%, ND-0.11 mg/kg). It can be concluded from the above that the detection rates of pesticide residues in grapes was very high. Liu et al. (2016) investigated the residues of nine pesticides in seven kinds of fruits including grapes and apples in Hangzhou of China from 2015 (Liu et al. 2016). The occurrence of pesticides in grapes was 56% (9 of 16 samples). Carbendazim was one of the most easily detectable residues in fruits, with a concentration range of 0.023–0.341 mg/kg. The comparison with the published research shows that the proportion of positive samples in grapes is usually very high, but the contamination level is variable and heterogeneous.

Our pesticide residues detection concentration was basically consistent with other reports, but the detection rates of several pesticides were much higher than other reports, including Dimethomorph, Pyrimethanil, and Difenoconazole. At present, in Shandong, there are two cultivation modes of grapes: greenhouse and open-air. The grapes cultivated in greenhouses are in an enclosed state during the flowering and young fruit period, and the low temperature and high humidity environment makes the occurrence of gray mould relatively heavy. The open-air cultivated grapes ripen during the rainy season in Shandong, (Shandong’s rainy season is July–September every year, which also happens to be the picking period of most of our grape samples), the high temperature and high humidity environment leads to the occurrence of downy mildew and other serious diseases. The picked grapes often rot due to gray mould during storage, transportation, and sales, which means that in different periods of grape growth, farmers use different pesticides. China implements a strict registration management system for pesticides. Through the search statistics of the pesticide registration data of China Pesticide Information Network, as of 7 January 2022, there were a total of 815 pesticide products registered in grape production in China, including 667 pesticides. There were 366 single agents and 301 mixtures of pesticides registered on grapes. The largest single-agent registrations was Dimethomorph with 48 registered products. Other pesticides registered on grapes related to our research were: eight Difenoconazole, twelve Procymidone, three Prochloraz, three Pyrimethanil, and two Thiophanate Methyl. Among them, the number of registered products of Dimethomorph accounted for the largest proportion of single doses of registered pesticide on grapes at 13.11%. There were 301 kinds of registered mixtures, 42 kinds of mixtures containing Dimethomorph, 45 kinds of Carbendazim, 24 kinds of Difenoconazole, and three kinds of Pyrimethanil. These data are consistent with the higher detection rates of pesticides including Dimethomorph and Difenoconazole in our research. As mentioned above, the common diseases of grapes are downy mildew, gray mold, and powdery mildew.

The above results were evaluated in accordance with the MRLs established by Chinese laws and regulations. Despite the high number of exposures, only six (6.98%) of the grape specimens contained pesticides above the MRLs. Among them, the concentration of Difenoconazole in four samples was 0.660–0.890 mg/kg, the concentration of Prochloraz in one sample was 3.30 mg/kg, and the concentration of Pyrimethanil in one sample was 8.70 mg/kg. These findings are very comparable to those of other studies. In a study of fruits’ pesticide residues from Turkey, 7.5% of grapes (55/728) showed multiple pesticide residues were higher than the values set by Turkish authorities (Soydan et al. 2021). While in the 2019 European Union report (Authority E. F. S. 2021), 28 of 291 grapes (9.6%) exceeded the MRLs. In Algeria, 12.5% samples with residues exceeding MRLs, which was higher than our research (Mebdoua et al. 2017). However, in some studies, the ratio of pesticide residues exceeding MRLs was much larger than that in our research. An investigation from Tunisia (Bouagga et al. 2019) found that at least one pesticide in 94% of grape samples exceeded the European MRLs. However, there were also situations that were contrary to our conclusion. For example, in the above mentioned study on pesticide residues in fruits in Shanghai, China (Si et al. 2021), although the detection rate of pesticide residues in grapes was 97.5%. According to GB2763-2019, the concentrations of all pesticides measured did not exceed the Chinese MRLs.

Figure 1 illustrates a range of pesticide residues. More than one pesticide contamination was found in 65 (75.6 percent) of these samples. Among them, a total of 15 (17.4%) samples of grapes that were examined contained two residues of pesticides, 25 (29.1%) samples were examined contained with three pesticide residues, and 25 (29.1%) samples were examined contained 4 or more pesticide residues. The number with the highest proportion of multiple pesticide residues was 3 residues, of which the most common combinations of pesticide residues were Dimethomorph, Pyrimethanil, and Difenoconazole. The sample with the most pesticides discovered has seven, which was proposed in Baša-Česnik et al. (2008). In Turkey, 42% of the positive fruit samples contained 2 or more residues, and grape was the fruit with the maximum number of residues. The results from Algria showed the majority of the analysed grape samples (57.5%) contained at least one pesticide, 25.0% were positive for one, 23.1% for two, 7.5% for three, and 1.9% for four or more pesticide residues (Mebdoua et al. 2017). Pesticide residues of more than one pesticide were identified in 49 percent of all recorded grapes during the period 2004–2011, according to the Danish monitoring program (Poulsen et al. 2017). According to Hjorth et al. (2011)., 71% of positive fruit and vegetable samples had more than one pesticide residue, with 13% of samples containing five or more pesticide residues (Hjorth et al. 2011). Two edible grape samples, both of which had nine pesticide residues, had the highest pesticide residues. The result was consistent with the findings of Bakirci et al. (2014) that grapes tend to contain four or more types of pesticide residues at the same time. The grapes are usually sprayed with pesticides every 7–10 days after the grapes have grown. Grape growers usually use a mixed pesticide formula because it can more effectively control a series of fungal diseases (Yang et al. 2020). However, if pesticides are mixed randomly, it will not only cause food safety hazards but can also easily lead to phytotoxicity. Excessive and frequent use of pesticides and random mixing should be avoided, and scientific and reasonable grape disease prevention and control technology should be established.

3.3. Health Risk Assessment

Pesticide levels in grapes (mg/kg day) were derived from consumption data, weight per capita, and our monitoring data (maximum residue and mean residue). Three scenarios were used for the chronic risk assessment in the report (Authority E. F. S. et al. 2021), including the lower, the middle and the upper bound scenario. In the upper and lower bound (UB and LB) scenarios, the unquantified results were replaced by 0 or value of LOQ. In addition, the middle bound (MB) scenario was calculated by assigning LOQ/2 to the result of the left truncation (Golge et al. 2018). The HQ of dietary risks from grapes contaminated with pesticides for adults and children is shown in Table 4.

Table 4

The short-term and long-term risks due to average daily intake of pesticides through grapes consumption in China.
Pesticide	Short-Term Risk						Long-Term Risk
	Maximum Residue (mg/kg)	ESTI		ARfD (mg/kg bw/day)	aHQ		Mean Residue (mg/kg)	EDI		ADI (mg /kg bw/ day)	cHQ
	Maximum Residue (mg/kg)	Adults (mg/kg day)	Children (mg/kg day)	ARfD (mg/kg bw/day)	Adult (%)	Children (%)	Mean Residue (mg/kg)		Children (mg/kg day)	ADI (mg /kg bw/ day)	Adults (%)	Children (%)
Carbendazim	0.156	8.73×10^− 5	1.51×10^− 4	0.1	8.73×10^− 2	1.51×10^− 1	0.053	2.97×10^− 5	5.14×10^− 5	0.03	9.89×10^− 2	1.71×10 − 1
Pyrimethanil	8.7	4.87×10^− 3	8.43×10^− 3	/	/	/	0.471	2.64×10^− 4	4.57×10^− 4	0.2	1.32×10^− 1	2.28×10 − 1
Metalaxyl	0.729	4.08×10^− 4	7.07×10 − ⁴	/	/	/	0.359	2.01×10^− 4	3.48×10^− 4	0.08	2.51×10^− 1	4.35×10 − 1
Dimethomorph(Z + E isomers)	4.27	2.39×10^− 3	4.14×10^− 3	0.6	3.98×10^− 1	6.90×10^− 1	0.524	2.93×10^− 4	5.08×10^− 4	0.2	1.47×10^− 1	2.54×10^− 1
Prochloraz	3.3	1.85× 10^− 3	3.20×10^− 3	0.1	1.85	3.20	0.71	3.97×10^− 4	6.88×10^− 4	0.01	3.97	6.88
Difenoconazole	0.89	4.98× 10^− 4	8.63×10^− 4	0.3	1.66×10^− 1	2.88×10^− 1	0.134	7.50×10^− 5	1.30×10^− 4	0.01	7.50×10^− 1	1.30
ThiophanateMethyl	1.18	6.60× 10^− 4	1.14×10^− 3	/	/	/	0.283	1.58×10^− 4	2.74×10^− 4	0.09	1.76×10^− 1	3.05×10^− 1
Triadimefone	0.005	2.80× 10^− 6	4.85×10^− 6	/	/	/	0.005	2.80×10^− 6	4.85×10^− 6	0.03	9.33×10^− 3	1.62×10^− 2
Procymidone	1.09	6.10× 10^− 4	1.06×10^− 3	0.1	6.10×10^− 1	1.06	0.225	1.26×10^− 4	2.18×10^− 4	0.1	1.26×10^− 1	2.18×10^− 1
ARfD and ADI were adopted from JMPR database and GB 2763 − 2019, China. The "/" indicated that no allowed value. The grape consumption value was calculated using the daily fruit intake recommendations of 38.17 g/day for adults and 42.18 g/day for children. (data from Survey on the total diet and health status of residents in Shandong Province, 2014).

All aHQs were below 10%, in which the adults’ value ranged from 0.0873% for Carbendazim to 1.85% for Prochloraz, while the children’s aHQs ranged from 0.151% for Carbendazim to 3.20% for Prochloraz. This means that the short-term risks with the consumption of the tested pesticides via grape consumption were low. The highest adult aHQ value was 1.85% and the highest children aHQ value was 3.20% from Prochloraz, followed by 1.06% from Procymidone. A study from China also found that the aHQ value of prochloraz from fruits was 3.2533%, which was basically consistent with our results (Liu et al. 2016). Among the 104 pesticides they studied, only Bifenthrin presented a health risk to children. However, in the research of pesticide residues in grapes from Tunisia (Bouagga et al. 2019), the acute exposure values for adults and children were 0.52–1016% and 1.10–2100% of ARfD, respectively. Their results indicated that consumers in Tunisia may face serious health risks.

For chronic risk assessment, the cHQ values were also all below 10%, in which the adults’ value ranged from 0.00933% for Triadimefone to 3.97% for Prochloraz, while the children’s cHQ values ranged from 0.0162% for Triadimefone to 6.88% for Prochloraz. The highest chronic risk came from Prochloraz, which was higher than previous studies of risk assessment in fruit. In that study, the cHQ for Prochloraz of adults and children were 0.053% and 0.213%, respectively. However, in the research of the risk assessment of prochloraz in bayberries (Zhao et al. 2019), cHQ was calculated for adults to be 0.001 and aHQ of 0.82, and children’s aHQ was 0.57, which indicated acceptable chronic risks. However, both adults and children face intolerable acute hazards.

From both aHQs and cHQs, children have a higher exposure risk with fruit than adults. This is because children are lighter in body weight than adults and their fruit consumption is relatively high. In the research of 284 pesticide residues in five local fruits, including grapes in Shanghai (Zhang et al. 2021), it was found that through fruit consumption, children face greater risks than adults. This conclusion is also consistent with some other reports (Si et al. 2021; Chu et al. 2020; Mojsak et al. 2018. According to research, pesticides with a high detection rate do not always represent a significant danger of exposure (Qin et al. 2021). In our research, the most detected pesticides were Dimethomorph with a rate of 75.6%, but the HQs were only from 0.147–0.690%. In short, all the risk scores were below 100% in this study, suggesting that the potential dietary risks of nine pesticides from grapes to Chinese consumers are not significant. Pesticide residue levels in all foods studied had no effect on consumer health.

It's worth mentioning that the dietary exposure calculated in this study only covered grape exposures and not any other foods. Therefore, the total exposure to the pesticides studied was underestimated. On the other side, the possible processing before eating fruit is ignored. Zhao et al. (2019) discovered that washing fruits with tap water for one minute effectively removes pesticide residues on the surface. Grapes, for example, are frequently washed, soaked, and peeled before eating, which can contribute to an overestimation of pesticide residues intake (Chen et al. 2011).

Shandong is one of the major grapes producing areas in China. Pesticide residues in table grapes pose a serious threat to consumer health. This study has provided pesticide residues in grapes from Shandong, China. This study clearly illustrated and documented the detection of pesticides in grapes; they are significant in that 80 of the 86 grape samples showed pesticide detected. However, the majority of the pesticide levels found were below the Chinese authorities' MRLs. Additionally, calculations of the EDI of each pesticide from the study has shown that the existing grape studies towards dietary intakes were much lower than the ADIs published. Although there is a negligible health risk from grape consumption of these pesticides, the large number of positive findings highlights the necessity for regular monitoring measures. The findings that are documented here are not an indication of being optimistic but highlighting the increased awareness that is needed from local authorities to establish a comprehensive monitoring program.

Author contributions: Conceptualization: Hongqian Nie, Jian Han; Methodology: Yanni Lu, Haimei Ye and Luping Yang; Formal analysis and investigation: Lijun Shao , Lianlong Yu and Xuancheng Lai; Writing - original draft preparation: Hongqian Nie and Jian Han; Writing - review and editing: Yanni Jiao; Funding acquisition: Yanni Jiao.

Competing Interests: The authors have no relevant financial or non-financial interests to disclose.

Funding: This work was supported by the Shandong Province Traditional Chinese Medicine Science and Technology Project (No.2021M221 and No.2021M222).

Data availability: All data generated or analysed during this study are included in this published article

Disclosure statement

The authors declare no conflict of interest.

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Occurrence of Nine Pesticide Residues in Table Grapes and Exposure Assessment in Shandong Province, China

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Abstract

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1. Introduction

2. Materials And Methods

2.1. Reagents

2.2. Sample Collection and Preparation

2.3. Chromatographic and Mass Spectrometer Conditions

2.4. Method Validation and Quality Control

2.5. Health Risk Assessment

3. Results

3.1. Validation Results

3.2. Detection of Nine Pesticide Pesticides in Grape Specimens

3.3. Health Risk Assessment

4. Conclusions

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