Dietary exposure to neonicotinoid insecticides and health risks in the Chinese general population through two consecutive total diet studies

Environment International 135 (2020) 105399

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Dietary exposure to neonicotinoid insecticides and health risks in the Chinese general population through two consecutive total diet studies ⁎

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Dawei Chena, Yiping Zhanga,b, Bing Lva, Zhibin Liua, Jiajun Hanc, , Jingguang Lia, , Yunfeng Zhaoa, Yongning Wua a

Food Safety Research Unit of Chinese Academy of Medical Science (2019RU014), NHC Key Lab of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing 100022, China b Technical Innovation Center for Utilization of Marine Biological Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China c Department of Chemistry, University of Toronto, Rm LM321, 80 St. George Street, Toronto, ON M5S 3H6, Canada

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Olga-Ioanna Kalantzi

Neonicotinoid insecticides are ubiquitous in food and the environment due to their wide use. Growing evidence suggests the adverse effects of neonicotinoids in many species, including mammals. Some studies have reported the urinary concentrations of neonicotinoids in human biological monitoring, but the potential risks of neonicotinoids on human health based on long-term chronic exposure studies in any general population have been rarely tackled. In this study, the dietary exposure to neonicotinoids of the Chinese adult population was studied on the basis of composite dietary samples collected from the 5th (2009–2012) and 6th (2015–2018) Chinese total diet studies (TDS). Residue levels of ten neonicotinoids were determined in 528 composite dietary samples from 24 provinces in China. Most of the samples (53.3% and 70.5% in the 5th and 6th TDS, respectively) that we analyzed contained the multi-residue of neonicotinoids. Imidacloprid and acetamiprid were the most frequently detected neonicotinoids, and thiamethoxam and clothianidin were increasingly used and found in the 6th TDS. The estimated daily intake (EDI) for total neonicotinoids was calculated to evaluate health risk of the Chinese adult population based on a relative potency factor assessment method. The mean EDIs of total neonicotinoids in the 5th and 6th TDS respectively reached 598.95 and 710.38 ng/kg bw per day. Although the mean EDIs of total neonics in 6th TDS was relatively higher than that in 5th TDS, no statistical difference was observed (p > 0.05). Vegetables were the main source of dietary exposure, but exposure via cereals and beverages and water must also be addressed in China. Although the average exposure for total neonicotinoids was much lower than the current chronic reference dose (57 μg/kg bw per day), the dietary exposure risks of a general population for total neonicotinoids should not be overlooked due to the ubiquity of neonicotinoids in food and the environment.

Keywords: Neonicotinoids Total diet study Dietary exposure Relative potency factor

1. Introduction Insecticides are widely used in agriculture for pest control. Insecticides are a major factor for increasing agricultural productivity. However, almost all insecticides can potentially affect the environment, and many of them are toxic to mammals including humans. Neonicotinoids (neonics) are a new type of insecticides that bind more strongly to nicotinic acetylcholine receptors in insects than in mammals, leading to its lower acute toxicity in mammals (Tomizawa, 2004; Tomizawa and Casida, 2005). Over the past two decades, neonics have gradually replaced organophosphates and carbamates as the most widely used insecticides. Neonics have been registered in more than 120 countries (Wang et al., 2019). In 2008, the global turnover of

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neonics accounted for 24% of the global insecticide market (Jeschke et al., 2011). Neonics are ubiquitous in food and the environment due to their wide use. The US Department of Agriculture reported that neonics are detected in almost all fruits and vegetables and 90% of honey samples, and many samples contain multiple residues of neonics (Chen et al., 2014). Neonic residues have been also found in milk, tea, drinking water, river, soil, bees, and feathers (Bonmatin et al., 2019; Dankyi et al., 2014; Humann-Guileminot et al., 2019; Ikenaka et al., 2018; Lachat and Glauser, 2018; Nicholls et al., 2018; Tong et al., 2018; Wan et al., 2019; Xiong et al., 2019; Zhou et al., 2018), apart from in vegetables and fruits (Lu et al., 2018), and honey (Lv et al., 2018; Mitchell et al., 2017; Song et al., 2018; Tao et al., 2019a). Even though neonics exhibit relatively low toxicity to mammals,

Corresponding authors. E-mail addresses: [email protected] (J. Han), [email protected] (J. Li).

https://doi.org/10.1016/j.envint.2019.105399 Received 15 October 2019; Received in revised form 6 December 2019; Accepted 6 December 2019 0160-4120/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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Table 1 Limit of detection (LOD), acceptable dietary intake (ADI), chronic reference dose (cRfD), and relative potency factors (RPF) for neonics. Neonics

NIT

DIN

THI

IMI

CLO

ACE-DE

ACE

THIA

IMID

CYC

LOD ADI (mg/kg/d) cRfD (mg/kg/d) RPF

0.010 0.530 –a 1.000

0.010 0.200 0.020 2.850

0.010 0.080 0.006 9.500

0.005 0.060 0.057 1.000

0.005 0.100 0.010 5.816

0.005 – – 0.803

0.010 0.070 0.071 0.803

0.005 0.010 0.004 14.250

0.010 0.025 – 1.000

0.010 – – –

Note:

a

No available data.

The South 1 (S1) region was composed of Shanghai (SH), Fujian (FJ), Jiangxi (JX), Jiangsu (JS), and Zhejiang (ZJ). The South 2 (S2) region was composed of Hubei (HuB), Sichuan (SC), Guangxi (GX), Hunan (HuN), and Guangdong (GD). In the 6th TDS, newly added provinces were included in the four regions; that is, Shanxi (ShX), Gansu (GS), Shandong (SD), and Guizhou (GZ) were added to the N1, N2, S1, and S2 regions, respectively. Each province constituted a small market basket and had two rural survey sites and one urban survey site. The respondents of the dietary survey included individuals and households. Individuals from at least 30 randomly selected households at each survey site were chosen as respondents for the food consumption survey. The consumption data in each survey site were collected in three consecutive days of household dietary survey and three days of 24 h recall questionnaire survey. The consumption data collected from the three survey sites in each province must represent the average dietary pattern of this province. A detailed description on dietary survey and consumption data can be found in the Section “dietary investigation“ from the supplementary Data. Food consumption per capita was aggregated into 13 dietary sample categories, namely, cereals, legumes, potatoes, meats, eggs, aquatic products, dairy products, vegetables, fruits, sugars, beverages and water, alcohols, and condiments. Among them, condiments were used in the preparation of 12 other sample categories; thus, the actual mixed dietary samples of each province covered 12 groups. A total of 12 types of dietary samples were clustered in accordance with the local dietary recipes and consumption of local residents. The food samples were collected at food purchase points in each sampling point and were then prepared in accordance with local dietary habits. After cooking, the prepared food was mixed to form a provincial composite sample for each food group. All of the samples were transferred to the laboratory as soon as possible through the cold chain and stored at −20 °C prior to analysis.

growing evidence suggests that the widespread use of neonics could negatively affect nontarget organisms, such as bees (Baron et al., 2017; der Sluijs et al., 2013; Woodcock et al., 2016), shrimps (Butcherine et al., 2019), birds (Eng et al., 2017; Hallmann et al., 2014) and other species (Morrissey et al., 2015). In response to these studies, the European Union banned three main neonics, namely, clothianidin (CLO), imidacloprid (IMI), and thiamethoxam (THI), except in greenhouses, in 2018 (Butler, 2018). Recently, the effects of neonics on human health have been widely reviewed (Cimino et al., 2017; Han et al., 2018; Zhang et al., 2018). The urinary concentrations of neonics in human biological monitoring in the USA, China, and Japan have also been extensively reported (Ikenaka et al., 2019; Osaka et al., 2016; Ospina et al., 2019; Tao et al., 2019b and 2019c; Ueyama et al., 2015; Zhang et al., 2019b). However, the potential risks of neonics on human health based on a systematic review have been rarely tackled due to the lack of long-term chronic exposure studies in any general population (Cimino et al., 2017). Non-occupational exposure to neonics through vegetable and fruit consumption has been limited to individual city or region in China (Chang et al., 2018; Lu et al., 2018; Zhang et al., 2019a). However, existing works did not consider other possible dietary sources, such as cereals, meats, aquatic products, and drinking water. In addition, such studies mainly used raw food products that had not been processed as ready-to-eat food by cooking which may lead to change of neonics residues. In the present study, the dietary exposure to neonics of the Chinese population was studied on the basis of composite dietary samples collected from Chinese total diet studies (TDS), which were recommended by the World Health Organization (WHO) to evaluate the dietary intakes of certain chemical substances (WHO, 1995). However, previous Chinese TDS focused only on exposure to organochlorine and organophosphate insecticides and did not consider exposure to neonics. Therefore, in the present study, composite dietary samples from different provinces in China were collected during the 5th and 6th TDS in 2009–2012 and 2015–2018, respectively. This study aims (a) to determine the residue levels of neonics in composite dietary samples from different food categories and understand the use of neonics in China and (b) to evaluate the health risk posed by total neonics in the Chinese population through dietary intake on the basis of consumption data from Chinese TDS.

2.2. Residue analysis of neonics The standards of 10 neonics, IMI, acetamiprid (ACE), THI, CLO, nitenpyram (NIT), dinotefuran (DIN), thiacloprid (THIA), imidaclothiz (IMID), cycloxaprid (CYC), and ACE-n-desmethyl (ACE-DE) were purchased from Dr. Ehrenstorfer (Augsburg, Germany). Three isotope labeling standards, namely, IMI-D4, CLO-D3, and THI-D3, were supplied by C/D/N Isotopes, Inc. (Pointe-Claire, Quebec, Canada), and ACE-D3 was obtained from TRC (North York, Ontario, Canada). All reagents used throughout this study were chromatographic or analytical grade. Our published analytical method for dietary samples was developed for the analysis of neonics with enhanced sensitivity and cleanup effects by liquid chromatography–high resolution mass spectrometry (Li et al., 2019). In brief, 5.0 g of dietary samples was weighed into Eppendorf tubes, and mixed isotope labeling standards were added. The ultrasonic extraction was performed by adding 9 mL of acetonitrile and an appropriate volume of water on the basis of the moisture content of different sample matrixes. After extraction and centrifugation, 2 mL of supernatant was subjected to cold-induced phase separation for 1 h at −20 °C. The upper layer solution was then pipetted out into a dispersive solid-phase extraction tube (50 mg of C18, 50 mg of PSA, and 5 mg of NanoCarb) for purification. The tubes were shaken for 30 s, and the supernatant was then filtered using a 0.2 µm syringe filter and

2. Material and methods 2.1. Food sampling and food consumption data The experimental design in the 5th and 6th Chinese TDS was similar to that in the 4th TDS conducted in 2000 (Zhou et al., 2012). The 5th TDS (2009–2012) was performed in 20 provinces in China, and the number of provinces was increased to 24 in the 6th TDS (2015–2018). In the 5th TDS, the 20 Chinese provinces were divided into four TDS regions on the basis of the geographical distribution of China and the dietary patterns of the residents. Each region representing a major market basket comprised five provinces. The North 1 (N1) region was composed of Heilongjiang (HLJ), Liaoning (LN), Hebei (HeB), Beijing (BJ), and Jilin (JL). The North 2 (N2) region was composed of Shaanxi (SX), Henan (HeN), Ningxia (NX), Neimenggu (NM), and Qinghai (QH). 2

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Table 2a The descriptive statistics of neonic residues and total neonics (expressed as IMIRPF) from the 5th TDS. Food category Total samples (N = 240) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Cereals (N = 20) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Legumes (N = 20) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Potatoes (N = 20) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Meats (N = 20) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Eggs (N = 20) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Aquatic products (N = 20) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Dairy products (N = 20) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Vegetables (N = 20) Frequency

NIT

DIN

THI

IMI

CLO

ACE-DE

ACE

THIA

IMID

IMIRPF

5.0% 0.015 0.055 < LOD 0.532 < LOD < LOD < LOD

0.4% 0.007 0.033 < LOD 0.512 < LOD < LOD < LOD

22.9% 0.426 2.506 < LOD 30.287 < LOD < LOD < LOD

60.8% 1.586 4.538 < LOD 33.214 < LOD 0.089 0.838

9.6% 0.100 0.497 < LOD 4.909 < LOD < LOD < LOD

31.3% 0.344 1.189 < LOD 11.322 < LOD < LOD 0.103

52.9% 3.423 11.070 < LOD 108.211 < LOD 0.044 0.800

2.1% 0.003 0.003 < LOD 0.048 < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

9.312 34.560 < LOD 337.229 < LOD 0.360 2.378

5.0% 0.015 0.044 < LOD 0.200 < LOD < LOD < LOD

5.0% 0.030 0.113 < LOD 0.512 < LOD < LOD < LOD

30.0% 0.046 0.084 < LOD 0.339 < LOD < LOD 0.069

85.0% 1.014 1.280 < LOD 4.942 0.065 0.424 1.670

30.0% 0.050 0.094 < LOD 0.373 < LOD < LOD 0.077

20.0% 0.037 0.077 < LOD 0.259 < LOD < LOD < LOD

65.0% 0.797 1.264 < LOD 4.870 < LOD 0.195 0.928

5.0% 0.003 0.002 < LOD 0.013 < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

2.559 3.159 < LOD 10.672 0.192 1.188 3.567

5.0% 0.010 0.023 < LOD 0.110 < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

25.0% 0.024 0.041 < LOD 0.166 < LOD < LOD 0.015

70.0% 0.155 0.206 < LOD 0.859 < LOD 0.110 0.212

0.0% 0.003 0.000 < LOD 0.003 < LOD < LOD < LOD

40.0% 0.078 0.152 < LOD 0.653 < LOD < LOD 0.110

60.0% 0.768 2.857 < LOD 12.874 < LOD 0.071 0.166

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

1.144 2.251 < LOD 10.465 0.297 0.491 1.089

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

25.0% 0.046 0.105 < LOD 0.455 < LOD < LOD 0.013

80.0% 0.388 0.363 < LOD 1.235 0.102 0.268 0.574

10.0% 0.025 0.087 < LOD 0.387 < LOD < LOD < LOD

10.0% 0.020 0.059 < LOD 0.246 < LOD < LOD < LOD

35.0% 0.091 0.188 < LOD 0.636 < LOD < LOD 0.079

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

1.120 1.433 < LOD 6.647 0.305 0.793 1.477

5.0% 0.005 0.001 < LOD 0.011 < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

20.0% 0.018 0.031 < LOD 0.102 < LOD < LOD < LOD

65.0% 0.356 0.749 < LOD 2.893 < LOD 0.071 0.287

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

30.0% 0.059 0.144 < LOD 0.629 < LOD < LOD 0.047

80.0% 0.909 2.096 < LOD 9.378 0.056 0.202 0.699

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

1.383 2.442 < LOD 11.051 0.271 0.505 1.447

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

5.0% 0.006 0.006 < LOD 0.031 < LOD < LOD < LOD

20.0% 0.019 0.034 < LOD 0.119 < LOD < LOD < LOD

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

15.0% 0.018 0.044 < LOD 0.185 < LOD < LOD < LOD

10.0% 0.015 0.041 < LOD 0.190 < LOD < LOD < LOD

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.180 0.120 < LOD 0.542 < LOD < LOD 0.145

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

15.0% 0.022 0.049 < LOD 0.196 < LOD < LOD < LOD

85.0% 0.398 0.691 < LOD 2.654 0.062 0.208 0.334

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

15.0% 0.069 0.202 < LOD 0.789 < LOD < LOD < LOD

75.0% 0.703 2.050 < LOD 9.259 0.045 0.111 0.327

5.0% 0.005 0.010 < LOD 0.048 < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

1.334 2.222 < LOD 10.012 0.331 0.505 1.353

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

5.0% 0.007 0.007 < LOD 0.037 < LOD < LOD < LOD

30.0% 0.051 0.126 < LOD 0.496 < LOD < LOD 0.024

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

10.0% 0.009 0.024 < LOD 0.104 < LOD < LOD < LOD

5.0% 0.010 0.024 < LOD 0.113 < LOD < LOD < LOD

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.204 0.194 < LOD 0.792 < LOD < LOD 0.158

15.0%

0.0%

75.0%

100.0%

50.0%

90.0%

100.0%

5.0%

0.0%

% multi-residue

53.3%

60.0%

70.0%

50.0%

75.0%

10.0%

75.0%

15.0%

100.0%

(continued on next page) 3

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Table 2a (continued) Food category

NIT

DIN

THI

IMI

CLO

ACE-DE

ACE

THIA

IMID

IMIRPF

Mean SD Min Max 25% Percentile Median 75% Percentile Fruits (N = 20) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Sugars (N = 20) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Beverages and water (N = 20) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Alcohols (N = 20) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile

0.046 0.125 < LOD 0.532 < LOD < LOD < LOD

0.005 0.000 < LOD < LOD < LOD < LOD < LOD

4.471 7.667 < LOD 30.287 0.044 0.254 6.028

9.508 9.319 1.486 33.214 2.929 6.680 10.609

1.000 1.450 < LOD 4.909 < LOD 0.133 1.919

2.341 2.786 < LOD 11.322 0.344 1.511 3.018

26.618 25.890 0.558 108.211 5.533 20.175 39.987

0.003 0.003 < LOD 0.017 < LOD < LOD < LOD

0.005 0.000 < LOD < LOD < LOD < LOD < LOD

81.162 91.215 2.058 337.229 19.829 52.506 107.206

20.0% 0.062 0.121 < LOD 0.411 < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

30.0% 0.388 1.125 < LOD 4.909 < LOD < LOD 0.082

90.0% 5.772 7.718 < LOD 32.773 1.343 2.656 6.840

20.0% 0.091 0.232 < LOD 0.930 < LOD < LOD < LOD

90.0% 1.266 1.861 < LOD 7.201 0.297 0.670 1.118

95.0% 6.465 6.400 < LOD 23.722 1.571 3.873 11.115

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

16.311 17.369 < LOD 61.209 3.448 11.026 22.150

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

5.0% 0.006 0.017 < LOD 0.078 < LOD < LOD < LOD

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

10.0% 0.011 0.019 < LOD 0.075 < LOD < LOD < LOD

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.139 0.030 < LOD 0.262 < LOD < LOD < LOD

10.0% 0.014 0.028 < LOD 0.115 < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

45.0% 0.070 0.144 < LOD 0.642 < LOD < LOD 0.076

65.0% 1.225 2.837 < LOD 12.514 < LOD 0.220 1.089

5.0% 0.006 0.014 < LOD 0.065 < LOD < LOD < LOD

45.0% 0.212 0.656 < LOD 2.970 < LOD < LOD 0.164

60.0% 4.597 12.025 < LOD 53.909 < LOD 0.211 3.668

5.0% 0.003 0.003 < LOD 0.014 < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD 0.005 < LOD < LOD < LOD

5.863 14.413 < LOD 64.998 < LOD 1.194 4.231

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

35.0% 0.139 0.307 < LOD 1.176 < LOD < LOD 0.055

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

10.0% 0.007 0.014 < LOD 0.048 < LOD < LOD < LOD

40.0% 0.088 0.163 < LOD 0.565 < LOD < LOD 0.092

5.0% 0.003 0.002 < LOD 0.012 < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.344 0.458 < LOD 1.790 < LOD < LOD 0.258

% multi-residue

90.0%

5.0%

60.0%

30.0%

contamination. The solvent blanks consisting of acetonitrile and spiked mixed ISs were analyzed before and after obtaining the calibration curve and after each province analysis (samples from 12 categories). As expected, all solvent blanks were required to be below the limits of detections (LODs) of all analytes. All the method LODs for the 12 types of dietary samples were normally determined in accordance with the suggestion of the Eurachem guide on analytical method validation, and the detailed description can be found in the Eurachem guide (Magnusson, & Örnemark, 2014) and our previous work (Li et al., 2019). The LODs of the 10 neonics ranged from 0.005 μg/kg to 0.01 μg/ kg (Table 1), and analytes below LOD were labeled as non-detected (ND). The average recoveries of the 10 neonics ranged from 70% to 120%, with relative standard deviations (RSDs) of 2.5%–6.4%. In this test, all of the samples were analyzed twice, and the detected levels of neonics should fall within the range of the calibration curve. The RSDs were < 20% for all duplicate samples.

analyzed on a Dionex Ultimate 3000 RSLC with a aQ C18 column (150 mm × 2.1 mm, 2.6 μm) at 40 °C and coupled to a Thermo QExactive high resolution mass spectrometry (HRMS, Bremen, Germany). The mobile phases were composed of water (A) and methanol (B) containing 0.1% formic acid and 5 mM ammonium formate. The mobile phase elution program was performed: 0–4 min, 2–20% B; 4–5.5 min, 20–40% B; 5.5–10.5 min, 40–100% B; 10.5–13 min, washing step at 100% B; 13–18 min, pre-equilibrium time at 2% B. The flow rate was set at 0.4 mL/min, and a 15 μL sample was injected coupled with 75 μL of the solvent mixer. The HRMS was equipped with an electrospray ionization probe in positive mode and operated in target single ion monitoring mode. The other detailed HRMS parameters are described in our previous work (Li et al., 2019). 2.3. Quality assurance and quality control To verify the accuracy and reliability of detected results, we performed laboratory blanking procedures and recovery experiments. Recovery tests were performed by spiking three different concentrations of neonic standards (0.1, 1.0, and 10.0 μg/kg) into four representative dietary samples before sample preparation with six replicates. In an analytical sequence, a low concentration solvent standard was tested to verify the accuracy of the standard curve calibration of the 12 food group samples in each province. A solvent blank specimen was tested to track potential carryover or system

2.4. Residue data analysis and relative potency factor of neonics A total of 528 dietary samples collected by the 5th and 6th TDS were analyzed for 10 neonics, including a metabolite (ACE-DE) of ACE. The US Environmental Protection Agency (USEPA) established a relative potency factor (RPF)-based assessment method for health risks associated with exposure to mixed chemicals with the same modes of action (USEPA, 2010). In this RPF approach, the potency of each chemical in 4

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Table 2b The descriptive statistics of neonic residues and total neonics (expressed as IMIRPF) from the 6th TDS. Food category Total samples (N = 288) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Cereals (N = 24) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Legumes (N = 24) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Potatoes (N = 24) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Meats (N = 24) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Eggs (N = 24) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Aquatic products (N = 24) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Dairy products (N = 24) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Vegetables (N = 24) Frequency

NIT

DIN

THI

IMI

CLO

ACE-DE

ACE

THIA

IMID

IMIRPF

6.9% 0.037 0.206 < LOD 2.338 < LOD < LOD < LOD

7.3% 0.024 0.121 < LOD 1.697 < LOD < LOD < LOD

54.9% 0.989 4.762 < LOD 71.309 < LOD 0.035 0.407

61.8% 0.915 2.890 < LOD 28.045 < LOD 0.055 0.345

52.8% 0.486 2.695 < LOD 39.009 < LOD 0.036 0.163

44.8% 0.282 1.248 < LOD 14.977 < LOD < LOD 0.058

56.9% 2.589 13.854 < LOD 185.533 < LOD 0.025 0.317

2.4% 0.010 0.086 < LOD 1.421 < LOD < LOD < LOD

0.3% 0.008 0.053 < LOD 0.897 < LOD < LOD < LOD

15.697 62.438 < LOD 916.132 0.174 0.916 7.551

8.3% 0.006 0.005 < LOD 0.027 < LOD < LOD < LOD

4.2% 0.008 0.014 < LOD 0.073 < LOD < LOD < LOD

62.5% 0.081 0.108 < LOD 0.385 < LOD 0.047 0.092

62.5% 0.119 0.155 < LOD 0.478 < LOD 0.058 0.150

70.8% 0.132 0.227 < LOD 1.111 < LOD 0.067 0.138

16.7% 0.009 0.020 < LOD 0.090 < LOD < LOD < LOD

58.3% 0.133 0.215 < LOD 0.770 < LOD 0.024 0.172

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

1.844 1.845 < LOD 7.466 0.232 1.289 2.557

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

58.3% 0.262 0.477 < LOD 1.821 < LOD 0.049 0.268

87.5% 0.185 0.242 < LOD 0.936 0.054 0.081 0.206

45.8% 0.086 0.190 < LOD 0.872 < LOD < LOD 0.076

66.7% 0.052 0.070 < LOD 0.311 < LOD 0.031 0.067

70.8% 0.223 0.562 < LOD 2.232 < LOD 0.041 0.119

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

3.448 5.716 < LOD 20.680 0.317 0.721 3.579

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

4.2% 0.012 0.033 < LOD 0.165 < LOD < LOD < LOD

83.3% 2.327 5.836 < LOD 22.785 0.069 0.250 1.708

95.8% 0.427 0.574 < LOD 1.826 0.094 0.140 0.502

87.5% 0.658 1.344 < LOD 5.894 0.053 0.166 0.349

16.7% 0.006 0.009 < LOD 0.035 < LOD < LOD < LOD

50.0% 0.124 0.237 < LOD 1.061 < LOD 0.009 0.146

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

26.547 61.899 < LOD 251.810 1.310 4.288 20.448

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

75.0% 0.518 1.002 < LOD 3.413 0.018 0.069 0.386

79.2% 0.183 0.374 < LOD 1.801 0.014 0.062 0.170

58.3% 0.164 0.381 < LOD 1.754 < LOD 0.051 0.119

62.5% 0.037 0.066 < LOD 0.321 < LOD 0.023 0.035

79.2% 0.287 0.653 < LOD 3.199 0.021 0.056 0.303

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

6.379 11.664 0.139 43.013 0.515 1.284 4.382

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

45.8% 0.622 1.328 < LOD 5.631 < LOD < LOD 0.689

37.5% 0.138 0.457 < LOD 2.217 < LOD < LOD 0.033

37.5% 0.112 0.262 < LOD 1.230 < LOD < LOD 0.089

33.3% 0.079 0.306 < LOD 1.508 < LOD < LOD 0.025

20.8% 0.023 0.062 < LOD 0.305 < LOD < LOD < LOD

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

6.836 14.383 < LOD 62.935 0.136 0.289 7.591

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

62.5% 0.377 0.735 < LOD 2.935 < LOD 0.030 0.429

66.7% 0.119 0.199 < LOD 0.849 < LOD 0.036 0.124

58.3% 0.125 0.299 < LOD 1.487 < LOD 0.041 0.123

12.5% 0.016 0.044 < LOD 0.197 < LOD < LOD < LOD

33.3% 0.255 0.788 < LOD 2.908 < LOD < LOD 0.037

4.2% 0.012 0.048 < LOD 0.235 < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

4.848 8.459 < LOD 36.618 0.395 1.019 4.815

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

8.3% 0.011 0.022 < LOD 0.101 < LOD < LOD < LOD

4.2% 0.003 0.004 < LOD 0.020 < LOD < LOD < LOD

4.2% 0.004 0.007 < LOD 0.036 < LOD < LOD < LOD

91.7% 0.429 1.480 < LOD 7.185 < LOD 0.030 0.114

54.2% 0.183 0.582 < LOD 2.828 < LOD < LOD 0.050

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.684 1.658 < LOD 8.164 0.141 0.166 0.341

58.3%

45.8%

100.0%

100.0%

100.0%

100.0%

100.0%

16.7%

4.2%

% multi-residue

70.5%

79.2%

91.7%

91.7%

91.7%

54.2%

70.8%

54.2%

100.0%

(continued on next page) 5

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Table 2b (continued) Food category

NIT

DIN

THI

IMI

CLO

ACE-DE

ACE

THIA

IMID

IMIRPF

Mean SD Min Max 25% Percentile Median 75% Percentile Fruits (N = 24) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Sugars (N = 24) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Beverages and water (N = 24) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile Alcohols (N = 24) Frequency Mean SD Min Max 25% Percentile Median 75% Percentile

0.356 0.641 < LOD 2.338 < LOD 0.131 0.310

0.195 0.382 < LOD 1.697 < LOD < LOD 0.193

6.568 14.263 0.279 71.309 0.963 2.882 5.360

4.162 5.597 0.822 28.045 1.474 2.710 4.204

3.823 8.651 0.062 39.009 0.313 0.839 2.637

1.993 3.586 0.032 14.977 0.193 0.626 1.299

22.010 39.178 0.371 185.533 3.936 8.193 20.045

0.076 0.291 < LOD 1.421 < LOD < LOD < LOD

0.042 0.182 < LOD 0.897 < LOD < LOD < LOD

110.106 180.871 5.372 916.132 38.559 62.300 115.684

12.5% 0.024 0.056 < LOD 0.236 < LOD < LOD < LOD

8.3% 0.009 0.014 < LOD 0.059 < LOD < LOD < LOD

95.8% 0.967 1.266 < LOD 6.127 0.235 0.558 1.238

100.0% 5.447 5.761 0.470 26.961 1.919 3.335 6.773

95.8% 0.680 0.533 < LOD 1.776 0.243 0.602 0.944

100.0% 0.733 0.608 0.024 2.317 0.348 0.567 0.920

100.0% 7.429 19.449 0.122 97.555 1.909 2.946 3.661

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

25.230 23.201 3.911 109.823 11.455 18.568 30.026

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

8.3% 0.009 0.014 < LOD 0.072 < LOD < LOD < LOD

8.3% 0.021 0.077 < LOD 0.378 < LOD < LOD < LOD

33.3% 0.019 0.027 < LOD 0.104 < LOD < LOD 0.034

4.2% 0.003 0.003 < LOD 0.015 < LOD < LOD < LOD

8.3% 0.027 0.087 < LOD 0.421 < LOD < LOD < LOD

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.295 0.333 < LOD 1.591 < LOD < LOD 0.329

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

25.0% 0.024 0.034 < LOD 0.108 < LOD < LOD 0.019

50.0% 0.119 0.191 < LOD 0.676 < LOD 0.013 0.131

62.5% 0.123 0.180 < LOD 0.717 < LOD 0.026 0.191

33.3% 0.027 0.042 < LOD 0.142 < LOD < LOD 0.032

33.3% 0.016 0.035 < LOD 0.173 < LOD < LOD 0.019

58.3% 0.362 0.641 < LOD 2.942 < LOD 0.116 0.576

8.3% 0.007 0.017 < LOD 0.083 < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

1.893 2.778 < LOD 9.389 < LOD 0.437 2.452

4.2% 0.013 0.038 < LOD 0.193 < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

8.3% 0.009 0.017 < LOD 0.087 < LOD < LOD < LOD

37.5% 0.052 0.108 < LOD 0.487 < LOD < LOD 0.057

8.3% 0.005 0.008 < LOD 0.034 < LOD < LOD < LOD

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

50.0% 0.018 0.017 < LOD 0.068 < LOD 0.007 0.029

0.0% 0.003 0.000 < LOD < LOD < LOD < LOD < LOD

0.0% 0.005 0.000 < LOD < LOD < LOD < LOD < LOD

0.252 0.230 < LOD 1.005 < LOD 0.160 0.259

% multi-residue

100.0%

12.5%

58.3%

41.7%

food consumption data for each food category from different provinces in China; Ct represents the corresponding concentration of each neonic or IMIRPF (total neonics) detected in the dietary samples; and bw (body weight) represents a 63 kg-bw basis for the average adult body weight based on the published references (Wu et al., 2018; Yang et al., 2018). Finally, EDI was calculated and reported as μg/kg bw per day. The health risks of each neonic were evaluated by comparing their respective available ADI, and the health risks of total neonics were evaluated by the RPF method using the cRfD value (57 μg/kg bw per day) of IMI.

the cumulative assessment group was standardized into a specific compound, which should have extensive toxicological data. Recently, this RPF approach has been used to evaluate the aggregate exposures and risk of total neonics to human health (Chang et al., 2018; Lu et al., 2018; Wan et al., 2019; Zhang et al., 2019a). In the present study, nine neonics and the metabolite ACE-DE were detected in the dietary samples. The available acceptable dietary intake (ADI), chronic reference dose (cRfD), and RPF are shown in Table 1. However, the cRfDs of NIT and IMID were not available in existing studies. Thus, the RPF calculations of NIT and IMID used the same cRfD as that used for IMI due to the similar structure to IMI. As a metabolite, ACE-DE used the same cRfD as that used for ACE. IMI was selected as the reference neonic (Chang et al., 2018; Lu et al., 2018; Zhang et al., 2019a). Thereafter, the RPF approach was used to integrate detected neonics that were expressed as a single measurement of IMIRPF.

2.6. Data treatment and statistical analysis Food sampling information, food consumption data, and aggregation analysis results were processed and calculated with Microsoft Office Excel 2007 and SPSS 18.0, and the details on this section are found elsewhere (Wu et al., 2018). Data and statistical analyses for residue levels and dietary exposure to neonics were performed using the GraphPad Prism (v.7.04). For all statistical tests, undetectable neonic concentration was assigned half of the LOD. The residue data of neonics in the dietary samples were not normally distributed. Therefore, Spearman tests were performed to test the possible correlations among the target analytes and dietary samples. Nonparametric Mann–Whitney U tests were carried out to compare the total neonic levels among different regions and different food categories via two-group

2.5. Dietary intake The estimated daily intake (EDI) of neonics for the Chinese adult population was estimated using the middle bound (MB) evaluation approach according to the recommendation by the WHO (WHO, 1995). Half of the LOD was assigned as a substitute for the results that were less than the LOD values to avoid missing values in the statistical analysis. The EDI of the neonics was then calculated using the following equation: EDI = Df × Ct/bw. In this equation, Df represents the average 6

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Fig. 1. Residue concentrations of neonics detected in dietary samples collected from the 5th TDS (A) and 6th TDS (B). Concentrations are plotted on a Box-andwhisker plot with four quartiles. Box limits are for the first and third quartiles, and the band inside the box is the second quartile (the median). Boxplot whiskers extend to 1.5 times the interquartile range. Outliers are represented by solid circles; “+“ indicates the mean value of residue data. The occurrence and distribution of each neonic in different provinces from the 5th TDS (C) and 6th TDS (D).

Data). In the 5th TDS, IMI was the most frequently detected neonic (60.8%), followed by ACE (52.9%), ACE-DE (31.3%), THI (22.9%), CLO (9.6%), NIT (5.0%), THIA (2.1%), and DIN (0.4%). The overall frequency of detection for the multi-residue of neonics (more than one neonic) was 53.3%. IMI (61.8%) and ACE (56.9%) were still the most frequently detected neonics in the 6th TDS, and the frequency was similar to that in the 5th TDS. However, an evident increase in the detected frequencies of ACE-DE (44.8%), THI (54.9%), CLO (52.8%), DIN (7.3%), and multi-residue of neonics (70.5%) was observed in the 6th TDS compared to the 5th TDS. Fig. S2 in Supplementary Data also shows the detection rates of the different numbers of detected neonics in a dietary sample. A high rate of five to eight neonics was observed in the 6th TDS. Eight neonics were detected in four vegetable samples. In total, the detected frequency of all detected neonics increased more obviously in the 6th TDS than in the 5th TDS. Among the 20 provinces in the 5th TDS, the average concentration ranges of the neonics in 12 food categories amounted to ND–26.618 μg/ kg. ACE, ACE-DE, IMI, CLO, and THI were the five dominant neonics in the dietary samples, and vegetables contained the highest average

comparisons. A p value lower than 0.05 (two-tailed) was considered as statistically significant. 3. Results 3.1. Neonic residues in dietary samples A total of 240 dietary samples were collected from 20 provinces in China between 2009 and 2012 (5th Chinese TDS), and 288 dietary samples were collected from 24 provinces in China between 2015 and 2018 (6th Chinese TDS). Residue data for each neonic in 12 food categories from the 5th and 6th Chinese TDS are listed in Excel S1 and S2, Supplementary Data. Table 2a and Table 2b respectively show the descriptive statistics of neonic residues and total neonics (expressed as IMIRPF). Among the 10 neonics analyzed, CYC was not detected in any of the dietary samples, and IMID was only detected in a vegetable sample from the 6th TDS with a residue level of 0.897 μg/kg. The detected frequencies of the 8 other neonics in the dietary samples from the 5th and 6th TDS are shown in Table 2 and Fig. S1 (Supplementary 7

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5th TDS, the highest concentration of neonics was detected in the dietary samples (based on the total concentration of each neonic) from GD province (194.678 μg/kg), followed by QH province (151.701 μg/ kg). In the 6th TDS, the highest concentration of neonics was detected in SH province (230.019 μg/kg). As for the food categories, IMI and ACE were detected in all 12 food categories in the 5th TDS; their mean concentration levels were 0.006–9.508 μg/kg and 0.011–26.618 μg/kg, respectively. ACE-DE is a metabolite of ACE that was also detected in 11 food categories with mean concentrations ranging from 0.007 μg/kg to 2.341 μg/kg. A strong correlation was also observed in the residue levels of ACE and ACE-DE (r = 0.749, n = 20, p < 0.0001). Among the mainly detected neonics, some correlations were found between IMI and ACE (r = 0.535, n = 20, p = 0.015), between IMI and ACE-DE (r = 0.522, n = 20, p = 0.018), and between IMI and THI (r = 0.466, n = 20, p = 0.039). Apart from IMI and ACE, THI and CLO were also detected in all 12 food categories in the 6th TDS. Similarly, ACE showed a significant positive correlation with ACE-DE and IMI in the 6th TDS. Regarding the high detection frequency of THI and CLO, the residue levels of THI and CLO were strongly correlated (r = 0.698, n = 24, p < 0.001). 3.2. Total neonic concentration In this work, the total neonic concentration was calculated using the RPF and expressed as IMIRPF. In the 5th TDS (Fig. 3A), the maximum of the total neonics in QH province (420.099 μg/kg) was > 43 times that of the minimum in HLJ province (9.637 μg/kg). This result indicated a large difference in neonic residues. As for the major market basket shown in Fig. 3B, the concentration of total neonics in the N1 region was significantly lower than that in the S2 region (Mann–Whitney U60,60 = 1111, p = 0.0002; meanN1 ± SD = 5.525 ± 20.355 μg/kg, medianN1 = 0.154 μg/kg; meanS2 ± SD = 11.421 ± 39.581 μg/kg, medianS2 = 0.879 μg/kg). Furthermore, the concentrations of total neonics for the N2 and S1 regions were similar (Mann–Whitney U60,60 = 1794, p = 0.9766; meanN2 ± SD = 12.274 ± 46.379 μg/ kg, medianN2 = 0.360 μg/kg; meanS1 ± SD = 8.027 ± 26.026 μg/ kg, medianS1 = 0.420 μg/kg), and although the concentration was relatively high in the S2 region, no statistical difference was observed (p > 0.05). The total neonics from different provinces in the 6th TDS are presented in Fig. 3D. The maximum of the total neonics was noted in GX province (916.132 μg/kg), and it was much higher than that in the 5th TDS. As for the major market basket, no significant difference was observed for the concentrations of total neonics in the four regions (Fig. 3E). As shown in Fig. 3C, the total neonic concentrations were predominantly distributed in vegetables (mean ± SD = 81.162 ± 91.215 μg/kg, median = 52.507 μg/kg) and fruits (mean ± SD = 16.311 ± 17.369 μg/kg, median = 11.027 μg/kg) in the 5th TDS, and their values were significantly higher than those in other food categories (p < 0.05). The 6th TDS also showed similar results (Fig. 3F), but the concentration of total neonics in the food categories was higher than those in the 5th TDS. Even though the average concentration of total neonics in other food categories was low, some outlier values were observed, and they included those in beverages and water (64.998 μg/kg, 5th TDS), potatoes (192.872 and 251.810 μg/kg, 6th TDS), and eggs (62.935 μg/kg, 6th TDS).

Fig. 2. Ratio profiling for each neonic residue level by comparing the 6th with 5th TDS. Heatmap shows relative changes in ratio value of each neonic from the 20 provinces between the 6th and 5th Chinese TDS, where blue and red indicate decreased and increased levels of neonic residue, respectively; white indicates no changes. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

levels of ACE (26.618 μg/kg), IMI (9.508 μg/kg), THI (4.471 μg/kg), ACE-DE (2.341 μg/kg), and CLO (1.000 μg/kg). Among the 24 provinces in the 6th TDS, a slight decline was noted in the average concentration ranges of neonics in the 12 food categories (ND–22.010 μg/ kg). For example, the highest average levels of ACE (22.010 μg/kg) and ACE-DE (1.993 μg/kg) were observed in vegetables, and those of IMI (5.447 μg/kg) were noted in fruits. A significant increasing trend was observed in the concentrations of CLO and THI, which rose from meanCLO ± SD = 1.189 ± 1.486 μg/kg and meanTHI ± SD = 5.108 ± 7.854 μg/kg in the 5th TDS to meanCLO ± SD = 5.834 ± 8.580 μg/kg and meanTHI ± SD = 11.870 ± 15.550 μg/kg in the 6th TDS (Fig. 1A and B). To better understand the change trends of each neonic from 20 provinces, we performed ratio profiling to map fold changes by comparing the ratio of each neonic residue level in the 6th with 5th TDS. Fig. 2 shows a downward trend or no change for residue levels of ACE and IMI, but an obvious upward trend for ones of CLO and THI. A significant decreasing trend was nearly observed for residue levels of each neonic in QH and GD provinces from the 5th to 6th TDS, but a relative high residue change of CLO was noted in HeN province. Fig. 1C and D also show the occurrence and distribution of each neonicotinoid in the different provinces from the 5th and 6th TDS, respectively. In the

3.3. Dietary intake The total dietary intake of each neonic from the 12 food categories in the average Chinese adult population is shown in Table 3 and Fig. 4. ACE, IMI, THI, and CLO had the highest EDIs. The comparison of the results of the 5th and 6th TDS indicated that the average EDI (EDIave) values of ACE (236.03–140.68 ng/kg bw per day) and IMI (96.23–33.67 ng/kg bw per day) decreased, whereas those of THI 8

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Fig. 3. Total neonics concentrations (expressed as IMIRPF) from dietary samples. (A and D) residue distribution from the 12 food categories in 20 provinces from the 5th TDS and 6th TDS, respectively; (B and E) residue concentration from the four regions (major market basket) from the 5th TDS and 6th TDS, respectively; (C and F) residue concentration from different food categories from the 5th TDS and 6th TDS, respectively. Concentrations are plotted on a Box-and-whisker plot with four quartiles. Box limits are for the first and third quartiles, and the band inside the box is the second quartile (the median). Boxplot whiskers extend to 1.5 times the interquartile range. Outliers are represented by solid circles; “+“ symbols indicate the mean value of residue data.

bw per day (GX province) in the 6th TDS. As for the major market basket, no significant difference in dietary exposure to total neonics was observed in the four regions (p > 0.05). Among the 12 food categories investigated, vegetables contributed most to the exposure to total neonics via the total dietary intake (Fig. S3 in Supplementary Data), making up 87.1% and 73.2% of the total intake of total neonics in the 5th and 6th TDS, respectively. In the 5th TDS, although the mean concentration levels of IMIRPF in beverages and water and in cereals were not high, their contribution to the total intake of total neonics ranked second (13.9%) and third (6.3%), respectively, due to their high consumption amounts.

(27.11–43.11 ng/kg bw per day) and CLO (6.29–23.14 ng/kg bw per day) increased. Among the 20 provinces from the 5th TDS, HLJ province had the lowest EDIs of ACE and IMI (6.28 and 16.05 ng/kg bw per day, respectively), and HuN province had the highest EDIs of the same neonics (636.47 and 376.68 ng/kg bw per day, respectively). As for THI, its highest EDI was observed in QH province at 203.88 ng/kg bw per day. Among the 24 provinces from the 6th TDS, the HLJ and GZ provinces had the lowest EDIs of neonics, and the SH and JX provinces had the highest EDIs of ACE and IMI at 1202.09 and 202.53 ng/kg bw per day, respectively. As for THI and CLO, the highest EDIs were noted in GX province at 402.01 and 222.72 ng/kg bw per day, respectively. In comparison with the current ADI proposed by the National Food Safety Standard of China (GB 2763-2016), the percentage contribution EDIave of each neonic to ADIs in Chinese adults obtained from this study was the highest for ACE (0.34%), followed by IMI (0.16%) and THI (0.05%) in the 5th and 6th TDS (Table 3). The RPF method was utilized in this work to obtain the total EDIs from a mixture of neonics in the 12 food categories. Table 3 and Fig. 5 provide an overview of the EDIs of total neonics in the Chinese adult population obtained using the MB approach. The average EDIs of total neonics in the 5th and 6th TDS respectively reached 598.95 and 710.38 ng/kg bw per day, which were much lower than the cRfD value of 57 μg/kg bw per day and were equivalent to 1.05% and 1.25% of the cRfD value, respectively. Although the mean EDIs of total neonics in 6th TDS was relatively higher than that in 5th TDS, no statistical difference was observed (p > 0.05). With regard to the different provinces in China, the EDIs of the total neonics varied from 24.28 ng/kg bw per day (HLJ province) to 2854.20 ng/kg bw per day (QH province) in the 5th TDS and from 60.44 ng/kg bw per day (HLJ province) to 5196.81 ng/kg

4. Discussion This study is the first to report the health risks of dietary exposure to neonics of the Chinese adult population. Among all detected dietary samples, IMI and ACE were the most widely used neonics in China with more than 50% detection rate. Several samples (31.3% and 44.8% in the 5th and 6th TDS, respectively) were also detected to have a residual contamination of a metabolite of ACE (ACE-DE). The third frequently detected neonics was THI, the detection rate for which increased from 22.9% in the 5th TDS to 54.9% in the 6th TDS. A similar finding was obtained for CLO, the detection rate for which increased from 9.6% in the 5th TDS to 52.8% in the 6th TDS. This result implied that THI and CLO were increasingly used in agricultural products. These results were similar or comparable to the reported detection frequencies in honey, pollen, soil, surface water, vegetable, and fruit samples from other Chinese studies (Table 4). Although the detection rates of THI and CLO were lower than those of IMI and ACE in this study, the calculated RPFs 9

Environment International 135 (2020) 105399 598.95 (1.05%) 710.38 (1.25%) – 0.47 (0.00%)

for neonics based on the toxicity of individual neonics relative to that of IMI must be addressed due to its low cRfD value. For example, the maximum IMIRPF concentration in THI was 297.221 μg/kg, which was the highest value in all dietary samples in the 5th TDS. In comparison with the detection frequencies for neonics reported in studies from other countries (Table 4), THI had a higher detection frequency than ACE in several samples. A worldwide survey of neonics in honey showed that IMI, ACE, and THI are predominantly detected in honey and revealed a higher detection frequency for ACE than for THI in Asia and Europe; the results were not the same for the other continents (Mitchell et al., 2017). To compare the total neonics across different studies, we used the RPF method. Among all the 12 dietary sample groups, vegetables and fruits had the highest total neonic residue concentrations; the 10 other groups were less contaminated with low concentrations of neonic residue. These results are similar to those detected by previous two cross-sectional studies conducted in vegetable and fruit samples (Lu et al., 2018). From the EDIave value of each neonic pesticide in the average population, the results of this study were far lower than the health guidance value recommended by GB 2763-2016 in China, indicating that the health risk of exposure to each neonic pesticide in China was low. However, it should be noted that the developmental neurotoxicity of neonic pesticides and their metabolites has not been established, so the health effects of long-term exposure to neonic pesticides on human health and neurodevelopment were not clear according to the report from the literature (Ikenaka et al., 2018). All of these introduce an uncertainty in the exposure assessment of each neonic pesticide. As for the dietary health risk of total neonics, this study results suggest a low health risk to the average adult population from current exposure level of total neonics in China. However, we must be aware of the need to reduce cRfD in the future due to the growing evidence of toxicological effects of neonics on mammals. This finding was attributed to the establishment of the existing cRfDs of neonics based on the observation endpoints of previous studies. However, new results obtained from experimental models may be highly correlated with public health. Compared with the studies on the EDI of total neonics reported in the US (Chang et al., 2018; Zhang et al., 2018), the current study found that the dietary exposure to total neonics in Chinese adults was greater than that in adults in the US. However, implementing a detailed comparison is difficult and inappropriate due to different food sources and cooking methods. In this study, the TDS samples were prepared by cooking or heating to produce ready-to-eat food products before analysis, whereas those in other studies were simply uncooked or obtained from environmental samples. The dietary food categories from TDS also comprised several individual food samples mixed together in specific proportions based on dietary habits. For example, luffa, Chinese cabbage, cauliflower, cucumber, and so on were aggregated into the vegetable category. However, the other studies focused on one or several food categories. For example, the EDI values of total neonics were obtained on the basis of the data from fruits and vegetables in the USA and Hangzhou City, China (Lu et al., 2018; Zhang et al., 2019a,b). In the correlation analysis, ACE-DE is the metabolite of ACE, so there was a strong correlation between them. As for the correlations between ACE and IMI, IMI and THI, THI and CLO, we originally speculated that they may be caused by using pesticide formulations that are not single molecules, but mixtures. However, through consulting the registration data of pesticides, there is no record of neonics used in combination by the pesticide formulations to be found in the existing registered pesticide products. These neonics pesticides were used mainly by single active molecule or in combination with other types of insecticides and fungicides. It also indirectly suggests the widespread use of neonics pesticides in China. As for the limitations of this work, the dietary exposure of total neonics to young children was not evaluated in current study. Even though the health of young children may possibly be affected sensitively by chemical contaminants in food, their dietary exposure to total

0.84 (0.00%) 2.02 (0.00%)

Note:

a

-: not mentioned.

5th 6th EDIs (Percentage contribution to ADIs or cRfD)

0.56 (0.00%) 1.46 (0.00%)

27.11 (0.03%) 43.11 (0.05%)

96.23 (0.16%) 33.67 (0.06%)

6.29 (0.01%) 23.14 (0.02%)

236.03 (0.34%) 140.68 (0.20%)

0.15 (0.00%) 0.56 (0.01%)

IMIRPF CLO IMI THI DIN NIT Neonics

Table 3 Estimated daily intake (EDI) (ng/kg bw per day) under MB approach to neonics and their percentage contribution to ADIs or cRfD.

ACE

THIA

IMID

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Fig. 4. Heatmap of the EDI values (ng/kg bw per day) for each neonic from different provinces based on the 5th TDS (A) and 6th TDS (B). Note: “Ave.“ indicates the average of EDI.

Fig. 5. EDI values of the total neonics in different provinces from the 5th TDS and 6th TDS.

residues measured in total dietary samples commonly consumed by Chinese adults. It is also the first to study the health risks of chronic exposure to neonics in China. Most of the samples (53.3% and 70.5% in the 5th and 6th TDS, respectively) that we analyzed contained more than one neonic. IMI and ACE were the most frequently detected neonic, and THI and CLO were increasingly used agricultural products in China. The high detection rates of neonics in the Chinese dietary composite samples provided a snapshot of the ubiquity of neonics in China. Vegetables were the main source of dietary exposure. Exposure via cereals and beverages and water must also be addressed due to the large consumption of these food products in China. The results of this study showed that the dietary exposure to total neonics poses an acceptable health risk to the average Chinese adult population on the basis of the current cRfDs. Given the ubiquity of neonics in the dietary samples and the uncertainty in the cRfDs of neonics with further studies on toxicological thresholds in mammals, the importance of routine measurements in food and the environmental samples, as well as the risks of the dietary exposure to total neonics of general populations, should not be overlooked.

neonics could not be obtained due to the different dietary composite samples. MB assumption was used for all of the ND samples in this study, and this approach might cause an overestimation of total neonics. Nevertheless, an ultra-sensitive analytical method was used in this study to obtain low LOD values and further reduce the number of NDs. This method made this risk assessment believable. Another limitation was that the inhalation exposure to total neonics from pollen was not available in this study. Hence, the total neonic exposure levels in the Chinese adult population may be underestimated. The average amount of pollen inhaled by adults is 0.005–0.08 mg/day on the basis of the data collected from three cities in China (Huang et al., 2009; Zhang et al., 2018). However, the inhalation exposure to total neonics based on the average amount of pollen inhaled by adults was far below the dietary exposure. Thus, dietary intake may still be the main source of exposure to neonics.

5. Conclusions In summary, this study is the first comprehensive survey of neonic 11

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Table 4 Comparison with detection rates (%) to neonics reported in studies in different countries or regions. Country

Source

Samples (No.)

IMI

ACE

THI

CLO

THIA

China (20 Provinces) China (24 Provinces) China (5 beekeeping areas) China (Tianjin City) China (Beijing City) China (Different Provinces) China (Zhejiang Province) China (Guangzhou City) China (Hangzhou City) China (Hangzhou City) USA (Cafeteria) Japan and Sri Lanka UK (3 regions) Swiss (Supermarkets) Ghana (Cocoa farms) Belize (Corozal district) Worldwide survey Africa Asia Europe North America South-America Oceania

This study (5th TDS) This study (6th TDS) Tong et al., 2018 Zhou et al., 2018 Lv et al., 2018 Song et al., 2018 Tao et al., 2019a Xiong et al., 2019 Zhang et al., 2019a Lu et al., 2018 Lu et al., 2018 Ikenaka et al., 2018 Nicholls et al., 2018 Lachat and Glauser, 2018 Dankyi et al., 2014 Bonmatin et al., 2019 Mitchell et al., 2017

Dietary samples (240) Dietary samples (288) Pollen (189)/beebread (226) Soil (68) Honey (66) Honey (30) Pollen (69)/honey (10) Surface water (22) Vegetables (83) and fruits (40) Vegetables and fruits (58) Vegetables and fruits (64) Tea (69) Pollen and nectar (233) Milk (19) Soil (52) Soil (40)/Sediment (34) Honey (198) Honey (37) Honey (41) Honey (53) Honey (22) Honey (28) Honey (17)

60.8 61.8 10.6/12.0 88.2 37.9 16.7 50.0/20.0 100 69.9/74.4 65.7 51.6 92.0 10.0 47.0 53.8 65.0/44.1 51.0 32.4 68.3 49.1 63.6 76.5 25.0

52.9 56.9 5.3/9.3 58.8 22.7 20.0 41.7/0.0 100 47.0/37.2 42.5 28.1 67.0 – 79.0 0.0 7.5/8.8 33.0 13.5 53.7 41.5 36.4 47.1 3.6

22.9 54.9 17.5/2.2 39.7 1.5 20.0 19.4/10.0 71.4 60.2/67.4 50.7 53.1 79.0 23.0 74.0 0.0 17.5/2.9 37.0 35.1 29.3 32.1 72.7 58.8 17.9

9.6 52.8 –a 17.6 0.0 16.7 33.3/10.0 100 27.7/4.7 19.4 35.9 74.0 – 63.0 9.6 22.5/2.9 16.0 5.4 9.8 22.6 50.0 17.6 0.0

2.1 2.4 – 2.9 3.0 13.3 19.4/0.0 0.0 12.0/4.7 9.0 3.1 79.0 15.0 23.0 0.0 0.0/0.0 24.0 8.1 17.1 52.8 18.2 17.6 10.7

Note:

a

-: not mentioned.

CRediT authorship contribution statement

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