Quantitative determination of pesticide residues in specific parts of bee specimens by nanoflow liquid chromatography high resolution mass spectrometry

Science of the Total Environment 715 (2020) 137005

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Quantitative determination of pesticide residues in specific parts of bee specimens by nanoflow liquid chromatography high resolution mass spectrometry David Moreno-González a, Victor Cutillas b, M. Dolores Hernando c, Jaime Alcántara-Durán a, Juan F. García-Reyes a,⁎, Antonio Molina-Díaz a a b c

University of Jaén, Analytical Chemistry Research Group, Department of Physical and Analytical Chemistry, Campus las Lagunillas s/n, 23071 Jaén, Spain University of Almería, Department of Physics and Chemistry, 04120 Almería, Spain National Institute for Agricultural and Food Research and Technology, INIA, 28040 Madrid, Spain

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Spatially resolved information of pesticides in individual bee specimens. • Miniaturized sample treatment based on ultrasonic-assisted extraction for pesticide analysis in head, thorax and abdomen. • No need to use pooled samples with various bee specimens. • Matrix effect was negligible in all cases using nanoflow LC-MS and ultrasonicassisted extraction.

a r t i c l e

i n f o

Article history: Received 8 November 2019 Received in revised form 27 January 2020 Accepted 28 January 2020 Available online 29 January 2020 Editor: Yolanda Picó Keywords: Nanoflow liquid chromatography High-resolution mass spectrometry Pesticides Honeybees Screening

a b s t r a c t The presence of pesticide residues in bees is of great interest, given the central role of bees as indicators for environmental assessment. The goal of this article is to propose a method to capture enhanced chemical information for these central environmental indicators. Most of the methods rely on the analysis of pooled samples rather than individual specimens due to practical sample preparation method considerations and limitations in sensitivity. This leads to miss information on the mapping of pesticides and actual amount of pesticide per specimen. In this article, a nanoflow liquid chromatography system coupled to high resolution mass spectrometry (using a hybrid quadrupole-Orbitrap instrument) has been applied for the development of a multiresidue pesticide method for the determination of 162 multiclass pesticides in specific part of honeybee samples (ca. abdomen, head or thorax). The reduced flow rate provided an enhancement in sensitivity and a strong reduction of matrix effects, thus only a quick and simple ultrasound assisted extraction using minute amount of sample was required. Satisfactory results were obtained for all tested analytes with concentration levels detected lower than 0.5 ng g−1 in all cases, thus being acceptable for monitoring purposes. Matrix effect was negligible for 94% of compounds. Extraction recoveries ranged from 70% to 105%, being within SANTE guidelines. Finally, the applicability of the method was demonstrated, by successful application to the analysis of contaminated honeybee samples, extracting useful information from specific bee parts of single specimens, thus, enabling pseudo spatially resolved chemical information. © 2020 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: Analytical Chemistry Research Group, Department of Physical and Analytical Chemistry, University of Jaén, 23071 Jaén, Spain. E-mail address: [email protected] (J.F. García-Reyes).

https://doi.org/10.1016/j.scitotenv.2020.137005 0048-9697/© 2020 Elsevier B.V. All rights reserved.

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D. Moreno-González et al. / Science of the Total Environment 715 (2020) 137005

1. Introduction Bee colony numbers decline is a worldwide concern. Notably, 37% of the 407 species for which there is information on population change are in decline in the European Union (EU) (Goulson et al., 2015). Besides this biodiversity issue, in economic terms, the role of bees as pollinators in agriculture is of paramount importance; approximately 80% of crops (and wild plant) in Europe depend directly on them (European Union, 2019). In addition, pollinators (honeybees included) contribute at least 22 billion € each year to the European agriculture industry, being also one of the most important economic insects due to the high production of honey, beeswax, pollen and propolis, used in food, cosmetics and medicine industries (Ediriweera and Premarathna, 2012). Recent studies have shown that pesticide misuse is one of the main factors involved in bee mortality by colony collapse disorder (European Parliament, 2012; Henry et al., 2012; Thompson et al., 2014). Pesticide exposure of honey bees causes a disruption of learning, memory, navigation and foraging activities (Samuelson et al., 2016). Worker bees could gather the nectar, water and pollen directly subjected to the action of pesticides or they may carry pesticide-contaminated pollen back to the hive and expose other honeybees. Thus, pesticide residues found in bees reflect the type of pesticides applied in the cultivated fields that surround their hives and can be used to evaluate environmental contamination with pesticides (Martel and Lair, 2011). In order to further assess how pesticide residues can affect honeybees, the development of analytical methods enabling the detection of these compounds in a specific part (abdomen, thorax or head) from one single specimen is of the utmost importance. Current analytical methods proposed for multi-residue the determination of pesticide in bees rely on the use of either gas chromatography (Walorczyk and Gnusowski, 2009; Gil-García et al., 2018; Kiljanek et al., 2016) or liquid chromatography (LC) (Gil-García et al., 2018; Kasiotis et al., 2014; Bargańska et al., 2014; Gbylik-Sikorska et al., 2015; Calatayud-Vernich et al., 2017) coupled to tandem mass spectrometry (MS/MS) in combination with different sample treatments including solid-phase extraction, ultrasound-assisted extraction or QuEChERS methodology. In most cases, pool of bees (ca. 3–10 g) are collected to provide enough sample to appropriately perform the generic sample treatment and the required sensitivity in these standard approaches (Bargańska and Namieśnik, 2010). However, bee may have different pesticide content and distribution. Enhancing the information gathered from chemical analyses using specific parts of single insects may be more informative for assessment purposes as well as in other studies conducted on these insects. For instance, pharmacokinetic studies involve extracting either whole bees or isolated tissues at different times after application (Cresswell et al., 2014). Therefore, studies contributing to afford a “spatially-resolved” information from pesticides in bees could be of interest. Nanoflow liquid chromatography/mass spectrometry is envisaged as an interesting alternative to conventional LC methodologies as it provides significant benefits in terms of sensitivity and matrix effects. The reduction of flow rates yields lower electrospray microdroplets diameter, and these smaller droplets contain fewer interfering species, which could compete for the ionization (Shen et al., 2002; Schmidt et al., 2003). So, an enhancement in sensitivity and matrix effect reduction could be reached at the same time, being a more effective ionization than pneumatically assisted standard ESI (Juraschek et al., 1999). These positive outcomes has been used for bioanalytical applications (Rigano et al., 2016; Wilson et al., 2015), in the field of food safety or for the determination of pesticides in pollen and nectar samples (MorenoGonzález et al., 2017; Moreno-González et al., 2018a; MorenoGonzález et al., 2018b), among others. In this work, a straightforward method based on nanoflow LC/ESI QOrbitrap-MS is proposed for the identification and simultaneous quantification of over 150 multi class pesticides in parts (abdomen, head or thorax) of individual specimens of honeybees, hence, enabling pseudo spatially resolved chemical information. A simple and miniaturized

sample treatment based only on ultrasound assisted extraction (UAE) and subsequent dilution of the extract was used in combination with nanoflow liquid chromatography high resolution mass spectrometry. To assess the feasibility of the proposed method for the determination of pesticides in different parts of a single honeybee, the proposed method was applied to the three parts of honeybee samples from different apiaries. 2. Experimental section 2.1. Chemicals and reagents HPLC-grade methanol (MeOH), HPLC-grade acetonitrile (MeCN) and HPLC-grade water were obtained from Sigma-Aldrich (Madrid Spain). Formic acid was supplied by Sigma-Aldrich and acetic acid was bought from J.T. Baker (Center Valley, PA, USA). Pesticide standards of the 162 multiclass pesticides selected (insecticides, herbicides, fungicides and acaricides) were supplied by SigmaAldrich or Dr. Ehrenstorfer (Augsburg, Germany). Each individual stock solution (ca. 500 μg mL−1) were prepared in MeCN or MeOH and kept at −20 °C. Then, a working solution containing the mixture of standards was prepared (5 μg mL−1) in MeCN and was also stored at −20 °C. To carry out the sample treatment, a Sonopuls HD 3100 ultrasonic system was used (Bandelin Electronic GmbH & Co. KG, Germany) beside SH 70G standard horn, GM3100 high-intensity generator (100 W), UW 3100 ultrasonic converter, and titanium MS73 probe. 2.2. Samples and sample treatment Honeybee samples were obtained from beekeepers from 3 apiaries in the southeast of Spain during the fall of 2018. The live honeybees were placed on labelled plastic vials. These samples were immediately transported at low temperature (in a cooler) to the laboratory and were frozen at −20 °C until analysis. According to Gil-García et al. (Gil-García et al., 2018), the effectiveness of the UAE without homogenization of the honeybee samples could be considered similar to other extraction approaches, where this homogenization step is mandatory. Thus, UAE was used as sample treatment in this study. Head, thorax including legs and wings, and abdomen of each specimen was weighed into and Eppendorf tube. The weight of each specific part/bee combination was ranged between 5.8 to 40 mg (Table S1). Then 300 μL of MeCN were added. The mixture was sonicated at a 75% amplitude for 140 s (ten extraction cycles of 12 s each plus a 2-s pause between them). Subsequently, it was centrifuged at 5000 rpm for 5 min. Finally, the extract was diluted 1:5 with Milli-Q water and filtered through a 0.45 μm nylon syringe filter. To avoid cross contamination among samples, 500 μL of water followed to 500 μL of MeCN was sonicated at a 75% amplitude for 140 s. 2.3. Nanoflow liquid chromatography high resolution mass spectrometry A Thermo Scientific EASY-nLC 1000 nano-LC system (Thermo Scientific, San Jose, USA) with an EASY-Spray PepMap® C18 column (75 μm × 150 mm, 3 μm particle size and 100 Å pore) with integrated emitter (Thermo Scientific, San Jose, USA) was used. Viper® zero-dead volume finger-tight fittings (Thermo Scientific, San Jose, USA) connections were used. The column was maintained at 25 °C, the injection volume was 1 μL and the flow rate was 200 nL min−1. The following linear gradient was used: 0–5 min 2% B, 5–15 min 30% B, 15–25 min 100% B, 25–28 min 100% B, 28–33 min 2% B and 33–37 min 2% B. Mobile phases A and B were Milli-Q water and MeCN respectively, both of them with 0.1% (v/v) formic acid. The nano-LC system was connected to a Thermo Q-Exactive Orbitrap mass spectrometer equipped with an Easy-Spray nano-electrospray ion source was used. To achieve the unambiguous identification of the pesticides studied, full scan (FS) in combination

D. Moreno-González et al. / Science of the Total Environment 715 (2020) 137005

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Table 1 Analytical performance characteristics of the proposed method for pesticide determination in bees. Compound

3-hydroxycarbofuran Acetamiprid Alachlor Aldicarb Aldicarb sulfone Aldicarb sulfoxide Ametoctradin Atrazine Azinphos methyl Azoxystrobin Benalaxyl Bifenazate Bitertanol Boscalid Bromuconazole 1 Bromuconazole 2 Bupirimate Buprofezin Carbaryl Carbendazim Carbofuran Carfentazone ethyl Chlorantraniliprol Chlorfenvinphos Chlorotoluron Chlorpyrifos methyl Clofentezin Clomazone Coumaphos Cyazofamid Cymoxanil Cyproconazole Cyprodinil Cyromazine Dazomet DEETc Demeton S methylsulphon Diazinon Dichlorvos Dicrotophos Diethofencarb Difenoconazole Diflubenzuron Dimethoate Dimethomorph E Dimethomorph Z Diniconazole Diuron Epoxiconazole Ethion Ethoprophos Etofenprox Fenamidone Fenamiphos Fenamiphos sulfone Fenamiphos sulfoxide Fenarimol Fenazaquin Fenbuconazole Fenhexamid Fenoxycarb Fenpropathrin Fenpropimorph Fenthion Fenthion sulfone Fenthion sulfoxide Fipronil Flonicamid Fluazifop Flufenacet Flufenoxuron

Rt (min)

16.74 17.83 25.24 26.10 3.36 11.48 23.43 22.35 23.76 24.06 25.46 24.51 12.80 24.09 23.91 24.42 22.62 24.96 22.00 3.30 21.56 24.75 23.28 25.32 22.07 25.89 26.12 23.25 25.99 25.32 24.07 23.64 22.84 3.24 22.09 22.32 4.11 25.86 20.64 14.26 23.75 25.56 21.68 17.33 23.17 23.39 25.07 22.54 24.21 26.82 24.32 25.42 24.08 23.98 21.28 21.67 23.75 27.33 19.54 31.02 25.5 23.99 19.19 25.70 22.97 22.97 25.29 30.54 21.99 24.91 27.05

R2

0.997 0.996 0.999 0.999 0.999 0.995 0.998 0.997 0.999 0.999 0.998 0.997 0.995 0.999 0.999 0.998 0.999 0.999 0.998 0.999 0.998 0.999 0.997 0.998 0.995 0.999 0.996 0.999 0.999 0.999 0.999 0.999 0.998 0.997 0.998 0.999 0.999 0.996 0.995 0.998 0.999 0.999 0.999 0.999 0.999 0.999 0.999 0.998 0.997 0.998 0.998 0.999 0.999 0.999 0.997 0.998 0.999 0.999 0.999 0.995 0.997 0.996 0.998 0.994 0.998 0.999 0.995 0.998 0.999 0.999 0.999

Precision

LCLa (pg·bee−1)

Recovery (n = 10)

Intraday (%, n = 10)

Interday (%, n = 10)

(%)

%RSD

5 8 4 5 6 4 5 6 5 8 5 5 7 8 5 6 5 4 3 9 6 6 3 4 5 7 8 5 5 5 6 4 7 10 8 5 5 6 5 5 3 5 8 5 6 4 6 4 7 8 5 6 5 5 6 5 4 8 6 7 4 6 6 4 5 5 8 4 8 5 6

11 14 8 8 18 6 9 9 7 10 9 10 10 9 8 9 8 9 8 12 9 9 8 7 10 12 12 8 7 9 9 8 11 13 14 10 12 10 9 8 9 9 13 12 10 9 12 9 11 12 9 9 8 9 9 10 9 12 11 10 8 10 13 12 9 12 12 9 14 9 12

82 89 93 91 72 71 93 72 84 93 85 96 94 96 86 85 97 81 91 82 93 93 85 92 96 93 93 78 93 99 96 89 82 94 96 96 91 86 98 76 87 85 89 89 81 83 87 79 94 92 85 72 88 79 83 73 85 76 89 86 93 75 81 92 99 94 96 91 96 97 89

5.8 4.9 7.2 8.9 5.1 8.2 5.6 8.2 9.3 9.3 10.2 9.2 9.3 9.2 10.5 9.3 8.5 7.1 8.3 11.5 10.5 9.5 7.3 8.3 7.5 8.2 8.6 7.6 7.6 8.3 11.2 8.9 7.3 12.3 12.3 8.8 10.6 10.8 9.8 9.3 8.4 10.1 15.1 12.3 9.9 8.8 11.2 8.2 9.3 12.3 8.8 10.1 10.1 10.2 9.91 10.9 10.2 10.2 9.9 9.9 9.3 11.0 9.6 12.1 10.2 11.3 11.2 8.9 12.5 8.3 10.1

6*102 6*102 6*103 6*102 6*101 6*102 6*101 6*102 6*102 6*101 6*101 6*102 6*103 6*103 6*103 6*103 6*101 6*103 6*102 6*101 6*102 6*103 6*102 6*101 6*102 6*103 6*103 6*102 6*102 6*102 6*102 6*101 6*102 6*102 6*102 6*102 6*101 6*102 6*101 6*101 6*102 6*102 6*102 6*101 6*102 6*102 6*101 6*101 6*102 6*102 6*102 6*102 6*102 6*101 6*102 6*102 6*102 6*103 6*101 6*101 6*101 6*102 6*103 6*103 6*102 6*103 6*102 6*103 6*102 6*103 6*103

S/N

158 456 421 80 245 101 702 623 153 251 180 852 103 202 623 585 563 256 125 153 103 256 235 158 263 123 202 96 296 321 254 92 259 213 125 102 199 377 156 263 96 296 255 256 305 123 156 856 301 563 369 253 372 356 125 196 56 96 658 356 56 56 236 256 199 156 156 235 589 232 956

Matrix effectb

−3 −5 8 −4 3 1 −3 −5 −1 4 1 0 −8 −5 −4 −4 −3 0 −6 0 0 2 −5 0 1 2 2 −2 0 −5 −1 −8 −4 −8 −3 −6 −8 −5 −3 −5 −1 −1 −6 −5 −8 −2 −9 −1 −6 −3 −4 −3 −4 −2 −5 −5 −8 −6 −5 −4 −3 −5 −10 −8 −9 −1 −4 −7 −5 −3 −3

(continued on next page)

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Table 1 (continued) Compound

Rt (min)

R2

Precision Intraday (%, n = 10)

Fluopyram Fluquinconazole Flusilazol Flutriafol Formetanate Fosthiazate Haloxyfop Hexaconazole Imazalil Imidacloprid Indoxacarb Iprodione Iprovalicarb Isocarbophos Isofenphos methyl Isoprocarb Isoproturon Isoxaflutole Kresoxim methyl Linuron Lufenuron Malaoxon Malathion Mandipropamid Mepanipyrim Metalaxyl Metconazole Methamidophos Methidathion Methiocarb Methiocarb sulfoxide Methomyl Methoxyfenozide Metobromuron Monocrotophos Myclobutanil Nitenpyram Norflurazon Omethoate Oxadixyl Oxamyl Oxyfluorfen Paclobutrazol Paraoxon methyl Penconazole Pencycuron Pendimethalin Penthiopyrad Phenthoate Phosalone Phosmet Phoxim Pirimicarb Pirimicarb desmethyl Pirimiphos methyl Prochloraz Procymidone Profenofos Propamocarb Propaquizafop Propiconazole Propoxur Propyzamide Pymetrozine Pyridate Pyrimethanil Pyriproxyfen Quinmerac Quinoclamine Quinoxyfen Rotenone Simazine Spirodiclofen

24.28 32.01 24.53 22.00 10.32 22.04 24.67 24.75 18.06 16.77 26.16 22.97 23.91 23.34 25.81 22.58 22.43 23.92 25.36 23.89 24.67 21.42 24.66 24.17 24.70 22.16 24.90 24.61 23.70 23.61 30.00 3.27 24.58 22.74 4.13 23.99 3.48 22.75 3.98 20.41 7.73 24.65 23.42 20.64 24.86 25.86 22.98 25.51 25.71 26.12 23.97 26.08 4.59 13.36 25.40 22.86 24.54 26.50 5.07 26.41 24.82 21.38 24.39 3.25 28.66 21.12 23.98 20.42 20.72 26.57 24.96 20.83 29.02

0.999 0.999 0.999 0.999 0.998 0.999 0.998 0.997 0.994 0.995 0.998 0.999 0.998 0.998 0.998 0.999 0.999 0.999 0.997 0.994 0.995 0.996 0.999 0.999 0.996 0.996 0.999 0.998 0.999 0.999 0.999 0.999 0.998 0.995 0.996 0.9998 0.997 0.997 0.998 0.998 0.999 0.999 0.999 0.999 0.999 0.997 0.998 0.9994 0.995 0.996 0.995 0.996 0.999 0.999 0.998 0.997 0.995 0.998 0.996 0.999 0.996 0.999 0.997 0.998 0.998 0.999 0.999 0.999 0.996 0.999 0.997 0.998 0.999

7 7 4 7 6 6 6 4 7 5 5 4 4 6 6 6 4 6 5 5 7 6 5 4 6 4 8 5 3 4 4 7 9 8 6 7 7 6 6 6 5 6 5 8 5 7 4 5 5 4 4 8 8 4 6 7 6 8 6 7 5 7 6 8 5 6 5 4 7 5 8 6 4

Recovery (n = 10) Interday (%, n = 10)

(%)

%RSD

12 9 8 12 14 12 10 8 10 9 9 9 8 8 13 12 9 12 10 9 12 12 10 8 14 9 14 8 9 10 9 14 12 13 12 10 14 10 10 9 9 8 8 12 13 12 9 10 8 9 9 12 12 8 12 12 11 15 12 12 10 10 9 12 11 9 12 9 14 14 14 13 8

85 89 83 93 76 83 97 81 76 93 85 89 92 92 97 90 93 96 92 91 85 78 91 89 93 88 96 95 96 93 96 82 83 92 80 93 89 95 90 76 92 96 89 71 82 86 77 95 96 95 93 93 88 89 81 75 90 83 78 96 86 92 89 85 91 87 78 81 92 83 89 89 72

12.3 9.3 9.1 10.1 12.3 9.2 10.3 8.2 10.5 10.2 8.8 8.6 8.2 10.2 12.1 9.6 9.7 12.1 11.5 8.6 11.2 10.9 9.3 9.1 12.9 8.9 12.2 8.9 5.7 9.5 10.3 12.3 10.2 12.5 11.3 11.0 12.3 11.2 10.2 8.2 10.5 8.2 12.5 7.2 8.9 10.5 10.1 8.2 8.3 9.3 8.9 11.9 10.2 9.3 10.2 13.2 10.5 14.2 9.2 8.2 9.3 8.9 10.1 11.5 10.5 8.2 9.3 6.2 15.1 9.2 8.4 10.2 14.5

LCLa (pg·bee−1)

S/N

Matrix effectb

6*102 6*101 6*102 6*102 6*102 6*102 6*102 6*101 6*102 6*101 6*103 6*102 6*102 6*102 6*102 6*102 6*101 6*102 6*102 6*103 6*101 6*102 6*102 6*102 6*101 6*102 6*101 6*102 6*102 6*102 6*102 6*102 6*102 6*102 6*102 6*101 6*102 6*102 6*102 6*103 6*103 6*103 6*102 6*102 6*102 6*102 6*102 6*103 6*102 6*103 6*102 6*102 6*102 6*101 6*103 6*102 6*102 6*103 6*103 6*103 6*102 6*102 6*102 6*102 6*102 6*103 6*102 6*102 6*103 6*103 6*102 6*102 6*103

56 123 99 10 369 356 286 78 356 256 302 369 923 752 256 56 932 301 96 156 156 263 253 168 69 156 452 121 123 129 236 51 456 256 56 230 56 296 256 156 233 259 523 123 303 302 99 263 102 296 723 451 236 263 231 93 256 156 236 658 125 256 156 189 123 423 325 530 201 956 59 1523 296

−2 −4 −4 −6 −10 −6 −5 −3 −8 −5 −5 −10 −3 −8 −6 −6 −8 −6 −2 −8 −7 −5 −10 −6 −8 −3 −5 −8 −8 −5 −5 −9 −3 −3 −9 −4 −8 −12 −8 −9 −8 −4 −3 −6 −8 −3 −6 −9 −8 −7 −4 −6 −6 −5 −8 −8 −6 −8 −6 −6 −2 −8 −3 −10 −5 −5 −8 −2 −6 −3 0 −2 −3

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Table 1 (continued) Compound

Rt (min)

R2

Precision Intraday (%, n = 10)

Spirotetramat Tebuconazole Tebufenpyrad Terbuthylazine Terbuthylazine desethyl TFNGd Thiabendazol Thiacloprid Thiamethoxam Thiobencarb Thiodicarb Triadimenol 1 Triadimenol 2 Triazophos Trichlorfon Triticonazole Zoxamide

23.60 24.48 26.30 23.83 21.1 4.44 21.00 19.86 14.42 21.48 26.13 23.31 23.53 24.87 33.67 23.68 25.53

0.999 0.999 0.998 0.999 0.999 0.997 0.995 0.996 0.999 0.999 0.997 0.998 0.999 0.996 0.998 0.999 0.999

4 4 5 5 6 5 5 5 8 5 7 6 5 4 7 4 5

LCLa (pg·bee−1)

Recovery (n = 10) Interday (%, n = 10)

(%)

%RSD

9 9 8 9 8 11 8 9 12 9 10 9 9 8 9 8 9

96 96 87 81 92 85 75 92 94 94 91 85 86 96 94 87 86

9.3 8.0 7.2 6.2 8.2 10.5 9.9 9.4 9.4 8.6 6.7 9.3 10.2 8.1 9.1 8.5 8.1

6*102 6*102 6*103 6*102 6*102 6*102 6*101 6*102 6*102 6*102 6*102 6*102 6*102 6*103 6*102 6*102 6*102

S/N

923 582 393 101 125 403 409 252 90 256 257 263 126 326 723 89 656

Matrix effectb

−3 1 −6 −3 −4 −3 −8 −3 −3 −3 −4 −5 −8 −5 −3 0 −5

a

Corresponds to the lowest concentration level tested giving S/N ratio equal or higher than 10. Matrix effect calculated as [(calibration curve slope in matrix/calibration curve slope in solvent) − 1] × 100. Zero result means that signal or suppression or enhancement has not been observed. c N,N-diethyl-meta-toluamide. d 4-(Trifluoromethyl)nicotinoyl glycine, N-[[4-(Trifluoromethyl)-3-pyridinyl]carbonyl]glycine. b

with all ion fragmentation (AIF) mode was selected, thus two product ions and a precursor ion -with a mass accuracy better than 5 ppmwere used. A resolution of 70,000 was set to obtain, in terms of selectivity and total cycle acquisition, satisfactory results, obtaining a FS experiment acquisition rate of ca. 5 Hz. The AIF mode was carried out at 30 V, this value was enough to obtain a fragmentation pattern for most of the studied compounds (Table S2). In all cases, the precursor ion was the protonated molecule [M + H]+. Other parameters were: S-lens RF level, 60; capillary temperature, 250 °C; spray voltage, 2.2 KV. A duty cycle of ca. 0.5 s/acquisition point was selected for acquiring FS and AIF experiments. Other parameters were as follow: automatic gain control (AGC) target at 1∙106; maximum injection time (IT), 200 ms and scan range, 100–750 m/z. Secondly was collision induced dissociation without precursor ion isolation or AIF mode at a resolution of 17,500, AGC target at 2∙105, maximum IT, 50 ms and scan range: 100–750 m/z. For qualitative and quantitative analysis Xcalibur 3.0 and TraceFinder 3.3 (Thermo Scientific) were used. 3. Results and discussion 3.1. Analytical performance Main analytical features including matrix effect, recovery rates, lowest concentration level (LCL) detected, and precision were studied to assess the performance of the proposed method using honeybees as representative matrix. These honeybees were obtained from an ecological apiary to ensure the absence of pesticide residues, according to guidelines (European Commission, 2017), being then employed for validation purposes. External (solvent) standard and matrix-matched calibration curves were studied over six levels of concentration (6*101, 6*102, 6*103, 6*104, 6*105 and 6*106 pg bee−1). As shown in Table 1, the linearity was appropriate for all pesticides studied, achieving regression coefficients (R2) better than 0.994 in all cases. It should be noted that, the high-resolution data from orbitrap usually yield higher S/N values due to negligible chemical background in extracted ion chromatograms with an m/z window of ±5 ppm. So, the S/N criterion was avoided to establish the LOQ. Therefore, the data from the LCL tested yielding S/N ratios distinctly higher than 10 was adopted as a more realistic criterion to display the sensitivity of the proposed method (Table 1). These LCLs were between 6*101 and 6*103 pg bee−1, thus

being appropriate to quantify the concentration of all pesticides studied far below their corresponding median lethal dose (LD50) (AERU, 2018). To evaluate the trueness of the proposed method, recovery studies in honeybee samples spiked at 6*103 pg bee−1 (n = 10) were also carried out. Recovery values between 70 and 105% with RSD values lower than 15% were obtained in all cases, being in agreement with established guidelines (European Commission, 2017). The precision of the proposed method was studied in terms of repeatability (intra-day precision) and intermediate precision (inter-day precision) using extracts from a honeybee pool spiked at 6*103 pg bee−1. Repeatability was evaluated over ten samples prepared and injected on the same day, under the same conditions. Intermediate precision was studied for ten consecutive days. Table 1 shows the results expressed as %RSD of peak area, obtaining %RSD lower than 20% in all cases. To evaluate and illustrate the feasibility to use external calibration for the determination of these pesticide in specific parts of honeybee samples instead of matrix matched standards, a pool of intact honeybees (not specific parts) was selected as representative matrix to study matrix effects. This effect was evaluated by comparison the slope of matrix-matched calibration curves and that of external standard calibration curves according to the following equation: [(calibration curve slope in matrix/calibration curve slope in solvent) – 1] × 100 (Matuszewski et al., 2003). This is a key parameter for obtaining reliable results, because of many coeluting interfering species could cause signal suppression of the analytes. 94% of compounds displayed a negligible reduction of the analytical response ([0%]–[−10%]), while soft/minor signal variation were obtained for the rest (between [−10%] and [−20%]) (Table 1). It can be concluded that no part of the honeybees have a remarkable influence on the analytical response. Therefore, matrix matched standards could be avoided, simplifying laboratory workflows by using external calibration for the determination of these pesticide in specific parts of honeybee samples. 3.2. Analysis of honeybees from Spanish apiaries 6 single specimen honeybee samples collected by beekeepers from three different apiaries were studied. 2 specimens per apiary of the same hive were analyzed. These apiaries are located in different regions of the southeast of Spain where the agricultural activity is widespread, so honeybee samples could be contaminated with pesticides, being

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Table 2 Pesticide residues in specific parts of honeybees from 3 apiaries (Head (H), Thorax (T) and Abdomen (A)). Pesticide residue

Type

LD50 (pg bee−1)

Bee toxicity group

No of Concentration of pesticide residues (pg part−1) positive Apiary 1 Apiary 2 per pesticide Specimen Specimen 2 Specimen 1 1 H T

Acetamiprid Ametoctradin Azoxystrobin Bifenazate Boscalid Bupirimate Carbofuran Chlorfenvinphos Cyazofamid Cyproconazole Cyprodinil Diazinon Difenoconazole Dimethoate Diuron Fenamidone Fenamiphos sulfone Fenhexamid Fenthion sulfoxide Flonicamid Fluopyram Flusilazol Formetanate Imidacloprid Iprodione Malathion Metalaxyl Myclobutanil Pendimethalin Propoxur Rotenone Tebuconazole Terbuthylazine Thiacloprid Thiamethoxam No of total pesticides per sample

1.5*107 1.1*108 2.0*108 8.5*106 1.8*108 5.0*107 1.6*105 5.5*105 1.0*108 5.5*108 7.5 *108 Insecticide 2. 6*105 Fungicide 1.3*108 Insecticide 1.0*105 Herbicide 1.5*108 Fungicide 1.0*108 Insecticide 4.5*105

Insecticide Fungicide Fungicide Acaracide Fungicide Fungicide Insecticide Insecticide Fungicide Fungicide Fungicide

Fungicide Insecticide Insecticide Fungicide Fungicide Insecticide Insecticide Fungicide Insecticide Fungicide Fungicide Herbicide Insecticide Insecticide Fungicide Herbicide Insecticide Insecticide

8

1.1*10 3.1*105 5.1*108 1.0*108 3.4*107 1.6*105 4.0*104 2.5*107 3.8*105 1.0*108 3.4*107 5.0*107 1.4*106 3.0*107 8.3*107 2.3*107 2.8*107 1.0*104

3

12

A

H

T

19

3 3 3 3 3 1 2 3 3 3

5 1 1 3 1 1 9 1 1 1

5

2

5

9

3 1 3 3 2

3 2 1 1 1

3 2 3 3 3 1 1 3 2 3 3 3 3 3 3 3 3 1

1 1 1 6 1 3 2 13 11 1 1 2 2 1 5 1 4 2 107

A

H

30 32

32

T

15

A 17

42

Apiary 3 Specimen 2 H 18

T 113

Specimen 1 A

H

16

T 15

Specimen 2 A

H

37

24

T

A 19

50

33 94

11 193 13

6383

95

7 4 1

27

7

13

17

45

13

55

35 3

1

7

7

9

13

7 1

9

1595

1 9 8 9 41 117 125 28 10 2

2

47

16

13

60

46 31 31 1063 0.26

91 1065 1401 1522 1640 5366 2020 672 887 1421 653 1591 1046 1 1 2 2 13 4 0.2 1 1 2 73 1 55

9

30

9 5 8

26

11

7

51

29 8 3

39 70 4 7

7

78 5 7

5

6

10

5

5

9 5

7

5

5

5

9 6

10

Group 1 - Highly Toxic: LD50 1.00*102–1.99*105 pg pesticide bee−1. Group 2 - Moderately Toxic: LD50 2.00*105–1.10*106 pg pesticide bee−1. Group 3 - Relatively Nontoxic: LD50 N 1.10*106 pg pesticide bee−1.

any disorder or high mortality reported by the beekeepers. The concentration found in the different parts of honeybees together with their corresponding LD50 are shown in Table 2. A total of 107 positives were found in the different samples studied and at least four pesticides were quantified in each sample. 16 fungicides, 14 insecticides, 3 herbicides and 1 acaracide were found, being acetamiprid, iprodione and malathion were the most frequently detected pesticides. 30, 29 and 41% of the pesticides were detected in head, thorax and abdomen respectively. However, the number of honeybee samples collected were not enough to conclude that some substances are accumulated more often in specific parts of honey bees. Thus, an extensive study of high number of samples would be interesting. However, this aspect is not the ultimate goal of this work. LD50 for honey bees is classified as highly, moderately and relatively nontoxic (see classification in Table 2) (Atkins et al., 1981). According to this classification 14%, 14% and 71% of the detected pesticides presented a high, medium or low bee toxicity respectively. However, the effects of many pesticides may be amplified by coexposure to other pesticides. For instance, acetamiprid or thiacloprid when mixed with demethylation inhibitors fungicides such as myclobutanil or tebuconazole may increase toxicity to bees (University of California, 2019). This scenario was shown in the sample

Apiary2-Specimen1-A1, were the concentration of acetamiprid and tebuconazole was 42 and 26 pg abdomen−1 respectively. On the other hand, the presence of hydrophobic pesticides such as diuron, fenhexamid, iprodione or tebuconazole in honeybee samples suggests a contamination from the comb since honey bee colonies are commonly treated with these pesticides for mite control (European Commission, 2010), such as Varroa destructor (Varroa mite). This external parasitic mite that attacks and feeds on the honey bees Apis cerana and Apis mellifera. Thiamethoxan was only found in one of the apiary. More specifically, thiamethoxan residue was found in the thorax in both specimens correspond to the Apiary1 with a concentration of 70 and 78 pg abdomen−1. This sentence has to be corrected. Tiso et al. have proved that an acute oral exposure of this neonicotinoid significantly altered honey bee thorax temperature at low concentrations (200 pg bee-1) (Tosi et al., 2016). Honeybee flight ability and consequent foraging behaviour depend on thorax temperature (Schmaranzer, 2000). So, the proposed methodology may be useful to conduct more in-depth studies concerning this matter. Nevertheless, the fact that thiamethoxan residues have only been found in this part of the bees may be related to thorax was analyzed together with the wings. Wings comprise a large fraction of a bee's body surface and therefore, represent a very

D. Moreno-González et al. / Science of the Total Environment 715 (2020) 137005

7

Apiary3 (A)

(B) Extracted ion chromatogram NL: 1.79E6

Relative Abundance

100 80

Apiary3-Specimen2-A 95 pg abdomen-1

60 40

343.0399

Full Scan

80 345.0369 60 40 344.0433 20

20

341.2504 0

0 100 Relative Abundance

Relative Abundance

24.09

100

C18H12Cl2N2O [M+H]⁺ 343.0399

340.5

341.0

341.5

342.0

NL: 0

342.5 343.0 m/z

343.5

344.0

344.5

345.0

80 60

Apiary3-Specimen1-A Not detected

40 20 0

23.0

23.5

24.0

24.5

25.0

25.5

Time (min)

Fig. 1. Nanoflow LC-HRMS analysis of pesticide in honeybees of the Apiary3. (A) Extracted ion chromatograms of boscalid in both thorax samples and (B) full-scan accurate mass spectrum in a positive sample.

significant route of exposure to pesticides (Poquet et al., 2015). Poquet and coworkers demonstrated that it is possible to induce mortality by thiamethoxan contact with only the wings of the honey bee (Poquet et al., 2015). So, the proposed methodology could be used to carry out more studies about this acute exposure of pesticides, which takes place on the surface of the specimen. The health of the honey bees is greatly influenced by the gut microbiota. Disruption of balance in gut microbiome or dysbiosis could result in accrued susceptibility of honey bees to diseases and pathogenic

microorganisms (Corby-Harris et al., 2014). Recent studies have shown that the chronic exposure to chemicals such as pesticides and antibiotics could affect the lability of the composition of the symbiotic gut microbes in honeybees (Nogrado et al., 2019). Therefore, the proposed methodology can be employed to detect pesticide residues in specific internal allocation of bees organs such as gut, muscle tissue in thorax or visceras. Another point that should be highlighted is that the most of the methods proposed in the literature are based on the use of a pool of

SAMPLE Apiary3-Specimen2-T (A)

NH2

100

3,5--DICHLOROANILINE

(B)

24.09

Relative Abundance

NL: 3.30E4 Relative Abundance

80

60

Cl

Cl

40

Cl

24.45

IPRODIONE

163.9650

40 160.9859

165.0910

163.1328

161

162

163

165.9626

164

m/z

165

166

167.1067

167

168

NL: 2.53E5

80

Relative Abundance

60

0

0 100

Full Scan

C6H5Cl2N+

80

20

20

161.9695

100

O HN

60

Cl

N

N O

O

40

20 0 23.0

23.5

24.0

24.5

25.0

25.5

26.0

Time (min)

Fig. 2. Nanoflow LC-HRMS analysis of pesticide in honeybees of the Apiary3 Extracted ion chromatogram of iprodione and 3,5-dichloroaniline in abdomen sample and (B) full-scan accurate mass spectrum of 3,5-dichloroaniline.

8

D. Moreno-González et al. / Science of the Total Environment 715 (2020) 137005

bees to obtain enough sample to allow a generic sample treatment. However, each part of a honeybee specimen may well have different content of pesticide residues. As an example, Fig. 1 shows two different honeybees of the same colony (Apiary 3). In Specimen1-A, boscalid fungicide was not observed, whereas in the second abdomen honeybee (Apiary3-Specimen2-A) this pesticide was present at a remarkably high concentration of 95 ng abdomen−1. Thus, the use of a pool of bees could lead to obtain less informative results (the mean value), as a consequence of the dilution of the analytes in the overall sample. Iprodione is a widely used fungicide. One of its main metabolites is 3,5- dichloroaniline, which has been detected in several samples where iprodione residue was also found (Fig. 2). It has been suggested that this metabolite may be associated with adverse health effects such as endocrine disruption (EFSA, 2019). Finally, the proposed analytical workflow could be also employed as alternative for pharmacokinetic studies including the uptake, metabolic fate, and excretion pesticides (Zaworra et al., 2019). As an example, Zaworra et al. showed that the penetration and uptake of 3 neonicotionids through honey bee cuticles is related with their polarity. So, this study could be expanded with other pesticides with the proposed methodology. 4. Concluding remarks In this work, a nanoflow LC/ESI Q-Orbitrap-MS multi-residue method combined with a very simple and miniaturized sample treatment is reported for the determination of 162 pesticides in specific part of honeybee body. The improvement in sensitivity and reduction of matrix effects obtained by the use of nano flows allowed to employing a very simple sample treatment based on UAE and dilution of the extract. The proposed method provided a negligible matrix effect for 94% of compounds studied and soft one for the rest. The sensitivity achieved allowed achieving LCL lower than their respective LD50. The analysis of bee specimens showed that a total of 107 positives could be quantified with the proposed procedure with different distribution in the honeybee body, thus making it possible to enable quantitative “spatially-resolved” mapping pesticides in these insects. It should be noted that the number of honeybee samples collected were not enough to obtain a representative sampling of these apiaries. The aim of this work has been to show the potential of nano flow LC for the determination of pesticides in specific parts of honeybees of a single specimen, rather than an occurrence study, which is not the aim of this work. So, it could be considered a proof of concept. The remarkable sensitivity obtained by the use of nano flow could be a useful tool to extend the knowledge on the impact of pesticides, as one of the main factors affecting bee colonies. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors acknowledge funding from the Spanish Ministerio de Economía y Competitividad (MINECO) through Project Ref. CTQ-201571321-P, partially co-financed with FEDER funds. The authors acknowledge Servicios Centrales de Apoyo a la Investigación (SCAI-UJAEN) from University of Jaén and Dr. Juan Castro Mármol (SCAI-UJAEN) for technical support. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2020.137005.

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