Science of the Total Environment 652 (2019) 562–572

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Science of the Total Environment

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Uptake and accumulation of emerging contaminants in soil and planttreated with wastewater under real-world environmental conditions inthe Al Hayer area (Saudi Arabia)

Yolanda Picó a,⁎, Rodrigo Alvarez-Ruiz a, Ahmed H. Alfarhan b, Mohamed A. El-Sheikh b,Samy M. Alobaid b, Damià Barceló b,c

a Environmental and Food Safety Research Group (SAMA-UV), Desertification Research Centre CIDE (CSIC-UV-GV), Moncada-Naquera Road Km 4.5, 46113 Moncada, Spainb Department of Botany and Microbiology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabiac Water and Soil Quality Research Group, Department of Environmental Chemistry, IDAEA-CSIC, Barcelona, 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

• Sensitive LC-QqTOF-MS method suc-cessfully identify 64 contaminants inwater.

• Method's performance verified for 40compounds in two matrices (cabbageand barley)

• Presence of 18 pesticides, pharmaceuti-cals and/or degradation products inplants verified

• Crops present more ECs (7) than soil(5) and at higher concentrations

E-mail address: Yolanda.Pico@uv.es (Y. Picó).

https://doi.org/10.1016/j.scitotenv.2018.10.2240048-9697/© 2018 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 13 September 2018Received in revised form 16 October 2018Accepted 16 October 2018Available online 17 October 2018

Editor: Jay Gan

In arid and semi-arid areas the use of treatedwastewater for crop irrigation and other agricultural practices, such as theuse of pesticides, increase the number of emerging contaminants (ECs) in crops. Hazards of these practices to humanbeing are largely unknown since there are few studies yet covering a short range of compounds and most of themunder non-realistic conditions. This study aims at assessing this problem that will become global soon in an area ofSaudiArabia heavily affectedby the reuseof treatedwastewater andpesticide inorder to ascertain its scale. Thenoveltyof the study relays in the large number of ECs covered and the variety of crops (cabbage, barley, green beans, eggplants,chili, tomato and zucchini) analysed. Extraction procedure developed provided an appropriate extraction yield (up to50% of the compoundswere recoveredwithin a 70–120% range), with good repeatability (relative standard deviationsbelow 20% inmost cases) and sensitivity (LOQ b 25 ng g−1) for themodel compounds. Determination by liquid chro-matography quadrupole time-of-flight (LC-QqTOF-MS) is able to identify N2000 contaminants. Sixty-four ECs wereidentified in wastewater but of the sixty-four compounds, six pharmaceuticals (atenolol, caffeine, carbamazepineand its metabolites 10,11-epoxycarbamazepine, gemfibrozil, and naproxen) and seven pesticides (acetamiprid, atra-zine deethyl, azoxystrobin, bupirimate, diazinon, malathion, pirimicarb and some of their metabolites) were detectedin plants. Furhermore, onemetabolite of the ibuprofen (not detected inwater or soil), the ibuprofen hexosidewas alsofound in plants. Up to our knowledge, this study demonstrate for the first time the accumulation of ECs in crops irri-gated with treated wastewater under real non-controlled environmental conditions.

© 2018 Elsevier B.V. All rights reserved.

Keywords:Treated waste water irrigationAgricultural practicesSoil accumulationPlant uptakeEmerging risk

⁎ Corresponding author.

563Y. Picó et al. / Science of the Total Environment 652 (2019) 562–572

1. Introduction

The current global change scenario predicts the future shortage ofgood quality water resources (Wortham et al., 2017). This is valuatingthe recycling of non-conventional water supplies whose use is increas-ing all around the world (Tahtouh et al., 2019). This new-coming situa-tion in many countries that raises concern to authorities, scientists andglobal public is since many years ago a reality in arid and semi-aridareas, such as the Middle East (Noorka and Heslop-Harrison, 2015;Quba'a et al., 2018; Shevah, 2017). Taking as an example the Arab re-gion, which is considered one of the most arid in the world, the prob-lems of water scarcity already affect N60% of the region forcing toimplement measures to preserve water resources (Al-Othman, 2015;DeNicola et al., 2015; HDR, 2011). The response of these countries is al-ready the extensive use of non-conventional water resources includingdesalination, wastewater reuse, rainwater harvesting, and long-distance water transfer (Chowdhury and Al-Zahrani, 2015; Djumaet al., 2016). Treated wastewater can be reused to irrigation (e.g. agri-culture, landscape, sport and recreation) and to recharge aquifers (El-Sheikh et al., 2018; Water, 2016).

Although agricultural production in these countries is far from satis-fying the needs of the population, this activity has increased in the lastyears and accounts for around 70% of the total water demand(Chowdhury et al., 2016; Djuma et al., 2016; Noorka and Heslop-Harrison, 2015). Globally, the use of wastewater for irrigation has a spe-cial significance due to the presence of an appreciable amount of cropnutrients (Ben Hassena et al., 2018; Tahtouh et al., 2019). However, itsmain problem is that in addition to nutrients, any contaminants or res-idues in the wastewater can accumulate in the soil during irrigationevents and could be hazardous affecting crops (Chaoua et al., 2018;Martínez-Piernas et al., 2018; Picó et al., 2017; Picó et al., 2018a;Riemenschneider et al., 2016). There are articles investigating variousaspects of the use of effluents from primary, secondary, and tertiarylevel wastewater treatment plants (Al-Saleh et al., 2017; Al Qarniet al., 2016; Ali et al., 2017; Alidina et al., 2014; Andrés-Costa et al.,2014; Campo et al., 2013; Campo et al., 2014; Farhadkhani et al., 2018;Maleksaeidi et al., 2018; McEachran et al., 2018). The identification ofpollutants and pathogens in wastewater and the formulation of stan-dards for its reuse have been part of already established processes inthe region that are nowadays being carefully observed even atEuropean Level (EC, 2018; EPA, 2012; Hong et al., 2018; Jampani et al.,2018; Orlofsky et al., 2016; Saliba et al., 2018; WHO, 2006). The reuseof non-conventional water resources has reached global dimensions.However, the potential uptake, translocation and accumulation of ECsin plants have been mostly established through laboratory studies athigh concentrations not always of environmental relevance (Di Baccioet al., 2017; Dudley et al., 2018; Fu et al., 2017; Madikizela et al., 2018;Montemurro et al., 2017; Picó et al., 2018a; Sun et al., 2019; Sun et al.,2018). Martínez-Piernas et al. (2018) investigated the uptake of ECs inradish and lettuce plants irrigated during 1.5 and 3 months consecu-tivelywith a secondary effluent from amunicipalwastewater treatmentplant at pilot scale under controlled greenhouse conditions. The resultsdemonstrated for thefirst time the accumulation of 5 ECs not previouslyreported: N-Formyl-4-aminoantipyrine, N-Acetyl-4-aminoantipyrine,hydrochlorothiazide, mepivacaine and venlafaxine. Furthermore,Riemenschneider et al. (2016) already indicated that 12 ECs and six car-bamazepinemetabolite could occur atmeasurable concentration (rang-ing from 1.7 to 216 ng g−1 of dry weight for all plant organs andvegetables).

Agriculture should sum-up to the contaminants from treated waste-water reuse, those from other agricultural practices (e.g. pesticides res-idues and fertilizers) (Osman et al., 2011; Osman et al., 2010; Wagenaand Easton, 2018; Zhang et al., 2018). Although the use of thesechemicals increases the production and quality of fruits and vegetable,they pose a risk to humanhealth and the environment. Particularly, pes-ticides and their transformation products can be found almost any time

of the year in streams draining extensive agricultural areas (Masiá et al.,2015; Metcalfe et al., 2019; Pascual Aguilar et al., 2017). The high tem-peratures of arid and semiarid areas increase pest populations and con-sequently also pesticides usage needed to control them with thesubsequent risk (Picó et al., 2018b). This also helps to forecast futurescenarioswithin the climate change, inwhich an increase in the amountof active ingredients applied to agricultural fields will increase theamount of active ingredients entering water bodies both through sur-face runoff and sub-surface leaching aswell as will increase the amountof residues on field crop (Chowdhury et al., 2016; DeNicola et al., 2015).

This study was aimed to assess the presence of ECs and pesticides insoils and crops irrigatedwith treatedwastewater in the area of Al Hayer(Saudi Arabia) to evaluate the potential of ECs plant uptake as well asthe presence of pesticides and/or other products used in agriculture.Up to our knowledge, no study has considered the effect of both prac-tices in crops and their surrounding environment. To achieve these ob-jectives, a multiresidue method based on methanol and solid-phaseextraction, covering a wide range of ECs and pesticides was optimizedand validate to extract plant materials. The determination of ECs andpesticides was carried out using liquid chromatography-mass spec-trometry with high resolutionmass spectrometry (HRMS) using a data-base in order to enlarge the scope of themethod beyond the compoundsselected to optimize the method. Accordingly, this methodology wasapplied to evaluate the uptake of ECs and the presence of pesticides inseveral plant materials irrigated wastewater under uncontrolled envi-ronmental conditions. Soils surrounding the crops and treated waste-water from the course of the irrigation channels were also analysed.

2. Materials and methods

2.1. Sampling and site description

For this purpose, the case study selected was the reuse of the waste-water effluent from the wastewater treatment plant of Al Hayer (SouthRiyadh). This effluent is used for natural recharge and is dischargedthrough a canal 40 km to the south in the Al Hayer area where it isstored in a pond and then infiltrates through the sandy soil to ground-water or also used to irrigate surrounding crops. Samples were takenin 4 different points in this areawith different influences, point 4 locateddirectly in the pond, point 1 affected by the release of an additionalwastewater effluent (secondary treatment) mixed rain water, point 2affected by the pond water and point 3 not affected by the treatedwastewater. Fig. 1 shows point location and samples taken in eachpoint. The whole area is a productive but fragile dryland agro-ecosystem set in a desert depression. The region produces date palms,other fruits (e.g., grapes), and vegetables (e.g., lettuce, carrots, tomatoes,cucumbers, and melons). The climate is characterized by hot and drysummers (average temperature 47 °C) and precipitations of 51 mmonly winter with temperatures ranging from −2 °C (night) to 22 °C(day). However, no precipitation was observed during the samplingperiod.

Samples were taken at the middle of February of 2017. Grab watersamples (2 L) were collected in clean polypropylene bottles, from themiddle of the channels width at ca. 25 cm depth. To take these samples,the presence of bridges near the crop fieldswas exploited. The samplingplan for soil involves a simple random sampling of each crop field fol-lowing the recommendations of the Swiss Agency for the Environment,Forests and Landscape (SAEFL, 2003). The soil was Calcic Fluvisol ac-cording to the FAO classification and its texture in the study area rangedbetween loamy fine sand and sandy loam. Five different samples wererandomly taken in each field separated 25–50 m depending on thefield extension. Soil samples of the upper 0–15 cm layer (taken into ac-count that soils were cultivated soils and that deepwas not toomuch insome areas)were collected from sampling points of 16m2 located in thesame field where crops were taken. In them, 5 sub-samples distributedrandomly were taken to get a composite sample. The composite

Fig. 1. Location of the wastewater treatment plant influencing water, sampling points and collected samples (WWTP: wastewater treatment plant, Loc: location).

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samples have an approximate weight of 1.5 kg to ensure homogeneity.Sampleswere collected in thick quality polyethylenebags. Crop samples(barley, cabbages, green beans, eggplants, tomatoes and zucchini) weretaken in the same 16m2 as soil. A composite sample of 10 plants of eachcommodity (including roots always that possible) randomly distributedwas also taken in thick quality polyethylene bags.

2.2. Sample extraction, clean-up and instrumental analysis

Water and soil samples were extracted using previously reportedand validated procedures (Carmona et al., 2017). Glass filtered watersamples (250 mL) were passed through of a cartridge Strata-X 33 UPolymeric Reversed Phase (200mg/6mL) from Phenomenex (Torrance,CA, USA) previously conditionedwith 6mLofmethanol and 6mLof dis-tilled water using vacuum (400 mbar hPa−1). Then, the cartridge was

dried under vacuum for 15 min. The analytes were eluted with 6 mLof methanol by gravity. The eluate was evaporated to dryness with aStuart air compressed evaporator and the residue reconstituted with250 μL ofmethanol–water (30:70, v/v). Lyophilized and sieved soil sam-ples (1 g d.w.) were added of 5 mL of methanol, 5 mL of distilled waterand 5 mL of McIlvaine-EDTA buffer. This mix was sonicated for 30 minand centrifuged for 6 min at 3000 rpm. The supernatant was dilutedto 200 mL with water in a volumetric flask and extracted by SPE aswater.

The method to extract crops was developed and validated for 40model compounds (Table 1). Vegetable samples (ca. 1 g w/w) werechopped, triturated, placed in a polypropylene tube of 15 mL and ex-tracted with 4 mL of methanol. Then, the tube was shaken, sonicatedfor 10min and centrifuged for 15min at 4000 rpm. This processwas re-peated three times, and the extracts were mixed obtaining a volume of

Table 1Lowest calibration level (LCLs), average recovery (%) and repeatability as RSD (in parenthesis) of three replicas of the trial (n= 3), obtained for target analytes after the application of thedeveloped method to barley and cabbage samples spiked at two concentration levels, matrix effect (ME %) and linearity.

Compound Cabbage Barley

LCLs (ng g−1) R (%) (RSDs) n = 3 ME (%) R2 LCLs (ng g−1) R (%) (RSDs) n = 3 ME (%) R2

25 ng g−1 250 ng g−1 25 ng g−1 250 ng g−1

Acid pharmaceuticalsChloramphenicol 25 71 (16) 78 (11) −49 0.993 25 71 (22) 80 (12) −24 0.990Chlofibric acid 20 72 (14) 77 (10) −25 0.991 20 75 (11) 83 (12) −20 0.998Diclofenac 20 74 (11) 82 (10) −33 0.992 25 82 (11) 85 (10) −28 0.994Flufenamic acid 15 75 (15) 82 (12) −18 0.996 10 78 (12) 83 (9) −15 0.997Gemfibrozil 20 71 (12) 81 (11) −55 0.994 20 76 (15) 80 (9) −52 0.995Ibuprofen 25 53 (18) 55 (15) −65 0.990 25 57 (19) 59 (15) −56 0.992Naproxen 25 72 (12) 79 (10) −38 0.992 25 75 (16) 78 (13) −26 0.992Salicylic acid 10 45 (17) 49 (14) −65 0.992 15 51 (18) 58 (16) −58 0.990Triclocarban 15 70 (15) 74 (12) −39 0.995 20 74 (16) 88 (9) −26 0.996Triclosan 15 65 (13) 71 (14) −59 0.994 20 76 (16) 86 (10) −37 0.992

Basic pharmaceuticalsAzithromycin 25 57 (18) 58 (15) −60 0.992 15 71 (14) 74 (10) −68 0.997Carbamazepine 10 92 (15) 89 (12) −22 0.998 5 93 (14) 94 (9) −17 0.999Clarithromycin 20 56 (19) 55 (15) −72 0.990 15 73 (16) 72 (12) −65 0.992Diazepam 20 75 (15) 78 (11) −37 0.991 15 82 (13) 91 (11) −31 0.991Fenofibrate 5 87 (16) 90 (10) −22 0.991 5 83 (14) 87 (10) −18 0.993Ofloxacin 20 82 (16) 91 (10) −59 0.994 20 83 (12) 89 (11) −33 0.996Paracetamol 20 45 (20) 50 (17) −55 0.996 20 48 (16) 52 (12) −62 0.998Sulfamethoxazole 15 54 (19) 58 (15) −35 0.995 10 74 (16) 76 (14) −40 0.996Telmisartan 15 59 (21) 55 (16) −22 0.992 5 53 (18) 57 (15) −22 0.995Trimethoprim 15 40 (17) 49 (18) −37 0.992 10 71 (19) 71 (14) −27 0.997

PesticidesAcetamiprid 10 85 (11) 86 (10) −30 0.999 10 88 (13) 80 (11) −25 0.999Atrazine 15 72 (12) 74 (8) −41 0.997 10 78 (12) 81 (12) −34 0.992Buprofezin 10 75 (14) 80 (11) −35 0.996 5 79 (12) 82 (8) −35 0.997Carbaryl 10 79 (15) 84 (10) −30 0.998 5 81 (14) 86 (12) −20 0.998Carbendazim 20 78 (13) 79 (10) −55 0.999 15 77 (16) 81 (10) −52 0.999Chlorpyrifos 10 81 (16) 85 (12) −52 0.996 10 80 (15) 90 (10) −53 0.997Coumaphos 5 74 (14) 81 (12) −15 0.995 5 79 (14) 81 (11) −15 0.996Deethylatrizine 15 73 (15) 82 (11) −65 0.994 10 76 (14) 79 (11) −62 0.995Deethylterbuthylazine 15 71 (17) 83 (13) −63 0.992 10 75 (13) 77 (10) −58 0.993Diazinon 15 73 (16) 79 (12) −15 0.997 15 74 (16) 79 (12) −20 0.998Dimethoate 20 76 (15) 83 (10) −35 0.998 20 80 (15) 87 (9) −25 0.998Diuron 25 75 (16) 79 (10) −68 0.992 25 84 (16) 92 (10) −59 0.991Ethion 25 79 (16) 85 (11) −30 0.995 25 85 (16) 92 (13) −40 0.996Hexythiazox 10 84 (14) 89 (12) 8 0.999 10 87 (17) 95 (9) −15 0.999Imazalil 20 69 (25) 67 (19) 25 0.991 20 63 (18) 64 (19) −10 0.992Imidacloprid 15 88 (12) 91 (11) −12 0.998 15 87 (15) 89 (15) −30 0.999Methiocarb 15 82 (12) 85 (10) −35 0.994 15 81 (14) 88 (13) −22 0.994Molinate 20 72 (13) 76 (11) −16 9.998 20 74 (13) 77 (10) −27 0.998Omethoate 15 74 (16) 79 (12) −33 0.998 15 81 (16) 80 (10) −38 0.998Terbuthylazine 20 81 (14) 87 (12) −61 0.994 20 88 (15) 90 (12) −52 0.995

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approximately 12 mL that was evaporated to 4 mL diluted to 200 mLwith Milli Q water. The supernatant was diluted to 200 mL with waterin a volumetric flask and extracted by SPE as water.

ECs in the extractswere determined as previously reported (Andrés-Costa et al., 2016). First, they were separated by reversed-phase chro-matography using an ultra-high performance liquid chromatography(UHPLC) system (model 1290; Agilent Technologies Inc., Waldbronn,Germany) consisting of a quaternary pump, a thermostatted columncompartment, and an autosampler. In positive ionmode, mobile phasesA and B were 0.1% formic acid in water and acetonitrile, respectively.Chromatographic separation was performed at a flow rate of 450μL min−1 using a UHPLC column (Acquity HSS T3, 1.8 μm, 2.1 mm× 100 mm;Waters Corp., Milford, MA) maintained at 45 °C. In negativeion mode, mobile phases A and B were 2.5 mM ammonium fluoride inwater and methanol, respectively. Chromatographic separation wasperformed at a flow rate of 200 μg mL−1 using the same column. Gradi-ent to separate the compounds was the same: 0–12 min, 30–95% B;12–18 min, 95% B; 18–19 min, 95–30% B; 12 min, 30% B.

SecondUHPLC eluent goes through a Duo Spray ion source (ABSciex,Warrington, UK) to mass spectrometric analysis performed with a Q-TOF MS (Triple TOF 5600+, AB Sciex). The instrument was operated

at a mass resolution of ∼30,000 for TOF MS scan (at m/z 300) and∼15,000 for product ion scan in the high sensitivity mode, and automat-ically calibrated every 10 sample injections using APCI positive or nega-tive (depending on the mode) calibration solution delivered via acalibration delivery system. The other experiment parameters wereset as follows: curtain gas, 30 (arbitrary units); ion source gas 1, 50 (ar-bitrary units); ion source gas 2, 50 (arbitrary units); temperature, 500°C; ion spray voltage floating, 5.5 kV; declustering potential, 80 V (thevoltages could be positive or negative depending on the ionizationmode.

HR-MS and HR\\tandemmass spectrometry (MS/MS) data for eachsample were acquired by information dependent acquisition. The IDAmethod (cycle time 750 ms) was composed of a TOFMS scan (accumu-lation time, 100ms; CE, 10 V) and 6 dependent product ion scans (accu-mulation time, 100 ms each; CE, 45 V with a spread of 35 V) in the highresolution mode with dynamic background subtraction. Data acquisi-tion and processing was carried out using software Analyst 1.7 (Fra-mingham, MA, USA), Peak View 1.2 with the application XIC managerand MultiQuant 2.0. The compounds were identified against a databasethat contains N500 pesticides and N2000 pharmaceuticals and othercompounds both in positive and negative ionization modes.

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2.3. Performance parameters of the methods and quality control

The performance parameters evaluatedwere linearity andmatrix ef-fect (ME), trueness (in terms of bias expressed as relative recovery),precision (expressed as relative standard deviation (RSD) in terms ofintra-day and inter-day precision) and sensitivity (as lowest calibrationlevel (LCL). As there are no specific guidelines for the determination ofemerging contaminants in plants, the SANTE Guidance document onanalytical quality control and method validation procedures for pesti-cide residues and analysis in food and feed was considered as a refer-ence (SANTE/11813/2017, 2017). Cabbage (as representative of highwater content sample) and barley (as representative of dry samples)to establish the performance parameters according to the guidanceSANTE/11813/2017 were acquired in an organic shop to ensure atleast lower levels of model compounds. A previous analysis was per-formed in order to ensure that they did not contain the target analytes.These samples were selected as a blank for validation purposes and forpreparing matrix matched calibration solutions.

The linearitywas studied using thematrix-matched calibration solu-tions prepared in blank extracts of cabbage and barley at seven concen-trations, ranging from 1 to 250 ng g−1. The peak area was selected asresponse. Satisfactory linearity was assumed when the determinationcoefficients (r2) were higher than 0.990.

ME was studied comparing the slope of the calibration curves pre-pared in pure solvent and in matrix, according to (1):

ME% ¼ Slope of the calibration curve in matrixSlope of the calibration curve in solvent

−1� �

� 100 ð1Þ

The negative values of ME indicate a signal suppression effect andthe positive ones, a signal enhancement. ME b 30% is considered lowME, between 30% and 50% moderate ME and N50% a strong effect.

For recovery studies, spiked samples of the twomatrices testedwereprepared by adding 100 μL of the solutions of a mixture of all theanalytes (at 250 and 2500 ng mL−1 each) in methanol to a portion of1 g of sample previously chopped and homogenized, and then the mix-ture was blended for 15 min prior to extraction. Recoveries were calcu-lated using the response (peak area) detected for the spiked samplesand compared with the response of matrix extracts spiked at the sameconcentration level after the extraction process. Recoveries were evalu-ated in triplicate (n = 3) at the 2 levels: 25 and 250 ng g−1, for eachcompound/matrix combination to provide information on analyticalperformance over a range of concentrations.

Method precision, determined as relative standard deviation (RSD,%),was calculated from the results of the recovery study. The lowest cal-ibration level (LCL) tested was the concentration of each target analytethat gives approximately an intensity ≥ 1.0 × 104 counts, as recom-mended elsewhere for LC-QqTOF-MS and orbitrap-MS (Andrés-Costaet al., 2016). LCL is defined in the guidance (SANTE/11813/2017,2017) as the lowest concentration (or mass) of the analyte withwhich the analytical system is successfully calibrated. It is equal orhigher to the limit of quantification. It is used when samples are moni-toring and surveyed over long periods of time. The LCL was determinedanalyzing by LC-QqTOF-MS extracts of spiked samples at these esti-mated concentration concentrations.

Water and soil extractionmethods were already published (Andrés-Costa et al., 2016; Carmona et al., 2017). Performance parameters ofthese methods were established following a laboratory procotol inagreement with several guidances including (SANTE/11813/2017,2017). The mean recoveries of target analytes in matrix spikes were56%–117% and 30%–121% for water and soil, respectively. The relativestandard deviations (RSDs)were b20% for all target analytes in the trip-licate samples. The LCLs were 10–25 ng L−1 in water and 15–50 ng g−1

d.w. for soils.A strict quality control was followed through the analytical proce-

dure. Field blanks (only forwater) andmethod blanks (water, sediment,

barley and cabbage) were treated identically to the actual samples inorder to avoid false positive samples. These signals of these blankswere subtracted from the signal of the chromatogram. Although quanti-ficationwas carried out using external calibrationwithmatrix-matchedstandards, 100 μL of a mixture of Ibuprofen-d3, acetaminophen-d3 andcarbamazepine-d2 at 500 ng mL−1 (internal standards, ISs from CDNIsotopes, Quebec, Canada)was added to the samples as labelled internalstandard. The area of these peaks was monitored to control extractionefficiency and avoid false negative. Furthermore, for each batch of 10samples of the same matrix analysed, the water field blank (only forwater), a method blank (of the same sample as the batch) and positivecontrol (for the matrix of the batch) obtained by spiking the matrix atthe LCLs, were routinely extracted and analysed under the same condi-tions as the ordinary samples. Samples were analysed in triplicate sam-ples and were only considered of the results were within 25%agreement.

3. Results and discussion

3.1. Analytical method optimization and validation

Methods previously used to extract ECs from plants have two mainsteps: extraction and clean-up. For the extraction, acetonitrile andmethanol have mostly been the extraction solvents of choice (DiBaccio et al., 2017; Martínez-Piernas et al., 2018; Montemurro et al.,2017; Picó et al., 2018a; Riemenschneider et al., 2016). Within thismethod several buffers, acids and salts can be added to control the pHand the ionic strength. The solvent (methanol or acetonitrile), pH (3,5, non-buffered) and the addition of salts (NaCl 20%) were tested.Analytes such as chloramphenicol, chlofibric acid, diclofenac,flufenamicacid, gemfibrozil, ibuprofen, naproxen, salicylic acid, sulfamethoxazole,triclocarban and triclosan presented slightly better recoveries using atacid pH, probably as due to the protonation of the acidic group. How-ever, as could be observed in Fig. 2 these differences are b20% whereasbasic pharmaceuticals are much better recovered at neutral pH. Phar-maceuticals yield is much more affected by pH than pesticides. No evi-dent differences in the recovery were observed between acetonitrileand methanol and with the addition of salt. Based on these results, theextraction with non-buffered methanol was selected for furtherexperiments.

The clean-up can also dramatically affect themethod's performance.Two main formats have been used: SPE and dispersive SPE (dSPE). Theformerwas performed as established in the experimental section. In thelatter, various combinations of sorbents have been recommended(Carmona et al., 2017; Martínez-Piernas et al., 2018). Commonly, inthese systems primary secondary amine (PSA) is used to remove fattyacids, polar organic acids, polar pigments and sugars, C18 for nonpolarinterferences, such as lipids, graphitized carbon black (GCB) for pig-ments like chlorophyll and steroids. The sorbents selected in thisstudy were a mix of GCB and PSA (1 mL of extract, 80 mg C18, 100 mgPSA) because cabbage and barley are rich in pigments but have lowlipid content (ca. 0.1%). Overall, the Strata X SPE showed better recover-ies in both matrices and cleaner extracts than dSPE. The differenceswere higher in the case of cabbage because the SPE eliminates more ef-fectively chlorophylls. However, for some compounds, such as paracet-amol or salicylic acid. Among them, salicylic presented recoveries forbothmatrices in a range from 70 to 75% (RSD ≤ 20%)with dSPEwhereasrecoveries were not higher than 54% with SPE. The same behavior wasfound for paracetamol, with recoveries of 80% (RSD ≤ 20%) by dSPE, incomparison with the values of 46% and 56% achieved by SPE. Finally,the clean-up step with SPE was selected since recoveries were betterfor the other compounds, extracts were cleaner and MEs lower.

Validation results obtained are shown in Table 1. The recoveries forthe model compounds taken as example were calculated as describedin the experimental section. Taking into account the large amount ofcontaminants under study and their different properties, the recoveries

Fig. 2. Recoveries obtained using methanol and methanol acidified (pH 3) for (A) pharmaceuticals and (B) pesticides.

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obtained were satisfactory. A total of 30 compounds out of 40 (75%) incabbage and 34 (85%) in barley presented recoveries within the rangeof 70–120% with RSDs ≤ 20% in the two concentration levels tested. Ingeneral, RSDs values b20% were obtained in the two matrices studiedregardless the recovery level, which proves the repeatability of themethod. Only paracetamol in barley and telmisartan and imazalil in cab-bage showed RSDs N 20% at the 25 ng g−1 spiking level, thus demon-strating good performance of the method.

Ibuprofen and salicylic yielded poor recoveries (b60%) in the twomatrices at any concentration. Triclosan showed recoveries between65 and 86%. For azithromycin and clarithromycin (aminoglycosides),

sulfamethoxazole and trimethoprim the recoveries were b60% in cab-bage, while they were properly recovery in barley (N70%). Differently,paracetamol and telmisartan showed poor recoveries in both cases(45–59%). All pesticides provided recoveries N70%, with the exceptionof imazalil that showed recoveries between 62 and 69%. The interactionof compounds with different physical-chemical properties in commod-itieswith varied compositions can lead to different results, one of the is-sues not enough studied yet is the effect of several viable enzymespresent in the plant. This could be achieved validating the analyticalmethod for each matrix analysed. However, validation involves hardwork and its time consuming. In this case, the guidance of the

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European Union (SANTE/11813/2017, 2017) classify plant commoditiesaccording to their physic-chemical properties to simplify the validationstep.

Appropriate linearity was observed using matrix-matched calibra-tion curves for all compounds, with correlation coefficients (R2) N 0.99in all cases. In barley, the 70% of the contaminants have a ME in low(47.5%) andmedium (22.5%) ranks but in cabbage, a 65%. Furthermore,the number of compounds that presented lowME (27.5) ismuch lower,thus evidencing the greater complexity of thismatrix, probably as a con-sequence of thehigher chlorophyll content thatmakes the SPE purifyingstep not so effective. In any case, a total of 65% of the compounds in cab-bage show low/medium ME. As shown in Table 1, signal suppressionwas the effect most commonly found for all matrices, only the 5% ofthe compounds showed an enhancement effect.

The LCLs determined were in the range of 2–25 ng g−1 for the twomatrices. Some examples of the extracted ion chromatograms as wellas the MS and MS/MS for cabbage spiked at 25 ng g−1 are given in theSupplementary material (Fig. S.1). These values are directly comparableto those of other authors thatworkwithhigh resolutionmass spectrom-etry either, orbitrap or QqTOF (Di Baccio et al., 2017; Picó et al., 2018a)and slightly higher (less than one magnitude order) of those workingwith triple quadrupole instruments (Martínez-Piernas et al., 2018;Montemurro et al.; Riemenschneider et al., 2016). Slightly worse LCLsare compensated by the wider range of ECs determinable.

3.2. Presence of contaminant in wastewater irrigated crops

The proposed method was applied to the analysis of barley, cab-bages, green beans, chili, tomatoes and zucchini crops grown in fields ir-rigated with treated wastewater taken at 4 different sampling pointsand with different unknown agrochemical treatments. The irrigationwater and the soils where the crop grew were also analysed. Com-pounds considered as tentatively identified were those that provideda MS/MS mass spectra that match that of analytical standard (whenavailable) or that experimental or theoretical recorded in any free data-base of MS/MS spectra (Metlin, ChemSpider, Pubchem, ChEBI, etc.). Theresults showed that the crops were exposed to a total of 53 out of the3500 ECs included in the database used (both ionization modes) (seeTable 2). Atenolol, atrazine-deethyl, caffeine, carbamazepine andnaproxen have been reported in four effluent wastewaters located inthewestern of the country (Alidina et al., 2014), two effuents of hospitalwastewater treatment plants located in Riyadh (Al Qarni et al., 2016)and in effluent-dominated Saudi Arabian coastal waters of the Red Sea(Ali et al., 2017). Clarithromycin was also found in hospital wastewater treatment plant effluent (Al Qarni et al., 2016). The concentra-tions of those that were possible to quantify (standard available in thelaboratory) ranging from 10 to 1933 ng L−1. The concentrations of thecompounds found in effluents in the previous studies were of thesame order of magnitude that those found in this one (Alidina et al.,2014; Al Qarni et al., 2016; Ali et al., 2017). If the analytical standardwas not available, the area under the peak was relativized in a numberof “+” signs in order to give an idea of the abundance. Some examples ofchromatograms,MS andMS/MS of the identified compounds are shownin the supplementary material (Fig. S2).

Furthermore, it is remarkable thatwater samples also present a largenumber of peak that provided themolecular ion but not MS/MS spectrathat could be identified by assigning theirm/z values to the most prob-able empirical formula. These peaks were not considered because inthose cases is very easy to make mistakes in the identifications. Consid-ering the number of compounds as well as their concentration, waterfrom sampling point 1 was the most contaminated, indicating clearlyan additional source of contamination in comparison with the othersamples.

The compounds found in the soils and crops irrigatedwithwastewa-ter treatment are summarized in Table 3. In the case of barley, separateanalyses on tails and rootswere carried out. The plants collected already

reached a representative size that allow to assess the potential accumu-lation in the crop.

Diazinon, atrazine deethyl, malathion, azoxystrobin, bupirimate,acetamiprid, imidacloprid and pirimicarbwere found in any of the sam-pling points. These pesticides arrive towater from the sourrounding soiland cultivars, as pointed out that concentrations are higher in thesourrounding soil and crops. It deserves remark the high amount ofimidacloprid found in soil from point 3, where not only imidaclopridbut also theirmetabolites 5-hydroxy imidacloprid and imidacloprid ole-fin were identified. As an example of the accurate quantification Fig. 3.shows results of identification for 3-hydroxy-imidacloprid, ametabolitelittle report in the studies. The parent pesticide has been used in to thetreatment of chili plants and their high concentration in soil can be ex-plained because it can be applied by soil injection and ground applica-tion as a granular or liquid formulation. Chlorfenvinphos was detectedin soil of point 2 but not in vegetables or water. This pesticide can alsobe applied directly to soils. Only in the sample of barley from the site4, pesticides were not detected. The lack of information on the treat-ment and the time remaining until the harvest of the vegetablesmakes little suitable to assess hazards derived from its consumptionsince these residues could dissipated before vegetables reach the con-sumer. Two previous studies on the presence of pesticides in vegetablesin Al-Qassim region, Saudi Arabia that is located near here alreadyshowed the presences of several pesticides (Osman et al., 2010;Osman et al., 2011). These studies were performed 7–8 years before,comparing the pattern of pesticides, it could be observed that organo-phosphorus pesticides (e.g. diazinon and malathion) are widely usedsince then. However, organochlorine compounds and carbamateswidely reported in the previous studies are less used nowadays beingdisplaced by newer types of pesticides, such as neonicotinoids andstrobilidium derivatives The impact of agricultural practices in the sur-face water has been widely reported in other countries (Campo et al.,2013; Jampani et al., 2018; Masiá et al., 2015; Metcalfe et al., 2019;Noorka and Heslop-Harrison, 2015; Pascual Aguilar et al., 2017). Ourstudy agree with previous results pointing out agricultural practices asmain sources of pesticides.

On the pharmaceuticals carbamazepine, 10,11-epoxycarbamazepine, caffeine, atenolol, gemfibrozil, naproxen and ibu-profen hexoside were identified in few vegetables at concentrationsranging from 35 to 125 ng g−1. Most contaminated vegetable was cab-bage from site 1. This could be expected becausewater on this site pres-ent the highest number of contaminants at the higher concentrations.Only caffeine, carbamazepine, gemfibrozil and atenolol considered asrecalcitrant were detected in soil but at lower concentration (up to96 ng g−1). This can be justified by a rapid degradation in soil thatcould be because of the physico-chemical conditions as the high tem-peratures that suffers the area aswell as biological since active degrada-tion by microorganisms can occur. Contrarily, metabolites as 10,11-epoxycarbamazepine and ibuprofen hexoside were only detected inplants. Fig. 4 shows results of the identification of 11,12-carbamazepine.Other identifications in plants are shown on the supplementary mate-rial (Fig. S4). Of the compounds found in this study, atenolol, carbamaz-epine and caffeine were already reported in crops irrigated withwastewater under controlled conditions for 3 and 1.5 months at con-centrations ranging from 0.03 to 57.6 ng g−1 (Martínez-Piernas et al.,2018). These authors used a more conventional target method that at-tains more sensitivity but it is restricted to target compounds, amongwhich metabolites were not. Our method has a wide scope because itis able to identify an unlimited number of compounds.Riemenschneider et al. (2016) were up to know the only authors thatdemonstrated the uptake of ECs and carbamazepine metabolites uponfield irrigation with treated municipal wastewater by crop and report10,11-epoxycarbamazepine as one of the most abundant compounds.The study was performed in the Zarqa River (Jordan), whose majorsource of water is the effluent of a wastewater treatment plant. This isa neighbouring area to the selected in our study.

Table 2ECs identified, at least by comparison of the MS/MS in a free database, in treated wastewater or treated wastewater mixed with water used for crop irrigation (concentrations calculatedusing analytical standards, compoundsmarked with * means that method was not validate for them, symbol + gives an idea of the relative intensity of the peak, rows shadowed in greymeans pesticides).

Compound Empirical formula Concentration ng L−1

W1 W2 W3 W4 4-Hydroxybenzaldehyde C7H6O2 ++ ++ ++ ++

5,6-Dehydro arachidonic acid C20H30O2 ++ ++ ++ ++

5-Aminosalicylic acid C7H7NO3 +++ +++ +++ +++8-Hydroxyquinoline C9H7NO ++++ N.D. ++++ ++++

Acetamiprid C10H11ClN4 N.D. N.D- 325 25

Adenosin C10H13N5O4 ++++ +++ ++++++ ++++ Atenolol C14H22N2O3 850 90 102 120 Atrazine-deethyl C6H10ClN5 N.D. N.D. 15 81.2

Azoxystrobin C22H17N3O5 25 124 800 600

Bisoprolol* C18H31NO4 118 N.D. N.D. N.D.

Bupirimate C13H24N4O3S N.D N.D. 251 N.D.

Caffeine C8H10N4O2 1406 636 125 1548

Captodiame C21H29NS2 +++ N.D N.D. N.D

Carbamazepine C15H12N2O 1933 25 N.D. 52 Carbamazepine 10,11-

Cedixope 15H12N2O2++ N.D N.D N.D

Clarithromicyn C38H69NO13 875 N.D. N.D. N.D

Climbazole C15H17ClN2O2 ++++ N.D. N.D. N.D.

Cotinine* C10H12N2O 1042 125 N.D. N.D. Crotetamide C12H22N2O2 +++ N.D. N.D. N.D.

Cryptopimaric acid C20H30O2 +++ N.D. N.D. N.D.

Dapiprazole C19H27N5 +++ N.D. N.D. N.D.

Demecarium C32H52N4O4 +++ N.D. N.D. N.D.

Denaverine C24H33NO3 +++ N.D. N.D. N.D.

Diazinon C12H21N2O3PS 384 57 25 152 Dicycloverine C19H35NO2 ++++ ++++ ++++ ++++

Dorzolamide C10H16N2O4S3 N.D N.D +++ N.D

Ecgonina methyl-ester* C10H17NO3 50 N.D N.D N.D

Eicosapentaenoic acid C20H30O2 N.D N.D ++++ N.D

Eprosartan* C23H24N2O4S 80 N.D N.D. N.D

Fenoxazoline C13H18N2O +++ N.D N.D N.D

Fexofenadine C32H39NO4 +++ ++ N.D +++ Gemfibrozil* C15H22O3 255 323 452 58

Guafecainol C16H27NO4 N.D N.D ++++ N.D

Hexamidine C20H26N4O2 +++ + N.D +++

Hydroxychloroquine C18H26ClN3O +++ N.D N.D N.D

Imidacloprid C9H10ClN5O2 25 N.D 1354 N.D

Imiquimod C14H16N4 N.D N.D N.D +++

Isopimaric acid C20H30O2 N.D N.D N.D +++

Malathion C10H19O6PS2 10 N.D N.D 10 Mianserin C18H20N2 ++ N.D N.D N.D

Morphine* C17H19NO3 22 N.D. N.D. N.D N,N-Diethyl-m-toluamide C12H17NO N.D N.D +++ N.D

Nabumetone C15H16O2 N.D N.D +++ N.D Nandrolone C18H26O2 N.D N.D +++ N.D Naproxen C14H14O3 252 N.D. N.D. N.D.

Nisoldipine C20H24N2O6 N.D N.D ++++ ++++ Ochratoxin A C32H39NO4 +++ N.D N.D N.D

Oxprendol* C15H23NO3 95 N.D N.D. N.D Pirimicarb C11H18N4O2 102 N.D N.D N.D Pseurotin A C17H24O4 +++ +++ +++ +++

Salbutamol C13H21NO3 ++++ N.D N.D N.D

Telmisartan C33H30N4O2 0 N.D N.D N.D

Terbuthylazine C9H16ClN5 87 20 21 22 Timolol C13H24N4O3S N.D N.D +++ +++ Toliprolol C13H21NO2 +++ N.D N.D N.D

Valsartan* C24H29N5O3 307 25 0 106

569Y. Picó et al. / Science of the Total Environment 652 (2019) 562–572

The hydroxyibuprofen hexoside found in this study was reported inlaboratory studies that involve the growth of the plant in pots for twospecies Lemma gibba and Vigna unguiculata (Di Baccio et al., 2017; Picóet al., 2018a). This metabolite is a Phase II metabolite form by conjuga-tion of the ibuprofen with monosaccharides of the plants. Up to ourknowledge, this metabolite was not previously reported under real en-vironmental field conditions.

The accumulation of carbamazepine, caffeine and atenolol could beexplained because all of them are neutral on a wide range of pH then,they pass cellmembranes easily than ionic compounds. On the contrary,

cell membranes are a barrier to the pass of ionic compounds. Ibuprofen,gemfibrozil and naproxen are ionic at neutral pH. However, their pres-ence in plants could be explained because some pH in the plants differsdepending on the organs. Then, pH could reach values of 4 at whichthese compounds would be mainly in the neutral form.

The selected crops are for human consumption. However, the con-centrations are so low in comparison with the therapeutic dose thatconsidering the current knowledge they do not represent a hazard forthe human being. However, more studies focus on chronic effects andon the effects of complex mixtures of contaminants would be needed.

Table 3ECs found in crops irrigated (concentrations calculated using analytical standards, symbol + gives an idea of the relative intensity of the peak).

Compound Empirical formula Concentration (ng/g)

Loc 1 Loc2 Loc3 Loc4

Soil Plant Soil Plant Soil Plant Plant

10,11-epoxycarbamazepine C15H12N2O2 N.D. ++ - Cabbage N.D. N.D. N.D. N.D. N.D.5-Hydroxy-imidacloprid C9H10ClN5O3 N.D. N.D. N.D. N.D. +++ ++ - Chili Plant N.D.Acetamiprid C10H11ClN4 N.D. N.D. N.D. N.D. 366 54,775 - Chili Plant N.D.Atenolol C14H22N2O3 N.D. N.D. 39 55 - Cabbage N.D. N.D. N.D.Atrazine deethyl C6H10ClN5 N.D. N.D. 125 42 - Eggplant N.D. N.D. N.D.Azoxystrobin C22H17N3O5 N.D. N.D. N.D. N.D. 282 892 - Chili Plant N.D.Bupirinate C13H24N4O3S N.D. N.D. N.D. N.D. 125 250 - Zucchini N.D.Caffeine C8H10N4O2 96 125 - Cabbage 25 48 - Cabbage N.D. N.D. N.D.Carbamazepine C15H12N2O 54 N.D. N.D. N.D. N.D. N.D. N.D.Chlorfenvinphos C12H14Cl3O4P 102 N.D. N.D. N.D. N.D. N.D. N.D.Diazinon C12H21N2O3PS 182 1204 -Barley root 172 35 - Eggplant

50 - Green Beans148 250-Tomato N.D.

Gemfibrozil C15H22O3 58 75 - Cabbage 38 45 - Green Beans34 - Eggplant72 - Cabbage

N.D. N.D. N.D.

Hydroxyibuprofen hexoside C19H28O8 N.D N.D. N.D. ++ - Eggplant N.D. N.D. N.D.Imidacloprid C9H10ClN5O2 108 425 - Cabbage N.D. N.D. 5650 2130 - Chili Plant

2531 - TomatoeN.D.

Imidacloprid olefin C9H8ClN5O2 N.D. N.D N.D N.D ++ N.D. N.D.Malathion C10H19O6PS2 25 1000 - Cabbage 320 125 - Cabbage

84 Chili Plant140 85 - Tomatoe N.D.

Naproxen C14H14O3 N.D. 38 - Cabbage N.D. N.D. N.D. N.D. N.DPirimicarb C11H18N4O2 N.D. N.D N.D. 98 - Cabbage N.D. N.D. N.D.

570 Y. Picó et al. / Science of the Total Environment 652 (2019) 562–572

4. Conclusions

A generic multi-residue method able to perform and exhaustiveextraction of pesticides, pharmaceuticals and personal care productshas been developed and validated for 50 model compounds in two

Fig. 3. Identification of 3-hydroxyimidacloprid

vegetable matrices (cabbage and barley). Furthermore, the use ofHR-MS allows to detect many other substances present in thesamples. The method is easy to apply and provides goodperformance characteristics in terms of linearity, sensitivity,accuracy and precision.

in soil (chromatogram, MS and MS/MS).

Fig. 4. Identification of 10, 11-epoxide carbamazepine in cabbage (chromatogram, MS and MS/MS).

571Y. Picó et al. / Science of the Total Environment 652 (2019) 562–572

The analysis of several crops irrigated with treated wastewater ortreated wastewater impacted water to estimate the potential effect ofpharmaceuticals in crops and assess the impact of pesticide residuesunder agricultural conditions have shown to provide valuable results.The assessment of cases (as that of Saudi Arabia) where these practicesare already routine implemented about the fate of these analytes in theenvironmentwill help us to determine the possible consequences of thereuse of wastewater for irrigation.

Results obtained in the study revealed the potential uptake and accu-mulation by crops of carbamazepine (as 10,11-carbazepine epoxide), aten-olol, caffeine, gemfibrozil and ibuprofen (as ibuprofen hexoside).Furthermore, this study also show the presence of several pesticides, suchas malathion at high concentrations. These data highlight the necessity ofhaving broad-spectrum analytical methods that allow a comprehensiveevaluation of the exposure of crops to the multiple chemical substancespresent in the irrigation water or used in the agricultural practices.

Further studies are needed to obtain a better estimate of the range ofchemical contaminants that can be present in crop under different cul-tivation condition as well as the public health risk that may arise notonly from the presence of contaminants and residues but also fromthe exposure to mixtures of substances or their metabolites, whichcould produce different toxic effects.

Acknowledgements

The financial support from the Distinguished Scientist FellowshipProgram (DSFP) from King Saud University, Saudi Arabia is gratefullyacknowledged. R. Álvarez acknowledge the SpanishMinistry of Science,Innovation and Universities for his FPI grant BES-2016-078612. Wethank to the mass spectrometry section of the Central Services of Sup-port to the Experimental Research (SCSIE) of theUniversitat deValènciafor providing us access to the Linear QTOF (Applied Biosciences) and toDr. Sales Galletero for her help.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2018.10.224.

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