Dynamics of chemical microcontaminants in peri-urban agriculture and evaluation of their potential impact on crops and human health

Universitat Politècnica de Catalunya Departament d’Enginyeria Agroalimentària i Biotecnologia Programa de Doctorat Tecnologia Agroalimentària i Biotecnologia Tesi Doctoral Dynamics of chemical microcontaminants in peri-urban agriculture and evaluation of their potential impact on crops and human health. Presentada per Anna Maria Margenat Mas Per a optar al títol de Doctora per la Universitat Politècnica de Catalunya Director Prof. Josep Maria Bayona i Termens (IDAEA-CSIC) Codirector Víctor Matamoros Mercadal (IDAEA-CSIC) Ponent Dr. Jordi Comas i Angelet (UPC) Dynamics of chemical microcontaminants in peri-urban agriculture and evaluation of their potential impact on crops and human health Anna Maria Margenat Mas ADVERTIMENT La consulta d’aquesta tesi queda condicionada a l’acceptació de les següents condicions d'ús: La difusió d’aquesta tesi per mitjà del repositori institucional UPCommons (http://upcommons.upc.edu/tesis) i el repositori cooperatiu TDX (http://www.tdx.cat/) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d’investigació i docència. No s’autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d’un lloc aliè al servei UPCommons o TDX. 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In the using or citation of parts of the thesis it’s obliged to indicate the name of the author. iii This Thesis was financially supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) through the national research project AGL2014-59353-R by the IDAEA through a predoctoral fellowship (BES-2015-075745). iv v Acta de qualificació de tesi doctoral Curs acadèmic: Nom i cognoms Programa de doctorat Unitat estructural responsable del programa Resolució del Tribunal Reunit el Tribunal designat a l'efecte, el doctorand / la doctoranda exposa el tema de la seva tesi doctoral titulada __________________________________________________________________________________________ _________________________________________________________________________________________. Acabada la lectura i després de donar resposta a les qüestions formulades pels membres titulars del tribunal, aquest atorga la qualificació: NO APTE APROVAT NOTABLE EXCEL·LENT (Nom, cognoms i signatura) President/a (Nom, cognoms i signatura) Secretari/ària (Nom, cognoms i signatura) Vocal (Nom, cognoms i signatura) Vocal (Nom, cognoms i signatura) Vocal ______________________, _______ d'/de __________________ de _______________ El resultat de l’escrutini dels vots emesos pels membres titulars del tribunal, efectuat per la Comissió Permanent de l’Escola de Doctorat, atorga la MENCIÓ CUM LAUDE: SÍ NO (Nom, cognoms i signatura) President/a de la Comissió Permanent de l’Escola de Doctorat (Nom, cognoms i signatura) Secretari/ària de la Comissió Permanent de l’Escola de Doctorat Barcelona, _______ d'/de ____________________ de _________ vi vii Acknowledgments Al llarg d’aquests tres anys han estat moltes les persones que de manera professional i/o personal han fet possible la realització d’aquesta Tesi. A totes elles, vull dedicar aquest treball i agrair la seva valuosa ajuda. En primer lloc, voldria expressar el meu profund agraïment als directors d’aquesta Tesi. Gràcies al Prof. Josep Maria Bayona per donar-me la oportunitat de realitzar el doctorat, pels coneixements transmesos, també pels valuosos comentaris, meticuloses revisions i suport al llarg de tot aquest temps. També vull agrair a en Dr. Víctor Matamoros per la seva total disponibilitat, per les continues idees i consells, i per donar-me els ànims necessaris per continuar endavant. Ambdós m’heu guiat en el procés i gràcies a la vostra dedicació ha estat possible la finalització d’aquest treball. Voldria agrair al Prof. Jordi Comas, tutor d’aquesta Tesi, per l’ajuda que m’ha dispensat al llarg d’aquests anys tant en qüestions professionals com en la gestió acadèmica. No podria faltar el meu agraïment al Dr. Sergi Díez, que també ha estat clau en aquesta Tesi aportant els seus coneixements i experiència amb els metalls. També a la Dra. Núria Cañameras per la seva ajuda en el treball i en les campanyes de mostreig. Gràcies a tots els meus companys de laboratori per tots els moments compartits, dies d’alegria i d’altres més estressants on sempre heu estat disposats a donar un cop de mà, formeu un marevellos grup. La meva gratitud a la Dra. Carmen Domínguez que sempre ha tingut la paciencia d’ajudar-me i m’ha endolçat molts dies amb els seus pastissos, ets una dona tot terreny tant en l’àmbit personal com en el laboral. També a la Yolanda pel seu suport en el laboratori i pels moments divertits que sempre dóna, part d’aquesta Tesi també és teva. A Sandra, la asturiana más salerosa, mil gracias por cuidarme y brindarme tu amistad, tu llegada fue como un soplo de aire fresco. A la Marta (Xaxi), dir-te que ets especial i contagies d’alegria als que estan al teu voltant. To Dorde, thanks for bringing many hilarious moments and having improved my English (part of my C1 certificate is due to you). To Rui, thanks for always wearing a smile and sharing your culture. A l’Edu, que vas venir pel TFG i et quedaràs fent el màster, molta sort i gràcies per l’alegria i l’interès posat en la feina. A la Dra. Inma Fernández, por brindarme tu apoyo y ser una más del grupo. A les més recents incorporacions que també fan que la experiència sigui molt agradable: Agnès, Laura i Alicia. També m’agradaria agrair a altres companys que han passat per un moment o altre pel grup: Yeidy, Igor, Giulia, Ale, Angela, Antía, Andrea y Carmen. També es mereixen un reconeixement especial les noies de Masses, Roser i Dori, que no han desistit d’arreglar-me l’equip tot i ser una àrdua feina, i sobretot pel tracte rebut. Agrair l’amistat i recolzament dels meus amics. Perdoneu per no posar un a un tots els noms, tot i que sou molts els que m’heu fet costat. Voldria destacar a les meves amigues de la infància Anna, Miriam, viii Ana, Mar i Marta per haver estat comprensives amb mi i haver-me acompanyat en aquest camí. També als meus amics de la carrera Adam, Agustí, Damià, Guillem, Lluís, Marc i Miki per tots els moments compartís. Em sento afortunada d’estar envoltada de tan bons amics. Finalment, el meu més especial i sentit agraïment a tota la meva família, que sou la part més important de la meva vida i la causa de ser qui sóc. Un record especial als meus avis, que sempre m’acompanyen i especialment a les meves àvies, Clemència i Paquita, que tan de bo haguéssiu pogut gaudir del final d’aquest camí. Als meus pares, Carme i Lluís, per la fe i suport incondicional que m’heu demostrat, sobretot quan en triar la carrera us vaig dir “o química, o res”. Al meu germà, Josep Lluís, gràcies per ser tan especial que dónes lliçons del veritable sentit de la vida, tan de bo pogués compartir aquesta celebració amb tu. Sempre hem sigut més que una simple família, formem un gran equip. Disculpeu-me si m’he oblidat de mencionar a algú. A tots, el meu més sincer agraïment. ix Abstract Peri-urban agriculture provides environmental, socio-economic functions and ecosystem services in the nearby urban areas. Nonetheless, crops grown in these areas are exposed to organic and inorganic contaminants from industrial emissions and road traffic, as well as the application of biosolids and the use of regenerated waters. In this regard, in the last years, there has been a growing concern about the presence of chemical contaminants in agricultural crops due to the evidence that plants are able to incorporate, translocate and accumulate them in their edible parts. Although the concentrations detected in food crops are generally low, little is known about the effects of these contaminants on human health. For this reason, field studies are necessary to properly evaluate their incorporation and potential risk to human health. Currently, there is no study that evaluates the exposure and presence of organic and inorganic contaminants simultaneously in agricultural crops in peri-urban areas. The few existing studies at field scale are based on the impact of regenerated water in agricultural crops, considering separately organic and inorganic contaminants. In this Thesis, it has been assessed the incorporation into plants in real field conditions of some trace elements and organic microcontaminants, chosen by their presence in the environment and their physicochemical properties. For this purpose, 4 agricultural parcels were selected located in the periurban area of Barcelona (NE Spain) and a rural agricultural plot far away from the periurban area, including different irrigation water quality and exposure to urban contamination. In this study, lettuce, tomato, cauliflower and beans were selected as a model plant (leaf, flower and fruit). The PhD dissertation is divided into six chapters. Chapter I gives an overview of the subject and presents the hypotheses and objectives of the PhD project. Chapter II assesses the occurrence of trace elements and organic microcontaminants in irrigation waters. Irrigation waters from peri-urban areas showed a higher abundance of the selected chemical contaminants than that water from the rural area. Nevertheless, none of the irrigation waters induced phytotoxic effects (seed germination, root elongation) or decrease crop productivity. Chapter III assesses the co-occurrence of these contaminants in soil and lettuce leaves, their bioaccumulation factors and how they affect lipid constituents and leaf sugars. The higher abundance of these contaminants in the irrigation waters and soils from the periurban area had no impact on the chlorophyll, carbohydrates and lipid content of lettuce leaves. Chapter IV shows the occurrence of the chemical contaminants in the edible parts of different model crops (lettuce, tomato, cauliflower and broad beans) and estimates the human health risk associated with their consumption. The results obained show that human health risks associated were low and similar among crops grown in peri-urban and rural areas. Chapter V is devoted to the general discussion of three previous chapters, whereas Chapter VI presents the main conclusions of the Thesis. xvi List of tables Table 1.1 Advantages and risks associated with the use of reclaimed water (Colon and Toor, 2016) . 30 Table 1.2. GRL values for metals and metalloids established in soils in Catalonia for protection of human health and ecosystems (Regional Decree 5/2017). .................................................................... 34 Table 1.3. Essential heavy metals for plants (Barker and Pilbeam, 2007; McCauley et al., 2011; Mengel and Kirby, 2004) ...................................................................................................................... 49 Table 1.4.Harmful effects of the chemicals studied on human health with oral exposure according to different sources (Ali et al., 2013; ATSDR, 2018; EPA, 2018; Fenner et al., 2013b; Pereira et al., 2015; World Health Organization, 2003) .............................................................................................. 54 Table 1.5. Anthropogenic sources of selected inorganic contaminants in the environment ................. 57 Table 1.6. Classification of organic analytes ......................................................................................... 58 Table 1.7. Summary of the physical-chemical properties of the selected organic contaminants .......... 61 Table 1.8. Occurrence of the selected OMCs in the aquatic environment ............................................ 62 Table 1.9. Occurrence of the selected contaminants in vegetable tissues ............................................. 63 Table 2.1 Minimum, maximum and average levels of general quality parameters in the studied irrigation waters. Levels below the LOD were replaced by ½ LOD. .................................................... 73 Table 2.2 Frequency of detection (FOD), minimum, maximum and average concentration of metals and metalloids in the water irrigation from the different studied plots. The threshold levels of trace elements for crop production of the Spanish Royal (SRD, 2007) and the Food and Agriculture Organization (FAO, 1985) are shown. Levels below the LOD were replaced by ½ LOD. .................. 74 Table 2.3 Frequency of detection (FOD), minimum, maximum and average concentration of CECs in the studied irrigation waters. Levels below the LOD were replaced by ½ LOD for the calculation of the average concentration. ..................................................................................................................... 76 Table 2.4 Variance explained and loadings for the two PCAs. ............................................................. 77 Table 2.5 Effect of irrigation waters on in vitro seed germination of lettuce (n=100). ......................... 80 Table 2.6 Fresh weight per unit, crop growing time and productivity for lettuce and tomatoes in the 5 farm plots studied. ................................................................................................................................. 81 Table 3.1 Concentration of TEs (mg/kg dw) in the agricultural soil from the different studied plots. The generic reference levels (GRL, mg/kg dw) of these elements for contaminated soils in Catalonia are shown. ............................................................................................................................................ 104 Table 3.2 Minimum, maximum and median concentration (mg/kg fw) of TEs in lettuce samples. Only Plots 3 and 4 were planted during the summer season. ....................................................................... 105 Table 3.3 Concentration of OMCs (ng/g dw) in soil samples during the summer campaign.............. 107 Table 3.4 Minimum, maximum and median concentration (ng/g fw) of the OMCs detected in lettuce samples during summer and winter campaigns. Only Plots 3 and 4 were planted during the summer season. Concentration values have been corrected by recoveries. ...................................................... 108 Table 3.5 Minimum, maximum and average levels of different lettuce quality parameters studied (n = 5). ................................................................................................................................................. 109 xvii Table 4.1 Average and 95th percentile concentration values of selected TEs in vegetables (mg/kg fw). Average and standard deviation of TEs in vegetables grown in rural and peri-urban agriculture. ..... 132 Table 4.2 . Average and 95th percentile concentration values of selected OMCs in vegetables (µg/kg fw). Average and standard deviation of TEs in vegetables grown in rural and peri-urban agriculture. Minimum, maximum and median concentrations of TEs (ng g-1 fw) in vegetable samples ............... 134 Table 4.3 HQ and THQ of TEs in vegetable samples for an adult (70 kg) and a child (24 kg). ......... 138 Table 4.4 The daily consumption (kg/day) required to reach TTC levels for the selected OMCs in an adult (70 kg) and in a child (24 kg). .................................................................................................... 140 Table 5.1 Details of water, lipid, carbohydrate, and fiber content of crops relevant to risk assessment ............................................................................................................................................................. 154 xviii List of supplementary tables Table S2.1 Physicochemical properties of the CECs of study .............................................................. 85 Table S2.2 Monitoring ions in GC-MS/MS of the underivatized contaminants ................................... 90 Table S2.3 Monitoring ions for derivatized compounds in GC-MS/MS .............................................. 91 Table S2.4 Limits of detection (LOD) and quantification (LOQ) of the selected ECs ......................... 92 Table S2.5 Recoveries of the surrogates ............................................................................................... 93 Table S3.1. General parameters of soil samples .................................................................................. 115 Table S3.2 Limits of detection (LOD) and quantification (LOQ) of soil samples .............................. 116 Table S3.3 Recoveries (%) of surrogates in soil samples ................................................................... 116 Table S3.4 Absolute recoveries (%) of analytes in soil samples ......................................................... 117 Table S3.5 Limits of detection (LOD) and quantification (LOQ) of lettuce’s samples ...................... 118 Table S3.6 Recoveries (%) of surrogates in lettuce’s samples ............................................................ 118 Table S3.7 Absolute recoveries (%) of analytes in lettuce’s samples ................................................. 119 Table S3.8 BCF for the TEs selected in this study .............................................................................. 120 Table S3.9 BCF of the CECs selected in this study ............................................................................ 120 Table S3.10 Loadings for PCA. .......................................................................................................... 121 Table S4.1 LODs and LOQs of the studied OMCs in vegetables (tomatoes, cauliflowers and broad beans). ................................................................................................................................................. 143 Table S4.2 Recoveries of the surrogates in vegetables ....................................................................... 144 Table S4.3 Recoveries of the studied OMCs in vegetables ................................................................. 145 Table S4.4 Daily consumption in Spain for each of the studied vegetables and population class ...... 147 Table S4.5 Maximum residue levels of pesticides in or on food and feed of plant and animal origin and amending council directive 91/414/EEC ............................................................................................. 148 Table S4.6 Loadings for PCA ............................................................................................................. 149 Table S4.7 EDI (mg/kg bw/day) of TEs in vegetables for an adult (70 kg) and a child (24 kg). *Calculated from the LOD/2 ............................................................................................................... 150 xix List of figures Figure 1.1 Classification of main air pollutants .......................................................................... 27 Figure 1.2. Countries that use major volumes of TWW per in-habitant for agricultural irrigation (Jiménez and Asano, 2008) ......................................................................................................... 29 Figure 1.3. Percentages of reuses of regenerated water in Catalonia (ACA, 2016) .................... 29 Figure 1.4. Diagram of contaminant sources in the peri-urban environment (Meuser, 2010). ... 32 Figure 1.5. Comparison of the total Pb concentrations in soils ranging from high-traffic urban roads to suburban low-traffic places in the city of Kampala (Nabulo et al., 2006) .................................. 33 Figure 1.6. Principal plant uptake pathways of chemical contaminants by plants (Collins, 2007)39 Figure 1.7. Transport proteins in the plasma membranes of root cells implicated in the movement of heavy metals from the rhizosphere to the xylem through the symplasm (White, 2012) VICC: voltatgeinsensitive cation channels, DACC: depolarization-activated calcium channels, HACC: hyperpolarization activated calcium channels (HACC), ZIP, IRT1: iron-regulated transporter (IRT)- like protein gene family, Z1P4, CTR, NRAMP: natural resistance associated macrophage protein gene family, SULTR: sulphate transporter gene family, YSL: yellow stripe 1 like, ........................... 40 Figure 1.8. Cross-sectional diagram of a root (Miller et al., 2016). ............................................ 42 Figure 1.9. Variation in the prediction of the transpiration stream concentration factor (TSCF) with KOW (Collins et al., 2007) ............................................................................................................ 43 Figure 1.10. Foliar pathways of heavy metal entrance to plants (Shahid et al., 2017a) .............. 44 Figure 1.11. Phases of ‘’green liver’’ model (Van Aken, 2008) ................................................. 45 Figure 1.12. Distribution of CBZ and its metabolites, 10,11-epoxycarbamazepine and 10,11dihydroxycarbamazepine in the bulk, soils, soil aqueous extracts, roots, and leaves of carros and sweet potatpes (Malchi et al., 2014) ...................................................................................................... 46 Figure 1.13. Effect of Cu on rice radicle elongation. Rice seeds were treated with the indicated concentrations of Cu for 4, 6 or 8 d. Values shown represent means + s.e. (n ¼ 3) for three different experiments. Means denoted by the same letter did not differ significantly (p ≥ 0.05 according to Duncan’s multiple range test) ...................................................................................................... 50 Figure 1.14. Box-plots of different agronomic parameters for different concentrations of pollutants in irrigated water (Hurtado et al., 2017). ......................................................................................... 51 Figure 0.1 Map of the sampling area. P1. Begues; P2. Prat de Llobregat P3. Sant Joan Despí; P4. Sant Boi de Llobregat; P5. Viladecans. ............................................................................................... 69 Figure 0.2 Principal Component Analysis (PCA) results. (a) PC 1 vs. PC2 score plot with all irrigation water samples (P1–P5), (b) PC1 vs. PC2 score plot excluding irrigation water samples from site P3. ......................................................................................................................................... 79 Figure 3.1 Principal Component Analysis (PCA) results. a) Scores plot PC1 vs PC2 (ID1=Plot1 winter, ID2=Plot2 winter, ID3=Plot3winter, ID4=Plot4 winter, ID5=Plot5 winter, ID6=Plot3 summer, ID7=Plot4 summer), b) Scores plot PC1 vs PC3 ...................................................................... 111 xx Figure 4.1 Principal Component Analysis (PCA) results. Scores plot PC1 vs PC3 (ID1= Plot1 lettuce, ID2= Plot3 lettuce, ID3= Plot3 lettuce, ID4= Plot1 tomato, ID5= Plot3 tomato, ID6= Plot4 tomato, ID7= Plot1 cauliflower, ID8= Plot3 cauliflower, ID9= Plot4 cauliflower, ID11=Plot 3 broad beans and ID12= Plot 4 broad beans)......................................................................................................... 135 xxi List of supplementary figures Figure S3.1 Dispersion diagram and linear correlation between nitrate content in water and in lettuce ............................................................................................................................................................. 122 Figure S4.1 Principal Component Analysis (PCA) results. Scores plot PC1 vs PC3. ........................ 151 xxii List of acronyms 2MBT 2mercaptobenzothiazole 5TTri 5-methyl-2H-benzotriazole BCF Bioconcentration factor BHA 2-tert-butyl-4-methoxyphenol BMDL Benchmark dose lower confidence limit BPA Bisphenol A BPB Butylparaben BPF Bisphenol F BT 1,3-benzothiazole BTri Benzotriazole C Concentration CBDZN Carbendazim CBZ Carbamazepine CECs Contaminants of emerging concern DEET N,N-diethyl-meta-toluamide dw Dry weight EC European Commission EPB Ethylparaben EPOCBZ 10,11-epoxycarbamazepine EU European Union fw Fresh weight GRL Generic residue levels HQ Hazard quotient Kd Soil-water partition coefficient KH Henry law’s constant Koa Octanol-air partition coefficient Koc Organic carbon soil - water partition coefficient Kow Octanol-water partition coefficient MPB Methylparaben MRL Maximum residue levels OHBT 1H-hydroxybenzotriazole OMCs Organic microcontaminants OP 4-tert-octylphenol xxiii PAHs Polycyclic aromatic hydrocarbons PCA Principal component analysis PCBs Polychlorinated Biphenyls PCPs Personal care products pKa Constant of acidity PM Particulate matter PPB Propylparaben RfD Reference dose ROS reactive oxygen species SI Supplementary information t1/2 Half-life TCEP (tris(2‐chloroethyl) phosphate) TCPP Tris(1-chloro-2-propyl) phosphate TEs Trace elements THQ Total hazard quotient TTC Threshold of toxicological concern TWW Treated wastewater VOCs Volatile organic compounds WWTPs Wastewater treatment plants 24 Motivations and structure of the Thesis Motivations In the recent years, a great progress has been achieved in the development of analytical technologies, which has allowed the identification and quantification of microcontaminants (organic and inorganic) in different environmental compartments at ultra-trace concentration levels. Several studies have demonstrated the incorporation of these compounds into crops irrigated with reclaimed waters or grown in polluted soils, as well as their incorporation and translocation into the edible parts of vegetables. Although a great progress has been made in assessing the mechanisms that may affect their incorporation, most of the existing studies on organic microcontaminangts (OMCs) have been performed in the laboratory or in greenhouses, which do not represent the common agricultural practices of commercial agriculture. Furthermore, in any case the simultaneous assessment of trace elements (TEs) and OMCs has been performed. Therefore, field studies are required to properly evaluate their incorporation and elaborate databases to assess the human health risk of the consumption of vegetables exposed to chemical contaminants. This figure could be more pronounced in peri-urban agriculture, where vegetables are exposed to TEs and OMCs through industrial and domestic activities such as the use of wastewater treatment plants ( WWTP) effluents for irrigation, soil amending with biosolids, and vehicular emissions, among others (Calderón-Preciado et al., 2011; Singh and Kumar, 2006). Nevertheless, there is no available study in the literature that assesses the effect of that exposure in terms of agricultural productivity and human health implications. This Thesis assesses for the first time in Spain the exposure and uptake of 50 chemical contaminants (OMCs and TEs) by food crops under real field-scale conditions. Furthermore, this is the first study worldwide that evaluates the implications of the peri-urban agriculture exposition to chemical contaminants on crop productivity and human health. For that propose, 4 farm fields located in the periurban area of the city of Barcelona and one rural site, outside the peri-urban area of influence, were selected. Whereas sites in peri-urban area were exposed to atmospheric pollution from the city and irrigated with reclaimed, ground or surface water, the rural site was less affected by urban pollution and irrigated with rainwater/groundwater. Lettuce, tomatoes, cauliflowers and broad beans were selected because of their importance in the agricultural production of the area. Structure of the Thesis The PhD dissertation is structured as follows. Chapter I provides an introductory overview of the concerns regarding plant uptake of contaminants in peri-urban horticulture, describes the relevant contaminants and their main sources as well as their known effects on plants and human health. Moreover, an overview of the selected compounds is detailed. The hypotheses and the objectives of the Thesis are stated 25 The results obtained along this Thesis as well as their discussion are compiled in chapters III, IV and V. Chapter II describes the occurrence of the selected contaminants (34 OMCs and 16 TEs), conventional quality parameters and nutrients in the irrigation waters used in the sampling area, which includes four peri-urban and one rural farm plots. Water samples were taken during the growing period of some crops of interest (lettuce and tomato), between February and September of 2016. Moreover, the effects of the presence of contaminants were assessed through a seed germination test (Lactuca sativa L.) and the crop productivity (Lycopersicon esculentum Mill. cv. Bodar and Lactuca sativa L. cv. Batavia). Data analysis, including PCA on the entire dataset that classifies the irrigation waters, is included. This Chapter is based on the paper Margenat, A., Matamoros, V., Díez, S., Cañameras, N., Comas, J., & Bayona, J. M. (2017). Occurrence of chemical contaminants in peri-urban agricultural irrigation waters and assessment of their phytotoxicity and crop productivity. Science of the Total Environment, 599–600, 1140–1148. In Chapter III, is provided the occurrence of these contaminants in soil and lettuce leaves grown in peri-urban and rural areas, and their bioaccumulation factors. Lettuce crops were sampled in two growing seasons, winter and summer, as they were planted in February-March and June of 2016 and harvested in May and June of 2016. The effects of the contaminants on the leaf constituents (e.g. chlorophyll, nitrate, lipid and carbohydrate contents) are also detailed and, finally, contains a PCA analysis that sheds light on the most relevant factors in the presence of contaminants in lettuce crops. This Chapter is based on the paper Margenat, A., Matamoros, V., Díez, S., Cañameras, N., Comas, J., & Bayona, J. M. (2018). Occurrence and bioaccumulation of chemical contaminants in lettuce grown in peri-urban horticulture. Science of the Total Environment, 637–638, 1166–1174. Chapter IV expands the study of the presence of the contaminants to the edible parts of different vegetables (lettuce, tomato, cauliflower and broad beans) grown in two peri-urban and one rural farm plots, and evaluates the potential risk of their consumption, individual and altogether, to human health. Statistical data analysis on the whole dataset is detailed. This Chapter is based on the paper under review Margenat, A., Matamoros, V., Díez, S., Cañameras, N., Comas, J., & Bayona, J. M. Occurrence and human health implications of chemical contaminants in vegetables grown in peri-urban agriculture. Environment International. Finally, a general discussion (Chapter V) and conclusions (Chapter VI) are included so as to provide an overview of the data obtained along the PhD. 32 ng·L-1 and μg·L-1 (Calderón-Preciado et al., 2013; Chen et al., 2005). Consequently, the use of reclaimed water for agricultural irrigation results in the crop exposition to EOCs. Soil Soil pollution can cause numerous detrimental effects on ecosystems, human, plants and animal health. These harmful effects may come from direct contact with polluted soil or from contact to other resources, such as water or food, which has been grown or been in direct contact with polluted soil. Sources of soil contamination can be differentiated between non-site related causes and site related ones (Figure 1.4). Figure 1.4. Diagram of contaminant sources in the peri-urban environment (Meuser, 2010). The non-site related causes involve extensive and linear contamination patterns. Extensive contamination can be caused by the presence of metals, metalloids and other TEs in geological materials and from dust deposition, mainly caused by industrial emission. Whereas, linear sources include emission along traffic roads and utility networks pipes as well as flood occurrences in alluvial floodplains. Nabulo et al. (2006) reported that soils closer to traffic roads contain a greater concentration of heavy metals such as Pb, Zn and Cd (Figure 1.5). Thereby the presence of TEs in peri-urban agriculture due to the traffic emissions needs to be considered as a contamination source. (Sewage) sludge application Site related Urban influence (contaminated land) Horticultural / agricultural influence Fertilizing Pesticide application Derelict sites Accident sites Deposits Non-site related Linear contamination Extensive contamination Bedrock / parent material Dust deposition Traffic routes Utility network pipes Floods 33 Figure 1.5. Comparison of the total Pb concentrations in soils ranging from high-traffic urban roads to suburban low-traffic places in the city of Kampala (Nabulo et al., 2006) Numerous contaminant sources are related to traffic. In the past, leaded gasoline emitted Pb and despite the introduction of unleaded gasoline, still trace levels of Pb can be found in soils due to its persistence (Borchers et al., 2010). Residues from tyre and brake wear (Cr, Cu, Ni) can be released to the environment, as well as heavy metals from products of corrosion (Cd, Cu, Zn) and new contaminants based on current car technology (catalytic filter systems) such as platinum and rhodium have arisen (Meuser, 2010). On the other hand, site related sources of contaminants comprise contaminants that have been used in a specific area (horticultural and agricultural activities) and contaminants generally site-specific such as contaminated derelict sites, heaps, etc. Finally, soils intended for horticultural purposes are potentially exposed to contaminants from fertilizers containing problematical mineral compounds and application of sewage sludge, wastewater and pesticides. Legislation In Spain, the Royal Decree 9/2005 (Ministerio de la Presidencia, 2005) establishes generic reference levels (GRL) for certain organic contaminants in soils for human health (industrial, urban or other uses) and ecosystems (agricultural and forest areas or the rest) protection. Additionally, the Regional Decree 5/2017 (Generalitat de Catalunya, 2017) provides the GRL levels for metals and metalloids (Table 1.2). 34 Table 1.2. GRL values for metals and metalloids established in soils in Catalonia for protection of human health and ecosystems (Regional Decree 5/2017). GRL values for protection of human health (mg/kg soil dw) GRL values for protection of ecosystems (mg/kg soil dw) Element Industrial use Urban use Other uses1 Agricultural and forest areas2 The rest3 Antimony (Sb) 30 * 6 ** 6 ** 6,0 6,0 Arsenic (As) 30 ** 30 ** 30 ** 30 30 Barium (Ba) 1.000 *** 880 500 500 270 Beryllium (Be) 90 40 10 10 4,5 Cadmium (Cd) 55 * 5,5 2,5 2,5 0,6 Cobalt (Co) 90 45 25 ** 25 25 Copper (Cu) 1.000 *** 310 90 90 55 Chromium (III) 1.000 *** 1.000 *** 400 400 85 Chromium (VI) 25 10 1 1,0 1,0 Tin (Sn) 1.000 *** 1.000 *** 50 50 7 Mercury (Hg) 30 * 3 2 ** 2,0 2,0 Molybdenum (Mo) 70 * 7 * 3,5 ** 3,5 3,5 Nickel (Ni) 1.000 *** 470 * 45 ** 45 45 Lead (Pb) 550 * 60 ** 60 ** 60 60 Selenium (Se) 70 * 7 * 0,7 0,7 0,5 Thallium (Tl) 45 * 4,5 * 1,5 ** 1,5 1,5 Vanadium (V) 1.000 *** 190 135 ** 135 135 Zinc (Zn) 1.000 *** 650 * 170 ** 170 110 1In the soils where NGRs are applicable to other uses in the protection of human health, the representative surface area of the soil will be that resulting from a homogeneous sample of the first 50 cm, once the natural coverage of the terrain has been removed (the first 5-10 cm); 2The NGR column defined as agricultural and forestry area will be applicable to all those soil subjected to agricultural fertilization practices. In this case the surface representative sample of the soil will be that resulting from a homogeneous sample of the first 50 cm, once the natural coverage of the ground (5-10 cm) has been removed; 3Reference levels: upper limit of the confidence interval of Percentile 95 calculated from natural soil samples. *In application of the criterion of contiguity; **in application of the reference values; ** in application of the reduction criterion Agricultural practices Agricultural practices might have influence in the chemical contamination of food crops. As mentioned previously, the use of TWW for irrigation, use of pesticides and fertilizers and soil amendments together with mulching may constitute an exposure to a wide group of chemicals. Mulching The use of field mulching alters the plant microenvironment in order to promote plant growth and thus, increases crop yield. It is useful as a water conservation technique, which increases water infiltration into the soil, reduces soil erosion and surface runoff (Prosdocimi et al., 2016). In addition, plastic mulching suppresses weed growth and reduces competition with weeds for water and nutrients (Abouziena et al., 2008). 35 The materials applied for mulching can be separated into three main categories: organic (e.g. plant products and animal wastes), inorganic (e.g. plastic and biodegradable plastic film) and special materials (e.g. sand and concrete, which are barely used) (Kader et al., 2017). The migration of heavy metals from soil to plant, influenced by mulching has been investigated up to a certain point. Li et al. (2010) reported that mulching (semi-transparent plastic film) affects the bioavailability of metals in soil as it slightly increased the bioconcentration factor (BCF) (calculated with the labile portion of metal) for most metals (by 16-58% for Fe, Zn and Cd). Pesticides Pesticide application is widespread in agricultural areas in order to provide plant protection. Approximately, from 1 to 2.5 million tons of active pesticides ingredients are applied worldwide every year. They are used to prevent crops from being harmed by disease and infestation and they comprise herbicides, fungicides, insecticides, acaricides, plant growth regulators and repellents. They are divided, based on the chemical constituents, into dithiocarbamates (7.1%), organophosphates (6.7%), phenoxy alkanoic acids (4.7), amides (4.2), bipyridyls (3.2), triazines (2.3), triazoles and diazoles (2%), carbamates (2%), urea derivatives (1.7%) and pyrethoids (1.3%) (Fenner et al., 2013a). Although their registration for use depends on their non-persistence in the environment after their period of use, their residues are found ubiquitously in the environment from ng·L-1 to low µg·L-1 and not only in ground water, but also in surface waters. Moreover, these substances are often harmful to non-target organisms through consumption of food crops (Li et al., 2010). Hence, the Regulation EC 396/2005 established maximum residue levels (MRL) for residues of pesticides in foodstuff in Europe. Agricultural soil amendment Soil amendment includes organic and inorganic substances mixed into the soil in order to improve soil conditions regarding plant productivity. Organic amendments are usually derived from vegetables, byproducts from processing plants or mills or waste disposal plants (e.g. processed sewage sludge, compost and biosolids). However, the enhanced physical, chemical and biological properties of soil may promote the migration of heavy metals from soil to crops and thereby constitute a pathway of heavy metal accumulation in crops (Li et al., 2010). On the other hand, the use of biosolids is one of the major environmental concerns worldwide as they usually contain many toxicants such as heavy metals, pesticides, EOCs, toxic organics, hormone disruptors, detergents and various salts in addition to organic material. Although the presence of nutrients improves the plant growth, a high accumulation of certain heavy metals (such as Cd, Pb and Ni) in seeds has also been reported (Singh and Agrawal, 2010). Therefore, it could serve as a pathway of entering toxic elements into the food chain (Alvarenga et al., 2015; Fijalkowski et al., 2017). 36 1.2 Plant uptake, translocation and metabolism 1.2.1 Key physical-chemical properties of contaminants Plant uptake of contaminants is affected by physicochemical properties of contaminants, but also by soil and plant physiology (Dolliver et al., 2007; Khan et al., 2015; Paterson et al., 1990; Trapp and Legind, 2011). The most relevant physical-chemical properties of contaminants and their implication in the plant uptake are detailed below.  Half-life (t1/2) It is defined as the time required by a certain amount of a compound to be reduced by half. Contaminants must be stable in the soil to be incorporated into the plant. Specifically, contaminants with half-life time greater than 14 days are more likely to be incorporated by plants (O’Connor, 1996). On the other hand, compounds with shorter half-lives may suffer degradation during treatment in WWTPs or in water supply networks.  Chemical Speciation Speciation is an important parameter to take into account during plant uptake of TEs. It refers to the distribution of an element amongst chemical species. It should be highlighted that the total metal contents in soil do not show the biogeochemical behaviour of a metal because its different chemical species have influence (Shahid et al., 2017b). For instance, chromium exhibits contrary effects among its different chemical forms, being Cr (III) and Cr (VI) the most stable and predominant in the nature. However, Cr (VI) is much more mobile in the soil and extremely toxic to organisms compared to Cr (III) as it is highly reactive with other elements (Amin et al., 2013). Meanwhile, Cr (III) is less toxic and mobile due to its precipitation at natural pH values (Shahid et al., 2017b). In addition, both chemical forms are incorporated through different mechanisms (active and passive) and Cr (VI) is known to interfere with the plant uptake of some essential nutrients due their ionic resemblance (e.g. K, Fe, Mn, Mg, Ca and P) (Gardea-Torresdey et al., 2004).  Solubility Solubility is the ability for a given substance (solute) to dissolve in a solvent and it is measured as the maximum amount of solute dissolved in a solvent at equilibrium. High water solubility compounds tend to have higher soil mobility and, hence, will be less likely to accumulate, bioaccumulate, volatilize and persist in the environment. Generally, the biodegradation and metabolization of these compounds by the microorganisms will be easier with respect to compounds with less solubility. 37  Octanol-water partition coefficient (Kow) One of the factors that most affects the distribution and bioavailability of a compound is its hydrophobicity, or tendency to dissolve preferably in a lipid phase. Hydrophobicity is measured from the octanol-water partition (Kow) coefficient and represents the distribution of a compound between two immiscible solvents, water (polar solvent) and octanol (relatively non-polar, which represents lipids). 𝐾𝑂𝑊 =𝐶𝑜𝑐𝑡𝑎𝑛𝑜𝑙 𝐶𝑤𝑎𝑡𝑒𝑟 (1.1) The translocation of organic contaminants in plants takes place with log Kow values between 1 and 4 (Calderón-Preciado et al., 2013; McCutcheon and Schnoor, 2004), with the maximum translocation around log Kow of 1.78 (Briggs et al., 1982). The compounds that have log Kow values in this range, have a higher probability of being incorporated by the plants because they would be sufficiently hydrophobic to mobilize through the lipid bilayer of the cell membranes and would be sufficiently soluble in water to be transported. A high value of log Kow (> 4) represents a high hydrophobicity and would indicate that the compound can be fixed to the organic matter of the soil. Therefore, it would be barely bioaccesible and would rarely be incorporated by the plant via root. On the contrary, if the hydrophilic compound were not set in organic matter, it would have a great mobility to the ground so that it could contaminate the aquifer (Calderón-Preciado et al., 2013). However, the octanol-water partition coefficient is important only for neutral compounds, since ionic compounds are usually more polar and soluble in water and have been observed to behave differently (Trapp and Legind, 2011). Other mechanisms such as attraction or electrostatic repulsion, and ion trap may affect their accumulation in roots (Wu et al., 2015). Therefore, the incorporation of ionic compounds via root cannot be related to their hydrophobicity, the acidity constants of compounds and variations in the pH of the medium (Trapp, 2004, 2000) are more important.  Constant of acidity (pKa) As it has mentioned above this value is of great importance since most of the pharmaceutical compounds are ionizable substances (Boxall et al., 2012). Depending on the pH conditions, these compounds can be neutral, cationic, anionic or zwitterionic by having different functionalities in the same molecule and therefore, it will change its possible incorporation and translocation into the plants.  Soil-water partition coefficient (Kd) It is the constant distribution of an organic substance between the soil and the water in equilibrium at a given temperature. 𝐾𝑑=𝐶𝑠𝑜𝑟𝑏𝑒𝑑 𝑖𝑛 𝑠𝑜𝑖𝑙 𝐶𝑑𝑖𝑠𝑠𝑜𝑙𝑣𝑒𝑑 𝑖𝑛 𝑤𝑎𝑡𝑒𝑟 (L·kg‐1) (1.2) 38  Organic carbon soil - water partition coefficient (Koc) It represents the capacity of a compound to be adsorbed by the organic matter present in the soil. Therefore, a high value of this parameter would indicate that the compound has a strong affinity to the soil and that a lower proportion of the compound can move through the interstitial water of the ground, so it would be unacceptable to be incorporated by the plants. 𝐾𝑂𝐶 =𝐾𝑑·100 % 𝑜𝑟𝑔𝑎𝑛𝑖𝑐 𝑐𝑎𝑟𝑏𝑜𝑛 𝑖𝑛 𝑠𝑜𝑖𝑙 (L · kg‐1) (1.3)  Henry constant (KH) It is the relation between the concentration of a compound in the air respect to its concentration in equilibrium in the water. Therefore, it indicates the volatilization potential of a compound from the water or soil. In addition, the higher the vapour pressure, the greater the potential of volatilization has a compound. 𝐾𝐻=𝑣𝑎𝑝𝑜𝑟 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑖𝑛 𝑙𝑖𝑞𝑢𝑖𝑑 𝑐𝑜𝑚𝑝𝑜𝑢𝑛𝑑 𝑠𝑜𝑙𝑢𝑏𝑖𝑙𝑖𝑡𝑦 (Pa·m3·mol‐1 o atm·m3·mol‐1) (1.4) 𝐾𝐻′ =𝐶𝑔𝑎𝑠 𝑝ℎ𝑎𝑠𝑒 𝐶𝑙𝑖𝑞𝑢𝑖𝑑 𝑝ℎ𝑎𝑠𝑒 (dimensionless) (1.5) Substances with KH'> 10-4 tend to move in the interstitial spaces of the soil, while with values of KH > 10-6 they move fundamentally in the water. Although the Henry constant is between these two values, the compound will be mobile in the air and in the water so that its potential for incorporation in plants is greater (Linde, 1994).  Octanol-air partition coefficient, Koa It is a measure of the distribution of a compound between the octanol and the gas phase. Octanol represents the tissue of plants and therefore, this parameter indicates the possible bioaccumulation of the compound in plants from the air. 𝐾𝑂𝐴 =𝐶𝑜𝑐𝑡𝑎𝑛𝑜𝑙 𝐶𝑎𝑖𝑟 (1.6) Aside from the specific factors of the physicochemical properties of the compound, there are also factors that depend on the plant. For example, the incorporation of contaminants may vary between plant species. It has been observed that the incorporation of contaminants from the soil is higher in root vegetables such as carrots than in fruit trees such as apples. However, the incorporation of contaminants from the air is considered to be greater in the opposite case (Trapp and Legind, 2011). Other plant factors 39 are the root system, the shape and size of the leaves and the lipid content. Some studies have shown that plants with a higher lipid content accumulate higher concentrations of contaminants such as PAHs (Simonich and Hites, 1995). 1.2.2 Uptake and translocation As mentioned above, the greatest concern about the presence of these contaminants in soils is the evidence that they can be incorporated into the plant and accumulated, not only in the roots, but also in the edible parts of plants (Bartha et al., 2010; Khan et al., 2015). Therefore, these contaminants will be incorporated into food chains, which represents a way of exposure for humans. Although the concentrations measured in vegetables are generally low, little is known about the long-term effects of these contaminants on human health (Boxall et al., 2006). The figure 1.6 shows the main plant uptake pathways of contaminants by plants: root uptake from soil solution, dry and wet deposition of particles and gaseous deposition to leaf via cuticle and stomata. Figure 1.6. Principal plant uptake pathways of chemical contaminants by plants (Collins, 2007) Root uptake Although the general uptake pathways are similar, plant uptake mechanisms by roots are very different for organic and inorganic contaminants. This difference remains in the fact that organic contaminants are usually xenobiotic to the plants, so there are no specific transporters for these compounds in the plant membranes (Pilon-Smits, 2005). 40 Inorganic contaminants In general, TEs such as metal ions are absorbed to soil particles in an insoluble form (e.g. Fe hydroxides in alkaline soil). However, plant roots can influence by releasing protons via membrane H+-ATPases, which acidify the rhizosphere and create a large membrane potential responsible of cation uptake. While the protons participate in the cation exchange, releasing divalent metal ions that are strongly bounded to soil particles, the acidified rhizosphere can release metals from their hydroxides (Alloway, 2012). Once heavy metals are incorporated into the roots from the soil solution, they can reach the xylem through same pathways as organic contaminants (symplastic or apoplastic pathways, see figure 1.7). Therefore, although they can migrate into the root apoplastic space, the impermeable Casparian strip in the endodermal cell layer will block this route and then, they have to be actively transported across plasma membrane into the symplast. In stark contrast to organic contaminants, inorganic transport is mediated by transport proteins in the xylem (Thakur et al., 2016). These transport proteins are naturally found in plants because inorganic elements can be nutrients or chemically similar to them, so they can also be incorporated (e.g. arsenate is incorporated by phosphate transporters, selenate by sulfate transporters). However, transition-metal cations are generally bound by organic ligands upon entry to the symplasm to protect essential cytoplasmic functions (Figure 1.7). Figure 1.7. Transport proteins in the plasma membranes of root cells implicated in the movement of heavy metals from the rhizosphere to the xylem through the symplasm (White, 2012) VICC: voltage-insensitive cation channels, DACC: depolarization-activated calcium channels, HACC: hyperpolarization activated calcium channels (HACC), ZIP, IRT1: iron-regulated transporter (IRT)-like protein gene family, Z1P4, CTR, NRAMP: natural resistance associated macrophage protein gene family, SULTR: sulphate transporter gene family, YSL: yellow stripe 1 like Heavy metals can be redistributed from stem and leaf cells through both the xylem and phloem. Selective movement of heavy metals in the phloem allows the delivery of essential elements to developing tissues, tubers, fruits and seeds, whilst toxic elements are retained in older leaves (White, 2012). 41 Organic contaminants Plants can incorporate organic contaminants from the soil through the roots (Paterson et al., 1990). The concentration in the soil water of a contaminant is determined by the Kd and as non-ionic organic contaminants are mainly sorbed onto the organic fraction of the soil’s solid phase, Kd can be defined in terms of the soil organic carbon content (Kd=Koc·foc, where foc=fraction organic carbon) (Collins et al., 2007). Correlations between Koc and Kow revealed that soil sorption increases with the Kow, reducing the availability of high Kow compounds for plant uptake (Karickhoff, 1981). Moreover, increases in the foc diminishes the total amount of contaminant absorbed by vegetation and the optimum Kow for plant uptake. The uptake of contaminants by plant roots usually is performed by diffusion, which is the simplest passive transport, as it does not require the cell to use energy (Calderón-Preciado et al., 2012; Trapp and Legind, 2011). By contrast, some hormone-like contaminants are incorporated by the active route although it is considered not to be the most important route in the incorporation of organic contaminants as it requires energy to move nutrients and contaminants through the cell membrane (Trapp and Legind, 2011). Uptake of non-ionic contaminants into plant roots consists of two steps: 1) “equilibration” of the aqueous phase in the plant root with the concentration in the surrounding solution, and 2) “sorption” of the chemical into the lipophilic root solids (lipids in membranes, cell walls, etc.) (Collins et al., 2007). Briggs et al. (1982) reported the linear relationship between the Kow of non-ionic contaminants and the observed root concentration factor (RCF=C in the root/ C in the external solution) in studies about the uptake and translocation of O-methylcarbamoyloximes and substituted phenylureas in barley plants. Furthermore, Wild and Jones (1992) published that non-ionic contaminants with logKow>4 have a high potential for retention in plant roots. The compounds enter the root with the flow of water and move through the plant through 3 possible pathways (Fig. 1.8): apoplastic (along cell walls through the intercellular space), symplastic (between cells through interconnecting plasmodesmata) and transmembrane route (between cells through cells walls and membranes) (Miller et al., 2016). 48 moves chemicals to sub-stomatal tissues. Moreover, it has been widely detected for inorganic contaminants, as volatile forms of these elements such as Se, As, and Hg (Limmer and Burken, 2016). Jia et al. (2012) reported the As volatilization from rice plants after the uptake of different methylated As species, which was positive related to the trimethylarsine oxide concentration in rice shoots and roots previously exposed to different As concentration levels. However, volatilization from the rice plants accounted just for 0.4 - 3.2% of the total As volatilized from the whole soil-plant system. Growth dilution The importance of this process has not been clearly established. There are two scenarios where it can be important: 1) where there is an acute exposure event a growth results in dilution of this peak concentration of contamination, and 2) where the uptake of this contaminants per unit mass is slower than the accumulation of dry matter per unit mass (Collins et al., 2007). Li et al. (2018) reported that the imidacloprid concentration in six leafy vegetable tissues varied significantly according to the plant variety and growth stage (seedling, rapid growth and maturation stages), probably due to growth dilution. It was observed a negative correlation between the daily transpiration, which increases with growth stages, and the log BCF, so it seems that higher daily transpiration values are related to larger shoot biomass, which resulted in lower concentration of imidacloprid in the shoots. 1.3 Effects of environmental contamination in horticulture and human health Plant uptake of diverse contaminants has become a global concern as contaminants have shown harmful effects to plants and human health directly by their occurrence in plants and/or indirectly, by causing changes in plant metabolism and therefore on nutritional values (Gaweda, 2007; Hurtado et al., 2017; Khan et al., 2015). This PhD Thesis is focused on the effect of the occurrence of TEs, CECs and pesticides in irrigation water and soil in crop productivity. The occurrence of POPs (PCBs, PAHs, dioxins…) has not been assessed as they are widely legislated, and they are not expected to be present in harmful concentrations. 1.3.1 Effects on plants Heavy metals, unlike organic substances, are non-biodegradable and hence tend to accumulate in the environment. Then, these elements can be accumulated in living organisms (bioaccumulation) and their concentrations increase as they pass from lower trophic levels to higher trophic levels (biomagnification). Therefore, heavy metals exert different toxicological effects on plants and animals depending on organism and metal (Khan et al., 2015). Nevertheless, some heavy metals are also essential nutrients for plant growth and development. Plants require 17 nutrients, including water, oxygen, carbon dioxide and 14 mineral elements that can be distinguished in two categories, depending on the relative amount need for plant growth. Macronutrients are generally found in plants at concentrations greater than 0.1% of dry weight (dw) tissue (N, P, K, 49 Ca, S and Mg), while micronutrients or TEs are generally found at concentrations less than 0.01% of tissue dw (Fe, Zn, Mn, Cu, B, Cl, Mo and Ni). The supply of the first three nutrients (C, H and O) is guaranteed by air and water. However, the remaining 14 mineral nutrients should be present in the plant growth medium in a proper concentration (Fageria et al., 2009). These mineral nutrients have numerous functions (Table 1.3) such as being structural components in macromolecules, as cofactors in enzymatic reactions, as osmotic solutes need to maintain proper water potential or as ionized species to provide charge balance in cellular compartments (Grusak, 2001). Table 1.3. Essential heavy metals for plants (Barker and Pilbeam, 2007; McCauley et al., 2011; Mengel and Kirby, 2004) Essential nutrients Chemical symbol Function Deficiency Boron B Cell wall component Chlorosis of young leaves and terminal bud death Calcium Ca Cell wall component Distorted and dark green leaves, weak stems and poor germination Chlorine Cl Photosynthesis reactions Chlorotic and necrotic spotting along leaves Copper Cu Chlorophyll production, respiration and protein synthesis Chlorosis in young leaves, stunted growth, delayed maturity, lodging and in some cases, melanosis (brown discoloration) Iron Fe Chlorophyll synthesis Interveinal chlorosis, stunted growth Potassium K Activation of enzymes, photosynthesis, protein formation and sugar transport Reduction of growth rate, chlorosis and necrosis in later stages, older leaves show mottled or chlorotic areas with leaf burn at margins Magnesium Mg Part of chlorophyll and co-factor for ATP production Interveinal chlorosis and leaf margins becoming yellow or reddish-purple Manganese Mn Activates enzymes, cofactor, chloroplast production Interveinal chlorosis in young leaves Molybdenum Mo Involved in N fixation and in enzyme activity Stunted growth and chlorosis Nitrogen N Proteins, nucleic acids (DNA and RNA) and chlorophyll Chlorosis of lower leaves, stunted growth and necrosis of older leaves Nickel Ni Component of enzymes Required for proper seed germination Chlorosis and interveinal chlorosis in young leaves that progress to plant tissue necrosis, poor seed germination and decreased crop yield Phosphorus P ATP (energy), sugars and nucleic acids Stunted growth, dark green plants Sulfur S Amino acids and proteins Light green, spindly and small plants Zinc Zn Hormone production and important for internode elongation Interveinal chlorosis, severe stunting In addition to essential nutrients, more than half of the elements in the periodic table have been detected in some plant tissues. Most of them do not have known benefits to the plant, and many, such as cadmium (Cd) or chromium (Cr), can be detrimental to plant growth (Grusak, 2001). Regarding their role in biological systems, heavy metals and metalloids are classified as essential and non-essential. Heavy metals needed by organisms in tiny quantities for vital physiological and biochemical functions (Fe, Mn, Mn, Cu, Zn, and Ni) are considered as essential, while the ones not needed (Cd, Pb, As, Hg and Cr) are non-essential (Ali et al., 2013). Although small concentrations of some heavy metals are essential for plant growth, high concentrations of them can exert harmful effects on plant growth, hampering the plant germination, growth and production (Ghosh and Sethy, 2013). 50 The effects on plants shown can be classified in, either seed germination and growth, plant structure or nutritional values. 1.3.2 Seed germination and growth Seed germination is one of the most important stages in crop development that influences in crop health, better growth and yield at the later growth stages. There are many factors affecting seed germination, such as the light, temperature, germination time, salinity, water availability and mineral composition of soil (Ahmad and Ashraf, 2011; Gray, 1975). The main effects that heavy metals exert on seeds consist on a decrease in seed germination, reduced root and shoot elongation, dry weight, membrane alteration, altered sugar and protein metabolism and nutrient loss among others, which result in seed toxicity and productivity loss (Ahmad and Ashraf, 2011). For instance, it has been reported that Ni and Cu inhibit the amylase and other enzymes involved in the breakdown of food reserves (e.g. starch and sucrose), thereby retarding seed germination of many crops (Ahmad et al., 2009; Zhang et al., 2009) (Figure 1.13). It must be stated that the assayed concentrations are much higher than most of the agricultural soils. Figure 1.13. Effect of Cu on rice radicle elongation. Rice seeds were treated with the indicated concentrations of Cu for 4, 6 or 8 d. Values shown represent means + s.e. (n = 3) for three different experiments. Means denoted by the same letter did not differ significantly (p ≥ 0.05 according to Duncan’s multiple range test) Even though low concentrations of some heavy metals have shown to improve seed growth, high levels are likely to be toxic to plants and inhibit their growth. In addition, high concentrations of some metals may interfere with mineral nutrient uptake. Among the most affected nutrients, the fact that Fe has several resemblances with other heavy metals regarding chemical structure, behaviour, and availability in soils or uptake by plant roots (e.g. Zn, Co, Ni, Cd and Mn) resulted in a Fe plant deficiency, being Zn the most inducing heavy metal (Lešková et al., 2017). On the other side, effects on seed germination by the occurrence of OMCs have also been studied. Moore and Kröger (2010) studied the effect of three insecticides (diazinon, fipronil, lambda-cyhalothrin) and two herbicides (atrazine, metolachlor) on germination, radicle (root) and coleoptile (shoot) of rice (Oryza sativa L.). Although no germination effects of pesticide exposure were observed, significant growth effects were detected between pesticide treatments. Coleoptile growth significantly (p ≤ 0.05) 51 diminished in most of the pesticide exposures, compared with controls. On the contrary, radicles of seeds were larger (p ≤ 0.05) compared to controls. 1.3.3 Plant structure Bini et al. (2012) showed that the presence of a cocktail of potentially toxic heavy metals (Cu, Fe, Pb, Zn) in soil and plants, is related to micro-morphological changes on the leaf anatomy, such as reduction in leaf thickness, changes in intercellular spaces and in cell structural organization. Balaguer et al. (1998) found that the tomato plant growth was negatively influenced by increasing levels of Ni in the nutrient solution (at 10 and 20 mg·L-1), being strongly altered the fresh weight of stem, branches and leaves but not the water content. Symptoms of Ni toxicity were also reported by Palacios et al. (1998) at nutrient solution containing 15 and 30 mg·L-1 of Ni, showing chlorosis, necrosis and stunted growth after 2 weeks of Ni treatments. In addition, Hurtado et al. (2017) observed that exposure of lettuce to CECs at significant environmental concentrations (0-50 μg·L-1) in irrigation water can cause metabolic alterations in plants as well as the associated morphological changes (height of the leaf and stem width) and variation in the chlorophyll content (Figure 1.14). Figure 1.14. Box-plots of different agronomic parameters for different concentrations of pollutants in irrigated water (Hurtado et al., 2017). Bellino et al. (2018) exposed tomato seeds (Solanum lycopersicum L.) to 5 mL of 0, 0.1, 1, 10, 100 and 1000 mg·L-1 of a mix of four antibiotics during 10 and 7 days for seed germination and root elongation tests, respectively. Results revealed that the four antibiotics could have phytotoxic effects on tomato root development but not on seed germination, at concentrations from 10 mg·L-1 (spectinomycin), 100 mg·L-1 (chloramphenicol) to 1000 mg·L-1 (spiramycin and vancomycin). This could be due to the reduced permeation of antibiotics through the seed coat, which aims to protect seeds from the noxious 52 effects produced by these molecules (An et al., 2009). Nevertheless, the concentrations tested (ppm) were much higher than the concentrations that are generally found in the environment. 1.3.4 Nutritional values Heavy metals frequently increase the production of reactive oxygen species (ROS) in plants, resulting in oxidative damages of proteins, lipids, and nucleic acids, which are responsible for several physiological disorders such as growth retardation, nutrient deficiency, reduced transport of nutrients, genotoxicity, and retarded photosynthesis (Khan et al., 2015).  Effects on carbohydrates Carbohydrate synthesis can be inhibited by an excessive build-up of toxic elements that might destroy the photosynthetic electron transport chain and production of ROS (Sandalio et al., 2001). Gaweda (2007) investigated changes in the carbohydrate content of six vegetable crop species (lettuce, spinach, radish, carrot, red beet and onion) with 0, 250 and 500 mg·kg-1 dw of Pb in the substrate; a higher Pb dose, caused a decrease in sucrose content and an increase in starch in the edible part of the plants. Moreover, at high Cd concentration, a general decrease in carbohydrate metabolism occurred (Rodríguez-Celma et al., 2010). On the other hand, Christou et al. (2019) reported the effects of three pharmaceutical compounds (diclofenac, sulfamethoxazole and trimethoprim), individually and mixed together (10 µg·L-1), on the quality of tomato fruits. It resulted in no significant alteration of crop productivity, but a significant increase in the soluble solids content and in the transcripts related to the biosynthesis and catabolism of sucrose like and consequently, an increase in the carbohydrate content, which is related to the taste of the fruits.  Effects on proteins and amino acids Nitrogen and sulphur are the essential nutrients required for synthesis of proteins and amino acids and plant growth. Therefore, the lack of them may affect the metabolic processes (Carfagna et al., 2011). High Cd concentration may impede protein metabolism by modifying physiological functions and synthetic activities (Sandalio et al., 2001) and can also have effects on the decomposition of protein contents (Z. Wu et al., 2014). Likewise, high metal concentrations inhibit protein synthesis by altering the pigment-lipoprotein complex accumulation in photosystems I and II (Wang et al., 2009) and effect ribulose-1,5-bisphosphate carboxylase/oxygenase enzymes (Krantev et al., 2008).  Effects on lipids Little knowledge is available on heavy metal impact on lipid content (Upchurch, 2008). Khanna-Chopra (2012) determined that heavy-metal-induced oxidative stress results in chloroplast degradation and lipid peroxidation affecting the nutritional status of the contaminated plant. 53  Effects on vitamins Although leafy vegetables are considered as a good source of nutrients (Gupta and Bains, 2006), they can be affected by the presence of heavy metals. Environment has a strong influence on vitamin contents and in extreme conditions with high heavy metal concentrations, temperature, and pH, the vitamin contents are significantly reduced (Ipek et al. 2005). Moreover, lipid peroxidation can also reduce vitamin content (Seven et al., 2012). Indeed, there is a negative correlation between heavy metals and vitamins (Widowati, 2012), as a Cd increase resulted in a decrease of 61.7% of vitamin A and a decline of 74.7% in vitamin C in three aquatic vegetables. 1.4 Effects on human health Entrance of TEs into human body is possible by different pathways such as consumption of contaminated food, drinking water and/or air. It has been highlighted the contribution of vegetables to the total metal intake in human diet, accounting for around 90%, while the other 10% is due to dermal contact and inhalation of dust contamination (Martorell et al., 2011). Moreover, these elements may accumulate in vital body organs such as liver, heart, kidney, and brain disturbing normal biological functioning. Some of these elements (e.g. Zn, Cu, Mg, Co…) as occurred for plants are essential for human body but ingested at higher concentrations may be toxic. By contrast, some other heavy metals (Pb, Hg…) do not have known favourable effects on human health and they become toxic once they are accumulated in the body (Rehman et al., 2018). Toxic heavy metals can cause different health problems depending on the heavy metal concerned, its concentration and oxidation state, etc. Table 1.4 shows some examples of harmful effects of selected heavy metals on human health. Moreover, OMCs constitute a broad family of compounds, but in this work only CECs and pesticides have been selected. The assessment of CECs in vegetables is important because the risk they might pose to human health is not fully understood. To date, CECs have not been included yet guidelines and regulations. However, information about human health effects resulting from the use of pesticides has been widely reported. The type of pesticide, the duration and route of exposure, and the individual health status are relevant to assess the possible health effect, also pesticides may be metabolized, excreted, stored, or bioaccumulated in the body fat (Pirsaheb et al., 2015). Furthermore, it should be noted that washing and peeling vegetables and fruits cannot completely remove pesticide residues (Reiler et al., 2015). The numerous of negative health effects (Table 1.4) that have been associated with chemical pesticides include, among other effects, dermatological, gastrointestinal, neurological, carcinogenic, respiratory, reproductive, and endocrine effects. 54 Table 1.4.Harmful effects of the chemicals studied on human health with oral exposure according to different sources (Ali et al., 2013; ATSDR, 2018; EPA, 2018; Fenner et al., 2013b; Pereira et al., 2015; World Health Organization, 2003) Chemical Harmful effects As As (V) (as arsenate) is an analogue of phosphate and thus interferes with metabolic processes such as ATP synthesis and oxidative phosphorylation Hyperpigmentation, keratosis and possible vascular complications B Most ingested boron is absorbed and leaves the body within 4 days. Decreased fetal weight (developmental) Ba Barium is a competitive potassium channel antagonist that block the passive efflux of intracellular potassium, results in a decrease of K in the blood plasma. Hypokalemia, which can result in ventricular tachycardia, hypertension and/or hypotension, muscle weakness, and paralysis. Cd Carcinogenic, mutagenic, and teratogenic; endocrine disruptor; interferes with calcium regulation in biological systems; causes renal failure and chronic anemia Co Has both beneficial and harmful effects on human health. It is a part of the vitamin B12, has been used for the treatment of anemia because it causes red blood cells. Cr Chromium is a human carcinogen mainly by inhalation exposure in occupational sceneries. Hair loss Cu Elevated levels have been found to cause brain and kidney damage, liver cirrhosis and chronic anemia, stomach and intestinal irritation Hg Anxiety, autoimmune diseases, depression, difficulty with balance, drowsiness, fatigue, hair loss, insomnia, irritability, memory loss, recurrent infections, restlessness, vision disturbances, tremors, temper outbursts, ulcers and damage to brain, kidney and lungs Li A single large dose may result in vomiting and diarrhea. Mn Central nervous system effects Mo Increases uric acid levels Ni Allergic dermatitis known as nickel itch; inhalation can cause cancer of the lungs, nose, and sinuses; cancers of the throat and stomach have also been attributed to its inhalation; hepatotoxic, immunotoxin, neurotoxic, genotoxic, reproductive toxic, pulmonary toxic, nephrotoxic, and hepatotoxic; causes hair loss Pb Its poisoning causes problems in children such as impaired development, reduced intelligence, loss of short-term memory, learning disabilities and coordination problems; causes renal failure; increased risk for development of cardiovascular disease. Sb Affects longevity, blood glucose, and cholesterol Zn Over dosage can cause dizziness and fatigue. Amide pesticides Their symptoms include abdominal cramps, anemia, ataxia, dark urine, cyanosis, hypothermia, collapse, convulsions, diarrhea, etc. 55 Bipyridyl herbicides The main effects are dehydration (resulted from vomiting), their high oxidative stress causes necrosis in the gastrointestinal tract, kidney tubules, liver, and lung; in the latter case, respiratory failure and pulmonary fibrosis may take place. Carbamate pesticides They poorly penetrate the blood-brain barrier. Their main symptoms of carbamates intoxication are miosis, salivation, sweating, tearing, rhinorrhoea, behavioural change, abdominal pain, vomiting, diarrhea, urinary incontinence, bronchospasm, dyspnea, and so on. Dithiocarbamate pesticides Low acute oral and dermal toxicity due to their slow absorption. The metabolite that derives from dithiocarbamates biotransformation is ethylenethiourea, which induces thyroid cancer and modifies thyroid hormones. Organophosphate pesticides The skin, conjunctiva, gastrointestinal tract, and lungs rapidly absorb most these compounds and their metabolites arise 12 to 48 h. The main symptoms are the muscarinic syndrome, nauseas, vomiting, and diarrhea; and provokes urinary incontinence, bronchospasm, miosis, and bradycardia. Phenoxy alkanoic acids herbicides They are mostly absorbed by the gastrointestinal tract rather than by the lungs or skin, and they are not stored in the fat. The main symptoms are nausea, dizziness, vomiting, burning in the mouth, constipation, abdominal pain, numbness, diarrhea, gastrointestinal bleeding, among others. Pyrethroid pesticides After their absorption, fast distribution occurs in the organism, where they undergo biotransformation via two mechanisms. Some of their injuring symptoms are tremors, spasms, incoordination, drooling, convulsions, and hypersensitivity to stimuli. Triazine herbicides Human exposure has been associated with carcinogenicity and endocrine disruption, but these effects are still debatable. Triazole, diazole pesticides Propiconazole was classified as a possible human carcinogen by EPA and its ingestion of can irritate the gastric mucosa. Urea derivative pesticides For example, isoproturon has been in commercial use for a short period and no cases of human poisoning have been reported. Regarding to the occurrence of CECs in edible parts of vegetables, although the effect they pose to human health risk is not fully understood, there are some studies that consider that the consumption of some vegetables could represent a risk to human health, principally due to the presence of genotoxic compounds. Malchi et al. (2014) observed, in a field study watered with TWW, higher concentrations of pharmaceuticals compounds in leaves rather than in roots and CBZ metabolites, mainly EPOCBZ (10,11-epoxycarbamazepine, genotoxic compound), rather than the parent compound. Although for CBZ and caffeine, hundreds of kilograms of carrots or sweet potatoes should be ingested by an adult to reach the threshold of toxicological concern (TTC), for lamotrigine and EPOCBZ the TTC could be surpassed easily. A child (25 kg) and an adult (70 kg) could reach the threshold of toxicological concern (TTC) by consuming half carrot (~60 g·day-1) and two carrots a day (~180 g·day-1), respectively. Thus, indicating that specific toxicity analysis of these contaminants is needed. Riemenschneider et al. (2016) reported the uptake of 28 microcontaminants and CBZ metabolites in 10 field-grown vegetable species irrigated with TWW and evaluated the human health risk associated to the consumption of these crops. 56 For most of the compounds assessed, no risk is shown according to the TTC approach as at least 9 kg of vegetable is allowable. However, for the genotoxic ciprofloxacin and EPOCBZ, further toxicological data are required. The TTC value for EPOCBZ and ciprofloxacin could be surpassed by an adult (70 kg) by consuming only one potato (~100 g·day-1) or half an eggplant (~177 g·day-1). Another field study (Christou et al., 2017b), reported that the estimated TTC and hazard quotient (HQ) values of tomatoes watered with TWW in three consecutive years, represents a de minimis risk to human health as low values (≤ 0.015) of HQ were obtained and the daily consumption of tomato by an adult (70 kg) or a toddler (12 kg) to reach the TTC at least is 9.04 and 1.55 kg·day-1, respectively. Therefore, in order to conduct a human health risk assessment on the occurrence of TEs, pesticides and OMCs in vegetables, hazard quotient (HQ) and TTC approaches can be used. For further details on HQ and TCC approaches, see Chapter 4 section 4.2.6. 1.5 Overview of the selected contaminants In this Thesis, chemical contaminants were selected based on their occurrence in the in peri-urban agriculture due to its proximity to relevant contaminant sources (e.g. WWTP effluents, industrial runoff and road networks), and their potential to be incorporated and accumulated into edible parts of plants. Nevertheless, pesticides applied by farmers to control pests, including weeds, were also included in the study, as well as TEs listed in the Spanish Royal Decree 1620/2007 for water reuse. In fact, consumption of vegetables has shown to be the main source of human exposure to heavy metals (Martorell et al., 2011). In addition, the selected OMCs comprise pesticides used in the area of study and CECs with a high plant uptake potential, presence in irrigation waters, persistence in the environment and potential harmful effects for human health. 1.5.1 TEs Many different sources contribute to the release of TEs into the environment. Some of the most significant natural sources consist on weathering of minerals, erosion and volcanic activity; while anthropogenic sources include mining, smelting, pesticides, fertilizers, sewage sludge, atmospheric deposition, among others (Ansari et al., 2016). From 58 elements analysed in this Thesis, only 16 were finally studied being the most frequent and relevant elements in irrigation waters (Table 1.5). 57 Table 1.5. Anthropogenic sources of selected inorganic contaminants in the environment Element Sources Reference As Pesticides, veterinary pharmaceuticals and wood preservatives 9 B Production of glass, ceramics, surfactants, fire retardants, pesticides, cosmetics, photographic materials and high energy fuels 10 Ba Petroleum and steel industry, production of semiconductors and medicinal uses 5 Cd Cd-Ni battery production, paints, pigments for plastics and enamels, fumicides, phosphate fertilizers and electroplating and metal coatings 7, 9 Co Steel and alloy production, paint and varnish drying agent and pigment and glass manufacturing 9 Cr Tanneries, steel industries, fly ash, chromium plating and allows in motor vehicles are considered to be a more probable source 4 Cu Pesticides, fertilizers, industry and sewage sludge, textile mills, cosmetic manufacturing and hardboard production sludge 4, 9 Hg Electrical apparatus manufacture, electrolytic production of Cl and caustic soda, pharmaceuticals, paints, plastics, paper products, Hg batteries, pesticides and burning of coal and oil 9 Li Lithium batteries 1 Mn Fertilizers, sewage sludge and ferrous smelters 4 Mo Super alloys, nickel base alloys, lubricants, chemicals, glass workings, ink, pigments and electronics 2 Ni Production of stainless steel, alloys, automobiles batteries, storage batteries, spark plugs, magnets and machinery 8, 9 Pb Emission from combustion of leaded gasoline in the past, battery manufacture, herbicides and insecticides 9, 11 Rb Used in electronics, special glass and in the production of semi-conductors and photocells. 3 Sb Plastics, pigments of paints, liners of automobile brakes, red rubber production, ceramics, fire retardants, electronics, and glass industries 6 Zn Mining, smelting and industrial processing of ores and metals, coal combustion, batteries, accumulators, plastics and paints 11 1. Aral and Vecchio-Sadus (2008); 2. Halmi and Ahmad (2014); 3. Kabata-Pendias and Mukherjee (2007); 4. Khan et al. ( 2007); 5. Kravchenko et al. ( 2014); 6. Mubarak et al. (2015); 7. Pulford and Watson (2003); 8 Tariq et al. (2006); 9. Thangavel and Subbhuraam (2004); 10. USEPA (2008); 11. Wuana and Okieimen (2011) 64 Carbendazim Lettuce 210 4 Chlorpyrifos Cauliflower 5-440 1, 6, 8, 11, 18 Lettuce 5.2-1524 4, 6, 12 Tomato 3.8-295 1, 4,6, 8, 12, 18, 21 Diazinon Tomato 0.3-29.5 21 Dimethomorph Lettuce 120 10 Indoxacarb Tomato 12-8 4 Pymetrozin Lettuce 80 10 Tomato 45-380 4, 10 Pyraclostrobin Lettuce 80 10 Tomato 10 10 Plasticizers (ng·g-1 fw) Bisphenol A Tomato 1.6-8.4 2 Lettuce 3.3-8.4 2 4-tert-octylphenol Tomato nd 20 Lettuce nd 20 TEs (mg·kg-1 fw) b As Cauliflower 0.01 15 As Lettuce 0.01-0.03 5, 22 As Tomato 0.002-0.003 22 Ba Vegetables 0.49a 7 Cd Cauliflower nd-0.09 15 Cd Lettuce nd-0.03 5, 15, 22 Cd Tomato 0.004-0.039 14, 22 Cr Vegetables 0.16a 7 Cr Cauliflower 0.04 15 Cr Lettuce 0.002-0.056 5, 15 Cu Vegetables 0.94a 7 Cu Cauliflower nd-1.47 16,17 Cu Lettuce 0.03-0.04 22 Cu Tomato 0.01-0.10 14, 22 Hg Cauliflower 0.001 15 Hg Lettuce 0.002 15 Mn Vegetables 2.24a 7 Mo Vegetables 0.16a 7 Ni Vegetables 0.36a 7 Ni Cauliflower 0.063 15 Ni Lettuce 0.05 15 Pb Cauliflower nd-0.02 15, 16 Pb Lettuce nd-0.08 5,15, 22 Pb Tomato 0.01-1.06 14, 22 Sb Vegetables 0.01a 7 Zn Vegetables 5.69a 7 Zn Cauliflower 1.85-6.28 16,17 Zn Tomato 0.24-0.73 14, 22 nd non-detected; a Median value is given; b data has been transformed to fw using as lettuce’s moisture 95.5%, tomato’s moisture 80.7% and 91.4% from cauliflower. 65 1. Alamgir et al. (2013); 2. Albero et al. (2017); 3. Aparicio et al. (2018); 4. Bakirci et al. (2014); 5. Chang et al. (2014); 6. Chen et al. (2005); 7. Generalitat de Catalunya (2015); 8. Latif et al. (2011); 9. LeFevre et al. (2017); 10. Lemos et al.(2016); 11. Lozowicka (2015); 12. Mac Loughlin et al. (2018); 13. Maher et al. (2018); 14. Mohod (2015); 15. Pan et al. (2016); 16.Singh and Singh (2014); 17.Singh and Kumar (2006); 18. Sinha et al. (2012); 19. Wu et al. (2014); 20. Yang and Ding (2005); 21. Yu et al. (2016); 22. Zhou et al. (2016) 1.6 Hypothesis and objectives Taking into consideration the evidence on the absorption of chemical contaminants by plants reported in the literature, the following hypothesis were proposed: 1. Peri-urban agriculture is exposed to a greater concentration of chemical contaminants through water irrigation, air or soil may end up with vegetables, which have high concentration of chemical contaminants. 2. Vegetables from peri-urban agriculture exposed to a higher concentration of contaminants may have negative yield and human health implications compared to rural farming. To address these hypotheses, the following objectives were established. The overall aim of this Thesis is the evaluation of the occurrence of several chemical contaminants in peri-urban agriculture (irrigation water, soil and vegetables) as well as their effect on crop productivity and human health, compared with rural agriculture. Therefore, in order to accomplish the general objective, this Thesis comprises the following specific objectives: 1. Evaluate the presence of the selected chemical contaminants (OMCs and TEs) in the irrigation waters used in the four peri-urban and one rural farm plots located in the Baix Llobregat Agrarian Park (Barcelona, Spain), 2. Assess the effect of the above-mentioned irrigation waters on seed germination and crop productivity. 3. Evaluate the effect of seasonality on the presence of the contaminants in soil and lettuce leaves and estimate their bioconcentration factors. 4. Assess the effects of the studied chemical contaminants in lettuce leave components (chlorophyll, nitrate, lipid and carbohydrate content). 5. Evaluate the concentration of contaminants (OMCs and TEs) in different food crops (i.e. lettuce, tomato, cauliflower and broad beans) grown under peri-urban and rural agriculture. 6. Evaluate the potential human health risk of the consumption of vegetables grown under periurban agriculture in comparison to those grown under rural agriculture by applying HQ and TTC approaches. 66 Chapter II: Occurrence of chemical contaminants in peri-urban agricultural irrigation waters and assessment of their phytotoxicity and crop productivity This chapter is based on the article: Margenat, A., Matamoros, V., Díez, S., Cañameras, N., Comas, J., & Bayona, J. M. (2017). Occurrence of chemical contaminants in peri-urban agricultural irrigation waters and assessment of their phytotoxicity and crop productivity. Science of the Total Environment, 599–600, 1140–1148. Water scarcity and water pollution have increased the pressure on water resources worldwide. This pressure is particularly important in highly populated areas where water demand exceeds the available natural resources. In this regard, water reuse has emerged as an excellent water source alternative for peri-urban agriculture. Nevertheless, it must cope with the occurrence of chemical contaminants, ranging from TE to OMCs. In this study, chemical contaminants (i.e., 15 TEs, 34 OMCs), bulk parameters, and nutrients from irrigation waters and crop productivity (Lycopersicon esculentum Mill. cv. Bodar and Lactuca sativa L. cv. Batavia) were seasonally surveyed in 4 farm plots in the peri-urban area of the city of Barcelona. A pristine site, where rain-groundwater is used for irrigation, was selected for background concentrations. The average concentration levels of TEs and OMCs in the irrigation water impacted by TWW were 3 (35 ± 75 μg·L−1) and 13 (553 ± 1050 ng·L−1) times higher than at the pristine site respectively. Principal component analysis was used to classify the irrigation waters by chemical composition. To assess the impact of the occurrence of these contaminants on agriculture, a seed germination assay (Lactuca sativa L.) and real field-scale study of crop productivity (i.e., lettuce and tomato) were used. Although irrigation waters from the peri-urban area exhibited a higher frequency of detection and concentration of the assessed chemical contaminants than those of the pristine site (P1), no significant differences were found in seed phytotoxicity or crop productivity. In fact, the crops impacted by TWW showed higher productivity than the other farm plots studied, which was associated with the higher nutrient availability for plants. 67 2.1 Introduction Water scarcity is increasing with global changes and already affects almost every continent and >40% of the world's population (UN-WATER 2016). Agriculture accounts for 70% of global water withdrawals, a figure that rises to 80% in arid and semiarid regions. In this context, the direct or indirect reuse of TWW for crop irrigation can be considered a reliable and strategic water supply, quite independent from seasonal drought and weather variability and able to cope with peaks in water demand. This can be very beneficial to farming activities that rely on a continuous water supply during the irrigation period, reducing the risk of crop failure and income losses (EC 2016). Appropriate nutrient appraisal in TWW could also reduce fertilization needs, resulting in environmental benefits and a reduction in production costs (Haruvy, 1997). This is particularly important in peri-urban agriculture, which is characterized by a high water demand and proximity to TWW (Kurian et al., 2013). However, TWW may contain pollutants and pathogens, which can constitute a threat to human health when the TWW is used for agricultural irrigation (Becerra-Castro et al., 2015; Prosser and Sibley, 2015). Those contaminants include so-called CECs, chemicals of a synthetic origin or deriving from a natural source that have recently been found to have possible harmful effects on environmental and public health, although the extent of the risk has yet to be determined (Naidu et al., 2016). Moreover, the use of TWW also raises the levels of metals, such as Cu, Zn, Fe, Pb, and Ni, in the receiving soils and, as a consequence, in the medium term can affect agricultural productivity and human health if they are uptaken by crops (Rattan et al., 2005). Therefore, although the occurrence of some metals in crops is already regulated in different countries due to their human health implications (Khan et al., 2015, 2013) CECs remain unregulated. Recent reports have shown that the occurrence of OMCs in irrigation waters is highly dependent on the source of water used. For instance, Calderón-Preciado et al. (2013) observed that irrigation water from a secondary TWW contains a higher concentration of OMCs such as pharmaceuticals and personal care products (772 ng L−1 on average) than groundwater (31 ng L−1 on average). Recent laboratory and greenhouse studies have shown that OMCs can produce phytotoxic, morphological and physiological changes in crop plants (Carter et al., 2015; Carvalho et al., 2014; Christou et al., 2016; Marsoni et al., 2014; Shahid et al., 2015), but until now there is no evidence of their effect on crop productivity at real field scale. Rattan et al. (2005) observed that irrigation with TWW for 20 years resulted in a significant build-up of extractable TEs such as Zn (208%), Cu (170%), Fe (170%), Ni (63%), and Pb (29%) compared to adjacent soil irrigated with tube-well water. According to the FAO, the threshold levels of TEs for crop production depend on the crop and the element (FAO 1985). For instance, As toxicity to plants ranges from 12 mg L−1 for Sudan grass to <0.05 mg L−1 for rice. Co is phytotoxic to tomato plants at 0.1 mg L−1 in nutrient solution, and Cu is phytotoxic at 0.1 to 1.0 mg L−1. Zn is phytotoxic to many plants at widely varying concentrations, while Ni is phytotoxic to a number of plants at 0.5 to 1.0 mg L −1; in both cases, toxicity is reduced at neutral or alkaline soil pH. Finally, while 0.2 mg L−1 of B in water 68 is essential for some crops, at concentrations of 1 to 2 mg L−1 it becomes phytotoxic. Recent findings suggest that the presence of macronutrients (e.g. N, P, K, Ca, Mg) and micronutrients (e.g. Cu, Fe, Zn, Mn, etc.) in the TWW leads to an increase in crop productivity (Li et al., 2015; Urbano et al., 2017). Notwithstanding these findings, there is no information about the co-occurrence of OMCs and TEs in irrigation waters impacted by TWW effluents and their potential effect on crop productivity in real field conditions. This study aimed to assess the occurrence of chemical pollutants (15 TEs, 34 OMCs, and nutrients) in irrigation waters, as well as their effect on crop phytotoxicity (i.e., seed germination) and productivity (i.e., lettuce and tomato) in 4 farm plots located in the peri-urban area of the city of Barcelona (NE Spain). The results were also compared with background concentrations from a farm plot located far away from the peri-urban area. 2.2 Material and methods 2.2.1 Description of the study area The area of study was located in the delta and low valley of the Llobregat River (NE Spain). This traditionally rich farmland, also known as the Baix Llobregat Agrarian Park (BLAP), is a protected farmland precinct spanning 3300 ha in the metropolitan area of Barcelona (Paül and McKenzie, 2013). In this region, farmland, the river, and natural or semi-natural sites exist side by side with urban sprawl, with the concomitant population pressure and environmental impact. The peri-urban area of the BLAP is characterized by a gradient of atmospheric and irrigation water pollution originating from industrial, urban, and agricultural activities. In this study, 5 plots were selected based on their irrigation water source. The plots included 4 farm plots located inside the peri-urban area (P2-P5, <50 m asl) and 1 pristine site (P1) located 400 m asl on a Karstic massif in the west of the BLAP (Fig. 2.1). 69 Figure 2.1 Map of the sampling area. P1. Begues; P2. Prat de Llobregat P3. Sant Joan Despí; P4. Sant Boi de Llobregat; P5. Viladecans. Each farm plot had a surface area of over 0.1 ha planted with different seasonal vegetables (i.e., lettuce, tomato, onion, and cauliflower). The soil in the peri-urban sites is formed by rich sediments deposited over the years by the river from which this area takes its name. All places have coastal Mediterranean climate. The soil analysis performed prior to lettuce cultivation indicated for the 5 sites an adequate level of fertility to grow vegetables (N-NO3 > 2 mg L−1; P > 15 mg L−1; K+ > 180 mg L−1) and similar electrical conductivity (2.2 - 2.8 dS m−1) and pH (7.6 -7.8). The 5 plots presented very permeable soils, P1, P3 and P4 soil's texture was sandy loam and P2 and P5 sandy. Most of the BLAP area (1240 ha) is watered with irrigation water from the Llobregat River. The river's average flow rate is 137 hm3 year−1, and it drains an area of 4948 km2. The Llobregat River and its two main tributaries, the Cardener River and Anoia Stream, receive discharges from 80 urban and industrial WWTPs. Furthermore, the central area of the basin receives brine leachates from natural salt formations and mining operations, which have caused an increase in water salinity downstream. The river water in the BLAP area flows through interconnected open-air concrete distribution channels (P2 and P4), but additional water sources such as well water are also used (P5). The irrigation water for site P3 originates in the Infanta Channel, which is mostly made up of TWW from 10 WWTP effluents (Rubí Creek). Therefore, whereas irrigation waters from P2 and 4 are a clear example of unplanned indirect water reuse, P3 is of planned indirect water reuse. Most of the WWTPs impacting irrigation waters (P2-P4) consisted of conventional activated sludge treatments without any additional polishing system. The sampling site located in Viladecans (P5) uses well water impacted by industrial and road runoff. 70 Additionally, a reference site (P1) was selected for the purposes of comparison in a pristine area in the Littoral Mountains where ground-rainwater is used for irrigation. Drip irrigation system was used in the reference plot (P1), while furrow irrigation was applied in the other farm plots (P2 - P5). The volume of irrigation water supplied to the cultures was similar among sites P2 - P5 (lettuce: 150 - 170 mm irrigation + 120 mm precipitation; tomato: 570 mm irrigation + 80 mm precipitation) and P1 (lettuce: 80 mm irrigation + 160 mm precipitation; tomato: 520 mm irrigation + 70 mm precipitation). 2.2.2 Sampling plan Irrigation water The sampling was carried out between February and September of 2016 during the growing period of the different crops of interest (lettuce and tomato). Fig.2.1 shows the location of both the sampling points in the irrigation network from the peri-urban area of the BLAP (P2 - P5) and the reference site (P1). In each farm plot, between four and ten irrigation water samples were analysed (Table 2.1). All water samples were collected directly from the irrigation canals, except in the P5 were water samples were collected in the irrigation pipeline, after the ferti-irrigation system. Water samples for TE determination were collected in acid-washed (2% HNO3) 125 mL fluorinated ethylene propylene (FEP) bottles (Thermo Scientific Nalgene, Rochester, NY, USA). After each field campaign, < 2h after their collection and once in the laboratory, the water samples were filtered (< 0.45 μm) with nylon membranes using a syringe filtration unit acidified with nitric acid (pH < 2). The samples were then stored in the fridge in a pre-cleaned bottle until analysis by ICP-MS and ICP-OES. Water samples for the determination of OMCs and conventional water quality parameters were collected in pre-cleaned 2.5 L amber glass bottles. All samples were kept refrigerated during transport to the laboratory, where they were stored at 4 °C until they were analyzed. Crops Lettuce (Lactuca sativa L. cv. Batavia) and tomatoes (Lycopersicon esculentum Mill. cv. Bodar) were harvested when they reached commercial size. The lettuce seedlings were planted in March 2016, and the plants were harvested in May 2016 (P1 - P5), whereas the tomato seedlings were planted in May - June and harvested in September 2016 (P1, P3, and P5). In each farm plot, 50 lettuces and 50 tomatoes fruits were randomly harvested, weighted over an area of 0.1 ha (P1 - P5). The same integrated management plan (fertilization and pesticide application) was used in all farm plots from the BLAP. 2.2.3 Analytical procedures Water quality parameters Conventional water quality parameters, including ammonium nitrogen (NH4+-N), NO3-N, total phosphorous (TP), and total suspended solids (TSS), were determined in all the water samples. The nutrients were measured with Hach Lange NH4+-N, NO3-N, and TP cell tests (LCK 303, 304, 339, and 71 349) on a spectrophotometer (Hach Lange DR 1900 Portable Spectrophotometer). Measurements of water pH, conductivity, temperature, and dissolved oxygen (DO) were taken using Hach Lange sensors. Trace elements The TEs were selected due to their inclusion in the Spanish Royal Decree 1620/2007 for water reuse, and since their accumulation by food crops, particularly vegetables, is of increasing concern because of the potential human health risks to the consumers (Khan et al., 2008). An inductively coupled plasma optical emission spectrometer (Thermo Scientific, iCAP 6500 ICP-OES) and an inductively coupled plasma mass spectrometer (Thermo Scientific, XSeries 2 ICP-MS) were used for the determination of TEs in the water samples. The most frequently found elements (i.e., 13 out of the 58 elements analyzed) were divided into three categories: major TEs (Ba, B, Mn, and Li), most common TEs (Mo, Pb, Zn, Cu, Ni, Co, and Cr), and other non-common TEs (Rb and Sb). Reagent water was used as a blank matrix, and laboratory reagent blank (LRB) was treated exactly the same as a sample, including exposure to all glassware, equipment, solvents, and reagents used with the other samples. A limit of detection (LOD) of 0.2 μg L−1 was determined from three times the standard deviation obtained from the analysis of ten runs of blank samples on the same day as the determinations. Similarly, the limit of quantification (LOQ) was calculated by multiplying the standard deviation by ten (0.67 μg L−1). Organic microcontaminants (OMCs) In this survey study, the prioritization of OMCs was based on the compounds' potential plant uptake, log Kow < 4.0 (Table S2.1), occurrence in irrigation waters, persistence, and potential harmful effects for human health (Banjac et al., 2015; Ginebreda et al., 2010; Prosser and Sibley, 2015). The determination of OMCs was performed as described by Matamoros and Bayona (2006). Briefly, 250 mL of filtered water samples was spiked with 100 ng of a surrogate standard mixture (see section 2.5.2). The samples were then percolated through a conditioned 200 mg STRATA X solid-phase extraction cartridge (Phenomenex, Torrance, USA). Elution was performed with 15 mL of ethyl acetate. After that, the eluted extract was evaporated under a gentle nitrogen stream until ca. 250 μL and 100 ng of triphenylamine was added. Derivatized and non-derivatized aliquots of the sample extracts were analyzed with an EI-GC–MS/MS Bruker 450-GC gas chromatograph coupled to a Bruker 320-MS triple-stage quadrupole mass spectrometer (Bruker Daltonics Inc., Billerica, MA, USA). The derivatization of samples was carried out by methylation of the acidic hydroxyl groups in a programmed temperature vaporizing (PTV) injector of the gas chromatograph by adding 10 μL TMSH to a 50 μL sample aliquot before injection. A volume of 5 μL was injected into a Bruker 450-GC gas chromatograph coupled to a Bruker 320-MS triple quadrupole mass spectrometer (Bruker Daltonics, Billerica, MA, USA) fitted with a 20 m × 0.18 mm ID, 0.18 μm film thickness Sapiens X5-MS capillary column coated with 5% diphenyl 95% dimethyl polysiloxane from Teknokroma (Sant Cugat del Vallès, Spain). The PTV injector was set at 60 °C for 0.5 min and then rapidly heated up to 300 °C at 200 °C 72 min−1 and held for 10 min. It was then cooled to the initial 60 °C at 200 °C min−1. The gas flow rate was set at 0.6 mL min−1. The ion source temperature and transfer line were both held at 250 °C. A solvent delay of 7 min was applied. Argon gas was used for CID at a pressure of 1.8 mTorr, and the optimum collision energy (CE) was selected for each transition. Qualitative and quantitative analyses were performed based on retention time and the selection reaction monitoring (SRM) mode of two product ions, and the ratio between the product ions was used for confirmation. The LOD and LOQ were defined as the mean background noise in a blank triplicate plus three and ten times, respectively, the standard deviation of the background noise from three blanks. The LOD ranged from 0.1 to 50 ng L−1, and the LOQ from 0.3 to 80 ng L−1, except for benzotriazole and derivatives, which exhibited an order of magnitude higher. The monitoring ions, CEs, LODs, LOQs, and recoveries can be found in the supporting information (Tables S2.2–2.5). 2.2.4 Seed germination bioassay and crop productivity The seed germination assay was performed as previously described by (Marsoni et al., 2014). Briefly, lettuce seeds (Vilmorin Jardin, St Quentin Fallavier Cedex, France) were sterilized with 2.5% sodium hypochlorite for 15 min and thoroughly washed with distilled water. Hydrated seeds were transferred to Petri dishes (100 mm diameter) containing a GF/F filter in the presence of 5 mL of distilled water (control) or irrigation waters. For each irrigation water, 10 dishes containing 10 seeds each (n = 100 per irrigation water type) were prepared and incubated in the dark at 25 °C. After 72 h, the germinated seeds were counted. Seeds were considered germinated when root elongation was >3 mm; a minimum of 80% germinated seeds in the control dishes was required. Lettuce and tomato yields were determined at commercial size in 0.1 ha plots in commercial fields. The time needed for them to reach commercial size was also recorded. Crop productivity for lettuce was calculated by multiplying the measured fresh weight per a survival factor of 0.8 by the number of crops per square meter (6.5 plants m−2). 50 tomatoes fruits (10 fruits × 5 sections × plot) were sampled to determine their average weight. 2.2.5 Data analysis The experimental results were statistically evaluated using the SPSS v. 22 package (Chicago, IL, US). All data sets were checked for normal distribution using the Kolmogorov–Smirnov test to ensure that parametric statistics were applicable. The comparison of means of the occurrence of chemical pollutants between farm plots was performed with a two-paired (Wilcoxon) signed-rank test (the concentration of each compound was compared between farm plots). A Mann-Whitney U test was used for the comparison of conventional quality parameters, and a one-way ANOVA was used for the productivity studies (n =50-100). Principal component analysis (PCA) was conducted on the concentration levels of TEs, OMCs, and nutrients. Once the data matrix had been completed, it was autoscaled to have zero mean and unit variance (correlation matrix). Statistical significance was defined as p ≤0.05. 73 2.3 Results and discussion 2.3.1 Conventional water quality parameters Table 2.1 shows the conventional water quality parameter values for the 5 irrigation plots studied. Irrigation waters from the peri-urban area of Barcelona had higher electrical conductivity than irrigation waters from the pristine zone (site P1). This can be attributed mainly to two factors: 1) the high impact of TWW on irrigation sites P2, P3, and P4) the impact of salt mining on the Llobregat River Basin (Momblanch et al., 2015). Additionally, the high electrical conductivity observed in the groundwater from the Llobregat Delta (site P5) may be due to seawater intrusion and agricultural activities (Miracle, 1989; Otero and Soler, 2002). Similarly, the concentration of TSS was higher at sites P2, P3, and P4 than at sites P1 and P5 (p < 0.05) and ammonium was lower at sites P1, P2, and P4 than at sites P3 and P5 (p < 0.05). This is in keeping with the fact that the irrigation waters of the former (i.e., P2, P3, and P4) originated from surface water bodies (i.e., Llobregat River and TWW effluents), whereas the irrigation water source of sites P1 and P5 was groundwater. The high concentration of TP, ammonium, and nitrates at site P5 was accounted for by the fact that the water samples were collected in the irrigation pipeline, after the ferti-irrigation system. When the P5 site was excluded, P3 showed the highest levels of conductivity and nutrients (p < 0.05). This is consistent with the fact that the water from the Infanta Channel is fed by Rubí Creek, a stream that is mainly composed of worse-quality TWW than that of the Llobregat River (González et al., 2012). Nevertheless, the irrigation water quality complied with the Spanish guidelines for water reuse in accordance with the general quality parameters assessed in this study (Royal Decree 1620/2007). In addition to these parameters, Spanish Royal decree also includes intestinal parasites, Salmonella sp., Escherichia coli (not included in this study), and the TEs listed in Table 2.2. Table 2.1 Minimum, maximum and average levels of general quality parameters in the studied irrigation waters. Levels below the LOD were replaced by ½ LOD. Plot 1 (n = 5) Plot 2 (n = 4) Plot 3 (n = 10) Plot 4 (n = 8) Plot 5 (n = 4)a Conductivity (μS cm− 1) (968–1211) 1049 (1519–1645) 1584 (1490–2148) 1944 (1255–1707) 1482 (1272–2370) 1663 NH4+–N (mg L− 1) (0.002–0.167) 0.05 (0.1–0.7) 0.3 (3–47) 14 (0.1–0.6) 0.2 (0.1–12.8) 4.2 Nitrates (mg L− 1) (2.8–4.6) 3.9 (1.8–2.7) 2.1 (3.4–7.4) 5.4 (1.5–2.5) 2.0 (4–175) 55 TP (mg L− 1) (0.03–3.0) 0.6 (0.2–2.4) 0.8 (0.6–2.5) 1.5 (0.2–0.7) 0.3 (0.6–6.2) 2.5 TSS (mg L− 1) (10–55) 21 (13–84) 33 (14–94) 46 (13–90) 63 (2–40) 18 pH (7.5–8.6) 8.1 (8.1–8.6) 8.4 (7.7–8.1) 7.9 (6.7–8.6) 8.1 (6.8–7.8) 7.4 TP: total phosphorous; TSS: total suspended solids. a Water samples collected from the irrigation pipeline (may contain chemical fertilizers due to ferti-irrigation). 2.3.2 Occurrence of trace elements (TEs) Table 2.2 shows that 13 out of the 58 TEs studied were detected above the LOQ in all irrigation waters. The TEs detected at the highest concentrations in all irrigation waters were B and Ba. This high abundance is consistent with their predominant geogenic origin (Kabata-Pendias and Mukherjee, 2007), 80 2.3.5 Phytotoxicity studies (lettuce seed germination) Table 2.5 shows that germination was higher in seeds watered with irrigation waters than with distilled water (88 vs. 98-99%). This may be explained by the higher concentration of macroand micronutrients such as nitrates and salts in the irrigation waters that would help break dormancy to facilitate seed germination (Hilton, 1985; Rezvani et al., 2014). The root elongation was higher at the sampling site P4 than in the distilled water (p = 0.01), but no differences were found among the irrigation waters from sites P1, P3, and P4 (p > 0.05). Likewise, no differences were observed between distilled water and the irrigation water for sites P1 and P3 (p > 0.05). Therefore, the higher FOD and concentration of OMCs, TEs, and nutrients in the irrigation water from site P3 did not affect seed germination. Similar results were reported by Marsoni et al. (2014), who observed that pharmaceutical compounds only affected seed germination at concentrations higher than 10 mg L−1. Table 2.5 Effect of irrigation waters on in vitro seed germination of lettuce (n=100). Control* Plot 1 Plot 3 Plot 4 Germination (%) 88 98 98 99 Root elongation (mm) 17.2 ± 0.9d 19.4 ± 0.8 19.5 ± 0.8 20.8 ± 0.9a *Seeds were grown in distilled water; Significant differences between plots are shown (control = a, P1 = b, P3 = c, and P4 = d), statistical differences at p = 0.05. 2.3.6 Crop productivity: a field study Many authors reported the importance of the pedological conditions related to agricultural and horticultural production systems (Andrews et al., 2002; Armenise et al., 2013; Bouma and Droogers, 1998; Mukherjee and Lal, 2014; Vasu et al., 2016), but in our study soil composition was similar; therefore, differences may be related to the water irrigation quality. Unfortunately, crop productivity can also be affected by other environmental conditions. For instance, the pristine site (P1) was located at 400 m asl, whereas the other plots were at sea level. Table 2.6 shows the crop productivity for lettuce and tomato fruits in the 5 farm plots (P1-P5). The productivity values for tomatoes fruits and lettuce were in keeping with those found in other studies. For instance, Serna et al. (2012) reported a lettuce plant yield of 34,000 ± 1000 kg ha−1. The results from farm plot P3 should be approached with caution, since they were harvested after 77 cropping days (yield of 96,428 Kg ha−1), whereas the other plots were harvested after 6169 days (yield of 40,178–52,187 Kg ha−1). Casals et al. (2010) observed an average tomato fruit weight of 138 g, which is in the range of the yield observed at the pristine site (P1), whereas the average tomato weight observed in the peri-urban area was slightly higher (189 to 208 g per fruit on average). Therefore, the results showed that lettuce and tomato productivity was significantly higher in the farm plot in which indirect water reuse with TWW prevails (P3) (p < 0.05), probably due to the higher nutrient content (Table 2.1). Suspended, colloidal, and dissolved solids present in TWW contain macronutrients (e.g. N, P, K, Ca, Mg) and micronutrients (e.g. Cu, Fe, Zn, Mn, etc.) required by many crops (Abu-Zeid., 1998). This is in keeping with the results found by Urbano et al. (2017) who observed 81 that the concentration of some soil nutrients (e.g. K, Ca, Al, and S) increased after irrigation with TWW and that lettuce production (in terms of fresh weight) was higher in lettuce cultivated in TWW than in those cultivated by conventional fertilization. Similarly, Li et al. (2015b) observed that irrigation with TWW increased tomato biomass and yield by 9%. Furthermore, the productivity results are consistent with the fact that the concentration levels of TEs in irrigation waters were between 10 and 100 times lower than the values observed to produce phytotoxicity (see Table 2.2). Table 2.6 Fresh weight per unit, crop growing time and productivity for lettuce and tomatoes in the 5 farm plots studied. Statistical differences at p = 0.05; super index letters show significant differences between plots (P1 = a, P2 = b, P3 = c, P4 = d, and P5 = e); f time required to reach commercial size. g fresh weight. Statistical assessment for lettuce yield is the same than for lettuce. These results and the non-effect of the presence of chemical contaminants on lettuce seed germination and crop productivity suggest that the use of water impacted by TWW effluents for crop irrigation is beneficial due to the reduction of fertilization costs and water availability over the production cycle. 2.4 Conclusions The results of this study show that the occurrence of OMCs, TEs, and nutrients in irrigation waters depends on the water source used (surface water vs. groundwater). Nevertheless, irrigation waters from peri-urban areas are more likely to contain chemical contaminants than those from pristine areas. The following key conclusions can be drawn: - The irrigation waters from the peri-urban area of the BLAP showed higher conductivity and nutrient levels than the pristine site. - Ba and B were the TEs with the highest concentration in all irrigation waters, whereas the irrigation water from the peri-urban area of the BLAP had the highest concentration levels of Zn and Mn. - Irrigation waters originating from surface water bodies had a higher FOD and concentration of OMCs than irrigation water originating from groundwater, and Surfynol 104 was the most abundant compound. - The irrigation water from site P3 (Infanta Channel) was the most impacted by nutrients, TEs, and OMCs since it is mainly made up of TWW. - The higher occurrence of TEs and OMCs in the peri-urban irrigation waters did not affect seed germination, root elongation, or crop productivity. Although our study shows that peri-urban agriculture is exposed to a higher concentration of TEs and OMCs and this did not affect crop productivity, further research is needed to exclude possible adverse Plot 1 Plot 2 Plot 3 Plot 4 Plot 5 Lettuce (g, fresh weight) 773 ± 38c,d 795 ± 37 c,d 1854 ± 71 a,b,d,e 1004 ± 44 a,b,c,e 805 ± 40c,d Lettuce (growing time, days)f 61 62 77c 69 66 Lettuce yield (kg ha− 1, fresh weight)g 40,178 ± 6342 41,332 ± 9640 96,428 ± 13,264 52,187 ± 6753 41,877 ± 12,332 Tomatoes (g, fresh weight per unit) 157 ± 41c,d – 207 ± 39a 189 ± 46a – 82 human health effects or nutritional crop changes associated with the use of irrigation waters containing these substances. 83 2.5 Supporting Information 2.5.1 Materials and Reagents Flame retardants (i.e, TCEP and TCPP), benzotriazoles and benzothiazoles (i.e., 1,3-benzothiazole, 2mercaptobenzothiazole, benzotriazole, 5-methyl-2H-benzotriazole and 1-hydroxybenzotriazole), parabens (methylparaben, etylparaben,butylparaben and propylparaben), antioxidant (i.e., butylated hydroxyanisole (BHA)), plastifiers (bisphenol A, bisphenol F and 4-tert-octylphenol), tensioactive (2,4,7,9-tetramethyl-5-decyne-4,7-diol (surfynol 104)), some pharmaceuticals (i.e., carbamazepine, diazepam, lamotrigine, lorazepam, primidone, oxazepam) and some pesticides (i.e., azoxystrobin, dymethomorph, pyraclostrobin, chlorpyrifos, diazinon, pymetrozin, indoxacarb, DEET) were purchased from Sigma-Aldrich (Bornem, Belgium). Other pesticides (i.e., carbamazepine-10,11-epoxide, carbendazim, atrazine and simazine) were supplied by Fluka (Buchs, Switzerland). Surrogates used were bisphenol A-d16, carbamazepine-13C6, diazepam-d5, 5,6-dimethyl-1Hbenzotriazole (XbTri), ethylparaben-13C and lamotrigine-13C15N4 purchased from Sigma-Aldrich (Bornem, Belgium) and caffeine-13C3 obtained from Fluka (Buchs, Switzerland). Internal standard triphenylamine (TPhA, 98%) was purchased from Sigma-Aldrich (St. Louis, MO, USA) and trimethylsulfonium hydroxide (TMSH) was obtained from Fluka (Buchs, Switzerland). Suprasolv® grade methanol, hexane, ethyl acetate were purchased from Merck (Darmstadt, Germany). Reagent water was deionized using the ultrapure water system Arium 611 from Sartorius (Aubagne, France). Strata-X solid phase extraction (SPE) cartridges (200mg / 6 mL) were purchased from Phenomenex (Torrance, CA, USA) and 0.70µm of glass filters 47mm in diameter were obtained from Whatman (Maidstone, UK). 2.5.2 Sample extraction CECs in wastewater and interstitial water samples were analyzed following a previously described methodology (Matamoros and Bayona, 2006). A sample volume of 250 mL was spiked at 0.25 ppb of a surrogate standard mix. The spiked sample was percolated through a polymeric solid-phase extraction cartridge, 200 mg Strata X from Phenomenex (Torrance, CA). Cartridges were conditioned with 6 mL of n-hexane, 6 mL of ethyl acetate, 10 mL of methanol and 10 mL of distilled water (pH=7). The spiked samples were percolated through the cartridges under vacuum, were allowed to dry for 30 min and eluted with 15 mL ethyl acetate. Then, the extract was evaporated until ca. 250 µL under a gentle nitrogen stream, and 25µL of triphenylamine (TPhA) as internal standard was added. 2.5.3 GC-MS/MS determination Aliquots of the sample extracts were analyzed with an EI-GC-MS/MS Bruker 450-GC gas chromatograph coupled to a Bruker 320-MS triple-stage quadrupole mass spectrometer (Bruker 84 Daltonics Inc., Billerica, MA, USA). The linearity range was from 0.80 to 500 µg·L-1. The correlation coefficients (R2) of the calibration curves were always higher than 0.99. Physicochemical properties of the CECs of study (Table S2.1), monitoring ions (Table S2.2 and Table S2.3), LODs and LOQs (Table S2.4) and recoveries of the surrogates used (Table S2.5) can be found in the supplementary information. 85 Table S2.1 Physicochemical properties of the CECs of study Name Molecular structure CAS Number Molecular Weight Molecular formula pKa1 Solubility (mg L-1) Log KOW2 Log DOW (pH=7.4) Log KOA2 Henry LC (atmm3/mole) Log KAW2 Parabens Methyl paraben 99-76-3 152.15 C8H8O3 8.50[0/-] 2500(a) 2.00 2.09 8.791 3.61E009 - 6.831 Propylparaben 94-13-3 180.21 C10H12O3 8.50[0/-] 500(a) 2.98 2.81 9.624 6.37E009 - 6.584 Ethylparaben 120-478 166.18 C9H10O3 8.50[0/-] 885(a) 2.49 2.48 9.178 4.79E009 - 6.708 Butylparaben 94-26-8 194.23 C11H14O3 8.47[0/-] 207(a) 3.57 3.12 10.032 8.45E009 - 6.462 Tert-butylphenols 2-tert-Butyl-4-methoxyphenol (BHA) 121-006 180.25 C11H16O2 10.57[0/-] 212.8 3.50 3.14 8.956 8.56E008 - 5.456 Benzotriazoles Benzotriazole (BTri) 95-14-7 119.12 C6H5N3 8.37[0/-] 5957 1.44 - 6.661 1.47E007 - 5.221 86 5-Methyl-2H-benzotriazole (5-TTri) 136-856 133.15 C7H7N3 0.77[+/0] 8.85[0/-] 1769 1.71 1.69 6.889 1.62E007 - 5.179 1-Hydroxybenzotriazole (OHBT) 259295-2 135.13 C6H5N3O 6.88[0/-] 2.258E+004 0.11 0.07 - - - Benzothiazoles 1,3-Benzothiazole (BT) 95-16-9 135.18 C7H5NS 2.28[+/0] 4300(a) 2.17 2.09 6.826 3.74E007 - 4.816 2-Mercaptobenzothiazole (2MBT) 149-304 167.24 C7H5NS2 10.90[0/-] 120(c) 2.86 2.21 8.249 3.63E008 - 5.829 Plasticizers Bisphenol A 80-05-7 228.29 C15H16O2 9.78[0/-] 10.39[-/2-] 120(d) 3.64 3.63 12.747 9.16E012 - 9.427 Bisphenol F 620-928 200.24 C13H12O2 9.84[0/-] 10.45[-/2-] 542.8 3.06 2.90 12.582 5.2E012 - 9.672 Anticonvulsants, antidepressants and its related metabolites Carbamazepine 298-464 236.28 C15H12N2O 13.9[0/-] 112(e) 2.25 2.28 10.805 1.08E010 - 8.355 Carbamazepine-10,11-epoxide 3650730-9 252.27 C15H12N2O2 15.96 [0/-] 276.8 0.95 1.31 11.503 6.84E013 - 10.553 Primidone 125-337 218.26 C12H14N2O2 2.36[+/0] 3.94[0/-] 5.42[-/2- ] 500(c) 0.73 0.61 9.011 1.94E010 - 8.101 87 Lamotrigine 8405784-1 256.10 C9H7Cl2N5 8.53[+/0] 9.21[0/-] 139.7 0.99 1.68 11.612 2.22E011 - 9.042 Diazepam 439-145 284.75 C16H13ClN2O 2.92[+/0] 50(c) 2.70 2.92 9.647 3.64E009 - 6.827 Lorazepam 846-491 321.16 C15H10Cl2N2O2 10.61[0/-] 12.46[-/2-] 80(g) 2.41 2.49 10.166 4.1E010 - 7.776 Oxazepam 604-751 286.71 C15H11ClN2O2 1.55[+/0] 10.9[0/-] 20.71 3.34 2.06 10.101 4.24E009 - 6.761 Chlorinated flame retardants Tris(1-chloro-2-propyl) phosphate (TCPP) 13674-845 327.57 C9H18Cl3O4P -9.8[0] 1200(b) 2.89 2.32 8.203 5.96E008 - 5.613 Tris(2-Chloroethyl) Phosphate (TCEP) 115-968 285.49 C6H12Cl3O4P -9.06[0] 7000(h) 1.63 1.42 5.311 3.29E006 - 3.871 Pesticides 88 Diazinon 333-415 304.35 C12H21N2O3PS 4.19[+/0] 40(i) 3.86 3.80 9.145 1.13E007 -5.335 Carbendazim 10605-217 191.19 C9H9N3O2 1.12[+/0] 2.76[0/-] 12.64[-/2-] 29(j) 1.55 1.61 10.582 2.12E011 -9.062 N,N-Diethyl-meta-toluamide (DEET) 134-623 191.27 C12H17NO - 666 2.18 2.24 8.250 2.08E008 -6.070 Simazine 122-349 201.66 C7H12ClN5 1.62[+/0] 6.2 2.18 2.30 9.594 3.37E009 -7.414 Atrazine 1912-24-9 215.68 C8H14ClN5 1.60 [+/0] 34.7 2.61 2.66 9.626 4.47E009 - 7.016 Chlorpyrifos 292188-2 350.59 C9H11Cl3NO3PS - 1.12(k) 4.96 4.78 8.882 2.52E006 - 3.922 Pymetrozin 12331289-0 217.23 C10H11N5O 4.37 [+/0] 11.4 [0/- ] 290(j) 0.89 - 11.729 3.54E013 - 10.839 Pyraclostrobin 17501318-0 387.82 C19H18ClN3O4 0.44[0/-] 0.08 5.45 4.07 18.778 1.15E015 - 13.328 Indoxacarb 14417161-9 527.83 C22H17ClF3N3O7 - 3.69 89 Dimethomorph 11048870-5 387.86 C21H22ClNO4 - 18.72 2.68 3.31 16.064 1.01E015 - 13.384 Azoxystrobin 13186033-8 403.39 C22H17N3O5 0.94 [+/0] 10(j) 2.50 3.54 14.049 8.01E014 - 11.549 Surfactants and derivatives Octylphenol (OP) 27193-288 206.33 C14H22O 10.30[0/-] 3.114 5.50 5.47 9.235 4.5E006 - 3.735 Surfynol 104 126-863 226.36 C14H26O2 13.15[0/-] 13.83[-/2-] 26.35 3.61 2.94 8.611 2.44E007 - 5.001 1 Dissociation reaction, [0]: neutral; [+]: cationic; [-]: anionic. 2 Log KOW, Log KOA, Henry LC and Log KAW from database provided by Episuite v4.11 (http://www.epa.gov/opptintr/exposure/pubs/episuite.htm) Note: Log KOW, Log KOA, Henry LC and Log KAW are estimate values. (a) Yalkowsky, S.H. (2003): Handbook of Aqueous Solubility Data. CRC Press. (b) Chemicals Inspection and Testing Institute (1992): Biodegradation and Bioaccumulation Data of Existing Chemicals Based on the CSCL Japan. Japan Chemical Industry Ecology - Toxicology and Information Center. (c) Yalkowsky, S.H., Dannenfelser, R.M. (1992): Aquasol database of aqueous solubility. Coll. Pharmacy, Univ. Arizona, Tucson, AZ. (d) Dorn PB et al. (1987): Chemosphere 16: 1501-7 (e) Ferrari, B., Paxéus, N., Giudice, R. Lo, Pollio, A., Garric, J. (2003): Ecotoxicological impact of pharmaceuticals found in treated wastewaters: study of carbamazepine, clofibric acid, and diclofenac. Ecotoxicol. Environ. Saf. 55, 359–370 (g) Budavari, S. (1996): The Merck Index: An Encyclopedia of Chemicals, Drugs, and Biologicals. Merck (h) Muir DCG (1984): Handbook of Environmental Chemistry. Germany: Springer-Verlag 3: 41-66 (i) Sharom MS et al. (1980): Water Res 14: 1095-100 (j) Tomlin, C.D.S. (1997): The Pesticide Manual, British Crop Protection Council, 11th ed., Surrey, UK. (k) Yalkowsky, S.H., He, Y., Jain, P. (2016): Handbook of Aqueous Solubility Data, Second Edition. CRC Press. 96 3.1 Introduction Peri-urban horticulture performs environmental and socioeconomic functions and provides ecological services to nearby urban areas. These include fresh vegetables with a low carbon footprint, as well as the provision of recreational, landscape structure, and other ecological services (Veenhuizen, 2007). Nevertheless, peri-urban agriculture is exposed to atmospheric and water pollution. For instance, air pollution associated with transportation infrastructure (airports, harbours, highways) or the use of reclaimed water containing OMCs and TEs for irrigation lead to their accumulation in soil and potentially in plants (Colon and Toor, 2016; Liacos et al., 2012). In particular, the application of manure and biosolids for soil amendment and of reclaimed water for irrigation have been reported as the main sources of OMCs in agriculture (Eggen and Lillo, 2012). On the other hand, as water scarcity is increasing due to climate change and population growth, the application of reclaimed water for crop irrigation in peri-urban agriculture is becoming a reliable alternative water supply, especially in arid and semi-arid regions (WHO, 1989). Despite the widespread occurrence of OMCs such as pharmaceuticals or personal care products in reclaimed water, their concentrations are generally low, ranging from ng L-1 to μg L-1; their continual release into the environment coupled with their transformation products makes them behave in a “pseudo-persistent” way (Daughton and Ternes, 1999). Reclaimed water irrigation is also one of the main sources of TEs in agriculture, along with biosolid and manure amendments and atmospheric deposition (Liacos et al., 2012; Lough et al., 2005; Nabulo et al., 2006). As a result, TEs have a significant impact on peri-urban agriculture (Singh and Kumar, 2006). Bioaccumulation of OMCs have been frequently observed in crops grown under field conditions irrigated with treated wastewater (X. Wu et al., 2014). The highest concentrations have been detected in the edible parts of leafy vegetables (carbamazepine and its metabolites at 347 ng g-1 dw lettuce) rather than in root or fruit-bearing vegetables (Riemenschneider et al., 2016). Knowledge about the bioaccumulation of OMCs in vegetables irrigated with treated wastewater under field conditions is scarce (Calderón-Preciado et al., 2011). One long-term study (3 consecutive years) revealed that a longer duration of treated wastewater irrigation may lead to a significant uptake and bioaccumulation of some OMCs (Christou et al., 2017b). Moreover, TEs such as Pb, Cd, and Zn have been found at greater concentrations in crops grown on the roadside in peri-urban areas than in those from rural sites (Nabulo et al., 2010, 2006). Therefore, concerns regarding human exposure to OMCs and TEs have arisen as they have been detected in the edible parts of plants (Khan et al., 2008; X. Wu et al., 2014). However, only field studies can fully assess the incorporation of these compounds in real scenarios (Colon and Toor, 2016), and to date few such studies exist (Malchi et al., 2014; X. Wu et al., 2014). Another important adverse effect of 97 the use of reclaimed water in agriculture is its high nitrate content. Nitrate content in vegetables is the major human dietary source of nitrate, and 5% of ingested nitrate is transformed into the toxic form nitrite (Santamaria, 2006). In this regard, nitrate fertilization and the reuse of treated wastewater contribute to nitrate uptake by food crops (Castro et al., 2009). The occurrence of TEs and OMCs in agricultural soils can also lead to morphological and physiological changes in the exposed plants. For instance, Hurtado et al. (2017) observed that the occurrence of OMCs in irrigation waters at environmentally relevant concentrations resulted in a decrease in the chlorophyll content, morphological changes, and alterations in the metabolic profile of lettuces. Carter et al. (2015) observed a reduction in biomass productivity and changes in hormone and nutrient content in zucchini due to the presence of carbamazepine and verapamil at environmentally relevant concentrations (0.005–10 mg L−1 in soil). The occurrence of heavy metals such as Pb in plants can inhibit chlorophyll biosynthesis and decrease vegetable carbohydrate content (Gaweda, 2007; Peralta-Videa et al., 2009), while the occurrence of Cd can cause lipid peroxidation (Monteiro et al., 2007; Rodríguez-Serrano et al., 2006). However, there is no available information about the impact of the co-occurrence of OMCs and TEs in crops in real-life scenarios. Furthermore, although the occurrence of TEs and pesticide residues in food products is regulated in the EU (EFSA), USA (FDA), China, and Australia-New Zealand, and the FAO has published international guidelines (Codex Alimentarius) (FAO, 2016), no regulations exist for the other detected OMCs. In a previous study, several chemical pollutants were detected in irrigation waters from the periurban horticultural area of the city of Barcelona (Margenat et al., 2017). The present study aims to assess the occurrence of these pollutants (16 TEs and 33 OMCs) in lettuce cultivation (soils and plants), as well as bioaccumulation factors and their potential impacts on leaf constituents (i.e. chlorophyll, nitrates, lipids, and carbohydrate content). The study was conducted in 4 farm fields located in the peri-urban area of Barcelona (NE Spain) and a remote organic farming plot located in a rural area. 3.2 Material and methods 3.2.1 Sampling site description Five locations in the Llobregat River delta and its lower valley (NE Spain) were sampled. Further information is described in section 2.2.1. 98 3.2.2 Sampling strategy Soil Soil was sampled in May of 2016 when the lettuce was harvested. A composite soil sample from a horizon of 0 to 20 cm was obtained from five subsamples in each farm plot. Soil samples were sieved through a 2.0 mm mesh and stored at -20 °C. According to the USDA (1987) classification, soil samples from farm plots P1, P3, and P4 were loamy sand, while the soils from farm plots P2 and P5 were sandy. The soil samples were also characterized by UNE-EN ISO/IEC 17025:2005 accredited laboratories. Table S3.1 provides information about the physicochemical properties of each of the studied soils. Lettuce Lettuce was selected for being one of the most cultivated vegetables in the peri-urban agricultural area of Barcelona. The lettuces (Lactuca sativa L. cv. Batavia) were harvested when they reached their commercial size. The lettuce seedlings were planted in February-March and June 2016, and the plants were harvested in May 2016 (P1-P5) and June 2016 (P3-4) for the winter and summer seasons respectively (Fig. 2.1). Each farm field was divided in 5 sections, and 10 lettuces were collected per section. For each section, a quarter of each lettuce was mixed and comminuted together using liquid nitrogen and a porcelain mortar. These samples were then stored at -20 °C until they were analyzed. Thus, 5 samples were obtained per plot. 3.2.3 Analytical procedures The chemicals and reagents used for the analytical methodologies described below are listed in section 3.5.1. Nitrate content in lettuce Extraction was performed according to the procedure recommended by the AOAC (AOAC, 1997; Guadagnin et al., 2005). Briefly, 40 mL of Milli-Q water were added to a tube containing 5 g of fresh weight (fw) lettuce leaves, and it was heated to 70 °C for 15 min. Once the extract was at room temperature, Milli-Q water was added to a total volume of 100 mL. It was then filtered through a filter paper (Whatman No. 4) prior to spectrophotometric measurement. Nitrate content was measured with a Hach-Lange spectrophotometer (DR 1900 Portable Spectrophotometer) at a wavelength of 460 nm as nitrate nitrogen (NO3-N). 99 Lipids and carbohydrate extraction in lettuce leaves The method was adapted from that described by (Yang et al., 2016). Extraction was carried out by adding 15 mL of ethanol/hexane (1:1, v/v) to a glass tube with 3 g of fw sample. The sample was sonicated for 15 min and centrifuged at 2500 rpm for 15 min. It was then filtered through a 0.22 μm nylon filter (Scharlab, Barcelona, Spain) into a preweighed glass tube. After removing the solvent by purging and drying with nitrogen gas, the tube and filter were weighed. The sample remaining in the tube was operationally defined as lipid content, whereas the sample on the filter was operationally defined as carbohydrates. Chlorophyll content in lettuce Chlorophyll content in lettuce was measured with a chlorophyll meter (CCM200Plus, OptiSciences) (Hudson, NH, USA) in triplicate based on the outer and inner leaf absorbance of each head of lettuce. A calibration curve was obtained to relate the chlorophyll content to the absorbance previously measured with the chlorophyll content meter. To this end, rounded samples of leaves (4 cm diameter) were extracted with 5 mL of N,N-dimethylformamide (DMF) and kept in the dark at 4 °C for 48 h before the spectrophotometric determination. The extracts were measured at two wavelengths, 647 and 664.5 nm, so that chlorophylls (a, b, and total) could be calculated using Inskeep and Bloom's coefficients (Inskeep and Bloom, 1985; Porra, 2002). Trace element (TE) extraction  Soil A slightly modified pseudo-total digestion method (Lee et al., 2006) using a strong acid (HNO3HClO4) was used. A portion of 0.1 g of homogenized, dried sample was sieved through 2 mm mesh (CISA, Spain) and placed in a polyethylene tube. Then, 10 mL of 65% HNO3 and 10 mL of concentrated HClO4 were added, and the mix was heated up to 135 °C for 16 h. The digested samples were then evaporated, resuspended with 3mL of HNO3, and then heated to ensure dissolution. A 1 mL aliquot was diluted with 24 mL of Milli-Q water, and the sample was filtered (0.2 μm) prior to analysis. An inductively coupled plasma optical emission spectrometer (Thermo Scientific, iCAP 6500 ICP-OES) and an inductively coupled plasma mass spectrometer (Thermo Scientific, XSeries 2 ICP-MS) were used for the determination of major and minor TEs respectively in both the soil and lettuce samples. The content of Hg was determined using an advanced mercury analyser (AMA-254, Altec, Prague, Czech Republic). 100  Lettuce A portion of 1 g of plant leaf tissues, dried and sieved, was digested with 4 mL (1:1) HNO3 and 10 mL of (1:4) HCl in a closed Teflon vessel using a six-position EvapoClean heating block (EvapoClean, Deltalabo, France) at 95 °C for at least 3 hosed. Afterwards, samples were transferred to a 100-mL volumetric flask and centrifuged; then, a 10 mL aliquot was diluted with 40 mL Milli-Q water prior to analysis. The determination method was the same as for the soil, but values are expressed in fw basis. The applied methodology was validated by NIST 1570a (Gaithersburg, USA), with certified values for As, B, Cd, Co, Cu, Mn, Hg, Ni, Zn in lettuce. For accuracy, excellent extraction efficiencies were noted for these elements (92-107%). Further information is provided in section 3.5.2. Organic microcontaminant (OMC) extraction Physicochemical properties (molecular weight, pKa, solubility, and log KOW,) of the studied OMCs are provided in Table S2.1.  Soil Soil extraction was adapted from a previously reported method (Xu et al., 2008). Briefly, 5 g of soil (fw), homogenized and sieved through 2.0mm mesh, were placed in a glass tube. It was fortified with 31.25 ng of a mixture of 6 surrogates and left to equilibrate for 30 min. The extraction was performed by sonication for 15 min three times with 5 mL of acetone/ethyl acetate (1:1, v/v). The extract was then centrifuged at 3100 rpm for 10 min and the supernatants were combined and evaporated to ca. 0.5 mL under a gentle stream of nitrogen. Next, 2 mL of methanol were added to the final extract, which was reconstituted with 250 mL of deionized water prior to percolation through previously conditioned SPE cartridges (STRATA X, 100 mg, 6 mL). The cartridges were dried under vacuum and eluted with 10 mL of ethyl acetate. The extracts were concentrated to ca. 250 μL under a stream of nitrogen and 37.25 ng of triphenylamine (TPhA) were added as an internal standard. Finally, a 50 μL aliquot was analyzed by GC-MS/MS without derivatization, and another 50 μL aliquot was analyzed derivatized with 10 μL of TMSH. The analytical quality parameters (LOD, LOQ, and recoveries) are provided in Tables S3.2-3.4 in the SI section.  Lettuce The extraction of OMCs from plant leaf tissues was performed according to Calderón-Preciado et al. (2009). Briefly, the extraction of samples (0.5 g fw) was performed with a matrix solidphase dispersion method previously spiked with 12.5 ng of a mixture of surrogates and 101 equilibrated for 30 min. Neutral-basic and acid fractions were obtained by solvent partitioning at neutral and acid pH, respectively. After clean-up, fractions were reduced to ca. 80 μL and 37.25 ng of TPhA were added. A 50 μL aliquot and another one derivatized with 10 μL of TMSH were analyzed in GC-MS/MS. The extraction of CBZ and EPOCBZ from lettuce samples was carried out by sonication followed by liquid chromatography tandem mass spectrometry (LC-MS/MS). To this end, 0.5 g of fw lettuce was spiked with 50 ng of carbamazepine-13C and left to stand for 30 min. Samples were sonicated with 10 mL of MeOH for 15 min and centrifuged 15 min at 3000 rpm. The extraction was performed twice, and the extracts were combined and reduced to ca. 1 mL with nitrogen gas and reconstituted with 10 mL of LiChrosolv water. The samples were then percolated through SPE cartridges (STRATA X, 100 mg·6 mL), previously conditioned with 1 mL of MeOH and water, respectively. The cartridges were washed with water/methanol (95:5, v/v) and eluted with 2 mL of a mixture of MeOH/ethyl acetate (1:1, v/v). The final extracts were reduced almost to dryness, resuspended in 1 mL of water, and filtrated (0.22 μm) prior to LCMS/MS analysis. The LODs and LOQs were calculated for each analyte as three and ten times the signal from the baseline noise (S/N ratio), respectively. Analytical quality parameters (LOD, LOQ, and recoveries) are provided in Tables S3.5-7 in the SI section. Further details on the GC– MS/MS and LC-MS/MS are provided elsewhere (section 3.5.2). 3.2.4 Data analysis Data values for the soil and plants are presented in dw and fw respectively. This is in agreement with legislated units for each of the studied matrices. The bioconcentration factor (BCF) was calculated for TEs and OMCs as the ratio between the concentrations (mg·kg-1 dw) in the edible parts of lettuce plants and soil content. The units used were mg/kg dw for TEs and μg/kg dw for OMCs. 𝐵𝐶𝐹 = 𝐶 𝑖𝑛 𝑒𝑑𝑖𝑏𝑙𝑒 𝑝𝑎𝑟𝑡 𝑜𝑓 𝑡ℎ𝑒 𝑝𝑙𝑎𝑛𝑡 𝐶 𝑠𝑜𝑖𝑙 (3.1) The experimental results were statistically evaluated using the SPSS v. 22 package (Chicago, IL, US). All data sets were checked for normal distribution using the Kolmogorov–Smirnov test to ensure that parametric statistics were applicable. The comparison of means of the occurrence of chemical pollutants between farm plots was performed with a two-paired (Wilcoxon) signed-rank test (the concentration of each compound was compared between farm plots). Principal Component Analysis (PCA) was conducted on the concentration levels of TEs, OMCs, lettuce 102 constituents (chlorophyll, nitrates, lipids, and carbohydrates), and soil properties. Once the data matrix had been completed, it was autoscaled to have zero mean and unit variance (correlation matrix). Statistical significance was defined as p ≤ 0.05. 103 3.3 Results and discussion 3.3.1 Occurrence of trace elements (TEs) Soil Table 3.1 shows the concentration of 16 TEs in the soil samples and their corresponding maximum values established for agricultural use by Catalan Law 5/2017 in accordance with Spanish Royal Decree 9/ 2005. The TE concentrations ranged from non-detectable (Cd in all plots except P3) to 802 mg/kg dw (Mn in P5). The most abundant TEs were Mn, Ba, Cr, Pb, Zn, Cu, and B in all the sampling sites. The total median concentration of TEs per site was as follows: 824 mg/kg dw (P1), 1353 mg/kg dw (P2), 1518 mg/kg dw (P3), 1623 mg/kg dw (P4), and 1896 mg/kg dw (P5). Based on these results, P1 was thus the least polluted site, and P5 the most polluted. A two-paired test showed that soil from the rural site (P1) was less polluted by TEs than any of the soils from the peri-urban area of Barcelona (P2-P5, p< 0.05). In fact, the concentration of Mo, Ni, Pb, Zn, and As in the soils of the peri-urban area (P2-P5) exceeded the maximum soil concentration limit established for agricultural use in the regional decree (Generalitat de Catalunya, 2017). These results are consistent with the abundance of Mn (103–13,584 mg/kg dw), Zn(23–214mg/kg dw), Cr (12–57 mg/kg dw), Ni (9–111 mg/kg dw), Cu (4–170 mg/kg dw), Cd (0.1–130 mg/kg dw), and Pb (20–86 mg/kg dw) reported in soil from the Baix Llobregat area (Zimakowska-Gnoińska et al., 2000). Finally, the high concentration of As in the peri-urban soil was in agreement with the fact that phosphatic fertilizers generally contain the highest concentrations of most heavy metal(loid)s including As, Cd, U, Th and Zn (Alloway, 2012), whereas in the rural site organic amending was used for soil fertilization. 104 Table 3.1 Concentration of TEs (mg/kg dw) in the agricultural soil from the different studied plots. The generic reference levels (GRL, mg/kg dw) of these elements for contaminated soils in Catalonia are shown. Plot 1 Plot 2 Plot 3 Plot 4 Plot 5 GRL agricultural use B 92 132 94 81 58 – Ba 86 257 226 305 412 500 Cd <0.56 <0.56 0.79 <0.50 <0.60 2.5 Co 5.7 11 9.7 10 17 25 Cr 26 42 45 44 62 400 Cu 33 68 89 178 89 – Li 10 27 26 28 43 – Mn 361 439 520 494 803 – Mo 2.0 3.7 3.6 2.0 2.9 3.5 Ni 17 31 57 33 49 45 Pb 15 83 216 164 77 60 Rb 28 35 37 39 58 – Sb <0.56 <0.56 <0.54 <0.50 <0.60 6.0 Zn 132 195 148 208 198 170 As 16 27 45 38 27 30 Hg 0.01 0.30 0.37 0.37 0.19 2 The high concentration levels of Pb are consistent with the fact that, in the past, Pb particles were widely released into the environment through vehicle emissions from leaded gasoline engines (half-life in soil of about 53,000 years). Industrial emissions and paints can also contribute to the release of Pb into the environment (Nabulo et al., 2006). Ba and Mn come mainly from natural sources in both urban and rural areas (Davis et al., 2009), while the source of B can be either geogenic or anthropogenic (fertilizers, households detergents, discharges from industrial plants, etc.) (Pedrero et al., 2010). Finally, Zn is naturally present in all soils in concentrations typically ranging from 10 mg/kg to 100 mg/kg; and human activities have enriched them through atmospheric deposition, fertilizers, and sewage sludge (Alloway, 2012). The proximity of road networks can also lead to considerable exposure to Zn through brake and tire wear, tailpipe emissions of motor oil, and anti-wear additives (Lough et al., 2005). Lettuce Table 3.2 shows the minimum, maximum, and median concentrations (mg/kg fw) of TEs in lettuce samples. B, Ba, Mn, and Zn were the most abundant TEs, in keeping with the occurrence of these elements in soil samples (Table 3.1). The detected levels were in the same range as those published for garden-grown vegetables (leafy greens, herbs, roots, and fruits) (McBride et al., 2014) for Ba (3.7 mg/kg fw) and Cd (0.028 mg/kg fw), but slightly higher for Pb (0.099 mg/kg fw). The values of Cd were compliant with Commission Regulation (EC) No 1881/2006 of 19 December 2006, which sets maximum levels for certain contaminants in food stuffs. In contrast, Pb in P3 in the summer season slightly exceeded the maximum legislated concentration. Since 105 the winter levels were below the regulated concentrations, further studies are needed to establish the significance of these data. Table 3.2 Minimum, maximum and median concentration (mg/kg fw) of TEs in lettuce samples. Only Plots 3 and 4 were planted during the summer season. Plot 1 winter Plot 2 winter Plot 3 winter Plot 3 summer Plot 4 winter Plot 4 summer Plot 5 winter Maximum value legislated B (1.50–2.12) 1.76 (1.43–1.89) 1.73 (1.54–2.44) 1.98 (1.62–2.02) 1.86 (1.56–2.43) 1.92 (1.58–2.42) 2.00 (1.30–1.97) 1.58 – Ba (0.62–1.16) 0.83 (0.56–0.66) 0.59 (0.38–0.66) 0.48 (0.55–0.80) 0.62 (0.44–0.71) 0.60 (0.48–0.85) 0.61 (0.40–0.58) 0.47 – Cd (0.004– 0.008) 0.006 (0.02–0.04) 0.03 (0.02–0.03) 0.02 (0.03–0.03) 0.03 (0.01–0.02) 0.02 (0.01–0.02) 0.01 (0.01–0.02) 0.01 0.20 Co (0.02–0.03) 0.02 (0.01–0.02) 0.01 (0.00–0.01) 0.01 (0.01–0.02) 0.01 (0.01–0.02) 0.01 (0.01–0.02) 0.01 (0.01–0.02) 0.02 – Cr (0.12–0.34) 0.20 (0.10–0.20) 0.14 (0.08–0.26) 0.15 (0.08–0.78) 0.25 (0.09–0.54) 0.20 (0.20–0.57) 0.33 (0.11–0.17) 0.13 – Cu (0.40–0.66) 0.49 (0.57–0.78) 0.67 (0.46–0.69) 0.59 (0.48–0.76) 0.65 (0.64–0.89) 0.78 (0.60–0.96) 0.78 (0.45–0.81) 0.57 – Li (0.04–0.07) 0.05 (0.05–0.05) 0.05 (0.02–0.04) 0.03 (0.04–0.08) 0.06 (0.03–0.05) 0.04 (0.03–0.05) 0.04 (0.04–0.07) 0.06 – Mn (2.77–4.91) 3.60 (1.29–1.77) 1.47 (2.65–4.20) 3.32 (3.29–4.28) 3.59 (1.50–2.46) 1.89 (1.73–2.79) 2.16 (1.89–2.70) 2.33 – Mo (0.02–0.03) 0.03 (0.02–0.03) 0.02 (0.02–0.04) 0.03 (0.02–0.06) 0.03 (0.02–0.05) 0.03 (0.03–0.06) 0.04 (0.02–0.03) 0.02 – Ni (0.05–0.16) 0.09 (0.05–0.09) 0.07 (0.04–0.10) 0.06 (0.08–0.61) 0.21 (0.05–0.41) 0.13 (0.19–0.32) 0.25 (0.05–0.12) 0.09 – Pb (0.03–0.08) 0.05 (0.09–0.15) 0.11 (0.08–0.19) 0.13 (0.13–0.45) 0.28 (0.13–0.21) 0.16 (0.13–0.25) 0.20 (0.13–0.22) 0.19 0.30 Rb (0.32–0.50) 0.37 (0.41–0.49) 0.45 (0.28–0.53) 0.40 (0.30–0.42) 0.35 (0.37–0.59) 0.50 (0.39–0.60) 0.50 (0.33–0.56) 0.44 – Sb (0.02–0.07) 0.03 (0.02–0.02) 0.02 (0.01–0.01) 0.01 (0.02–0.03) 0.02 (0.01–0.01) 0.01 (0.02–0.02) 0.02 (0.01–0.02) 0.01 – Zn (1.00–1.52) 1.23 (2.36–3.41) 2.78 (1.73–2.92) 2.28 (1.30–1.96) 1.71 (2.15–3.12) 2.53 (2.01–2.95) 2.50 (1.47–2.69) 1.91 – As (5.75·10−5– 6.51·10−4) 4.10·10−4 (5.10·10−4– 8.55·10−4) 6.77·10−4 (2.16·10−4– 9.36·10−4) 6.01·10−4 (5.67·10−4– 2.30·10−3) 1.54·10−3 (4.45·10−4– 1.17·10−3) 8.35·10−4 (4.19·10−4– 1.10·10−3) 6.88·10−4 (2.67·10−4– 1.40·10−3) 8.43·10−4 – Hg (3.01·10−4– 4.59·10−4) 3.65·10−4 (3.65·10−4– 1.67·10−3) 1.06·10−3 (5.20·10−4– 1.12·10−3) 8.37·10−4 (6.95·10−4– 1.45·10−3) 1.07·10−3 (6.90·10−4– 1.56·10−3) 9.74·10−4 (5.26·10−4– 8.56·10−4) 7.45·10−4 (6.36·10−4– 1.66·10−3) 1.01·10−3 – A two-paired test was used to compare differences between sampling points regarding the TEs present in lettuce samples. No statistical differences (p > 0.05) were obtained for Cr, Mn, or Ba content at the study sampling sites. In contrast, they were obtained among sites for the rest of the TEs. The most polluted crops were found in site P3 (total concentration of 192 mg/kg fw in winter and 170 mg/kg fw in summer), whereas crops least polluted by TEs (total concentration 140 112 biosolids and pesticides. This makes this farm plot the most polluted site. Fig. 3.1b shows the score plots of PC1 vs PC3. As can be seen, the farm plots from the peri-urban area can be grouped into 3 clusters: the plots irrigated with river water (group IV, P2 and P4), the farm plot irrigated with reclaimed water (group III, P3), and the farm plot irrigated with groundwater (group II, P5). Therefore, the main differences observed in this second score plot (Fig. 3.1b) with regard to pollution (organic and inorganic) in soil and lettuce are due to the quality of the irrigation water. 3.4 Conclusions The results of this study demonstrate that peri-urban pollution increases the occurrence of pollutants but does not affect lipid and carbohydrate content. - The concentrations of TEs and OMCs ranged from 0.790 to 803 mg/kg dw and from <0.1 to 397 ng/g dw, respectively, in the peri-urban soil, whereas they ranged from 6·10−5 to 5 mg/kg fw and from b0.1 to 193 ng/g fw, respectively, in the lettuce crops. - The concentrations of metals in the soil from the rural area were always below the Catalonian guidelines, but limits were exceeded for Mo, Ni, Pb, and As in the soils of the peri-urban area. However, their occurrence in lettuce complied with human food safety standards (except for Pb in peri-urban area). - The many fungicides (carbendazim, dimethomorph, and MPB) and chemicals released by plastic pipelines (TCPP, BPF, and 2-MBT) used in agriculture were prevalent in the soil or the edible parts of the lettuce. In contrast, chemicals from irrigation waters (carbamazepine, surfynol 104) were not. - The BCFs of TEs ranged from 0.0002 to 2, whereas for OMCs such as pesticides and plastic-related compounds it depended on whether or not they came into direct or indirect (use of plastic pipelines) contact with the lettuce leaf surface. - Chlorophyll, lipid, and carbohydrate content in crops grown in the peri-urban area were not affected by soil or irrigation water pollution, whereas nitrate content depended on the irrigation water quality. - PCA showed that peri-urban pollution and water irrigation quality could explain a large share of the variance in the dataset. Although the present study showed that lettuce exposure to peri-urban pollution did not affect lipid and carbohydrate content, further studies are necessary to assess changes in agri-food quality associated with peri-urban pollution. Similarly, further work is needed on the potential effects of chemical pollutants released by plastic irrigation pipes on crops. 113 114 3.5 Supporting Information 3.5.1 Materials and reagents Most of the reagents, OMCs and surrogates are described in section 2.5.1. N,N-dimethylformamide was obtained from Merck and and 0.70 µm of glass-fiber filters 47 mm in diameter were obtained from Whatman (Maidstone, UK). 3.5.2 Analytical determination of chemical pollutants in soil and crop samples ICP-MS and ICP-OES determination An inductively coupled plasma optical emission spectrometer (Thermo Scientific, iCAP 6500 ICP-OES) and an inductively coupled plasma mass spectrometer (Thermo Scientific, XSeries 2 ICP-MS) were used for the determination of TEs. Major elements were determined by ICP-OES (Ba and Mn), while the rest of TEs were determined by ICP-MS. Reagent water was used as a blank matrix, and laboratory reagent blank was treated exactly the same as a sample. A limit of detection (LOD) of 0.2 μg/L in the solution analyzed was determined from three times the standard deviation obtained from the analysis of ten runs of blank samples on the same day as the determinations. GC-MS/MS determination (SI) GC-MS/MS determination is described in section 2.2.3. LODs, LOQs, recoveries of the surrogates and recoveries of the targeted compounds are reported in Tables S3.2-3.4 and Tables S3.5-3.7. LC-MS/MS determination Samples extracted by sonication, were analyzed with a Waters Acquity Ultra-Performance liquid chromatography system coupled to a Waters TQ-Detector (Manchester, UK). Autosampler was set at 15ºC and a volume of 10 µL of sample was injected to the liquid chromatography system fitted with an Ascentis Express RP-Amide column (5 cm x 2.1mm, 2.7 µm particle size, Supelco, Bellefonte, USA) and with a guard column (0.5 cm x 2.1 mm) containing the same packing material. The flow rate was 0.35 mL/min and the gradient conditions of mobile phase A (acetonitrile 0.1% formic acid) and mobile phase B (water + 0.1% formic acid) were set as follows: 0-1 min 3% of A, 1-7 min 3-25% of A, 7-10 min 25-95% of A, 10-15 min 95% of A, 15-17% min, 15-17 min 95-3%, 17-23 min 3% of A. Column oven was set at 25ºC. Ions were generated with an 115 electrospray in positive mode (ESI+). Source and desolvation temperature were set to 80ºC and 350ºC, respectively. Qualitative analysis was performed as in GC-MS/MS, quantitative analysis was performed through matrix-matched calibration. Table S3.1. General parameters of soil samples Plot 1 Plot 2 Plot 3 Plot 4 Plot 5 Methodology Humidity at 105ºC (%) <1 <1 <1 <1 <1 Gravimetry Nitrogen-nitric (mg/Kg) 9 19 10 2 12 Colorimetry Phosphorous (mg/Kg) 64 15.6 67 35 102 Spectrophotometry UV-VIS Potassium (mg/Kg) 375 183 309 346 512 Spectrophotometry ICP-OES Calcium (mg/Kg) 2984 6422 6498 6561 6598 Spectrophotometry ICP-OES Magnesium (mg/Kg) 379 330 390 360 510 Spectrophotometry ICP-OES Sodium (mg/Kg) 32 152 161 136 109 Spectrophotometry ICP-OES Cation exchange capacity (mS·cm-1) 7.5 8.0 9.3 9.7 11.5 Volumetric titration pH 7.6 7.8 7.6 7.7 7.8 Potentiometry Electrical conductivity (mS·cm-1) 2.3 2.3 2.8 2.2 2.3 Conductimetry Texture Sandy loam Sand Sandy loam Sandy loam Sand Granulometry 116 Table S3.2 Limits of detection (LOD) and quantification (LOQ) of soil samples Analyte LOD (ng/g dw) LOQ (ng/g dw) Atrazine 0.44 0.46 Azoxystrobin 0.36 0.37 2-tert-Butyl-4-methoxyphenol (BHA) 0.03 0.04 Chlorpyrifos 0.04 0.06 N.N-Diethyl-meta-toluamide (DEET) 0.19 0.22 Diazepam 0.11 0.12 Diazinon 0.36 0.37 Dimethomorph 0.29 0.30 Indoxacarb 0.29 0.30 Simazine 0.37 0.38 Surfynol 104 0.96 1.01 Tris(2-Chloroethyl) Phosphate (TCEP) 0.17 0.18 2-Mercaptobenzothiazole (2-MBT) 4.33 4.51 5-Methyl-2H-benzotriazole (5-TTri) 0.12 0.13 Bisphenol A (BPA) 4.22 4.24 Butylparaben (BPB) 0.14 0.16 Bisphenol F (BPF) 9.57 10.06 1,3-Benzothiazole (BT) 4.04 4.09 Benzotriazole (Btri) 0.39 0.40 Carbamazepine (CBZ) 0.46 0.47 Carbendazim 0.22 0.23 Ethyl paraben (EPB) 3.09 3.63 Lorazepam 5.91 5.92 Methyl paraben (MPB) 6.18 6.92 1-Hydroxybenzotriazole (OHBT) 10.8 11.0 Octylphenol (OP) 0.63 0.64 Oxazepam 0.51 0.52 Propyl paraben (PPB) 0.20 0.21 Primidone 0.17 0.18 Pymetrozin 0.88 0.89 Pyraclostrobin 0.04 0.06 Carbamazepine-10,11-epoxide (EPOCBZ) 0.21 0.40 Tris(1-chloro-2-propyl) phosphate (TCPP) 20.9 21.4 Table S3.3 Recoveries (%) of surrogates in soil samples Surrogate R(%) at 7 ng/g dw Bisphenol A-d16 88±5 Caffeine-13C3 10±0.9 Carbamazepine-13C6 65±5 Diazepam-d5 68±8 5,6-dimethyl-1H-benzotriazole (XbTri) 50±6 Etylparaben-13C 67±3 117 Table S3.4 Absolute recoveries (%) of analytes in soil samples Compound R(%) at 7 ng/g dw Atrazine 39±5 Azoxystrobin 71±0.3 Chlorpyrifos 32±4 N.N-Diethyl-meta-toluamide (DEET) 49±5 Diazepam 38±5 Diazinon 33±4 Dimethomorph 61±9 Indoxacarb 46±1 Simazine 62±1 Surfynol 104 44±5 Tris(2-Chloroethyl) Phosphate (TCEP) 50±0.8 5-Methyl-2H-benzotriazole (5-TTri) 54±5 Bisphenol A (BPA) 72±9 Butylparaben (BPB) 42±0.6 Bisphenol F (BPF) 66±21 1.3-Benzothiazole (BT) 76±16 Benzotriazole (Btri) 10±0.8 Carbamazepine (CBZ) 58±3 Carbendazim 75±7 Ethyl paraben (EPB) 54±4 Lorazepam 68±6 Methyl paraben (MPB) 120±22 1-Hydroxybenzotriazole (OHBT) 85±13 Octylphenol (OP) 46±2 Oxazepam 56±3 Propyl paraben (PPB) 53±2 Carbamazepine-10,11-epoxide (EPOCBZ) 84±6 Tris(1-chloro-2-propyl) phosphate (TCPP) 87±13 nd: non detected 118 Table S3.5 Limits of detection (LOD) and quantification (LOQ) of lettuce’s samples Compound LOD (mg/kg fw) LOQ(mg/kg fw) Atrazine 0.11 0.18 Azoxystrobin 0.22 0.35 2-tert-Butyl-4-methoxyphenol (BHA) 0.025 0.028 Chlorpyrifos 0.56 0.94 N.N-Diethyl-meta-toluamide (DEET) 0.32 0.7 Diazepam 0.03 0.05 Diazinon 0.35 0.63 Dimethomorph 0.03 0.06 Indoxacarb 0.49 0.83 Simazine 0.19 0.3 Surfynol 104 4.1 6.5 Tris(2-Chloroethyl) Phosphate (TCEP) 0.82 1.5 2-Mercaptobenzothiazole (2-MBT) 0.66 1 5-Methyl-2H-benzotriazole (5-TTri) 0.26 0.43 Bisphenol A (BPA) 0.24 0.39 Butylparaben (BPB) 0.16 0.22 Bisphenol F (BPF) 0.51 0.77 1.3-Benzothiazole (BT) 1.2 1.7 Benzotriazole (Btri) 0.96 1.6 Carbamazepine (CBZ) 0.05 0.15 Carbendazim 0.24 0.33 Ethyl paraben (EPB) 0.39 0.66 Lorazepam 0.82 1.3 Methyl paraben (MPB) 0.81 0.99 1-Hydroxybenzotriazole (OHBT) 0.38 0.6 Octylphenol (OP) 0.11 0.18 Oxazepam 0.38 0.63 Propyl paraben (PPB) 15 24 Primidone 0.26 0.33 Pymetrozin 3.2 5.1 Pyraclostrobin 3.7 6.6 Carbamazepine-10,11-epoxide (EPOCBZ) 0.1 0.3 Tris(1-chloro-2-propyl) phosphate (TCPP) 16 23 Table S3.6 Recoveries (%) of surrogates in lettuce’s samples Surrogate R(%) at 10 ng/g fw Bisphenol A-d16 38±3 Caffeine-13C3 46±4 Carbamazepine-13C6 111±14 Diazepam-d5 15±2 5.6-dimethyl-1H-benzotriazole (XbTri) 73±7 Etylparaben-13C 74±8 119 Table S3.7 Absolute recoveries (%) of analytes in lettuce’s samples Compounds R(%) at 10 ng/g fw 1-hydroxybenzotriazole (OHBT) 76±5 2-mercaptobenzothiazole (2MBT) 85±6 2-tert-Butyl-4-methoxyphenol (BHA) 74±4 4-tert-octylphenol (OP) 48±3 5-methyl-2H-benzotriazole (5TTri) 71±1 Azoxystrobin 35±8 Benzothiazole 66±1 Benzotriazole 86±3 Bisphenol A 34±2 Bisphenol F 20±2 Butylparaben 39±4 Carbamazepine 152±6 Carbamazepine-10.11-epoxide (EPOCBZ) 95±14 Carbendazim 40±2 DEET 46±3 Dimethomorph 35±3 Etylparaben 60±4 Indoxacarb n.a. Lamotrigine n.a. Lorazepam n.a. Methylparaben 58±8 Oxazepam 96±2 Primidone 52±5 Propylparaben 72±3 Pymetrozin 70±6 Pyraclostrobin n.a. Simazine 41±1 Surfynol 104 89±13 Tris(1-chloro-2-propyl) phosphate (TCPP) 44±6 Tris(2-chloroethyl) phosphate (TCEP) 37±4 n.a. not evaluated 120 Table S3.8 BCF for the TEs selected in this study Plot 1 winter Plot 2 winter Plot 3 winter Plot 3 summer Plot 4 winter Plot 4 summer Plot 5 winter B 0.36 0.28 0.50 0.41 0.54 0.47 0.62 Ba 0.19 0.05 0.05 0.06 0.05 0.04 0.03 Cd -* - 0.69 0.79 - - - Co 0.07 0.03 0.02 0.02 0.02 0.02 0.03 Cr 0.12 0.06 0.07 0.06 0.06 0.15 0.05 Cu 0.28 0.20 0.15 0.16 0.10 0.08 0.13 Li 0.09 0.04 0.03 0.04 0.03 0.03 0.03 Mn 0.19 0.07 0.15 0.14 0.09 0.08 0.07 Mo 0.22 0.14 0.16 0.13 0.28 0.46 0.18 Ni 0.07 0.04 0.03 0.04 0.05 0.17 0.05 Pb 0.06 0.03 0.01 0.03 0.02 0.02 0.06 Rb 0.24 0.27 0.25 0.19 0.29 0.24 0.17 Sb -* - - - - - - Zn 0.17 0.29 0.35 0.25 0.28 0.23 0.22 As 0.00045 0.0005 0.0003 0.0008 0.0006 0.0004 0.0002 Hg 0.64 0.07 0.06 0.07 0.05 0.04 0.12 *soil concentration below LOD Table S3.9 BCF of the CECs selected in this study Plot 1 winter Plot 2 winter Plot 3 winter Plot 3 summer Plot 4 winter Plot 4 summer Plot 5 winter Dimetomorph * * 64 * * * 1 2-MBT * * * 30 * * * BPF 1 * 200 6 * * 2 Carbamazepine * * 10 6 53 24 8 Carbendazim * * * * * * 375 MPB * 30 * * * * 85 *soil concentration below LOD 121 Table S3.10 Loadings for PCA. Component 1 2 3 4 5 6 PPB (ng/g) soil .980 -.175 -.040 -.080 .016 -.037 Carbendazim (ng/g) soil .978 -.176 -.039 -.092 .015 -.040 Carbendazim (ng/g) lettuce .976 -.152 .026 -.111 .105 -.021 Mn (ng/g) soil .974 .206 .081 .013 .048 .028 Moisture (%) soil .972 -.154 -.058 .164 .031 .029 Rb (ng/g) soil .969 .184 -.144 .069 -.020 .034 BPB (ng/g) soil .945 .296 -.090 -.099 .041 .020 Mg (mg/Kg) soil .928 -.209 .301 .046 .028 -.029 BT (ng/g) soil .918 .071 .273 -.024 -.030 .278 Co (ng/g) soil .901 .334 -.214 -.170 .035 .014 CAP.Interc.Cat. (meq/100g) soil .868 .349 .025 .322 .070 .128 Cr (ng/g) soil .866 .489 -.040 -.068 .054 .049 Li (ng/g) lettuce .847 -.050 .052 -.418 -.220 -.234 Li (ng/g) soil .843 .488 -.205 -.061 .049 .061 TCEP (ng/g) soil .830 -.545 .042 -.072 -.009 -.087 Dimethomorph (ng/g) soil .829 .119 .522 -.150 .039 -.051 Btri (ng/g) soil .814 .173 .386 .001 -.048 .396 K (mg/Kg) soil .770 -.407 .154 .461 .031 .060 Ba (ng/g) soil .770 .524 -.298 .081 .142 .131 P (mg/Kg) soil .712 -.310 .621 .087 .025 -.051 B (ng/g) soil -.696 .168 -.160 -.663 -.055 -.141 MPB (ng/g) lettuce .030 -.987 .011 -.113 -.011 -.107 TCPP (ng/g) soil -.070 -.981 .115 .031 -.058 -.123 BPF (ng/g) soil .167 -.971 .112 .010 -.054 -.116 Hg (ng/g) soil -.113 .967 -.006 .149 .064 .160 Na (mg/Kg) soil -.050 .939 .290 -.157 .054 .061 TOC(%) soil .258 .926 .206 -.157 .062 .066 Ca (mg/Kg) soil .357 .916 -.125 -.022 .064 .119 Hg (ng/g) lettuce .357 .829 .376 -.194 .018 -.081 2MBT (ng/g) lettuce -.277 -.821 .320 .076 -.314 -.205 As (ng/g) soil .004 .799 .056 .393 -.449 .052 Zn (ng/g) lettuce .037 .786 -.458 -.042 .412 -.016 Cu (ng/g) lettuce -.127 .783 -.343 .495 .059 .068 Cd (ng/g) lettuce -.190 .774 .415 -.336 -.034 -.279 Pb (ng/g) soil -.053 .768 .064 .422 -.471 .049 B (ng/g) lettuce -.216 .713 .235 .123 .609 -.061 Carbamazepine epoxide (ng/g) lettuce -.050 -.680 -.091 .620 .063 -.373 Co (ng/g) lettuce .644 -.676 -.308 -.158 -.034 -.087 Zn (ng/g) soil .306 .568 -.541 .089 .470 .249 As (ng/g) lettuce .411 .504 .428 .158 -.474 -.381 Mn (ng/g) lettuce .090 -.166 .968 .087 .131 -.044 Carbamazepine (ng/g) lettuce -.127 .182 .952 -.036 -.209 -.001 Pymetrozin (ng/g) soil .234 -.071 .951 -.159 .018 -.104 Conductivity (mS) soil -.107 .291 .931 -.179 .028 -.063 Lorazepam (ng/g) soil -.181 .360 -.900 .108 .000 .125 128 extracts were combined and reduced to ca. 1 mL with nitrogen gas and reconstituted with 10 mL of LiChrosolv water. The samples were subsequently percolated through SPE cartridges (STRATA X, 100 mg/6 mL), previously conditioned with 1 mL of MeOH and water, respectively. The cartridges were washed with water/methanol (95:5, v/v) and eluted with 2 mL of a mixture of MeOH/ethyl acetate (1:1, v/v). The final extracts were reduced almost to dryness, resuspended in 1 mL of water, and filtered (0.22 µm) prior to LC-MS/MS analysis. LODs and LOQs were calculated for each analyte as three and ten times the signal from the baseline noise (S/N ratio) of 3 blank samples, respectively. Further details on the GC-MS/MS and LC-MS/MS and analytical quality parameters (LOD, LOQ, and recoveries) are provided elsewhere (section 4.5.2 and tables S4.1-4.3). It is important to notice that the extraction method used for the determination of OMCs in lettuce, as well as the analytical quality parameters are likewise reported in section 3.2.3. 4.2.4 Human health risk assessment TEsHazard Quotient (HQ) The potential risk to human health resulting from consumption of TEs in vegetables was conducted through the hazard quotient (HQ) approach, which was calculated as follows: HQ= EDI RfD (4.1) where RfD (reference dose) is the maximum tolerable daily intake (µg/kg bw/day) of a specific metal (EPA, 2015; WHO, n.d.) that does not result in any harmful health effect, and EDI is the estimated daily intake (µg/kg bw/day). The EDI was calculated as follows: EDI= DI×CM BW (4.2) where DI denotes the daily intake of edible parts of vegetables in g per day (the DI for each of the vegetables and population classes is given in Table S.4.4), CM is the 95th percentile value for the concentration of each TE in the vegetable tissue (µg/g fresh weight (fw)), and BW is the body weight (kg) of the target individual. The DI of fresh vegetables in Spain used in the calculations was taken from the EFSA’s Comprehensive Food Consumption Database. For these data, all varieties of lettuce were considered, as well as tomatoes and tomato by-products, cauliflower, and all types of beans. An EDI value for a specific TE in excess of the corresponding RfD (i.e., HQ>1) implies a potential risk to consumers. RfD values are obtained from chronic oral exposure studies (IRIS, n.d.). Finally, the total 129 hazard quotient (THQ) of each sampling site was calculated as the sum of all the HQs for all the chemicals to which an individual might be exposed. The values for selected metals are shown in Table 4.3. The RfD value for Cr was calculated considering the Cr3+ form, as it is the main chromium species present in lettuce (Asfaw et al., 2017). OMCs - Threshold of toxicological concern (TTC) The human health risk associated with the intake of the selected OMCs through the consumption of the vegetable crops was assessed with the threshold of toxicological concern (TTC) approach using Toxtree software (Toxtree V2.6.13), which is based on the decision trees of Cramer and Kroes (Cramer et al., 1976; Kroes et al., 2000). This method is suitable for evaluating chemical compounds found at low concentrations in food products without toxicity data (Kroes et al., 2000). As a result, the OMCs were classified into three categories (I, II, III), corresponding to increasing toxicity or their potential genotoxicity and carcinogenicity. Class I contains substances with simple chemical structures with efficient modes of metabolism, suggesting a low order of oral toxicity. Class II consists of intermediate compounds and substances. Class III includes those that allow for no strong initial presumption of safety or may even suggest significant toxicity. The selected TTC values were 30, 9, and 1.5 μg/kg bw/day for Classes I, II, and III, respectively (Munro et al., 1996). For the genotoxic substances, a TTC value of 0.0025 mg/kg bw/day was selected (Kroes et al., 2004). TTC values were calculated based on analysis of the chronic toxicity data of chemicals in three structural classes identified according to the Cramer decision tree (Cramer et al., 1976). The intake of OMCs above TTC values could pose a potential risk of exposure and requires a specific toxicity analysis of the targeted OMCs. In this study, the average body weights of a Catalan male adult (20-65 years) and child (4-9 years) were used, i.e. 70 and 24 kg, respectively (Generalitat de Catalunya, 2015). The daily consumption (DC, kg/day) by an adult or child to reach the TTC was calculated as follows: 𝐷𝐶 =𝑇𝑇𝐶 ×𝐵𝑊 𝐶𝑂𝑀𝐶 (4.3) where COMC is the 95th percentile value for the concentration of each OMCs in the vegetable tissue (µg/kg fw). 4.2.5 Data analysis The experimental results were statistically evaluated using the SPSS v. 22 package (Chicago, IL, US). All data sets were checked for normal distribution using the Kolmogorov–Smirnov test to ensure that parametric statistics were applicable. The overall comparison of the occurrence of chemical contaminants between farm plots (rural vs peri-urban) was performed with a paired-sample t-test 130 (dependent samples), whereas the comparison of the concentration of each chemical contaminant between plots was analyzed by independent samples t-test. Statistical significance was defined as p ≤ 0.05. Principal component analysis (PCA) was performed on the concentration of TEs and OMCs in vegetables by using a correlation matrix. 4.3 Results and discussion 4.3.1 Occurrence of trace elements (TEs) Table 4.1 shows the occurrence of TEs in vegetable crops from different sampling sites. Different vegetable species and cultivars differ in their ability to uptake, accumulate, and tolerate heavy metals. The concentration of TEs in vegetables ranged from non-detectable to 17 mg/kg fw. Zn, B, and Mn were the most abundant, each of them with a concentration higher than 1 mg/kg fw. No statistical differences were generally observed between the rural and peri-urban sites (paired t-test taking into account all compound differences between sites, p-value>0.05). Nevertheless, the concentrations of As, Cd, Pb, Mo, and Hg were greater in vegetables harvested in the peri-urban area (p-value <0.05; broad beans were not included due to the lack of data for the rural site), except for Ba, Co, Mn and Sb which showed higher abundance in vegetables from rural agriculture. These findings are consistent with a previous study conducted in the same area for lettuce, in which Cd and Pb showed greater concentrations in the peri-urban site than in the rural one (Margenat et al., 2018). They were also similar to those of other studies carried out in urban gardens (0.014 mg/kg fw Cd in fruit and 0.028 mg/kg fw Cd in leafy vegetables) (McBride et al., 2014). The total concentration of TEs per vegetable was as follows (calculated as the average of the total concentration of TEs in the 3 plots): 26 mg/kg fw (broad beans), 19 mg/kg fw (tomato fruits), 9.0 mg/kg fw (lettuce), and 8.9 mg/kg fw (cauliflower). Zn, Cu, B, and Mn, were the most abundant TEs in broad beans (14 mg/kg fw for Zn, 4.0 mg/kg fw for Cu, 2.9 mg/kg fw for B, and 3.0 mg/kg fw for Mn, on average) and tomato fruits (8.0 mg/kg fw for Zn, 3.1 mg/kg fw for Cu, 4.1 mg/kg fw for B, and 2.5 mg/kg fw for Mn, on average), whereas Cu and B were lower than 0.7 and 2.0 mg/kg fw respectively in lettuce and cauliflower. Mn concentration was similar in all of the studied vegetables. These results are in keeping with those of other studies conducted in the Basque Country (Spain) (Trebolazabala et al., 2017), which showed that Zn and Cu were detected in tomato fruits in a similar range of concentrations (12.43-26.7 mg/kg fw for Zn and 3.563-16 mg/kg fw for Cu). Similarly, in a study carried out in Turkey (Bagdatlioglu et al., 2010), Cu was detected at a higher concentration in tomato fruits than in lettuce. Furthermore, in the same study broad beans showed maximum levels for both Cu and Zn. Another study conducted in Bangladesh observed that the concentration of Cu was higher in tomato fruits than in the rest of vegetables studied (brinjal, bean, carrot, green chilli, onion, andpotato) (Shaheen et al., 2016). 131 Finally, it should be noted that Li, Cd, Sb, and As were only detected in lettuce, due to their higher accumulation in the edible parts of this crop compared to root and fruit vegetables (Singh, 2012). The values of Cd and Pb were compliant with the maximum levels for these TEs in foodstuffs set out in Directive 1881/2006/EC of 19 December 2006 (0.2 mg/kg and 0.05 mg/kg fw for Cd in lettuce and the other vegetables, respectively, and 0.3 mg/kg and 0.10 mg/kg fw for Pb in lettuce and cauliflower and in tomatoes and broad beans, respectively). 132 Table 4.1 Average and 95th percentile concentration values of selected TEs in vegetables (mg/kg fw). Average and standard deviation of TEs in vegetables grown in rural and peri-urban agriculture. * broad beans were not included for the statistical analysis, when values were <LOD, LOD/2 value has been considered ** p-value<0.005. Lettuce Tomato Cauliflower Broad beans Rural* Peri-urban* Plot 1 Plot 3 Plot 4 Plot 1 Plot 3 Plot 4 Plot 1 Plot 3 Plot 4 Plot 3 Plot 4 As 3.38·10-4 (6.28·10-4) 5.94·10-4 (9.17·10-4) 8.41·10-4 (1.14·10-3) <10-4 <10-4 <10-4 <5·10-5 <5·10-5 <5·10-5 <10-4 <10-4 1.38·104±2.08·10-4 2.73·104±3.68·10-4** B 1.77 (2.11) 2.00 (2.38) 1.91 (2.33) 3.98 (4.14) 3.77 (4.85) 4.60 (5.18) 2.35 (2.74) 1.69 (1.84) 1.99 (2.13) 3.12 (3.99) 2.62 (2.94) 2.70±1.00 2.66±1.21 Ba 0.83 (1.09) 0.50 (0.63) 0.60 (0.70) 0.19 (0.21) 0.16 (0.20) 0.21 (0.33) 0.21 (0.23) 0.19 (0.23) 0.21 (0.23) 0.28 (0.30) 0.17 (0.19) 0.41±0.33 0.31±0. 19** Cd 0.01 (0.01) 0.02 (0.03) 0.02 (0.02) <0.008 <0.008 <0.008 <0.004 <0.004 <0.004 <0.008 <0.008 0.004±0.002 0.009±0.009** Co 0.02 (0.03) 0.01 (0.01) 0.01 (0.02) <0.008 <0.008 <0.008 <0.004 <0.004 <0.004 <0.008 <0.008 0.009±0.010 0.005±0.003** Cr 0.20 (0.32) 0.15 (0.24) 0.18 (0.46) 0.18 (0.19) 0.14 (0.16) 0.17 (0.18) 0.15 (0.20) 0.13 (0.14) 0.12 (0.13) 0.27 (0.28) 0.28 (0.29) 0.17±0.06 0.15±0.08 Cu 0.49 (0.62) 0.61 (0.69) 0.76 (0.88) 3.28 (4.77) 2.83 (3.54) 2.87 (3.55) 0.61 (0.75) 0.44 (0.48) 0.46 (0.54) 3.89 (4.09) 4.01 (4.21) 1.57±1.57 1.33±1.13 Hg 3.66·10-4 (4.43·10-4) 8.34·10-4 (1.09·10-3) 9.76·10-4 (1.47·10-3) 3.15·10-4 (4.36·10-4) 5.33·10-4 (5.82·10-4) 2.80·10-4 (3.31·10-4) 3.55·10-4 (6.12·10-4) 1.74·10-4 (2.02E-04) 1.54·10-4 (2.44E-04) 1.67·10-4 (1.86·10-4) 2.10·10-4 (3.50·10-4) 3.50·104±1.36·10-4 4.92·104±3.63·10-4** Li 0.04 (0.06) 0.03 (0.04) 0.04 (0.05) <0.008 <0.008 <0.008 <0.004 <0.004 <0.004 <0.008 <0.008 0.013±0.018 0.014±0.016 Mn 3.59 (4.73) 3.33 (4.05) 1.89 (2.38) 3.00 (3.61) 2.12 (2.40) 2.38 (2.99) 1.56 (1.87) 2.13 (2.30) 1.64 (1.74) 3.31 (3.61) 2.67 (2.81) 2.76±1.05 2.25±0.64** Mo 0.03 (0.03) 0.03 (0.04) 0.03 (0.05) <0.008 0.07 (0.12) 0.12 (0.16) <0.004 <0.004 <0.004 1.18 (1.46) 1.04 (1.17) 0.01±0.01 0.04±0.05** Ni 0.09 (0.15) 0.06 (0.09) 0.11 (0.34) <0.008 0.07 (0.12) 0.12 (0.19) <0.004 <0.004 <0.004 0.38 (0.44) 0.29 (0.32) 0.03±0.05 0.06±0.09 Pb 0.05 (0.07) 0.14 (0.18) 0.16 (0.20) <0.008 <0.008 0.15 (0.19) <0.004 <0.004 <0.004 <0.008 <0.008 0.02±0.03 0.08±0.08** Rb 0.37 (0.47) 0.40 (0.51) 0.50 (0.59) 0.95 (1.33) 1.17 (1.27) 0.60 (0.70) 0.32 (0.39) 0.29 (0.33) 0.29 (0.31) 0.40 (0.44) 0.24 (0.29) 0.57±0.37 0.54±0.31 Sb 0.02 (0.03) 0.01 (0.01) 0.01 (0.01) <0.008 <0.008 <0.008 <0.004 <0.004 <0.004 <0.008 <0.008 0.009±0.010 0.005±0.003** Zn 1.23 (1.51) 2.29 (2.85) 2.50 (3.01) 7.45 (9.61) 6.98 (7.36) 9.13 (11.3) 3.79 (4.17) 3.56 (3.82) 4.60 (4.85) 14.03 (14.70) 14.09 (16.60) 4.16±2.59 4.85±2.6 133 4.3.2 Occurrence of OMCs Only 10 of the 33 OMCs assessed in the vegetables were detected in at least one site. Overall, carbamazepine was the most frequently detected OMC (75%) (Table 4.2). This is consistent with the demonstrated ubiquity of this compound in surface water samples, as well as its high plant uptake in greenhouse and field-grown experiments. Due to its neutral molecular form in a wide range of pH values and its log Kow (2.45), meaning it is rapidly uptaken by plants and accumulated at higher concentrations (Franklin et al., 2016; Goldstein et al., 2014; Riemenschneider et al., 2016). The concentration of OMCs in vegetables ranged from non-detectable to 256 µg/kg fw (dimethomorph). Dimethomorph (110-256 µg/kg fw for tomato fruits in the peri-urban site P3), methyl paraben (106-193 µg/kg fw for lettuce in P1), bisphenol F (32-92 µg/kg for tomato fruits in P4), and TCEP (97-124 µg/kg fw for tomato fruits in P4) were among the OMCs detected at the highest concentrations. This is consistent with the fact that direct application of fungicides, WWTP effluents (methylparaben), and chemicals released by plastic pipelines (bisphenol F) have been demonstrated to be the main sources of pollution in lettuce (Margenat et al., 2018). Similarly, dimethomorph has already been detected in tomato fruits due to its direct application in agriculture (Walorczyk, 2013). Although the paired t-test did not reveal statistical differences between the concentrations of OMCs in vegetables from the rural and peri-urban areas (pvalue>0.05), individual analyses showed that the concentrations of dimethomorph, TCEP, and MPB were different between the two areas (p-value<0.05). The higher concentrations of dimethomorph and TCEP in the peri-urban area can be explained due to the application of fungicide and the wet/dry deposition of the fire-retardant compound on the vegetable surface, respectively. The occurrence of fireretardant compounds in the atmosphere in areas close to cities has already been demonstrated (Ren et al., 2016), mostly in the particle phase (gas phase <5%), and it has been found that rainfall can lead to a scavenging effect, promoting the wet deposition of these compounds. The total concentration of OMCs per vegetable was as follows (calculated as the average of the total concentration of OMCs in the 3 plots): tomato fruits (163 µg/kg fw), lettuce (104 µg/kg fw), broad beans (19 µg/kg fw), and cauliflower (5 µg/kg fw). The highest average concentrations were found in tomato fruits for TCEP (64 µg/kg fw) and bisphenol F (33 µg/kg fw). Finally, it is important to note that the concentration of pesticides (dimethomorph, carbendazim, and indoxacarb) in vegetables complied with the maximum residue levels of pesticides in or on food and feed of plant and animal origin under Council Directive 91/414/EC (EC, 2006) (Table S4.5). 134 Table 4.2 . Average and 95th percentile concentration values of selected OMCs in vegetables (µg/kg fw). Average and standard deviation of TEs in vegetables grown in rural and peri-urban agriculture. Minimum, maximum and median concentrations of TEs (ng g-1 fw) in vegetable samples Lettuce Tomato Cauliflower Broad beans Plot 1 Plot 3 Plot 4 Plot 1 Plot 3 Plot 4 Plot 1 Plot 3 Plot 4 Plot 3 Plot 4 Dimethomorph <0.29 18.7 (20.4) <0.29 12.9 (17.7) 172 (247) 0.58 (0.98) <0.03 <0.03 <0.03 2.04 (2.91) <0.03 Surfynol 104 <4.07 7.57 (8.04) <4.07 <4.1 <4.1 <4.1 <4.1 <4.1 <4.1 <4.1 <4.1 2mercaptobenzothiazole 18.3 (37.2) <0.66 <0.66 <4.80 <4.80 <4.80 <4.80 <4.80 <4.80 <4.80 <4.80 Bisphenol F 1.56 (1.88) 61.6 (101) 22.83(30.35) 55.6 (55.7) 50.5 (62.2) 65.2 (88.9) <0.51 <0.51 <0.51 <0.51 <0.51 Carbamazepine 0.20 (0.23) 0.23 (0.31) 0.12 (0.17) 0.16 (0.19) 0.12 (0.14) 0.13 (0.21) <0.05 <0.05 <0.05 0.15 (0.22) 0.18 (0.30) Carbendazim <0.22 1.00(2.46) <0.22 <0.24 <0.24 <0.24 <0.24 <0.24 <0.24 <0.24 <0.24 Methylparaben 150 (192) 28.5 (31.7) 34.1 (46.2) 23.3 (30.2) 12.5 (17.2) 15.4 (22.5) <0.81 <0.81 <0.81 <0.81 28.7 (34.9) Carbamazepine epoxide 0.21 (0.23) 0.06 (0.07) 0.16 (0.24) <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 TCEP <0.17 <0.17 <0.17 <0.82 64.3 (78.8) <0.82 <0.82 <0.82 <0.82 <0.82 <0.82 Indoxacarb <0.29 <0.29 <0.29 <0.49 <0.49 <0.49 0.80 (0.89) 3.63 (6.11) <0.49 <0.49 2.15 (2.92) * FOD: frequency of detection 135 4.3.3 Sources and Distribution of chemical pollutants (PCA) Principal component analysis (PCA) was performed on the whole data set to gain further insight into the sources and distribution behavior of the various parameters assessed in the irrigation waters (Table S4.6). The PCA reduced the 23 measured variables to 6 principal components with eigenvalues > 1, which explained 75% of the total variability observed. Components explaining small data variance (i.e., < 10%) were not retained and were assumed to be mostly due to background and noise contributions. Therefore, only the first three principal components, accounting for 55% of the total variability, were studied. The first principal component (PC1), which accounted for 23% of the variance, had high positive loading values (> 0.6) for B, Cu, and Mo, but negative loadings for Co, Ba, and MPB. This negative correlation between parameters indicates that while the positive variables were not accumulated in plants, the negative ones were. This fit perfectly with the lettuce plants. The second component explained 18% of the variance and had positive loadings (>0.6) for Cr, Mn, Co, Ni, Mo, carbamazepine, and MPB. This component correlated with the chemicals that were highly abundant in lettuce and broad beans. The third component accounted for 13% of the variance and had positive loadings (>0.6) for B, Rb, TCEP, and BPF. It correlated with the chemicals found to be most abundant in tomato fruits. Fig. 4.1 shows the score plots for PC1 vs PC2 (PC1 vs PC3 is provided in Fig. S4.1). Both plots grouped samples in 4 groups depending on the vegetable (lettuce, tomato, cauliflower, or broad bean). Hence, the abundance of chemicals in the various samples collected depended on the vegetable rather than the location. Figure 4.1 Principal Component Analysis (PCA) results. Scores plot PC1 vs PC2 (ID1= Plot 1 lettuce, ID2= Plot 3 lettuce, ID3= Plot 4 lettuce, ID4= Plot 1 tomato, ID5= Plot 3 tomato, ID6= Plot 4 tomato, ID7= Plot 1 cauliflower, ID8= Plot 3 cauliflower, ID9= Plot 4 cauliflower, ID11=Plot 3 broad beans and ID12= Plot 4 broad beans). 136 4.3.4 Potential human health risk associated with the consumption of vegetables Risk assessment for TEs exposure The estimated daily intake (EDI) of TEs was compound and vegetable dependent. On average, Zn was the compound with the highest intake (9.0  10-3 and 1.0  10-2 mg/day for adults and children, respectively), whereas tomatoes had the highest concentration of TEs on average per farm plot (4.3 - 7.2 mg/kg fw). Oral reference doses (RfDs) were used to assess the human health risk (Table 3), except for Co, Hg, Li, and Rb, for which the oral RfD was not available. Therefore, the risk assessment was evaluated for 12 TEs. HQs ranged from 4.8  10-8 to 0.2 for As in broad beans and Pb in tomato fruits (P3), respectively. Of these 12 TEs, on average, Pb posed the greatest health risk to adults and children, followed by Zn, As, Mn, B, Mo, Cd, Cu, Ni, Ba, As, and Cr. These findings are consistent with those of other studies carried out in China and Ethiopia (Chang et al., 2014; Dziubanek et al., 2017; Woldetsadik et al., 2017), where food crops irrigated with either river water or treated wastewater in rural and urban sites did not pose risks to human health. Pb was also assessed with three Benchmark Dose Lower Confidence Limits (BMDLs) to evaluate the effects of lead exposure in humans: BMDL10 for chronic kidney disease development at 0.63 μg/kg bw/day, BMDL01 for systolic blood pressure effects at 1.5 μg/kg bw/day, and BMDL01 for developmental neurotoxicity (which would apply to fetuses and infants) at 0.5 μg/kg bw/day, as determined by the WHO (1986) and EFSA (2010). The EDI values found for Pb in the present study due to the ingestion of these vegetables (Table S4.7) were in the range of 5.2· 10-5 to 5.2 ·10-1 μg/kg bw/day. These values were lower than all three of the aforementioned BMDLs, except for tomato fruits in Plot 3. This is in agreement with the high lead content found in the soil from this plot (164 mg/Kg dw) in a previous study (Margenat et al., 2018). The THQs obtained for all age groups were less than 1 for all vegetables and sites studied. Tomato fruits showed the highest THQ on average (0.40 and 0.57 on average for adults and children, respectively), followed by lettuce (0.23 and 0.30 for adults and children, respectively), cauliflower (0.02 and 0.003 for adults and children, respectively), and broad beans (0.0001 and 0.005 for adults and children, respectively). The fact that the highest THQ was observed for tomatoes is probably due to the high consumption of this fruit in Spain compared to the other studied vegetables (see the Material and Methods section). Nevertheless, no statistical differences were observed in the individual and total HQs between vegetables grown in the peri-urban area and rural site (paired t-test, p > 0.05). Although for some TEs such as Pb in tomatoes and Zn in lettuce the HQs were greater in the peri-urban agriculture, statistical analysis, comparing rural and peri-urban vegetables (including all vegetables), resulted in no statistical differences between both areas for any of the studied TEs. The total sums of the THQs (for 137 adults) for each farm plot, taking into consideration all TEs and the consumption of all the studied vegetables, were as follows: 0.82 (P4), 0.59 (P3), and 0.54 (P1). Farm plots irrigated with unplanned reclaimed water (de facto reuse) showed the highest risk (P3-4), although it was still less than 1 for adults; hence no risk was assumed (Zeng et al., 2018). These findings are consistent with previous studies, which have found that farm plots irrigated with treated wastewater result in a higher risk due to the occurrence of TEs than control plots irrigated with groundwater (Khan et al., 2008). Although the THQs obtained were lower than 1 in all sites, it should be taken into account that the assessment included only the consumption of the analyzed vegetables. The values could be higher if other vegetables are considered. Therefore, future studies should evaluate the risk associated with other vegetables consumed per person and day in a real-life scenario. 144 Table S4.2 Recoveries of the surrogates in vegetables Surrogate R (%) tomato in tomato R (%) cauliflower R (%) broad beans Bisphenol A-d16 52±4 54±1 48±6 Caffeine-13C3 53±8 60±1 52±9 Carbamazepine-13C6 72±12 64±7 83±14 Diazepam-d5 52±8 78±2 69±7 5.6-dimethyl-1H-benzotriazole (XbTri) 62±10 55±5 71±3 Etylparaben-13C 57±7 58±8 52±7 145 Table S4.3 Recoveries of the studied OMCs in vegetables Compounds R (%) tomato in tomato R (%) cauliflower R (%) broad beans 1-hydroxybenzotriazole (OHBT) 43±2 66±5 53±3 2-mercaptobenzothiazole (2MBT) 89±5 81±8 85±1 2-tert-Butyl-4-methoxyphenol (BHA) 91±16 7.7±10 89±9 4-tert-octylphenol (OP) 84±2 55±9 55±2 5-methyl-2H-benzotriazole (5TTri) 54±3 42±10 47±2 Atrazine 71±5 54±12 78±4 Azoxystrobin 90±8 47±5 98±1 Benzothiazole 54±14 38±13 40±3 Benzotriazole 43±6 58±6 38±3 Bisphenol A 51±8 59±10 66±9 Bisphenol F 44±6 57±11 55±7 Butylparaben 51±2 58±2 76±2 Carbamazepine 92±26 152±24 52±10 Carbamazepine-10.11-epoxide (EPOCBZ) 134±31 95±14 93±4 Carbendazim 110±10 88±5 84±3 Chlorpyrifos n.d. n.d. 41±10 DEET 83±7 86±5 73±5 Diazepam 53±8 81±5 66±1 Diazinon 96±9 79±8 87±8 Dimethomorph 94±1 87±5 89±7 Etylparaben 73±4 84±10 75±4 Indoxacarb 69±5 65±3 70±7 Lorazepam n.d. 35±5 61±1 Methylparaben 92±11 81±7 86±4 Oxazepam 70±11 60±4 78±4 Primidone 49±3 51±3 52±6 Propylparaben 54±3 52±4 12.967 5.967 48 51 13.714 6.714 54 13.536 6.536 52 58±1 146 Pymetrozin n.d. n.d. n.d. Pyraclostrobin n.d. n.d. 47±7 Simazine 66±5 54±12 77±2 Surfynol 104 86±6 97±9 82±3 Tris(1-chloro-2-propyl) phosphate (TCPP) 98±9 103±2 99±2 Tris(2-chloroethyl) phosphate (TCEP) 73±1 64±10 77±5 147 Table S4.4 Daily consumption in Spain for each of the studied vegetables and population class Vegetable Population class Dietary survey Nº subjects Nº consumers Value Consumption (g/day) Lettuce Adult AESAN-FIAB 981 662 P95 86.7 Other children Food patterns of Spanish school children and adolescent 156 45 P95 38.0 Tomato Adult AESAN 410 370 P95 196 Other children Food patterns of Spanish schoolchildren and adolescent 156 92 P95 94.3 Cauliflower Adult AESAN-FIAB 981 88 P95 24.4 Other children Nutrition survey2005 399 7 Mean 1.20 Broad beans Adult AESAN-FIAB 981 567 P95 0.02 Other children Nutrition survey2005 399 256 P95 0.31 148 Table S4.5 Maximum residue levels of pesticides in or on food and feed of plant and animal origin and amending council directive 91/414/EEC Vegetable Carbendazim and benomyl (sum of benomyl and carbendazim expressed as carbendazim) Indoxacarb (sum of indoxacarb and its R diastereomer) Dimethomorph (sum of isomers) Tomato 0.3 0.5 1 Cauliflower 0.1 0.3 0.6 Lettuce 0.1 3 15 Beans (without pods) 0.1 0.02 0.04 149 Table S4.6 Loadings for PCA Component 1 2 3 4 5 6 B 0.6 0.0 0.6 -0.2 -0.1 -0.2 Cr 0.3 0.7 -0.1 0.1 -0.2 0.2 Mn -0.1 0.8 0.2 0.0 0.3 -0.2 Co -0.7 0.6 0.2 -0.2 0.0 0.1 Ni 0.4 0.7 -0.1 0.3 -0.2 0.2 Cu 0.8 0.4 0.3 0.0 0.1 0.0 Zn 0.9 0.4 0.0 0.1 0.0 0.0 Rb 0.3 -0.1 0.8 -0.3 0.3 0.2 Mo 0.6 0.6 -0.3 0.2 0.0 0.1 Ba -0.8 0.5 0.1 0.0 -0.1 0.1 Pb -0.4 0.1 0.5 0.4 -0.5 0.0 Hg -0.4 -0.2 0.3 0.5 0.0 0.5 Indoxacarb 0.2 0.0 -0.4 0.0 0.0 0.0 Dimetomorph 0.2 -0.2 0.5 -0.2 0.4 0.4 TCEP 0.3 -0.1 0.6 0.0 -0.5 -0.4 Surfynol -0.2 -0.1 0.0 0.6 0.4 -0.3 MBT -0.5 0.4 0.0 -0.4 0.1 -0.2 BPF 0.0 -0.1 0.6 0.2 -0.3 0.0 Carbamazepine 0.0 0.6 0.4 0.2 0.2 0.0 EpoxyCBZ -0.5 0.3 0.1 -0.2 -0.1 0.2 Carbendazim -0.2 0.0 0.1 0.6 0.4 -0.3 MPB -0.6 0.6 0.1 -0.3 0.1 -0.1 150 Table S4.7 EDI (mg/kg bw/day) of TEs in vegetables for an adult (70 kg) and a child (24 kg). *Calculated from the LOD/2 Lettuce Tomato Cauliflower Broad beans Plot 1 Plot 3 Plot 4 Plot 1 Plot3 Plot 4 Plot 1 Plot 3 Plot 4 Plot 3 Plot 4 As Adult 7.77· 10-7 1.14· 10-6 1.41· 10-6 1.40·1 0-7* 1.40·1 0-7* 1.40·1 0-7* 1.74·1 0-8* 1.74·1 0-8* 1.74·1 0-8* 1.43·1 0-11* 1.43·1 0-11* Child 9.94· 10-7 1.45· 10-6 1.80· 10-6 1.96·1 0-7* 1.96·1 0-7* 1.96·1 0-7* 2.50·1 0-9* 2.50·1 0-9* 2.50·1 0-9* 6.46·1 0-10* 6.46·1 0-10* B Adult 2.62· 10-3 2.94· 10-3 2.88· 10-3 1.16·1 0-2 1.35·1 0-2 1.45·1 0-2 9.55·1 0-4 6.40·1 0-4 7.43·1 0-4 1.15·1 0-6 8.45·1 0-7 Child 3.35· 10-3 3.76· 10-3 3.68· 10-3 1.63·1 0-2 1.90·1 0-2 2.03·1 0-2 1.37·1 0-4 9.18·1 0-5 1.07·1 0-4 5.16·1 0-5 3.80·1 0-5 Ba Adult 1.35· 10-3 7.86· 10-4 8.67· 10-4 5.83·1 0-4 5.51·1 0-4 9.36·1 0-4 8.12·1 0-5 8.07·1 0-5 8.15·1 0-5 8.72·1 0-8 5.40·1 0-8 Child 1.73· 10-3 1.01· 10-3 1.11· 10-3 8.19·1 0-4 7.75·1 0-4 1.32·1 0-3 1.17·1 0-5 1.16·1 0-5 1.17·1 0-5 3.93·1 0-6 2.43·1 0-6 Cd Adult 9.74· 10-6 3.74· 10-5 2.25· 10-5 1.12·1 0-5* 1.12·1 0-5* 1.12·1 0-5* 6.96·1 0-7* 6.96·1 0-7* 6.96·1 0-7* 1.15·1 0-9* 1.15·1 0-9* Child 1.25· 10-5 4.78· 10-5 2.88· 10-5 1.57·1 0-5* 1.57·1 0-5* 1.57·1 0-5* 1.00·1 0-7* 1.00·1 0-7* 1.00·1 0-7* 5.17·1 0-8* 5.17·1 0-8* Cr Adult 3.91· 10-4 3.00· 10-4 5.72· 10-4 5.19·1 0-4 4.34·1 0-4 5.07·1 0-4 6.86·1 0-5 4.75·1 0-5 4.39·1 0-5 8.10·1 0-8 8.41·1 0-8 Child 5.00· 10-4 3.83· 10-4 7.31· 10-4 7.30·1 0-4 6.10·1 0-4 7.13·1 0-4 9.86·1 0-6 6.82·1 0-6 6.30·1 0-6 3.65·1 0-6 3.79·1 0-6 Cu Adult 7.70· 10-4 8.51· 10-4 1.09· 10-3 1.33·1 0-2 9.90·1 0-3 9.92·1 0-3 2.62·1 0-4 1.69·1 0-4 1.89·1 0-4 1.17·1 0-6 1.21·1 0-6 Child 9.84· 10-4 1.09· 10-3 1.39· 10-3 1.87·1 0-2 1.39·1 0-2 1.39·1 0-2 3.77·1 0-5 2.42·1 0-5 2.71·1 0-5 5.28·1 0-5 5.44·1 0-5 Mn Adult 5.86· 10-3 5.01· 10-3 2.95· 10-3 1.01·1 0-2 6.71·1 0-3 8.36·1 0-3 6.51·1 0-4 8.00·1 0-4 6.07·1 0-4 1.04·1 0-6 8.06·1 0-7 Child 7.49· 10-3 6.41· 10-3 3.77· 10-3 1.42·1 0-2 9.43·1 0-3 1.18·1 0-2 9.35·1 0-5 1.15·1 0-4 8.71·1 0-5 4.66·1 0-5 3.63·1 0-5 Mo Adult 4.07· 10-5 4.51· 10-5 5.95· 10-5 1.12·1 0-5* 3.39·1 0-4 4.53·1 0-4 6.96·1 0-7* 6.96·1 0-7* 6.96·1 0-7* 4.19·1 0-7 3.34·1 0-7 Child 5.20· 10-5 5.76· 10-5 7.61· 10-5 1.57·1 0-5* 4.76·1 0-4 6.37·1 0-4 1.00·1 0-7* 1.00·1 0-7* 1.00·1 0-7* 1.89·1 0-5 1.51·1 0-5 Ni Adult 1.82· 10-4 1.16· 10-4 4.23· 10-4 1.12·1 0-5* 3.42·1 0-4 5.33·1 0-4 6.96·1 0-7* 6.96·1 0-7* 6.96·1 0-7* 1.25·1 0-7 9.32·1 0-8 Child 2.33· 10-4 1.48· 10-4 5.41· 10-4 1.57·1 0-5* 4.82·1 0-4 7.50·1 0-4 1.00·1 0-7* 1.00·1 0-7* 1.00·1 0-7* 5.64·1 0-6 4.20·1 0-6 Pb Adult 9.23· 10-5 2.18· 10-4 2.51· 10-4 1.12·1 0-5* 1.12·1 0-5* 5.22·1 0-4 6.96·1 0-7* 6.96·1 0-7* 6.96·1 0-7* 1.15·1 0-9* 1.15·1 0-9* Child 1.18· 10-4 2.78· 10-4 3.21· 10-4 1.57·1 0-5* 1.57·1 0-5* 7.34·1 0-4 1.00·1 0-7* 1.00·1 0-7* 1.00·1 0-7* 5.17·1 0-8* 5.17·1 0-8* Sb Adult 3.72· 10-5 1.79· 10-5 1.63· 10-5 1.12·1 0-5* 1.12·1 0-5* 1.12·1 0-5* 6.96·1 0-7* 6.96·1 0-7* 6.96·1 0-7* 1.15·1 0-9* 1.15·1 0-9* Child 4.75· 10-5 2.29· 10-5 2.08· 10-5 1.57·1 0-5* 1.57·1 0-5* 1.57·1 0-5* 1.00·1 0-7* 1.00·1 0-7* 1.00·1 0-7* 5.17·1 0-8* 5.17·1 0-8* Zn Adult 1.87· 10-3 3.53· 10-3 3.73· 10-3 2.68·1 0-2 2.06·1 0-2 3.15·1 0-2 1.45·1 0-3 1.33·1 0-3 1.69·1 0-3 4.22·1 0-6 4.76·1 0-6 Child 2.39· 10-3 4.52· 10-3 4.77· 10-3 3.77·1 0-2 2.89·1 0-2 4.43·1 0-2 2.09·1 0-4 1.91·1 0-4 2.43·1 0-4 1.90·1 0-4 2.14·1 0-4 *Calculated from the LOD/2 151 Table S4.8 HQ of the studied OMCs in the vegetable samples Lettuce Tomato Cauliflower Broad beans Plot 1 Plot 3 Plot 4 Plot 1 Plot 3 Plot 4 Plot 1 Plot 3 Plot 4 Plot 3 Plot 4 Dimethomorph 9.7·106* 1.4·103 9.7·106* 0.04 0.56 2.2·103 7.1·106* 7.1·106* 7.1·106* 1.7·104 8.7·107* Carbendazim 1.1·103* 0.02 1.1·103* 9.0·104* 9.0·104* 9.0·104* 3.4·104* 3.4·104* 3.4·104* 2.8·106* 2.8·106* Indoxacarb 4.8·105* 4.8·105* 4.8·105* 1.1·103 1.1·103 1.1·103 8.4·104 0.01 2.3·104* 1.9·104 3.4·104 *Calculated by using the LOD/2 value Figure S4.1 Principal Component Analysis (PCA) results. Scores plot PC1 vs PC3 (ID1= Plot 1 lettuce, ID2= Plot 3 lettuce, ID3= Plot 4 lettuce, ID4= Plot 1 tomato, ID5= Plot 3 tomato, ID6= Plot 4 tomato, ID7= Plot 1 cauliflower, ID8= Plot 3 cauliflower, ID9= Plot 4 cauliflower, ID11=Plot 3 broad beans and ID12= Plot 4 broad beans). 152 Chapter V: General Discussion Peri-urban agriculture performs environmental, social and economic functions and services to the nearby urban areas (FAO, 2011). Nevertheless, industrialization and irrigation with TWW have led to an increase of the peri-urban agricultural exposure to chemical contaminants. For example, heavy metal accumulation in soil caused by industrial run-off, airborne pollution by traffic or industrial emissions, or the use of reclaimed water with a high content of CECs (Nabulo et al., 2010; X. Wu et al., 2014). Concerns regarding human exposure to chemical contaminants have arisen since they have been detected in the edible parts of food crops at detectable levels (Pan et al., 2014), but the risk that accumulated residues may pose to humans via crop consumption is still not well documented. Furthermore, most of the current studies available in the literature are conducted in hydroponic cultures, or greenhouse settings, but real scale conditions have barely been studied for OMCs (Riemenschneider et al., 2016). Currently, there are only few field studies done at real scale, none of them in Spain, which demonstrate the uptake of micropollutants, needless to say the co-occurrence of organic and inorganic contaminants, in vegetables irrigated with reclaimed water (Christou et al., 2017b). Therefore, this Thesis aimed to evaluate the presence of OMCs and TEs in irrigation water and later in the soil-plant system in different farming conditions and evaluates their possible plant and human health risk effects. This study has been conducted in four farm plots located in the peri-urban area of Barcelona (NE Spain) and a pristine farm plot far away from the peri-urban area for comparison. A summary of the principal distinctive of each farm plot is provided in Chapter II. .Many studies reported the presence of OMCs in TWW due to an incomplete removal of these chemicals in WWTPs as well as their posterior plant uptake (Calderón-Preciado et al., 2011; de Jongh et al., 2012; Gonzalez-Rey et al., 2015; Masiá et al., 2015). In this regard, Chapter II conducted an assessment of the occurrence of the selected chemical contaminants in irrigation waters (TEs and OMCs) from peri-urban area of Barcelona. As it was expected, Plot 3, located in the peri-urban area, showed the highest concentration levels for TEs and OMCs (Tables 2.2 and 2.3) due to their irrigation water impacted by WWTP effluents and the proximity to a traffic network. On the contrary, Plot 1, outside the peri-urban area, showed lower concentration levels of TEs and FOD of OMCs. Additionally, PCA analysis provided an insight into irrigation water nutrients and contaminants and classified these waters in four main groups: ground water (P1), surface water (P2 and P4), surface water impacted by WTTP effluents (P3) and groundwater impacted by industrial run-off (P5). However, the irrigation waters analyzed did not affect either seed germination, root elongation and crop productivity, indicating the suitability of using TWW for agricultural irrigation. Gvozdenac et al., (2016) assessed the phytotoxicity of the water from the Stara Tisa meander (Serbia), which contained low TEs and pesticides levels (<2 µg L-1) in 6 vegetable seedlings and observed that while some species (sunflower, cucumber, maize and white mustard) are sensitive to abiotic stress, 153 others (cabbage and radish) are tolerant showing little alterations. D’Abrosca et al. (2008) also observed different sensitivity among plant species in phytotoxicity assays probably due to different plant physiology. Therefore, in view of these studies, other seed species should be tested to assess the phytotoxicity of irrigation waters on seed germination. Once chemical contaminants reach agricultural soil, their fate and behaviour depend on several factors. OMCs can undertake chemical changes or degrade into products with different toxicity than the parent compound. Whereas TEs cannot break down, their characteristics can be altered and thereby, their ability to be taken up by plants. Chapter III provides co-occurrence of TEs and OMCs in soil and lettuce irrigated with the irrigation waters reported in Chapter II. Two-thirds of the farm plots (P1, P3 and P4) sampled contained sandy loam soils with a smaller proportion of clay particles. The soils in the sampling area had the following TOC content: 0.77% for P1, 2.86% for P2, 2.65% for P3, 3.53% for P4 and 1% for P5. Examining table S3.8 about TEs’ BCF, it would seem that generally food crops grown in soils with lower TOC content (P1 and P5) bioaccumulate TEs more than soils with a lower TOC content (P2P4). This is consistent with the fact TEs present in the soil phase are able to undergo precipitation and decomposition, ionic exchange and adsorption and desorption; depending on the pH and the presence of clay minerals, humic substances, iron oxides and hydroxides, and manganese found in the soil (Petruzzelli et al., 2010). The importance of clay minerals and soil texture in the distribution of TEs between soil solid and liquid phases, affects the TEs bioavailability of plants. In sandy soils, TEs are more soluble and available for plant uptake than in clay soils. However, the organic fraction has a great influence on metal mobility and bioavailability due to the tendency of metals to bind with humic compounds in both the solid and solution phases in soil. Similarly, the mobility of OMCs in the soil compartment will depend on compounds and soil characteristics and basically consists on adsorption or desorption processes on solid soil phase. Generally, compounds with high KOW values and low solubility will be mostly retained in the soil and consequently, less available for plant uptake. Indeed, compounds with log KOW > 4.0 hardly mobilize. On the contrary, organic contaminants with log KOW<1 will not be retained in soil, so they could be mobile and finally found in aquifers. In general, BCF for OMCs were lower in summer than in winter cultivation campaigns, this might be due to the rapid growth of summer crops (1 month) compared to winter crops (4-5 months) which could have cause growth dilution. PCA analysis determined the most relevant factors determining the presence of OMCs and TEs in lettuce crops, which were soil pollution, fungicide application, and irrigation water quality. Finally, in Chapter IV, the occurrence of chemical contaminants in different vegetables crops and the human health risk by their consumption were examined. As it has been reported in the literature, vegetable species differ widely in their ability to take up and accumulate TEs, even among cultivars and varieties within the same species (Zhou et al., 2016). For example, Säumel et al. (2012) reported that Zn