Evaluation of the airborne pollution by emerging contaminants using bitter orange (Citrus aurantium) tree leaves as biosamplers

Science of the Total Environment 677 (2019) 484–492

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

Evaluation of the airborne pollution by emerging contaminants using bitter orange (Citrus aurantium) tree leaves as biosamplers Pedro José Barroso, Julia Martín, Juan Luis Santos ⁎, Irene Aparicio, Esteban Alonso Departamento de Química Analítica, Escuela Politécnica Superior, Universidad de Sevilla, C/ Virgen de África 7, E-41011 Seville, Spain

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Tree leaves were used as biosamplers of emerging contaminants (EC) in urban air. • High concentrations of surfactants and plasticizers were found in tree leaves. • No significant spatial and seasonal variations in concentrations were found. • High concentrations of HBCDD were found in tree leaves located close to fire events.

a r t i c l e

i n f o

Article history: Received 22 December 2018 Received in revised form 22 April 2019 Accepted 26 April 2019 Available online 29 April 2019 Editor: Thomas Kevin V Keywords: Air pollution biomonitoring Surfactants Plastizicers Perfluorinated compounds Parabens

a b s t r a c t In this work, an analytical method has been applied to biomonitor airborne emerging pollutants in urban areas using bitter orange (Citrus aurantium) tree leaves, which is an evergreen species widely extended in the Mediterranean region, as biosampler. Leaves, from trees located in 20 different locations from Seville City (South of Spain) were sampled during one year period. Sampling sites were located in six highly populated areas, in seven lowly populated areas, in six urban parks and in one industrial area. Fifteen of the target compounds were detected in the analysed samples. The highest concentrations corresponded to plasticizers (up to 852 ng/g dry matter (dm)) and surfactants (up to 752 ng/g dm), especially di(2-ethylhexyl)phthalate and nonylphenol. Spatial distribution allowed assessing the influence of populated areas in the concentration of some of the studied compounds, such as plasticizers and perfluorinated compounds, and the influence of industrial areas, in the concentration of surfactants. No clear influence of the climatic conditions (temperature, solar radiation and rainfall) on the concentrations of studied compounds was observed. This fact could be due to the presence of diffuse sources of these compounds. In the case of the brominated flame retardant, the measured concentrations could be related with two fire episodes in the vicinity, but until now it has not been possible to rigorously demonstrate a causal relationship. This fact could reveal the suitability and valuable use of Citrus aurantium tree leaves for biomonitoring atmospheric pollutants, especially from unexpected emissions in atmospheric pollution episodes. © 2019 Elsevier B.V. All rights reserved.

1. Introduction ⁎ Corresponding author at: Department of Analytical Chemistry, University of Seville, C/ Virgen de África, 7, 41011 Seville, Spain. E-mail address: [email protected] (J.L. Santos).

https://doi.org/10.1016/j.scitotenv.2019.04.391 0048-9697/© 2019 Elsevier B.V. All rights reserved.

Nowadays, thousands of organic pollutants are daily released to the atmosphere from urban and industrial activities such as car exhausts,

P.J. Barroso et al. / Science of the Total Environment 677 (2019) 484–492

spraying of pesticides, flaring activities and from industrial sectors such as chemical, textile, municipal solid wastes, and others (Annamalai and Navasivayam, 2015; Al-Alam et al., 2017). Contaminants usually monitored in urban air are persistent organic pollutants such as polycyclic aromatic hydrocarbons (PAHs) (Niu et al., 2017), polychlorinated biphenyls (PCBs) (Annamalai and Navasivayam, 2015; Dumanoglu et al., 2017) or organochlorine pesticides (Pucko et al., 2017). However, there is a significant increase in the production and usage of new products in plastic or textile materials and in personal care products that has led to the continuous emission of hundreds of new organic contaminants, the so-called emerging pollutants, to the atmosphere. Therefore, the evaluation of the presence and distribution of these hazardous pollutants in the atmosphere is of great interest to preserve the air quality (Al-Alam et al., 2017). The evaluation of this kind of contamination is usually carried out using active or passive samplers. Active samplers are connected to a pump to pull air into the collection device that consists on a filter, for the collection of atmospheric particulate matter, and/or in a solid adsorbent, for the collection of the gaseous fraction. In passive samplers, also known as diffusive samplers, contaminants are retained by molecular diffusion through the diffusive surface of an adsorbent. Active samplers can be used to obtain information about gas and particle phase distribution. However, due to their high cost and electricity, they are not suitable for extensive and long-term monitoring (Liu et al., 2016; Niu et al., 2017). Passive samplers, due to their low cost and electricity independence, are most appropriate for long-term monitoring of organic pollutants. In the last decade, the use of plants as biosamplers or bioindicators of atmospheric pollution has received significant attention (Al Dine et al., 2015; St-Amand et al., 2009). Pollutants may penetrate into the plant tissue by gaseous equilibrium partitioning between the vegetation and the surrounding air and by dry or wet particle-bound deposition on the leaves (primary pathway) or uptake via the roots (secondary pathway) (Bartrons et al., 2016). However, generally the foliage represents the main route for plant uptake from air. The lipid layer that covers the surface of the leaves allows the accumulation of hydrophobic compounds. On the other hand, the stomata present in the leaves, and also in shoots and stems, are responsible for the diffusion of gaseous molecules. Regarding the uptake from the soil, hydrophobic compounds have shown no evidence of transport in plants to above ground parts (Hellström, 2004). The use of tree leaves as biosamplers can provide several advantages over active and passive samplers. For example, trees widely distributed in urban areas can provide not only information of unexpected contamination events but also can provide information about the most suitable locations for the placement of active or passive samplers. Most of the studies reported in the literature about the use of tree leaves as biosamplers have focused on the determination of persistent organic pollutants. For example, the distribution of PAHs in urban atmosphere has been studied using several tree species as biosamplers, such as pine needles (Al Dine et al., 2015), gingko (Yin et al., 2011), eucalyptus, populus (Rodriguez et al., 2012), quercus (Orecchio, 2007) or bitter orange leaves (Citrus aurantium) (Fasani et al., 2016). Holt et al. (2016) evaluated the atmospheric distribution and potential sources of polychlorinated dibenzo-p-dioxins/furans in different countries in Europe using scot pine needles as biosamplers. Al Dine et al. (2015) studied the distribution of several families of organic pollutants, including pesticides, PCBs and PAHs in urban, suburban and rural areas in France by the analysis of pine needles. In addition to the above mentioned studies, only a few papers have been published about the use of tree leaves for biomonitoring of emerging pollutants in the atmosphere. These studies have focused on particular areas such as coastal areas, using coastline plant species (Ribeiro et al., 2017) and along ski tracks (Chropeňová et al., 2016) but only a few studies have evaluated the atmospheric contamination by emerging

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pollutants as PFCs (Zhang et al., 2015) or synthetic musks (Silva et al., 2015; Busso et al., 2018) in rural, remote or urban areas using tree leaves as biosamplers. Citrus aurantium is an evergreen species with a height up to 10 m. It is grown in Asia, North Africa and it is particularly widespread cultivated in the southern region of Europe, especially in the south of Italy and Spain. For instance, in Seville city (South Spain), there are about 30,000 Citrus aurantium trees (Bonells, 2003) what makes this tree species the most extended ornamental tree in the city (Oliva et al., 2008). Due to these characteristics, C. aurantium is a suitable species, not only for the evaluation of the presence of airborne pollutants in urban areas, but also for the evaluation of pollutant accumulation through long-term exposure. The aim of this work was to evaluate the distribution of emerging pollutants in urban areas using bitter orange leaves as bioindicator. For this purpose tree leaves were collected from 20 sampling points located in Seville City (South of Spain) and analysed by a previously validated analytical method (Barroso et al., 2018). The selected contaminants were nonylphenol ethoxylates (NPE, including nonylphenol mono- (NP1EO) and diethoxylate (NP2EO) and nonylphenol (NP)), plasticizers (di(2-ethylhexyl)phthalate (DEHP) and bisphenol A (BPA)), parabens (methylparaben (MeP), ethylparaben (EtP) and propylparaben (PrP)), perfluorinated compounds (PFCs) (perfluorobutanoic acid (PFBuA), perfluoropentanoic acid (PFPeA), perfluorohexanoic acid (PFHxA), perfluroroheptanoic acid (PFHpA), perflurorooctanoic acid (PFOA), and perfluorooctanesulfonic acid (PFOS)), and a brominated flame retardant (hexabromocyclododecane (HBCDD)). Target compounds were selected taking into account their toxicity, transport, persistence (Annamalai and Navasivayam, 2015; Naidu et al., 2016; Shan et al., 2014; Zhang et al., 2015) or their inclusion in the Stockholm Convention list of persistent organic pollutants. 2. Material and methods 2.1. Standards and reagents HPLC-grade water, acetone, methanol and hexane were provided by Romil (Barcelona, Spain). Acetic acid was purchased from Scharlab (Barcelona, Spain). Ammonium acetate, florisil sorbent, BPA (≥99%), MeP (≥99%), EtP (≥99%), PrP (≥99%), PFOS (≥98%), PFOA (96%), PFHpA (99%), PFHxA (≥97%), PFPeA (97%), PFBuA (98%), HBCDD (95%) and propyl 4-hydroxybenzoate-13C6 (PrP-13C6) (50 μg/mL in acetone) were obtained from Sigma-Aldrich (Steinheim, Germany). Bisphenol A-d14 (BPA-d14) (50 μg/mL in acetone) was purchased from Dr. Ehrenstorfer (Augsburg, Germany). Perfluoro-n-[1,2,3,4-13C4]octanoic acid (MPFOA) (50 μg/mL in acetone) was supplied by Cambridge Isotope Laboratories (MA, USA). BPA-d14, PrP-13C6 and MPFOA were used as internal standards (I.S.). Nylon syringe filters were provided by Scharlab (0.45 μm, 13 mm, Scharlab, Barcelona, Spain). Individual stock standard solutions of each compound (1000 mg/L) were prepared in methanol and stored at 4 °C. Working solutions, containing a mixture of the target compounds at 10 mg/L, were prepared by dilution of the stock standard solutions in methanol. An internal standard (I.S.) mixture working solution, containing 500 μg/L of PrP-13C6 and MPFOA and 1000 μg/L for BPA-d14, was prepared by dilution of commercial I.S. solutions in methanol. 2.2. Studied area and sampling Samples were collected from different locations from Seville city (South of Spain) covering a superficial area of about 28 km2. Fig. 1 shows the location of sampling points. Considering the population data from 2010 to 2017 (http://www.seville.org), twenty sampling points were fixed (Fig. 1). Sampling points were located in highly populated locations (n = 6) (HP; from 50,000 to 100,000 inhabitants), in

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HP5 P6 HP6

P3

HP2 HP1 LP2 HP3 LP3

LP7

LP6

HP4 P4

P1

IA1

LP4 LP5

P2 LP1

SPAIN N

High density population !

Low density population

P5

Industrial estate Parks

E

W

2 km

Seville

S

Fig. 1. Studied area and sampling sites.

lowly populated locations (n = 7) (LP; from 25,000 to 50,000 inhabitants), in urban parks (n = 6) (P) and in one industrial area (IA). The six HP areas, in increasing order of population density, were: Torneo (HP1), Santa Clara (HP2), Centro (HP3), Menéndez y Pelayo (HP4), Medina y Galnarés (HP5), Ronda Urbana Norte (HP6). The seven LP locations were, in increasing order of population density: Palmera (LP1), Cristo de la Expiración (LP2), Ronda Triana (LP3), Ciudad Jardín (LP4), Diego Martínez Barrio (LP5), Ranilla (LP6) and Luis Montoto (LP7). The urban parks selected were Los Príncipes (P1), María Luisa (P2), Miraflores (P3), Amate (P4), Paseo de Europa (P5) and Alamillo (P6). The industrial estate was El Pino (IA). Each sampling point was evaluated by collecting leaves from at least 25% of trees sited in the studied location. Sampled trees were in similar growth status. Trees with anomalies, gummosis or putrefaction of the neck of the root, infections caused by viruses or presence of parasites were discarded. Five subsamples, one from each cardinal point and one from the centre of the tree were collected from each tree. Subsamples were composed of 3–5 young leaves (up to the 3rd node from the top of the shoots) and 3–5 old leaves (from the basal parts of the shoots), according to Sarrou et al., 2013. One sample composed of the samples collected from each tree was obtained from each sampling point. Samples were collected in each season: in November 2016, January 2017, April 2017 and July 2017 during three consecutive days in each month. Predominant wind directions in Seville city are SW, from April to September, and NE from October to March (Fig. 1) (MoreauGuigon et al., 2016). Meteorological conditions are shown in supplementary material (Table S1). Samples were transported to the laboratory on aluminium foil. Then, they were cut and crushed using a crusher, lyophilised at 0.01 mbar vacuum after being frozen at −18 °C for 24 h, pulverised and sieved (b1 mm). 2.3. Analysis of emerging pollutants A previously optimized and validated analytical method was used for the determination of the selected contaminants (Barroso et al., 2018). The method is based on sonication-assisted extraction of 0.5 g of sample with acetone:acetonitrile (70:30, v/v) as extraction solvent and extract clean-up by dispersive solid-phase extraction (d-SPE) by the addition of 0.28 g of Florisil to sample extract. Analytical

determination was carried out by liquid chromatography-triple quadrupole mass spectrometry on an Agilent 1200 series liquid chromatograph (Agilent, USA) coupled to a 6410 triple quadrupole (QqQ) mass spectrometer (MS) equipped with an electrospray ionisation source (Agilent, USA). Separation was carried out using an HALO C18 (50 × 4.6 mm i.d.; 2.7 μm) analytical column (Teknokroma, Spain) protected by an HALO C18 (5 × 4.6 mm 1.d.; 2.7 μm) guard column (Teknokroma, Spain). Analytes were separated by gradient elution with methanol (A) and 10 mM ammonium acetate aqueous solution (B) at 0.6 mL/min. Column temperature was kept at 35 °C. Gradient conditions applied to the separation of the target compounds are showed in supplementary material (Table S2). Mass spectrometry analysis was carried out applying the following settings: MS capillary voltage, 3000 V; drying gas flow rate, 9 L/min; drying gas temperature, 350 °C; and nebulizer pressure, 40 psi. Instrument control and data acquisition were carried out with MassHunter software (Agilent, USA). Detection was performed in multiple reaction-monitoring mode (MRM). Optimized MS/MS parameters for MRM analysis are shown in supplementary material (Table S3). 2.4. Method performance and quality control The method was validated by the determination of recovery, precision, matrix effect, linear range and limits of detection and quantification. A description of the validation procedure and parameters (Table S4) are included in supplementary material. Acceptable recoveries, determined at three concentrations levels (low, medium and high levels), were achieved. Mean recoveries were up to 87% for PFCs and surfactants; up to 88% for parabens and plasticizers and up to 47% for plasticizers and HBCDD. Precision was lower than 10% for most of the target compounds. Limits of quantification were in the range from 0.02 ng/g dry matter (dm) to 0.30 ng/g dm, except for BPA, DEHP, NP, and HBCDD (28.9, 8.00, 28.3, and 15.9 ng/g dm, respectively). In order to ensure the quality of the results, calibration standards, spiked samples, procedural blanks and standard quality controls were included in each batch of samples. Matrix effect and recoveries were determined in each batch and compared with values obtained in the validation process.

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been reported in countries such as Germany (Berkner et al., 2004; Xie et al., 2006), France (Teil et al., 2006) or China (Wang et al., 2008) and, in the case of NPEs, the concentrations measured in air samples are similar or even lower than those reported in the case of phthalates, reaching concentrations up to 68 ng/m3 (Van Ry et al., 2000; Berkner et al., 2004). However, considering the other studied compounds (BPA, PFCs and parabens) the concentrations reported in air have been substantially lower than those reported for phthalates or NPEs (Berkner et al., 2004; Matsumoto et al., 2005; Ferrey et al., 2018; Kim and Kannan, 2007; Stock et al., 2007). Moreover, in the case of PFCs, the measured concentrations were in concordance with concentrations measured in other tree species in China (from 36 to 150 ng/g depending on the tree species) (Zhang et al., 2015), Norway and Slovakia (from 10 to 25 ng/g) (Chropeňová et al., 2016). No information about the concentrations of the other target compounds has been found in the literature; therefore no comparison can be done.

2.5. Statistical analysis Graphical and statistical analysis was used for the discussion of the results obtained in this work. Assuming a normal distribution of the concentrations of emerging pollutants in tree leaves, according to composed samples taken, parametric statistical tests were applied for the treatment of the data. The comparison of concentrations obtained in the studied sampling sites was carried out using student's t-test for the comparison of mean values. Statistical techniques, correlation and factorial analysis, were used to evaluate the existence of relations between concentrations of emerging pollutants and climatic conditions as well as relations between the studied sampling sites. Statistical analysis was carried out using Statistical 10.0 software for Windows. 3. Results and discussion 3.1. Concentrations of emerging pollutants in bitter orange leaves

3.2. Spatial distribution of the target compounds in the studied area Studied compounds were found, at least once throughout the year, in the different types of sampling points (HP, LP, P and IE). The exception to that behaviour were NP2EO, that was not detected neither in samples collected from parks nor from the industrial area; HBCDD, that was not detected in the industrial area; and EtP that was not detected neither in the lowly populated areas nor in the industrial area (Table 1). The abundance patterns of the studied families of contaminants were similar in the different sampling points. Plasticizers, especially DEHP (up to 789 ng/g dm), and NPE, especially NP (up to 752 ng/g dm), were the families of compounds at the highest concentration levels. This fact could be explained by the widespread use of the plasticizers DEHP and BPA in plastic materials, and of NPE in detergents, pesticides, resins, paints, etc. (Bao et al., 2015; Giovanoulis et al., 2018). PFCs and parabens were found one order of concentration lower than plasticizers and NPE. The distribution found in this work (plastizicers N NPEs N parabens ≈ PFCs) was in concordance with the relative abundance of these compounds usually found in air (Salgueiro-González et al., 2015; Salapasidou et al., 2011; Zhang et al., 2015). For example, concentrations of phthalates of several ng/m3 (up to 77 ng/m3) have

Fig. 2 shows box-and-whisker plots of the concentrations of the studied families of compounds in each type of sampling locations. Lines in each box show concentrations corresponding to the 5 percentile, median and 95 percentile. Lines from each box, named whiskers, represent the highest and lowest concentrations measured. Points inside each box show the average concentrations. Concentrations of each family of contaminants in each sampling point are showed in supplementary material (Fig. S1). The influence of the local sources in the air concentrations of these kinds of pollutants has been described previously. For example, Cahill et al. (2007) measured concentrations of PBDEs around an automotive shredding and a metal recycling facility in USA. They found an increase of the concentrations of PBDEs from 360 to 820 pg m−3, when both production facilities were operating. Other study carried out by Chropeňová et al. (2016) analysed pine needles as bioindicator of the contamination by PFCs in ski tracks. They found the highest concentrations in the sampling sites nearby to ski tracks, identifying ski waxes as the potential source of these pollutants in this area. However, due to the

Table 1 Ranges and mean concentrations of emerging contaminants in bitter orange tree leaves from highly and lowly populated areas, parks and Industrial area. Emerging contaminant

Highly populated area

Lowly populated area

Parks

Industrial area

Mean (ng/g dm)

Frequency (%)

Range (ng/g dm)

Mean (ng/g dm)

Frequency (%)

Range (ng/g dm)

Mean (ng/g dm)

Frequency (%)

Range (ng/g dm)

Mean (ng/g dm)

Frequency (%)

2.18 2.22 1.76 2.09 3.49 3.62

71 58 37 6 62 92

0.09–4.63 0.17–5.89 0.11–10.8 0.18–25.8 0.51–2.98 0.10–6.32

1.00 1.39 2.06 4.27 1.37 3.69

50 69 69 53 62 94

0.34–2.41 0.14–0.70 0.15–0.81 0.13–7.34 0.34–3.55 0.18–16.3

1.14 0.36 0.40 2.87 1.37 5.16

30 35 30 35 55 80

0.23a 0.30a 0.73a 0.21a 0.13–1.58 0.28–4.59

0.23 0.30 0.73 0.21 0.66 3.15

25 25 25 25 25 100

Plasticizers BPA DEHP

1.34–72.3 40.5 5.23–647 309

42 87

1.16–160 6.52–524

53.7 273

91 100

10.3–52.9 39.6 3.07–256 140

65 75

60.0–68.7 63.1 33.9–789 532

100 100

Surfactants NP NP1EO NP2EO

104–752 288 0.31–2.94 1.97 0.11a 0.110

100 71 4

110–599 231 0.43–12.1 1.91 0.77-0.92 0.84

100 75 6

106–633 291 0.21–4.96 1.58 bMDL bMDL

100 70 0

125–377 265 1.84–3.45 2.65 bMDL bMDL

100 75 0

Brominated flame retardants HBCDD 15.1–20.7 18.5

13

0.64a

3

284–313

299

15

bMDL

0

Preservatives MeP EtP PrP

83 12 33

0.31–17.5 11.3 bMDL bMDL 0.75–0.90 0.83

62 0 12

0.41–19.9 10.1 8.63–9.35 8.99 2.34–8.11 5.23

90 10 10

0.38–12.3 7.62 bMDL bMDL 0.52a 0.52

Range (ng/g dm)

Perfluorinated compounds PFBuA 0.25–6.43 PFPeA 0.11–7.87 PFHxA 0.19–4.23 PFHpA 0.19–6.97 PFOA 0.32–12.7 PFOS 0.14–7.67

a

0.30–19.2 13.5 8.21–9.09 8.73 0.46–9.76 5.33

Compound detected in just one sample.

0.64

bMDL

100 0 50

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1000 Concentration (ng/g dm)

Plastizicers 750

500

250

0

Concentration (ng/g dm)

400

NPE 300

200

100

0

Concentration (ng/g dm)

8

Parabens 6

4

2

0 60 Concentration (ng/g dm)

PFC 40

20

0 HP

LP

P

IE

Fig. 2. Concentrations of the studied emerging pollutants (ng/g dm) in tree leaves from the four types of sampling sites (HP: highly populated area (n = 24), LP: lowly populated area (n = 28); P: Parks (n = 24); IA: Industrial area (n = 4)).

wide use of the compounds studied in this work, it is difficult to accurately assess the influence of emission sources in the concentrations of these contaminants, especially in urban areas. PFCs were found in all sampling points at mean concentrations from 2.52 to 37.4 ng/g dm. This fact could be explained by diffusive sources of these compounds in urban areas where consumer products containing PFCs are widely used (Wang et al., 2012). Similar mean concentrations were measured in samples collected from HP (up to 38.4 ng/g dm in 95% of the HP locations) and LP locations (up to 39.0 ng/g dm in 95% of the LP locations) (Student t-test: tcal = 0.642, ttab = 2.01, p b 0.05) whereas concentrations in P (up to 17.6 ng/g dm in 95% of the studied parks) and IA (up to 4.61 ng/g dm) were significantly lower (Student t-test: tcal = 4.115 and 6.343, respectively; ttab = 2.01 and 2.04, respectively, p b 0.05). The compound with the highest detection frequency was PFOS, between 80% and 100%, which also coincides with the highest

mean concentrations (up to 5.16 ng/g dm) (Table 1). The highest concentrations were measured in HP3, LP7 and P1 locations (Fig. S1 in supplementary material) what could suggest the presence of local pollution sources, as the usage of products containing PFCs, such as polymeric and surfactants materials, in facilities close to these sampling points. For example, there are about ten facilities, among carpet cleaning business, laundry services, hotels, etc. 500 m around sampling points HP3, LP7 and P1. The concentration pattern of plasticizers was similar to that of PFCs in HP, LP and P sampling points. Concentrations measured in HP (mean concentrations from 113 to 487 ng/g dm) were similar to those measured in LP (from 223 to 458 ng/g dm) sampling points (Student t-test: tcal = 1.219, ttab = 2.01, p b 0.05) and higher than those found in P (76.7 to 184 ng/g dm) (Student t-test: tcal = 6.350, ttab = 2.01, p b 0.05). However, the highest concentrations of plasticizers were

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measured in the IA sampling point (Student t-test: tcal = 2.672, ttab = 2.09, p b 0.05). This distribution was mainly due to the concentration of DEHP, up to 20 times higher than those found in the case of BPA (Fig. S2 in supplementary material) and could be attributed, as the same as PFCs, to the high use of products containing these compounds. The lower concentration found in parks could be explained by the lower population density of these areas in comparison with urban areas. In the particular case of BPA (mean concentrations from 1.34 to 91.3 ng/g dm), no clear accumulation was observed in any of the studied areas. NP was the most abundant NPE, while NP1EO and NP2EO were found at concentrations up to 12.0 and 0.92 ng/g dm. NPE concentrations were similar in all the studied sampling points, while relatively high concentrations were found in HP1, LP5 and P3 sampling points that could be affected by nearby industrial areas located approximately at 3 km from the studied areas. As observed for NPE, the concentrations

489

of parabens were similar in all the studied sampling points. However, slightly higher concentrations were found in the samples collected from HP area than in those collected from LP and P. This fact could be due to the major population in HP sampling points and, potentially, to a higher use in this area of products containing parabens (Fig. S1 in supplementary material). In the case of HBCDD, its concentrations were lower than detection limits in all studied sampling points, except in HP5 and P4, where high concentrations (18.5 and 106 ng/g dm, respectively) were measured. These high concentrations found in the two sampling points could be explained by the coincidence of two fire episodes in the vicinity (about 10 m of sampling site P4 and about 50 m of HP5), due to a volatilization of the compounds due to the high temperatures reached and their subsequent emission into the air (Sun et al., 2018), but until now it has not been possible to rigorously demonstrate a causal relationship. 120

Concentration (ng/g dm)

Plastizicers 100 750 80 500

60 40

250 20 0

0 120

Concentration (ng/g dm)

NPE 100 600 80 400

60 40

200 20 0

0

Concentration (ng/g dm)

Parabens

35

Rainfall (L/m2), temperature (0C) and solar radiation (10-1 MJ/m2) )

120

40

100

30

80

25 20

60

15

40

10

20

5 0

0

120

PFC 100 40

80 60

20

40 20

0

0 Autumn

Winter Temperature

Spring

Summer

Rainfall

Fig. 3. Seasonal variability of the concentrations of the studied emerging pollutants.

Radiation

Rainfall (L/m2), temperature (0C) and solar radiation (10-1 MJ/m2)

60 Concentration (ng/g dm)

Rainfall (L/m2), temperature (0C) and solar radiation (10-1 MJ/m2)

800

Rainfall (L/m2), temperature (0C) and solar radiation (10-1 MJ/m2)

1000

490

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3.3. Seasonal distribution of organic compounds in the studied area Fig. 3 shows seasonal evolution of the studied families of organic compounds. The lowest concentrations of plasticizers were measured in winter (January 2017) (mean concentration 127 ng/g dm), while similar concentrations were measured in the other seasons (mean concentrations ranged from 285 to 369 ng/g dm). This behaviour has been previously described for other organic contaminants. For example, Muñoz-Arnanz et al. (2016) and Diefenbacher et al. (2015) found a similar seasonal distribution for PCBs with the highest concentrations in the warmer seasons (spring and summer). De la Torre et al. (2018) found the highest concentrations of PBDEs in Madrid (Spain) urban air in spring and summer. This seasonal behaviour, similar to that found for DEHP in this work, could be related to an increase of its vaporization at elevated temperatures. However, the influence of temperature is not completely clear, due to the contribution of DEHP diffuse sources. The seasonal variability of NPE was lower than DEHP variability (Fig. 3). This behaviour may be due to different sources of these contaminants to the urban air at each location and season. Similar patterns were observed in the case of PFCs and parabens. The lowest concentrations of both families of compounds were measured in autumn whereas the highest concentrations were found in winter and spring. However, this pattern could not be explained considering the climatic conditions (Fig. 3). In the case of HBCDD (Fig. S3 in supplementary material), this compound was only detected in the sampling sites HP5, LP5 and P4 in January 2017 and in April 2017, respectively. This temporal trend could be related with fire events occurred during these periods. 3.4. Statistical analysis Statistical analysis was carried out in order to evaluate possible relationships among the studied compounds, sampling sites and climatic conditions. Both correlation and factorial analysis were carried out. Statistical analysis was used to evaluate the existence of relationships between target compounds and sampling sites. Regarding to the relations between the studied emerging contaminants, the correlation matrix was defined using mean concentrations of target compounds in each sampling site as variables and the sampling sites as cases. Correlation matrix did not reveal relations between the studied families of organic pollutants (Table S5 in supplementary content). Only relations among PFCs were observed. This fact was clear from the results of factorial analysis. Only two factors were obtained

with eigenvalues higher than 1 (Factor 1: 4.65; Factor 2: 2.30), and only the 47% of the total variability was accounted (Table S6 in supplementary content). The first factor was mainly composed of PFCs and, in a lower extent, by other compounds (DEHP and PrP). The second factor was only composed of EtP and, in a lower extent, of PrP and HBCDD. These results could be explained by the different emission sources of these compounds to the urban atmosphere. The representation of Factor 1 versus Factor 2 corroborated these results (Fig. S4 in supplementary material). In order to evaluate the potential relation between the studied urban areas and the potential sources of these contaminants, correlation and factorial analysis were carried out. The contribution of each sampling site to the total concentration of each compound was considered as variables and target compounds were considered cases. Strong correlations (N0.90) were obtained between most of the studied sampling sites (Table 2). Only P2 and P4 were not significantly correlated to the other sampling sites. HP1, HP2, LP3, LP6 and P6 showed significant correlations between them but not with other sampling sites (HP1 with P6, HP2 with LP6 and P6 with HP1 and LP3). This behaviour may be due to the wide use of these compounds in everyday products, which results in diffuse sources of pollution to the studied area. These results were corroborated by the factorial analysis. Three factors with eigenvalues N1 (Factor 1: 13.4; Factor 2: 3.19; Factor 3:1.37) accounting for 89.8% of the total variability were identified (Table 3). The first factor was loaded by most of the studied sampling sites (HP3, HP4, HP5, HP6, LP1, LP2, LP4, LP5, LP7, P1, P3, P5 and IA), factor 2 was loaded by HP1, LP3, P2 and P6 and factor 3 was loaded by HP2 and LP6. These factors could represent contamination grades, classifying the sampling sites in more contaminate locations, sited mainly in the centre of the city of Seville, and less contaminated locations, sited mainly on the outskirts of the city. The plots of the obtained factors (Figs. S5 to S7 in supplementary material) show these groupings. Three groups were observed, the first group was composed of the majority of the sampling points, the second was composed of sampling points HP1, LP3, P2 and P6 and, the third, was composed of HP2 and LP6. The first group could be due to a similar grade of contamination. In the case of the second group, the relation between the other sampling sites could be related with low contamination levels. For example, Figs. S4 to S6 show a group composed of three of the largest parks in the city as well as two sampling points located in broad avenues (HP1, LP3, P2, P4 and P6). In the case of sampling points HP2 and LP6, this relation could be due to the proximity of these sampling points, among them and to a located source of pollution. Considering each family of emerging contaminants, the relations between studied

Table 2 Correlation matrix of the sampling sites (considering the contribution of each sampling site to the total concentration of each target compound as variables and target compounds as cases).

HP1 HP2 HP3 HP4 HP5 HP6 LP1 LP2 LP3 LP4 LP5 LP6 LP7 P1 P2 P3 P4 P5 P6 IA

HP1

HP2

HP3

HP4

HP5

HP6

LP1

LP2

LP3

LP4

LP5

LP6

LP7

P1

P2

P3

P4

P5

P6

IA

1.00

0.34 1.00

−0.21 −0.32 1.00

−0.24 −0.37 0.99 1.00

−0.21 −0.36 0.99 0.98 1.00

−0.20 −0.31 1.00 0.99 1.00 1.00

−0.24 −0.37 0.98 0.96 0.99 0.98 1.00

−0.17 −0.30 1.00 0.98 0.99 1.00 0.98 1.00

0.56 0.36 −0.21 −0.24 −0.19 −0.21 −0.23 −0.16 1.00

−0.18 −0.31 1.00 0.99 0.99 1.00 0.98 1.00 −0.17 1.00

−0.14 −0.29 1.00 0.98 0.99 1.00 0.98 1.00 −0.16 1.00 1.00

0.23 0.81 −0.29 −0.33 −0.33 −0.29 −0.34 −0.26 0.27 −0.28 −0.27 1.00

−0.21 −0.31 1.00 0.99 0.99 1.00 0.98 1.00 −0.21 1.00 1.00 −0.29 1.00

−0.21 −0.32 1.00 0.99 0.99 1.00 0.98 1.00 −0.21 1.00 1.00 −0.29 1.00 1.00

0.54 0.42 −0.21 −0.22 −0.22 −0.20 −0.24 −0.19 0.41 −0.18 −0.17 0.10 −0.21 −0.20 1.00

−0.18 −0.31 1.00 0.99 0.99 1.00 0.98 1.00 −0.19 1.00 1.00 −0.28 1.00 1.00 −0.19 1.00

−0.20 −0.41 −0.14 −0.10 −0.09 −0.14 −0.16 −0.15 −0.23 −0.15 −0.15 −0.38 −0.14 −0.14 −0.25 −0.14 1.00

−0.19 −0.30 1.00 0.99 0.99 1.00 0.98 1.00 −0.19 1.00 1.00 −0.27 1.00 1.00 −0.20 1.00 −0.14 1.00

0.71 0.49 −0.26 −0.30 −0.27 −0.25 −0.30 −0.21 0.75 −0.23 −0.21 0.37 −0.26 −0.26 0.52 −0.24 −0.26 −0.23 1.00

−0.16 −0.30 1.00 0.98 0.99 1.00 0.97 1.00 −0.15 1.00 1.00 −0.25 1.00 1.00 −0.18 1.00 −0.16 1.00 −0.20 1.00

Significant correlations (higher than 0.70 for p b 0.05) are marked in bold.

P.J. Barroso et al. / Science of the Total Environment 677 (2019) 484–492 Table 3 Results of the factorial analysis (considering the contribution of each sampling site to the total concentration of each target compound as variables and target compounds as cases). Correlations higher than 0.70 (p b 0.05) are considered significant and are marked in bold.

HP1 HP2 HP3 HP4 HP5 HP6 LP1 LP2 LP3 LP4 LP5 LP6 LP7 P1 P2 P3 P4 P5 P6 IA Expl.Var Prp.Totl

Factor 1

Factor 2

Factor 3

−0.12 −0.24 0.99 0.97 0.98 0.99 0.97 1.00 −0.12 1.00 1.00 −0.22 0.99 0.99 −0.13 0.99 −0.22 0.99 −0.16 1.00 12.97 0.65

−0.85 −0.30 0.10 0.13 0.10 0.10 0.14 0.05 −0.80 0.06 0.04 −0.09 0.10 0.10 −0.72 0.07 0.22 0.08 −0.86 0.04 2.85 0.14

−0.08 −0.84 0.07 0.12 0.12 0.07 0.10 0.05 −0.16 0.07 0.06 −0.89 0.06 0.07 −0.11 0.06 0.67 0.05 −0.25 0.05 2.13 0.11

sampling points were slightly modified. Factorial analysis carried out considering the contribution of each sampling site to the total concentration of PFCs as variables and PFCs as cases revealed the same results obtained in the global statistical analysis, therefore, PFCs were the compounds with the highest contribution to these groupings (Fig. S8 in supplementary material). Considering plasticizers and NPE, factorial analysis revealed three groupings (Fig. S9): the first one, composed of the majority of the sampling points; the second group, mainly composed of sampling sites located in the East and North area of the city (HP2, HP6, LP6, P4, P5 and P6); and the third one, formed by two sampling points sited in the North (HP5) and South (LP1) areas of the city. These groupings could indicate the existence of a local source of these contaminants sited in the East of the city, in which several industrial sectors, such as a surfactant industry, are established. In the case of flame-retardants and parabens, no clear relation between sampling points was obtained (Fig. S10). It could be due to the uniform concentrations of these contaminants in all studied areas. In the case of parabens, average total concentrations were 14.1, 8.10, 9.72 and 7.75 ng/g in HP, LP, P and IE sampling sites, respectively. HBCDD concentrations were lower than the detection limit of the analytical method except in HP5 and P4. 4. Conclusion A previously developed and validated analytical method was applied to biomonitor emerging contaminants in bitter orange (Citrus aurantium) tree leaves from Seville City. A total of 20 sampling sites including highly and lowly populated areas, parks and industrial areas were sampled. The abundance pattern of the studied families of contaminants was similar in most seasons and sampling locations. The highest concentrations were for plasticizers and NPE, followed by PFCs, parabens and HBCDD. Spatial distribution did not allow a clear identification of local sources of pollution. The highest concentrations of plasticizers and PFCs were measured in HP and LP areas, what could be due to the wide use of these compounds in populated areas, whereas the highest concentrations of NPE measured in some of the studied sampling points could be explained by the proximity of an industrial area. Similar patterns of seasonal variability were observed for DEHP and NPE and for PFCs and parabens. This distribution could be related to

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ambient temperature, in the case of DEHP and NPE, however, in the case of PFCs and parabens, the observed pattern could not be explained considering the climatic conditions what could be due to diffuse sources of these compounds at each location and season. In the case of the high concentration of HBCDD, it could be speculated that its presence was related to two fires occurred near to some of the sampling points, although it could not be demonstrated. This study highlights the potential of bitter orange tree leaves as biosamplers of atmospheric emerging pollutants. However, a further study, involving the measurement of atmospheric concentrations of the pollutants using bitter orange tree leaves as bioindicators and active or passive samplers, is necessary to establish the relation between the concentrations of organic contaminants measured in bitter orange leaves with their respective atmospheric concentrations. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.04.391.

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