Spatial distribution and historical deposition behaviors of perfluoroalkyl substances (PFASs) in sediments of Lake Chaohu, a shallow eutrophic lake in Eastern China

Ecological Indicators 57 (2015) 1–10

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Spatial distribution and historical deposition behaviors of perfluoroalkyl substances (PFASs) in sediments of Lake Chaohu, a shallow eutrophic lake in Eastern China Yanjie Qi a,b , Shibin Hu a,∗ , Shouliang Huo b,∗∗ , Beidou Xi b , Jingtian Zhang b , Xiaowei Wang b a b

College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China

a r t i c l e

i n f o

Article history: Received 5 October 2014 Received in revised form 9 April 2015 Accepted 12 April 2015 Keywords: Perfluoroalkyl substances Perfluorooctane sulfonic acid Perfluorooctanoic acid Spatial distribution Historical deposition behaviors

a b s t r a c t Sediment samples were collected from the Chaohu Lake basin to investigate the spatial distribution and historical deposition behaviors of 17 perfluoroalkyl substances (PFASs). Concentrations of the total PFASs  ( PFASs) in limnetic sediments ranged from 0.719 to 2.429 ng/g dry weight (dw), with an average of 1.449 ng/g dw. A clear gradient in the spatial distribution was observed from west to east in surface sediments of Lake Chaohu. Perfluorooctane sulfonic acid and perfluorooctanoic acid were  predominant in limnetic sediments, with an average of 0.383 and 0.275 ng/g dw, respectively. The PFASs concentrations in riverine sediments were the highest in the Shiwuli River, followed by the Nanfei River. PFASs in riverine sediments indicated that industrial discharge and urban runoff played key roles in PFAS distribution and pollution levels. In vertical profiles, concentrations of the PFASs and PFAS congeners in three  sediment cores generally increased with decreasing depths, indicating that the input history of the PFASs could be attributed to the development of industrialization and urbanization in the Chaohu Lake basin. The correlations between PFASs and sedimentary characteristics, organic carbon, nutrients and inorganic salts suggested that both hydrophobic and electrostatic effects played important roles in PFAS distribution and pollution levels in sediments of Lake Chaohu. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction The production and usage of perfluoroalkyl substances (PFASs) have sharply increased for a wide array of industrial, commercial and consumer goods applications since the late 1940s (Kim et al., 2012). PFASs have been found to be ubiquitous in land (Zareitalabad et al., 2013), oceans (Ahrens et al., 2009a), polar regions (Schenker et al., 2008), wildlife (Loi et al., 2011) and humans (Zhang et al., 2013). Due to their persistence, bio-accumulative properties and potential toxicity, PFASs become environmental contaminants that have already attracted great concern globally. In May 2009, the United Nations Environment Programme officially classified “perfluorooctane sulfonate” and its salts as persistent organic

∗ Corresponding author at: Northwest A&F University, No. 3, Taicheng Road, Yangling 712100, Shaanxi, China. Tel.: +86 29 8708 0056; fax: +86 29 8708 0056. ∗∗ Corresponding author. Tel.: +86 29 8493 7970; fax: +86 29 8491 3805. E-mail addresses: [email protected] (S. Hu), [email protected] (S. Huo). http://dx.doi.org/10.1016/j.ecolind.2015.04.015 1470-160X/© 2015 Elsevier Ltd. All rights reserved.

pollutants (POPs) at the Conference of the Parties 4 of the Stockholm Convention (Buck et al., 2011). PFASs can be released into the aquatic environment from direct sources such as the manufacturing of PFASs and the use of products containing PFASs through various pathways, as well as indirect sources such as the degradation of precursors (Kim et al., 2012; Kim and Kannan, 2007; Prevedouros et al., 2006). It was inferred that a large proportion of emissions would be released to the surface waters, and that sediment burial is one of the most important environmental sinks for PFASs, implying a very long residence time in the environment (Prevedouros et al., 2006). The vertical variations of PFAS concentrations in sediments with depth are able to illustrate the temporal trends of PFASs in sediments. Thus, studies on PFASs in sediments are of great importance. The occurrence of PFASs in sediments has been found in several countries and areas such as the Pearl River Delta (Liu et al., 2014; Pan et al., 2014a; Zhao et al., 2014), Yangtze River (Pan et al., 2014b; Pan and You, 2010), and Bohai Sea in China (Gao et al., 2014; Wang et al., 2011; Zhu et al., 2014), the Cantabrian Sea in north Spain (Gomez et al., 2011), Tokyo Bay in Japan (Ahrens et al., 2009b), and

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three lakes in the Canadian Arctic (Stock et al., 2007). PFAS concentrations in sediments varied greatly, ranging from below the limits of detection (LODs) to several hundred nanograms per gram of dry weight (dw). Several studies on PFASs in limnetic sediments of China have also been reported, such as Lake Tangxun (Zhou et al., 2013), Lake Taihu (Pan et al., 2014c; Yang et al., 2011), and Lake Baiyangdian (Shi et al., 2012). However, the available studies concerning PFASs in limnetic sediments of China are still scarce as a whole, especially those regarding the historical deposition behaviors of PFASs in sediment cores. Vertical distribution of PFASs in sediment cores, which clearly differed from the “classical” pollutants, was influenced by various factors, such as the carbon chain length of PFASs and the functionality of their head groups (Ahrens et al., 2009b), metal ions (Chen et al., 2012), particle sizes (Zhao et al., 2012) and total organic carbon (TOC) (Ahrens et al., 2009b). The factors influencing PFAS distribution in limnetic sediments also need to be further investigated. Lake Chaohu (31◦ 25 –31◦ 43 N, 117◦ 16 –117◦ 51 E), one of the five largest freshwater lakes of China, is situated in Anhui Province of eastern China and is a typical semi-enclosed shallow eutrophic lake. In recent decades, the land-use pattern in the Chaohu Lake basin has experienced the conversion of agricultural land to industrial and urban land. Since 1950, the urban population of the Chaohu Lake basin has greatly increased from approximately 200 000 people to approximately 1 600 000 by 1985, and then 8 000 000 by 2009. Quantities of fertilizer use have also rapidly increased, accelerating the inputs of agricultural runoff in the catchment. Due to the increasing anthropogenic activities in the Chaohu Lake basin, the lake has suffered from severe pollution and eutrophication, and there is a trophic gradient between the two half-lake regions (Zan et al., 2012). Previous studies performed in Lake Chaohu have mainly focused on nitrogen and phosphorus (Zan et al., 2012), heavy metals (Zan et al., 2011), polycyclic aromatic hydrocarbons (Li et al., 2014), and polybrominated diphenyl ethers (Wang et al., 2013). Information on PFAS concentrations, however, has not been available for Lake Chaohu until now. The aims of this study were to (1) investigate PFAS levels and spatial distribution in sediments from the Chaohu Lake basin; (2) trace the historical evolution of PFASs in sediments of Lake Chaohu over nearly 60 years; and (3) elucidate the relationship between PFAS concentrations and various influencing factors in sediments of Lake Chaohu.

2. Material and methods 2.1. Study area and sampling Lake Chaohu has a surface area of 780 km2 , a mean depth of approximately 3 m and a catchment area of 12 938 km2 (Fig. 1). The western region (ca. 1/3 of lake area) is mainly surrounded by Hefei City (the capital city of Anhui Province) and the eastern region (ca. 2/3 of lake area) by Chaohu City. Seven main inflowing rivers, including five rivers were located in the western lake region and two rivers were located in the eastern lake region, account for more than 80% of the runoff volume of the Chaohu Lake basin (Wang et al., 2013). The rivers Nanfei, Shiwuli, and Paihe are the three most polluted rivers within the territory of Hefei City. Two large wastewater treatment plants are situated beside the Nanfei River and the Shiwuli River. The rivers Hangbu and Fengle hold the largest water flows and join together downstream. A total of 28 surface sediment samples (top 0–5 cm) were collected using a stainless steel grab sampler from the Chaohu Lake basin in December 2011. Sixteen sampling sites were located in Lake Chaohu. Two sampling sites were situated in Lake Wanfohu as the background region. Ten surface sediment samples were

collected along the five main inflowing rivers (Fig. 1). Detailed information is given in the supplementary information (SI) and is listed in Table SI-1. The sediment cores were collected using a handdriven stainless steel corer (50 cm long and 8 cm i.d.) at M1, M2 and M3 in July 2009 and sliced at 2 cm intervals up to 20 cm after siphoning the overlying water. The three sediment core samples from M1, M2 and M3 were named core-1, core-2 and core-3, respectively. All of the sediment samples were stored in polypropylene (PP) bags after removing the sundries and were kept in freezers. The samples were then immediately transferred to the laboratory and stored at −20 ◦ C until analyzed. 2.2. Chemicals and standards The internal standards of sodium perfluoro-1-[1,2,3,4-13 C4 ] octanesulfonate (13 C4 -PFOS, MPFOS) and perfluoron-[1,2,3,4-13 C4 ] octanoic acid (13 C4 -PFOA, MPFOA) and a mixture of 17 native perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkane sulfonic acids (PFSAs) were purchased from Wellington Laboratories (Guelph, Ontario, Canada) (see Table 1 for details). All stock standards and solutions were prepared in methanol (HPLC grade, Fisher Scientific, Hampton, NH, USA) and stored in PP tubes or vials at 4 ◦ C. Ammonium acetate (HPLC grade), ammonium hydroxide (NH4 OH, HPLC grade; v/v, 50%) and acetic acid (HPLC grade, >99.8%) were purchased from Alfa (Ward Hill, MA, USA). Copper powder (analytical grade, <75 ␮m, 99.9%) was purchased from Aladdin Industrial Corporation (China). Other chemicals used in the study were of reagent grade and were used as received. Solid phase extraction (SPE) columns (Oasis® WAX, Weak Anion Exchange, 6 cc, 150 mg, 30 ␮m) were purchased from Waters Corporation (Milford, MA, USA). Ultrapure water (18.2 M*cm) provided by Milli-Q Advantage A10 system (Millipore, Billerica, MA, USA) was used for all of the experiments. 2.3. Sample pretreatment All sediment samples, except for the sub-samples for particle size analysis, were freeze-dried, homogenized and passed through a 150 ␮m stainless steel mesh sieve prior to analysis. The samples were pretreated as described elsewhere with some minor modifications (Zhou et al., 2013). In general, 2 g of the sediment samples with 1 ng of internal standards were sonicated in 30 mL of methanol at 60 ◦ C for 30 min. For the elimination of sulfur in sediments, activated copper powder was added before the extraction. After shaking for 16 h at a rate of 250 r/min and centrifugation, the supernatant was concentrated to 0.5 mL under a gentle nitrogen stream and diluted to 50 mL with ultrapure water. The dilution was then loaded onto an Oasis® WAX single-use cartridge, which was preconditioned with 4 mL of 0.1% NH4 OH (in methanol), 4 mL of methanol and 5 mL of ultrapure water. Cartridges were washed with 4 mL of 25 mmol/L buffer solution (pH 4) and then centrifuged at 3000 r/min for 10 min to remove the residual water. Target compounds were then eluted with 4 mL of methanol and 4 mL of 0.1% NH4 OH (in methanol). The eluent was concentrated to 200 ␮L under a gentle nitrogen stream for injection. 2.4. Instrumental analysis Analyses were performed for 17 PFASs using ultra-high performance liquid chromatography (UPLC, Waters Corporation, Milford, MA, USA) equipped with an electrospray ionization tandem mass spectrometer (ESI/MS/MS, Xevo TQD, Waters Corporation, Milford, MA, USA) operated in negative ion and MRM mode. PFASs were separated on an Acquity UPLC® BEH C18 column (2.1 mm × 50 mm, 1.7 ␮m) with an aliquot of 10 ␮L injections. Nitrogen was used as

Y. Qi et al. / Ecological Indicators 57 (2015) 1–10

3



Fig. 1. Map of sampling sites including the distribution of the total perfluoroalkyl substances (

the nebulizer gas and argon as the collision gas. Capillary voltage was operated at 2.7 kV in negative mode, extractor lens at 3.0 V, and RF lens at 2.5 V. The source and desolvation temperatures were set to 150 and 400 ◦ C, respectively. Cone and desolvation gas flows were 30 and 900 L/h, respectively. A 10 min dualistic gradient of 0.1% NH4 OH (in methanol) (A) and 25 mmol/L buffer solution (B) delivered at a flow rate of 1 mL/min was started with 25% B, ramped to 50% B in 0.5 min, 85% in 4.5 min, 100% in 2 min, held for 1.5 min, then returned to the initial conditions gradually within 0.1 min and finally held constant for 1.4 min. TOC was determined using a TOC analyzer (multi N/C 2100, Analytik Jena AG, Jena, Germany). Total nitrogen (TN) was measured by a Vario El elemental analyzer (Elementar Corporation, Hanau, Germany). Total phosphorus (TP) was analyzed by the ammonium molybdate spectrophotometric method (Zan et al., 2012). Metal analyses were performed using an inductively coupled plasma optical emission spectrometer (ICP-OES, iCAP 6000, Thermo Scientific, Waltham, MA, USA) after microwave digestion in a mixture of HNO3 :HCl:HF (v/v, 1:3:6). The grain size distribution of the

PFASs) in the Chaohu Lake basin, China.

samples was determined using a Mastersizer 2000 particle size analyzer (Malvern Instruments Limited, Malvern, UK; measuring range: 0.02–2000 mm).

2.5. Quality assurance/quality control (QA/QC) To avoid contamination, polytetrafluoroethylene or other fluoropolymer materials were removed during the sample collection, pretreatment and analytical procedure. Glass materials were also avoided because of their irreversible adsorption with PFASs (Moody and Field, 1999). The sampling tools, containers, and various materials were all rinsed at least three times with methanol and ultrapure water prior to use. Procedural blanks, spiked blanks and spiked matrixes, were processed for each set of extractions. Next, 5 ng/mL of mixed standard sample was measured to check for instrumental drift after every 12 injections. The calibration curve was used for further quantification only when the quality control standard was within ±20% of its initial value. Otherwise, samples

Table 1 Target analytes of 17 perfluoroalkyl substances (PFASs) measured in this study with QA/QC information including mass transitions, retention times, limits of detection and matrix spiked recoveries. Analyte

Acronym

Precursor/product ion

Retention times

Limits of detection (ng/ml)

Matrix spiked recovery (%, n = 5)a

Perfluorobutanoic acid Perfluoropentanoic acid Perfluorohexanoic acid Perfluoroheptanoic acid Perfluorooctanoic acid Perfluorononanoic acid Perfluorodecanoic acid Perfluoroundecanoic acid Perfluorododecanoic aicd Perfluorotridecanoic acid Perfluorotetradecanoic acid Perfluorohexadecanoic acid Perfluorooctadecanoic acid Perfluorobutane sulfonic acid Perfluorohexane sulfonic acid Perfluorooctane sulfonic acid Perfluorodecane sulfonic acid

PFBA PFPeA PFHxA PFHpA PFOA PFNA PFDA PFUnDA PFDoDA PFTrDA PFTeDA PFHxDA PFODA PFBS PFHxS PFOS PFDS

212.9/168.9 262.9/218.9 312.9/269 362.9/318.9 412.8/369, 412.8/168.9 462.9/419.1 512.8/469.1 562.8/519 612.9/569 662.9/618.9 712.9/669 812.9/768.9 913/869.1 298.9/79.9, 298.9/99 398.8/79.9, 398.9/99 498.9/79.9, 498.9/99 598.9/79.9, 598.9/99

1.68 2.19 2.78 3.41 4.00 4.53 5.03 5.48 5.83 6.15 6.42 6.85 7.22 2.29 3.44 4.56 5.46

0.04 0.04 0.07 0.05 0.10 0.04 0.07 0.04 0.06 0.04 0.06 0.02 0.36 0.03 0.02 0.03 0.03

83 97 101 99 112 109 94 93 110 88 79 72 67 97 106 94 96

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

8.4 12.2 8.3 10.0 8.1 9.8 12.8 8.4 10.0 5.7 10.1 5.9 14.0 7.0 10.5 3.9 5.6

a The matrix spiked recoveries of PFASs were calculated using the equation: Matrix spiked recovery = (Csample+spiked − Csample )/Cspiked × 100%, where Csample+spiked was the concentration of PFASs in a spiked sample, Csample was the concentration of PFASs in the sample (same as above without spiked target compounds), Cspiked was the concentration of the spiked target compound (1 ng of target compounds spiked). The matrix spiked recovery was shown in the form of “mean ± SD”, that was, “arithmetic mean ± standard deviation”. n indicates the number of samples analyzed.

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were reanalyzed with a new calibration curve within the normal range. Quantification was based on an internal standard calibration curve (0.02, 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50 ng/mL). A strong linearity was shown with correlation coefficients >0.99. When the sample concentration exceeded the linear dynamic range of the calibration line, the sample was re-treated. LODs were considered to be the lowest concentrations resulting in a signal-to-noise ratio (S/N) of 3 for each compound, which varied from 0.02 to 0.36 ng/mL. Procedural blanks were conducted for each sample batch, and the blank values were below LODs. Blank spiked recoveries (n = 5) ranged from 95% to 104% with standard deviations of <10% for each compound, which showed excellent reproducibility. Matrix spiked recoveries (n = 5) ranged from 67% to 112%. Detailed information about QA/QC is shown in Table 1. 2.6. Dating of the sediment cores The sediment cores collected from M2 and M3 were chosen for dating using 137 Cs and excess 210 Pb (210 Pbex ). 137 Cs activities were determined using ␥-spectrometry on a Canberra S-100 multichannel spectrometer mated to a GCW3022 H-P Ge coaxial detector (efficiency 50%). 210 Pbex activity was obtained by subtracting the 226 Ra activity from total 210 Pb activity that was derived from 210 Po. 210 Po activities in sediments were analyzed by ␣-spectrometry on a Canberra S-100 multi-channel spectrometer with a PIPS Si detector, and 226 Ra activities were determined by ␥-spectrometry on a Canberra S-100 multi-channel spectrometer coupled to a GCW3022 H-PR Ge well detector. Details on dating of the sediment cores were described in the study of Zan et al. (2012), and the average sediment accumulation rates were 0.224 g/(cm2 y) for core-2 and 0.242 g/(cm2 y) for core-3. The timing of PFAS deposition at site M1 was estimated by comparing the concentration-depth profiles of PFASs at site M2. 2.7. Data analysis All of the results were calculated according to the dry matter. Statistical analyses were performed with EXCEL 2010 (Microsoft Incorporation, Redmond, WA, USA), SPSS 20.0 (SPSS Incorporation, Chicago, IL, USA) and MassLynx V4.1 (Waters Corporation, Milford, MA, USA). Concentrations below LODs were assigned as zero during the calculations. A Spearman rank correlation analysis (2-tailed) was used to examine the possible correlations between PFASs and various sediment parameters at significance levels of 0.01, with no special instructions. The sediment dating, TOC, TN, TP, heavy metals and particle size data of the sediments, as well as the dry weights and sedimentation rates of each sediment slice, were used for this study (for details, see the reports of Zan et al., 2011, 2012). 3. Results and discussion 3.1. Spatial distribution of PFASs in surface sediments The spatial distribution of PFASs in surface sediments of Lake Chaohu is shown in Fig. 2(a). Concentrations of the total PFASs  ( PFASs) in limnetic sediments ranged from  0.719 to 2.429 ng/g PFASs concentrations dw, with a mean of 1.449 ng/g dw. The ranged from 1.182 to 2.429 ng/g dw with a mean of 1.683 ng/g dw in the western lake region (S1–S6, M1, M2) and from 0.719 to 2.390 ng/g dw with a mean of  1.214 ng/g dw in the eastern PFASs concentrations had a lake region (S7–S12, M3, M4). The decreased tendency from west to east. Maximum concentrations of PFASs in the lake region were observed at site S2 near the outlet of the Shiwuli River, and then PFAS concentrations gradually declined along the water flow direction, with a substantial increase

from S11 to S12 adjacent to Chaohu City. The value was approximately 10 times higher than the average PFASs concentration of Lake Wanfohu, which is the important drinking-water source for Shucheng  County and is less influenced by human activities. The PFASs concentrations in riverine sediments ranged from 0.870 to 19.102 ng/g dw. As shown in the inset of Fig. 2(b), the Shiwuli River had the highest concentrations of 19.102 ng/g dw, which could be attributed to the direct discharge of large amounts of untreated sewage and industrial wastewater and possibly the pollution sources such as fluorochemical plants (Bao et al., 2011; Kim et al., 2012). PFAS concentrations in sediments of the Nanfei River increased slightly before reaching Hefei City, rose dramatically after flowing through the city, and subsequently decreased downstream. PFAS concentrations were also significantly higher in sediments of the Paihe River adjacent to Feixi County and decreased downstream. It could be preliminarily concluded that PFASs were derived from municipal wastewater and domestic sewage from the urban area (Bao et al., 2010). From Lake Wanfohu to the Hangbu River, PFAS concentrations increased sevenfold, which implied that the potential pollution sources should be present along the river and may include industrial sources (Yu et al., 2011). PFAS concentrations had a slight decrease along the Fengle River and were the lowest in the downstream sediments, which suffered mainly from agricultural runoff and rural activities. This result was consistent with the minor contribution of agricultural and non-industrial activities to PFAS levels (Jin et al., 2009; Wang et al., 2012). In surface sediments of the Chaohu Lake basin, 16 out of 17 target PFASs were detected including C4-C14-, and C16-PFCAs, and C4-, C6-, C8-, and C10-PFSAs. As illustrated in Fig. 2, PFAS concentrations of the limnetic sediments were lower than those of the riverine sediments. In limnetic sediments, the long-chained PFCAs (C9-C13-) were detected with high detection frequencies of 100%. Only approximately 67–72% of the short-chained PFCAs (C5-C7) contained detectable concentrations. Average concentrations of C9-C13-PFCAs were approximately 3.5 times higher than those of C4-C7-PFCAs, suggesting that the long-chained PFCAs seemed to be more prone to partition into the sediment than the short-chained PFCAs (Lasier et al., 2011; Yeung et al., 2013; Zhang et al., 2012). Composition profiles of PFASs in limnetic sediments and riverine sediments from the Chaohu Lake basin are shown in Fig. 3. The dominant PFAS congener in limnetic sediments was PFOS, with a contribution of 27% to the PFASs, which was also the most predominant PFAS compound in sediments from several areas (Naile et al., 2010; Yeung et al., 2013). PFOS concentrations from Lake Chaohu were higher than those in the Yangtze River Estuary (Pan et al., 2014b) and Tokyo Bay, Japan (Ahrens et al., 2010), comparable with those in Lake Dianchi (Zhang et al., 2012), Lake Baiyangdian (Shi et al., 2012), Lake Michigan (Codling et al., 2014), and rivers and lakes in Korea (Lam et al., 2014), and generally 2 orders of magnitude lower than those observed in Lake Taihu (Pan et al., 2014c), Lake Ontario (Yeung et al., 2013) and Lake Tangxun (Zhou et al., 2013). Other major components in sediments from Lake Chaohu were PFOA (19%), PFUnDA (13%) and PFTrDA (10%). The situation was quite different in riverine sediments with the contribution of PFOS being only 15%, whereas the contribution of PFOA was 37%. For the Shiwuli River, PFOA was the dominant PFAS congener, with an extremely  high contribution of more than 53%. The PFASs concentrations in surface sediments from Lake Chaohu were comparable to those from most areas (in cases where various PFAS congeners are considered) such as Lake Dianchi (Zhang et al., 2012), Lake Baiyangdian (Shi et al., 2012), Lake Michigan, USA (Codling et al., 2014) and Tokyo Bay, Japan (Ahrens et al., 2010 and 2011). Compared to Lake  Tangxun near the high-tech PFASs concentrations in Lake industrial development zone, the Chaohu were significantly lower (Zhou et al., 2013). In general, PFAS concentrations in Lake Chaohu were comparable to concentrations

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Fig. 2. (a) Concentrations and compositions of PFASs in limnetic sediments from Lake Chaohu and Lake Wanfohu; (b) concentrations and compositions of PFASs in riverine PFASs in different sediments from the Chaohu Lake basin. Notes: W1 and W2 represent two sediment samples of Lake Wanfohu. Box plots show the distribution of the sites. The upper right box plot in (a) is displayed for Lake Chaohu only. The upper edge, median bar (line in the middle) and lower edges of the box represent the 75th, 50th and 25th percentiles, respectively. The upper and lower limits of the whiskers show the maximum and minimum values. The symbol of “” represents the outliers, which exceed 3 times the percentiles.

Fig. 3. (A) Composition of PFASs in the Shiwuli River, and (B) percentage composition of PFASs in limnetic and riverine sediments.

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found elsewhere in China and other countries. PFAS concentrations found in the present study and in previous studies are provided in Table SI-2.

ΣPFASs concentrations (ng/g dw) 0.0



The PFASs concentrations in cores-1, 2 and 3 ranged from 0.480 to 2.660 ng/g dw (core-1), 0.280 to 2.210 ng/g dw (core-2) and 0.210 to 1.705 ng/g dw (core-3), with averages of 1.881, 1.378 and 1.062, respectively (see Tables SI-3–5 for more details). The  PFASs in cores-1, 2 and 3 had similar trends with higher levels in the upper slices than those in the bottom samples (Fig. 4), and usage in which was consistent with the enhanced production  China in recent years (Wang et al., 2014). The PFASs concentrations in cores-1, 2 and 3 increased sharply since the early 1950s until the mid- to late 1970s, which was synchronized with the

Year

3.2. Historical deposition behaviors of PFASs in sediment cores

2010 2005 2000 1995 1990 1985 1980 1975 1970 1965 1960 1955 1950

0.5

1.0

1.5

2.0

2.5

3.0

M1 M2 M3

Fig. 4. Vertical distribution of PFAS concentrations in sediment cores-1, 2 and 3.

Fig. 5. Vertical distribution of PFBA, PFPeA, PFHxA, PFOA, PFUnDA and PFOS in sediment cores-1, 2 and 3.

0.723** 0.618** −0.586** −0.595** 0.211 −0.651** −0.510* 0.866** 0.789** −0.576** −0.794** 0.150 −0.819** −0.708** 0.448* 0.292 −0.423 −0.260 0.150 −0.338 −0.233 0.406 0.466* −0.378 −0.408 −0.111 −0.406 −0.406 0.661** 0.565** −0.235 −0.551* 0.326 −0.624** −0.464* −0.338 −0.378 −0.139 0.378 −0.076 0.378 0.378 – – – – – – – 0.529* 0.466* −0.439 −0.443 0.282 −0.489* −0.363 0.489* 0.355 −0.168 −0.321 0.503* −0.401 −0.194 0.845** 0.681** −0.599** −0.666** 0.291 −0.731** −0.563** 0.824** 0.704** −0.508* −0.689** 0.259 −0.745** −0.595** 0.791** 0.736** −0.457* −0.739** 0.114 −0.758** −0.675** 0.844** 0.714** −0.653** −0.713** 0.126 −0.764** −0.633** 0.884** 0.783** −0.679** −0.792** 0.065 −0.829** −0.726** *

**

Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).

0.693** 0.109 0.613** −0.008 −0.408 0.217 −0.607** 0.013 0.323 0.494* −0.639** −0.062 −0.479* 0.080 0.386 0.189 −0.408 −0.165 0.120 −0.260 −0.167 0.314 0.144 −0.496* −0.114 0.062 −0.188 −0.105 0.398 0.242 −0.495* −0.208 0.095 −0.280 −0.171 Particle sizes <4 ␮m 4–8 ␮m 8–16 ␮m 16–64 ␮m >64 ␮m Median particle size Mean particle size

0.580** 0.567** 0.615** 0.615** 0.505* 0.626** 0.734** 0.620** 0.836** 0.726** 0.780** 0.869** 0.760** 0.870** 0.920** 0.851** 0.357 0.439 0.429 0.351 0.314 0.370 0.448* 0.378 0.345 0.150 0.323 0.406 0.172 0.408 0.466* 0.466* 0.594** 0.605** 0.802** 0.690** 0.859** 0.822** 0.777** 0.728** −0.378 −0.259 −0.338 −0.259 −0.298 −0.219 −0.259 −0.338 – – – – – – – – 0.445* 0.540* 0.582** 0.542* 0.445* 0.569** 0.629** 0.588** 0.522* 0.466* 0.726** 0.668** 0.837** 0.769** 0.693** 0.694** 0.865** 0.798** 0.899** 0.900** 0.872** 0.930** 0.929** 0.930** 0.855** 0.791** 0.905** 0.927** 0.883** 0.933** 0.932** 0.940** 0.804** 0.724** 0.746** 0.869** 0.668** 0.817** 0.867** 0.815** 0.753** 0.651** 0.678** 0.724** 0.708** 0.762** 0.808** 0.752** 0.761** 0.615** 0.640** 0.727** 0.659** 0.725** 0.805** 0.734** 0.066 0.226 0.399 0.152 0.503* 0.303 0.226 0.199 0.587** 0.535* 0.636** 0.645** 0.538* 0.614** 0.698** 0.593** 0.290 0.468* 0.415 0.265 0.304 0.343 0.379 0.307 0.326 0.408 0.379 0.329 0.250 0.331 0.411 0.344 Heavy metals Fe Co Cr Cu Mn Pb Zn Ti

0.191 0.307 0.247 0.209 0.167 0.284 0.334 0.226

0.688** 0.635** 0.561* 0.836** 0.829** 0.827** 0.460* 0.399 0.305 0.294 0.406 0.372 0.881** 0.886** 0.859** 0.260 −0.338 −0.378 0.247 – – 0.437* 0.590** 0.476* 0.831** 0.812** 0.746** 0.892** 0.904** 0.848** 0.907** 0.920** 0.883** 0.664** 0.751** 0.785** 0.816** 0.745** 0.676** 0.566** 0.720** 0.683** 0.645** 0.444* 0.399 0.715** 0.572** 0.535* 0.445* 0.358 0.263 0.261 0.323 0.229

0.324 0.253 0.144

7

TOC TN TP



C9-C14C4-C8  PFCAs PFCAs PFDS PFOS PFHxS PFBS PFHxDA PFTeDA PFTrDA PFDoDA PFUnDA PFDA PFNA PFOA PFHpA PFHxA PFPeA PFBA

The influences of TOC, TN, TP, heavy metals and particle sizes on PFAS distribution in sediment cores were investigated in this study. Physical and geochemical characteristics of the sediment profiles were variable, as described in previous studies (Zan et al., 2011, 2012). Spearman’s correlation coefficients between PFASs and TOC, TN, TP, heavy metals and particle sizes in sediment cores are summarized in Table 2. Positive correlations were observed between several PFAS congeners and TOC in three sediment cores, which mainly focused on the long-chained PFAS congeners (C9 C14-). C9-C14- PFCAs were correlated significantly with  TOC, with a correlation coefficient of 0.836, whereas C4-C8- PFCAs were correlated with TOC with a correlation coefficient of 0.460 at a significance level of 0.05. The correlation between PFOS and TOC was stronger than that between PFOA and TOC. Organic content was recognized as the dominant sediment parameter

Parameters

3.3. Influence of sedimentary characteristics on PFSA distribution

Table 2 Spearman’s correlation coefficients between PFASs and TOC, TN, TP, heavy metals and particle sizes in sediment cores.

mass production and wide use of PFASs. PFASs have been applied to various daily consumer goods and industrial products since the late 1940s (Senthil Kumar et al., 2009). Since the late 1970s, the PFASs in cores-1, 2 and 3 have been temporal trends of the slightly different from one another. PFBA, PFHxA, PFHpA, PFOA, PFUnDA and PFOS were the dominant PFAS congeners in cores1, 2 and 3, and the sum levels of these six congeners accounted for at least 69% of the PFASs. Fig. 5 illustrates the composition of the six dominant PFAS congeners in cores-1, 2 and 3. Concentrations of the six PFAS congeners had a significant increase from the early 1950s to the mid- to late 1970s. Since then until the mid-2000s, PFOA and PFOS still had an increase in concentrations. On the contrary, concentrations of PFBA, PFHpA and PFHxA in the mid-2000s decreased relative to the mid- to late 1970s. Since the early 1990s, PFUnDA concentrations in cores-1, 2 and 3 consistently declined, suggesting the phase-out of PFUnDA in the locality in recent years. The inconsistent temporal trends among the regions sampled and the congeners detected might be representative of differences in the influx of pollutants from source regions or in environmental dynamics (Butt et al., 2010; Zushi et al., 2010). It is worth mentioning that PFBA, PFPeA and PFHxA, as the substitute compounds for the long-chained PFASs, contributed more to PFASs concentrations in cores-1, 2 and 3 than PFUnDA. Studthe ies showed that the short-chained PFASs might be very persistent and much more mobile than the long-chained PFASs (Ahrens et al., 2009b; Washington et al., 2010; Zhou et al., 2013). Although the short-chained PFASs are not as bioaccumulative, more attention should still be paid to their ecological risks because of the greater mobility and contribution (Dreyer et al., 2012; Ritter, 2010). As reported in the study of Zan et al. (2012), both the 210 Pb estimated age and the 137 Cs peak were consistent in assigning an age of 1963 to the ca. 16–17 cm layer, which was used in Lake Chaohu. Sediment fluxes of PFASs in two sediment cores were calculated using the dry sediment concentrations multiplied by the densitycorrected yearly sedimentation rate (Ahrens et al., 2009b). The deposition fluxes of PFASs  are illustrated in Fig. 6.2The highest flux was observed for the PFASs with 0.306 ng/(cm y) (1975–1982)  in core-2 and 0.240 ng/(cm2 y) (2003–2006) in core-3. The PFASs before 1975 fluxes in core-2 decreased to 0.041 (1952–1960)  and 0.126 ng/(cm2 y) (1988–1993) after 1982. The PFASs fluxes in core-3 continued to decrease to 0.020 ng/(cm2 y) (1952–1961) before 2003, with minor increases in 1977–1983 and 1994–1998. Exponential increases were observed in deposition fluxes in cores2 and 3 between 1952 and 1975, with first–order rate constants of 0.082 and 0.080 y−1 (R2 = 0.983 and 0.968), respectively. Deposition fluxes in core-2 (1988–2006) and core-3 (1983–2006) showed a trend of linear correlation with correlation coefficients of 0.774 and 0.803, respectively.

PFASs

Y. Qi et al. / Ecological Indicators 57 (2015) 1–10

8

Y. Qi et al. / Ecological Indicators 57 (2015) 1–10

ΣPFASs fluxes (ng/(cm2·y)) 0.1

Fluxes (ng/(cm2·y))

Year

0.0 2010 2005 2000 1995 1990 1985 1980 1975 1970 1965 1960 1955 1950 0.4

0.2

0.4

M2 M3

a

0.3 0.2

0.3

y = 0.028x - 54.509 R² = 0.774

y = 19.028-71e0.082x R² = 0.983

0.1 b

0.0 1950

1960

1970

1980 Year

1990

2000

2010

Flux (ng/(cm2·y))

0.3 y = 0.0038x - 7.4013 R² = 0.803 0.2 y = 4E-70e0.080x R² = 0.968 0.1 c

0.0 1950

1960



Fig. 6. Deposition fluxes of the distribution of the and 3, respectively.

1970

1980 Year

1990

2000

2010



PFASs in sediment cores-2 and 3. (a) Vertical

PFASs fluxes; (b and c) stages of fluxes in sediment cores-2

influencing adsorption of anionic PFAS surfactants (under the premise that PFASs are present in water as anions and have a surface-active character), which suggested the importance of hydrophobic interactions (Ahrens et al., 2011; Higgins and Luthy, 2006; Myers et al., 2012). Both the length of the perfluorocarbon tail and the functionality of the head group had an important impact on the adsorption of anionic PFAS surfactants to sediment materials (Higgins and Luthy, 2006). The hydrophobicity of PFASs increased with the length of the perfluorocarbon tail. As reported in a previous study, the distribution coefficients of both PFCAs and PFSAs were increased by CF2 moiety (Higgins and Luthy, 2006). It has been reported that most sediment organic carbon exists in a charged state due to the ionization of its own carboxylic acid and phenolic groups, meaning that the sediment organic carbon is apt to compete the adsorptive sites with anions (Schwarzenbach et al., 2003). Thus, adsorption between the less polar C9-C14-PFCA anions and sediment organic carbon could be stronger, compared with C4-C8PFCA anions. Adsorption was stronger for PFSAs than PFCAs of equal chain length, which might be attributed to the slightly larger size of the sulfonate moiety, compared to the carboxylate moiety or specific electrostatic interactions. Significant positive correlations were also observed between  PFASs C9-C14-PFCAs and TN and TP in sediment cores, and the and C7- and C16-PFCAs also had a significant positive correlation with TN. Likewise, PFOS also showed a stronger correlation with

TN and TP than PFOA. Interactions between POPs and eutrophication were conducted by the Swedish Environmental Protection Agency as early as 1995 (Skei et al., 2000). Results showed that levels of polychlorinated biphenyls in sediments from the Baltic Sea increased linearly with the degree of eutrophication (Meili et al., 2000). Di-2-ethylhexyl phthalate in a small eutrophic lake also had a significant positive correlation with TP (Chi et al., 2003). Interactions between POPs and eutrophication may be achieved mainly by acting on the biomass in aquatic ecosystems, such as phytoplankton, benthic organisms, etc. (Greer et al., 1998).  The PFASs and C7-, C14-, and C16-PFCAs were correlated with several heavy metals. The C9-C14- PFCAs, C9-C13-PFCAs and C8PFSA had positive correlations with all of the heavy metals studied. Metal cations in solution may reduce the negative charge, leading to reduced repulsion of the PFAS anion, which would favor adsorption and produce  isotherm nonlinearity (Chen et al., 2012). The PFASs, C9-C14- PFCAs, C7-, C9-C13-PFCAs and C8-PFSA were positively correlated with the percentage of sediment particles sized <4–8 ␮m and in contrast were negatively correlated with the percentage of particles sized 8–64 ␮m, the median particle size, and the mean particle size of the sediments. Similarities are also apparent in the distribution of TOC, TN and TP in relation to sediment particle sizes (Zan et al., 2012), which might be related to the compositional properties of clay and slit, such as the mineral composition (Johnson et al., 2007), organic content (Ahrens et al., 2011), etc. Although the result was consistent with a previous study that showed the lowest adsorption capacity for a sandy sediment with a low content of organic carbon, and higher adsorption capacities for muddy sediments with higher contents of organic carbon, it was demonstrated that particle sizes and electrostatic effects played predominant roles in PFAS partitioning when organic content was low (Ahrens et al., 2011). Particles sized <4–8 ␮m were classified as clay and fine silt. In other words, the long-chained PFAS congeners preferred to absorb onto the fine grains. Myers et al. (2012) also found that PFASs, especially the long-chained PFASs, were predominant in fine-grained sediments from major depositional basins in sediments from Lake Ontario. This observation had important environmental implications because some benthic organisms prefer to ingest fine particles. However, adsorption of PFASs in sediments is a very complicated process and is affected by many factors, not only organic carbon and nutrients, but also heavy metals and particle sizes (Chen et al., 2012). Both the organic and inorganic materials might influence the transport and fate of PFASs in sediments, suggesting that both hydrophobic and electrostatic effects would play a role in PFAS distribution and pollution levels in sediments (Higgins and Luthy, 2006). Further study should be conducted to investigate which factor dominates the partitioning behaviors of PFASs.

4. Conclusions This work revealed the concentrations and spatial and vertical distribution of PFASs in sediments collected from the Chaohu Lake basin. The PFASs concentrations in limnetic sediments ranged from 0.719 to 2.429 ng/g dw and were higher in the western half-lake region adjacent to the industrial areas and Hefei City, were decreased along the water flow direction of Lake Chaohu, and had a significant increase close to the densely populated  Chaohu City. The highest PFASs concentrations of 19.102 ng/g dw detected in the Shiwuli River sediment were higher than nearly any other concentrations reported worldwide. PFAS distribution in riverine sediments indicated that industrial and urban activities played an important role in PFAS concentrations, but non-industrial and agricultural activities played lesser roles. The historical

Y. Qi et al. / Ecological Indicators 57 (2015) 1–10

deposition behaviors of PFASs were related to the industrialization and urbanization in the Chaohu Lake basin. Spearman rank correlation analysis showed that the PFASs and individual PFAS congeners were positively correlated with TOC, TN, TP, heavy metals and sediment particles sized <4–8 ␮m in sediment cores. These phenomena would be of particular interest in modeling PFAS fate and distribution, and further research is needed to investigate how various factors affect the partitioning behavior of PFASs in the lake environment. Acknowledgments This study was supported by the Mega-projects of Science Research for Water Environmental Improvement (Program No. 2012ZX07101-002), the National Natural Science Foundation of China (No. 41303085) and the China Postdoctoral Science Foundation (Grant No. 2014M550784). The authors declare no competing financial interest. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecolind.2015. 04.015 References Ahrens, L., Felizeter, S., Ebinghaus, R., 2009a. Spatial distribution of polyfluoroalkyl compounds in seawater of the German Bight. Chemosphere 76 (2), 179–184. Ahrens, L., Yamashita, N., Yeung, L.W.Y., Taniyasu, S., Horii, Y., Lam, P.K.S., et al., 2009b. Partitioning behavior of per- and polyfluoroalkyl compounds between pore water and sediment in two sediment cores from Tokyo Bay, Japan. Environ. Sci. Technol. 43 (18), 6969–6975. Ahrens, L., Taniyasu, S., Yeung, L.W.Y., Yamashita, N., Lam, P.K.S., Ebinghaus, R., 2010. Distribution of polyfluoroalkyl compounds in water, suspended particulate matter and sediment from Tokyo Bay, Japan. Chemosphere 79 (3), 266–272. Ahrens, L., Yeung, L.W.Y., Taniyasu, S., Lam, P.K.S., Yamashita, N., 2011. Partitioning of perfluorooctanoate (PFOA), perfluorooctane sulfonate (PFOS) and perfluorooctane sulfonamide (PFOSA) between water and sediment. Chemosphere 85 (5), 731–737. Bao, J., Liu, W., Liu, L., Jin, Y.H., Ran, X.R., Zhang, Z.X., 2010. Perfluorinated compounds in urban river sediments from Guangzhou and Shanghai of China. Chemosphere 80 (2), 123–130. Bao, J., Liu, W., Liu, L., Jin, Y.H., Dai, J.Y., Ran, X.R., et al., 2011. Perfluorinated compounds in the environment and the blood of residents living near fluorochemical plants in Fuxin, China. Environ. Sci. Technol. 45 (19), 8075–8080. Buck, R.C., Franklin, J., Berger, U., Conder, J.M., Cousins, I.T., de Voogt, P., et al., 2011. Perfluoroalkyl and polyfluoroalkyl substances in the environment: terminology, classification, and origins. Integr. Environ. Assess. Manage. 7 (4), 513–541. Butt, C.M., Berger, U., Bossi, R., Tomy, G.T., 2010. Levels and trends of poly- and perfluorinated compounds in the arctic environment. Sci. Total Environ. 408 (15), 2936–2965. Chen, H., Zhang, C., Yu, Y.X., Han, J.B., 2012. Sorption of perfluorooctane sulfonate (PFOS) on marine sediments. Mar. Pollut. Bull. 64 (5), 902–906. Chi, J., Huang, G.L., Lu, X., Ma, D.G., Wang, Y., 2003. DEHP enrichment in the surface microlayer of a small eutrophic lake. Water Res. 37 (19), 4657–4662. Codling, G., Vogt, A., Jones, P.D., Wang, T.Y., Wang, P., Lu, Y.L., et al., 2014. Historical trends of inorganic and organic fluorine in sediments of Lake Michigan. Chemosphere 114, 203–209. Dreyer, A., Thuens, S., Kirchgeorg, T., Radke, M., 2012. Ombrotrophic peat bogs are not suited as natural archives to investigate the historical atmospheric deposition of perfluoroalkyl substances. Environ. Sci. Technol. 46 (14), 7512–7519. Gao, Y., Fu, J.J., Zeng, L.X., Li, A., Li, H.J., Zhu, N., et al., 2014. Occurrence and fate of perfluoroalkyl substances in marine sediments from the Chinese Bohai Sea, Yellow Sea, and East China Sea. Environ. Pollut. 194, 60–68. Gomez, C., Vicente, J., Echavarri-Erasun, B., Porte, C., Lacorte, S., 2011. Occurrence of perfluorinated compounds in water, sediment and mussels from the Cantabrian Sea (North Spain). Mar. Pollut. Bull. 62 (5), 948–955. Greer, C.W., Fortin, N., Roy, R., Whyte, L.G., Lee, K., 1998. Indigenous sediment microbial activity in response to nutrient enrichment and plant growth following a controlled oil spill on a freshwater wetland. J. Soil Contam. 7 (1), 69–80. Higgins, C.P., Luthy, R.G., 2006. Sorption of perfluorinated surfactants on sediments. Environ. Sci. Technol. 40 (23), 7251–7256. Jin, Y.H., Liu, W., Sato, I., Nakayama, S.F., Sasaki, K., Saito, N., et al., 2009. PFOS and PFOA in environmental and tap water in China. Chemosphere 77 (5), 605–611. Johnson, R.L., Anschutz, A.J., Smolen, J.M., Simcik, M.F., Penn, R.L., 2007. The adsorption of perfluorooctane sulfonate onto sand, clay, and iron oxide surfaces. J. Chem. Eng. Data 52 (4), 1165–1170.

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