Partition and source identification of organophosphate esters in the water and sediment of Taihu Lake, China

Accepted Manuscript Title: Partition and Source Identification of Organophosphate Esters in the Water and Sediment of Taihu Lake, China Authors: Xiaolei Wang, Lingyan Zhu, Wenjue Zhong, Liping Yang PII: DOI: Reference:

S0304-3894(18)30630-7 https://doi.org/10.1016/j.jhazmat.2018.07.082 HAZMAT 19591

To appear in:

Journal of Hazardous Materials

Received date: Revised date: Accepted date:

1-2-2018 14-7-2018 23-7-2018

Please cite this article as: Wang X, Zhu L, Zhong W, Yang L, Partition and Source Identification of Organophosphate Esters in the Water and Sediment of Taihu Lake, China, Journal of Hazardous Materials (2018), https://doi.org/10.1016/j.jhazmat.2018.07.082 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Partition and Source Identification of Organophosphate Esters in the Water and Sediment of Taihu Lake, China

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Xiaolei Wang1, Lingyan Zhu1,2,*, Wenjue Zhong1, Liping Yang1

1. Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of

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Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University,

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Tianjin, P.R. China 300350

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2. College of natural resources and environment, Northwest A&F University,

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*

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Yangling, Shanxi, P.R. China 712100

Correspondence and requests for materials should be addressed to L. Zhu (email:

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[email protected]). Phone: +86-22-23500791. Fax: +86-22-23500791.

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Graphic Abstract

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OPEs

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ies Cl

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O Cl

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O Cl

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Ma nuf act or

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P < 0.05

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Log Koc

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5 Km

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6 Log Kow

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1200 - 2000 > 2000

20 - 40 > 40

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∑OPEs(ng/L) ∑OPEs(ng/g dw) in water in sediment 100 - 400 2 -5 400 - 800 5 - 10 10 - 20 800 - 1200

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Highlights

High level of OPEs was detected in the northwest shore of Taihu Lake.

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TCIPP and TCEP predominated in the Taihu Lake waters.

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OPEs decreased from northwest to southeast Taihu Lake.

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Calculated logKoc of OPEs displayed significantly correlation with their logKow.

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OPEs manufacturing in Yixing was an important point source of OPEs in Taihu

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Lake. 2

Abstract

Taihu Lake is the third largest freshwater lake in China, and has been heavily polluted by surrounding industrial activities. This study aimed to investigate the sources

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of organophosphate esters (OPEs) in Taihu Lake, and their partitioning behaviors between sediment and water. The total concentrations of the eleven target OPEs

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(∑OPEs) in the water and sediment of Taihu Lake were 166 - 1,530 ng/L and 2.82 47.5 ng/g dw, respectively. The OPEs in both water and sediment generally decreased from northwest to southeast. Extremely high level of OPEs (1,410 - 15,300 ng/L) was

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found in the flow-in rivers passing through the OPE manufacturing regions in Yixing.

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In both water and sediment, tris(2-chloroisopropyl) phosphate and tris(chloroethyl)

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phosphate were the predominant OPEs. The sediment-water partitioning coefficients,

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log Koc, of OPEs were calculated based on paired sediment and water samples, and they

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displayed strong correlation with their log Kow (octanol-water), suggesting that their partition was dominated by hydrophobic interaction. Principle component analysis

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indicated that OPE manufacturing in Yixing was an important point source of OPEs, especially of TCIPP in Taihu Lake. Many OPE-related industries, such as electronic,

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textile, machine and plastic industries around Taihu Lake also made contributions to OPEs in the Lake.

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Keywords: Organophosphate esters, Water, Sediment, Sources, Partition

1. Introduction

With the restrictions on polybrominated diphenyl ethers (PBDEs), the most 3

commonly used brominated flame retardants (BFRs), in the early 2000’s, many substitutes emerged in the market [1]. Organophosphate esters (OPEs) are one of these substitutes and widely applied in plastics, furniture, textile and many other industrial products [2]. The production of OPEs increased ~10% in western Europe from 2001 to 2006 [3], and the global consumption of OPEs was estimated to be 680,000 tonnes in

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2015 [4]. Similar to PBDEs, OPEs are additive flame retardants without chemical

bonding and may easily escape from the products to surrounding environment during

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manufacturing, application, aging and disposal of OPEs-containing products [2]. Consequently, OPEs are ubiquitously detected in various environmental compartments,

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19], soil [20, 21], organisms [17, 22-27], and so on.

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such as indoor and outdoor dust [5-7], atmosphere [8-11], water and sediment [1, 12-

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OPEs are median to highly hydrophobic with log Kow (octanol-water partitioning

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coefficients) values in the range of 1.44 - 9.49 [2]. Once released to aquatic systems, OPEs would partition between water and sediment, which is an important process

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affecting the transport, fate and risks of OPEs in the environment. There have been many studies on the occurrence of OPEs in aquatic environment, but very few

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investigated the partition of OPEs between water and sediment [1, 12-19]. Cao et al. [13] studied the partitioning behaviors of only three OPEs, including tris(2-

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chloroisopropyl) phosphate (TCIPP), tri(butyl) phosphate (TNBP) and tris(2butoxyethyl) phosphate (TBOEP), based on nine sediment samples. Previous studies

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reported that the organic carbon normalized distribution coefficient (Koc) of perfluoroalkyl substances (PFASs) between sediment and water was correlated with the chemical structures, such as carbon chain length [28]. Interaction between polychlorinated biphenyls and marine humic substances was dependent on their solubility, Kow and the molecular surface area [29]. Considering that OPEs have a 4

variate chemical structures and properties, extensive studies are necessary to fully understand the impacts of physicochemical properties of OPEs on their partitioning behaviors. With the water surface area of 2,338 km2 and mean water depth of 1.9 m, Taihu Lake is the third-largest freshwater lake in China [30]. It is located in the most industrialized,

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urbanized and densely populated area, the Yangtze River Delta, in China. Many upstream rivers of Taihu Lake pass through the industrialized cities, such as Wuxi,

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Yixing, Suzhou, and bring lots of anthropogenic chemicals into the Lake. As a result, a

variety of organic pollutants, such as pesticides [31], bisphenols [32] and PFASs [28],

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were identified at considerable levels in the Lake. Surrounding the Lake, there are many

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OPE-related industries, such as furniture, vehicle, textile treatment, electronic, rubber,

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paint and plastics industries [33, 34], which are possible sources of OPEs in Taihu Lake.

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It is worth noting that several branch factories of Jiangsu Yoke Technology Co., Ltd., the largest organophosphate flame retardants manufacturer in China, are close to the

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northwest of Taihu Lake [35], which could be point sources of OPEs in the Lake. Identification of the sources of contaminants in the Lake is essential for risk assessment

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and efficient pollution control. It was reported that several OPEs were present in the water and sediment samples in the northern part of the Lake [36, 37], where serious

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pollution of anthropogenic chemicals was reported. But the impact of production of OPEs around Taihu Lake on their pollution in Taihu Lake has long been ignored. Sparse

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information is available for their spacial distribution in the whole lake, especially in the northwest shore of the Lake. In June 2016, paired water and sediment samples were collected from Taihu Lake. Several water samples were collected from the rivers in Nanhe river system, which went through the OPE manufacturing regions in Yixing city, and may transport OPEs to 5

Taihu Lake. The objectives of this study were to: 1) investigate the occurrence and spacial distribution of OPEs in the water and sediment samples of the whole Taihu Lake; 2) investigate the partitioning behaviors of OPEs between water and sediment and impacts of the physicochemical properties on their partition; and 3) explore the impacts of OPE manufacturing on their pollution in the Lake and identify possible

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contamination sources.

2. Materials and methods Sample collection

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2.1.

Water, suspended particulate matter (SPM) and sediment samples were collected

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from 29 sampling sites (T1-29) in Taihu Lake in June 2016, and detailed information

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about sampling sites is displayed in Table S1 and Fig. 1. Industries which may be related

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to OPEs, such as vehicle, furniture, textiles, electronics, rubber, paint and plastics,

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within ~ 10 km of the shore of Taihu Lake are also displayed in Fig. 1. Information about OPEs applications is listed in Table S2. Nine river samples (R1-9) going through

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Yixing were collected at their mouths entering Taihu Lake (Fig. 1). Surface water was

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collected at least 5 m off the shore at a depth of 0.5 m, and collected with 4 L brown glass containers which were rinsed twice with water previously. Surface sediment (0 -

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10 cm) was collected with a stainless steel hand piston sediment sampler. The inner portion of the sediment without contacting the sampler was used for analysis. Only water samples were collected from rivers since sediment was not available at these sites.

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4 L of surface water was filtered through a glass fiber filter (142 mm dia., 0.7 μm pore size; Whatman, UK) [19] and collected in a new amber glass bottle, then stored at 4 °C until pretreatment with solid-phase extraction (SPE) cartridges. SPM and sediment samples were wrapped in aluminum foil and stored in PP plastic bags. All SPM and sediment samples were frozen immediately after being transported to the laboratory and 6

stored at -18 °C until they were processed. The SPM content remaining on the membrane was determined by weight difference of the membrane before and after filtration. All used bottles and sampling equipment were rinsed with methanol (MeOH) and Milli-Q water before sampling to eliminate contamination.

2.2.

Chemicals and materials

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The standards of eleven OPEs, i.e., tri(isopropyl) phosphate (TIPP); TNBP and tri(isobutyl) phosphate (TIBP), which are two isomers of tributyl phosphate (TBP);

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TBOEP; tris(chloroethyl) phosphate (TCEP); TCIPP; tris(1,3-dichloropropyl) phosphate (TDCIPP); triphenyl phosphate (TPHP); 2-ethylhexyl diphenyl phosphate

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(EHDPP); tris(2-ethylhexyl) phosphate (TEHP); and tri(methylphenyl) phosphate

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(mixture of isomers) (TMPP), were purchased from AccuStandard Inc. (USA). Four

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deuterium-labeled OPEs were used as recovery and internal standards: TNBP-d27 was

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bought from Cambridge Isotope Laboratories (UK), and TDCIPP-d15, TCEP-d12 and TPHP-d15 were provided by Toronto Research Chemicals Inc. (Canada).

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HPLC-grade dichloromethane (DCM), acetonitrile (ACN) and MeOH were provided by J&K Scientific Ltd. (Beijing, China). Formic acid (HPLC grade) was

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purchased from CNW Technologies GmbH (Germany). The SPE cartridges

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Supelclean™ ENVI-18 (6 mL, 500 mg) were supplied by Supelco (USA). Milli-Q water was used throughout the study.

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2.3.

Sample pretreatment

The procedures for extraction and clean-up of the water, SPM and sediment samples

followed a previously described method with minor modifications [15, 28, 38, 39]. The target analytes in the water were extracted using a SPE cartridge (ENVI-C18, 6 mL, 500 mg; Supelco, USA). The SPM and sediment samples were extracted with ACN using ultrasonication and the following processes were identical with that of water 7

pretreatment. The detailed information about the sample pretreatment is provided in the SI. The total organic carbon (TOC) content of the sediment samples was measured as CO2 in acid treated samples using a Solid TOC Analyzer (multi N/C 3100, Analytik Jena, Germany) [38].

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Instrumental analysis

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As detailed elsewhere [6], an ultraperformance liquid chromatography-tandem

electrospray-triple quadrupole mass spectrometry system (Xevo TQ-S; Waters, Milford,

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MA, USA) was used to quantify OPEs in the samples. Chromatographic separation of OPEs was accomplished on a Waters BEH C18 column (2.1 mm × 50 mm, 1.7 μm)

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coupled with a VanGuard Pre-column (C18 column, 2.1 mm × 5 mm, 1.7 μm). The

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injection volume was 10 μL, and the column temperature was 55 °C. For the gradient

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elution, a binary eluent of water (A) and ACN (B), both containing 0.1% formic acid,

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was used for the separation of analytes at a flow rate of 0.4 mL/min. The gradient was set as follows (with reference to B): 0 min 10% B, 1-3.5 min 50% B, 3.6 min 40% B,

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4.1-6 min 50% B, 7-9 min 100% B and 10 min 10% B. Chromatograms were recorded using positive ion mode and multiple reaction

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monitoring. Nitrogen was applied as desolvation gas and argon as collision gas. Other

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operation parameters for MS were set as follows: capillary voltage 3.5 kV; source temperature 150 °C; probe temperature 400 °C; cone gas flow 150 L/h; and desolvation gas flow 800 L/h. The detection parameters of each pollutant are listed in Table S3.

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2.5.

QA/QC

Multi-level calibration curves were created for quantification, and strong linearity

(r2 > 0.995) was achieved. Field blanks of pure water were transported with the real samples during the sampling campaign. Method blanks and solvent blanks were included with each batch of twelve samples. The standard solution of the analytes, at 8

20 ng/L, was checked every ten injections to ensure analysis stability. All OPEs were detected in the procedural blanks, except TEHP in water and TIPP in sediment. The method detection limits (MDLs) were defined as the concentrations with a signal-tonoise ratio of three if specific OPEs were not detected in the blanks. For the analytes detected in the blanks, the MDLs were defined as the mean blank concentration plus

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three times the standard deviations (displayed in Table S4). The MDLs for the target

compounds in water, SPM and sediment were in the range of 0.013-3.93 ng/L, 0.005-

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0.747 ng/L and 0.004-0.8 ng/g dw, respectively. To validate the analytical procedure,

standards of the selected OPEs were added to Milli-Q water (20 ng/L), SPM (20 ng/g

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dw) and sediment (20 ng/g dw). The recoveries of OPEs in the water, SPM and sediment

Data analysis and statistical analysis

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2.6.

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were 72.3-138%, 79.4-115% and 74.4-124%, respectively (Table S4).

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The field partition coefficients of OPEs between water and sediment (or SPM) were estimated using the concentrations in paired water and sediment (or SPM) samples. The

follows [32]:

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water-sediment (or water-SPM) distribution coefficients Kd (cm3/g) were calculated as

(1)

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Kd = Cs / Cw × 1000

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where Cs and Cw are the OPE concentrations in sediment (or SPM) (ng/g dw) and water (ng/L) samples, respectively. The field-based Koc (cm3/g) of OPEs between water and sediment were calculated

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using the following equation: Koc = Kd × 100 / foc

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where foc is the percentage of organic carbon in the sediment (%). Though the partition of OPEs between water and sediment in field is a dynamic partitioning process and may be affected by many field factors, the field-based Koc was 9

also calculated, which can still reflect the partition behavior of OPEs between water and sediment according to the study of Chen et al. [28]. In addition, partition coefficients of OPEs between water and SPM (Kd water-SPM) were also calculated and compared with those between water and sediment (Kd water-sediment). All statistical analyses were performed with IBM SPSS Statistics v21. Correlation

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analysises were performed to evaluate correlation between the Kd of individual OPEs and sediment foc, and correlations between concentrations of OPEs. Student's t-test was

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applied for assessing variance of the OPE concentrations and partition coefficients at different sampling sites. Principal component analysis (PCA) and hierarchical cluster

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analysis (HCA) were performed using SIMCA 13.0 to determine possible sources of

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OPEs in Taihu Lake. For statistical purposes, all concentrations lower than the MDLs

3.1.

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3. Results and discussion

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were set as one-half of the MDLs.

OPEs in the water of Taihu Lake and flow-in rivers within Yixing

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The distribution of OPEs in the dissolved water phase of Taihu Lake is illustrated in

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Fig. 2A, S1A and Table S5. Among the target compounds, TCIPP, TCEP, TDCIPP, TBOEP, TIBP, TNBP and TIPP were detected in all the samples, while TMPP, TEHP,

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TPHP and EHDPP were detected with frequency of 55.2, 48.3, 37.9 and 13.8%, respectively. Within Taihu Lake, the total concentrations of all target OPEs (∑OPEs) ranged from 166 to 1,530 ng/L. The amount of SPM in the water phase was very low

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(~ 0.05 g/L), leading to low detection frequency (8-36%) of several compounds, such as TBOEP, TMPP, TPHP and TIBP. In addition, many compounds were close to the detection limits, such as TEHP, TMPP, EHDPP, TPHP, TIBP, TNBP and TIPP (Table S6). Thus, SPM bound OPEs were not included in the water samples. The ∑OPEs in Taihu Lake were comparable or higher than those in lakes in Beijing [16] and USA [40], 10

fresh surface water in Germany [41] and Austria [42] and volcanic lakes in Italy [43], but lower than that in the Pearl River Estuary, China [44]. The ∑OPEs in the lake displayed a decreasing trend from northwest to southeast. Relatively high concentration of ∑OPEs was observed in Zhushan Bay (T1-3) and Meiliang Bay (T4-10), which are located in the northwest and north of Taihu Lake. Many previous studies indicated that

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these two bays always had relatively high level of anthropogenic chemicals, such as

perfluorinated chemicals [28] and bisphenol analogues [32], since they are close to the

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heavily industrialized and urbanized cities, such as Wuxi [28, 32]. Different from these chemicals, very high level of ∑OPEs was observed at the sites near the west shore of Taihu Lake. This could be due to the impacts of OPE manufacturing in Yixing, which

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will be discussed later. Relatively high level ∑OPEs was also observed in Gonghu Bay

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(T11-14), which could be derived from Wangyu River and upstream effluents from

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WWTPs which collected waste water from electronic, plastic and machine industries (Fig. 1). The lowest level of OPEs were observed at T18-22, which were situated in

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southern Taihu Lake.

In Taihu Lake, TCIPP predominated in all the water samples with a contribution of

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32.5 - 73.7% to the ∑OPEs, followed by TCEP (11.8 - 41.7%), shown in Fig. 2A. TCIPP and TCEP are heavily used as flame retardants and plasticizers in vehicle, furniture,

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electronic, cable, rubber and polyurethane foam (PUF) and so on [2]. Many related industries are located surrounding the Taihu Lake, which may explain the predominance

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of TCIPP and TCEP in the Lake. A previous study reported that TCEP (259 - 2,406 ng/L) was predominant in Meiliang Bay of Taihu Lake with a contribution of > 80%, while TCIPP (7.7 - 19.1 ng/L) was 1-2 orders of magnitude lower than that in this study (60 - 1,063 ng/L) [36]. Similar situation of increasing concentration of TCIPP was also observed in Europe and America in recent years [2, 13], which was attributed to the 11

industrial replacement in these regions considering the neurotoxicity and carcinogenicity of TCEP. This replacement could also take place in China although there are no public documents reporting this. The result was also consistent with the production of chlorinated OPEs in Jiangsu Yoke Technology Co., Ltd., which was the largest manufacturer of organophosphate flame retardants in China [35]. The

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production of TCIPP by Yoke was two orders of magnitude higher than TDCIPP, while obvious production of TCEP was not reported in any public documents [35].

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Among the non-chlorinated OPEs, TBOEP was predominant at level of 5.08 - 138

ng/L (0.8 - 17.7% of ∑OPEs), which was followed by TIBP and TNBP, with

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concentrations of 16.4 - 72.5 and 3.61 - 36.1 ng/L, respectively. The concentrations of

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TBOEP and TIBP were comparable or higher than other lake waters around the world

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[16, 40, 41] except for the Albano and Vico Lakes in Italy [43]. Different from other

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lakes [16, 41, 43], the level of TIBP was higher than TNBP, which could be due to the more use of TIBP as an antifoaming agent in the textile, printing and concrete industries

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around Taihu Lake (Fig.1 and Table S2).

In the nine rivers passing through the manufacturing regions, all targeted OPEs were

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detected (Fig. 2A). An extremely high level of ∑OPEs (15,300 ng/L) was observed at R3, which was the highest reported level in surface waters worldwide, excluding the

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water from the Aire River, UK, which was close to a wastewater treatment plant [45]. The ∑OPEs at other river sites (437 - 3,910 ng/L) were higher than most of the samples

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in Taihu Lake, and represented a high surface water level in the world. Specially, the levels of ∑OPEs at R1-5 were higher than those at R6-9, because more industrial parks and factories are located in northern Yixing close to R1-5 [34]. Similar to Taihu Lake, TCIPP was the predominant compound in the rivers with a contribution of 39.1 - 82.2%, followed by TCEP (6.0 - 42.6%) (Fig. 2A). The high level of ∑OPEs at R1-5 was in 12

accordance with the relatively high level at T28-29, suggesting that these lake sites were affected by the flow-in rivers.

3.2.

Partitioning of OPEs between water and sediment in Taihu Lake

In the sediment of Taihu Lake, the level of ∑OPEs ranged from 2.82 to 47.5 ng/g dw, with a mean concentration of 17.6 ng/g dw (Fig. S1B and Table S7). The concentrations

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of ∑OPEs were lower than those of Laizhou Bay [18] and Pearl River Delta in China [46-49], but higher than those of Bohai and Yellow Seas in China [50], Great Lakes in

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the USA [13], Lake Bracciano and Lake Martignano in Italy [51] and Western Scheldt estuary in the Netherlands [27]. Similar to the lake water, ∑OPEs level in the sediment

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decreased from northwest to southeast, and significantly high levels were observed in

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Zhushan Bay, Meiliang Bay, Gonghu Bay and the west part of Taihu Lake (Fig. 1). The

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∑OPEs level in the whole lake (2.83 - 47.5 ng/g dw) was higher than that in Meiliang

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Bay, Gonghu Bay and Xukou Bay in 2011 (3.38 - 14.3 ng/g dw) [37], implying that the contamination of OPEs in Taihu Lake was becoming more heavy possibly due to

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increasing usage in recent years.

All targeted OPEs were detected in the sediment with 100% detection frequency,

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except TIPP (96%), TPHP (93%) and EHDPP (72%). Similar to the water samples, the

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predominant compound in the sediment was TCIPP (0.48 - 21.7 ng/g dw), followed by TCEP (0.33 - 7.62 ng/g dw), and their summed contribution to ∑OPEs was 16.9 - 75.2% (Fig. 2B). This was similar to the results in the sediment of Bohai and Yellow Seas [50]

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and the sediment core in Laizhou Bay [18]. In Taihu Lake sediment in 2011 [37], TBOEP and TDCIPP were the dominant compounds while TCIPP made less contribution. This difference in sediment was consistent with that in water and perhaps due to shift of production to TCIPP in recent years. The summed contribution of TCIPP and TCEP in the sediment (16.9 - 75.2%) was much lower than that in the water (54.9 13

- 91.9%), while the proportions of TEHP, TBOEP and TMPP in the sediment (3.5 44.1%, 4.8 - 40.1% and 0.5 - 16.3%, respectively) were significantly higher than in the water (0 - 0.02%, 0.8 - 17.6% and 0 - 0.2%, respectively, Fig. 2B). The different compositional profiles of OPEs in sediment and water could be influenced by their partitioning behaviors.

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To describe the partition of OPEs between water and sediment or water and SPM, distribution coefficients (Kd) were calculated, and the results are displayed in Table S6

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and Figure S2. For the compounds with detection frequencies lower than 40%, its Kd

was not calculated. There was a strong positive correlation between the log Kd water-SPM

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and log Kd water-sediment with a slope of 1.01 and R2 = 0.98. This suggested that it was

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plausible to study the water-sediment partition using the paired water sediment samples

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in filed. Strong positive correlations were observed between the Kd water-sediment values

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and foc contents of the sediment samples (Table S8), suggesting that organic carbon played an important role on the sorption of OPEs in sediment [50]. Due to the very low

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amount of SPM in the water samples, foc content was not measured in the SPM samples, and organic carbon normalized distribution coefficients (Koc) were not calculated for

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SPM. Thus, the Koc of OPEs were only calculated for sediment, and average results are listed in Table 1.

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Table 1 also lists the log Koc values of OPEs obtained with different methods,

including soil adsorption and theoretical calculation techniques [52-54]. Generally, the

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obtained log Koc values in this study, especially those of TDCIPP, TIBP, TNBP, TBOEP and TEHP, were comparable to those based on laboratory tests and computational estimations. While those of TCEP and TCIPP, TPHP and TMPP were slightly higher than other studies [52-54]. Similarly, higher log Koc values were obtained in the Lake Michigan for TCIPP and TNBP [13]. The field based partition is a dynamic process and 14

the differences in compositions and properties of sediment, water chemistry, temperature and other factors may affect the determined values [13, 56]. As shown in Fig. 3, the calculated log Koc displayed a strong correlation with their log Kow (p < 0.05, r2 = 0.845). This provided evidence that partition of OPEs between sediment and water was strongly influenced by hydrophobic interaction. The calculated

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log Koc was correlated to the chemical structures of OPEs. For alkyl-OPEs, the log Koc increased with an increase in carbon atom number, such as the log Koc of TEHP, which

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has eight carbon atoms, was significantly higher than those of TBP and TIPP, which have four and three carbon atoms, respectively. The log Koc of TMPP was higher than

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that of TPHP because of the three extra methyl groups on TMPP. The log Koc of

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branched compound, such as TIBP, was lower than that of straight-chain isomer, such

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as TNBP, since the straight-chain isomer has higher log Kow than the corresponding

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branched isomers. The log Koc of chlorinated OPEs increased with the increase in the number of chlorine atoms, such as the log Koc was in the order of TDCIPP > TCIPP >

3.3.

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TIPP, although the difference was minor.

Sources of OPEs in Taihu Lake

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To understand the impacts of OPE manufacturing and the OPE-related industries on

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OPE contamination in the Lake, PCA and HCA were performed. EHDPP was excluded from the data analysis since the total detection frequency was very low (21%) in the water samples. This resulted in a dataset of ten OPEs at 38 sampling sites. Before

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performing PCA and HCA, the data were normalized by transformation to a percent metric to reduce concentration/dilution effects. The data were then mean centered and scaled using a Z-transform to prevent high concentration variables from dominating the analysis [57]. The first principal component accounted for 29.9% of the variation and the second principal component accounted for 24.0%. The scores and loading plot are 15

presented in Fig. 4. The dendrogram is displayed in Fig. S3. Sampling sites in the score plot were divided into three groups as shown in Fig. 4A according to the HCA results. The scores and loading plot of PCA based on the logarithm transformed concentrations of OPEs in the water samples are displayed in Figure S4. Table S9 lists the correlation coefficients among the OPE concentrations. Except TIPP and TIBP, strong positive

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correlation was observed among TNBP, TCEP, TCIPP, TPHP, TMPP, TBOEP,

TDCIPP and TEHP, which could be used as plasticizers and flame retardants in plastic,

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rubber and paints. The detailed applications of these compounds are summarized in SI.

The first group mainly contained river samples R1-6 and Taihu samples T1-9 and

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T29 (Fig. 4A), most of which were situated in the north and northwest of the Lake. The percentage of TCIPP in the first group was significantly higher than in other groups.

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The ∑OPE concentrations at these sites were also higher than other sites and contained

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more TCIPP, TCEP, TDCIPP, TBOEP, TNBP and TPHP (Fig. S4). Since R1-5 went through the OPE manufacturing regions, such as Jiangsu Yoke Technology Co., Ltd.

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which produces large amount of TCIPP [35], it was suspected that the manufacturing of OPEs in Yixing was the most important point source of OPEs in Taihu Lake. In

PT

addition, sub-factories of Yoke are located in Zhuqiao Industrial Park, Zhoutie town and Chenqiao town in Yixing near the northwest of Taihu Lake [35]. The production of

CC E

OPEs in these factories present as important source of OPEs in the surrounding rivers and the northwest of Taihu Lake. Meiliang Bay is connected to Zhushan Bay via

A

Nanhuandi River, thus the OPE contamination in Meiliang Bay could also be affected by the manufacturing factories. Surrounding Meiliang Bay, there are many machinery, plastics, electronics and rubber industries (Fig. 1), which could also explain the high levels of TCIPP and ∑OPEs in Meiliang Bay. The second group included the sites in Gonghu Bay and the center and east areas of 16

Taihu Lake, in which the proportions of TIPP, TCEP and TIBP were higher than others and the percentage of TCIPP was lower than the first group. These might be caused by the complex influences of OPE-related industries, such as electronic, textile, machine and plastic industries around the east and southwest of Taihu Lake, as illustrated in Fig. 1. According to Fig. S4, TIBP made more contribution in Cluster 2. The concentration

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ratio of TIBP and TNBP in the Lake was significantly higher than that in the flow-in

rivers in Yixing (p < 0.01) (Fig. 5). No obvious difference was observed in chemical

SC R

degradation or biodegradation of TIBP and TNBP [58, 59], the different ratios of TIBP/TNBP in the lake and river water were mainly caused by their different sources.

U

This indicated that rivers in Yixing were not the main source of TIBP in the Lake. TIBP

N

is widely applied in concrete industries to adjust concrete pore size [2], and also as an

A

antifoaming agent in textile and printing industries [60] (Table S2). The abundant textile

M

industries located in Tiaoxi sub-basin and concrete industries nearing Wangyu River might be the main sources of TIBP in Taihu Lake (Fig 1 and S5). In Gonghu Bay, the

ED

proportions of TBOEP, TNBP and TPHP in T13 and T14 were relatively higher than other sites, which could be affected by the nearby Shuofang International Airport in

PT

Southern Jiangsu (Fig. 1). It was reported that TNBP and TPHP were used in aircraft hydraulic fluid and aircraft oil [61].

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The third group included R7-9 and T18-20 with more proportions of TBOEP, TMPP

and TPHP, and the TCIPP proportion was significantly lower than the first and second

A

groups. R7-9 are located in southern Yixing and are far from the OPE manufacturing sites [34]. Hence, the OPE compositions in R7-9 were different from R1-6. The ∑OPEs level in R7-9 was lower than the nearby lake site T28, suggesting that these rivers were not the major sources of OPEs in Taihu Lake. T18-20 are situated in the south of Taihu Lake, which are far from the heavy industries and close to the outlets of Taihu Lake. 17

Thus, the ∑OPEs level was the lowest in the whole Tiahu Lake.

Conclusions Results of this study demonstrated serious pollution of OPEs in the water and sediment of Taihu Lake, with TCIPP and TCEP as the main pollutants. Higher

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concentrations of ∑OPEs were observed in the northwest of the Lake and the flow-in rivers in Yixing. The calculated log Koc of the OPEs displayed a positive correlation

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with their log Kow, suggesting that partition of OPEs between sediment and water were dominated by hydrophobic interaction. It was verified that the OPE manufacturing in

U

Yixing presented as a potential point source of OPEs in the northwest of Taihu Lake. In

N

addition, many OPE-related industries and WWTPs also influenced the OPE

A

contamination in Taihu Lake significantly. The results provided scientific information

M

for long term risk assessment and pollution control of OPEs in Taihu Lake.

ED

Acknowledgments

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The authors gratefully acknowledge the financial support from the Natural Science Foundation of China (NSFC 21737003, 21577067, 46103095), Tianjin Municipal

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Science and Technology Commission (17JCYBJC23200, 15JCZDJC40700), Yangtze

A

River scholar program, and 111 program, Ministry of Education, China (T2017002).

Appendix A. Supplementary data Table S1-S9 and Figure S1-S5 are listed in Supplementary material.

18

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28

Table Legends Table 1. The obtained log Koc of OPEs and comparison with the experimental or calculated results in other studies.

Figure Legends

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Figure 1. Sampling sites, spatial distribution of total OPEs in water and sediment samples, and possible sources (within ~ 10 km off the lakeshore) in Taihu

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Lake, China.

Figure 2. Profiles of OPEs in the water (A) and sediment (B) of Taihu Lake.

U

Figure 3. Correlation between the log Koc values of OPEs and their log Kow.

N

Figure 4. Score plot (A) and loading plot (B) of PCA based on the percentage

M

flow-in rivers in Yixing.

A

transformed concentrations of OPEs in the water samples of Taihu Lake and

Figure 5. The concentration ratio of TIBP and TNBP in Taihu Lake and flow-in rivers

A

CC E

PT

ED

in Yixing.

29

Nanhe River System R1 R2 R3 R4 R5

Chenqiao Town Zhoutie Town

T29

R6

Zhuqiao Town

R8 T28

T6 T7

T5

T8

T2

T13

MeiliangBay

T14

Wangyu River

GonghuBay

T1

T9

T10

T12

T4

R1 R2 R4R3 R5 T29 R6 R7 R8 T28 R9

R7

R9

ZhushanBay

T11

T3 T27

T15 T25

T26

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DongguiLake

T16 T24

T23 T22

T21 T20

T19

Tiaoxi River System

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T17 T18

∑OPEs (ng/L) ∑OPEs (ng/g dw) in water (up) in sediment (down) 2-5 100 - 400 5 - 10 400 - 800 10 - 20 800 - 1200

Electronic

Plastic

Rubber

Machine

Wastewater treatment

Traffic Tool

Textile

Furniture

Flooring

Concrete

1200 -2000 > 2000

RPUF

20 - 40 > 40

Paint and Glue

FRs manufacturer( the Jiangsu Yoke Technology Co., Ltd.)

N

Print and Pack

U

5 Km

A

Figure 1. Sampling sites, spatial distribution of total OPEs in water and sediment

A

CC E

PT

ED

M

samples, and possible sources (within ~ 10 km off the lakeshore) in Taihu Lake, China.

30

A ED

PT

CC E

100

40

20 20

0 0

31

IP T

80

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T21 T22 T23 T24 T25 T26 T27 T28 T29 R1 R2 R3 R4 R5 R6 R7 R8 R9

60

Composition (%)

A

Figure 2. Profiles of OPEs in the water (A) and sediment (B) of Taihu Lake.

SC R

U

N

A

M

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T21 T22 T23 T24 T25 T26 T27 T28 T29 R1 R2 R3 R4 R5 R6 R7 R8 R9

Composition (%) 100

B

80

60

TIPP TIBP TNBP TCEP TCIPP TPHP EHDPP TMPP TBOEP TDCIPP TEHP

40

8 y = 0.5218 x + 1.8773 R2 = 0.8450

7

TEHP

IP T

TMPP

5

TCEP TIPP

TPHP

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TBOEP

4

TNBP TDCIPP

3

TCIPP TIBP

2 2

4

6

N

0

U

Log Koc

6

8

M

A

Log Kow

A

CC E

PT

ED

Figure 3. Correlation between the log Koc values of OPEs and their log Kow.

32

10

A

B

T25

0

T4

T27 T23 T13

T14 T9

T8

T24

T16

T12

T10 T26 T28

T17

T15

T20

R7 T22

T3

R9

T7 T2 R1 R3

-2

R5

R2 T1

T18 R8

R6

TIBP

0.4

0.2

0 TBOEP TNBP

T29

T6

TPHP

T5

R4

-0.2

Group 3 T19

Group 1

TEHP

TCIPP

-4

0

2 PC1(29.9%)

4

-0.4

6

-0.4

-0.2

0 PC1(29.9%)

SC R

-2

IP T

T11

2

River Zhushan Bay Meiliang Bay Gonghu Bay East South Center West

T21

PC2(24.0%)

Group 2

PC2(24.0%)

TCEP

TIPP

4

0.2

Figure 4. Score plot (A) and loading plot (B) of PCA based on the percentage

U

transformed concentrations of OPEs in the water samples of Taihu Lake and flow-in

A

CC E

PT

ED

M

A

N

rivers in Yixing.

33

TDCIPP TMPP

0.4

12

10

6

4

IP T

TIBP/TNBP

8

SC R

2

0

River

U

Lake

N

Figure 5. The concentration ratio of TIBP and TNBP in Taihu Lake and flow-in rivers

A

CC E

PT

ED

M

A

in Yixing.

34

Table 1. The obtained log Koc of OPEs and comparison with the experimental or calculated results in other studies. log Koc [13]

log Koc [2]

log Koc [53] log Koc [52]

log Kow

(the

(bulk

water

(soil

(calculated by

Compounds

( sorption on [2]

present

and sediment

adsorption

1.44

ACD/Labs

experiment)

connectivity with QSAR)

study)

partition)

Software)

3.23±0.23

-

2.48

-

2.18

3.09±0.34

-

2.83

-

2.83

2.12 TIPP

(molecular (estimated

Peat soil)

TCEP

log Koc [54] log Koc [54]

3.25±0.24

4.00-5.20

2.71

2.23

TDCIPP

3.80

3.32±0.29

-

2.35

2.83

TPHP

4.59

4.22±0.42

-

3.72

3.45

TIBP

3.60

2.93±0.27

-

3.05

-

TNBP

4.00

3.59±0.24

4.70-5.30

3.28

TBOEP

3.65

3.93±0.19

3.80-4.80

4.38

TMPP

5.11

5.37±0.39

-

TEHP

9.49

6.92±0.23

-

-

-

2.44

2.44

3.11

3-3.16

2.96

3.96

3.42

3.42

3.72

2.99

2.99

3.05

-

3.56

3.13

3.28

-

3.01

3.01

5.67

4.35

-

3.67

3.67

4.37

6.87

-

5.79

5.79

6.36

M

A

U

2.59

2.48

N

TCIPP

2.04

SC R

[55]

estimate)

IP T

log Koc

ED

log Kow: n-octanol/water partition coefficient; log Koc: soil adsorption coefficient

A

CC E

PT

obtained by different method.

35