Distribution, source apportionment and ecological risks of organophosphate esters in surface sediments from the Liao River, Northeast China

Chemosphere 250 (2020) 126297

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Distribution, source apportionment and ecological risks of organophosphate esters in surface sediments from the Liao River, Northeast China Qing Luo *, Leiyan Gu , Zhongping Wu , Yue Shan , Hui Wang , Li-na Sun Key Laboratory of Regional Environment and Eco-Remediation of Ministry of Education, College of Environment, Shenyang University, Shenyang, 110044, China

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


The 13 targets OPEs were detected in surface sediment samples from Liao River with 100% detection frequency.
The OPEs pollution was increasing from upstream to downstream of Liao River.
Liao River has been seriously contaminated by OPEs, especially TNBP and TBOEP.
TNBP was the most abundant OPEs, followed by TBOEP and TPPO.
EHDPP was the main substance causing risk.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 November 2019 Received in revised form 7 February 2020 Accepted 19 February 2020 Available online 21 February 2020

A total of 24 surface sediment samples were collected from Liao River, Northeast China. The concentration, spatial distribution, potential source, and ecological risk of 13 organophosphate esters (OPEs) flame retardants and plasticizers were analyzed. The total concentrations of OPEs varied considerably, ranging from 19.7 to 234 ng g1 dry weight (dw), with the mean concentrations of 64.2 ± 52.2 ng g1 dw. The OPEs pollution was increasing from upstream to downstream of Liao River. Compared with other sediments of rivers and lakes all over the world, Liao River has been seriously contaminated by OPEs, especially tributyl phosphate (TNBP) and tri-butoxyethyl phosphate (TBOEP). TNBP was the most abundant OPEs, followed by TBOEP and triphenylphosphine oxide. Their mean relative contributions were 26.3%, 12.4% and 11.6%, respectively. Positive matrix factorization indicated that OPEs in sediments from Liao River might be derived from plastic, textile, and polyurethane foam, anti-foam agent, hydraulic fluids, and coatings, indoor release, and chemical process emission. The risk of potential adverse effects of each individually OPEs on aquatic organisms were low (risk quotient less than 0.1). 2-Ethylhexyl diphenyl phosphate was the main substance causing risk. © 2020 Elsevier Ltd. All rights reserved.

Handling Editor: Magali Houde Keywords: Liao river Organophosphate esters Spatial distribution Positive matrix factorization Ecological risk assessment

1. Introduction

* Corresponding author. E-mail address: [email protected] (Q. Luo). https://doi.org/10.1016/j.chemosphere.2020.126297 0045-6535/© 2020 Elsevier Ltd. All rights reserved.

Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers in industrial and household products, such as plastics, textiles, electronic equipment, furniture and building

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materials (Van der Veen and De Boer, 2012). Chlorinated OPEs, for example, tris-(2-chloroethyl) phosphate (TCEP), tris-(1-chloro-2propyl) phosphate (TCIPP), and tris [2-chloro-1-(chloromethyl) ethyl] phosphate (TDCPP) are dominantly applied as flame retardants, while non-chlorinated OPEs such as triphenyl phosphate (TPHP), 2-ethylhexyl diphenyl phosphate (EHDPP), and tri (2ethylhexyl) phosphate (TEHP) are used as plasticizers in different applications (Yadav et al., 2018a). OPEs began to be used in the early 20th century, and then the use of OPEs increased rapidly after 1940s and lasted for decades (USEPA, 1976). Especially during 2000s, the production and consumption of OPEs as the major replacement of polybrominated diphenyl ethers (PBDEs) are increased significantly, due to the restriction and phase-out of PBDEs (Stapleton et al., 2012). Consumption of OPEs in the global market was 370, 000 t in 2013, accounting for 19% of consumption of the total annual flame retardants (Zhang, 2014). Among, consumption of OPEs in China was up to 100, 000 t in 2011, and it continued to increase at an average annual growth rate of 15% (Wang et al., 2015). Unlike some other additives, OPEs are generally added into rather than chemically bonded to products, which makes them easy to release to the environment through volatilizing, fray or leak from the products (Fan et al., 2014). Moreover, the flame retardant can account for more than 10% of the weight of the final product (Leisewitz et al., 2001), which will form a considerable pollution source. At present, OPEs have been detected in various environmental matrices, including air (Yadav et al., 2017), air particulate matter (Lai et al., 2015), dust (Langer et al., 2016; Yadav et al., 2019), water (Shi et al., 2016; Khan et al., 2016), sludge (Zeng et al., 2014), sediment (Cao et al., 2017; Wang et al., 2018), soil (Yadav et al., 2018a, 2018b; Luo et al., 2018a), and biological samples including human hair and blood, breast milk, and placenta (Kim et al., 2014; He et al., 2018). Unfortunately, most OPEs have been proved to induce toxic effects on the neurological system, the reproductive system, the endocrine system, and the immune system (Fernie et al., 2015; Noyes et al., 2015). Especially, many toxic effects occurred in the environmental concentrations (Zhu et al., 2015; Yu et al., 2017). Therefore, OPEs pollution has been becoming a global concern. Sediment is the “source” and “sink” of water pollutants, which plays a very important role in aquatic ecosystems and water environment quality (Zhu et al., 2005). However, limited data is available of OPEs in sediments. Especially in China, the researches on OPEs in sediments were mainly concentrated on Taihu Lake and Pearl River Delta (Cao et al., 2012; Liu et al., 2018; Wang et al., 2018; Tan et al., 2016; Hu et al., 2017). Liao River, located in Liaoning Province, Northeast China, is a seasonal plain river. It is one of the seven major rivers in China and once one of the most polluted rivers in China. Liao River, with a total length of 516 km, starts at the intersection of East and West Liao River in Fudedian, Tieling City, flows through Tieling, Shenyang, Anshan and Panjin, and finally enters Bohai Sea. Previous studies have found acidic pharmaceuticals, antibiotics, polycyclic aromatic hydrocarbons, and other pollutants in water and sediments of Liao River (Zhou et al., 2011; Lv et al., 2014). Moreover, OPEs were detected in water from the estuary of Liao River (Wang et al., 2015). They were also found in sediments from Hun River, which is close to Liao River (Zeng et al., 2018). Therefore, it is necessary to clarify the levels, distribution and source of OPEs in sediments from Liao River. In the present study, sediment samples from Liao River in Northeast China were collected and analyzed for 13 OPEs. The objectives of this study were to (1) study the contamination levels and spatial distribution of OPEs; (2) investigate the composition profile and potential sources of OPEs; (3) evaluate the potential ecological risk of the target OPEs.

2. Materials and methods 2.1. Chemicals and reagents The standards of eleven OPEs, i.e., triethyl phosphate (TEP), tripropyl phosphate (TPP), tributyl phosphate (TNBP), TCEP, TCIPP, TDCPP, tri-butoxyethyl phosphate (TBOEP), TPHP, EHDPP, TEHP, and tricresyl phosphate (TMPP, mixture of isomers) were purchased from Dr. Ehrenstorfer (Augsburg, Germany); tri-iso-butyl phosphate (TIBP) and triphenylphosphine oxide (TPPO) were obtained from Toronto Research Chemicals (Toronto, Canada). The two deuterium labelled OPEs, TNBP-d27 and TPHP-d15, were purchased from CDN Isotopes Inc. (Pointe-Claire, QC, Canada). The detail information of all target OPEs is listed in Table S1. Acetone and n-hexane (GC grade) were purchased from Fisher (Fair Lawn, NJ, USA). Silica gel (100e200 mesh) was obtained from CNW Technologies (Düsseldorf, Germany), and activated at 200  C for 24 h and then deactivated by adding 3% (w/w) water before use. Copper powder (200 mesh) was obtained from Sinopharm (Shanghai, China), and activated by diluted hydrochloric acid and then washed by pure water and acetone sequentially before use. Ultra-pure water (UPW, 18.2 MU) was produced with a Milli-Q Gradient system (Millipore, Bedford, USA). 2.2. Sample collection A total of 24 surface sediment samples were collected from 24 sampling sites (S1-24) in Liao River in June 2018, using a stainlesssteel grab into an aluminum container (Fig. 1). After transported to the laboratory on ice-bath, the sediment samples were freezedried, ground fine, sieved through 1 mm, and stored in aluminum foil bags at 20  C for further extraction. 2.3. Total organic carbon analysis The total organic carbon (TOC) analysis refers to the method published by Yadav et al. (2018a). Briefly, about 1e2 g of freezedried, sieved and homogeneously mixed sediment was treated with 3 mL of 3% HCI and allowed for 8 h to remove inorganic carbon. The sediment was then washed with Milli-Q water (three times) and dried overnight in an oven at 45  C. A portion of this was used to determine the TOC by a CHN elemental analyzer (Elementar, Germany). The TOC values were listed in Table S2. 2.4. Sample preparation and analysis The OPEs extraction and analysis was according to the method described by our previous studies with some modifications (Luo et al., 2018b and 2018c). Briefly, accurately weighted 10 g sediment sample was spiked with 20 ng TNBP-d27 and TPHP-d15 as internal standards and the mixture was then loaded into the 34 mL stainless steel extraction cell which preloaded 5 g silica gel and 2 g copper powder as the purification materials. After that, the samples were exacted by the accelerated solvent extraction (ASE, Dionex, Sunnyvale, CA, USA). The ASE operating conditions were the extraction solvent was n-hexane: acetone (1: 1, v: v), the extraction temperature was 100  C, the extraction pressure was 1500 psi, the static extraction time was 10 min, the flush volume was 60%, the nitrogen purge time was 60 s and the number of extraction cycles was 2. The extracts were evaporated and blown down to dryness, then the residue was redissolved with 1 mL of n-hexane. The samples were analyzed by Thermo Trace 1300 GC coupled with a Thermo fisher TSQ 8000 Evo triple quadrupole mass spectrometer (Thermo fisher, USA). The GC column was the TG-5SILMS

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Fig. 1. Locations of sampling sites and concentrations and spatial distributions of the total and different substituents of OPEs in the surface sediments of Liao River, Northeast China.

capillary column (30 m  0.25 mm  0.25 mm). The carrier gas was helium and the flow rate was 1 mL/min. The oven temperature program: 50  C for 1 min, 10  C min1 to 180  C and hold for 8 min, 20  C min1 to 240  C and hold for 8 min, 3  C min1 to 255  C, 30  C min1 to 300  C and kept at that temperature for 5 min. The sample was injected in the splitless mode with a pulse pressure of 20 psi for 1 min and the injected volume was 1 mL. The temperatures of injection port, interface and ion source were set at 250, 280 and 280  C, respectively. The MS detection was in selective reaction monitoring (SRM) mode at electron impact energy of 70 eV. The solvent delay time was set at 6 min. The instrumental parameters were shown in Table S3. 2.5. Quality assurance/quality control (QA/QC) In order to minimize possible contamination, plastic and rubber material were avoided to use in the processes of sampling, storage, transport, and extraction. To minimize the losses of OPEs by photodegradation, only amber-colored glassware was used. All used glassware was baked at 450  C for 4 h, and all used vessels were rinsed with acetone and ethyl acetate and covered with aluminum foil. The quantitation of OPEs was performed using the internal standard method. The method detection limit (MDL) was determined according to the US EPA Regulation 40 CFR Part 136

(Appendix B) method (USEPA, 1986). Eight replicates of hexane/ acetone (1:1, v/v)-washed sediment samples spiked with a mixture of target compounds (0.5 ng g1) were processed through the entire sample preparation and instrumental analysis procedures. The three times of the measured standard deviation was used to estimate the MDL, ten times of the measured standard deviation was used to calculate the method quantitation limit (MQL). The MDL and MQL of OPEs were in the range of 0.013e0.083 ng g1 dry weight (dw) and 0.043e0.28 ng g1 dw, respectively (Table S3). In order to check for the method background pollution and extraction efficiency, a series of procedural blank, blank spiked and matrix spiked samples were run along with the analysis process. The results are shown that the background pollution was existed. The main background was TEHP, TDCPP, and TIBP, but their concentrations were less than MQL. TCEP, TPPO, TPHP, TNBP, and TCIPP were also detected in procedural blanks, but their concentrations were less than MDL. All the background values were deducted from samples. The recoveries of spiked experiments were 82.5e104% for thirteen target OPEs. The matrix effects were over the range of 81.2e112%. The relative standard deviations (RSDs) were less than 12%. 2.6. Positive matrix factorization analysis Positive matrix factorization (PMF) was an effective tool for

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multivariate factor analysis, which has been successfully used to identify the possible source of many pollutants (Luo et al., 2019). PMF 5.0 was used in this study. The concentration matrix (C) and uncertainly matrix (U) of concentration were used as the input data on the model. In the concentration matrix, the lost concentration data was replaced by the average concentration of this species, the concentration less than MDL was replaced by half of MDL. In the uncertainty matrix, the concentration less than MDL, U ¼ 5/6 MDL; otherwise, U ¼ [(sj  Cij)2þ(MDL)2]1/2, where sj is the RSD of the concentration of species j. At first, the species were categorized as “Strong”, “Weak”, or “Bad” according to the signal-to-noise ratio. Then, those categories would be adjusted according to the simulation results. The purpose of this was to make the model run stably. The model was considered as run stably, when the robust Q value is close to the theoretical Q value and the scaled residuals were between 3 and þ3. If the simulation results shown that there was a weak correlation between the measured and the predicted values of species, this species should be down-weighted or excluded from the model. More detailed information about PMF, including principles and instructions, could be obtained from the user’s manual and previous studies (USEPA, 2015). 3. Results and discussion 3.1. Levels and distribution of OPEs in surface sediments The concentrations of 13 individual OPE, including six nonchlorinated alkyl OPEs (TEP, TPP, TIBP, TNBP, TEHP, and TBOEP), three chlorinated alkyl OPEs (TCEP, TCIPP, and TDCPP), three aryl OPEs (TPHP, EHDPP, and TMPP) and the synthetic intermediate TPPO in the surface sediment samples from Liao River are listed in Table 1, while the specific data are shown in Table S4 in the Supplementary data. All 13 targets OPEs were detected in surface sediment samples with 100% detection frequency. The concentraP tions of 13OPEs varied considerably, ranging from 19.7 to 1 234 ng g dw, with the median and mean concentrations of 52.3 and 64.2 ± 52.2 ng g1 dw, respectively. Among 13 OPEs, the contents of TNBP and TBOEP were relatively high, the mean concentrations were 16.0 ± 16.2 and 11.3 ± 17.4 ng g1 dw, respectively. The mean concentrations of other OPEs were below 5 ng g1 dw. The content of TPHP was the lowest, the mean concentration was 2.13 ± 1.93 ng g1 dw. In the four different types of OPEs, the contents of alkyl-OPEs were the highest, ranged from 9.75 to 133 ng g1 dw, with the median and mean concentrations of 32.6

Table 1 Summary of the concentrations of OPEs (ng g1 dw) in surface sediments of Liao River, Northeast China. OPEs

Min

Max

Median (n ¼ 24)

Mean (n ¼ 24)

SD (n ¼ 24)

TEP TPP TIBP TNBP TEHP TBOEP TCEP TCIPP TDCPP TPHP EHDPP TMPP TPPO P Alkyl-OPEs P Cl-OPEs P Aryl-OPEs P 13OPEs

0.56 0.78 0.30 2.88 1.36 1.11 1.15 0.44 0.48 0.55 1.19 0.53 1.10 9.75 2.45 2.95 19.7

11.4 12.6 12.7 49.1 20.2 69.0 30.1 13.3 27.7 6.35 16.4 11.1 12.3 133 71.1 24.9 234

1.80 1.40 3.33 6.95 2.44 2.73 1.55 2.33 0.93 1.76 2.71 1.31 3.49 32.6 5.07 5.33 52.3

2.89 2.75 3.79 16.0 4.99 11.3 3.63 2.98 4.00 2.13 3.35 2.26 4.07 41.8 10.6 7.74 64.2

2.91 3.17 3.21 16.2 5.27 17.4 6.03 2.94 6.82 1.93 3.63 2.85 3.09 35.8 14.9 6.18 52.2

and 41.8 ± 35.8 ng g1 dw. Then followed by Cl-OPEs, aryl-OPEs, and TPPO, the mean concentrations were 10.6 ± 14.9, 7.74 ± 6.18, and 4.07 ± 3.09 ng g1 dw, respectively. The reason for this pollution characteristic might be the alkyl-OPEs contained more compounds in this study. Another more important reason was that related to local OPEs emission. The same pollution characteristic was found in soil from Shenyang (Luo et al., 2018a), the largest city that Liao River flowed through. The spatial distribution of the total and different substituents of OPEs in the surface sediments from Liao River was shown in Fig. 1. As can be seen from the figure, the OPEs pollution was increasing from upstream to downstream. This indicated that the OPEs pollution in Liao River was mainly related to the industrial activities and domestic wastewater discharge. The upstream of Liao River were mainly engaged in agricultural production with a small population, while the mid- and downstream were mainly engaged in industrial activities with a large population. The concentrations of P 13OPEs in the upstream of Liao River (S1eS8) varied little, ranged from 19.7 to 28.3 ng g1 dw, with the mean concentration of 25.2 ± 3.22 ng g1 dw. However, the concentration variation of P 13OPEs in the midstream (S9eS17) was large, ranged from 30.0 to 234 ng g1 dw, with the mean concentration of 91.4 ± 72.6 ng g1 dw. The highest concentration occurred at the sampling site S14 (194 ng g1 dw) and S17 (234 ng g1 dw). S14 was located in the Qiandangpu Town, Xinmin City, Shenyang City, the main mining area of Liaohe Oilfield. In addition, the electric light source product was the main industry of this town, with more than 40 production enterprises. S17 was located at the junction of Xinmingtun Town, Liaozhong District, Shenyang City and Daniu Town, Tai’an County, Anshan City. This area was the Shenxi industrial corridor with medicine, chemical industry and smelting as the main industries. These industrial activities might be the reason for the high concentration of OPEs at these two sampling sites. The concentrations P of 13OPEs in the downstream (S18eS24) varied little, ranged from 59.8 to 90.1 ng g1 dw, with the mean concentration of 74.0 ± 11.5 ng g1 dw. The spatial distribution of OPEs in the surface sediments of Liao River was different from that of PCBs and PCDD/ Fs (Zhang et al., 2010), which might be due to their different pollution sources. This increasing distribution from upstream to downstream was be also occurred in the OPEs pollution in the
n, sediments of Hun River in China (Zeng et al., 2018), and Arga, Nalo s in Spain (Cristale et al., 2013). However, this distribution and Beso pattern may not be occurred in other rivers. For example, the P concentration of OPEs in the sediments of Nakdong River in Korea was decreasing from upstream to downstream (Choo et al., P 2018). The concentration distribution of OPEs in the sediments of Bagmati River in Nepal was irregular (Yadav et al., 2018a). The difference in the distribution pattern was related to the surrounding environment of the river, such as the industrial type.

3.2. Global comparison of OPEs in sediments To evaluate the pollution levels of OPEs in the surface sediments of Liao River, the comparison of the concentrations of OPEs in surface sediments with other studies worldwide was shown in Fig. 2 and in detail in Table S5 in the Supplementary data. The mean P concentrations of OPEs in surface sediments from Liao River were much higher than those reported in sediments from Nakdong River in Korea (Choo et al., 2018), Evrotas River in Greece (Giulivo et al., 2017), Great Lakes in USA (Cao et al., 2017), and Ocean (Ma et al., 2017; Zhong et al., 2018), but significantly lower than those reported in sediments from Lake Shihwa in Korea (Lee et al., 2018),  s River in Bagmati River in Nepal (Yadav et al., 2018a), and Beso Spain (Cristale et al., 2013). They are similar to the concentrations of

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Fig. 2. Summary of OPEs concentrations in surface sediments around the world.

OPEs in sediments from Adige River in Italy (Giulivo et al., 2017), Arga River in Spain (Cristale et al., 2013), Pearl River Delta in China (Tan et al., 2016; Hu et al., 2017), and Estuarine in Europe (Wolschke et al., 2018). Due to the different number of OPEs selected in different studies, some individual OPEs analyzed in most studies were further compared, such as TNBP, TBOEP, and TCEP. The concentrations of TNBP and TBOEP measured in this study were higher than most of previously reports, such as Luoma Lake, Hun River and Taihu Lake in China (Xing et al., 2018; Zeng et al., 2018; Cao et al., 2012; Liu et al., 2018; Wang et al., 2018). The concentration of TEHP was similar or higher those reported in sediments from Lake
n River in Spain (Cristale Shihwa in Korea (Lee et al., 2018), Nalo et al., 2013), Sava River in Serbia and Evrotas River in Greece (Giulivo et al., 2017), but significantly lower than Bagmati River in s River in Spain Nepal (Yadav et al., 2018a), Arga River and Beso (Cristale et al., 2013). The concentrations of TCEP, TCIPP and TDCPP were lower than most of previously reports, such as Yangtze River in China (Zha et al., 2018), Lake Shihwa in Korea (Lee et al., 2018),
n River, Arga River and Beso s River in Spain (Cristale et al., and Nalo 2013). These results indicate that Liao River is seriously contaminated by OPEs, especially TNBP and TBOEP. Therefore, some effective measures should be taken to reduce the emissions of OPEs and control the existing OPEs pollution. 3.3. Composition profiles and correlations of OPEs in sediments Composition profiles of OPEs in the surface sediments from all sampling site were illustrated in Fig. 3. In general, alkyl-OPEs were the predominant compounds in sediments of Liao River. The relative contributions were ranged from 34.6% to 86.3%, with the mean relative contribution of 60.9%. Cl-OPEs, aryl-OPEs, and TPPO accounted for a minor contribution to the total concentrations, the mean relative contributions were 14.4%, 13.2%, and 11.6%, respectively. For the individual OPE, TNBP was the most abundant chemical measured in sediments of Liao River, the relative contributions were ranged from 6.41% to 68.9%, with the mean relative contribution of 26.3%. Then followed by TBOEP and TPPO, the mean relative contributions were 12.4% and 11.6%, respectively. The mean relative contributions of other OPEs were all less than 8.0%. The

share of TPP was the lowest, and the mean relative contribution was 3.91%. This result was not similar as the composition profiles of OPEs in other sediments, such as Lake Superior in United States (Cao et al., 2017), Taihu Lake in China (Chen et al., 2018), Nakdong River in Korea (Choo et al., 2018), and Evrotas River in Greece (Giulivo et al., 2017). In sediments of Lake Superior in United States, TIBP was the most abundant compound (Cao et al., 2017). TEHP was the predominant chemical in sediments of Taihu Lake in China, accounting for 90.4% of the total OPEs (Chen et al., 2018). TBOEP and TCIPP were the most abundant compounds in sediments of Nakdong River in Korea (Choo et al., 2018). In sediments of Evrotas River in Greece, EHDPP and TCIPP were two of the most abundant OPEs (Giulivo et al., 2017). The most abundant OPEs in different river sediments were different. The reason for this variation might be that these different researches selected different target OPEs. Another reason might be that different regions have different industrial structure, and then lead to different sources of OPEs. TIBP and TNBP were the important components of hydraulic fluids, lubricants, transmission fluids and motor oils (Regnery et al., 2011), could be released from machinery and equipment. In addition, TBP was also extensively employed as an antifoaming agent in concrete, as a wetting agent in casein glue and as a pasting agent in pigment paste (Marklund et al., 2005). TBOEP was widely used as additives in plastics, rubber, and floor wax, as well as in the production of cables and electrical appliance (Marklund et al., 2003). TPPO was widely used as a synthetic intermediate in organic synthesis and pharmaceutical products, and also as the ligand for many transitional metals (Hu et al., 2009). The release of these related products was the reason of high concentration of TIBP, TBOEP, and TPPO in sediments of Liao River. In addition, the distribution differences of composition profiles of OPEs were found in sediments collected from different sampling sites. These distribution differences also appeared in Lake Michigan and Lake Ontario in United Stated (Cao et al., 2017). In the upstream of Liao River (S1eS8), alkyl-OPEs and TPPO were the most abundant OPEs. The concentration of TPPO was decreasing along the river flow direction. On the contrary, the concentration of alkylOPEs was increasing. TNBP was the most abundant alkyl-OPEs. In the mid- and downstream of Liao River (S9eS24), alkyl-OPEs were

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Fig. 3. Composition profiles of OPEs in the surface sediments of Liao River.

the predominant OPEs, with the mean relative contribution of 67.4%. TNBP and TBOEP were two of the most abundant alkyl-OPEs. The contribution of TPPO was decreased significantly, and the mean relative contribution was only 3.59%. Near the end of Liao River (S22, S23), the contributions of TDCPP and TMPP were increased obviously, the mean relative contributions were 20.8% and 13.8%, respectively. Correlations between individual OPE in the environmental medium could be applied to explore the possible common sources and the environment behavior of these compounds. The Spearman correlation coefficients and significance level were listed in Table S6 in the Supplementary data. It can be seen from the table that nine OPEs, including TEP, TPP, TIBP, TEHP, TBOEP, TCEP, TCIPP, TPHP, and EHDPP, had a significant correlation between each other. However, TNBP, TDCPP, TMPP, and TPPO were not related to these nine OPEs. And there was no correlation among these four OPEs. It should be noted that there were significant correlation between some OPEs, but the correlation coefficients were low. A more strict statistics criterion, the correlation coefficient greater than 0.68 means strong or high correlation (Taylor, 1990), was usually used to identify the strong correlation. According to this criterion, only TEP, TIBP, TBOEP, and TCIPP had a strong correlation with each other. TPP, TEHP, TCEP, TPHP, and EHDPP had a strong correlation with some OPEs. The strong correlation means that these compounds might have the common sources or similar environment behavior (Chen et al., 2015; Langer et al., 2016). Therefore, TEP, TIBP, TBOEP, and TCIPP might have the common sources in this study area. TNBP, TDCPP, TMPP, and TPPO might be released from different pollution sources or had different environment behavior with other OPEs. 3.4. Possible sources The concentration and uncertainty matrices (24  13, 24 sediment samples and 13 individual OPE) were loaded as the input files into the PMF 5.0 model in this study. Firstly, the model was running with the default parameters. Then, some parameters were adjusted to ensure the model can run stability according to the simulation results. In this study, when the model runs stably, the absolute

residues of 13 OPE in most sample were acceptable, only 1.92% of the absolute residues were beyond the acceptable range. And the predicted values had good correlations between the observed values for most OPEs. The correlation coefficients of other OPEs were higher than 0.76 except TNBP and TMPP. The correlation coefficients of TNBP and TMPP were 0.61 and 0.64, respectively. The results of model simulation, including source profiles and contributions, were shown in Fig. 4. Factor 1 was heavily dominated by TPHP and TEP, and moderately weighted by TCIPP and TIBP. TPHP was used as flame retardant and plasticizer in hydraulic fluids, polyvinyl chloride (PVC), electronic equipment, casting resins, glues, engineering thermoplastics, phenylene-oxide-based resins, and phenolics resins (Van der Veen and De Boer, 2012). And it was detected as the most prevalent component in liquid-crystal display television (LCD-TV), laptop computers, curtains, electrical outlets, insulation boards wallpaper, and building materials (Kajiwara et al., 2011). In addition, TPHP could be continuously emitted into indoor air during the normal operations of a computer (Carlsson et al., 1997). TEP was widely added to PVC, polyester resins, and polyurethane foam (Van der Veen and De Boer, 2012). TCIPP was widely added to polyurethane foam, and TIBP was predominantly used as plasticizers, lubricants and to regulated pore sizes (Van der Veen and De Boer, 2012). And TCIPP and TIBP were the most detected OPEs in building and decoration materials (Wang et al., 2017). Hence, Factor 1 was identified as source of release from indoor. Factor 2 was dominated by TPPO. TPPO was a chemical intermediate that used for various chemical reactions and products, such as formulating certain flame retardants (Hu et al., 2009). TPPO was also used as a crystallizing agent in chemical reactions (Sternbeck et al., 2012). In addition, it was the by-product formed in certain industrial organic syntheses (Dsikowitzky et al., 2016). TPPO was frequently found in effluents from petrochemical and pharmaceutical industries (Emery et al., 2005; Botalova et al., 2009). Therefore, Factor 2 represented the contribution of chemical process. Factor 3 was predominantly loaded on TNBP, and moderately weighted by TBOEP, TIBP, and TEHP. TBP, including TNBP and TIBP, was widely used in anti-foam agent, hydraulic fluids, coatings,

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Fig. 4. Source profiles and source contributions of OPEs obtained from PMF analysis.

extractant for metal complexes, and plastic (Van der Veen and De Boer, 2012). TNBP was the important constituent of aircraft hydraulic fluids, accounting for up to 79% (ATSDR, 1997). In addition, TNBP was also widely used as extreme pressure additive and antiwear agent in lubricants and transmission oil (ATSDR, 1997; Marklund et al., 2003). TBOEP was extensively used in anti-foam agent, floor polish, coatings, plastic, and rubber, while TEHP was used in PVC, cellulose, paints and coatings, rubber, and polyurethane foam (Van der Veen and De Boer, 2012). To sum up, antifoam agent, hydraulic fluids, and coatings were the common and main products added TNBP, TBOEP, TIBP, and TEHP. Thus, Factor 3 was labelled as the combined contribution from anti-foam agent, hydraulic fluids, and coatings. Factor 4 was heavily dominated by TDCPP, TCEP, and TMPP, and moderately weighted by EHDPP, TEHP, and TPP. TDCPP and TCEP were difficult to degrade, and widely used in plastic, textile, and polyurethane foam (Van der Veen and De Boer, 2012). In addition, TCEP was also used in PVC, cellulose, coatings, and polyester resins (Andresen et al., 2004). TMPP was extensively used in hydraulic fluids, PVC, cellulose, cutting oils, plastic, polystyrene, thermoplastics, and transmission fluids (Van der Veen and De Boer, 2012). EHDPP was the major component of food packaging and paints (Brommer, 2014), and it was also added as plasticizer in hydraulic fluids (Wei et al., 2015). TPP has been applied together with halogenated and non-halogenated flame retardants in polyurethane foam (Van der Veen and De Boer, 2012).

In summary, plastic, textile, and polyurethane foam were the common and main products added TDCPP, TCEP, TMPP, EHDPP, TEHP, and TPP. Therefore, Factor 4 was regarded as combined contribution from plastic, textile, and polyurethane foam. The relative contributions of the four factors were 25.9% for Factor 1 (indoor release), 10.5% for Factor 2 (chemical process emission), 28.7% for Factor 3 (anti-foam agent, hydraulic fluids, and coatings), and 34.9% for Factor 4 (plastic, textile, and polyurethane foam). 3.5. Ecological risk assessment The risk quotient (RQ) value was commonly applied to evaluate the risk of OPEs in water or sediments on aquatic organisms (Xing et al., 2018; Zeng et al., 2018; Liu et al., 2018). The RQ value was defined as the ratio of the measured environmental concentration and the predicted no-effect concentration.

RQi ¼

MECi MECi ¼ PNECi LðEÞC50 =f

where RQi is the RQ of each compound; MEC is the measured environmental concentration; PNEC is the predicted no-effect concentration which equal to the toxicological relevant concentration (L(E)C50) for algae, crustacean, and fish, dividing by an security factor (f) of 1000 (Yan et al., 2017). In this study, the RQ value

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was calculated only for those compounds which the L(E)C50 is available (Table S7). For the ecological risk assessment of pollutants in sediment, pore water in sediment was considered as the main exposure route for aquatic organisms (Yadav et al., 2018a). Thus, the concentrations of OPEs in pore water were used to calculate the RQ of OPEs in sediments in this study. The concentration of pollutant in pore water was estimated using the equilibrium partitioning approach recommended by Di Toro et al. (1991).

MECpore water ¼

1000  Cs;i KOC  %total organic carbon

where KOC values are the organic carbon partitioning coefficients (Table S1); Cs,i is the concentration of each compound in sediments, ng g1 dw. The total organic carbon (TOC) was listed in Table S2. The possibility of ecological risk was assessed based on the magnitude of RQ. The common ecological risk ranking criteria were low risk (0.01 < RQ < 0.1), moderate risk (0.1 < RQ < 1), and high risk (RQ > 1) (Blair et al., 2013). The mean RQ values of OPEs in the surface sediments from Liao River for the different aquatic organisms, including alga, crustacean, and fish were shown in Fig. 5A. More detailed data were shown in Table S7 and Fig. S1. As shown in Fig. 5A, the mean RQ values of 12 OPEs were below 0.1, indicated that OPEs in sediments from Liao River have low risk of potential adverse effects on aquatic organisms. However, the risk of EHDPP for crustacean should be alerted. Its mean RQ value was up to 0.075. And the RQ values of five sampling sites were higher than 0.1 (Fig. S1B), the highest RQ value was 0.70. In addition, TNBP and TDCPP also should be concerned, because of their mean RQ values were relatively high and

the RQ values of some sampling sites were more than 0.1. In sediments from Taihu Lake, EHDPP also showed higher risks on daphnia (Liu et al., 2018). EHDPP also was the most significant contributor of the ecological risk of OPEs in surface water of Luoma Lake (Xing et al., 2018). This might be due to the high acute toxicity of EHDPP to aquatic organisms. The distribution of the total RQ values of OPEs in sediments from Liao River was shown in Fig. 5B. It can be seen from the figure that the risk of potential adverse effects were low in the up- and downstream of Liao River, their total RQ values were below 0.1. However, there are five sampling sites in midstream with moderate risk of potential adverse effects on aquatic organisms. Among them, two sampling sites (S11 and S17) were for all three aquatic organisms, and three sampling sites (S14, S16, and S18) were only for crustacean. It should be noted that the risk of potential adverse effects in sampling sites S17 for crustacean was high, and the total RQ value was 1.30. In addition, the risks of adverse effects in all sediment samples for crustacean were higher than algae and fish. This was mainly caused by the high RQ value of EHDPP to crustacean. Due to the lack of toxicity data of EHDPP to algae and fish, its risk of potential adverse effects to algae and fish were not considered in this study. Therefore, the ecological risk caused by EHDPP should be required more attention. 4. Conclusion In this study, 13 target OPEs were detected in all surface sediment samples from Liao River. The concentration of alkyl-OPEs, mainly TNBP and TBOEP, were the highest. The OPEs pollution was increasing from upstream to downstream of Liao River. By comparison, Liao River has been seriously contaminated by OPEs, especially TNBP and TBOEP. TNBP was the most abundant OPEs, followed by TBOEP and TPPO. The possible sources of OPEs in sediments from Liao River were plastic, textile, and polyurethane foam, anti-foam agent, hydraulic fluids, and coatings, indoor release, and chemical process emission. The risk of potential adverse effects of each individually OPEs on aquatic organisms were low. EHDPP was the main substance causing risk. Declaration of competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement Qing Luo: Conceptualization, Writing - original draft, Funding acquisition. Leiyan Gu: Investigation, Formal analysis. Zhongping Wu: Investigation, Formal analysis. Yue Shan: Investigation, Formal analysis. Hui Wang: Validation, Formal analysis. Li-na Sun: Project administration. Acknowledgements We are grateful to the National Natural Science Foundation of China (NO. 41807384), the China Postdoctoral Science Foundation (NO. 2018M630304) and the Natural Science Foundation of Liaoning Province (NO. 20170520384). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Appendix A. Supplementary data

Fig. 5. The mean RQ values (A) and total RQ values (B) of OPEs in the surface sediments from Liao River for the different aquatic organisms, including alga, crustacean, and fish.

Supplementary data to this article can be found online at

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