Ecotoxicology and Environmental Safety 164 (2018) 540–547
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Occurrence, distribution and ecological risk of ultraviolet absorbents in water and sediment from Lake Chaohu and its inﬂowing rivers, China
Zhenwu Tanga,b, Xue Hanb, Guanghui Lic, Shulei Tiand, Yufei Yangd, , Fuyong Zhongb, Yu Hana, ⁎ Jun Yange, a
College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China c China Merchants Ecological Environmental Protection Technology Co. Ltd., Chongqing 400067, China d State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China e Center for Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Organic ultraviolet absorbent (UVA) Distribution Source Risk Lake Chaohu
The available information is insuﬃcient to enable a reliable understanding of the global distribution and eﬀect of organic ultraviolet absorbents (UVAs) on ecosystems. Little is known about the pollution of China's lakes by these chemicals. We conducted a survey of UVAs in water and sediment from Lake Chaohu and its inﬂowing rivers. The UVAs were widely present in this area and the concentrations of total 12 UVAs (Σ12 UVAs) ranged between 162 and 587 ng/L in water and 9.70–178 ng/g in sediment. Benzophenone and benzophenone-3 were dominant in water, and benzophenone and octocrylene dominated in sediment. Higher concentrations of benzophenone were detected in the investigated water samples, although the contamination levels of UVAs in this study were comparable to those investigated in other areas. In addition to the inputs from the UVAs used as ﬁlters in cosmetics, the discharge from industries using UVAs as stabilizers also contributed much to the pollution in the study waters. Generally, the risk to aquatic organisms from exposure to UVAs in this area was low, but further research is needed to elucidate the fate of UVAs and to understand bioaccumulation and associated risks.
1. Introduction Organic ultraviolet absorbents (UVAs), including organic UV ﬁlters and UV stabilizers, are beneﬁcial in protecting against UVA (315–400 nm) and UVB (280–315 nm) radiation (Balmer et al., 2005). Organic UV ﬁlters are widely used in sunscreens and other cosmetics, and UV stabilizers are mainly used in textiles, plastics, paints, adhesives and other polymer materials to prevent the aging and degradation of polymers under sunlight irradiation (Ao et al., 2017; Kameda et al., 2011; Ramos et al., 2015). Some UV ﬁlters have also been used as UV stabilizers in industrial products such as textile and polymeric materials (Kameda et al., 2011). In recent years, the production and use of organic UVAs have increased dramatically and at an unprecedented rate. For example, the consumption of benzotriazole-type UV stabilizers in the United States was 9000 t/year in 2004 (Liu et al., 2011). In United States, the amount (in pounds) of bumetrizole (UV-326), 2,4-ditert-Butyl-6-(5-
chlorobenzotriazol-2-yl) phenol (UV-327) and octrizole (UV-329) produced and imported in 2006 reached 500 K–1 M, less than 500 K and 1–10 M, respectively (USDHHS, 2011). From 2010 to 2013, the use of drometrizole (UV-P) and UV-326 in Japan reached 1000 t/year (NITE, 2017). Some organic UVAs are lipophilic, indicated by their logKow, implying their potential for accumulation in biota, and some have been reported to show estrogenic activity, genetic toxicity and reproductive toxicity (Fent et al., 2014; Paredes et al., 2014; Ramos et al., 2015). For these reasons, contamination by organic sun-blocking agents, and their associated risks, are attracting increasing attention. Organic UVAs can enter the aquatic environment either directly by being washed from skin and clothes during swimming and other recreational activities, or indirectly via wastewater or swimming pool water. The extensive production and use of these chemicals have resulted in various UVAs being observed in water, sediment and organisms worldwide, and even in the human body (Apel et al., 2018; Blüthgen et al., 2014; Gago-Ferrero et al., 2015; Tsui et al., 2015; Zhang
Corresponding authors. E-mail addresses: [email protected]
(Z. Tang), [email protected]
(X. Han), [email protected]
(G. Li), [email protected]
(S. Tian), [email protected]
(Y. Yang), [email protected]
(F. Zhong), [email protected]
(Y. Han), [email protected]
(J. Yang). https://doi.org/10.1016/j.ecoenv.2018.08.045 Received 27 May 2018; Received in revised form 13 August 2018; Accepted 14 August 2018 0147-6513/ © 2018 Elsevier Inc. All rights reserved.
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et al., 2011, 2013). The aquatic environment is not only an important sink for UVAs but also a major route of migration of these chemicals. However, available information on the occurrence of organic UVAs in the aquatic environment mainly focuses on wastewater treatment plants, the larger rivers and coastal environments (Ao et al., 2017; Jiang et al., 2014; Peng et al., 2017a, 2017b; Pintado-Herrera et al., 2017; Ramos et al., 2016; Wick et al., 2016). Kameda et al. (2011) investigated the occurrence of 18 UVAs in water and sediment collected from 22 rivers, four sewage treatment plants and three lakes in Japan. The results indicated that benzyl salicylate, benzophenone-3 (BP-3), 2ethylhexyl 4-methoxycinnamate (EHMC) and octyl salicylate (EHS) were dominant in municipal wastewater treatment plants, and 2-(3,5di-tert-amyl-2-hydroxyphenyl) benzotriazole (UV-328), BP and EHMC were dominant in other surface waters except background sites. In sediment, UV-328 and 2-(benzotriazol-2-yl)-4,6-bis-(2-phenylpropan-2yl) phenol (UV-234) were the most prevalent compounds. Cuderman and Heath (2007) found that BP-3 was the most abundant chemical among UVAs in water samples from lakes, rivers and other recreational waters. However, the pollution of water by organic UVAs is far from being well studied and available data are still limited. There may be a signiﬁcant discrepancy in pollution by UV ﬁlters and stabilizers in different regions, and in their production and use; and the removal eﬃciency by wastewater treatment varies greatly (Gago-Ferrero et al., 2012; Ramos et al., 2015). In China, more than 20 organic UV ﬁlters have been added to cosmetics, and the consumption of UV stabilizers reached 7000 t in 2010 (Gao et al., 2011). This may lead to extensive pollution of water by UVAs. To date, such pollution has been investigated in only a few areas such as the Pearl River Estuary, the Huangpu River and the Songhua River (Peng et al., 2017a; Wu et al., 2017; Zhao et al., 2017). Little is known about pollution by these chemicals in China's lakes. The transport of diﬀerent UVAs in riverine runoﬀ and the inputs from diﬀerent tributaries to the lakes remain unclear. Lake Chaohu is the ﬁfth largest freshwater lake in China, with an area of 760 km2. It is located in one of the most developed areas in China, the Yangtze River Delta Economic Zone. Rapid urbanization of the surrounding area has led to the lake suﬀering from serious pollution (Qin et al., 2014; Tang et al., 2015; Wang et al., 2013; Yang et al., 2012). The levels of polybrominated diphenyl ethers and tetrabromobisphenol A in this lake have been in the high ranges of global concentrations in water environments (Wang et al., 2013; Yang et al., 2012). Antibiotic pollution in Lake Chaohu has placed aquatic organisms at great ecological risk (Tang et al., 2015). Source assessments in these studies indicated that the input of domestic sewage and industrial wastewater from Hefei (which has a population of 7.87 million and houses developed chemical, textile and plastics industries) is the key source of the lake's pollution (Qin et al., 2014; Wang et al., 2013; Yang et al., 2012). Organic UVAs in the environment might be attributed to emissions from these industries as well as to domestic sewage. However, the current status and risks of UVA contamination in Lake Chaohu and its inﬂowing rivers remain unknown. This study focused on the occurrence and variations in concentration of selected organic UVAs in Lake Chaohu and its inﬂowing rivers in order to track their distribution, transport and potential sources. The potential ecological risk they pose in the lake and its tributaries was then assessed based on the existing toxicity data. The results will be helpful in identifying the potential sources of organic UVA contamination in this region, as well as in developing pollution prevention measures to control future releases of organic UVAs into Lake Chaohu.
Fig. 1. Sketch map of the study area with locations of the sampling sites for water and sediment.
samples was collected from Chaohu Lake. The detailed locality of the sampling sites is shown in Fig. 1. To evaluate the primary source of the UVAs in Lake Chaohu, 24 water samples and 27 sediment samples were obtained from the ﬁve major inﬂowing rivers: the Nanfei, Paihe, Tangxi, Banqiao and Dianbu. The principal pollutants entering the lake come from these rivers, which discharge domestic and industrial wastewater and non-point sources from Hefei City into the western portion of the lake (Wang et al., 2013). The density of sampling sites was increased along the Nanfei River, the most polluted river ﬂowing into the lake (Wang et al., 2011). Sampling sites located at river estuaries, the conﬂuences of tributaries and the outlets of wastewater treatment plants were important locations. At each sampling site, we used cylindrical samplers to take 20 L water from 0.5 m below the air–water interface at 3–5 horizontal points along a river cross section. Subsequently, water samples were ﬁltered through 0.45 µm polytetraﬂuoroethylene ﬁlters (Ø100 mm, Beijing Beifang Weiye Ltd., China) under vacuum. Water temperature was measured at all sites. The sediment was collected in a Van Veen grab (Eijkelamp, Netherlands). Approximately the top 5 cm of sediment was taken oﬀ with a stainless-steel spoon and placed in a pre-cleaned aluminum box. Each sediment sample consisted of four sub-samples taken randomly from the surroundings of each site. All sediment samples were freeze-dried before being ground, homogenized and stored at − 20 °C prior to analysis. 2.2. Chemicals and reagents HPLC dichloromethane, n-hexane and methanol were obtained from J.T. Baker (Phillipsburg, PA, USA). Analytical grade anhydrous sodium sulfate (Na2SO4) and silica gel (100–200 mesh) were purchased from Sinopharm Chemical Reagent Co. Ltd. (Beijing, China). Na2SO4 was baked at 450 °C for at least 4 h and silica gel (100–200 mesh) was baked at 160 °C for 12 h before using. UV-327 (> 98%) and UV-329 (> 98%) standards were purchased from Tokyo Chemical Industry (Tokyo, Japan). 2-(2′-hydroxy-3′,5′-di-tert-butylphenyl) benzotriazole (UV-320, 99.1%) was purchased from Dr. Ehrenstorfer (Augsburg, Germany). UV326 (99.2%) was purchased from Chiron (Trondheim, Norway). Octocrylene (OC, ≥ 98%) was purchased from Sigma-Aldrich (St. Louis, MO, USA). Benzophenone (BP, 99.5%) was obtained from Chem Service (West Chester, PA, USA). 2-Ethylhexyl salicylate (EHS, 99.4%), homosalate (HMS, 100%), benzophenone-3 (BP-3, 100%), 4-methylbenzylidene camphor (4-MBC, 100%), octyl dimethyl p-aminobenzoic acid (OD-PABA, 97%), ethylhexyl methoxycinnamate (EHMC, 97.5%)
2. Materials and methods 2.1. Sampling In September 2016, a total of 17 water samples and 33 sediment 541
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9.98 ± 2.37 1.01 ± 0.60 2.41 ± 2.13 0.44 ± 0.29 0.53 ± 0.55 < 0.08 3.54 ± 6.12 0.18 ± 0.23 < 0.15 0.47 ± 0.56 1.86 ± 2.33 0.64 ± 0.83 21.2 ± 10.8
and an internal standard phenanthrene-d10 (> 98.6%) were purchased from Accustandard (New Haven, CT, USA). 2.3. Sample preparation
7.51 4.86 8.02 3.02 6.12 0.52 1.53 0.52 1.86 3.76 15.5 4.12 57.7 7.96 ± 3.05 3.74 ± 4.20 7.49 ± 8.25 2.97 ± 4.11 3.65 ± 4.15 1.75 ± 3.31 2.56 ± 1.59 1.38 ± 1.88 2.48 ± 3.13 10.7 ± 15.1 16.4 ± 20.2 4.00 ± 3.25 651 ± 53.5
8.42 ± 0.57 1.03 ± 1.02 2.01 ± 2.19 0.43 ± 0.64 0.46 ± 0.22 < 0.08 0.80 ± 0.45 1.07 ± 1.72 1.20 ± 2.15 4.54 ± 8.56 6.64 ± 8.89 1.39 ± 1.53 28.0 ± 26.2
8.98 1.06 1.62 0.77 1.42 0.28 0.40 0.10 0.70 6.99 4.87 6.35 33.5
± ± ± ± ± ± ± ± ± ± ± ± ±
0.64 0.64 1.01 0.51 1.13 0.29 0.23 0.12 0.72 4.95 3.21 4.90 16.0
The methods for analyzing water samples have been described in previous studies (Tang et al., 2013). Brieﬂy, 1.0 L of the water sample was extracted with a solid phase extraction (SPE) system by using Oasis HLB cartridges (500 mg, 6 mL, Waters). After vacuum drying, the cartridges were eluted with 6.0 mL dichloromethane/methanol (V/V, 9:1). The volume of the extracts was reduced by a gentle stream of nitrogen and adjusted to a volume of 0.2 mL with hexane. Internal standard was added for the GC analysis. A 10.00 g freeze-dried sediment sample was extracted with 20 mL methanol in an ultrasonic bath for 20 min. The extract was centrifuged at 4000 r/min for 5 min and the supernatant was collected. The extraction was repeated twice, and the supernatants were combined. The extracts were evaporated to 0.5 mL and passed through a silica gel chromatographic column, and then were eluted using 30 mL dichloromethane/methanol (V/V, 1:1). The eluates were concentrated under a nitrogen stream and diluted to a ﬁnal volume of 0.5 mL with hexane. An internal standard (phenanthrene-d10) was added to the extract prior to analysis by gas chromatography–mass spectrometry (GC–MS).
± ± ± ± ± ± ± ± ± ± ± ± ±
2.27 5.79 8.48 2.61 6.67 0.63 0.49 0.82 1.62 2.57 10.3 3.72 33.0
Banqiao River (n = 4) Nanfei River (n = 13)
2.4. Sample analysis
0.07 0.14 0.28 0.13 0.09 0.08 0.10 0.08 0.15 0.12 0.14 0.08
6.4 0.39 1.84 0.59 0.32 0.85 1.06 < 0.08 0.67 0.34 0.4 0.28 13.2
Analysis of the target compounds was performed by GC–MS (Agilent 7890A–5975, USA) equipped with silica capillary column (DB-5MS, 30 m × 0.25 mm × 0.25 µm, Agilent). The GC oven temperature program was 80 °C for 1 min, increasing at 10 °C/min to 190 °C, and then at 20 °C/min to 300 °C; it was held at this temperature for 15 min. The injector temperature was 280 °C, and the helium mobile phase ﬂow rate was 1.0 mL/min. For analysis, 1-μL aliquots of samples were injected. The injector was used in the splitless mode. The quadrupole and ion source temperatures were held at 150 °C and 230 °C, respectively. The ionization of GC–MS was conducted under 70 eV. Injections were made in both SCAN and SIM modes. The m/z values used as quantiﬁcation and conﬁrmation are provided in Tang et al. (2018). The total organic carbon (TOC) in the sediment was measured in a LiquiTOC analyzer (Elementar, Germany) at a combustion temperature of 950 °C.
255 22.7 21.7 33.2 10.0 2.71 8.96 2.33 < 0.45 4.73 30.9 1.26 394
2.5. Quality assurance and quality control The concentrations of UVAs were quantitatively determined by the internal standard method using peak areas of the composite standards. Linearity was tested in the concentration range 10–400 μg/L, by injecting replicate standards at seven diﬀerent concentration levels. All target compounds fulﬁlled the linearity with R2 higher than 0.99. Recoveries were in the range of 85.6–112% for water and 71.9–131% for sediment, depending on individual UVAs. Estimations of limits of detection (LODs) were based on a signal to noise ratio of 3. The LODs of UVAs were 0.45–2.43 ng/L for water samples and 0.07–0.28 ng/g dry weight (dw) for sediment samples. Procedural blanks and samples spiked with the standard mixture were used to monitor performance of the method and matrix eﬀects.
Limits of detection (LOQ).
2.6. Statistics and risk evaluation Data were processed using SPSS 18.0 (SPSS, Chicago, IL, USA). Groups were compared using a nonparametric test because the data did not consistently meet the normality assumption. Correlation hypotheses were tested by two-tailed Pearson's correlation analysis. Diﬀerences were considered signiﬁcant at p < 0.05. To meet the requirements of
159 ± 67.2 24.4 ± 5.25 43.5 ± 36.5 46.1 ± 16.7 15.8 ± 4.76 0.76 ± 0.54 12.7 ± 14.7 1.61 ± 2.62 0.60 ± 0.58 10.2 ± 5.77 23.8 ± 11.4 1.70 ± 1.28 340 ± 84.6 329 20.8 26.6 61.5 7.94 2.17 32.3 < 0.57 1.67 4.62 17.2 3.64 507 BP EHS HMS BP-3 4-MBC OD-PABA EHMC UV-320 UV-326 UV-329 OC UV-327 Σ12 UVAs
0.87 0.61 2.43 2.38 1.69 0.70 0.47 0.57 0.45 0.94 0.57 0.85
205 ± 28.5 29.4 ± 4.30 48.4 ± 10.2 95.9 ± 22.1 16.6 ± 4.86 0.88 ± 0.33 28.1 ± 29.8 1.08 ± 1.77 < 0.45 5.76 ± 0.93 32.6 ± 19.1 1.00 ± 0.94 465 ± 78.4
164 ± 67.9 19.8 ± 5.13 39.8 ± 6.23 28.7 ± 2.51 8.02 ± 3.12 1.25 ± 0.47 6.09 ± 2.17 2.55 ± 1.33 < 0.45 2.90 ± 2.60 18.4 ± 7.18 < 0.85 292 ± 82.9
172 ± 63.3 13.4 ± 3.15 23.6 ± 13.4 37.7 ± 30.7 1.81 ± 1.26 < 0.35 12.7 ± 19.5 < 0.57 < 0.45 3.04 ± 1.21 14.8 ± 6.43 0.81 ± 1.08 281 ± 89.0
Tangxi River (n = 1) LOQa Paihe River (n = 2) Dianbu River (n = 5) Banqiao River (n = 5) Nanfei River (n = 10) Tangxi River (n = 2) LOQa
Table 1 Concentrations of 12 UVAs in water and sediment from Lake Chaohu and its inﬂowing rivers.
Chaohu Lake (n = 17)
Dianbu River (n = 5)
Paihe River (n = 4)
Chaohu Lake (n = 33)
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the statistics, concentration values < LOD were assigned as 1/2 LOD and the data was log-transformed to obey normal distribution. According to the Risk Assessment Technical Guidelines (ECB, 2003), the ecological risks were assessed according to the calculated risk quotient (RQ) based on the predicted no-eﬀect concentrations (PNEC). The estimation of PNEC values is described in detail in Supplementary material. The RQ for individual UVAs in the water was calculated and typically classiﬁed as: RQ < 0.01, no risk; 0.01 ≤ RQ < 0.1, low risk; 0.1 ≤ RQ < 1, moderate risk; RQ ≥ 1, high risk (Hernando et al., 2006; Tsui et al., 2014). In the sediment, the following RQ values gave an indication of ecological risk for the chemicals with 3 < log Kow < 5: RQ < 0.1, no risk; 0.1 ≤ RQ < 1, low risk; 1 ≤ RQ < 10, moderate risk; RQ ≥ 10, high risk (Chen et al., 2010; Tang, 2012). Chemicals with log Kow > 5, RQ ≥ 10 indicated existing ecological risk; chemicals with log Kow < 3 (not easily absorbed by sediment) were not considered a risk (ECB, 2003).
Water samples, rivers
Water samples, lake
Total water samples
Sediment samples, rivers
Sediment samples, lake
Total sediment samples 0%
Fig. 2. Composition of the 12 UVAs in water and sediment from Lake Chaohu and its inﬂowing rivers. UVs includes UV320, UV326, UV329 and UV327; other includes 4-MBC, OD-PABA and EHMC.
3. Results and discussion
rivers. In some previous studies, BP and BP-3 had also been reported as the main pollutants in waters from other areas, which agreed well with our result (Cuderman and Heath, 2007; Kameda et al., 2011; Moeder et al., 2010). In addition to the diﬀerences in inputs, this may be related to the low log Kow (< 5) of BP and BP-3, which are hydrophilic substances, but high log Kow (> 5) of UV320, UV326, UV327 and ODPABA, which are hydrophobic chemicals. OD-PABA, UV-320 and UV-326 were detected in 43.3%, 50.0% and 40.0% of the sediment samples, respectively. The detection rates of the other UVAs were all greater than 80%, reﬂecting wide occurrence of these chemicals in the sediment from Lake Chaohu and its inﬂowing rivers. Fig. 2 shows the relative abundance of diﬀerent UVAs in the sediment. BP and OC were the predominant pollutants in the surface sediment, contributing an average of 48.5% and 60.7% of the Σ12 UVA concentration in the rivers and lake, respectively. Overall, OD-PABA, and UV-320 contributed less than 1.0% of the Σ12 UVAs concentration in the sediment, respectively. The UVA proﬁles in sediment from the lake and its inﬂowing rivers were similar. BP was also found to be the main pollutant in the riverine sediment from Korea and Japan (Jeon et al., 2006; Kameda et al., 2007), which is consistent with the results of the present study. In China, BP and OC have been widely used in polymer-based products (Zhang, 2014, 2016). The use of UV-320 and the derivatives of p-aminobenzoic acid (PABA) have, however, been gradually reduced or excluded in personal care products because of their potential carcinogenicity and toxicity to aquatic species (Radke et al., 2010). This may explain the UVA distribution pattern in the study sediment to a certain extent.
3.1. Occurrence of UVAs in water and sediment Table 1 shows the UVA concentrations in surface water and sediment. In water, the concentrations of total 12 UVAs (Σ12 UVAs) ranged from 162 to 587 ng/L, with a mean of 375 ng/L in rivers and 266 ng/L in the lake. To better understand the UVA contamination status in the study area, we compared UVA concentrations found in this study with those observed in previous studies in other regions. Inconsistencies in the numbers of UVAs analyzed in diﬀerent studies made it diﬃcult to draw comparisons, so simple comparisons were made using the same chemicals, where this was possible. The concentrations of BP-3, 4-MBC, EHMC and OC found in water from diﬀerent regions are shown in Table S2. The concentrations of BP-3 and EHMC in the study water samples were lower than the concentrations observed in the Kolpa River in Slovenia (Cuderman and Heath, 2007). The EHMC and OC concentrations in the present study were lower than those found in rivers in central Spain (Madrid) and in Lake Cospude, Germany, but higher than the concentrations reported in the Glatt River and Lake Greifensee, Switzerland (Balmer et al., 2005; Gómez et al., 2009). In Lake Chaohu, the maximum concentration of 4-MBC was 4.17 ng/L, which placed it in the lower detection levels in lake waters. However, BP concentrations in the water from Lake Chaohu and its inﬂowing rivers were 3–4 times higher than the concentrations found in the surface water from Japanese rivers (31–82 ng/L) (Kameda et al., 2011). In the sediment, the concentrations of Σ12 UVAs ranged from 9.75 to 178 ng/g dw (average, 50.5 ng/g dw) in the rivers and from 12.2 to 57.4 ng/g dw (average, 21.2 ng/g dw) for the lake. In the study rivers, BP-3, 4-MBC, EHMC and OC were the predominant UVA compounds in the sediment. The levels of BP-3 and EHMC were lower than those detected in the sediment from the Ebro River Basin in Spain (GagoFerrero et al., 2011). EHMC and OC were detected in most sediments and their concentrations were similar to the results for sediment from the rivers in Saitama-ken, Japan (Kameda et al., 2011). The concentration of 4-MBC in the present study was lower than that reported in the Magdalena River, Colombia (Barón et al., 2013). Compared with other areas, the sediment in Lake Chaohu has been contaminated with relatively low levels of BP-3, 4-MBC and OC. In the water samples, the detection rates of 4-MBC, OD-PABA, UV320, UV-326 and UV-327 were 75.6%, 39.0%, 39.0%, 19.5% and 43.9%, respectively; and the other UVAs were detected in all water samples. Fig. 2 shows that BP was the predominant UVA in the surface water, contributing an average of 55.3% of the Σ12 UVA concentrations. BP-3 was also found in relatively high abundance compared to the other chemicals in most water samples. OD-PABA, UV320, UV326 and UV327 each represented less than 1% of the Σ12 UVA concentration. The UVA proﬁles in Lake Chaohu were similar to those found in the inﬂowing
3.2. Spatial distribution and possible sources The concentrations of Σ12 UVAs in waters from Lake Chaohu were signiﬁcantly (p < 0.05) lower than those of the ﬁve major inﬂowing rivers (the Nanfei, Paihe, Tangxi, Banqiao and Dianbu). Among the rivers, the highest mean concentration of Σ12 UVAs (507 ng/L) was found in the Tangxi River, which ﬂows through densely populated areas, suggesting that UVA contamination was mainly derived from anthropogenic inputs. The highest concentrations for most of the UVA contaminants were observed at sampling site W15, close to eﬄuent outfalls from the two Technical Economic Development Areas of Hefei, and so likely receiving large industrial wastewater discharges. In the study, higher concentrations of Σ12 UVAs were generally observed in downstream sites in the vicinity of wastewater treatment plant (WWTP) outfalls, such as site W5 (490 ng/L). High levels of multiple classes of UVAs have been reported in the small rivers receiving inputs from wastewater treatment plants (Balmer et al., 2005; Kameda et al., 2011; Ramos et al., 2016). The results therefore demonstrated WWTP eﬄuent to be a major source of the UVA contaminants in the Lake Chaohu catchment. In the lake, however, relatively high concentrations of Σ12 543
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signiﬁcantly correlated with one another (p < 0.01), suggesting that these UVAs had common sources. These three chemicals have been widely used in cosmetics as ultraviolet ﬁlters (Yang et al., 2015) and in China, EHS, 4-MBC and OC are recommended for use in sunscreen cosmetics; their maximum allowable doses (in terms of acid) are up to 5%, 4% and 10%, respectively (NHFPC, 2015). Li et al. (2007) reported that the removal eﬃciency of 4-MBC, OC and four other organic UV ﬁlters ranged from 28% to 43% in WWTPs in Tianjin, China. The total removal rate of 4-MBC and OC only reached from 59% to 94% with secondary biochemical treatment and advanced wastewater treatment units, indicating that a portion of the UV ﬁlter had still been transferred into the environment (Huang et al., 2015). Therefore, PC1 likely reﬂects the pollution of UVAs used in cosmetics via wastewater discharges. In the loading plot (Fig. 3(A)), BP, UV-326, UV-327 and OD-PABA formed a group, with similar loading to PC2. Signiﬁcant linear correlations (p < 0.05) were found among the concentrations of these four UVAs, with the exception of the weak correlation between OD-PABA and UV-327 (p > 0.05), suggesting a similar source of these UVAs. These chemicals are widely used in both personal care products and other industrial products. A photoinitiator, BP is widely used in printing inks for food packaging materials (Sagratini et al., 2008; Van Hoeck et al., 2010). Some benzophenone-type UV stabilizers have been using in a variety of plastic products, such as building materials, automobile components, wax, paint, adhesive agents, ﬁlm and some sports equipment, to mitigate yellowing and degradation of the products (Zhang et al., 2011). Benzotriazoles are high-production volume chemicals, used in various industrial processes as corrosion inhibitors, in de-icing ﬂuids for aircraft and in automotive antifreeze cooling systems (Cancilla et al., 2003; Gruden et al., 2001). Hefei, the industrial center of Anhui Province, has developed plastics, coating and automotive industries. These facts suggested that the UVA pollution represented by PC2 was related to the discharge from industries using UVAs as stabilizers. For the sediment, PC1 and PC2 explain 51.1% and 17.5% of the total variance, respectively. PC1 was dominated by EHS, HMS, BP-3, UV-320, UV-326, UV-329, OC and UV-327. The concentrations of the six UVAs were signiﬁcantly correlated with one another (p < 0.01), suggesting that these chemicals had common sources. These UVAs, especially benzotriazoles and benzophenones, have been widely used in industrial products. Benzotriazole-type UVAs can eﬀectively absorb ultraviolet with the wave length of 270–380 nm, and they are widely used in various plastic products to prevent the degradation and yellowing caused by light (Pintado-Herrera et al., 2017). They are also widely used in multiclass synthetic materials because of their good compatibility and stability with polymers (Zhang et al., 2011). In 2009, the production of several major UV stabilizers in China reached 10500 t (Li, 2010). Of this total, benzotriazoles and benzophenones were the dominant chemicals. In the present study, we observed relatively high levels of Σ12 UVAs at site S9 (166 ng/g), where many manufacturing plants involved in plastics, building materials and textiles are located. Benzophenone-type UVAs have also been used in textiles to avoid the aging of cotton and hemp ﬁber; these chemicals can form a strong hydrogen bond with the ﬁbers (Shen, 2013). Salicylate-type UVAs are used in polymer processing and fabric ﬁnishing, as they can rearrange esters by the photo-Fries reaction and then form 2-hydroxy benzophenone under the long-term inﬂuence of light. This results in their marked ability to abort UV with wavelengths of 280–310 nm (Fang, 2011). OC is also used in polymer-based products and some paints (Kameda et al., 2011). PC1 may, therefore, reﬂect the discharge of UVAs from plastic manufacturing and textile processing. PC2 was dominated by 4-MBC, OD-PABA and EHS. These three UVAs were signiﬁcantly correlated with each other (p < 0.01), suggesting a similar source of these chemicals. In China, 4-MBC, OD-PABA and EHS can be added into cosmetics in the proportions 4%, 8% and 5%, respectively (NHFPC, 2015). Thus, PC2 likely reﬂects emissions
UVAs were also found at sites W30 and W31, near the tourist attraction of Zhongmiao Town rather than the estuaries (Fig. 1), implying other sources of these emerging contaminants, including recreational activities. In the sediment, there was a signiﬁcant positive correlation (Table S3) between TOC content and Σ12 UVAs (r = 0.822, p < 0.01) concentrations. The concentrations of an individual UVA were also signiﬁcantly related to TOC content (p < 0.05), except for BP and EHMC, indicating that TOC in sediment has an important eﬀect on the distribution of pollutants. Based on the TOC-normalized concentrations, the highest concentration of Σ12 UVAs was found at site S16, from the Nanfei River, the most polluted river in Hefei. A high level of UVAs may be related to the input of large amounts of domestic wastewater discharge. In Lake Chaohu, the highest levels of TOC-normalized Σ12 UVAs were found at site S45, near the estuary. In the present study, there were no signiﬁcant diﬀerences (p = 0.062) in TOC-normalized UVA concentrations between Lake Chaohu and its inﬂowing rivers, which implies that complex sources and diﬀerent factors contributed to UVA pollution in this sediment. Principal component analysis (PCA) was conducted on the UVA concentrations to better understand the contamination pattern and to identify the possible sources of the UVAs. The results of the Kaiser–Meyer–Olkin test for water and sediment samples were 0.64 and 0.70, respectively; results of the Bartlett's sphericity test were all signiﬁcant at p < 0.01, which indicated that the UVA concentrations were suitable for PCA. The loading plot of PCA is shown in Fig. 3. In water, all UVAs were well represented by principal component 1 (PC1) and principal component 2 (PC2), which accounted for 35.9% and 15.6% of the total variance, respectively. PC1 was dominated by EHS, 4-MBC and OC. The concentrations of these chemicals were
Fig. 3. Loading plots of the principal component analysis (PCA) for UVAs in the (A) water samples and (B) sediment samples from Lake Chaohu and its inﬂowing rivers. 544
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Fig. 5. Risk quotient of organic ultraviolet absorbents to aquatic organisms in sediment from Lake Chaohu and its inﬂowing rivers. For BP-3, EHMC and 4MBC, RQs based on algaes (red), Daphnia magna (black) and shrimp (blue), respectively; for OD-PABA, RQs based on algaes (red); for BP, RQs based on nematodes (black) and Pimephales promelas (blue), respectively; for others, RQs based on algaes (red), Daphnia magna (black) and ﬁsh (blue), respectively. The lower and upper limits of the whiskers indicate 5% and 95% values, respectively; boxes extend from 25th to 75th percentiles; horizontal lines within the boxes represent medians; asterisks below or above the whiskers indicate outlier values. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.).
Fig. 4. Risk quotient of organic ultraviolet absorbents to aquatic organisms in water from Lake Chaohu and its inﬂowing rivers. For BP-3, EHMC and 4-MBC, RQs based on algaes (red), Daphnia magna (black) and shrimp (blue), respectively; for OD-PABA, RQs based on algaes (red); for BP, RQs based on nematodes (black) and Pimephales promelas (blue), respectively; for others, RQs based on algaes (red), Daphnia magna (black) and ﬁsh (blue), respectively. The lower and upper limits of the whiskers indicate 5% and 95% values, respectively; boxes extend from 25th to 75th percentiles; horizontal lines within the boxes represent medians; asterisks below or above the whiskers indicate outlier values. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.).
Tollefsen, 2011). There is also uncertainty in the values of assessment factors (AFs) used in the PNEC. In this study, the actual bioavailability of UVAs in water and sediment was not considered, which might result in an overestimation of the potential risks. In addition, randomness was still present in this investigation, although the representativeness of the collected samples was fully considered.
from the UVAs used in cosmetics. 3.3. Ecological risk assessment Fig. 4 shows evaluation of the ecological risks of 11 UVAs using the available data on acute toxicity to the target aquatic organisms, based on the concentrations in water investigated in this study. The results showed that the potential ecological risks related to UVAs in the lake and its inﬂowing rivers were similar. BP-3 in the water represented a moderate risk to algae. HMS and OC represented a low to moderate risk to algae. EHMC in 14.6% of water samples also represented a low to moderate risk to algae (Table S1). EHS may pose a low to moderate risk to ﬁsh. In the study area, there was no risk (or only a low risk) to aquatic organisms from exposure to the six other UVAs in water. Fig. 5 depicts the risk quotients of UVAs to aquatic organisms in sediments. BP-3 in 64.0% of river samples and 36.4% of lake samples may pose a low risk to algae. In the sediment, BP in 43.3% of sediment samples may pose a low risk to Pimephales promelas. Chemicals with log Kow > 5, OD-PABA, 4-MBC EHMC, EHS, HMS, OC, UV-326, UV-329 and UV-327 in sediment may not individually represent an obvious ecological risk to aquatic species both in rivers and in lake. The ﬁndings suggest that the risks to aquatic organisms from exposure to multiple UVAs in water and sediment from Lake Chaohu and its inﬂowing rivers were generally at low levels. The results are, however, still uncertain because of the nature of the risk assessment, which was associated mainly with estimates of toxicity and exposure parameters (Tang et al., 2017). The parameter values selected in our evaluation were derived from the relevant literatures, and the calculations were made using ECOSAR software (US EPA), which may result in LC50 or LE50 having a certain deviation from the actual values. The actual LC50 or LE50 values of individual UVAs may be underestimated if there were harmful eﬀects on aquatic organisms resulting from synergy with other, coexisting pollutants such as antibiotics and endocrine disruptor chemicals in water and sediment (Kim and Choi, 2014; Petersen and
4. Conclusions The present report describes the occurrence of 12 UVAs in water and sediment from the Lake Chaohu catchment. To the best of our knowledge, this is the ﬁrst study of organic UVAs in this lake and its inﬂowing rivers. The report provides not only information on the occurrence and regional distribution of these UVAs but also a global comparison with similar ﬁgures from other countries. Our results show that commonly used UVAs were widely present in the rivers and lake in the study area. Comparison of our results with those from studies in other regions indicates that the predominant UVAs detected in water and sediment—the ﬁlters benzophenones, OC and EHMC—are almost the same worldwide. High concentrations of BP were universally detected in the investigated water samples A source assessment suggested the discharge from industries using UVAs as stabilizers to be an important source of their occurrence in aquatic environments, in addition to the direct source of domestic sewage resulting from the wide use of UVAs in cosmetics. Our assessment of the individual chemicals suggests that the risks to aquatic organisms from exposure to UVAs in this area were generally within low levels. However, there is insuﬃcient information available on the eﬀect of these compounds in organisms, and in particular in the food chain. These risks may be ampliﬁed through bioaccumulation and trophic transfer. Further studies are needed to investigate the ecotoxicological implications of exposure of aquatic species to these emerging contaminants.
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