Polycyclic aromatic hydrocarbons (PAHs)

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biological impairment in some locations; nevertheless, the mean effects range median quotient suggested that the ecological risk of multiple PAHs was quite low ...

Environmental Science and Pollution Research https://doi.org/10.1007/s11356-018-1421-8

RESEARCH ARTICLE

Polycyclic aromatic hydrocarbons (PAHs) in multi-phases from the drinking water source area of the Pearl River Delta (PRD) in South China: Distribution, source apportionment, and risk assessment Yunjiang Yu 1 & Ziling Yu 1 & Zhengdong Wang 1 & Bigui Lin 1,2 & Liangzhong Li 1 & Xichao Chen 1 & Xiaohui Zhu 1 & Mingdeng Xiang 1,2 & Ruixue Ma 1 Received: 13 November 2017 / Accepted: 29 January 2018 # Springer-Verlag GmbH Germany, part of Springer Nature 2018

Abstract Sixteen priority polycyclic aromatic hydrocarbons (PAHs) were investigated in the water dissolved phase (DP), suspended particulate matter (SPM), and sediment collected from the water source zone of the Pearl River Delta region. The sum of 16 PAH concentrations ranged from 92.8 to 324 ng/L in the water DP, from 28.8 to 205 ng/L in the SPM, and from 55.7 to 381 ng/g (d.w.) in the sediment. Compared with other areas globally, the PAH levels were considerably moderate in the DP and SPM and relatively low in the sediment. Spatial distribution of PAHs was site-specific, and relatively higher PAH levels were found in the areas with dense population and heavy traffic. The PAH profile was dominated by two- and three-ring PAHs, and PAH pollution was identified of pyrolytic origins. Based on risk quotient, the ecological risk in water was ranked as moderate, but the adverse health risk associated with water ingestion was minimal. Naphthalene and fluorene of the sediment samples showed potential biological impairment in some locations; nevertheless, the mean effects range median quotient suggested that the ecological risk of multiple PAHs was quite low (less than 10% incidence of adverse effects). Keywords Polycyclic aromatic hydrocarbons (PAHs) . Spatial distribution . Source apportionment . Risk assessment . Toxic equivalent quantity (TEQ)

Introduction

Responsible editor: Hongwen Sun Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11356-018-1421-8) contains supplementary material, which is available to authorized users. * Yunjiang Yu [email protected] * Mingdeng Xiang [email protected] 1

State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China

2

State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous toxic organic pollutants that are mainly attributed to anthropogenic sources, such as incomplete combustion or pyrolysis of organic materials (Kim et al. 2013; Ma and Harrad 2015; Shen et al. 2013; Xia et al. 2012). PAHs have attracted much scientific and regulatory attention owing to their toxic, mutagenic, and carcinogenic effects and tendency toward bioaccumulation (Liu et al. 2017; Ma and Harrad 2015; Yu et al. 2011). The occurrence of PAHs in aquatic environments can be ascribed to diverse pathways that include atmospheric processes; municipal, hospital, and industrial wastewaters; agricultural effluents; and nonpoint source pollution (Lu et al. 2016; Melymuk et al. 2014; Witter and Nguyen 2016). Once they have been released into the environment, they may bioaccumulate and deposit in the bottom sediment (Liu et al. 2017; Ma and Harrad 2015; Yu et al. 2011). PAHs are potential proxies for the reconstruction of change in human activities (Ma et al. 2017; Zhang et al. 2017b). Generally, serious

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PAH pollution is mainly distributed in densely populated areas, major manufacturing districts, intensive agricultural basins, and other industrial centers (Han and Currell 2017; Liu et al. 2017). Thus, increasing public and scientific concern has been focused on the occurrence of PAHs (Han and Currell 20 17 ; M a a nd H ar ra d 2 01 5) . T h e U n i t e d S t a t es Environmental Protection Agency (US EPA) has classified 16 PAHs as priority pollutants and seven of those as probable human carcinogens. In China, PAHs are of great concern owing to high emission density, wide occurrence, and toxic effects on ecosystem and human health (Yin et al. 2017; Yu et al. 2011; Zheng et al. 2016). It was estimated that the total atmospheric emissions of the 16 priority PAHs in China was 114 Gg y−1), contributing 29% of the global total PAH emissions (Hong et al. 2016). High levels of PAHs have been measured in several major water systems in China, including the Liaohe River (range 946–13,448 ng/L, mean 6471 ng/L, (Guo et al. 2007)), Haihe River (range 1765–35,210 ng/L, mean 14,066 ng/L, (Cao et al. 2005)), Yellow River (range 144.3–2361 ng/L, mean 662 ng/L, (Sun et al. 2009)), and Pearl River Estuary (range 691–6941 ng/L, mean 3941 ng/L, (Luo et al. 2004)), where concentrations generally exceed the US EPA guideline value of 200 ng/L, the EU standard of 100 ng/L, and the Chinese guideline value of 2000 ng/L. In addition, it was reported that rivers in China typically showed significantly higher levels than those in Europe, the USA, and Australia (Han and Currell 2017; Xia et al. 2012). Thus, it is of great importance to investigate the environmental fate, behavior, and ecological and health effects of PAHs in China. The Pearl River Delta (PRD), located in South China, is one of the most industrialized, densely populated, and economically prosperous regions in China (Tan et al. 2016). High levels of PAHs in this aquatic system have been extensively detected in dust (Wang et al. 2011), water (Chen et al. 2016; Luo et al. 2004; Zhang et al. 2017a), sediment (Luo et al. 2008; Sun et al. 2016), and organisms (Gu et al. 2016), with most of the PAHs concentrated in hot spots and areas, such as e-waste sites, the Dongjiang (DJ) River, the Zhujiang River, the Pearl River Estuary, and coastal areas. Limited data, however, is available on the quality of the source water with regard to PAH levels and their association with human health risk. The city of Heyuan is located in the northeastern region of Guangdong province, in the upper watershed of the PRD. The city is in rich in water resources, with 640 km2 of water area, including the largest lake in Guangdong: Xinfengjiang (XFJ) Reservoir. Heyuan plays a key role in the drinking water supply and source water for agricultural and industrial activities in several major cities of the PRD, with a total population about 40 million (Wu and Chen 2013), including Guangzhou, Dongguan, Huizhou, Shenzhen, and Hong Kong. In recent years, Heyuan has developed into a major area for industrial

transfer (Liu 2012). Various kinds of industry, such as mechanical, textile, electronics, and biopharmaceutical, have settled there. Consequently, the immense pressures on the ecological integrity and freshwater resources occurred via the process of industrial transfer have received increasing concern. The quantitative identification of possible sources and contributions of PAH contaminants among the dissolved, particulate, and sediment are essential for better understanding of the cycling and fate of PAHs and are of great significance regarding the control of PAHs in aquatic environments. Therefore, the objectives of this work were to (i) determine the PAH concentrations and spatial variation in hierarchical rivers; (ii) evaluate the sources of PAHs and their composition pattern in the multi-phases; and (iii) estimate the implications of PAH loadings to ecosystem and human health.

Materials and methods Sample collection The sampling campaign was performed in June 2016, the flooding period, with 24 water and 16 sediment samples collected (Fig. 1). Of these samples, DJ1–DJ6 were located in the mainstem of the DJ River, which mainly flows through urban areas, whereas XFJ1–XFJ10 were collected from the XFJ Reservoir, which is a tributary of the DJ river and supplies drinking water to the PRD region (Wu and Chen 2013). WSC1–WSC8 also were collected from tributaries of the DJ River that receive the discharges of domestic wastewater or effluents. Water samples (2.5 L) were collected 15–30 cm below the surface of the water in pre-cleaned amber glass bottles. Sediment samples (0–5 cm) were collected using a box core sampler. All the samples (water and sediment) were immediately transported to the laboratory and directly treated without storage.

Sample extraction Water samples were directly filtered through a GF/F glass fiber filter (Whatman, Maidstone, UK; baked at 450 °C for 4 h). Target compounds (PAHs) in the water dissolved phase (DP) were extracted using solid phase extraction (SPE) with an Oasis HLB cartridge (6 mL, 500 mg; Waters), and the SPE procedure was performed using a modified method as reported by Liu et al. (2016). First, the HLB column was conditioned with 10 mL of dichloromethane, 10 mL of methanol, and 15 mL of water. Then, the water samples were spiked with naphthalene-8, acenaphthene-d10, phenanthrene-d10, chrysene-d12, and perylene-d12 and passed through the cartridge at a flow rate of 5 mL/min. After that, the cartridges were eluted with 10 mL methanol, 10 mL dichloromethane, and

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Fig. 1 Map of the study areas and sampling sites

10 mL hexane. Finally, the eluate was concentrated to 500 μL for PAH analysis. Sediment samples and the suspended particulate matter (SPM) separated via GF/F filter were freeze-dried and then soxhlet-extracted using dichloromethane (150 ml) for 24 h followed by 150 mL of a mixture of acetone and hexane (1:1, v:v) for 24 h. The combined extracts were purified via a self-packet silica-alumina column (300 mm × 10 mm, from top to bottom: anhydrous sodium sulfate, 2 cm, baked at 450 °C for 4 h; neutral silica, 12 cm, 80–100 mesh, activated at 180 °C for 12 h, and deactivated with 3% water; neutral aluminum oxide, 6 g, 100–200 mesh, activated at 250 °C for 12 h, and deactivated with 3% water; Fig. S1). Elution was carried out using 70 mL hexane-dichloromethane (7:3, v/v). The collected eluate was evaporated to near dryness, and then the solvent was changed to hexane. Finally, the collected samples were concentrated to 500 μL for PAH analysis.

Instrumental analysis PAH analysis was performed on an Agilent 6890 gas chromatograph coupled to a 5975B mass spectrometer operating

in electron impact mode (Agilent Technologies, Santa Clara, CA, USA). A DB-5 MS capillary column (60 m × 0.25 mm × 0.25 μm) was used. The mass spectrometer was operated in selective ion monitoring mode. The instrumental parameters are described in detail in the Supplementary material. The 16 USEPA priority PAHs were analyzed: naphthalene (Nap), acenaphthylene (Acy), acenaphthene (Ace), fluorene (Fl), phenanthrene (Phe), anthracene (An), fluoranthene (Fla), pyrene (Pyr), benzo[a]anthracene (BaA), chrysene (Chr), benzo(k)fluoranthene (BkF), benzo(b)fluoranthene (BbF), benzo(a)pyrene (BaP), indeno(1, 2, 3-cd)pyrene (IP), dibenzo(a, h)anthracene (DahA), and benzo(g, h, i)perylene (BghiP).

Risk assessment In water Risk quotient (RQ), proposed by Kalf et al. (1997), was applied to evaluate the eco-toxicity of water contamination. The negligible concentrations (NCs) and the maximum permissible concentrations (MPCs) of individual

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PAHs were used as the quality values (Table 3) (Kalf et al. 1997; Cao et al. 2010). Ci NCs Ci ¼ MPCs

RQNCs ¼ RQMPCs

where RQNCs and RQMPCs are RQs of the NCs and MPCs for individual PAHs, respectively. Generally, RQNCs < 1 indicates that the ecological risk of individual PAHs might be negligible; RQNCs > 1 and RQMPCs < 1 indicate that the ecological risk of individual PAHs might be moderate; and RQMPCs > 1 indicates the ecological risk of individual PAHs might be high.

69.8% for Nap to 121% for Phe in the water samples and from 73.6% for Nap to 111% for Chr in the sediment samples. The detection limits were in the range 0.10–0.16 ng/L for water and 0.01–0.20 ng/g for sediment. Reported concentrations were recovery corrected.

Data analysis The sum of the 16 PAH congeners was expressed as ∑16PAHs. Statistical analyses were performed using SPSS 18.0 for Windows (SPSS Inc., IL, USA), and the figures were created using Origin 8.0 software (OriginLab Inc., USA). A nonparametric Kruskal–Wallis test was applied to compare the variations in PAH concentration among the sampling sites at a P = 0.05 significance level.

In sediment The concentrations of the effects range low (ERL) and effects range median (ERM) were applied to evaluate the impact of individual PAHs on the aquatic sediment ecosystem (Montuori et al. 2016; Sun et al. 2016). Principally, the concentrations were compared with the ERL and ERM supplied by Long et al. (1995). In addition, the mean ERM quotients (MERMQ), which are calculated using the following equations, were also implemented in the present study to further investigate the combined ecological risk of multiple PAHs.

Results and discussion PAH occurrence in DP, SPM, and sediment

n where, CERMi is the corresponding quality value of the ERM for individual PAHs. According to Long and Macdonald (1998), the MERMQ was divided into four categories (< 0.1, 0.11–0.5, 0.51–1.50, and > 1.50), which were identified as low, medium-low, medium-high, and high-priority risk, respectively, and coincided with ≤ 10, 25–30, 46–53, and ≥ 75% incidences of ecological risk, respectively.

The concentrations of the 16 individual PAHs and sum of 16 PAHs (∑16PAHs) in DP, SPM, and sediment samples are depicted in Table 1. The concentrations of ∑16PAHs were respectively 92.8–324 (181 ± 54.0, mean ± SD) ng/L in the DP (n = 24), 28.8–205 (48.8 ± 35.4) ng/L in the SPM (n = 24), and 55.7–381 (186 ± 79.8) ng/g in the sediment (n = 16). All 16 PAH congeners were detected in 100% of the samples. Nap was the most abundant PAHs in both the DP and SPM, followed by Phe and Fl, which appeared as major contributors (contributing 90.0 and 67.0%, respectively). However, the highest level was found for Nap in the SPM, followed by Phe and BbF, which contributed 60% of ∑16PAHs. The DP, SPM, and sediment PAH concentrations found in this study and those reported in previous studies for areas worldwide are summarized in Table 2. Overall, comparing these data indicates that the concentrations of PAHs in the DP and SPM were moderate and somewhat low in the sediment.

Quality control

PAH spatial distribution in DP, SPM, and sediment

Quantification was performed via the internal standard method using naphthalene-8, acenaphthene-d10, phenanthrened10, chrysene-d12, and perylene-d12 (4 μg/mL, AccuStandard, USA). All solvents used were chromatographic grade (Fisher Chemical, USA). Laboratory blanks were taken throughout the analysis and used to correct the analytical results. Surrogate recoveries (mean ± standard deviation) were 61 ± 5% for naphthalene-8, 71 ± 8% for acenaphthene-d10, 94 ± 10% for phenanthrened10, 89 ± 10% for chrysene-d12, and 85 ± 12% for perylened12, respectively. Recovery of individual PAHs ranged from

The concentration of ∑16PAHs varied significantly among sampling locations and phases (Fig. 2). In the DP, the highest concentration of ∑16PAHs measured was from sampling site DJ 1 (324 ng/L), which was threefold higher than the lowest, observed in DJ3 (92.8 ng/L). High concentrations of ∑16PAHs were also observed in XFJ6 (294 ng/L) and WSC1 (265 ng/L), with the high concentrations largely due to Nap (Fig. 2a). In sediment, ∑ 16PAH concentrations were highest at DJ5 (381 ng/g), followed by DJ4, WSC2, WSC3, WSC5, and XFJ5 (all > 200 ng/g). In comparison, ∑16PAH concentrations in the SPM showed relatively less variations (Fig. 2b), except

Ci C ERMi

ERMQi ¼

n

∑ ERMQi MERMQ ¼

i

Environ Sci Pollut Res Table 1 PAHs

Concentration of the 16 individual PAHs and ∑16PAHs in the DP, SPM, and sediment DP (n = 24, ng/L)

SPM (n = 24, ng/L)

Sediment (n = 17, ng/g, d.w.)

Range

Mean

Median

Range

Mean

Median

Range

Mean

Median

Nap Acy

57.3–283 1.96–4.33

146 2.59

130 2.26

5.61–184 1.23–1.65

21.4 1.31

14.0 1.26

25.6–201 1.40–4.76

85.5 2.10

60.4 1.76

Ace

1.68–3.31

2.44

2.44

1.33–8.73

2.01

1.42

1.74–5.62

2.50

2.17

Fl Phe

3.26–10.2 4.45–15.9

5.99 10.8

5.73 10.8

1.65–15.3 3.92–26.1

3.47 7.86

1.98 5.46

2.20–23.4 3.70–29.6

9.69 14.9

8.11 12.9

Ant Fla

1.84–2.98 0.86–4.30

2.47 1.98

2.59 1.74

1.58–2.00 0.62–3.34

1.65 1.31

1.62 1.07

1.66–6.28 1.86–36.4

2.69 10.2

2.23 6.99

Pyr

0.63–5.02

1.57

1.35

0.54–7.64

1.50

1.08

0.95–31.5

8.32

4.88

BaA Chr

0.44–0.97 0.57–1.66

0.54 0.71

0.52 0.66

0.48–1.84 0.64–2.37

0.63 0.95

0.55 0.81

0.76–25.0 1.39–35.1

6.01 7.73

2.79 3.71

BbF BkF

0.91–1.31 0.79–0.92

0.98 0.82

0.97 0.82

1.08–3.81 0.84–2.26

1.43 1.02

1.29 0.96

1.94–43.4 0.95–16.0

11.2 4.55

6.26 2.65

BaP DahA

0.73–6.52 1.06–1.09

1.24 1.07

0.80 1.07

0.72–1.58 1.06–1.31

0.88 1.10

0.84 1.09

1.07–23.4 1.05–5.70

6.11 2.04

3.14 1.55

InP BghiP ∑16PAHs

0.97–1.09 0.81–0.93 92.8–324

1.00 0.84 180

1.00 0.84 168

1.03–2.24 0.89–2.22 28.8–205

1.18 1.09 48.8

1.13 1.01 38.2

1.69–19.5 1.67–24.8 55.7–381

5.85 6.64 186

3.72 4.02 179

for site DJ5 (205 ng/L), which was the most contaminated sample, with a concentration about one order of magnitude higher than those of the others. Because they are located in the center of the city of Heyuan, which is a commercial and residential area, DJ5 and DJ4 were exposed to heavy traffic, residential heating, and significant discharge of urban waste, which may increase the amount of PAHs more at this location than at the others (McDonough et al. 2014; Wang et al. 2011). WSC2 and WSC5 were situated downstream of direct discharge outlets, where a huge amount of wastewater was discharged into the river, which might contribute to the very high concentrations of PAHs (Chen et al. 2016). PAHs entering the aquatic environment exhibit a high affinity for suspended particulates in the water column because of their hydrophobic nature (Doong et al. 2004; Guo et al. 2007). The concentration of PAHs in the SPM could be attributed to the total suspended solids (TSS) content (Luo et al. 2004). In the present study, water samples were collected from the source water area of Heyuan. The TSS contents were relatively low, which may explain the relatively lower PAH concentrations in the SPM. PAHs in the DP can be significantly affected by direct PAH inputs, such as surface runoff, wastewater, and atmospheric dry and wet deposition (Zhang et al. 2017a). Meanwhile, PAHs in the sediment can reenter the water column through physical, chemical, and biological processes (Montuori et al. 2016; Wang et al. 2015). However, the SPM in the present study had relatively uniform TSS content, which might contribute to the lower variation in the SPM than in the DP and sediment.

Clear differences were also evident among samples grouped as the reservoirs (XFJ1–XFJ10), the mainstem (DJ1–DJ6), and tributaries (WSC1–WSC5). In SPM, the ∑16PAH concentrations averaged 75.2 ng/L for the mainstem, which was about twofold higher than those of the reservoirs (41.3 ng/L) and major tributaries (38.3 ng/L). Similarly, patterns in sediment, most ∑ 16 PAH concentrations in the mainstem (212 ng/g) and the tributaries (211 ng/g) were greater than those in the reservoirs (143 ng/g), while no apparent difference for PAH concentrations in the water phase among hierarchical rivers were observed. As suggested by McDonough et al. (2014) and Ruge et al. (2015), air-water diffusive exchange was an important source of PAHs to the surface waters. Generally, PAHs displayed net air-to-water deposition near populated regions or industrialized (point) sources (Ruge et al., 2015). In addition, the PAH source, as described later in BSource apportionment,^ was identified as of pyrolytic origin (fossil fuels, biomass, residential heating, wildfires, etc.), leading to a ubiquitous presence and continued atmospheric deposition. Thus, atmospheric deposition might be a major source of gaseous and particle-bound PAHs to the DP. In summary, PAH pollution in our studied hierarchical rivers showed: the mainstem > tributaries > the reservoirs. Our results showed similar trends to those in previous studies, in that higher concentrations of PAHs were found in the areas with relatively denser population, more traffic congestion, and greater industrial activities (Hong et al. 2016; Ruge et al. 2015). Moreover, because of the land use pattern for drinking water sources and vast agricultural activities (Ding et al. 2016;

Environ Sci Pollut Res Table 2

Concentration ranges and mean values of PAHs in the DP, SPM, and sediment collected from different rivers worldwide #of PAHs

Range ∑PAHs

Mean ∑PAHs

Reference

Rivers in Heyuan

16

92.8–324

180

Present study

Rivers in Northern France-Belgium

16

n.d.-6610



(Rabodonirina et al. 2015)

Wyre River, England

28

2.7–20



(Moeckel et al. 2013)

Tiber River, Italy

16

1.75–608

90.5

(Montuori et al. 2016)

Rivers in Shanghai

16

46.5–460

113

(Liu et al. 2016)

Daliao River

16

71.1–4255

749

(Zheng et al. 2016)

Maozhou River, Shenzhen

16

13–1212

292

(Zhang et al. 2017a)

Rivers in Heyuan Rivers in Northern France- Belgium

16 16

28.8–205 810–4940

48.8 –

Present study (Rabodonirina et al. 2015)

Paranagua Estuarine, Brazil

16

10.0–89.2



(Cardoso et al. 2016)

Tiber River, Italy

16

4.53–473.4

111.5

(Montuori et al. 2016)

Rivers in Shanghai

16

2930–37,957*

9313*

(Liu et al. 2016)

Daliao River

16

1970–11,612*

4016*

(Zheng et al. 2016)

Maozhou River, Shenzhen

16

120–133,859*

8516*

(Zhang et al. 2017a)

Region DP (ng/L)

SPM (ng/L)

Sediment (ng/g) Rivers in Heyuan

16

55.7–381

186

Present study

Rivers in Northern France- Belgium

16

3750–22,300



(Rabodonirina et al. 2015)

Huveaune River, France

16

572–4235

1966

(Kanzari et al. 2014)

Paranagua Estuarine, Brazil

16

0.6–63.8



(Cardoso et al. 2016)

Tiber River, Italy

16

36.2–545.6

155.3

(Montuori et al. 2016)

Rivers in Shanghai

16

249–36,198

3328

(Liu et al. 2016)

Daliao River

16

375–11,588

3700

(Zheng et al. 2016)

Maozhou River, Shenzhen

16

28–1051

458

(Zhang et al. 2017a)

Dongjiang River, China

16

100–3400

880

(Zhang et al. 2011)

Erjien River, Taiwan

16

22–28,622

737

(Wang et al. 2015)

Note: n.d.: < LOQ (limit of quantification); B–^: no data available; *: ng/g

Wu and Chen 2013), the reservoirs were less contaminated by PAHs (Launay et al. 2016; Liu et al. 2017).

PAH composition and source apportionment PAH composition profile The composition profiles of PAHs in DP, SPM, and sediment are illustrated in Fig. 3, which indicates that the lowmolecular-weight PAHs (LMW PAHs; with two and three rings) were abundant at most sampling sites, representing on average for 93.6%, 73.9%, and 66.4% of total PAHs in the DP, SPM, and sediment, respectively. In contrast, the highmolecular-weight PAHs (HMW PAHs; with four, five, and six rings) were present in low proportion, accounting for only 6.4%, 26.1%, and 33.6% of all PAHs in the DP, SPM, and sediment, respectively. The prevalence of LMW PAHs in DP could be attributed to their high water solubility (Doong and

Lin 2004). Moreover, partitioning toward the DP is also favored by the lower octanol–water partition coefficients (Kow) of the LMW PAHs (log Kow < 4.50; Table S1). Clear variation in composition pattern by ring size was also identified (Fig. 3). As can be seen, two-ring PAHs showed much higher contributions in the DP (74.5%) than in the SPM and sediment (contributing 36.5% and 46.3%, respectively). However, the proportion of three-ring PAHs partitioned into the SPM was 37.4%, about twofold higher than in the DP and sediment, where it was 14.2% and 18.6%, respectively. In addition, four-ring PAHs were much more abundant in the sediment (16.0%) and in the SMP (9.90%) than in the DP (2.84%). The different PAH patterns in the water and sediment may be explained by hydrophobic characteristics and microbial degradation (Liu et al. 2017). Generally, aqueous HMW PAHs are less soluble and more hydrophobic than LMW PAHs, so they tend to associate with SPM and become incorporated into the sediment. Indeed, the

Environ Sci Pollut Res Fig. 2 Spatial concentration profiles of total PAHs in the DP (a), SPM (b), and sediment (c). XFJ1 to XFJ10 were categorized as reservoirs, DJ1 to DJ6 as the mainstem, and WSC1 to WSC8 as tributaries

partition coefficients (Kp, defined as the ratio of PAHs in the SPM (Cs; mg/kg) to PAHs in the DP (Cw; mg/L) showed an increasing trend of SPM partitioning of the HMW PAHs (Table S2). Additionally, HMW PAHs are more resistant to degradation, so they were prevalent in the sediment and SPM, which protect against microbial attack. The spatial distribution of PAH composition exhibited site-specificity in the SPM and sediments. The highest proportions of two-ring PAHs (Fig. 3) in the SPM were found in DJ5, which was close to the center of the city of Heyuan, suggesting that urban centers are a primary source of two-ring PAHs (McDonough et al. 2014; Wang et al. 2011). In addition, relatively high proportions of two-ring PAHs in the sediment were observed for sites WSC1, WSC2, and WSC5, which are in close proximity to discharge

outlets, from which a huge amount of wastewater is discharged into the river. Source apportionment The PAH diagnostic ratio is the preferred method for determining the likely PAH origins in various environmental media, owing to its simplicity and validity (Yunker et al., 2002; Tobiszewski and Namiesnik, 2012). In the present study, An / (An + Phe), BaA / (BaA + Chr) and Fla / (Fla + Pyr) were utilized for widely used (Montuori et al. 2016; Shen et al. 2013). Based on the values of PAH isomer ratios, an An / (An + Phe) ratio > 1 indicates pyrolytic origin; < 0.1 a petrogenic source, a Fla / (Fla + Pyr) ratio < 0.40, suggests a

Environ Sci Pollut Res Fig. 3 Composition profiles of PAHs by ring size in DP (a), SPM (b), and sediment (c)

pyrolytic source, from 0.40 to 0.50 petroleum combustion, and > 0.50 either biomass or coal combustion; and a BaA / (BaA + Chr) ratio < 0.20 demonstrates PAH from predominantly petrogenic sources, for 0.20 to 0.35 from mixed sources, and > 0.35 only pyrolytic origin. The cross plots of An / (An + Phe) and BaA / (BaA + Chr) versus Fla / (Fla + Pyr) for the studied samples are shown in Fig. 4. In the DP, all ratios of An / (An + Phe) and BaA / (BaA + Chr) obtained in the DP were in the ranges of 0.15– 0.29 and 0.36–0.52, respectively, which indicated pyrolytic sources. Moreover, the Fla / (Fla + Pyr) ratios for all sampling locations, excluding WSC6, were greater than 0.5, which corresponds to pyrolytic origins. In the SPM phase, the ratios of BaA / (BaA + Chr) fitted the idea of pyrolytic sources, as all samples were higher than 0.35, and the ratios of An / (An + Phe) corresponded to pyrolytic origins, with most samples greater than 0.1. Furthermore, 21 of 24 SPM

samples had ratios of Fla / (Fla + Pyr) greater than 0.4, confirming that fuel combustion was the main possible source. In the sediment phase, ratios of An / (An + Phe) > 0.1 and BaA / (BaA + Chr) > 0.35 were obtained for most of the sampling locations, which implied pyrolytic sources. It was similar for Fla / (Fla + Pyr), as all the ratios were greater than 0.4, corroborating sources of petroleum and biomass combustion. In summary, PAHs in the DP, SPM, and sediment of this study mainly originated from pyrolytic processes.

Toxicity and risk assessment Toxicity equivalency factors (TEFs) TEFs, as suggested by the USEPA (2010, 2012), were used to evaluate the toxicity of and assess the likely risks for

Environ Sci Pollut Res Fig. 4 Cross plots of the values of Fla / (Fla + Pyr) versus An / (An + Phe) (a) and Fla / (Fla + Pyr) versus BaA / (BaA + Chr) (b)

Table 3 RQs for maximum permissible concentrations (MPCs) and negligible concentrations (NCs) for the individual and total PAHs in water

PAHs

TEF

NCs (ng/L)

MPCs (ng/L)

RQNCs

RQMPCs

Range

Mean

Range

Mean

Nap Acy Ace Fl Phe Ant Fla Pyr BaA

0.001 0.001 0.001 0.001 0.001 0.01 0.001 0.001 0.1

12 0.7 0.7 0.7 3 0.7 3 0.7 0.1

1200 70 70 70 300 70 300 70 10

6.93–26.4 4.64–7.94 5.01–15.9 8.61–29.5 3.75–12.9 4.93–6.77 0.66–1.91 2.07–12.9 9.86–23.5

13.9 5.57 6.36 13.5 6.23 5.89 1.10 4.39 11.7

0.069–0.26 0.046–0.079 0.050–0.16 0.086–0.30 0.038–0.13 0.049–0.068 0.007–0.019 0.021–0.13 0.099–0.235

0.14 0.056 0.064 0.135 0.062 0.059 0.011 0.044 0.12

Chr BbF BkF BaP DahA InP BghiP ∑16PAHs

0.01 0.1 0.1 1 0.1 1 0.001

3.4 0.1 0.4 0.5 0.5 0.4 0.3 27.2

340 10 40 50 50 40 30 2720

0.37–0.88 20.2–47.5 4.16–7.64 3.01–14.6 4.25–4.76 5.09–8.11 5.67–10.3 89.2–231

0.49 24.0 4.61 4.25 4.35 5.47 6.43 118

0.004–0.009 0.20–0.48 0.042–0.076 0.030–0.15 0.043–0.048 0.051–0.081 0.057–0.10 0.89–2.31

0.005 0.24 0.046 0.043 0.043 0.055 0.064 1.18

Environ Sci Pollut Res

environmental mixtures of PAHs. Toxic equivalent (TEQ) values referred to BaP were calculated as follows: TEQ ¼ ∑C i  TEFi where Ci represents the concentration of individual PAHs, and TEFi is the TEQ factor for individual PAH compounds (Table 3). As shown in Table S3, the TEQ concentrations were 2.24–8.04 (2.79 ± 1.21, mean ± SD) ng/L in the DP (n = 24), 2.13–4.83 (2.55 ± 0.53) ng/L in the SPM (n = 24), and 3.93– 52.5 (14.6 ± 14.2) ng/g in the sediment (n = 16). Among the 16 PAH congeners, BaP was the major contributor to the TEQ concentrations in both the DP and sediment (accounting for 44.5% and 41.9%, respectively), followed by InP (36.0% and 40.1%, respectively). However, InP was the major contributor to the SPM (46.3%), followed by BaP (34.7%) and BbF

Fig. 5 Concentrations of ERL and ERM for the individual PAHs (a) and MERMQ for the total PAHs (b) in sediment

(5.59%). Based on TEQ analysis, BaP and InP are the principle risk culprits in this aquatic environment.

Risk assessment Existing RQNCs and RQMPCs for PAHs in water are presented in Table 3. For individual PAH, the mean values of RQNCs for Chr were < 1, which indicated negligible eco-risk effects of Chr to aquatic organisms. Meanwhile, the mean values of RQMPCs of the other individual PAHs were all < 1 and RQNCs > 1, suggesting moderate biological effects. Based on the cancer risk assessment detailed in the Supplementary material, the incremental lifetime cancer risk (ILCR) values in adults ranged from 1.39 × 10−7 to 3.21 × 10−7, with a mean of 1.65 × 10−7. The ILCR values in all the water samples were

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