Introduction

As a group of hazardous organic compounds of concern, polycyclic aromatic hydrocarbons (PAHs) are continuously input into the environment from variety of sources, including natural events (i.e., forest fires, volcanic eruptions) and human activities (i.e., fossil fuel combustion) (Wilcke, 2007; Zhang & Tao, 2009). With the development of industrialization and urbanization, the emissions of PAHs are increasing, and the increasing human activities have become the main contribution to PAHs pollution into the environment (Shen et al., 2013). As the most populous country and second largest economy in the world, China has also become one of the largest energy-consuming countries, with the highest emissions of PAHs soaring from 18,000 tons in 1980 to 125,000 tons in 2012 (Cui et al., 2022). PAHs are aromatic rings consisting of two or more carbon and hydrogen atoms fused in different configurations. These compounds not only have the properties of long-distance migration, lipophilic and bioaccumulation, but also has the toxicity of carcinogenesis, teratogenesis and mutation (MacDonald et al., 2000). Numerous medical researches have confirmed an association PAH exposure with cancer prevalence (Choi et al., 2006; Montuori et al., 2016). As a result, 16 PAHs were listed as priority contaminants by the US Environmental Protection Agency (USEPA), and seven of them were listed as potentially carcinogenic to humans by the International Agency for Research on Cancer (Montuori et al., 2016). Therefore, study of the pollution and risk of PAHs in the environment has attracted global concern.

As a semi-volatile chemical, it can be found throughout different environmental matrices (atmosphere, water, sediment, soil and organisms) by large airflow, dry and wet deposition, surface runoff, industrial and municipal wastewater and biological enrichment behavior, and enter into aquatic environment eventually. PAHs in water are further deposited into sediments due to their lipophilicity, which are considered to be major sinks. Therefore, the study of PAHs in water and sediment is helpful to understand the migration, transformation and deposition process of these contaminates in the environment.

Occurrence of water–sediment systems in river, lake and marine environments has been well documented. PAHs were detected at high concentrations in several developed countries, such as in water of the Mississippi River (62.9–144.7 ng/L) in the USA (Zhang et al., 2007) and the Tiber River (23.9–72.0 ng/L) in Italy (Patrolecco et al., 2010), as well as in the sediments of Brisbane River (148.4–3079 ng/g) in Australia (Duodu et al., 2017) and the Tiber River (36.2–545.6 ng/g) in Italy (Montuori et al., 2016). Notably, some developing countries like China have experienced rapid industrialization, resulting in severe contamination of the aquatic environment with PAHs. The total concentration of 24 PAHs in water and sediment of the Dalian coast after oil spill was in the ranges of 15–160 ng/L and 64–2100 ng/g, respectively (Liu et al., 2013). An investigation around chemical parks in the Yangtze River showed that 19 PAHs were present in the water and sediment at concentration ranges of 32.98–286 ng/L and 15.14–5355 ng/g, respectively (Jia et al., 2021). Zhao et al. (2021) investigated PAHs in the middle and lower reaches of the Yangtze River and found that PAHs were 2.4–2227 ng/L and 85.7–4075 ng/g in water and sediment, respectively. Generally, the concentration of PAHs was higher concentrations in developed economic regions and mega-cities and were strongly correlated with economic parameters (Gong et al., 2022). As a result, the aquatic environment in China suffers from serious PAH contamination, which may threaten the survival of aquatic organisms. Research on PAHs pollution and ecological risks in critical water environments is of great significance to the management of aquatic ecosystem in China.

As the third largest river basin worldwide, the Yangtze River flows through many provinces, cities and economic zones in China, and its ecological and environmental problems will seriously affect the public health of about 600 million people in China. Wuhan is the core city along the Yangtze River basin and a transportation hub of China. Therefore, an investigation on PAHs pollution of Wuhan section in the Yangtze River (WYR) is also helpful to understand the evolution trend of PAHs in developing cities. Studies on PAHs along the Yangtze River have been documented, such as the Wuhan section in 2005 (Feng et al., 2007), the Yangtze Estuary (Wang et al., 2013), and Hubei to Jiangsu section (Zhao et al., 2021). Nevertheless, previous studies on the WYR did not focus much on the contribution of pollution sources. For example, the work of Feng et al. (2007) and Dong et al. (2018) along the Yangtze River (Wuhan section) only provided qualitative diagnosis of the contamination sources from 16 PAHs in water and sediment, but did not obtain quantitative results of the sources, which is helpful to understand the proportion of different energy structures. Yang et al. (2022) showed that ecological risk of PAHs in the Yangtze River mainstem has been effectively reduced through the implementation of the Yangtze River protection policy based on the comparison of the ecological risk changes in the Yangtze River over the years. However, few of these studies presented trends in the evolution of PAHs, a traditional class of hazardous organic compounds, over the years in terms of composition, distribution, and pollution inputs to the WYR. Moreover, quantifying the contaminant fluxes across rivers should be valuable for understanding the sources, transport, and fate of contaminants in a region. Wang et al. (2007) determined annual fluxes of Σ27PAHs and Σ15PAHs in the Pearl River based on monthly sampling (12-month). At present, a few studies have estimated the dry and wet deposition fluxes and sedimentation fluxes of PAHs in the Yangtze estuary and coastal region, but few studies have assessed runoff fluxes of PAHs through monthly monitoring in WYR or even in the Yangtze River. Detailed information on PAHs pollution in the core area of the Yangtze River Economic Belt (Wuhan section) is still lacking. For these reasons, it is necessary to conduct detailed sampling for the WYR based on different seasonal and geographical characteristics and quantify its pollution sources, which will help to understand the evolution trend on typical hazardous organic compounds along the Yangtze River (historical legacy or recent pollution input) and is essential for the future pollution prevention and control in the Yangtze River.

Therefore, the main objectives of this study are to investigate the occurrence, spatiotemporal variation, sources and ecological risks of 16 PAHs in the trunk stream of WYR. This study is the first effort to simultaneously determine the presence of large amounts of PAHs in both water and sediment from the mainstream of the Yangtze River (Wuhan section) at various locations during two seasons (dry and wet seasons), particularly provide the first dataset on the assessment of the annual loads of PAHs by monthly monitoring (12 months) in the mid-reaches of Yangtze River.

Materials and methods

Sample collection

Water and sediment samples were collected at 18 sites (S1–S18) from the WYR in January 2021 (dry season) and May 2021 (wet season), presented in Fig. 1. Detailed information on the sampling sites, including geographic location and land use, is in Table S1. The collected water samples were stored in 1 L pre-cleaned glass bottles at below 4 °C (extraction within 48 h). Surface sediment (< 10 cm) was sampled by stainless steel samplers as well as preserved at − 20 °C. A series of measurements of temperature, pH, conductivity, dissolved oxygen for water was conducted by portable water quality monitor (YSI, 3090, USA), as shown in Table S2. Other water quality parameters such as chemical oxygen demand (COD), dissolved organic carbon (DOC), total nitrogen (TN), total phosphorus (TP), and ammonium nitrogen (NH3–N) and sediment physicochemical parameters including pH, total organic carbon (TOC), TN, TP, and NH3–N are presented in Table S3, respectively.

Fig. 1.
figure 1

Sampling sites in the WYR

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Sample extraction and instrument analysis

The 16 PAHs target PAHs are classified into two-ring [Naphthalene (Nap)], three-ring [Acenaphthylene (Acy), Acenaphthene (Ace), Fluorene (Flu), Phenanthrene (Phe), and Anthracene (Ant)], four-ring [Fluoranthene (Fla), Pyrene (Pyr), Benz[a]anthracene (BaA), and Chrysene (Chry)], five-ring [Benzo[b]fluoranthene (BbF), Benzo[k]fluoranthene (BkF), Benzo[a]pyrene (BaP), and Dibenz[a,h]anthracene (DahA)] and six-ring PAHs {Indeno[l,2,3-cd]pyrene (InP) and Benzo[g,h,i]perylene (BghiP)}. Details of target PAHs and internal standards and sample pretreatment methods for water and sediment are provided in text S1 and S2. A method used to extract PAHs from water and sediment was derived from previous study of Zhang et al. (2004). Pretreated water samples were extracted with the solid phase extraction system (SPE) (Supelco, USA). Briefly, the C18 SPE cartridges (500 mg, Agela) were conditioned with 5 mL dichloromethane (DCM), 10 ml methanol and 10 ml ultrapure water after SPE system pre-cleaned by DCM. And then 1L water samples passed through the C18 columns at a rate of 5–10 mL/min. The cartridges were dried at 30 min, followed by being eluted with 10 mL DCM. The eluate was dehydrated by anhydrous Na2SO4 and then, blown to 100 μL in vials under a gentle N2 stream for analysis.

The extraction method of PAHs in sediments was derived from the previous study by (Cui et al., 2020b; Zhang et al., 2004; Zhou et al., 2000). Briefly, 1 g sediment sample was added with 5 mL (0.5 μg/g) internal standards and saponified by 8 mL 1 M ethanolic potassium hydroxide. The samples heated in a 90 °C water bath for 8 h were cooled before being added 3 mL of ultrapure water, and the analytes were extracted into 3 × 10 ml of n-hexane. Then, the obtained extract was evaporated to about 2–3 mL under nitrogen and loaded onto a composite silica gel column (9 g silica gel, 1 g acidified silica gel and 1 g anhydrous Na2SO4) which was pre-conditioned with 40 mL n-hexane in advance. 10 mL of DCM/n-hexane (1:4, v/v) eluent was discarded, and then, 2 × 20 mL of DCM/n-hexane (1:4, v/v) eluent was added to the column with a glass pipette. Then, eluate was collected in a bottom flask, followed by being concentrated by rotary evaporation and transferred to GC vial, and the volume was adjusted to 100 μL for analysis by GC–MS.

The Agilent 7890A linked to 5977B (GC–MS, Agilent, USA) was used for 16 PAHs analysis with selective ion-monitoring (SIM) mode. Compounds were separated using ZB-SemiVolatiles fused silica capillary column (30 m × 0.25 mm i.d. × 0.25 μm film thickness, Phenomenex, USA). The carrier gas is high-purity Helium (≥ 99.999%) with a flow rate of 1 mL/min. The injection volume was 1 μL using splitless injection mode. The samples were analyzed under a temperature program: the initial temperature was 70 °C for 3 min, increased at 5 °C/min to 250 °C and held for 1 min, then ramped to 300 °C at 6 °C/min for 6 min, and finally to 325 °C at 10 °C/min for 5 min. The analyte parameters are shown in Table S4.

Quality assurance and quality control

Target PAHs were quantified using GC/MS under optimized conditions, recognized primarily by the specific ions and their retention times. Before sample analysis, the relevant standards were analyzed to check instrument performance, peak height and resolution. For each batch of samples to be analyzed, solvent blanks, reference standard mixtures, quality control samples, and procedural blanks were run sequentially to check for contamination, instrument performance, peak identification, and quantification. Procedural blanks were processed in the same steps described for the experimental samples. The PAHs identified in the water and sediment were subtracted by procedural blanks. The detailed recoveries and limit of detection (LOD) for each PAH are summarized in Table S5.

Flux calculation

Fluxes (F) of the target PAHs were estimated at the S11 by using the monthly monitoring data from March 2021 to February 2022 following Eq. (1) (Zhang et al., 2016, 2018):

$$F = C \times Q \times t$$
(1)

C (ng/L) is the concentration detected at site 11. Q (m3/s) is flow data for the site 11 obtained at the Hubei Provincial Hydrological and Water Resources Center. Correspondingly, t is the exposure time (days) for each month. Monthly PAHs concentration data at site 11 are provided in Table S6, and average flow rate is indicated in Fig S1. Values below the LOD were instead by 1/2*LOD. Specifically, the river water period is divided into the dry season (December, January and February) and wet season (May, June, July, August and September), which is mainly based on their flow rates and the climate (e.g., rainfall and temperature, etc.). Afterward, the annual fluxes for PAHs were estimated by accumulating the mass loads of each month.

Positive matrix factorization

Multivariate receptor model is commonly applied to examine the distribution of species concentrations and sources among chemical mass balances (CMBs). Positive matrix factorization (PMF) is such a modeling and data analysis tool that decomposes a matrix of specified sample data into two matrices, i.e., factor contribution and factor profile to understand the factors or sources that contribute to specified sample data (Brown et al., 2015). This study used PMF 5.0, which was developed by the US Environmental Protection Agency (USEPA), to determine the sources of PAHs in the WYR. The specific factor analysis model is represented by the following Eq. (2), (3) and (4):

$$x_{ij} = \mathop \sum \limits_{k = 1}^{p} g_{ik} f_{kj} + e_{ij}$$
(2)

where xij, eij, gik and fkj represent elements of the sample concentration matrix, residual matrix, contribution matrix and source component spectrum matrix. p represents different sources of PAHs

$$Q = \mathop \sum \limits_{i = 1}^{n} \mathop \sum \limits_{j = 1}^{m} \left[ {\frac{{x_{ij} - \mathop \sum \nolimits_{k = 1}^{p} g_{ik} f_{kj} }}{{u_{ij} }}} \right]^{2}$$
(3)

where Q is a critical parameter for PMF.

$$u_{ij} = \left\{ {\begin{array}{*{20}c} {\frac{5}{6} * {\text{MDL}}\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;,} & {x_{ij} \le {\text{MDL}}} \\ {\sqrt {\left( {{\text{RSD}} * x_{ij} } \right)^{2} + \left( {{\text{MDL}}} \right)^{2} } ,} & {x_{ij} > {\text{MDL}}} \\ \end{array} } \right.$$
(4)

uij is uncertainty value matrix, respectively. In addition, MDL is the method detection limit of the compound.

Water–sediment partitioning

Fugacity (f) is a measure of the chemical potential or partial pressure of a chemical species in a specific medium, which controls the transfer of chemical between phase (Mackay & Paterson, 1991). Therefore, the fugacity model was used to evaluate their equilibrium in both phases regarding their migration and diffusion in water–sediment (Cui et al., 2020a; Zhu et al., 2022). Fugacity fraction (ff) of a chemical in specific media can be converted by concentration, according to the following Eq. (5):

$${\text{ff}} = \frac{{f_{{\text{s}}} }}{{f_{{\text{s}}} + f_{{\text{w}}} }} = \frac{{\frac{{1000C_{{\text{s}}} }}{{0.41 * f_{{{\text{oc}}}} * K_{{{\text{ow}}}} }}}}{{\frac{{1000C_{{\text{s}}} }}{{0.41 * f_{{{\text{oc}}}} * K_{{{\text{ow}}}} }} + C_{{\text{w}}} }}$$
(5)

fs and fw are the fugacity (Pa) of chemical in sediment and water, respectively. Cs (ng/g, dw) and Cw (ng/L) are the concentration of chemical in sediment and water, respectively. Kow is the n-octanol–water partition coefficient of the chemical. foc is the organic carbon content of sediment. According to the uncertainty study, the ff values of < 0.3 and > 0.7 represent migration of chemicals from the water to sediment phase (net deposition) and diffusion from the sediment to water phase (net diffusion), respectively. The value of 0.3 ≤ ff ≤ 0.7 is considered as equilibrium state.

Risk assessment

Risk quotient (RQ) method was further used to assess the risk level of PAHs in the WYR. The RQ was obtained from Eq. (6):

$${\text{RQ}}_{i } = C _{{i {\text{PAHs}}}} / C _{{i {\text{QV}}}}$$
(6)

where Ci is the concentration of certain PAH congener and CiQV is the risk standard quality value of PAH monomer in water or sediment (ng/L or ng/g). The detailed standard quality values are shown in Table 1. The specific standard risk value adopts the negligible concentrations value (NCs) and the maximum permissible concentrations value (MPCs) proposed by (Kalf et al., 1997), as the following Eq. (7) and (8):

$${\text{RQ}}_{{i{\text{NCs}}}} = C_{{i{\text{PAHs}}}} / C_{{i{\text{QV}} \left( {{\text{NCs}}} \right)}}$$
(7)
$${\text{RQ}}_{{i{\text{MPCs}}}} = {\text{C}}_{{i{\text{PAHs}}}} /{\text{ C}}_{{i{\text{QV}}\left( {{\text{MPCs}}} \right)}}$$
(8)

where RQiNCs and RQiMPCs represent low risk and high risk quotients. CiQV (NCs) and CiQV (MPCs) represent the minimum and maximum risk concentrations of PAH monomers in water or sediment. The above RQ calculation method was first applied to the risk assessment of only 10 monomeric ecosystems of PAHs. Cao et al. (2010) further developed the approach with a toxicity equivalent factor that could be used to evaluate risks of other six PAHs monomers (Acy, Ace, Flu, Pyr, BbF and DahA). The CiQV(NCs) and CiQV (MPCs) of the above PAHs were determined from their toxicity equivalence factors, as shown in Table 2.

Table 1 Ecological risk levels of PAHs monomers and ∑16PAHs
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Table 2 Risk quotient (RQ) values of PAHs in water and sediments of the WYR in different seasons
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Results and discussion

Contamination of PAHs in water and sediment

The concentrations and detection frequencies (DFs) of PAH in water and sediment across the WYR are presented in Fig. 2. Among 16 PAH congeners, the DFs of 10 PAH congeners in water were greater than 72%, whereas all 16 PAH congeners were detected in sediment at DFs of > 72%. The remaining 6 PAH congeners (BaA, BbF, BkF, BaP, DahA and BghiP) were rarely detected at the ranges of less than 53% in water, which belong to high molecular weight (HMW) monomers with four to six benzene rings that generally tend to absorb to sediments (Jia et al., 2021). In view of further exploring the concentration distribution characteristics of PAHs, target PAHs were divided into five categories: two-ring, three-ring, four-ring, five-ring and six-ring PAHs. In terms of surface water, total PAH concentrations (Σ16PAHs) were in the ranges of 2.51–102.5 ng/L (mean: 21.41 ng/L, median: 15.53 ng/L). Only NaP (two-ring) exhibited the highest concentration with the ranges of < LOD-88.74 ng/L (median 7.24 ng/L). Other PAH congeners were identified at low concentration ranges of < LOD-8.68 ng/L. Two-ring PAHs were the dominant chemicals in water, with the contribution of 0–87.7% (median: 47.8%) of Σ16PAHs (Fig. 3). Followed by three- and four-ring PAHs, accounting for 3.2–66.1% (median: 29.1%) and 5.7–72.2% (median: 18.7%), respectively. The Log Kow could be a significant driver governing the dynamics for PAHs in water environment. In our study, the lowest Log Kow values of target PAHs were two-ring PAHs (3.37), followed by three-ring PAHs (3.94–4.57) and four-ring PAHs (5.33–5.91). Five-ring PAHs and six-ring PAHs exhibited greater hydrophobicity because of high log Kow values of 6.30–6.84 and 7.23–7.66. Nevertheless, in the sediment samples, the contribution of four-ring PAHs was the dominant, constituting 0.5–44.9% (median: 35.4%) of Σ16PAHs, as well as three- and five-ring PAHs, with the proportion of 2.9–43.7% (median: 22.3%) and 0–32.5% (median: 20.9%), and two-ring PAHs had the lowest proportion (median: 8.8%) of Σ16PAHs. As the molecular weight of PAHs increases, the saturated vapor pressure of PAHs decreases, which leads to the easy detection of low molecular weight (LMW) PAHs (two- and three-rings) in water, while HMW PAHs have a tendency to be deposited in sediments (Zhang et al., 2022). Additionally, the proportions of different categories of PAH may also depend on their emissions. Σ16PAHs were identified in the sediments varying from 5.90 to 2926 ng/g (mean: 325 mg/g, median: 216.9 ng/g). BbF was the predominant congeners in sediments ranging from < LOD-404.7 ng/g (median: 26.04 ng/g), followed by Phe (median: 30.39 ng/g), Fla (median: 26.02 ng/g), Pyr (median: 23.57 ng/g) and Chry (median: 20.42 ng/g). LMW PAHs like Acy (median: 1.03 ng/g) and Ace (median: 1.33 ng/g) were the lowest abundance in the sediment samples. All of these findings further confirmed that HMW PAHs were more prone to be accumulated in sediments, which was consistent with previous investigations in Chinese lake sediment (Gong et al., 2022).

Fig. 2.
figure 2

Box plots of PAH concentrations in surface water (a) and sediment (b). The numbers above the boxes represent the detection frequencies (%) of each compound

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Fig. 3.
figure 3

Composition of PAHs in water (a) and sediment (b) during dry season and wet season of the WYR

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Compared to other regions (Table 3), the levels of Σ16PAH observed in water of WYR were significantly lower than the results of Σ18PAHs in the Daliao River (946.1–13,449 ng/L) (Guo et al., 2007a, b), Σ16PAHs of the Yellow River (144.3–2361 ng/L) (Sun et al., 2009), Σ16PAHs of the Damodar River in India (ND-36000 ng/L) (Ambade et al., 2021). Nevertheless, PAH contamination in sediments was inconsistent with that in water. The sediment concentration of Σ16PAHs in this study was higher than results of the Luan River (20.9–287 ng/g) (Cao et al., 2010; Wang et al., 2016a, b), Σ16PAHs of the Yellow River (Henan section) (16.4–1358 ng/g) (Sun et al., 2009), Σ18PAHs of the Daliao River (61.9–840.5) (Guo et al., 2007a, b), and Σ16PAHs of the Tiber River in Italy (36.2–545.6 ng/g) (Montuori et al., 2016). However, the observed concentration in our study was significantly lower than the level of Σ16PAHs in the Huai River (mean: 7955 ng/g) (Zhang et al., 2017) and Songhua River (mean: 1180 ng/g) (Yang et al., 2020), Σ15PAHs Brisbane River in Australia (mean: 849.0 ng/g) (Duodu et al., 2017), Σ35PAHs of Susquehanna River in the USA (74–18,073 ng/g) (Ko et al., 2007), and Σ16PAHs of Mahakam River of Indonesia (mean: 611.1 ng/g) (Hadibarata et al., 2019). Generally, PAHs concentration in water and sediment in the WYR is at an intermediate level with respect to global rivers.

Table 3 Comparison of concentrations of PAHs in water and sediment with other rivers
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Meanwhile, compared to historical level in Yangtze River, Σ16PAHs in water of our study were significantly lower than observed level of WYR in 2005 (322–6235 ng/L) (Feng et al., 2007), Yangtze River (Hubei section) in 2017–2018 (47.6–1208 ng/L) (Zhao et al., 2021), and WYR in 2019–2020 (50–1051 ng/L) (Yang et al., 2022), while closed to the results of the WYR in 2016 (20.8–90.4 ng/L) (Dong et al., 2018; Wang et al., 2016a, b). In comparison, PAHs observed in water also consisted predominantly of two- to four-rings. However, concentrations for Σ16PAHs in water of Yangtze River showed a significant decreasing trend with compared to previous years. Over recent decades, with the rapid economic and industrial development in Wuhan, fossil energy has been manufactured and consumed extensively. Table S7 summarizes the energy consumption and industrial products in Wuhan for the last 15 years. It is notable that the use of coal and coke and the production of iron and steel in Wuhan peaked in 2013 and then, decreased year by year. This is probably due to the implementation of the ‘Air Pollution Prevention and Control Action Plan’ by the Chinese government in the same year (The State Council, 2013). In this plan, many initiatives were proposed, such as phasing out decentralized coal combustion, promoting VOC pollution control, strengthening traffic pollution prevention and management, and adjusting energy structure. With the adjustment of energy structure and the execution of air pollution regulations, emission of PAHs may be controlled accordingly, and thus, the pollution of PAHs in water bodies has decreased significantly compared with that before 2013.

In terms of sediments, the PAHs in present study were lower compared to the results of Σ16PAHs from the Yangtze River (Wuhan section) in 2005 (mean: 1335 ng/g) (Feng et al., 2007), as well as Yangtze Delta in 2019 (mean: 787 ng/g) (Jia et al., 2021). Moreover, concentrations observed for Σ16PAHs in this study were higher than results of Σ15PAHs from the Yangtze River (a site of Wuhan) in 2009 (80.43 ng/g) (Huang et al., 2016), Σ16PAHs of WYR in 2016 (mean: 191.8 ng/g) (Dong et al., 2018), as well as Σ16PAHs of the Yangtze River (Hubei section) in 2018 (mean: 269.2 ng/g) (Zhao et al., 2021). It is interesting that PAHs in sediments are generally dominated by high rings (four to six rings) in recent years. According to a previous study on historical trends of PAHs in sediments (Feng et al., 2019), the proportion of two–three-ring PAHs in sediments showed a decreasing trend from 1912 to 2007, ranging from 70 to 40%, four-ring PAHs were relatively stable, while five–six-ring PAHs showed an increasing trend year by year, rising from 10 to 40%. It can be seen that input of HMW PAHs to the environment in sediments presented a trend of increasing year by year, which was also observed in the Elbe River (Li et al., 2019). According to previous studies (Wolska et al., 2012), fossil fuels and their products tended to generate four-ring and higher-ring PAHs in their combustion, yet two- and three-ring PAHs were generated by the combustion of industrial and household fuels. The composition of PAHs in sediments reflected that fossil fuels currently accounted for a significant portion of the energy structure. The statistics indicated that the production for gasoline and diesel in Wuhan increased by 98.71% and 33.44% in 2019, respectively, compared to 2007 (Table S7). Even if the economy of Wuhan has suffered a significant impact from the COVID-19 in 2020, the annual gasoline and diesel fuel use increases by 49.96% and 3.38% in comparison with 2007. The rapid development of transportation, shipments and industries after the contained pandemic may be responsible for the release of many PAHs into the environment.

Distribution of PAHs

Water–Sediment Partitioning

Water–sediment exchange is an important process for the transport and diffusion of PAHs in aquatic environments, especially through which further insight into the role assumed by organic pollutants in secondary discharges and deposition can be gathered. We analyzed the equilibrium of water–sediment exchange, using fugacity fraction (ff) (Eq. (4)).

As shown in Fig. 4, the ff (mean) of each PAH monomer between water and sediment was in the ranges of 0.03–0.82. Concretely, ff of Nap and Phe was > 0.7, indicating that both monomers diffused primarily from sediment to water, with sediment being a secondary release source, and Nap followed this pattern in 83.3% of the sampling sites. ff for BkF, BaP, InP, DahA and BghiP had average values of < 0.3, indicating that these monomers were prone to deposition from the water to sediment, and InP and DahA tended to transfer to the sediment phase at all sampling sites. The average ff values of the remaining PAHs monomers, including Acy, Ace, Flu, Ant, Fla, Pyr, BaA, Chry and BbF, ranged from 0.3 to 0.7, which was the indication that these monomers were mainly in equilibrium in both media. Similar results were observed in sediment–water interface of the Yangtze River (Hubei to Jiangsu section) (Zhao et al., 2021). That is, the sediments can be considered as potential secondary release sources of two- to four-ring PAHs and sinks for five- to six-ring congeners. There was a significant negative correlation between ff and the molecular weight of each PAHs (p < 0.05).

Fig. 4.
figure 4

The fugacity fraction (ff) between water and sediments in the WYR

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Spatiotemporal variation

In terms of seasonal and spatial profiles of PAHs, the concentration of Σ16PAHs in water varied from 4.65 to 37.98 ng/L during the dry season, with the median concentration of 17.61 ng/L, while ranged from 2.48 to 102.5 ng/L during wet season, with the median concentration of 13.43 ng/L (Fig. 5). Σ16PAHs and two- to six-ring PAHs in the water of the WYR did not indicate significant seasonal variations (two pair, p > 0.05). Nevertheless, the coefficient of variation (CV) of LMW PAHs (i.e., Nap and Ace) in the dry season is lower than result of wet season, which could be associated with the following factors for this difference. These chemicals generally enter surface waters through dry and wet deposition and runoff. On the one hand, abundant rainfall/runoff may enhance the transport of PAHs from terrestrial and atmosphere to aquatic environments during the wet season (summer). For example, the average flow of our studied rivers (e.g., S11) is 35,026 m3/s in May, which is much higher than in January (12,894 m3/s). On the other hand, higher temperatures in summer improve the solubility of LMW PAHs, further promoting their transport between the gas and sediment phases to the aqueous phase. As shown in the above Sect. "Water–Sediment Partitioning", sediments can be potential sources of two–four-ring PAHs. Similar temporal distribution characteristics of PAHs were observed in the Pearl River Estuary, i.e., PAHs concentrations in water were highest in summer despite the lowest concentrations in winter (Niu et al., 2018).

Fig. 5.
figure 5

Spatiotemporal of PAHs in water (a) and sediment (b) of the WYR

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In the sediment, Σ16PAHs concentrations ranged from 5.90 to 898.8 ng/g during the dry season (median: 133.0 ng/g) and from 10.87 to 2926 ng/g during the wet season (median: 295.1 ng/g). PAHs in the sediment presented the maximum concentrations in summer, consistent with the seasonal distribution of PAHs in water. Likewise, no significant difference for Σ16PAHs and two- to six-ring PAHs was also observed between dry and wet season (p > 0.05). The CVs of HMW PAHs in wet season (summer) are higher than those of the dry season, suggesting rainfall/runoff and high temperature have probably facilitated the transport of these chemicals from terrestrial and atmospheric to aqueous environments (Ontiveros-Cuadras et al., 2019). As rainfall, runoff erosion and wastewater discharge further intensified, much of PAHs bonded to particles in water reached WYR and were ultimately deposited in the bottom sediment. On the other hand, strong hydrological conditions during the wet season could have resulted in extensive sediment resuspension and migration with water flow. It is noted that more than 80% of the annual average sediment in the Yangtze River is discharged into the Yangtze estuary (Guo et al., 2007a, 2007b). These may contribute to the large differences in sediment PAHs concentrations and also explain the lack of significant differences in Σ16PAHs and two–six-ring PAHs between the dry and rainy seasons (p > 0.05).

In spatially, the most serious contamination of PAHs in water was observed at S12 during the wet season, followed by S13 and S14. Notably, PAHs in these areas were dominated by two-ring PAHs (Nap), with accounting for 81.7–87.7% of Σ16PAHs concentration. The elevated Nap concentrations in these areas might be largely due to their proximity to large industrial areas (i.e., steel plants, petrochemical plants, and chemical plants) in Wuhan City. Nap is extensively derived from petrochemicals, fossil fuel combustion and coking industries, etc. (Stogiannidis & Laane, 2015). In accordance with previous studies, areas with high PAHs pollution in inland cities were also usually located in oil extraction areas or/and developed industrial cities (He et al., 2021). In the sediment, the most polluted area was at S16, dominated by four-ring and five-ring PAHs. This area is located downstream of the wharf and the sluice gate; on the one hand, the movement and docking of a large number of vessels may lead to PAHs being released into the surrounding environment. On the other hand, a large amounts of sediment deposition lead to high concentration due to the interception of water by the locks.

Quantification of PAH sources

Before determining specific control measures, the source of pollutants should be determined. The methods commonly used for source analysis include diagnostic ratio method and principal component analysis (PCA). The former can only be used for qualitative analysis and cannot specifically distinguish the type and contribution rate of pollution sources (Katsoyiannis & Breivik, 2014). PCA is a dimensionality reduction method that provides characterized insights on potential pollution sources with simple data. Nevertheless, its application process will produce negative factor loading, and only non-negative values have physical significance, which brings difficulties to the analysis of pollution (Yu et al., 2015). In contrast, the positive matrix factorization (PMF) method not only provides more details for source resolution of PAHs in samples and validates source information, but also evaluates the contribution from different sources to PAHs for quantitative description of PAHs sources. Thus, the PMF method in this study was used to analyze specific PAHs to determine the potential sources.

Four factors were obtained in water and sediment based on PMF analysis, and the species contribution is indicated in Fig. 6. In the water, source 1 was dominated by Acy, Ace, Flu and accounted for 15.9% of total factor contribution. Acy was the indicators of straw and firewood combustion; hence, source 1 probably came from the wood combustion (Khalili et al., 1995). Source 2 was consisted with Phe, Ant and Fla, which were also major releases in the coal combustion and steel industry (Duval & Friedlander, 1981; Larsen & Baker, 2003), contributing 14.0% of the total sources; therefore, source 2 was primarily associated with coal combustion and industrial sources. Interestingly, source 3 showed the most significantly contribution of total PAHs about 58.5% and Nap had the highest weight in factor 3 accounting for about 90%. This may be mainly associated with oil spills and industrial sources as Nap was a marker of oil spills and industrial waste gas (Larsen & Baker, 2003; Ravindra et al., 2008). Source 4 was strong in its relevance to HMW PAHs including Ace, Ant, Fla, Pyr, BaA, Chry, BbF, BkF, BaP, InP, and BghiP, accounting for 11.6% of the sources. It is generally known that most of these compounds originated from the combustion of gasoline and diesel (i.e., engine combustion emissions) (Han et al., 2019; Liu et al., 2018). Conversely, PCA indicated that combustion of coal and coke (dominated by Ana, Flu, Phe, Ant, Pyr and BaP) was the most significant source of PAHs in water of the Yangtze River (Hubei to Jiangsu section) (67.0%), followed by natural gas, gasoline and diesel combustion sources (Zhao et al., 2021). Differences in assessment may be explained to some extent by the use of different source resolution methods. In addition, the differences in energy and industrial structures in different regions could be responsible for the variations in the composition of local PAH pollution (Gong et al., 2022).

Fig. 6.
figure 6

Source compositions of PAHs in four factors obtained using PMF model in the water (a) and sediment (b) of the WYR

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In terms of sediment, source 1 was weighted at 15.5% of total factor contribution and was characterized by wood combustion, mainly composed of Nap, Acy and Ant (Khalili et al., 1995). Source 2 mainly included Nap, Flu and Phe. Oil spills commonly release some LMW PAHs such as Nap (Larsen & Baker, 2003). Also, Flu can be used to index coking and steel production (Wang et al., 2009). For this reason, PMF source 2 showed that mixture combustion including oil spills and industrial production (22.2% of the total factor). The PMF source 3 exhibited the highest weight (44.6% of total factor), indicating strong relationship with HMW PAHs, i.e., BbF, BkF, BaP, InP, DahA and BghiP (Simcik et al., 1999; Wang et al., 2014; Yu et al., 2019). This constituted mainly a traffic emission source. Source 4 was of significant weight in coal sources like chemicals Ace, Flu, Phe, Ant, Fla and Pyr (Sofowote et al., 2008; Wu et al., 2019), accounting for 17.7% of the total factor. Gong et al. (2022) used PMF to resolve the sources of PAHs in lake sediments in southern China, with the dominant source being the mixed sources of biomass combustion and industrial production (38.9%), followed by diesel and gasoline combustion sources (25.3%), and coal combustion sources (35.9%). The discrepancy in the main sources is probably due to the diversity of traffic and energy consumption structures between river and lake. More shipments and higher density vehicles in the surrounding area emitted more HMW PAHs in the river. Additionally, abundant shipping in the Yangtze River may be the primary reason for oil spills resulting in high LMW PAHs content.

Comparatively speaking, some differences were generally observed in the composition and proportion for PAHs sources from water and sediment. Specifically, oil leakage and industrial production were the dominant pollution sources for PAHs of water, and the compositions were mainly LMW PAHs (i.e., Nap), while PAHs in sediment were primarily from traffic pollution sources, and HMW PAHs were the predominant components. As mentioned before, the chemical properties of the compounds, i.e., the n-octanol water distribution coefficients, precipitation, and hydraulic conditions, lead to different distribution characteristics in water and sediment. Overall, anthropogenic activities, i.e., traffic emissions, oil and industrial production have the greatest impact on PAH pollution in the current study area.

Flux of PAHs in WYR

Mass flux is considered as an important indicator for water quality evaluation, prediction and water pollution control. With the purpose of further understanding the PAHs contamination in the WYR and the mass load of PAHs from study area, the PAHs annual fluxes were estimated using monthly measured concentrations (12 months). As in Eq. (1), the PAHs mass flux was governed by both the concentration and influx into water streams. Under large hydraulic conditions, the PAHs flux remains elevated even when the PAHs concentration is low. Nonetheless, flux information can enable regulatory surveys and monitoring networks to better understand river pollution and implement more effective control and remediation strategies (Poulier et al., 2015).

In this study, site 11 was chosen for the calculation of the PAHs flux in the WYR. Overall, the seasonal characteristics of the studied area were 90 days, 153 days and 122 days, for dry, wet and normal seasons, respectively. Seasonal fluxes for Σ16PAHs at site 11 were evaluated with Eq. (1), as shown in Fig. 7. It showed that the total annual flux of Σ16PAHs was estimated to 28.77 t, with the largest load in the wet season (21.13 t), accounting for about 73.5% of the annual fluxes. In addition to the length of seasonal period, the difference of the river flow rate (wet season > dry season) might also contribute to this. The estimated annual load for individual PAHs ranged from 0.04 t (BkF) to 20.93 t (Nap). Notably, two-ring PAHs (Nap) with the extensive contribution accounted for 72.7% of the total fluxes. In some studies, PAH fluxes were obtained by multiplying their concentrations in water and suspended matter with river flow. However, the variation of sand content in water is often greatly affected by the differences in geographical characteristics and hydrological conditions at different areas, which may lead to overestimation of the PAHs flux through suspended solids. The previous study evaluated the fluxes of PAHs in Pearl River runoff based on monthly monitoring of PAHs in the water and suspended solids using spot sampling. The annual loads of Σ27PAHs and Σ15PAHs were estimated to be 60.2 and 33.9 metric tons, respectively (Wang et al., 2007). Similarly, the wet season (April to October) contributed about 81% of the total mass flux driven by the high flow of the season. These values were significantly higher than the estimation in our study.

Fig. 7.
figure 7

Fluxes of PAHs in the WYR

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In addition, the monitoring methodology used to estimate mass flux in this study was spot sampling and it could only reflect the instantaneous pollution in the water and fail to capture the concentration changes in the real time, which might overestimate or underestimate the actual pollution status. To better understand the pollution in the river, integrated sampling tool (e.g., passive sampling or auto-sampling) might be useful to be employed for more closely reflecting the water pollution loads (Zhang et al., 2016). On the other hand, PAHs represent a class of volatile and lipophilic chemicals that can transport between multiple media, so future systematic flux studies may need to involve different media (atmospheric, biological, soil).

Risk assessment

The ecological risks level of water and sediments in the WYR was evaluated using the RQ method [Eq. (6) and (7)] to determine the environmental risk of each PAHs. Table 2 indicated the average values of RQNCs and RQMPCs for each PAH monomer and Σ16PAHs. The risks for the 16 PAH monomers in WYR water were low (RQ(NCs) < 1) except for Acy, Ace, Flu and Pyr, which were at median risks (RQ(NCs) ≥ 1 and RQ(MPCs) < 1) during the dry season. In contrast, Nap, Flu, Pyr, and BbF were among the PAHs of water with medium ecological risks (RQ(NCs) ≥ 1 and RQ(MPCs) < 1) in the wet season, and the remaining PAHs presented low risks. The RQNCs values of Σ16PAHs in the dry and wet seasons were 12.51 and 10.93 (both > 1 and < 800), respectively, while the RQMPCs values of Σ16PAHs were 0.13 and 0.11 (both < 1), respectively, indicating that PAHs in water from the WYR were at low risk level overall.

In terms of sediment, PAHs monomers in the dry season of the WYR were at medium risk level (RQ(NCs) ≥ 1 and RQ(MPCs) < 1), except for Chry, BkF, BaP, InP, DahA and BghiP, which were at low risk. As for PAH monomers in wet season sediments, they were also at medium risk, except for Chry, BkF, InP, DahA and BghiP, which were at low ecological risks. Regarding the RQ of Σ16PAHs, the RQNCs of the Σ16PAHs in the dry season sediment were in the ranges of 3.91–297.6 (mean: 72.04), with the values of 1 < RQ < 800. Also, the RQMPCs of the Σ16PAHs ranged from 0.04 to 3.46 (mean: 0.85). Of these, the RQMPCs values of the Σ16PAHs with 22.2% of the sampling sites (i.e., S3, S7, S8 and S16) were all greater than 1, indicating that the PAHs at these areas were at medium risk level. In the wet season, the RQNCs of the Σ16PAHs in the sediments ranged from 4.97 to 592.1 (mean: 104.4), while the RQMPCs values of the Σ16PAHs values ranged from 0.06 to 7.54 (mean: 1.26), with 44% of the sites at medium risk level 2 (RQMPCs > 1). The remaining sites had RQMPCs values of Σ16PAHs less than 1, which were at low risks.

In comparison, the ecological risk of PAHs in the WYR was relatively lower than the risk of PAHs in the previous. Among the 16 PAHs studied of the Yangtze River Delta (Jia et al., 2021), the average values of RQNCs in water (15 PAHs) and sediments (14 PAHs) were > 1. And the water and sediments at one of the sites had high risks (RQNCs > 800 and RQMPCs > 1 for Σ16PAHs). In the Yangtze River (Wuhan section) (Feng et al., 2007), the mean values of RQMPCs in water (for Ant, Acy and Σ16PAHs) and sediments (Acy, Ace, Flu, Ant and Pyr) were > 1, indicating high ecological risks in this region. Among the 16 PAHs, 8 PAHs of water and 15 PAHs of sediment posed high risks to the region. Moreover, the risks of PAHs in the water and sediment of the WRY were significantly lower than that of the Luan River (Cao et al., 2010). Nine out of 16 PAHs posed high risks to water and sediment in the Luan River. In these studies, although the risk levels of PAHs were inconsistent, three and four-ring PAHs were the primary risk burden to ecosystems, with some degree of ecosystem risk to water and sediment from two-rings. Also, the risk of this study was consistent with this characteristic. Five and six PAHs contributed to a certain percentage of PAH concentrations, yet their proportions in water were usually much lower. Additionally, although five and six PAHs exhibit highly mutagenic and carcinogenic, with less mutagenic for two- to four-ring PAHs, the latter have lower ecological effect concentrations in water and sediments than the former (see Table 2), which may lead to higher ecosystem risks for two- to three-ring PAHs. Overall, PAHs with two to three rings usually posed median risks to WYR thus requiring attention to these substances. PAHs posed higher risk to sediments than that to waters. Therefore, it is recommended that appropriate management measures should be taken to control the sources of PAHs according to the pollution characteristics in the WYR.

Conclusion

The spatial and temporal distribution, source identification and risks of PAHs in the water and sediment along the WYR were studied. The concentrations of Σ16PAHs in water ranged from 2.51 to 102.5 ng/L (mean: 21.41 ng/L). Two-ring PAHs was the dominant chemicals in water, with the contribution 47.8% (median) of Σ16PAHs, followed by three-ring (median: 29.1%) and four-ring PAHs (median: 18.7%). Σ16PAHs were identified in the sediment ranging from 5.90 to 2926 ng/g (mean: 325 mg/g). The contribution of four-ring PAHs was the dominant in the sediment, accounting for 35.4% (median) of Σ16PAHs, followed by three-ring (median: 22.3%) and five-ring PAHs (median: 20.9%). In addition, WYR sediments were a secondary source of low-molecular PAH emissions and a sink for high-molecular congeners as indicated by the ff. PAHs in the WYR were dominated by anthropogenic activities, with oil and industrial production being the most important sources in water while traffic pollution was the main sources in sediments. Based on seasonal and spatial distribution, PAH pollution levels were prominent in industrially developed regions, and rainfall/runoff was considered as the main transport pathway for PAHs. The total annual flux of Σ16PAHs was estimated to 28.77 t. The risk quotient indicated that Σ16PAHs posed low risks in surface water, and 44% of the sites in the sediment of WYR were at medium risks at least. Therefore, the ecological risks caused by PAHs in WYR are expected to require attention.