Introduction

Polycyclic aromatic hydrocarbons (PAHs) have become a worldwide environmental issue in recent decades due to their toxicity and prevalence in various environmental media (Bai et al., 2022a, b; Li et al., 2021). Some PAHs have strong teratogenic and carcinogenic effects, and exposure to these hydrocarbons can cause a variety of negative health effects (Cao et al., 2020; Li et al., 2022). Thus, the United States Environmental Protection Agency (USEPA) listed 16 of them as the main pollutants of concern in 1976 (USEPA) (Zhang et al., 2022).

PAHs are unavoidable products resulting from the incomplete combustion of carbon-containing materials; thus, they can come from natural sources or those related to human activities. In particular, anthropogenic PAH sources have increased the contents of PAHs through either combustion (e.g., industrial pyrolysis and shipping-related and urban coal) or petrogenic sources (Han et al., 2021). Due to their hydrophobic and lipophilic properties (Corminboeuf et al., 2021), PAHs are preferentially first adsorbed onto suspended particles or deposited in surface sediments after entering aquatic environments, and sediments are considered one of their main environmental fates. The migration and transformation of PAHs in sediments have significant effects on aquatic ecology (Škrbić et al., 2018). Therefore, much attention has been given to the PAHs in sediments from different aquatic environments. PAH residues have been observed in estuarine sediments (Li et al., 2021; Montuori et al., 2016), lake sediments (Gu et al., 2017), intertidal sediments (Li et al., 2017), and bay sediments (Bateni et al., 2022; Shi et al., 2022a, b).

Source analyses of environmental pollutants are the basis for quantifying their impacts on the environment. Source-apportionment methods developed for PAHs in water environments mainly include diagnostic ratio and statistical analyses. The diagnostic ratio method directly uses the chemical properties or some chemical parameters of PAHs to identify pollution sources. To some extent, the composition of PAHs in media can reflect their main sources and input pathways. In general, 2- or 3-ring low molecular weight PAHs originate from petroleum sources, and 4- to 6-ring high molecular weight PAHs originate from combustion sources (Deng et al., 2013; Han et al., 2021; Hong et al., 2022). However, it is difficult to determine the sources and relative contribution rates of PAHs with similar compositions. Statistical analyses use mathematical means to identify sources with methods such as principal component analysis (PCA), in which multiple variables are transformed into a few comprehensive variables (Sun et al., 2022; Wang et al., 2021; Li et al., 2021; Ciucure et al., 2023). Positive matrix factorization (PMF) is one of the most widely used research methods in recent years because it uses the weighted least-squares method to perform iterative calculations and quantify the contributions of source components without measuring the source component spectrum (Gao et al., 2012; Hong et al., 2022).

To clarify whether PAHs can produce adverse effects on the ecological environment, it is necessary to assess their ecological risks. Various methods have been used to evaluate the ecological risks of PAHs in past studies, such as sediment quality guidelines (SQGs) and the quotient value method. SQGs can be used to qualitatively assess the possible ecological risk based on the contents of each PAH monomer found in the sediments, whereas the quotient value method can provide quantitative estimations based on ecotoxicity or potential carcinogenicity. The most widely used quotient value methods include the mean effects range median quotient (M-ERM-Q), ecological risk quotient (RQ), and toxicity equivalence quotient (TEQ) methods. Although all of these methods are intended to be used for risk assessments, they differ from each other in their factor-selection and assessment criteria. M-ERM-Q is used mainly to quantitatively predict the comprehensive ecotoxicity of various PAHs (Han et al., 2021; Lin et al., 2018), whereas RQ uses monomer risk standard values to estimate the ecological risks of 16 PAH monomers and ∑16PAHs (Zhang et al., 2022). TEQBaP uses the toxicity equivalence factor (TEF) based on benzo[a]pyrene (BaP) to evaluate the carcinogenic potencies of 16 PAH monomers, thus achieving quantitative risk assessment (Ihunwo et al., 2021). These methods have been widely applied in risk assessments of PAHs in sediments. To our knowledge, however, most of the existing relevant studies have used these methods to conduct independent analyses but have failed to consider multifactor combinations.

Baiyang Lake (BYL) is the largest freshwater lake on the North China Plain. Since China announced the plan to create the Xiongan New Area, a new economic zone, special research attention has been given to the important ecological function of this lake. In the last 10 years, many people have studied the pollutants in the aquatic environment of BYL, including pollution associated with pharmaceuticals and personal products, fluorescent whitening agents, quinolone antibiotics, and perfluoroalkyl substances (Liu et al., 2019; Wu et al., 2021; Yang et al., 2021; Zhang et al., 2020). Heavy metals and microplastics are also present in lake sediments (Ji et al., 2019; Zhou et al., 2021). Previous studies only qualitatively analyzed the sources of PAHs in Baiyang Lake using the molecular diagnostic ratio method (Hu et al., 2010), while quantitatively identifying and evaluating the sources of PAHs and the contribution of each source in the study area is still unclear.

Therefore, this study aimed to (1) investigate the latest spatial distribution and composition characteristics of PAHs in the sediments covering BYL, (2) identify qualitatively and quantitatively the sources of the PAHs using the molecular diagnostic ratio (MDR) method and PMF model, (3) estimate the ecological risk of PAHs by various methods, and finally (4) rank the priority pollutants of PAH monomers according to their physicochemical properties, abundance, and ecological risk levels. This work provides a method for establishing a list of priority pollutants in sediment to promote effective water pollution control, while also providing more accurate data support for PAH pollution control in sediment of BYL.

Materials and methods

Study area and sampling

BYL, the largest freshwater, shallow lake with an herbaceous marsh wetland ecosystem in China, is located in the southeastern Xiongan New Area (39°04′–40°04′ N, 113°39′–116°11′ E). This lake plays an important role in promoting local economic development and transportation and maintaining ecological balance (Wang et al., 2020). In the past, nine rivers flowed directly into the lake (Zhang et al., 2020). However, the monsoon climate and extensive water conservancy project construction have led to seasonal droughts and a reduction in river surface runoff (Zhao et al., 2021). Now, the Fu River is the stable replenishment stream of BYL, which mainly accepts the effluent from the upstream Baoding sewage treatment plant (Wang et al., 2014). There are 89 villages with a permanent population of approximately 279,000 around the lake. The water quality and ecosystem of the lake have been affected by domestic sewage and garbage carried by rivers and discharged by residents in the lake area, as well as by water transportation and villagers’ enclosure farming activities (Yang et al., 2021).

According to the national environmental automatic monitoring points and provincial monitoring points set by China, a total of 22 sampling sites were set up in BYL (numbered as per shown in Fig. 1). In this study, all sampling was conducted in October 2021. Surface sediment (0–10 cm) was collected using a stainless-steel grab sampler, packaged in a double-layer self-sealing bag covered with tin paper on the surface and placed into a refrigerator until being transported to the laboratory. After returning to the laboratory, the sediment samples were freeze-dried for the first time, ground with an agate mortar, poured through a 100-mesh copper sieve, and stored in a freezer until analysis.

Fig. 1
figure 1

Map of sampling sites in the BYL

Full size image

Chemicals and materials

HPLC-grade n-hexane and dichloromethane (Tianjin, China) were used for the sample extraction and purification procedures. Analytical pure anhydrous sodium sulfate purchased from Kermel (Tianjin, China) and copper flakes purchased from Sinopharm were used for dewatering and removing sulfur in the extraction process, respectively. Neutral silica gel columns, purchased from Agilent (CA, USA), were activated with ultra-pure water. The sixteen PAH standards were purchased from Alta (Tianjin, China).

PAH extraction and analysis

All samples were extracted by ultrasonication. Briefly, 5 g of the samples was accurately weighed and mixed with anhydrous Na2SO4, and extracted for 0.5 h with 30 mL of n-hexane/dichloromethane (1:1, V/V) and 0.5 g copper powder. The extract was incubated for 0.5 h, and then the supernatants were transferred into a conical flask. The extraction was repeated 3 times. Subsequently, the extract was purified by neutral silica gel columns and eluted with 20 mL of dichloromethane/n-hexane (4:6, V/V) three times. The eluent was mixed, rotated, and evaporated until nearly dry, and then 10 mL of n-hexane concentrated to 1 mL was added for replacement. The final solution was filtered through a 0.22-μm filter membrane before next analysis.

Sixteen PAHs were identified using an Agilent 7890-5977A gas chromatography-mass spectrometer (GC–MS) (CA, USA). The oven temperature was set from an initial temperature of 50 °C for 2 min, ramped to 250 °C at a rate of 20 °C·min−1, held for 2 min, increased to 290 °C at 5 °C·min−1, and held for 5 min. Ultra-pure helium was used as the carrier gas for analysis at a flow rate of 1 mL·min−1 with an HP-5 fused silica capillary column (15.0 m × 0.25 mm × 0.25 μm) (Folsom, CA). Selected ion monitoring (SIM) mode was applied for analysis, and the electron bombardment energy was 70 eV. The temperatures of the sample inlet and ion source were set to 290 °C and 270 °C, respectively.

Quality assurance/quality control (QA/QC)

The tinfoil paper was soaked in dichloromethane for 24 h in advance and then dried until sampling to avoid contamination by impurities affecting the experimental analysis results. The accuracy of the standard curve was ensured before each test using the PAH standard mixture. A procedural blank was set after every 5 samples. The external standard method was used for the quantitative analysis and blank analysis to calculate the limit of detection (LOD) and limit of quantification (LOQ). Through repeated analysis, the LODs were set at 3 times the signal-to-noise ratio. In the sediment samples, the LODs and LOQs ranged from 0.10 to 0.45 ng·g−1 and from 0.04 to 0.18 ng·g−1, respectively. The recovery of each sample was determined to be between 71 and 115%. The relative standard deviation (RSD) of the repeated test sample was low (< 10%). The ∑16PAH contents were measured based on dry weight and were not corrected for surrogate recoveries.

Source identification of PAHs

The PMF model uses the weighted least-squares iterative calculation method to quantify the contribution rate of each major source (Ma et al., 2020). The model used in this study was EPA PMF5.0. The principle is shown in Eq. (1) as follows:

$${X}_{ij}={\sum}_{k=1}^p{G}_{ik}{F}_{kj}-{E}_{ij}$$
(1)

where Xij is the content of the jth PAH compound of the ith sample, p is the number of factors, Gik is the contribution of factor k to sample i, Fkj is the content of species j in factor profile k, and Eij is the residue of the jth PAH in sample i.

The minimizing objective function Q sums all sample residuals and their uncertainties to obtain each contribution and composition of factors and then obtain the optimal solution. The Q value is obtained using following equations as Eq. (2) (Huang et al., 2021; Riaz et al., 2019):

$$Q=\sum \limits_{i=1}^n\sum \limits_{k=1}^m\left[\frac{E_{ij}-{\sum}_{k=1}^p{G}_{ij}{F}_{ij}}{U_{ij}}\right]$$
(2)

where n is the number of samples and m is the number of chemical components.

The PMF needs to be imported into the uncertainty data before running. If the PAH contents were below the method detection limit (MDL), Uij was equal to the MDL; otherwise, Uij was calculated using Eq. (3) as follows:

$${U}_{ij}=\sqrt{{\left( RSD\times {X}_{ij}\right)}^2+{\left(0.5\times MDL\right)}^2}$$
(3)

where Xij is the measurement matrix of species j in sample i and RSD is the relative standard deviation of the PAH content.

Ecological risk assessments

Sediment quality guidelines (SQGs)

MacDonald et al. (1996) optimized the weight of evidence of SQGs and proposed the effects range low (ERL) and effects range median (ERM). The SQGs are used to determine three concentration ranges of chemicals by comparing the contents of target pollutants with the ERL or ERM values (Li et al., 2021). When the PAH content is lower than the ERL value, effects on the biological system rarely occur. When the PAH content is between the ERL and ERM values, adverse biological effects occasionally occur. Once the PAH content in the sediment exceeds the ERM value, adverse biological effects may frequently occur (Cui et al., 2022; Han et al., 2021).

Mean effects range median quotient (M-ERM-Q)

The risk level was classified for the sediments at each sampling site of the M-ERM-Q model (Lin et al., 2018). This method is usually used to estimate the ecological risk level of PAH mixtures in sediments. The calculation model is expressed with Eq. (4) as follows:

$$M- ERM-Q=\frac{1}{n}\sum \limits_{i=1}^n\frac{C_{\textrm{i}}}{ERM_{\textrm{i}}}$$
(4)

where Ci is the content of PAH i in sediments, ERMi is the ERM of the corresponding PAH i, and n is the number of PAHs. The ERM data of the PAHs are shown in Table S1.

Ecological risk quotient (RQ)

The RQ was summarized by Kalf et al. (1996), who used the values of negligible contents (NCs) and maximum permissible contents (MPCs) to obtain the reference contents of 16 kinds of PAHs. The ecological risk of PAHs in sediments was evaluated by calculating risk entropy. The calculation method of the RQ is provided with Eq. (5) and Eq. (6) as follows:

$${RQ}_{(NCs)}={C}_{PAHs}/{NC}_S$$
(5)
$${RQ}_{(MPCs)}={C}_{PAHs}/{MPC}_S$$
(6)

where CPAHs is the content of PAHs and CQV(NCs), CQV(MPCs), NCS, and MPCS are the lowest and the highest risk standard and quotient values, respectively. The specific ecological risk classifications of individual PAHs and ∑PAHs are listed in Table S2.

Toxicity equivalent quotient (TEQBaP)

The TEQ through potential toxicity was used to estimate the potential ecological risk of PAHs. The TEQ was calculated using Eq. (7) (Li et al., 2015):

$$T\textrm{o} talTEQ=\sum {C}_i\times {TEF}_i$$
(7)

where Ci is the content of PAH i in the sediment and TEFi is the toxicity equivalent factor of compound i. The TEF data of 16 PAH monomers are shown in Table S1.

Priority pollutant ranking

To more accurately assess the adverse effects of the target pollutants in the regional sediment on the ecological environment, the multi-index comprehensive evaluation method was used to establish a list of priority pollutants. This method sets four different types of evaluation criteria: (1) environmental exposure to pollutants; (2) persistence; (3) bioaccumulation; and (4) ecological risk (Zhao et al., 2021).

The environmental exposure of pollutants is based on the detected contents of pollutants. The persistence of pollutants is mainly measured by the biodegradation coefficient (BioWIN). The smaller the BioWIN value, the stronger the persistence of the pollutant. In addition, the octanol water partition coefficient (Kow) is used to evaluate the bioaccumulation of pollutants. The BioWIN and Kow values of all selected pollutants were obtained from the EPA EPI Suite software. Ecological risk is based on the environmental ecological risk values of the pollutants, mainly including M-ERM-Q, RQ, and TEQBaP.

Oc, BioWIN, Kow, M-ERM-Q, RQ, and TEQBaP were divided into five grades according to the geometric progression method and then assign points according to the (2/3) cumulative rank method (USEPA) (Table S3), with a full score of 100 for each standard. In calculating the weight of importance, equal weight was given to each criterion to avoid any artificial judgment deviations. The comprehensive score of each PAH monomer was the sum of the environmental exposure, persistence, bioaccumulation, and ecological risk scores, where ecological risk is the average M-ERM-Q, RQ, and TEQBaP score.

The list of priority pollutants was generated from the highest to the lowest comprehensive score. Then, the priority pollutants were further divided into three grades according to the method of arithmetic progression, among which the compounds in the first grade were the highest priority pollutants. In the RQ assignment process of PAH monomers, if RQ(MPCs) < 1, only the RQ(NCs) score was taken; however, if RQ(MPCs) > 1, the average score of RQ(NCs) and RQ(MPCs) was taken.

Results and discussion

PAH content levels and compositions in sediment

PAH content levels

The detected contents of each monomer of sixteen PAHs in sediments were identified by the proposed GC-MS method. The performance of this method is listed in Table S4. The contents of PAHs detected in sediments of BYL are listed in Table 1.

Table 1 The contents of PAHs detected in sediments of BYL
Full size table

The contents of ∑16PAHs in sediments ranged from 114 to 1010 ng·g−1, which were higher than the contents detected by Hu et al. (2010). The contents of detected 2-ring PAHs, 3-ring PAHs, 4-ring PAHs, 5-ring PAHs, and 6-ring PAHs ranged from 5 to 44 ng·g−1, 0.01 to 46 ng·g−1, 0.2 to 84 ng·g−1, 0.2 to 157 ng·g−1, and 3 to 173 ng·g−1, respectively. The contents of 2-, 3-, 4-, 5-, and 6-ring PAHs contributed 17.3%, 6.4%, 16.9%, 26.8%, and 32.6% to the ∑PAHs, respectively (Fig. S1). We observed that most of the sites had abundant 4-, 5-, and 6-ring PAHs, as shown in Fig. S1. This characteristic composition was similar to the levels measured in the sediments of Taihu Lake (Sun et al., 2022). The average BbF content was the highest (62 ng·g−1), followed by those of BghiP (48 ng·g−1), BaP (47 ng·g−1), IcdP (44 ng·g−1), and Chr (28 ng·g−1). Such a dominant composition was close to the levels measured in the sediments from the river mouths of Kaohsiung Harbor, Taiwan (Chr, BbF, BaP, IcdP, and BghiP contents of 23, 84, 45, 48, and 46 ng·g−1, respectively) (Dong et al., 2014). The high ratio of high molecular weight PAHs in sediments may have been due to the hydrophobic and lipophilic properties and bacterial degradation. High molecular weight PAHs have a higher affinity for esters and are more resistant to degradation, enabling most of them to be effectively transported to and easily accumulate in sediments (Corminboeuf et al., 2021; Škrbić et al., 2018). In contrast, low-cyclicity PAHs are found mainly in the aquatic phase due to their low octanol partition coefficient and microbial degradation reducing their enrichment in sediments (Montuori et al., 2016).

Compared to other water environments that have been studied globally (Table S5), the ∑16PAH contents in sediments from the BYL (114–1010 ng·g−1) were similar to those found in the Serbian rivers and canals (Serbia) by Škrbić et al. (2018), in the Pearl River Delta (China) by Li et al. (2021), in the Marano and Grado Lagoon (Italy) by Acquavita et al. (2014), and in the Durance River (France) by Kanzari et al. (2014). The ∑16PAH contents measured in the present study in sediment samples were higher than those found in the Yangtze River estuary, China (Liu et al., 2020), Awassa Lake, Ethiopia (Mekonnen et al., 2015), and the Amazon estuary, Brazil (Rodrigues et al., 2019) and lower than those found in the Niger River, Assai River, and Forcados River, Nigeria (Iwegbue et al., 2021), Mitty River, India (Singare, 2015), Brisbane River, Australia (Duodu et al., 2017), and Huviorn River, France (Kanzari et al., 2014). These comparisons show that the PAH contents in the sediments in our study area were at a moderate level globally.

Spatial distribution of sediment samples

Since the RSDs of the 16 kinds of PAHs were all greater than 50%, and some were even greater than 100%, the changes in the PAH contents varied greatly among different sites. The enormous content alterations of PAHs in sediments were closely related to human activities along the river because the samples were collected at different sites (Ciucure et al., 2023). The spatial variations in PAH contents in the surface sediments of rivers and lakes were attributed mainly to human activities, including traffic emissions, fuel consumption, and untreated urban wastewater emissions (Dong et al., 2022; Ciucure et al., 2023). Based on ArcGIS 10.7 software, the inverse distance-weighting method was used to interpolate the ∑16PAH contents at different sampling points and obtain a content contour map. The spatial pattern of ∑16PAHs in sediments from BYL is presented in Fig. 2.

Fig. 2
figure 2

Spatial distribution of ∑16PAHs in sediments of the BYL area

Full size image

As shown in Fig. 2, the ∑16PAH contents of BYL were high in the southeastern and western lake regions, low in the southern and northern lake regions, and highest at site S19, which was located in the Fu River estuary close to villages and the traffic trunk road. A small peak in the PAH contents was observed at site S4 (935 ng·g−1), which was located east of Quantou Village at one of the main river courses in the north–south direction. This river course receives most of the water flowing from the southern direction of lake at sites such as S7, S12, and S16, with frequent water traffic. Another peak occurred at site S16 (823 ng·g−1), which was adjacent to a village wharf with multiple large ships and frequent personnel activities. Several water flows converged downstream, which decreased the ∑16PAH contents at sites S11 (125 ng·g−1) and S6 (245 ng·g−1) at the outlet of the BYL. Other relatively high ∑16PAH contents were detected at sites S1, S5, S7, and S8, which were located near villages with high populations and frequent production and living activities. Sites S10, S15, S17, and S21, which were located along the edges of the lake area, far from villages and near the wide-open water surface with little human activity, had low ∑16PAH contents. These results showed that the sediments around the Fuhe River estuary and the village were seriously polluted by PAHs, especially in areas with extensive human activities (Hu et al., 2010). PAH pollution in BYL was affected mainly by human activities.

Source identification of PAHs in the sediments

PAH sources identification by MDR

Different ratios of Fla/(Fla+Pyr), Ant/(Phe+Ant), IcdP/(IcdP+BghiP), and BaA/(BaA+Chr) correspond to different sources, and intuitive judgments can often be made by making ratio cross-plots (Dudhagara et al., 2016; Masood et al., 2018). Briefly, Fla/(Fla+Pyr) ratios < 0.4 indicate petroleum spills, ratios between 0.4 and 0.5 suggest petroleum combustion, and ratios > 0.5 indicate the dominance of biomass combustion. Ant/(Phe+Ant) and ∑LMW/∑HMW values < 0.1 and > 0.1 have been determined to correspond to petroleum leakage and combustion, respectively. BaA/(BaA+Chr) ratios < 0.2, between 0.2 and 0.35, and over 0.35, as well as IcdP/(IcdP+BghiP) ratios < 0.2, between 0.2 and 0.5, or > 0.5, are attributed to petroleum spills, petroleum combustion, and biomass combustion, respectively (Han et al., 2021; Lin et al., 2018).

In this study, the Ant/(Ant+Phe) ratios at all sites (except S20) were above 0.1 (Fig. 3a), and the fountainhead of PAHs could thus be attributed to combustion sources. The BaA/(BaA+Chr) ratios of most sampling points were greater than 0.2, indicating combustion sources, while the ratios of some points were greater than 0.35, suggesting that mixed sources, such as petroleum, liquid fossil fuel, biomass, and coal combustion, were responsible for the PAHs (Fig. 3b) (Montuori et al., 2016). Similar conclusions were reached by observing the ratio of IcdP/(IcdP+BghiP), which indicated combustion sources (Fig. 3c). Concurrently, the ∑LMW/∑HMW ratios indicated that the PAHs at all points were sourced from pyrogenic sources (Li et al., 2017) (Fig. 3d).

Fig. 3
figure 3

PAH molecular diagnostic ratios in the sediments of BYL. a Ant/(Phe+Ant) versus Fla/(Fla+Pyr); b BaA/(BaA+Chr) versus Fla/(Fla+Pyr); c IcdP/(IcdP+BghiP) versus Fla/(Fla+Pyr); and d ∑LMW/∑HMW versus Fla/(Fla+Pyr)

Full size image

The above results obtained through different MDRs were related to the specific pollution conditions in BYL. The Fu River, one of the main rivers flowing into the lake, passes through Baoding, a light industrial city, where textile mills, paper mills, agricultural areas, and the effluent from sewage plants are mixed halfway. The exhaust gas and atmospheric particles produced by factories and intensive agricultural activities also adsorb PAHs and then may be deposited to Fu River and transported into the lake (Hu et al., 2010). Therefore, the polluted water carried by the Fu River and the mixed combustion source emissions from the densely populated area of the Lake District resulted in PAH pollution caused mainly by pyrolytic sources in these areas.

PAH sources apportionment by the PMF model

To explore the possible sources of PAHs in the surface sediments of BYL, the PMF model was used to allocate the potential source according to the measured PAH contents. A dataset with 22 sampling points ×16 PAH elements was introduced to the PMF 5.0 model. By combining the possible input channels of target pollutants in the study area, four factors were finally selected to perform a quantitative analysis of the PAH sources. The Qrobust/Qtrue ratio exceeded 0.98, indicating that the model was both credible and reliable (Huang et al., 2021).

The results of the PMF model are shown in Fig. 4. Factor 1 accounted for 22% of the total factor contribution and consisted mainly of BkF, IcdP, and DbA (high-cyclic PAHs). Many studies have shown that BkF and IcdP are derived from diesel combustion (Huang et al., 2021; Li et al., 2015), and DbA is also commonly considered to be associated with transport emissions (Zheng et al., 2017). To date, many ships are still using diesel engines in BYL. Thus, factor 1 represented diesel engine emissions.

Fig. 4
figure 4

Factor profiles derived from the PMF models used to predict the PAH sources in the BYL sediments

Full size image

Factor 2 accounted for 32% of the total variance, and the main components were BkF and BaP, followed by Nap, Ace, DbA, and BghiP. BkF, BaP, and BghiP are markers of gasoline engine emissions (Huang et al., 2021). Nap and Ace, low cyclic weight PAHs, are generally regarded as indicators of the volatilization or leakage of oil-related products (Hu et al., 2013; Ma et al., 2020). Thus, factor 2 represented the traffic sources of gasoline engines, including land cars and small boats on water.

Factor 3 was characterized by high loadings of 3- to 4-ring PAHs and accounted for 24% of the total variance, mainly including Acy, Pyr, and Fla, followed by Ant, Flu, and Phe. Acy and Pyr are considered to be an indicator of wood and straw combustion (Shi et al., 2023; Zhang et al., 2021), and the comparatively high Fla load indicated wood combustion (Balgobin & Singh, 2019). Ant, Flu, and Phe are indicators of wood combustion (Zhang et al., 2021). Therefore, factor 3 represented the wood combustion source.

The main components of factor 4 were BaA, Chr, and Flu, which are the main PAHs emitted during coal combustion (Sofowote et al., 2008; Huang et al., 2023; Zhang et al., 2021). In northern rural areas, coal is widely used for household heating and cooking, so there is great consumption for household coal. Factor 4 was therefore concluded to indicate coal combustion, and accounted for 22% of the total variance.

Based on the PMF analysis results, four sources were successfully identified: (1) diesel engine emissions sources (22% of the total), (2) gasoline engine emissions (accounting for 32% of the total factor contribution), (3) wood combustion sources (accounting for 24% of the total), and (4) coal combustion sources (accounted for 22% of the total variance).

Ecological risk assessment of PAHs in the sediments

The SQG results indicated that the contents of 16 PAHs detected in all sediment samples of BYL were much lower than the ERM values (Table S1). That is, the PAHs at these points are not expected to cause serious ecological risks. Thus, the main consideration was whether the PAH monomer content at each point exceeded the ERL value. In the study area, in relation to the individual compounds, Flu had the highest frequency of contents over the corresponding ERL value (32%) and was sourced mainly from biomass combustion. The DbA level in the samples collected from Stations S16 and S19 also exceeded the ERL values, and some studies had shown that DbA was derived mainly from vehicle emissions (Zheng et al., 2017).

According to the above assessment results, PAHs produced by biomass combustion in villager production and life have potential biological toxicity effects at certain sites in lake networks, and DbA emitted from ships and vehicles may constitute a potential ecological risk to lake sediments. In general, the ecosystem risk of PAHs in the BYL sediments is low.

As shown in Fig. 5a, in the study region, the M-ERM-Q values were lower than 0.1 in all 22 samples, showing that the comprehensive ecotoxicity of PAHs in the sediment samples of BYL was low.

Fig. 5
figure 5

The M-ERM-Q values (a) and TEQBaP values of ∑16PAHs and ∑7PAHs (b)

Full size image

The RQ was used to evaluate the ecological risk of PAHs in sediments by calculating risk entropy, and the RQ(NCs) and RQ(MPCs) values were calculated using Eqs. (5 and 6). The RQ(NCs) and RQ(MPCs) values of individual PAHs and ∑PAHs were calculated. The details of the RQ(NCs) and RQ(MPCs) values in sediment are listed in Table S6. The mean RQ(MPCs) values were < 1, while the mean RQ(NCs) values of PAHs (except Chr, BkF, DbA, IcdP, and BghiP) were > 1, indicating that these PAHs posed moderate ecological risks and might have certain toxicity to aquatic organisms. For the ∑PAH risk in BYL, the overall RQ∑PAHs(NCs) values were between 1 and 800, and 41% of the samples posed moderate risks. These results showed that the ecological risks were at moderate and low levels in BYL.

The TEQBaP values of ∑16PAHs and ∑7PAHs (BaA, Chr, BbF, BkF, BaP, IcdP, and DbA) are shown in Fig. 5b. The TEQBaP values of ∑16PAHs were in the range of 26–220 ng·g−1, with an average value of 85 ng·g−1. The TEQBaP value range of ∑7PAHs was 26–218 ng·g−1, accounting for 98.6–99.9% of ∑16PAHs, indicating that carcinogenic PAHs play a decisive role in the potential toxicity of the environment in this area. Among them, the BaP contents were the highest, ranging from 17 to 121 ng·g−1 and accounting for 31.2–80.5% of ∑7PAHs. The second was DbA, which ranged from 0.2 to 106 ng·g−1, accounting for 0.6–48.5% of ∑7PAHs. Some studies have shown that BaP and DbA are mostly emitted by vehicles (Huang et al., 2021; Zheng et al., 2017), so the ecological risks in the studied lake may be caused mainly by the burning of liquid fossil fuels.

The TEQ ΣPAH values obtained in the BYL sediments were similar to those found in the sediments of the Fuxian Lake in China (83–262 ng·g−1) (Gu et al., 2017). The TEQ ΣPAH values of BYL were higher than the values reported in the sediment samples from Romania Olt River dam reservoirs (0.1–106 ng·g−1) (Ciucure et al., 2023), Taihu Lake in China (1–63 ng·g−1) (He et al., 2023), and Wang Lake Wetland, China (1–69 ng·g−1) (Shi et al., 2022a, b), but lower than the values reported in the sediment samples of the Poyang Lake, China (33–369 ng·g−1) (Lu et al., 2012) and Erhai Lake, Southwest China (940–2539 ng·g−1) (Huang et al., 2023).

Priority list of PAHs in BYL sediments

Different from previous research literature that only evaluated the ecological risks of PAHs in the study area (Gu et al., 2017; He et al., 2023; Hu et al., 2010; Lu et al., 2012; Shi et al., 2022a, b), priority pollutants posing serious hazards were ranked and identified from 16 PAHs in this study based on the comprehensive scoring method of multi-index parameters (Zhao et al., 2021). All 16 PAHs were detected, and their comprehensive scores were greater than 0. The priority levels of PAHs were divided into three levels and the detailed grade allocation is listed in Table S3. Grade I PAHs included two pollutants defined as high-priority pollutants. Grade II PAHs comprised a total of 9 pollutants defined as medium-priority pollutants. Grade III contained a total of 5 substances defined as low-priority pollutants. Table 2 shows that among the 16 PAHs measured in the BYL sediments, the PAHs that should be considered for priority control were BbF and BaP, followed by DbA, BghiP, and BkF; these PAHs arise mainly from the combustion process of diesel and gasoline from traffic sources. The continuous input of these PAHs in the environment leads to the high environmental exposure level of these five PAHs, and they also have high bioaccumulation capacities and high toxicity levels. The ecological risks generated after these PAHs enter the aquatic ecosystem cannot be ignored. In the relatively late stage, the supervision and management of traffic sources (mainly ships) should be strengthened.

Table 2 Ranked results of priority PAH pollutants in BYL sediments
Full size table

Conclusions

In this study, the pollution characteristics, sources, and ecological risk assessments of PAHs in BYL sediments were comprehensively researched. The PAH contents in the study area were at moderate levels compared to those measured globally. The proportion of high molecular weight PAHs in the sediment samples was the highest. The MDRs and PMF analysis showed that PAH pollution was mainly the result of pyrolysis sources, and the important contribution source of this pollution was mainly the combustion process of biomass and liquid fossil fuels. In addition, the contents of Flu and DbA in some places were above the corresponding ERL values and thus may occasionally cause negative ecological effects. The M-ERM-Q, RQ, and TEQBaP values of PAHs suggested that the ecological risks of PAHs at the measured sites were at low-moderate levels. The priority pollutant list showed that BbF and BaP were the highest priority pollutants, and 80.0% of 4-ring PAHs were medium-priority pollutants. This study found a slightly higher risk of PAH pollution in areas near villages and shipping lanes, which might be influenced by villagers’ production and living activities, as well as by fishing boats and tourist vessels. Therefore, the local government should pay more attention to adjusting the residential energy use structure and to reasonable tourism development while maintaining systematic and continuous PAH monitoring.