Introduction

Polycyclic aromatic hydrocarbons (PAHs) have potential toxic, mutagenic, and carcinogenic characteristics to the environment and human beings (Tong et al. 2018; Chen et al. 2020), with semi-volatility and continuous degradation proprieties. In addition, PAHs can be transported over a long distance through the air, surface water, sediment, soil, biota, and other environmental compartments (Khanal et al. 2018; Liu et al. 2021a, b). Therefore, PAHs have attracted increasingly more attention from researchers in recent years. Scholars have conducted a lot of research on PAHs in the environment, pointing out that PAHs in the natural environment are mainly derived from human activities (e.g., industrial production, agricultural waste, automobile exhaust, coal combustion, and petrochemical product emission) and reach the surrounding environmental media, more particularly water bodies, through atmospheric dry and wet deposition, industrial sewage discharge, and surface runoff (Montuori et al. 2016; Zhang et al. 2022a, b). The maximum threshold concentration of PAHs in the water environment was fixed by the United States Environmental Protection Agency (USEPA) and the Canadian Council of Ministers of the Environment (CCME) (Meng et al. 2019). In recent years, China’s total energy consumption and coal mining activities have significantly increased, resulting in a significant increase in PAHs emissions. Therefore, it is necessary to monitor and assess the PAHs pollution levels in coal mining areas and energy and chemical industry areas.

In recent years, the impacts of exposure to PAHs on the ecological environment have generated considerable concern in coal mining areas. Several researchers have studied the abundance, distribution, source, and health risks of ambient PM10-bounded PAHs in coal mining cities (Zheng et al. 2019). In addition, numerous studies were carried out on PAHs in coal waste material, mine water, underground sludge, and groundwater in coal mining areas to investigate the contamination levels, distributions, and potential sources of PAHs, as well as their associated ecological risks (Ribeiro et al. 2012; Shao et al. 2014; Hao et al. 2018; Chen et al. 2019; Wu et al. 2021; Jiang et al. 2022), whereas other studies have investigated the concentrations, spatiotemporal variations, and origins of PAHs in soils and sediments in the vicinity of coal mines, highlighting the great effects of human activities on PAHs (Orecchio et al. 2015; Ugwu et al. 2016; Liu et al. 2017; Zhang et al. 2022a, b). However, a few studies have investigated PAHs in the surface water of mining areas.

The northern Shaanxi mining area is China’s national energy and chemical industry base and one of the seven largest coalfields worldwide. However, with the rapid industrialization of the region, the Kuye River, as the main river in this mining area, is becoming increasingly polluted by PAHs from mining production activities, including coal mining, transportation, coal washing, combustion power generation, and coal coking. Surface water is not only an important source of water in the lives of surrounding residents but also an important irrigation water source for farmlands. To date, there is little information available on the content and spatial distribution of PAHs in surface water in mining areas. To better understand the effects of coal industries on the levels and distributions of PAHs in surface water, northern Shaanxi, which is a typical coal mining area, was selected in this study to assess the contents and spatial distributions of PAHs in the Kuye River. In addition, diagnostic ratios and the positive matrix factorization (PMF) model were used to analyze the sources of PAHs in the study area. Finally, the ecological risks of PAHs were evaluated to provide a basis for the protection, development, and utilization of water resources in the study area and similar areas.

Materials and methods

Study area

The Yulin region in northern Shaanxi is an important coal production area and a national energy and chemical industry base in China. It is located in the border zone between the Loess Plateau and the Mu Us Desert. The region has a typical semi-arid inland climate area with poor water resources (Dong et al. 2020). In recent years, the coal industry in Yulin has developed rapidly. In 2021, coal mining volume exceeded 550 million tons, which stimulated the development of related industries. Kuye River as the main river in the northern Shaanxi mining area is the first-class tributary of the Yellow River, and numerous coals are being mined in the upper reaches, whereas the middle reaches consist of Ningtiaota Industrial Park, as well as energy and chemical production activities, such as thermal power generation, coking, and coal products manufacturing. The downstream consists mainly of rural residential areas. PAHs generated by human activities, such as coal production and processing emissions in the region, are accumulated in the Kuye River (Liu et al. 2019a, b). Moreover, the significant decrease in the natural runoff of the Kuye River, the high regional evaporation rates, the increase in the industrial water consumption, and the irrigation water return flows have affected the PAHs concentrations and compositions in the study area (Wu et al. 2020; Han et al. 2019).

Water sampling

In total, 59 sampling sites in the Shaanxi section of the Kuye River and the mainstream of the Yellow River were selected for water sampling, including 57 sampling sites in the Kuye River and 2 sampling sites in the mainstream of the Yellow River (Fig. 1). In this study, the water samples were collected in the wet season (October 2021), characterized by high precipitation amount and surface runoff, promoting the pollutant entry from industrial and agricultural activities and traffic into the surface water. The sampling sites are located in the coal mining, Lantan, coking, and other mining active industrial areas in the upper and middle reaches of the Shaanxi section of the Kuye River, as well as the rural residential and agricultural areas in the lower reaches. The sampling was carried out by collecting a 2-L water sample at 0.5-m depth from each sampling site using the JK-8008 water sampler (stainless steel, JKD, China). The water samples were sealed in brown glass bottles and stored.

Fig. 1
figure 1

Study area and the sampling sites in Kuye River Basin, China

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Chemicals and instruments

In this study, the solid-phase extraction (SPE) and high-performance liquid chromatography (HPLC) were used to quantitatively detect 16 PAHs types at 59 sampling sites of the typical river in the northern Shaanxi mining area—Kuye River.

The instruments and equipment used in this study were Agilent1200 high-performance liquid chromatography-diode array detector-fluorescence detector (HPLC-DAD-FLD, Agilent, USA), solid-phase extraction device (Mediwax, USA), rotary evaporator (N-1000, Eyela, Japan), and nitrogen blower (TTL-DCII, Tongtailian, China), whereas the analytical reagents were liquid chromatography pure reagents, including acetonitrile, methanol, dichloromethane, and decafluorobiphenyl with 99% purity, which obtained from the Aladdin Holdings Group Co., Ltd. (Beijing, China). The standard mixture solution for PAHs contained 16 components, namely naphthalene (Nap), acenaphthylene (Acy), fluorene (Flu), benzo[g,h,i]perylene (BghiP), acenaphthene (Ace), phenanthrene (Phe), anthracene (Ant), indeno[1,2,3-cd]pyrene (InP), fluoranthene (Fla), pyrene (Pyr), chrysene (Chry), benzo[a]anthracene (BaA), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), and dibenzo[a,h]anthracene (DahA), and was obtained from AccuStandard, Inc. (New Haven, CT, USA).

Pretreatment method and instrument analysis

The 1 L collected water samples were first passed through a 0.45-μm glass fiber filter membrane (surface waterman, UK) and then were enriched by a solid-phase extraction device. C18 column (Supelco, 1000 mg/6mL) was used in the solid-phase extraction. Before the water samples were enriched, the C18 column was pre-washed with 10 mL dichloromethane and then activated using 10 mL methanol and 10 mL water twice respectively. After sample enrichment, the C18 column was washed with 10 mL water and blown with high-purity nitrogen for 10 min, followed by dichloromethane elution and dilution to 0.5 mL using acetonitrile after concentration.

The PAHs concentrations in water samples were determined using a high-performance liquid chromatography-diode array detector-fluorescence detector. The diode array detector was used in this study at a wavelength of 238 nm since the acenaphthylene concentrations cannot be determined using the fluorescence detector. The concentrations of the remaining 15 PAHs monomers were determined using the fluorescence detector using multiple wavelength gradients (Jin et al. 2022). The chromatography column was a specific column for polycyclic aromatic hydrocarbons (5 μm × 250 mm, waters). The acetonitrile-water mobile phase was used, with a flow rate of 1.2 mL/min. The elution was performed by increasing the acetonitrile gradient from 65 to 100% at 2.5%/min until reaching the peak capacity.

Quality assurance and quality control

The multi-point calibration external standard curve method of high-performance liquid chromatography was used to determine the concentrations of PAHs in the samples. The linear coefficient of marking (R2) values varied from 0.9937 to 0.9990. The accuracy of the experimental process was ensured using experimental blanks, parallel samples, and matrix labeling (Zhang et al. 2007). On the other hand, the relative standard deviation of the 16 PAHs monomer concentrations in water samples ranged from 0.6 to 2.4%. The detection limit and spiked recovery ranges were 0.4~1.5 ng/L and 79.6~101.7%, respectively. In addition, 10% double parallel determinations and 2 experimental blanks for each batch of samples were carried out, and the determination results of blank experiments were lower than the detection limit, thus meeting the quality control requirements.

PMF model

The PMF receptor model (EPA PMF5.0) of the US Environmental Protection Agency was used to quantitatively assess the PAHs source compositions and their contributions to the pollution of the Kuye River in the study area (Liu et al. 2019a, b; Ren et al. 2021). The input data were the PAHs concentrations and its corresponding uncertainty at various points.

When concentration xik was lower than the method detection limit (MDL) and σik is uncertainty and calculated with Eq. (1):

$${\sigma}_{ik}=5/6\times \textrm{MDL}$$
(1)

When concentration was higher than the detection limit, the uncertainty is calculated with Eq. (2):

$${\sigma}_{ik}=\sqrt{{\left(\textrm{EF}\times {\textrm{x}}_{\textrm{ik}}\right)}^2+{\left(0.5\times \textrm{MDL}\right)}^2}$$
(2)

Ecological risk assessment

The Kalf risk quotient method was used in this study to assess the ecological risk of PAHs in the Kuye River. A risk quotient value greater than 1 suggests an ecological risk of PAHs. The higher the value, the greater the risk (Li et al. 2021; Wang et al. 2010).

The risk quotient of PAHs monomer was calculated using Eqs. (3) and (4):

$${\textrm{RQ}}_{\textrm{MPCs}}={\textrm{C}}_{\textrm{PAHs}}/{\textrm{C}}_{\textrm{QV}\left(\textrm{MPCs}\right)}$$
(3)
$${\textrm{RQ}}_{\textrm{NCs}}={\textrm{C}}_{\textrm{PAHs}}/{\textrm{C}}_{\textrm{QV}\left(\textrm{NCs}\right)}$$
(4)

where RQMPCs and RQNCs are the risk quotient of the highest and lowest allowable concentrations of PAHs, respectively; CQV(MPCs) and CQV(NCs) are the maximum and minimum risk standard concentrations of PAHs monomers, respectively; CPAHs is the measured concentration of each monomer.

The maximum and minimum ecological risk quotient values of ΣPAHs at each sampling site were calculated using Eqs. (5) and (6):

$${\mathrm{RQ}}_{\sum\mathrm{PAHs}\left(\mathrm{MPCs}\right)}=\sum\nolimits_{\mathrm i=1}^{16}{\mathrm{RQ}}_{\mathrm i\left(\mathrm{MPCs}\right)}\;\left({\mathrm{RQ}}_{\mathrm i\left(\mathrm{MPCs}\right)}\geq1\right)$$
(5)
$${\mathrm{RQ}}_{\sum\mathrm{PAHs}\left(\mathrm{NCs}\right)}=\sum\nolimits_{\mathrm i=1}^{16}{\mathrm{RQ}}_{\mathrm i\left(\mathrm{NCs}\right)}\;\left({\mathrm{RQ}}_{\mathrm i\left(\mathrm{NCs}\right)}\geq1\right)$$
(6)

The ecological risk quotient classification of ΣPAHs is shown in Table S2 of the Supplemental material.

Data processing and analysis

The PAHs concentration data were corrected for recovery. The undetected concentrations in water samples were assumed to be equal to 0. Data calculations and descriptive statistics were performed using Excel 2019. In addition, PMF 5.0 software was used to perform the positive definite matrix factorization analysis. The spatial distribution maps of PAHs concentrations in the study area and other related plots were generated using ArcGIS 10.1 and Origin 8.0, respectively.

Results and analysis

Occurrence of PAHs in river water

The observed concentrations of PAHs in the study area and their national standard values for surface water are reported in Table S1 of the Supplemental material. In total, 16 PAHs monomers were detected in most samples, with variation coefficients greater than 0.4, suggesting significant spatial differences of 16 monomers concentrations. In addition, the results showed mostly higher mean values than median values, indicating positive skew distributions. This finding suggests significant impacts of human activities on the accumulations of PAHs in river water in the study area. The concentration range of ΣPAHs was 50.06~278.16 ng/L, with mean and median values of 128.22 ng/L and 116.34 ng/L, respectively. Chry showed the highest concentration, with a mean value of 36.58 ng/L and a median value of 23.59 ng/L, followed by BaA and Phe. The detection rates of all PAHs monomers were above 70%, of which 12 monomers were 100%, and BbF and BkF revealed detection rates of 77.97% and 76.27%, respectively. The relatively low detection rates of BbF and BkF were related to the fact that these two components are high molecular weight monomers characterized by high hydrophobicity (Liang et al. 2020; Liu et al. 2018). Moreover, BbF and BkF showed the highest variation coefficients of 1.39 and 1.08, respectively, suggesting significant spatial differences.

In order to further assess the 16 PAHs monomers concentrations in the river water of the study area, the concentrations of each monomer at 59 sampling sites were compared with national standards (detailed data listed in Table S1). However, China’s Environmental Quality Standards for Surface Water (GB3838-2002,2002) reported only the BaP concentration standard. The National Recommended Water Quality Criteria of the USEPA (USEPA 2015) and the Canadian Environmental Quality Guidelines (CCME 2015) highlighted stricter concentration limits of all PAHs monomers in water. Therefore, the stricter threshold in USEPA and CCME water quality criteria were used in this study to assess the concentrations of PAHs. The maximum detectable concentration of the BaP monomer at all the sampling sites was 13.35 ng/L, significantly exceeding the Chinese standard. The concentrations of BaA, InP, BbF, BaP, and DahA exceeded the USEPA threshold values at 100%, 93.2%, 50.8%, 91.5%, and 93.2% sampling sites (Fig. 2). These monomers have potential carcinogenicity. Indeed, the BaP monomer can cause lung and gastric cancers and teratogenesis, as well as endangering the immune and reproductive systems (Taghvaee et al. 2018). In addition, the concentrations of ΣPAHs at all 59 sampling sites were higher than those reported in the USEPA safety guidelines for exposure of aquatic organisms to PAHs in surface water, thus suggesting that PAHs in the river water in the study area may affect human health through biological enrichment (Wang et al. 2018). Therefore, the human exposure risk to PAHs in the study area cannot be ignored.

Fig. 2
figure 2

PAHs monomer concentrations at the 59 sampling sites in the study area. Red lines indicate the USEPA threshold values

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Spatial distribution characteristics of PAHs

The spatial distribution of ΣPAHs at 59 sampling sites in the Kuye River is shown in Fig. 3. The highest ΣPAHs concentrations ranged from 227.74 to 278.16 ng/L and were observed at sampling sites S36~S40, near Ningtiaota Industrial Park. The highest proportions were attributed to Chry and BaA, with concentrations of 86.91~121.22 ng/L and 45.68~58.10 ng/L, respectively, whereas S53, S54, S57, and S45~S48, in the densely populated residential areas (Shamao and Shenmu Town), revealed the second-highest ΣPAHs concentrations range content of 153.95~207.16 ng/L. In addition, ΣPAHs concentrations at the sampling sites S13 and S14 of Daliuta Town were 142.16 and 153.96 ng/L, respectively, whereas the ΣPAHs concentrations at the sampling sites S5, S6, S7, S19, and S20 were relatively low, which were located in remote areas, far away from areas with industrial and human activities. On the other hand, the sampling site S59, located downstream of the Kuye River entrance into Yellow River, showed ΣPAHs concentration (87.09 ng/L), indicating that the PAHs pollution in the Kuye River did not significantly affect the water quality at the main stream of the Yellow River.

Fig. 3
figure 3

ΣPAHs and 5 PAHs monomers concentration distributions of water samples in study area. The bubble size and color depth in the figure represent the concentration

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The obtained results showed 5 PAHs monomers exceeding the USEPA threshold concentrations at multiple sampling sites in the Kuye River, namely BaA, InP, BbF, BaP, and DahA (Fig. 2). The spatial distributions of these 5 monomers are shown in Fig. 3. Among them, BaA showed the highest concentration in Ningtiaota Industrial Park, followed by Shamao Town, whereas InP and BaP showed similar distribution characteristics, except for the upstream area, where Shenmu Town showed high concentrations. The BbF concentration in the area above Daliuta Town was significantly higher than that in other areas, while DahA high concentrations were widely distributed and revealed slight spatial differences. The spatial distributions of the remaining 11 monomers are shown in Fig. S1 of the Supplemental material.

According to these results, the high-value point of each monomer concentration and ΣPAHs were similar; these were mainly distributed near mining active industrial areas and towns. In fact, Lantan production, thermal power generation, coking, and coal products manufacturing accounted for a large proportion of production structures in Ningtiaota Industrial Park. PAHs, generated from coal combustion, entered the rover water through atmospheric dry and wet deposition and surface runoff. In addition, industrial wastewater from coal mining, coal chemical industry, magnesium aluminum industry around Daliuta and Shenmu Town, tail gas emission from coal-heavy truck transportation vehicles, and agricultural production, living, and vehicle exhaust emissions in densely populated residential areas may have also contributed to the increase in the concentrations of PAHs in the river water. Mining production and coal-fired industries, as pillar industries in the local area, had a significant impact on the spatial distribution of PAHs, which requires particular attention.

Component characteristics

The 16 PAHs monomers were classified in this study according to their ring numbers. Figure 4 shows the ternary composition diagram of PAHs in the Kuye River. The results revealed large relative abundances of 4-ring PAHs, namely Fla, Pyr, Chry, and BaA, at the 59 sampling sites, ranging from 38.59 to 70.85%, with an average value of 51.41%. The 2- and 3-ring PAHs were the second most abundant PAHs in the study area, accounting for 9.44~50.79%, with an average value of 33.22%, whereas the relative abundances of the 5- and 6-ring PAHs were low, mostly below 20%. Indeed, 5- and 6-ring high molecular weight PAHs are generally carcinogenic, with toxicity equivalent factors often higher than those of 2–4 rings. In addition, the results showed relative abundances close to 50% for only the high ring PAHs in the upstream (S1–S8 sampling sites), while medium and low ring PAHs, with low toxicity equivalent factors, were mainly observed at the remaining sampling sites, suggesting low toxicity risk.

Fig. 4
figure 4

Ternary composition diagram of PAHs in the surface water of Kuye River

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Comparison with other water bodies

Compared with other surface water in similar regions worldwide (Table 1), the concentrations of ΣPAHs in the Kuye River (50.06–278.16 ng/L) were much higher than those observed in the surface water of the Fengfeng coal mining area (Hao et al. 2018), Huainan mining area (Chen et al. 2005), and Jianghan oilfield river (Li et al. 2020). In addition, the concentrations of ΣPAHs in the Kuye River were slightly similar to those observed in the surface water of the Shilong coal mining area (Yang et al. 2007) and Amu Darya Basin (Jin et al. 2022) and significantly lower than those observed in the Heshan coalfield river (Huang et al. 2016), Shaying River (Du et al. 2020), and Fenhe Basin (Zhao et al. 2017). Therefore, the concentrations of ΣPAHs in the study area were at moderate levels compared to those revealed in previous related studies.

Table 1 Concentrations of PAHs and their associated ecological risk values in surface water of mining areas in China and other countries
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Discussion

Source identifications of PAHs using diagnostic ratios

The identification of PAHs sources in the natural environment is relatively complex since PAHs monomers can be derived from several anthropogenic sources, including oil, oil combustion, and coal and biomass combustion. Indeed, the isomer ratios of PAHs, showing stable chemical proprieties, can be used to determine their related anthropogenic sources and transport, based on the distinct composition characteristics of PAHs from different sources (Tu et al. 2018). In this study, PAHs isomer ratios, namely Fla/(Fla+Pyr) and Ant/(Ant+Phe), were used to identify the pollution sources, as shown in Fig. 5. It is generally believed that when Fla/(Fla+Pyr) is greater than 0.5, PAHs pollution mainly comes from the combustion of biomass and coal. A Fla/(Fla+Pyr) ratio between 0.4 and 0.5 indicate the combustion of petroleum, whereas a Fla/(Fla+Pyr) ratio of less than 0.4 suggests leakage of petroleum. In addition, Ant/(Ant+Phe) ratios of greater than 0.1 and less than 0.1 suggest combustion sources and leakage of petroleum, respectively (Wu et al. 2011; Maioli et al. 2011).

Fig. 5
figure 5

PAHs source identifications by diagnostic ratios

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According to the results obtained (Fig. 5), Fla/(Fla+Pyr) and Ant/(Ant+Phe) ratios of 7% sampling sites of Kuye River suggested leakage of petroleum. Indeed, these sampling sites were adjacent to the coal mine and were vulnerable to be impacted by the leakage of machine petroleum in mining. In addition, about 38% of the sampling sites showed mixed pollution sources of petroleum leakage, combustion of biomass and coal, and combustion of petroleum. PAHs pollution in other regions was mainly caused by combustion, of which 16% of sampling sites revealed a petroleum combustion source. In fact, most of these sample sites were close to the main channel of coal transportation. In addition, frequent traffic congestion on national highways caused a large amount of exhaust from motor vehicles. The remaining 39% of samples suggested combustion sources of biomass and coal. It should be noted that the observed Chry, BaA, and Phe concentrations in the Kuye River were relatively high, which is described in the “Occurrence of PAHs in river water” section. Chry is a gasoline combustion and coal combustion characteristic compound, while BaA and Phe are specific characteristic compounds of coal combustion (Qu et al. 2020; Zhang et al. 2022a, b). The relatively high concentrations of these three compounds suggested a more coal combustion source in the study area, with significant pollution from heavy-truck vehicle exhaust, which conformed to the characteristics of the research area as a national energy and chemical industry base and one of the seven largest coalfields in the world. The PAHs diagnostic ratios allowed the identification of the pollution sources in the study area, providing a basis for quantitative analysis of PAHs sources.

Source apportionment of PAHs using PMF model

The PMF model was used to further quantitatively assess the PAHs source compositions and their contributions to the pollution of the Kuye River in the study area. The error factor (EF) ranged from 0.05 to 0.3. In this study, the EF value was assumed to be 0.2. Different factor numbers from 3 to 6 were sequential operations. In addition, the smaller dQ(Robust) value, the larger R2, and the clearly interpretable factors in this region were considered in this study. The final factor number was 4, and the optimal number of model iterations and dQ(Robust) were the third time, −0.29, respectively, with a monomer R2 range of 0.45–0.99, suggesting good operation results of the model. The final source profiles obtained are shown in Fig. S2 of the Supplemental material.

It can be seen from Fig. S2 that the main identified components of factor 1 were InP, BbF, and BaP. These PAHs compounds are common characteristic emission monomers of gasoline and diesel engine emissions (Riaz et al. 2019; Ili et al. 2021). This factor was related to the vehicle emission source due to the presence of many national roads in the region, which are the main transportation routes for coal and heavy traffic. On the other hand, the main load monomers of factor 2 were Phe, Chry, and BaA, which were characteristic emissions of coal combustion (Cao et al. 2020; Huang et al. 2016), thereby, this factor was related to the coal combustion source. Coal mining, as pillar industries in the local area, has also driven the development of the coal chemical industry and some related industries in the study area, such as cogeneration, thermal power generation, and metal smelting. As a combustion energy, coal is widely used in this region. Moreover, the study area is rich in coal, and coal combustion is the main heat source for residents in this area. The main load monomer of factor 3 was Ant, which was generally used as an identification component of fuel-wood combustion source (Hellén et al. 2008); therefore, this factor was related to the fuel-wood combustion source. The study area contains a high proportion of the rural population. In fact, biomass (e.g., wood and straw) are one of the living fuels in rural areas. On the other hand, factor 4 revealed higher loads on Nap, Acy, Flu, and Ace, all of which were 2~3 ring PAHs, and these low rings’ PAHs were related to coking emission sources (Liu et al. 2021a, b). Also, Nap, Acy, and Flu are indicators of petrochemical leakage (Miao et al. 2018; Qiao et al. 2021). So they are identified as coking/petroleum sources. There are several coking companies in the study area, so PAHs in coking waste gas and fly ash can enter the water through dry and wet depositions. The coal mining industry in the study area is mainly mechanical mining, and certain petroleum substances will leak during mechanical mining. In addition, industrial wastewater discharged by coal chemical enterprises will also cause petroleum pollution.

According to the average contribution rate of each pollution source, calculated using PMF5.0, coking/petroleum sources revealed the highest contribution (37.91%) to PAHs in the Kuye River, followed, respectively, by coal combustion sources (36.31%), vehicle emission sources (13.93%), and fuel-wood combustion sources (11.85%). Indeed, the coal industry is the pillar industry in Yulin, where there are hundreds of coal mines, coal chemical companies, and thermal power plants, which is consistent with source apportionment results. The PAHs in the study area were mainly derived from coal-related activities.

In order to comprehensively assess the spatial impact of different pollution sources on the river water of the study area, the contribution rates of each pollution source to PAHs concentration at the 59 sampling sites were calculated using the PMF method (Fig. 6). The highest contribution rates of coal combustion and coking/petroleum sources to the PAHs concentrations in the river water were 92% and 87%, respectively, and were mainly observed near the mining activity area, indicating that the coal industry had a great impact on the quality of the surrounding waters. On the other hand, a small number of sampling sites located at the coal transportation hub were greatly affected by vehicle emission sources, while sporadically distributed sampling sites were greatly affected by fuel-wood combustion sources. In addition, vehicle emission and fuel-wood combustion sources showed contribution rates of less than 40% at most other sampling sites, suggesting insignificant impacts of these sources on water PAHs pollution in the study area.

Fig. 6
figure 6

Contribution rations of each pollution source to 59 receptor samples

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Ecological risk assessment of PAHs in the Kuye River

The calculated risk quotient of PAHs monomer (Fig. 7a) showed that the RQMPCs and RQNCs values of Nap were less than 1, suggesting a low-risk level, whereas RQMPCs and RQNCs values of BaA were 1.99 and 199.28, respectively, significantly higher than those of the other monomers, suggesting a high-risk level, which indicating that BaA significantly affected the ecological environment of the Kuye River in the study area. Therefore, prompt actions should be taken to reduce the concentration of this monomer. On the other hand, RQMPCs values of other monomers were all less than 1, and RQNCs values were all greater than 1, suggesting medium risks. Therefore, surveillance of these monomers should be strengthened in the Kuye River to prevent subsequent increases in their concentrations.

Fig. 7
figure 7

PAHs ecological risk in the study area. a Ecological risk quotient of PAHs monomer. b ΣPAHs ecological risk quotient of 59 sampling sites

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The results of the ecological risk quotient of ΣPAHs at each sampling site (Fig. 7b) showed that the RQ∑PAHs(MPCs) values of S5, S6, S11, S19, and S25–32 were equal to 0, while those of RQ∑PAHs(NCs) were less than 800, indicating low ecological risk at these sampling sites. On the other hand, RQ∑PAHs(MPCs) and RQ∑PAHs(NCs) values of the remaining sampling sites were greater than 1 and less than 800, respectively, indicating medium- to high-risk levels. Among them, RQ∑PAHs(NCs) value at S36 was 748.40, which was close to the high ecological risk threshold. This sampling site is located in the Ningtiaota Industrial Park, and the PAHs produced during industrial production process exposed the surrounding waters to a high ecological risk. Therefore, it is urgent to formulate a prevention and control plan in the study region.

By considering the concentrations of PAHs in other surface water of mining areas in the Kalf risk quotient method, the high-risk monomers and ecological risk quotient of ΣPAHs were obtained for each water body (Table 1). Compared with other surface water, the ecological risk of ΣPAHs in the Kuye River was at a moderate level. In addition, as can be seen from Table 1, BaA and BbF in rivers exhibited high-risk levels in many mining areas. Therefore, it is important to develop effective measures in future related studies on the ecological environment in mining areas to control the sources of these two monomers.

The detected PAHs in the surface water of the Kuye River presented certain ecological risks. Indeed, PAHs pollution may become increasingly serious in the future due to the continuous development of coal industries, thereby further affecting negatively the ecological environment and endangering human health. Therefore, researchers should monitor surface water regularly and collect soil, air, and groundwater PAHs samples in the study area to improve the accuracy of source distribution analysis in future research, thereby contributing to the assessment of pollution control plans for ensuring sustainable development of coal industries.

Conclusions

In this study, the statistical analysis and PMF model were used to analyze the characteristic of PAHs in surface water in the Kuye River, northern Shaanxi mining area, China. The results showed a ΣPAHs concentration range of 50.06–278.16 ng/L, with an average value of 128.22 ng/L, indicating an average pollution level compared to other rivers in mining areas. In addition, the middle ring PAHs accounted for a large proportion. There were substantial spatial variations in the concentrations of PAHs in the study area. Moreover, the high-value point of each monomer concentration and ΣPAHs were similar, particularly near active mining areas and towns. The PMF model results showed that coking/petroleum and coal combustion were the major sources contributing to the increase in the concentrations of PAHs in the Kuye River, with average contribution rates of 37.91 and 36.31%, respectively, followed by vehicle emission (13.93%) and fuel-wood combustion (11.85%). The ecological risk assessment results showed a high BaA-related ecological risk, affecting seriously the ecological environment of the Kuye River. On the other hand, the ecological risk of ΣPAHs in the Ningtiaota Industrial Park was close to the high ecological risk threshold. This work should be valuable for revealing the fates of PAHs and also provide support for the control and management of persistent organic pollutants in mining area river.