Introduction

Coal is the main form of energy utilized in China1, playing a vital role in the energy security2 and economic development of the region3. However, during the processes of raw coal-mining, extraction, loading, stacking of coal gangue4 and transportation, large quantities of coal dust, coal-burning gas emissions5, engine wear, and other pollutants are produced. The pollutants generated by these processes pose risks to ecological systems6,7 and public health8, with exposure occurring through various routes such as wastewater, exhaust gas, and waste residue emissions. As a key component of the river system, the sediment provides an important sink for pollutants, with the absorption and storage of pollutants within the sediment driving their migration and transformation9,10. Furthermore, the long-term accumulation of various pollutants (including HMs) within sediments forms an environmental pollution reservoir that cannot be ignored. Therefore, determining the pollution level and assessing the risk posed by HMs in river sediments of coal-mining hinterlands is important for the protection of ecological and public health.

To date, several indices for environmental contamination have been introduced to assess HM pollution in sediments, including the geo-accumulation index (Igeo)11,12 and the Nemerow comprehensive pollution index1. In addition, the ecological risk index (RI)13,14 and public health risk assessment (HRA) [Including total non-carcinogenic risk (THQ) and total carcinogenic risk (TCR)]4,15 have been used to quantify the environmental and public health effects of HMs. Consequently, a comprehensive analysis (using two or more evaluation methods) of HM pollution in coal-related industrial areas allows a thorough representation of sediment pollution characteristics16,17. However, the above-mentioned models have been used to determine the specific contribution of pollution sources to pollutant concentrations without providing insights into associated ecological health risk levels. In fact, the contribution of the same pollution source to ecological health risks is not often positively correlated with associated pollutant concentrations due to the varying degrees of toxicity between HMs17. Some pollution sources can significantly affect ecological and human health risks even at low contribution rates to associated pollutant concentrations. Relying solely on source apportionment results based on receptor concentrations for pollution source control may lead to overlooking the importance of pollution sources in ecological health risk assessments18. It is, therefore, crucial to integrate PMF source apportionment results into ecological health risk assessment models in order to comprehensively evaluate HMs-associated ecological health risks and to determine their pollutant sources in river sediments, thereby enabling the implementation of effective control measures.

In recent years, probabilistic risk assessments have been increasingly used for sediment pollution risk assessments, and random simulations can comprehensively account for the impact of HMs on ecological and public health. The Monte Carlo simulation (MCS) model is the most commonly used probabilistic risk analysis method18,19, which can simulate the actual physical process, generate probability distribution data, and determine the pollution risk probability value. In previous studies, the MCS model has been applied to estimate the probable risks associated with HMs in reservoir surface water20, sediment19, road dust21,22, vegetables23,24, and typical mining soil1,25.

The Shenfu coal field is one of the largest coal fields worldwide and the largest in China, accounting for 15% of China’s coal reserves26,27. The upper reaches of the Kuye River are located in the hinterland of the Shenfu coal field, where coal-mining, coal-fired power generation, coal chemical industries, and related industrial activities have been developed, with frequent human activities, causing serious pollution levels within the Kuye River basin. Previous studies utilized the positive matrix factorization (PMF) approach to quantify HM pollution sources affecting the sediment of the Kuye River28. The results showed that the main HM pollution sources included coal-mining, industry, traffic emissions, and agriculture, with moderate ecological risk levels. However, to determine the priority pollution sources and target HMs for control, Igeo, along with the ecological and human health hazards posed by these pollution sources in the Kuye River basin, were assessed using random simulation to avoid the limitations of a single-method evaluation. Moreover, few studies have assessed the health risks of river sediment for four distinct population groups (boys, girls, adult males, and adult females). Therefore, the aim of this study was to establish a pollution-based ecological health risk assessment methodology for the priority control pollution sources and target HMs in river sediments within coalfield hinterland. The spatial and temporal variations of HMs (Cu, Cd, Zn, Pb, Ni, Hg, Cr, and As) were analyzed during dry and wet periods to determine the HM pollution levels within sediments and the resulting risks to ecological and human health across four groups using the MCS method. This analysis allows for the comprehensive identification and management of key sources of control pollution and the corresponding HMs. This study consisted of three main aims (Fig. S1): (1) To determine the low-pollution season during both the wet and dry season, and to discuss the seasonal variations and spatial distribution of HM pollution; (2) To analyze the risk posed by the accumulation of HMs using various indices (Igeo, RI, and HRA based on MCS); and (3) To identify the risk level posed by HM pollution sources using PMF-based on RI and HRA coupled models, allowing for the identification of major pollution sources and priority control HMs related to ecological health risk.

Materials and methods

Study area

Shenfu Coal field has the largest proven reserves of high-quality thermal coal and gasification coal in China and is one of the major high-quality thermal coal and gasification coal fields worldwide3, playing an important role in the international energy market29. Upper reaches of the Kuye River lie in the hinterland of the coal field, which is surrounded mainly by coal resource exploitation and its ancillary industries, as shown in Fig. S2. Three large industrial parks were operational (Daliuta industrial parks, Yanjiata industrial parks, and Ningtiaota industrial parks), which were mainly involved in the extraction and processing of bituminous coal and anthracite, along with coking operations, cement fabrication, and the production of various coal-based goods. Shenmu City, a major coal-producing city, is located in the middle reaches of the river basin30. In recent years, with the large-scale exploitation of coal resources and the development of ancillary industries (e.g., coking, coal product manufacturing, and thermal power), water resources have been rapidly depleted, and water quality has deteriorated due to extensive water use and pollution from industry. This has led to ecological destruction and increased risks to both the ecological environment and public health within the basin area.

Sampling and preparation

Taking account of the hydrogeological conditions of the Kuye River, coal-mining areas, industrial park sewage outlets, livestock and poultry breeding facilities and densely populated areas, sediment sampling surveys were conducted in October 2021 (representing the wet season, with an average temperature of 13℃) and February 2022 (representing the dry season, with an average temperature of -12℃). To avoid the impacts of temporary disturbances, such as precipitation and sediment resuspension, the sampling campaign was conducted in a period without precipitation events over two consecutive days, the turbidity of the surface water bodies (turbidity control threshold ≤ 10 NTU) was measured in situ31. A Peterson mud sampler was used to collect 59 river surface sediment (0–10 cm) samples. From each sampling site, 3 subsamples were collected and combined, with large impurities removed before being packed into clean sterile plastic bottles, which were then transported to the laboratory and stored in cold storage. The collected sediment samples were freeze-dried, ground, sieved through a 100-mesh screen, and stored in sealed bags for later testing. Pretreatment was conducted according to the methods proposed in the literature20,31. The concentrations of Cu, Zn, Pb, Cd, Ni, Cr, and As were determined by inductively coupled plasma mass spectrometry (ICP-MS) (Agilent 7850, USA), Hg contents were detected using a chromatography atomic fluorescence spectrometer (Jitian AFSS922-SA20, China), with three parallel samples tested for each sediment sample and the standard GBW07423 used for quality control. The results indicated that the recovery rate was 100%±20%, which was within the acceptable range according to national standards.

Methods of contamination assessment

Geo-accumulation index method

The Igeo method12,32, proposed by the German scholar Muller (also known as the Muller index), is an evaluation method based on the relationship between the measured value of HMs in sediments and the regional background value (BV), allowing the degree of HM pollution in river sediments to be determined as described by Eq. (1), as follows:

$${\text{I}}_{{geo}} = \log _{2} \frac{{C_{i} }}{{kC_{0} }}$$
(1)

where k is a correction factor used for the watershed BV, which in this case was 1.5; Ci denotes the measured content of HMs within the sediment (mg·kg− 1); and C0 corresponds to the BV in local topsoil33. The classification of sediment pollution levels according to Igeo is shown in Table S1.

PMF-based RI coupling model

According to the characteristics and environmental behavior of HMs, Hakanson proposed a method to evaluate HM pollution in sediments from the perspective of biological HM toxicity34,35, which quantitatively classifies potential ecological risk levels using Eq. (2) as follows:

$$RI = \sum\limits_{{p = 1}}^{m} {E_{p} } = \sum\limits_{{p = 1}}^{m} {} \sum\limits_{{i = 1}}^{8} {E_{i} } = \sum\limits_{{p = 1}}^{m} {} \sum\limits_{{i = 1}}^{8} {T_{i} } \times \frac{{C_{p} \cdot k_{{pi}} }}{{C_{0} }}$$
(2)

where RI is the comprehensive potential ecological risk index; Ep/Ei is the potential ecological risk index of a kind of pollution source p or a single element i; Ti represents the toxicity response coefficient for HM element i, with values of 5 for Cu, 1 for Zn, 5 for Pb, 30 for Cd, 5 for Ni, 2 for Cr, 40 for Hg, and 10 for As13,36; Cp is the concentration contributed by pollution source p (mg·kg− 1) as determined by the PMF; kpi is the proportion of HM element i attributed to pollution source p; m is the total number of pollution sources; i is the number of HM elements. The ecological risk assessment was conducted using the RI and Ep/Ei (Table S1).

PMF-based HRA coupling model

Health risks were evaluated across four demographic groups: boys, girls, adult males, and adult females, employing the USEPA-recommended human health risk assessment (HRA) models37,38. Human intake through three exposure routes (ingestion, dermal contact, and inhalation pathways) was established to assess non-carcinogenic and carcinogenic health risks. The daily exposure dose was calculated according to Eqs. (3)–(5), while the non-carcinogenic and carcinogenic indices were calculated using Eqs. (6) and (7), respectively16,17.

$${\text{ADD}}_{{ing}} = \frac{{{\text{C}}_{p} \times {\text{k}}_{{pi}} \times {\text{R}}_{{ing}} \times {\text{EF}} \times {\text{ED}}}}{{{\text{BW}} \times {\text{AT}}}} \times 10^{{ - 6}}$$
(3)
$$ADD_{{der}} = \frac{{C_{p} \times k_{{pi}} \times SA \times AF \times ABS \times EF \times ED}}{{BW \times AT}} \times 10^{{ - 6}}$$
(4)
$$ADD_{{inh}} = \frac{{C_{p} \times k_{{pi}} \times IR_{{inh}} \times EF \times ED}}{{PEF \times BW \times AT}}$$
(5)
$$THQ = \sum {HQ_{i} } = \sum {\frac{{ADD}}{{RfD_{i} }}}$$
(6)
$$TCR = \sum {CR_{i} } = \sum {ADD \times SF_{i} }$$
(7)

where EF is the exposure frequency (350 d·a− 1); AT was the average exposure time, corresponding to ED×365 d for carcinogenesis and 70 × 365 d for non-carcinogenesis; PEF is the settling factor of suspended particles (1.36 × 109 m3·kg− 1); ADD represents the exposure dose39,40; ABS represents the skin absorption factor (0.001). The parameters used in these equations are thoroughly outlined in Table S2. Non-carcinogenic reference doses (RfD) and carcinogenic slope factors (SF) are shown in Table S3. HQi and THQ indicate the single and combined non-carcinogenic risk indices, respectively; CRi and TCR represent the single and combined carcinogenic risk indices, respectively.

Data analysis method

Excel 2016 was used for data sorting, analysis, and statistical characterization. ArcGis Pro was used for spatial feature analysis and mapping20,41. Origin 2024 was used for related graphics. PMF 5.0 was used for source analysis. Oracle Crystal Ball software 11.1.2.4 was used to conduct MCS, with the risk levels expressed as probability distributions.

Results and analysis

Concentration levels and spatial distribution of HMs

HM concentrations measured in the surface sediments of the Kuye River are presented in Fig. 1a and Table S4. In the dry season, the concentrations of HMs varied as follows, 8.51–216.20 mg·kg− 1 for Cu, 29.76–387.56 mg·kg− 1 for Zn, 5.28–124.30 mg·kg− 1 for Pb, 0.08–1.41 mg·kg− 1 for Cd, 18.04–197.55 mg·kg− 1 for Ni, 16.71–209.56 mg·kg− 1 for Cr, 0.00–0.04 mg·kg− 1 for Hg, and 3.72–66.94 mg·kg− 1 for As. In contrast, wet season concentrations varied as follows, 9.47–85.72 mg·kg− 1 for Cu, 41.03–341.43 mg·kg− 1 for Zn, 8.94–57.20 mg·kg− 1 for Pb, 0.00–0.27 mg·kg− 1 for Cd, 17.04–151.97 mg·kg− 1 for Ni, 28.62–199.52 mg·kg− 1 for Cr, 0.00–0.25 mg·kg− 1 for Hg, and 6.55–42.12 mg·kg− 1 for As. In both the dry and wet seasons, the measured HM concentrations exceeded their corresponding BVs (consistent with those in Eqs. 1 and 2). The mean sum content of all eight HMs was 488.38 mg·kg− 1 in the dry season and 412.79 mg·kg− 1 in the wet season (Fig. 1b), which was consistent with previously reported literature42,43 showing elevated HM levels during dry periods compared to wet periods13,44. The average Hg contents showed a substantial decreasing trend from 0.10 to 0.01 mg kg− 1 over the wet-dry period. On the other hand, the average values of the remaining HMs exhibited significant increasing trends, which were consistent with the changing patterns of the total HM contents. These differences might be attributed to the higher saturated vapor of Hg than those of the other HMs, with comparatively stronger volatility at room temperature42,45.

Additionally, the coefficients of variation (CV) for Cu, Pb, and As were higher in the dry periods than wet periods, while all other elements exhibited little difference between the two seasons, which may be due to the increased flow velocity in the wet season affecting the release and migration of some HMs from sediments46. In the dry season, water flow rates are relatively slow, allowing suspended substances to deposit easily and HM concentrations to increase significantly. However, with the acceleration of water flow in the wet season, sediments are easily washed out, promoting the migration of HMs from sediments to the overlying water and their dispersion within the river system47,48. Water temperatures are usually very low during dry season, limiting microbial activity and chemical reaction rates49, thereby affecting the solubility of HMs, making them more likely to accumulate in sediments. Furthermore, most of the plants in the study area were found to die-off during the dry season, resulting in a higher abundance of waste plant biomass and subsequently, the accumulation of organic matter. High loads of organic matter in sediments can form complexes with HM ions, affecting their overall migration50.

The spatial distribution of HMs within the study area is shown in Fig. 1b. During dry season, HM concentrations in surface sediments were higher in middle and downstream areas of the river basin, while lower in upper reaches. With the exception of Hg, all other HMs were mainly concentrated in the middle reaches of the Ningtiaota Industrial Park (S36-S40), while Cd, Cu, and Zn also exhibited high concentrations in the Shamao Town (S52-S55) area. Moreover, Hg presented a distinct pattern of distribution as compared to the other tested HMs, with negligible variations observed across the whole basin area. However, in the wet season, the high Hg concentration sites were mainly concentrated in the upper reaches (S1-S18, 0.118–0.233 mg·kg− 1) and the middle reaches (S36-S48, 0.094–0.184 mg·kg− 1) of the river basin, as well as the urban area of Shenmu City (S46-S47, 0.164–0.172 mg·kg− 1). The highest contents of Pb, Cd, Ni, Cr, and As were mainly concentrated in the upstream coal-mining (S1-S15) and industrial park areas (S35-S39). In summary, the areas with high concentrations of HMs in the wet season were mainly concentrated in the coal-mining area, three large industrial parks, and traffic trunk road of Shenmu City and Shamao Town. Relevant departments should pay attention to the impact of coal-mining, coal-burning enterprises, and urban traffic on the surrounding environment. In the wet season, peak HM concentrations occurred more frequently, and the pollution sources were more widespread compared to the dry season42,44. However, the mean HM concentrations were lower during the wet season compared to the dry season. Considering this decline, along with the broader distribution of pollution sources during the wet period, the minimum ecological and public health risks associated with HMs in the Kuye River sediments were explored in following paragraphs based on the HM content during the wet season.

Fig. 1
Fig. 1
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HMs contents and spatial distribution.

Probabilistic HM pollution levels

The Igeo value was employed to determine the HM pollution level using the MCS method, as shown in Fig. 2 and Table S5. Results showed that the simulated values were highly consistent with real measured values (correlation coefficient > 0.978)31, with the HM concentrations ranked in the descending order of: Hg (1.493) > Zn (0.608) > Ni (0.602) > As (0.260) > Cu (0.259) > Pb (-0.148) > Cr (-0.214) > Cd (-0.505). Analysis of the Kuye River surface sediment showed that sediments were not contaminated with Pb, Cr, and Cd, but exhibited at least moderately contaminated with Hg, Zn, Ni, As, and Cu. According to the results of MCS analysis, 22.27%, 39.44% and 23.16% of sites contaminated with Hg were moderately to heavily, moderately, and uncontaminated to moderately contaminated, respectively, while 18.05%, 30.74%, 25.08%, 7.92% and 17.98% of sites contained moderate levels of Cu, Zn, Ni, Cr and As contamination and 43.94%, 45.93%, 45.93%, 30.97%, and 43.89% were uncontaminated-moderately contaminated, respectively. In general, the study area exhibited notable levels of HM contamination, among which Hg was the main contributor, corresponding to a 69.06% probability of moderate Hg contamination levels or above, and the expected maximum Igeo value reached 5.988, suggesting severe Hg enrichment levels.

Fig. 2
Fig. 2
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Cumulative probability distribution of Igeo values of HMs in Kuye River surface sediments based on MCS (Monte Carlo simulation): (a ~ h) Cumulative probability distribution of single HM Igeo value; (i) Probability occurrence of different levels of Igeo value. The MCS data were obtained through calculation using Crystal Ball 11.1.2.4 (https://www.oracle.com/applications/crystal-ball/), and the figures were drawn using Origin 2024 (https://www.originlab.com/).

The results revealed significant different HM enrichment characteristics in the rivers of this study area from those in other coal mining and industrial areas (Table S7). In particular, the Igeo (1.493) of Hg was significantly higher than the corresponding value in the coal mining area of the upper reaches of the Yellow River in the Ningxia section (-0.27)51 and Krishna River, South India (-0.30)52. However, the Igeo of Cd (-0.505) was lower than that in the Ningxia section of the upper reaches of the Yellow River (0.73)51 and the Guihua River (3.16)53. Importantly, the Igeo values of Pb (-0.148) and Zn (0.608) in the study area were relatively similar to those revealed in the Beijing Qingshui River (-0.14)54 and Guihua River (0.85)53, respectively. It can, therefore, be concluded that HM pollution in the study area was at a medium level when compared with previous related studies.

Probabilistic ecological risk of HMs

Content-oriented ecological risk

The MCS method was used to estimate the single potential ecological risk index (Ei) for HMs, based on their total content in the Kuye River, as shown in Fig. S3 and Table S6. For Cu, Zn, As, Cr, Ni, and Pb, the Ei values remained < 40 at least 96.61% of the sites, indicating that they present a low level of ecological risk. In 3.39% and 20.08% of sediment samples, As and Cd presented a moderate level of ecological risk, while the Ei values for Hg exhibited Ei values between 0 and 329.59, with an average of 57.93, corresponding to a moderate ecological risk level (Ei values ranging from 40 to 80). The range of RI values was 26.15 to 467.39, and the mean reached 162.19, in which samples exhibited a 41.56% cumulative probability of RI values representing a moderate risk (RI values ranging from 150 to 300) and a 11.25% cumulative probability of RI values representing a considerable risk (RI values ranging from 300 to 600), implying that half of the HMs samples exhibited at least a moderate level of ecological risk. Among these, Hg was the main contributor (35.72%) to the comprehensive ecological risk (RI) posed by HMs in the sediment of the Kuye River, mainly due to Hg having the highest toxicity coefficient13,55 and a high RI value.

The results of this study highlighted different individual ecological risk levels from those in other areas (Table S7). The Ei value of Hg (57.93) was higher than that of the coal mining area in the Ningxia section of the upper reaches of the Yellow River51 (2.62) and lower than the corresponding values of the main stream of the Yellow River in Shandong (555.79)56 and the Guihua River (757.10)53. Whereas the Ei value of Cd was 25.45, significantly lower than that of the Ningxia section of the upper reaches of the Yellow River (123.00)51, the Qingshui River in Beijing (48.39)54, the Guihua River (402.40)53, and the Euphrates (Iraq) (222.66)57. In contrast, the obtained Ei values of As and Cr reached 13.12 and 2.87, which were close to those of the Qingshui River in Beijing (7.44)54 and the Ningxia section of the Yellow River (5.20)51, respectively, highlighting relatively similar potential ecological risks of As and Cr between coal mining areas. Therefore, compared with previous related studies, the single ecological risks of Hg in the study area were at a medium level.

Source-oriented ecological risks

The MCS was further applied to evaluate probabilistic ecological risks of HMs in the Kuye River’s surface sediments using the PMF-based RI coupling model. This model was, in fact, constructed based on previous PMF analysis results28 (Text S1 and Fig. S4). The mean EP values of coal-mining, traffic, agriculture, and industrial sources were 107.86, 76.06, 9.07, and 28.07, respectively (Fig. 3a), indicating risk levels of considerable, moderate, and low (for both agriculture and industry). Coal-mining sources accounted for 48.79% of the total RI value, making them the dominant source of ecological risk, whereas traffic, industrial, and agricultural sources contributed 34.41%, 12.70%, and 4.10%, respectively (Fig. 3b). Additionally, statistical analysis (Fig. 3c and Table S8) demonstrated that the probability of coal-mining sources Ep values exhibiting a moderate, considerable, or high risk were 38.06%, 9.12%, or 1.28%, respectively. The Ep value for traffic sources was much lower compared to coal-mining, with only 8.95% and 1.24% probabilities of posing moderate and considerable risks, respectively. Among the four sources, the industrial and agricultural sources contributed the least to ecological risk, exhibiting low-risk probabilities of 98.98% and 99.28%, respectively, indicating that their risks can be ignored.

Fig. 3
Fig. 3
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Cumulative probabilistic ecological risk assessment based on MCS: (a) Cumulative probability distribution of source-oriented RI values; (b) Contribution ratio of different pollution sources to ecological risk; (c) Occurrence Probability of different ecological risk levels across all pollution sources.

Total source ecological risk index varied between 26.30 and 472.23, averaging at 210.49. Samples exhibited a 47.95% cumulative probability of RI values representing moderate risk (RI values ranging from 150 to 300) and a 21.09% cumulative probability of RI values representing considerable risk (RI values ranging from 300 to 600), implying that 73.08% of the HM samples exhibited at least a moderate level of ecological risk (Fig. 3c). Considerable risk samples were mainly collected from large-scale coal-mining areas, which were rich in coal resources and had large daily traffic volumes, resulting in river sediments within these mining hinterlands exhibiting HM risk characteristics. Therefore, in terms of pollution risk management, controlling raw coal-mining, coal gangue stacking, and transportation emissions could significantly alleviate the risk of HM pollution in river sediments within the study area.

Probabilistic health risks posed by HMs

Content-oriented health risk

The non-carcinogenic health risks posed by HMs to four distinct population groups were evaluated using the MCS probabilistic risk model (Fig. S5a and Table S9). The mean THQ for boy, girl, adult male, and adult female groups were 0.441, 0.436, 0.181, and 0.197, respectively, all of which were < 1 and therefore, HMs in the survey area presented a low non-carcinogenic risk to human health. The non-carcinogenic health risks were higher for children than for adults, with 7.45% of THQ values being > 1 for children at least, while the probability of unacceptable risk was close to zero for adults, which was consistent with previous related findings31 for fine particulate pollution in a northern Chinese industrial city.

The total carcinogenic health risks posed by HMs are shown in Fig. S5b. The TCR value of HMs indicated an intolerable level of carcinogenic risk (> 1.0E-04) in Kuye River surface sediments, with a probability of at least 94.59% (Table S9). The mean TCR values were ranked as boys (6.608E-04) > girls (6.555E-04) > adult females (2.842E-04) > adult males (2.622E-04), indicating there was no significant variation in mean TCR values between males and females within the same age group. However, with increasing age the risk of cancer exhibited a downward trend, emphasizing the importance of age-related indicators when evaluating carcinogenic risk, such as body weight, oral ingestion frequency, skin adhesion coefficient in children, and the increased sensitivity of younger children to toxic substances14,52.

Source-oriented health risks

Through PMF-based HRA(THQ/TCR) coupling model analysis, source-specific probabilistic health risk assessments showed significant differences in the TCR values for different pollution sources across boys (6.38E-4), girls (6.33E-4), adult males (2.52E-4), and adult females (2.73E-4) (Fig. 4a and Table S10), indicating that the risk of cancer was highest in boys and girls, consistent with the carcinogenic risk-sensitive group identified in the content-oriented results. Results showed that the overall risks of exposure to HMs in river sediments in the study areas were greater for children than adults, which is in agreement with previously reported studies15,17. In addition, children face greater carcinogenic risks than adults, warranting increased attention. Additionally, the probability of TCR values > 1.0E-4 was 99.61%, 99.59%, 94.07%, and 95.28% for boys, girls, adult males, and adult females, respectively (Table S10). These findings show that the carcinogenic risks posed by HM pollution were very high for all population groups residing around the Kuye River basin.

Fig. 4
Fig. 4
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Results of human health risk assessment of HM pollution sources: (a) Source-directed TCR (total cancer risk) cumulative probability distributions of four populations. (b) Carcinogenic risk index of human health under different pathways. (c) Contribution of different sources to source-directed TCR.

Among the three possible exposure pathways, the ingestion pathway presented the most significant level of carcinogenic risk58 (Fig. 4b and Table S11), with the contribution to risk ranging from 68.03 to 81.66%. In contrast, the risk posed by dermal contact was lower than the risk from ingestion, ranging from 18.09 to 31.81%. In contrast, the risk from inhalation was close to zero and therefore was considered to be negligible. Due to the high risk posed by ingestion, reducing HM exposure through this pathway is essential to significantly minimize the carcinogenic risks posed by HMs in coal field regions14. Therefore, guidance should be actively strengthened regarding the lifestyle habits and behaviors of children to decrease their daily exposure to HMs, with a focus on personal protection to mitigate the resulting health risk42,59.

Carcinogenic risk levels, as reflected by average TCR values, were 3.70E-05 for coal-mining, 2.71E-04 for traffic, 1.21E-05 for agriculture, and 3.40E-04 for industrial sources (Fig. S6 and Table S12). Industrial sources contributed most to the TCR value for carcinogenic risk (51.48%), followed by traffic emissions (41.09%), with relatively small contributions from coal-mining (5.61%) and agricultural sources (1.83%) (Fig. 4c), both of which posed a negligible carcinogenic risk within the exposed population. The probability of exposure to an unacceptable risk from coal-mining sources (TCR > 1 × 10− 4) was 11.47%, from traffic sources was 94.42%, from agricultural sources was 0.12%, and from industrial sources was 97.70%. Therefore, the control of industrial sources may be the most important factor in reducing public health risks in the Kuye River basin area, followed by traffic sources.

Risk contribution and management from various sources

The assessment of source-oriented risks established a clear link between pollution sources and the ecological health risks associated with HMs in sediments, giving a thorough description of the risk contributions from various sources. The results helped identify the key targets for controlling regional sediment HM pollution, developed the formulation of integrated risk management strategies, and improved the overall effectiveness of the risk control process. The comprehensive potential ecological risk of HMs considers their environmental toxicity45,60, whereas health risk mainly considers the effect of HMs on human health31,61. The contributions of various sources to sediment contamination, ecological and public health risks in the study area, are shown in Fig. 5, exhibiting significant differences in the contribution rates. Industrial and traffic sources contributed most to sediment pollution levels, accounting for 48.23% and 31.06% respectively, followed by agricultural and coal-mining sources, which accounted for 11.84% and 8.87% respectively. The four pollution sources contributed 12.70%, 34.41%, 4.10%, and 48.79%, respectively, to the total ecological risk, while their contributions to carcinogenic risk were 51.48%, 41.09%, 1.83%, and 5.61%, respectively. Results showed that specific pollution sources contributed to varying degrees of sediment pollution, ecological and carcinogenic risks. The contribution of coal-mining sources to ecosystem risk was the largest, while its contribution to sediment pollution and carcinogenic risk was relatively low. In contrast, the contribution of industrial sources to ecological risk was the smallest, while their contribution to HM concentrations and carcinogenic risk was the largest.

Fig. 5
Fig. 5
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Contribution of specific sources to sediment pollution, ecological and carcinogenic risk.

Considering the overall impact of HMs in river sediments on the ecological environment and human health, coal-mining and industrial sources were determined to be priority factors for the control of regional HMs risks, with their element contribution rates shown in Fig. 5. Hg was the largest contributing factor to the ecological risk of HMs, reaching 98.46%, although it did not exhibit any obvious carcinogenic risk. The results of probabilistic carcinogenic risk evaluation showed that Ni had the largest impact on human health risk, reaching 82.65%, mainly due to the high SF value for Ni exposure through dermal contact and ingestion pathways58,62. The elevated likelihood of these elements causing adverse health effects, such as neurological and lung diseases, has been observed in populations exposed to such pollutants61,63. Therefore, it is crucial to regulate the release of Hg and Ni from coal-associated industries along the Kuye River to protect public health and maintain the sustainability of ecological health.

Fig. 6
Fig. 6
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Risk values of HM specific sources in the Kuye River sediment (The bubble size and color depth represent the risk values) (a) Ecological risk; (b) Carcinogenic risk.

Compared with the results of traditional pollution source analysis, which relies mainly on pollutant concentrations, these coupled models can control pollution sources more reasonably. Through the comprehensive assessment of the ecological RI and public carcinogenic risk, considering the corresponding BV, element toxicity, source contributions, exposure pathways, and the characteristics of the population affected by HMs in the Kuye River sediments, high-risk areas and pollution sources were identified and prioritized. The results of comprehensive ecological risk calculations for HMs in sediments (Fig. 6a) showed that RI values varied between 26.30 and 472.23, averaging at 210.49. At least 25.13% of the samples exhibited a considerable risk (Table S8), mainly located in the section where the Kuye River enters Shaanxi, specifically at sampling sites S1 to S4, and the area around Daliuta Industrial Park, corresponding to sites S13 to S15. The upstream region on both sides of the river is known for its abundant coal reserves, and HMs in coal mine solid waste and coal dust can migrate and be released into the surrounding river surface sediment through weathering and precipitation runoff, causing serious HM pollution in the Kuye River sediment64. Moreover, 47.95% of the sampling sites exhibited a moderate risk, mainly located in Yanjiata (S16 ~ S18), Ningtiaota (S36 ~ S40) industrial Park, and the Shenmu-county area (S46 ~ S47). Coal burning during industrial production processes (such as coking and coal production emissions, the deposition of coal coke particles during screening, transportation, and handling, waste gas produced by gas combustion, and high-temperature treatment of coal in the coke oven)65, along with heavy truck exhaust emissions14, tire wear15, and braking18, indirectly increases the risk posed by HMs in sediments. The ecological and human health risks in the Kuye River were attributed to the release of HMs from different anthropogenic sources, such as coal chemical enterprises and transportation. The pollutants are persistent and cannot be degraded through natural processes, accumulating in sediments through chemical precipitation and adsorption on clay minerals. However, they can be desorbed and released from sediments into water bodies under the effects of changing external environmental conditions (e.g., pH decreases or redox potential changes)8. The pollution of the Kuye River’s water environment negatively affected plankton production, disrupting the balance of the river ecosystem46,49. Nevertheless, the Kuye River is considered the main water source for agricultural irrigation in the study area. HMs in the sediments can be transferred into the human body through the food chain (sediment - water body - crops - humans), potentially affecting human health63,67. Furthermore, the high TCR value areas of carcinogenic risk were consistent with those of ecological risk in the upper and middle sections of the basin (Fig. 6b), while high carcinogenic risk areas also appeared in the downstream residential area. These results indicated that the ecological and carcinogenic risks posed by coal resource exploitation and industrial facilities were highly consistent, while some differences were observed in non-industrial areas. Therefore, targeted risk management strategies must be developed based on specific ecological risk and carcinogenic risk characteristics.

Long-term coal resource exploitation and coal-burning industrial processes have led to substantial HM accumulation within the study area, resulting in the need for mining and industrial discharge management practices to be strengthened and standardized across the region. Additionally, traffic pollution also significantly disrupts the ecological balance and human health within an environment, which may exacerbate the risk posed to ecological environment and public health. However, the element identified as representative of ecological risk during source apportionment may not be the main contributor to health risk. Therefore, strict regulations on coal solid waste stacking, coal dust prevention, and industrial emission standards should be formulated, with improved monitoring of vehicle exhaust emissions, roadside management, and public education. Based on the above, managing the HMs risk posed by the Kuye River sediments, particularly in mixed ‘mining-industrial- traffic’ areas, posing a multi-faceted challenge. In conclusion, policymakers must carefully balance the relationship between environmental protection and regional socio-economic development. By optimizing energy structure and enhancing the use of clean energy, the environmental quality of large mining areas and the health of surrounding residents can be significantly improved3,66.

Limitations of this study

This study analyzed the impact of HM accumulation on the ecological and public health of the sediment of the Kuye River, during the process of coal resource exploitation and subsequent coal-burning industries, determining the priority risk factors for the control of HMs. However, this study has some important limitations. Firstly, only eight HMs were considered in this study, including Cu, Zn, Pb, Cd, Ni, Cr, Hg, and As, which have been designated as priority pollutants by the Joint Environmental Protection Agency, while the pollution levels and risks posed by the remaining elements were not considered. Secondly, when exposed through ingestion, dermal contact, and inhalation pathways, only a fraction of the HMs in river sediments enter the digestive or circulatory systems of the human body (the available portion ranges from 1% to 100%)67,68, adversely causing lesions in human tissues or organs. Therefore, the carcinogenic risk assessment methodology used in this study may result in conservative risk assessments, and future carcinogenic risk assessments should also consider the bioavailability of HMs.

Conclusions

The probability of Igeo and the ecological health risks posed by HMs were investigated in the Kuye River surface sediment, which lies within China’s largest known coal field. This study analyzed the regional effects of HMs in different seasons. The results showed that the average total HM contents were significantly higher in the dry season compared to the wet season. Significant differences were observed in the distribution of HMs, with the wet season exhibiting a higher peak frequency of total HM concentrations than the dry season. During the dry season, higher HM concentrations were observed in the middle and downstream of the basin and lower concentrations were observed in the upper reaches, while high concentration areas were mainly concentrated in the upper and middle reaches in the wet season. Therefore, subsequent conclusions were drawn based on the wet season, which had the lowest risk and the widest range of pollution sources. The results showed that pollution and ecological risks in the Kuye River sediments were moderate, with Hg emissions from coal-mining being the primary contributor to concentration pollution and ecological risk. Meanwhile, the carcinogenic risks for the four population groups (boys, girls, adult males, and adult females) were extremely serious, as industrial sources released carcinogenic HMs (especially Ni) into the environment, posing a great threat to human health. Boys were identified as the most vulnerable group, given their heightened sensitivity to HM exposure. The effects of HMs on ecological and carcinogenic risks were highly consistent in coal-mining areas, industrial parks, and along major traffic routes. However, significant differences were observed in non-industrial and densely populated areas, where ecological risk was low, but carcinogenic risk remained high.

This study integrates MCS, source-specific ecological risk, and health risk assessment methods to support pollution management, aiding better decisions and improving treatment effectiveness. The RI value for source-oriented ecological risk (210.49) was higher than that for content-oriented risk (162.19). Meanwhile, the difference in carcinogenic risk between source-oriented and content-oriented approaches was negligible, with a very high probability of unacceptable risk. Additionally, coal-mining and industrial sources were found to be the major sources of ecological risk and carcinogenic risk, respectively. The extent to which a pollution source contributes to carcinogenic risk differed significantly from its impact on ecological risk and receptor concentration. Therefore, in order to manage receptor concentration contamination, as well as the ecological and carcinogenic risks posed by HMs in river sediments, risk source control and targeted management measures, such as coal-related industrial source control, ecological restoration, waste resource utilization, and other measures, should be implemented according to the specific pollution situation. These findings provide a valuable reference for risk control and environmental management strategies of river sediments in regions with large coal-related industries.