
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12332-8
10.1016/j.heliyon.2024.e36301
e36301
Research Article
Systematic assessment of source identification and ecological and probabilistic health risks of potentially toxic elements (PTEs) in soils of a typical coal mining area in Guanzhong region
Chen Daokun ab
Li Xinbin lixinbin@mail.cgs.gov.cn
ac⁎
Wang Zhanbin solohike@163.com
a⁎⁎
Kang Chengxin a
He Tao d
Liu Hanyuan a
Jiang Zhiyang b
Xi Junsheng a
Zhang Yao a
a Xi'an Center of Mineral Resources Survey, China Geological Survery, Xi'an, 710100, China
b School of Earth and Environment, Anhui University of Science & Technology, Huainan, 232001, China
c Qinling--Loess Plateau Transition Zone Observation and Research Station for Coupling of Soil and Water Elements and Conservation of Biological Resources, China
d Research Center of Applied Geology of China Geological Survey, Chengdu, 610036, China
⁎ Corresponding author. Xi'an Center of Mineral Resources Survey, China Geological Survery, Xi'an, 710100, China. lixinbin@mail.cgs.gov.cn
⁎⁎ Corresponding author. solohike@163.com
16 8 2024
15 9 2024
16 8 2024
10 17 e3630128 5 2024
5 8 2024
13 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Mining activities may cause the accumulation of potentially toxic elements (PTEs) in surrounding soils, posing ecological threats and health dangers to the local population. Therefore, a comprehensive assessment using multiple indicators was used to quantify the level of risk in the region. The results showed that the mean values of the nine potentially toxic elements in the study area were lower than the background values only for Cr, and the lowest coefficient of variation was 17.1 % for As, and the spatial distribution characteristics of the elements indicate that they are enriched by different factors. The elements Hg and Cd, which have substantial cumulative features, are the key contributors to ecological risk in the study region, which is overall at moderate risk. APCS-MLR model parses out 4 possible sources: mixed industrial, mining and transportation sources (53.98 %), natural sources (24.56 %), atmospheric deposition sources (12.60 %), and agricultural production sources (8.76 %). The probabilistic health risks show that children are more susceptible to health risks than adults; among children, the safety criteria (HI < 1 and CR < 10−4) were surpassed by 29.29 % of THI and 8.58 % of TCR. According to source-orientated health hazards, the element Ni significantly increases the risk of cancer. Mixed sources from industry, mining, and transportation are important sources of health risks. The results of this research provide some scientific references for the management and decrease of regional ecological and health risks.

Keywords

Potentially toxic elements
Ecological risk
APCS-MLR model
Probabilistic health risk assessment
Monte Carlo simulation
==== Body
pmc1 Introduction

The foundation for advancing industrial development and socioeconomic expansion is coal resources. Large amounts of potentially toxic elements are released during the mining or processing of coal, as well as in other associated businesses, endangering human health and the surrounding ecology [1]. Such as coal mining activities to destroy the ground surface, the coal mine stone, surrounding rock and other products exposed to the air environment by the weathering leaching precipitation of potentially toxic elements, as well as coal mine stone transport and industrial coal-fired power generation activities such as the release of pollutants will be in the soil environment will be enriched, the pathway also led to the soil environment convergence of potentially toxic elements greatly exceeded the background, thus triggering the coal development of polluted environments have been important concerns [[2], [3], [4]].

The enrichment of pollutants in the environment is influenced by the natural geological environment and anthropogenic factors[[5], [6], [7], [8]]. Pollutants build up in soil and water ecosystems, and during environmental transit transformation, they may create new hazardous compounds [9].PTEs are highly cumulative, toxic and non-degradable [10], a higher concentration of metal ions will increase the permeability of the soil and cause long-lasting pollution of the soil environment [11]. Human activity disturbance of the soil environment can result in major risks such as soil degradation and nutrient change and release [12,13]. Pollutants cause degradation of the regional environment and health hazards through inhalation, ingestion and dermal intrusion [14,15]. The USEPA has designated Cd, Cr, As, Pb, Cu, Zn, and Ni as priority pollutants due to their potential ecological and human health risks [16]. Such as Pb is highly toxic to human organs, disrupts blood circulation, damages the nervous system and is extremely harmful to children's intellectual development [17]; Cd causes dysfunction in human kidneys [18]. PTEs in the soil cause regional ecological and environmental risks, vulnerability to human health hazards, and even regional risks of irreversible health disorders [19,20]. Consequently, it is advantageous to ascertain the extent of the accumulation status of PTEs in the region in order to assess the ecological and human health hazards. This is crucial for the identification of priority contaminants that must be controlled in soil risk management.

The current multi-perspective characterization of risk is a significant trend among academicians in the evaluation of PTEs hazards [21]. The geo-accumulation index method, potential ecological hazards, and enrichment factors are frequently employed to evaluate the quantification of contamination levels of potentially toxic elements[[22], [23], [24]]. Pearson's correlation, cluster analysis, and principal component analysis are employed to qualitatively evaluate the sources of contaminants, as well as geostatistical analysis of spatial interpolation to characterize differences in the spatial distribution of elements for initial source identification. Although these methods are capable of elucidating the contamination levels of potentially toxic elements and conducting qualitative analyses of the sources of contaminating substances, they are unable to quantify the priority contaminating factors and sources that must be controlled [25]. However, the APCS-MLR receptor model could be employed to quantitatively resolve potential sources of contaminants [26]. As a result, this investigation integrated the APCS-MLR model with Pearson's correlation combined and spatial location attributes of geostatistical analyses to conduct a thorough evaluation of pollutant sources. Traditional human health risk assessment modeling is a deterministic approach that is predicated on the content of the contaminant and the parameters established by the evaluation model. It fails to account for the varying sensitivities of the population to the contaminant, which may result in misleading assessment results that overestimate or underestimate the hazards of the contaminant [27]. Probabilistic health risk assessment is an excellent way of avoiding this problem by using Monte Carlo simulation that takes into account parameter uncertainty while calculating human health risks in areas exposed to contaminants [28]. To provide effective information for the identification of the primary health hazards, as well as for the control and prevention of regional health risks, by establishing a source-directed health relationship between potentially toxic elements, pollution sources, and health risks [29,30]. Consequently, source-orientated health risks are crucial for the suppression and reduction of pollution from sources that make a substantial contribution.

The main objectives of this study are as follows: (1) To quantify the ecological hazards and investigate the spatial distribution characteristics and accumulation status of potential toxic elements in the study area. (2) The identification and quantification of contamination sources through the use of Pearson correlation in conjunction with APCS-MLR receptor modeling. (3) Employ Monte Carlo simulation to conduct a probabilistic health risk assessment and assess the health risk of potentially toxic elements from various sources in conjunction with the APCS-MLR receptor model.

2 Materials and methods

2.1 Study area and sample collection

Hancheng City is abundant in coal mining resources and is situated on the western bank of the Yellow River in eastern Shaanxi and the northeastern region of the Guanzhong Basin. Hancheng City's terrain is characterized by high elevation in the northwest and low elevation in the southeast. The city features diverse landforms, including deep mountainous terrain over 900 m in the northwest, loess hills in the center, and loess flat topography in the east. The region has a warm-belt continental monsoon climate with four distinct seasons and an average annual temperature of 13.5 °C. It is also surrounded by several different nations. The rainy season occurs predominantly from July to September, with an annual rainfall of 559.7 mm. Extended periods of sunshine and a frost-free span of 208 days are beneficial for agricultural productivity. The regional agro-industrial structure is rich in food and cash crop industries. The coal, power, and metallurgical industries serve as the backbone of the local economy. Additionally, the region boasts an extensive and sophisticated transportation infrastructure, which includes a well-integrated network of roads, railways, and waterways.

The study area is located in the vicinity of the Xiangshan coal mine in Hancheng City, with its southeastern border next to the urban center of Hancheng City. The mine boasts a rich historical tapestry of mining activities, with operations initially commencing in the 1970s. Geological strata in the area are richly exposed, with the Carboniferous Permian being the ore-bearing strata, and the area is controlled by multi-directional geological structures. In this study, GPS is used to determine sampling locations following the "plum blossom method." Five sample points are combined in equal amounts to create a 1 kg sample. The sample number, latitude and longitude coordinates, and other pertinent information are recorded on the record card. 23 sample sites were gathered from the 0–20 cm topsoil layer (Fig. 1). The samples were dried naturally at ambient temperature to eliminate contaminants including sand, gravel, and plant residue. The samples were mixed using the tetrad technique, placed in plastic sample vials, labeled for preservation, and then delivered to the laboratory for examination.Fig. 1 Location and sampling map of the study area.

Fig. 1

2.2 Chemical analysis

Soil samples were desiccated naturally and homogenized in the laboratory. The elements Pb, Zn, and Cr were determined using powder compact X-ray fluorescence spectrometry. The sample was dissolved in HNO3–HCl–HF–HClO4 after weighing 0.2 g. After the volume was fixed, 25 ml of the solution was taken for the determination of elemental V using inductively coupled plasma emission spectrometry. The elements Co, Ni, and Cd were determined using inductively coupled plasma mass spectrometry after 20 ml of the solution was taken for dilution. The atomic fluorescence spectrometer was employed to ascertain the elements of As and Hg by weighing 0.5g of the sample and dissolving it in aqua regia. The laboratory analysis process uses 12 blank samples of continuous measurement and calculates the standard deviation of three times as the detection limit of elemental analysis, for Co, Cd, Cr, Ni, As, Zn, Pb, Hg and V elemental analysis method detection limit were: 0.2, 0.02, 5, 1, 1, 1, 2, 2, 0.02, 5 μg/kg, respectively. Twelve analyses were carried out using eight (high, medium and low content) national-level standards of different categories (GBW series) to calculate and evaluate the accuracy and precision of the analyses for quality control of regulatory assay analyses. The accuracy of the analytical method for each element was within 0.11, and the RSD of precision was within 10 % of the quality control requirements.

2.3 Assessment methods

In order to accurately describe the risk profile of potentially toxic elements in the study location. The geo-accumulation index method (Igeo), the potential ecological risk index method (PERI), the absolute principal component score-multiple linear regression (APCS-MLR), and the Monte Carlo simulation were employed in this study to evaluate probabilistic health risks. Systematic assessments evaluate the risk hazards of potential toxic elements in terms of their accumulation, ecological risks, identification of sources, and probabilistic health risks.

2.3.1 Geo-accumulation index

The geo-accumulation index is a widely used technique for evaluating soil components by considering both natural processes and human activities. The cumulative risk of an element is evaluated by comparing its measured value with the background value [31]. The Igeo are calculation formulae are as Eq. (1):(1) Igeo=log2Cn1.5Bn

The details of the Igeo are presented in the supporting data file Table S1.

2.3.2 Potential ecological risk index

The potential ecological risk index (RI) method assesses the negative impact of pollutants on the ecosystem from a toxicological perspective [32]. Potential ecological risk levels in the environment were determined by statistically evaluating RI for various components and potential ecological risk coefficients (Eir) for each element [33]. The RI are calculation formulae are as Eq. (2):(2) RI=∑i=1nEri=∑i=1nTri×Cfi=∑i=1nTri×CiCni

The details of the PERI are presented in the supporting data file Table S2.

2.3.3 APCS-MLR source resolution model

Absolute principal component score-multiple linear regression combines the methods of absolute principal component score (APCS) and multiple linear regression (MLR) [34]. The model assumes that all pollution sources have a linear connection with the ultimate pollution concentration at the receptor location, allowing for the quantification of each source's contribution to the pollution [35]. The APCS-MLR receptor model standardized the elemental concentration sample data, converted them into absolute principal component factor scores (APCS) as independent variables based on PCA results, and conducted multiple linear regression using the measured concentrations of potentially toxic elements as dependent variables to quantitatively assess the contributions of different pollution sources. APCS-MLR receptor modeling is a beneficial approach to address the constraint of Principal Component Analysis (PCA) being able to only qualitatively identify the origin of pollution without providing a visual quantification of the impact [36]. The APCS-MLR model are calculation formulae are as Eqs. (3), (4), (5), (6):(3) Zij=Cij−C‾iσi

(4) (Z0)i=−0−C‾iσi=−C‾iσi

(5) Ci=bi0+∑k=1pbki×APCSk

(6) Pci=∣bki×APCS‾k∣∑k=1p|bki×APCS‾k|

The above equation Cij is the measured concentration of the PTEs; ‾Ci and σi are the mean concentrations of the elements and their standard deviations, respectively; bi0 is the multiple linear regression constant terms for potentially toxic element i; bki is the regression coefficient of source k on element i; APCSk is the absolute principal component factor score; bki × APCSk denotes the amount of source k contribution to Ci; The average of all samples bki × APCSk represents the average absolute contribution of source k to Ci; Pci is the contribution of element i to source k.

2.3.4 Probabilistic health risk assessment

The human health risk assessment model, which is derived from the USEPA, is employed to quantify and evaluate the severity of carcinogenic and non-carcinogenic hazards to human health posed by potentially toxic elements [37]. Assessing the likelihood of cancer-causing and non-cancer-causing dangers from potentially harmful elements to human populations by examining intake by ingestion, inhalation, and skin contact in adults and children, using Monte Carlo simulations [38,39]. The evaluation model are calculation formulae are as Eqs. (7), (8), (9), (10), (11):(7) ADDing=Cs×IngR×EF×EDBW×AT×10−6

(8) ADDinh=Cs×InhR×EF×EDPEF×BW×AT

(9) ADDder=Cs×AF×SA×ABS×EF×EDBW×AT×10−6

(10) HI=∑HQi=∑ADDing+ADDinh+ADDderRfDi

(11) CR=∑CRi=∑(ADDing+ADDinh+ADDder)×SFi

In the above formula, ADDing、ADDinh and ADDder are the average intake by oral、respiratory and dermal routes, respectively. Cs is the actual measured concentration of potentially toxic elements. Details of the remaining parameters (IngR, InhR, EF, ED, BW, AT, PEF, SA, AF, and ABS) are given in the supporting data file Table S3.

The HI is the hazard risk index for all potentially toxic elements in soil, and when HI < 1 it means that the non-carcinogenic health risk of potentially toxic elements in soil is negligible. CR represents the total carcinogenic risk index for all potentially toxic elements in soil. When CR < 1.0 × 10−6, it indicates that the local population is less disturbed by cancer risk; when CR > 1.0 × 10−4, it indicates a significant increase in the probability of cancer risk. SF denotes the reference dose in carcinogenic risk; RfDi is the reference dose for the corresponding element of a specific exposure pathway. Exhaustive information on SF and RfDi for different pathways of uptake of potentially toxic elements is in the supporting data file Table S4.

Many researchers employ Monte Carlo simulation, which involves combining mathematical formulae with probabilistic random sampling to approximate solutions to problems in uncertainty risk assessment [40]. Utilizing Monte Carlo simulation in conjunction with the human health risk model helps accurately assess the risk of hazardous health risks in soil, preventing the risk from being inaccurately estimated owing to individual variations [41]. For computational simulation, 10,000 iterations with 95 % confidence intervals were built up using Crystal Ball software. Detailed information on additional parameters is included in the supporting data file Table S5.

2.4 Statistical analyses

This study utilized Excel 2021 for descriptive analysis of potentially toxic elements, ArcGIS 10.8 for inverse distance spatial interpolation to depict the spatial distribution of the elements, IBM SPSS 26 for correlation analysis and source resolution of the APCS-MLR model, Monte Carlo simulation for calculating probabilistic health risks using Crystal Ball software, and generated study-related plots in OriginPro 2021.

3 Results and discussion

3.1 Soil PTEs content characteristics and spatial distribution

Table 1 provides descriptive statistics regarding the concentration of PTEs in the soil environment of the study area. The soil contained an average of 13.63 mg/kg, 0.23 mg/kg, 54.87 mg/kg, 30.60 mg/kg, 11.41 mg/kg, 80.33 mg/kg, 26.77 mg/kg, 0.133 mg/kg, and 81.70 mg/kg of Co, Cd, Cr, Ni, As, Zn, Pb, Hg, and V elements, respectively. The maximum values of all potentially toxic elements in the study area were higher than the background values when compared with the background values in Shaanxi Province. The mean values of all elements, with the exception of Cr, were also higher than the background values. The values of the kurtosis of the potentially toxic elements are all greater than zero, suggesting that the elements in the study area may be influenced by exogenous factors to varying degrees [42]. The coefficient of variation (CV) indicates the average variability of the sample sites in the research region and the dispersion of potentially toxic elements [43]. Only the element As exhibits weak variability. The coefficients of variation for the remaining elements are greater than 20 %, and the content of Hg is high, with a coefficient of variation of 97.9 %, suggesting that the Hg content is not evenly distributed in the sample sites, leading to significant spatial variations that could be influenced by external factors associated with human activities [44]. Compared with the elements of the surface soil of the Shigetai coal mine in northern Shaanxi [45], the mean values of Ni, As, Pb, Zn and Hg elements of the surface soil of Shigetai coal mine were lower than those of the present study area, and only the mean values of Cd and Cr were higher than those of the present study area. The different concentrations of PTEs in surface soils of different coal mining areas may be influenced by the spatial heterogeneity of regional human activities and changes in soil properties [46].Table 1 Descriptive statistics of PTEs in soils of the study area (mg/kg).

Table 1Elements	Co	Cd	Cr	Ni	As	Zn	Pb	Hg	V	
Min	1.8	0.05	7.6	4.4	5.73	15	4.6	0.031	11.9	
Max	20.4	0.36	74.6	38.5	13.8	130.4	43.3	0.53	107	
mean	13.63	0.23	54.87	30.60	11.41	80.33	26.77	0.133	81.70	
SD	3.06	0.07	11.61	6.46	1.95	22.14	8.07	0.13	16.75	
CV(%)	22.5	29.6	21.2	21.1	17.1	27.6	30.2	97.9	20.5	
Skewness	−2.30	−0.69	−3.07	−3.16	−1.19	−0.48	−0.34	1.84	−3.37	
Kurtosis	11.37	0.88	13.51	13.00	1.76	3.16	1.70	2.92	14.95	
BV	10.6	0.094	62.5	28.8	11.1	69.4	21.4	0.03	66.9	
Shigetai [45]	–	1.03	76.22	16.11	4.73	46.94	23.8	0.128	–	
Note: BV: Background value of Shaanxi Province [47].

The geochemical map is a crucial tool for visually identifying the distribution features of high and low levels of elements in the studied region [48]. Using the geostatistical analysis module in ArcGIS 10.8 software to map the spatial distribution features of nine potentially toxic soil elements in the research region by inverse distance interpolation, with the findings displayed in Fig. 2. The study area predominantly exhibits a pattern of high values in the perimeter and low values at the centers, with variations in the distribution of regions with high values for each element. Element As is mainly located in the central part of the study area, particularly in the vicinity of coal mining shafts. On the other hand, Hg is notably concentrated in the western and southeastern corners, indicating that Hg levels are affected by power plants in the west, agricultural activities in the south, and human activities in the eastern peri-urban areas. Co, Cr, Ni, and V are predominantly found in the northwestern, eastern, and southern areas, whereas Pb, Cd, and Zn are largely concentrated in the western, eastern, and southern regions. The geographical distribution of elements shows that potentially toxic elements in the research region are enriched due to human activity, maybe from numerous sources [49].Fig. 2 Spatial distribution of nine PTEs in the study area.

Fig. 2

3.2 Contamination evaluation of PTEs

3.2.1 Evaluation of the geo-accumulation index

Fig. 3 displays the findings of the geo-accumulation index for PTEs. The average geo-accumulation index of PTEs in decreasing order is as follows: Hg (1.063) > Cd (0.647) > Co (−0.304) > Pb (−0.356) > V (−0.369) > Zn (−0.455) > As (−0.570) > Ni (−0.573) > Cr (−0.849). The Igeo values of Cr, Ni, and As elemental samples are uncontaminated at level 0. The percentages of Co, V, Pb, and Zn elements at level 0 were 91.3 %, 95.65 %, 91.3 %, and 73.91 % respectively, indicating that these elements are practically uncontaminated. However, individual points may show slight to moderate contamination due to human activities. Cd is classified in the Igeo standard at levels 0 to 2. 60.87 % and 30.43 % of the sample sites are in uncontaminated to moderately contaminated and moderately contaminated categories, respectively. Only 8.7 % of the sample sites are in uncontaminated practical status, suggesting a significant accumulation of Cd due to specific human activities. Hg Igeo values range from −0.538 to 3.558, falling within the range of uncontaminated practically to heavily contaminated. Only 17.39 % of the sample sites are uncontaminated, whereas 43.48 % are somewhat contaminated, and 21.74 %, 13.04 %, and 4.35 % are moderately polluted, moderately to strongly contaminated, and heavily contaminated, respectively.Fig. 3 Box plot of PTEs accumulation in the study area.

Fig. 3

3.2.2 Ecological risk assessment of PTEs

The ecological risk assessment results for PTEs in this specific research region are displayed in Fig. 4. Fig. 4a displays the ecological risk coefficients of individual toxic elements. The results indicate that the average and highest Er values for Co, V, Cr, Ni, As, Zn, and Pb are below 40, suggesting that these elements pose very low or negligible ecological risk to the soil in the research area. The Cd element with an Er value of 74.93 is considered to pose a moderate ecological danger, yet the Cd element overall falls within the range of low to considerable ecological risk, with Er values ranging from 15.96 to 114.89. The element Hg has an average Er value of 177.85, indicating high ecological risk. The overall Er value ranges from 41.33 to 706.67. Of the sample sites, 30.43 % are at moderate ecological risk, 30.43 % at considerable ecological risk, 21.74 % at high ecological risk, and 17.39 % at very high ecological risk. The results from Fig. 4b indicate that the potential ecological risk index in the research region is mostly affected by the components Hg and Cd, accounting for 62.2 % and 26 % respectively. Fig. 4c displays the spatially interpolated distribution of the prospective ecological risk index, with high values concentrated in the western and southeastern corners of the study region. The research region's RI values varied from 128.84 to 824.13, with an average of 286.42, indicating a moderate ecological risk and an overall high ecological risk in the study area.Fig. 4 Evaluation of the potential ecological risk of PTEs in the study area; a- Box plot of potential ecological risk coefficients of PTEs; b- Percentage of contribution of each element to the potential ecological risk index; c- Characteristics of the spatial distribution of potential ecological risk coefficients.

Fig. 4

The research region's soil accumulation of PTEs was analyzed using the geo-accumulation index method. The ecological risk status of the area was determined by combining the potential ecological risk index with the toxicity coefficients of the elements. The analysis of the results indicates that both the geo-accumulation index method and the potential ecological risk index method show a similar trend. They reveal that the study area is primarily impacted by the elements Cd and Hg, aligning with previous research findings [50]. Hg and Cd concentrations in the soil can build in animals and crops, posing significant health concerns to people through long-term consumption via the food chain[[51], [52], [53]]. It is crucial to enhance the management and control of the ecological threats posed by the elements Hg and Cd in the research region.

3.3 APCS-MLR source analysis

Incorporating Pearson correlation analysis together with APCS-MLR receptor modeling for a comprehensive investigation to precisely identify sources of PTEs and statistically evaluate their impact. The study split source identification into three basic components. Pearson correlation analysis was initially employed to assess the correlation between PTEs. Subsequently, the APCS-MLR receptor model was utilized to quantify the primary sources of PTEs. Finally, the source factors' contribution was integrated with the Pearson correlation coefficients of the PTEs to conduct a thorough analysis and validate the source outcomes [54].

Principal component analysis was conducted on the nine PTEs, resulting in a KMO value of 0.654, exceeding the threshold of 0.5. Additionally, Bartlett's test of sphericity yielded a value of 0.000, indicating a statistically significant connection among the components and their suitability for factor analysis. After the orthogonal rotation of the factors using the Kaiser standardized factors and the maximum variance approach, four main components with eigenvalues larger than 1 were retrieved. The overall cumulative explanatory rate was 94.76 %. The linear regression analysis of observed and anticipated PTE concentrations in the research region yielded R2 values ranging from 0.888 to 0.996, suggesting that the APCS-MLR receptor model provided precise and dependable calculation findings.

The APCS-MLR receptor model identifies four sources based on the data shown in Fig. 5. Fig. 5a indicates that the primary loading components in APCS1 are Co, Cr, Ni, Zn, Pb, and V, with Cd elements following. Fig. 5c results indicate a significant correlation among the elements Co, Cr, Ni, Zn, Pb, and V. V, Pb, and Zn serve as indicators of transportation origins [55,56], and transport source marker elements in this study are relatively rich in areas close to well-developed road and railway networks [57]; Cr is vulnerable to coal mining and industrial production [58], Co and Ni are intricately connected to industrial manufacturing [59]. Fig. 2 shows that the enrichment characteristics of Cr、Co and Ni elements are obvious in coal mine shaft areas and coal industry areas such as power plants. The carboniferous-Permian system is a favorable coal-producing environment in the paleogeography, and the accumulation of large amounts of coal may lead to the enrichment of PTEs in coal and thus affect the soil environment [60]. Therefore, APCS1 is a mixed source of industry, mining and transport; The primary loading element in APCS2 is Cd. However, the significant association between Cd and Co, Cr, Ni, Zn, Pb, and V (Fig. 5c) indicates that the origin of Cd is complicated and might have numerous sources. The average Cd elemental concentrations are significantly beyond the background levels, with both Igeo and RI indicating a substantial accumulating trait and ecological danger associated with Cd elemental. The high Cd-rich areas shown in Fig. 2 are primarily located near farmland. This is due to the high Cd content found in pesticides and fertilizers used in agricultural practices, which have been continuously applied over a long period, resulting in the accumulation of Cd in the topsoil layer [61,62]. Thus, APCS2 can be identified as an agricultural production source; APCS3 main loading element is element As, and As is not strongly correlated with other elements. While the mean values of elemental As were slightly higher than the background values, the coefficients of variation were low, both Igeo and RI values indicated that the ecological risk regarding As was low and in a non-polluted state, suggesting that it was less affected by human activities. With rocky outcrops in the northern part of the study area, natural conditions such as weathering and soil erosion are relatively prominent [63,64]. So, APCS3 can be interpreted as a natural source; In APCS4, the main loading element is Hg, which is not strongly correlated with other elements and is negatively correlated with Co, Cr, Ni, As and V (Fig. 5c). Elemental enrichment of Hg is clearly characterized by high accumulation characteristics and ecological risks based on the Igeo and RI results, indicating that Hg mainly originates from human activities. Spatially Hg is distributed in the power plant in the west, and the enrichment of elemental Hg in the south-east may be due to enrichment caused by the influence of the regional north-easterly wind direction over a long period of time, as well as by the terrain's low relief. Chinese coal has a relatively high Hg content [65], and elemental Hg has a low boiling point and is volatile, easily entering the atmosphere in the form of mercury vapor, so the gas and dust emitted from the combustion process of coal mining are the main sources of Hg [66,67]. It follows that APCS4 can be interpreted as a source of atmospheric deposition.Fig. 5 Map of correlation and source contribution of PTEs in the study area; a - contribution of each source of PTEs; b - percentage of each source factor; c - correlation between the content of PTEs and source factors.

Fig. 5

In Fig. 5b, the result shows that APCS1 (mixed industrial, mining and transportation sources) is the major source contributing to this study area with a high percentage of 53.98 %; followed by APCS3 (natural sources) sources with a percentage of 24.65 %; APCS4 (atmospheric deposition sources) contributing to the harmful substances with a percentage of 12.60 %; and APCS2 (agricultural production sources) contributing the least to the pollution of the harmful substances with a percentage of only 8.76 %. Therefore, mixed industrial, mining and transportation sources are the priority source pathways for hazardous substances to be controlled in this study area.

3.4 Probabilistic health risk assessment

3.4.1 Concentration-orientated health risk assessment

In this study, Monte Carlo simulation was used for probabilistic health risk assessment of PTEs for both adult and child populations, and the characteristics of the probabilistic distribution of non-carcinogenic and carcinogenic risks for both populations are shown in Fig. 6, Fig. 7. Probabilistic health risk results indicate that children are more vulnerable to risk compared to adults. This may relate to the fact that children are more likely to be exposed to pollutants in the environment [68]. From the non-carcinogenic risk, it can be seen that the HI values of the nine PTEs in the study area were all in the safe range, indicating that the single elements did not pose a non-carcinogenic risk to the population. Adult and children's mean THI values were 9.86E-2 and 7.41E-1, respectively, both of which were within the range of the safety value of 1. Adult THI values were in the safe range, however, the 95th percentile THI value for children was 1.55, indicating that PTEs pose some non-carcinogenic risk to children in the region. According to the results of carcinogenic risk, Pb and Cd did not pose a carcinogenic risk to adults and children, while the mean CR values of Ni, Cr and As exceeded 10−6 but did not exceed the risk threshold of 10−4, indicating that these three elements are subject to a certain degree of carcinogenicity, with Ni posing the highest carcinogenicity risk. Both adults and children had mean TCR values of 2.71E-5 and 5.26E-5, respectively, indicating that both were subject to some carcinogenic risk. It is noteworthy that the 95th% TCR value of 1.09E-4 exceeded 10−4 for the children group only, indicating a significant carcinogenic risk for children.Fig. 6 Health risk assessment of non-carcinogenic probability of PTEs.

Fig. 6

Fig. 7 Health risk assessment of carcinogenic probability of PTEs.

Fig. 7

3.4.2 Source-oriented health risk assessment

Combine the concentrations of potentially toxic elements with the results of source identification and health risks to create a Sankey diagram of the relationship between the three of them. According to the results in Fig. 8, the different sources contributed similarly to the health risks for both adults and children. The order of magnitude of the contribution of the four sources to health risk in this study is APCS1>APCS3>APCS2>APCS4. Results showed that mixed industrial, mining and transportation sources (APCS1) contributed the most to the health risk of the population, followed by natural sources (APCS3). The higher carcinogenic risk from mixed industrial, mining and transport sources is mainly due to the prominent contribution of elemental Ni. Remarkably, human factors have a significant impact on ecological and health risks in environmental risk management, and natural factors are often unavoidable [69]. Therefore, emphasis should be placed on strengthening the control of pollution from the industrial, mining and transport mixed sources. It is worthwhile to find out that the level of PTEs is a prominent contributor to a particular source, but not necessarily the main driver of its associated health risk [70]. As shown in Table 1 the mean Cr element content is lower than the background value, but it is not the first contributing factor to APCS1, while Cr element is a significant factor for carcinogenic health risk. So it can be clearly concluded that there is not necessarily a linear relationship between a high level of elemental enrichment and its carcinogenic risk, but the route of intake and toxicity of the element are important factors that should not be ignored in the health risk assessment [71,72].Fig. 8 Sankey diagram of the relationship between PTEs, sources and health risks (width proportional to level of contribution).

Fig. 8

4 Conclusion

This study assessed the cumulative risk and source contribution of PTEs in a standard coal mining region. A Monte Carlo probabilistic method was utilized to evaluate the health risk status and was integrated with the APCS-MLR receptor model to analyze the health risk from various sources both qualitatively and quantitatively. The results are as follows: only the mean value of Cr for the nine PTEs in the study area does not exceed the background value for Shaanxi Province, and the characteristic spatial distribution of high values for each element indicates that each element is subject to anthropogenic influences of varying magnitude and factors. Igeo and RI show that regional topsoil PTEs are at moderate ecological risk, compared with other elements, Hg and Cd showed higher accumulation characteristics and stronger ecological risks, and the ecological risks in the western and southeastern parts of the study area should be emphasized.

The APCS-MLR receptor model identified four sources, namely, mixed industrial, mining and transport sources(53.98 %), natural sources(24.65 %), atmospheric deposition sources(12.60 %), and agricultural production sources(8.76 %), and contribution of industrial, mining and transport mixed sources is relatively high, so it is necessary to focus on controlling the continuous input of pollution from industrial, mining and transport sources. Probabilistic health risk results showed higher risk hazards for children than for adults, with a significant increase in multi-element composite compared to single-element risk. 95 % confidence intervals for THI and TCR results indicate that adults have no significant health risk while the child population has some health risk, so the health risk of the child population should be focused on. Monte Carlo simulation and human health risks were combined with the APCS-MLR model to perform a source-contribution-oriented probabilistic health risk assessment and to identify priority sources of mixed industrial, mining and transport pollution for control. The results of this study provide some reference for the development and implementation of regional environmental protection policies, and in this way to protect the regional ecological and health security. Future source analysis studies of potentially toxic elements in soil should take into account the type of land use and its circulation under socio-economic development so that traceability results can be more accurate.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Daokun Chen: Writing – original draft, Visualization, Software, Methodology, Formal analysis, Conceptualization. Xinbin Li: Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition. Zhanbin Wang: Writing – review & editing, Supervision, Project administration, Data curation. Chengxin Kang: Investigation, Data curation. Tao He: Visualization, Methodology. Hanyuan Liu: Investigation. Zhiyang Jiang: Visualization, Methodology. Junsheng Xi: Investigation. Yao Zhang: Investigation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Acknowledgments

This study was supported by the China Geological Survey (DD20242461,DD20220868 ).

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36301.
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