Soil Heavy Metal Pollution and Health Risk Assessment Based on Monte Carlo Simulation: Case Study of Xicheng Lead-Zinc Mining Area

Abstract

To investigate the status of heavy metal pollution and the associated ecological and health risks in farmland surrounding the Xicheng lead and zinc mining area, we collected soil samples (0–20 cm) and measured the level of As, Ni, Cu, Zn, Cd, Pb, Hg, and Cr. The characteristics of soil heavy metals, pollution levels, and ecological, and health risks were quantitatively assessed using Monte Carlo simulation in conjunction with the geo-accumulation index (Igeo), pollution index (Pi), ecological risk index (Er), and human health risk assessment model. The results indicated that the average concentrations of As, Ni, Cu, Zn, Cd, Pb, Hg, and Cr in the soil of the study area were 26.92, 39.46, 31.18, 340.23, 1.13, 184.61, 0.34, and 71.15 mg·kg⁻¹, respectively. These values were significantly higher than the soil background levels reported for Gansu Province. The average Igeo for Hg was 3.27, and the average Er was 679.13, indicating that Hg had the highest accumulation and posed the greatest ecological risk in the study area. The average single-factor pollution index for Cd was 2.52, marking it as the heavy metal with the highest pollution level. The cumulative probability of health risk derived from Monte Carlo simulation indicates that the non-carcinogenic hazard indices for adults and children were 2.79 × 10⁻¹ and 1.67, respectively, with 94.82% of children exceeding the non-carcinogenic risk threshold. The carcinogenic risk indices (TCR) for adults and children were 8.09 × 10⁻⁵ and 4.60 × 10⁻⁴, respectively, and 100% of the samples exceeded the TCR standard for children. As and Cd were identified as the primary contributors to both carcinogenic and non-carcinogenic risks. The findings enhance our understanding of heavy metal contamination in farmland soils and establish an empirical framework for developing targeted remediation approaches and sustainable land management practices.

1. Introduction

As a cornerstone of terrestrial ecosystems, soil integrity critically underpins socioeconomic progress by safeguarding food systems and population health outcomes—key determinants of a nation’s sustainable development trajectory. Soil is also the foundation of ecological systems and green development [1]. However, the rapid advancement of urbanization, industrialization, and agricultural activity has led to increasingly severe soil pollution, posing a significant threat to ecosystem stability and human health [2,3]. Within the spectrum of environmental contaminants, heavy metal contamination has emerged as one of the most challenging environmental issues in the realm of soil pollution, due to its high toxicity, concealment, non-degradability, and difficulty in management [4]. This issue has also become a prominent topic of research both domestically and internationally in the past few years. The situation regarding heavy metal pollution in soil across China is particularly alarming [5]. According to national statistics, over 1.0 × 10⁶ ha of land have been contaminated by heavy metals [6]. Notably, the heavy metal pollution levels in farmland soil exceed the standard rate by 19.4%, posing a considerable risk to soil health in agricultural land [7]. The extraction, treatment, and transportation of mineral resources are the primary sources of heavy metal contamination in the soils of mining regions [8]. These activities release substantial amounts of heavy metal elements into the environment through the discharge of wastewater, residue, and gas. Once introduced into the soil system, heavy metals can be readily absorbed by crops, thereby entering the food chain, and may also enter the human body through dust, atmospheric particles, or skin contact [9]. Prolonged exposure to heavy metals can result in acute or chronic poisoning, organ dysfunction, and even severe diseases, such as cancer [10]. Previous studies have demonstrated that the average concentrations of Pb, Zn, Cd, and Cu in farmland soils surrounding 49 metal mining areas in China were 1536, 1371, 19.8, and 19.8 mg·kg⁻¹, respectively [11,12]. These concentrations are significantly elevated compared to the baseline soil values in these areas, posing a substantial threat to the surrounding ecosystems and the well-being of local inhabitants.

Furthermore, in certain high-risk areas, heavy metal pollution factors cannot be degraded by natural processes in the short term, and their long-term cumulative effects may further exacerbate the risks to ecological systems and human health. Therefore, investigating the current state of soil contamination and conducting risk assessments in agricultural land surrounding the mining areas is of great significance. This type of research can quantitatively reveal the spatiotemporal distribution characteristics of pollution, identify its main sources, and pinpoint the key pollution factors and their primary impact pathways on both ecosystem and human health. Additionally, comprehensive evaluation methods based on multiple indicators—such as pollution and potential ecological risk indices, health risk assessment, and Monte Carlo simulation—can offer a foundational framework for developing tailored pollution mitigation measures.

Metallic mining areas are significant contributors to heavy metal pollution in agricultural land. Researchers have focused extensively on the evaluation, occurrence features, and ecological risk assessment of heavy metal contamination in farmland surrounding metal mining sites, including manganese ore, gold ore, pyrite, and lead–zinc ore [13,14,15,16,17,18,19]. Researchers have also examined the distribution characteristics, as well as the mechanisms of migration and transformation of heavy metals [18], the source–sink relationships of heavy metals, and their migration and transformation between soil and crops [20,21], along with analyses of origins of heavy metal contamination [22,23,24]. However, in comparison to the numerous studies on heavy metal pollution and evaluation, few studies have examined the potential health risks that heavy metals in the soil of mining areas may pose to humans. Furthermore, conventional methods such as the local cumulative pollution index, Nemerow index, enrichment factor, potential ecological hazard index, and health risk assessment have been predominantly employed in studies on soil heavy metal pollution and assessment [13,14,15,16,17,18,19,24].These methods rely on deterministic parameters and indicators to appraise heavy metal pollution and its associated hazards. However, the evaluation parameters and indicators exhibit inherent variability, which can lead to increased uncertainty in the evaluation results, as evidenced by the variability in high or low outcomes [25,26,27,28]. In Monte Carlo probabilistic risk assessment, the likelihood of pollutants exceeding risk thresholds is calculated and health risk factors assessed; in this way, pollutants that require limited control can be identified [25]. With this approach, researchers can effectively conduct probabilistic risk analysis, quantify and mitigate the uncertainties associated with conventional evaluation methods, and enhance the accuracy and scientific rigor of risk assessments for soil heavy metal pollutants [29,30]. During the research process, it is essential to focus on the following core scientific questions: What are the distribution characteristics and sources of major heavy metals in mining soil? Are the results from different evaluation methods consistent? What are the correlations and differences between ecological and health risks?

We focused on the farmland surrounding a lead–zinc smelter in the Xicheng lead-zinc mining area to address these scientific challenges. We analyzed the contents of a number of heavy metals in soil: As, Ni, Cu, Zn, Cd, Pb, Hg, and Cr. The analysis involved Monte Carlo simulation in conjunction with employment of the ground accumulation index, comprehensive pollution index, potential ecological risk index, and human health risk model to assess the features of contamination, potential ecological risks, and human health risks associated with heavy metals in in the context of mining and smelting activities. The overall aim of the study was to provide scientific guidance and a theoretical foundation for the effective management of ecological risks and the protection of human health in the region.

2. Materials and Methods

2.1. Study Area and Sampling Site

The Xicheng mining area is a significant lead–zinc resource base in China and serves as a typical representative of large lead–zinc-rich deposits within the Qinling metallogenic belt. This mining area is situated in the Pb-Zn-Cu (Fe)-Au-Hg-Sb metallogenic belt of the West Qinling Mountains. The primary ore-bearing rock series consists of Middle Devonian clastic rock–carbonate formations. The representative lithologies include biotite quartz schist, light-colored quartzite–quartz schist, crystalline limestone, marble, dolomite, and phyllite. These lithologies are distributed across the Pb-Zn deposits of Changba-Lijiagou, Dengjiashan, Bijiashan, Luoba, and Jianjiagou. The study area is situated in the agricultural land surrounding a Pb and Zn smelter in Chengxian County (Figure 1). The geographical coordinates are 105°40′48″~105°44′19″ E and 33°54′39″~33°57′48″ N. The climate is classified as warm temperate, semi-humid, and semi-arid, characterized by four distinct seasons and ample sunlight. The annual mean temperature is 11.9 °C, and the annual mean precipitation is 620 mm, with precipitation mainly occurring from July to September. According to the Chinese Soil Taxonomy system, the predominant soil types in the study area are yellow–brown earth and brown earth, characterized by a thin soil layer and low fertility. The terrain in this area is complex, with elevations ranging from 1110 to 1702 m—higher in the northeastern region and lower in the southwestern region—and exhibits a high degree of erosion and denudation. The prevailing winds are from the southeast. The land cover type is primarily woodland, with residential areas and farmlands scattered in the western and northern parts of the gully and along both sides of the road. The arable land is mainly dryland, with maize and wheat as the principal crops. The region has a long history of mining, with numerous non-ferrous metal smelters. A mining industry chain encompassing extraction, beneficiation, smelting, and comprehensive resource utilization has gradually developed. Several villages are located around the smelters. Due to atmospheric subsidence and other factors, metal smelting activities pose a significant threat to the health of the surrounding population.

2.2. Sample Collection and Analysis

Based on the topography, landforms, and land use types of the study area, and taking into account the accessibility of roads and uniform distribution of sampling points, 50 sampling points were established using the equidistant point layout method. These points were selected from farmland located 2 to 20 km around the Dengjiashan Pb-Zn mining area, extending along the Nan River from north to south, and concluding at the Guojiagou Pb-Zn mining area to the east. Sampling was conducted over a period of seven consecutive sunny days in July 2023 (Figure 1). Samples were randomly distributed based on the size of the plots, adhering to the principles of equal quantity and multi-point mixing. Within a 70-m radius of each sampling point, three surface soil mixed samples, each within 0–20 cm in depth, were collected vertically using a stainless steel barrel soil drill. After quartering, no less than 1.0 kg of samples were obtained, placed into sample bags, and labeled with sampling numbers. The coordinates (latitude and longitude) of each sampling point were recorded using GPS, along with the sampling date and the land use surrounding the sampling location.

After the soil samples had been transported to the laboratory and air-dried at room temperature, debris such as plant roots, leaves, and gravel were removed. The samples were then ground using an agate mortar, screened with a 100-mesh nylon, and stored for later use. The pH value of the soil was determined using the potentiometric method with a Shanghai Lei-magnetic pH meter (PH3-3C). The analysis of heavy metal content in the soil was conducted at Qingdao Kemai Analysis and Testing Technology Service Co., Ltd., Qingdao, China. Following digestion using the HNO₃-HF-HClO₄ method at a volume ratio of 5:5:3, the contents of Ni, Pb, Zn, Cd, Cu, and Cr in the samples were measured using an inductively coupled plasma mass spectrometer (ICP-MS, ThermoFisher ICAP-RQ01441, Erlangen, Germany). The detection limits (in mg·kg⁻¹) were Ni, 1.0; Pb, 0.1; Zn, 4.0; Cd, 0.01; Cu, 4.0; Cr, 0.2. The digestion of As and Hg was performed using a HNO₃-HF mixture (in a volume ratio of 5:1) with 0.5% H₂O₂ additive under controlled thermal conditions, comprising a 30-min pre-digestion phase at 85 °C followed by a 40-min main digestion phase at 95 °C. Subsequent quantification of As and Hg concentrations was performed via atomic fluorescence spectrophotometry (AFS-9700, Beijing Haiguang Instrument Co., Ltd., Beijing, China), achieving method detection limits of 0.01 mg·kg⁻¹ for As and 0.002 mg·kg⁻¹ for Hg, consistent with DZ/T 0295–2016 quality assurance criteria [31]. Quality control measures included the addition of blank samples, parallel samples, and a national standard soil sample (GBW07405) [32]. Three parallel samples were analyzed for each sample. The relative standard deviation (RSD) for the eight elements ranged from 1.8 to 4.2%, and the recovery rate was between 91.5 and 104.5%.

2.3. Methods

2.3.1. Geo-Accumulation Index

The geo-accumulation index (Igeo) is a quantitative method used to evaluate the extent of heavy metal contamination in sediments and other materials. In recent years, it has become widely adopted for assessing heavy metal pollution levels in soil [14]. This method primarily reflects the enrichment of individual heavy metals in the soil. The calculation formula is as follows:

$$\mathrm{I}\mathrm{g}\mathrm{e}\mathrm{o}={\log }_2[C_i/(K\times C_o\left)\right]$$

(1)

where $C_i$ represents the measured content of heavy metals (mg·kg⁻¹) and $C_O$ denotes the evaluation standard value. The background value of soil heavy metal content in Gansu Province (mg·kg⁻¹) is given in Guo et al. [33]. A correction coefficient set at 1.5 [14] was applied to account for changes in the background value due to diagenesis. The classification standards for pollution levels corresponding to the values of the Igeo are presented in

Table 1. The classification criteria for heavy metal pollution in soil.

Igeo	Value range	$I_{geo}\le 0$	$0<I_{geo}\le 1$	$1<I_{geo}\le 2$	${2<I}_{geo}\le 3$	$3<I_{geo}\le 4$	$I_{geo}>5$
	Contamination degree	Safe	Mild-moderate	Mild-strong	Moderate	Strong	Strong–heavy
${\mathrm{P}}_{\mathrm{i}}$	Value range	$P_i\le 1$	$1<{\mathrm{P}}_{\mathrm{i}}\le 2$	$2<P_i\le 3$	$P_i>3$
	Pollution grade	Clean	Slight	Moderate	Heavy
$\mathrm{P}$	Value range	$P\le 0. 7$	$0.7<\mathrm{P}\le 1.0$	$1.0<\mathrm{P}\le 2.0$	$2.0<\mathrm{P}\le 3.0$	$\mathrm{P}>3.0$	—
	Pollution grade	Clean	Warning	Slight	Moderate	Heavy	—
${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$	Value range	${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}<40$	$40\le {\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}<80$	$80\le {\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}<160$	$160\le {\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}<320$	${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}\ge 320$	—
	Pollution grade	Slight	Medium	Relatively high	High	Extremely high	—
$\mathrm{R}\mathrm{I}$	Value range	$RI<150$	$150\le RI<300$	$300\le RI<600$	$RI\ge 600$	—	—
	Pollution grade	Slight	Medium	Relatively high	High	—	—
$\mathrm{I}\mathrm{E}\mathrm{R}$	Value range	$IER<0$	$0\le IER<1$	$1\le IER<3$	$3\le IER<5$	$IER>5$	—
	Pollution grade	Safe	Warning	Slight	Moderate	Heavy	—

.

2.3.2. Comprehensive Pollution Index

The comprehensive Pollution Index (P), a holistic pollution index derived from single-factor pollution indices [17], is calculated as follows:

$$\mathrm{P}=\sqrt{{\displaystyle \frac{{\left(ave\right(P_i\left)\right)}^2+{\left(\max (P_i\right))}^2}{2}}}$$

(2)

where ${\mathrm{P}}_{\mathrm{i}}={\displaystyle \raisebox{1ex}{${\mathrm{C}}_{\mathrm{i}\mathrm{j}}$}\!\left/ \!\raisebox{-1ex}{${\mathrm{S}}_{\mathrm{j}}$}\right.}$ denotes the single-factor pollution index for a sampling point, $C_{ij}$ is the measured concentration (mg·kg⁻¹) of a pollutant at the sampling point, and ${\mathrm{S}}_{\mathrm{j}}$ is the evaluation standard (mg·kg⁻¹) of the pollutant. The risk screening value from GB15618-2018 [32] was used as the evaluation standard to calculate each P_i value. $ave\left(P_i\right)$ represents the average value of each P_i at the sampling point, and $\max (P_i)$ indicates the maximum value of each P_i at the sampling point. The standard for classification for this pollution index is presented in Table 1.

2.3.3. Pollution Risk Assessment

(1)

Potential Ecological Risk Index

The potential ecological risk index (RI) reflects not only the pollution level of individual heavy metals but also the cumulative effects of all heavy metal pollutants. In this comprehensive approach, several factors are considered, namely concentration, toxicity levels, ecological sensitivity, and synergistic interactions among multiple elements. As a result, the index is a widely utilized quantitative tool for assessing the potential ecological risks related to heavy metal contamination in sediments [17].

The index is calculated as:

$$\mathrm{R}\mathrm{I}={\sum }_{i=1}^n{E}_r^i={\sum }_{i=1}^n(T_r^i\times {\displaystyle \frac{C_i}{C_O}})$$

(3)

where $\mathrm{R}\mathrm{I}$ represents the potential RI of heavy metals and $E_r^i$ denotes the potential ecological risk factor of a single heavy metal. The term $C_i$ refers to the measured content of heavy metals in soil (mg·kg⁻¹), and $C_O$ indicates the soil background reference value for Gansu Province (mg·kg⁻¹). The biological toxicity response factors (dimensionless) for different metals are as follows: As, 10; Ni, 5; Cu, 5; Zn, 1; Cd, 30; Pb, 5; Hg, 40; and Cr, 2 [34]. The evaluation and grading standards for $RI$ and $E_r^i$ are presented in Table 1.

(2)

Ecological Risk Early Warning Index

Ecological risk early warning refers to the system established to identify potential depletion or crises related to natural resources or ecological risks. The index is calculated as:

$$\mathrm{I}\mathrm{E}\mathrm{R}={\sum }_{i=1}^n{\mathrm{I}\mathrm{E}\mathrm{R}}_i={\sum }_{i=1}^n({\displaystyle \frac{C_i}{C_0}}-1)$$

(4)

where the ecological risk early warning index IER for a single heavy metal is denoted as ${\mathrm{I}\mathrm{E}\mathrm{R}}_i$. $C_O$ is the critical concentration limit value of the i-th heavy metal (mg·kg⁻¹), and this limit value is based on the baseline levels in Gansu Province as the reference standard. The detailed categorization standards for IER are presented in Table 1 [3].

2.3.4. Human Health Risk Assessment of Heavy Metals

Heavy metals in farmland soils surrounding lead–zinc mining areas enter the human body primarily through oral ingestion, skin contact, and respiratory inhalation, posing potential risks to human health [35]. The health risk assessment model advocated by the United States Environmental Protection Agency (USEPA) was employed to assess both non-carcinogenic and carcinogenic health risks for adults and children in the study area [36]. The daily intakes for adults and children via oral, skin, and respiratory exposure routes were calculated according to Equations (5)–(7), and the non-carcinogenic and carcinogenic risks associated with soil heavy metals for different populations were determined using Equations (8) and (9).

$${\mathrm{A}\mathrm{D}\mathrm{D}}_{\mathrm{L}\mathrm{n}\mathrm{g}}={\displaystyle \frac{C_i\times IngR\times EF\times ED}{\mathrm{B}\mathrm{W}\times \mathrm{A}\mathrm{T}}}\times {10}^{-6}$$

(5)

$${\mathrm{A}\mathrm{D}\mathrm{D}}_{\mathrm{D}\mathrm{e}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}}={\displaystyle \frac{C_i\times \mathrm{S}\mathrm{A}\times \mathrm{A}\mathrm{F}\times ABS\times EF\times ED}{\mathrm{B}\mathrm{W}\times \mathrm{A}\mathrm{T}}}\times {10}^{-6}$$

(6)

$${\mathrm{A}\mathrm{D}\mathrm{D}}_{\mathrm{L}\mathrm{n}\mathrm{h}}={\displaystyle \frac{C_i\times InhR\times EF\times ED}{\mathrm{P}\mathrm{E}\mathrm{F}\times \mathrm{B}\mathrm{W}\times \mathrm{A}\mathrm{T}}}$$

(7)

$$\mathrm{H}\mathrm{I}=\sum HQ=\sum {\displaystyle \frac{{ADD}_i}{{RfD}_i}}$$

(8)

$$\mathrm{T}\mathrm{C}\mathrm{R}=\sum CR=\sum {ADD}_i\times {SF}_i$$

(9)

where ADD_Lng, ADD_Lnh, and ADD_Dermal (mg·(kg·d)⁻¹) quantify the mean daily internal exposure load via ingestion, respiration, and cutaneous absorption routes, respectively, and $C_i$ (mg·kg⁻¹) indicates the concentration of heavy metals in soil; HQ and HI are the single and integrated non-carcinogenic risk indices, respectively; RfD denotes the daily reference dose for heavy metals; CR and TCR are the single and integrated carcinogenic risk indices, respectively; and SF refers to the carcinogenic slope factor. The definitions and values of the parameters used in Equations (5)–(7), alongside the probability distribution of exposure parameters obtained through Monte Carlo simulation, are presented in Table 2. Additionally, the parameters’ definitions and values applied in Equations (8) and (9) are listed in Table 3. Carcinogenic risk calculations excluded pathways lacking internationally recognized slope factors to ensure methodological rigor.

An HI/HQ value ≤ 1 indicates that soil heavy metals are not linked to non-carcinogenic risks for humans; and HI/HQ > 1 indicates the presence of a non-carcinogenic risk. A TCR/CR < 1 × 10⁻⁶, is considered no carcinogenic risk; a 1 × 10⁻⁶ < TCR/CR < 1 × 10⁻⁴, is considered a carcinogenic risk within an acceptable range for humans; and a TCR/CR > 1 × 10⁻⁴, is considered a higher carcinogenic risk [37].

Table 2. The parameter values used in the probabilistic risk assessment model.

Parameter	Meaning	Unit	Probability Distribution	Adult	Child	Data Source
$IngR$	Dust intake	mg·d−1	Triangular	4, 30, and 52	66, 103, and 161	[38]
$EF$	Exposure frequency	d·a−1	Triangular	180, 345, and 365		[38]
$ED$	Exposure period	a	Point	24	6	[35]
$\mathrm{S}\mathrm{A}$	Exposed skin area	cm2	Point	0.54	0.23	[35]
$\mathrm{A}\mathrm{F}$	skin adhesion coefficient	mg·cm−2	Logarithmic	0.49 and 0.54	0.65 and 1.2	[38]
$ABS$	Inhalation factor	—	Point	0.001 (non-carcinogenic risk)		[35]
				0.01 (carcinogenic risk)
$InhR$	Air intake	m3·a−1	Point	19	8.6	[39]
$\mathrm{B}\mathrm{W}$	Average body mass	kg	Logarithmic	57.03 and 1.18	16.68 and 1.48	[40]
$\mathrm{A}\mathrm{T}$	Action time	d	Point	365 × ED (non-carcinogenic risk)		[40]
				365 × 70 (carcinogenic risk)
$\mathrm{P}\mathrm{E}\mathrm{F}$	Particulate emission factor	m3·kg−1	Point	1.36 × 109	1.36 × 109	[40]

Table 2. The parameter values used in the probabilistic risk assessment model.

Parameter	Meaning	Unit	Probability Distribution	Adult	Child	Data Source
$IngR$	Dust intake	mg·d−1	Triangular	4, 30, and 52	66, 103, and 161	[38]
$EF$	Exposure frequency	d·a−1	Triangular	180, 345, and 365		[38]
$ED$	Exposure period	a	Point	24	6	[35]
$\mathrm{S}\mathrm{A}$	Exposed skin area	cm2	Point	0.54	0.23	[35]
$\mathrm{A}\mathrm{F}$	skin adhesion coefficient	mg·cm−2	Logarithmic	0.49 and 0.54	0.65 and 1.2	[38]
$ABS$	Inhalation factor	—	Point	0.001 (non-carcinogenic risk)		[35]
				0.01 (carcinogenic risk)
$InhR$	Air intake	m3·a−1	Point	19	8.6	[39]
$\mathrm{B}\mathrm{W}$	Average body mass	kg	Logarithmic	57.03 and 1.18	16.68 and 1.48	[40]
$\mathrm{A}\mathrm{T}$	Action time	d	Point	365 × ED (non-carcinogenic risk)		[40]
				365 × 70 (carcinogenic risk)
$\mathrm{P}\mathrm{E}\mathrm{F}$	Particulate emission factor	m3·kg−1	Point	1.36 × 109	1.36 × 109	[40]

Table 3. The RfD and SF values for heavy metals ⁽¹⁾.

Elements	Reference Dose (RfD)/mg·(kg·d)−1			Slope Factor (SF)/(kg·d)·mg−1
	Digestive System	Respiratory System	Skin Contact	Digestive System	Respiratory System	Skin Contact
As	3.00 × 10−4	1.23 × 10−4	1.23 × 10−4	1.50	1.51	3.66
Ni	2.00 × 10−2	2.06 × 10−2	5.40 × 10−3	—	8.40 × 10−1	—
Cu	4.00 × 10−2	4.02 × 10−2	1.20 × 10−2	—	—	—
Zn	3.00 × 10−1	3.00 × 10−1	6.00 × 10−2	—	—	—
Cd	1.00 × 10−3	1.00 × 10−5	1.00 × 10−5	6.10	6.30	—
Pb	3.50 × 10−3	3.52 × 10−3	5.25 × 10−4	8.50 × 10−3	—	—
Hg	3.00 × 10−4	8.57 × 10−5	2.10 × 10−5	—	—	—
Cr	3.00 × 10−3	2.86 × 10−2	6.00 × 10−5	8.50 × 10−3	4.20

⁽¹⁾ SF values were strictly sourced from USEPA IRIS. Dashes (—) indicate pathways without established slope factors per current toxicological consensus.

Table 3. The RfD and SF values for heavy metals ⁽¹⁾.

Elements	Reference Dose (RfD)/mg·(kg·d)−1			Slope Factor (SF)/(kg·d)·mg−1
	Digestive System	Respiratory System	Skin Contact	Digestive System	Respiratory System	Skin Contact
As	3.00 × 10−4	1.23 × 10−4	1.23 × 10−4	1.50	1.51	3.66
Ni	2.00 × 10−2	2.06 × 10−2	5.40 × 10−3	—	8.40 × 10−1	—
Cu	4.00 × 10−2	4.02 × 10−2	1.20 × 10−2	—	—	—
Zn	3.00 × 10−1	3.00 × 10−1	6.00 × 10−2	—	—	—
Cd	1.00 × 10−3	1.00 × 10−5	1.00 × 10−5	6.10	6.30	—
Pb	3.50 × 10−3	3.52 × 10−3	5.25 × 10−4	8.50 × 10−3	—	—
Hg	3.00 × 10−4	8.57 × 10−5	2.10 × 10−5	—	—	—
Cr	3.00 × 10−3	2.86 × 10−2	6.00 × 10−5	8.50 × 10−3	4.20

⁽¹⁾ SF values were strictly sourced from USEPA IRIS. Dashes (—) indicate pathways without established slope factors per current toxicological consensus.

In the Monte Carlo simulation, a substantial number of random samples, conforming to a specific probability distribution, were input into the mathematical model as parameters for calculation. The simulation iterations were set to 10,000 to determine the probability distribution of the risk associated with soil heavy metal exposure to human health.

2.3.5. Data Processing

Descriptive statistics and processing of the original heavy metal data were conducted using Microsoft Excel. Monte Carlo simulations were performed with Oracle Crystal Ball version 11.1.24. Sampling point mapping was executed using ArcGIS 10.8, while other maps were created with Origin 2020.

3. Results

3.1. Descriptive Statistical Analysis of Heavy Metal Contents and Soil pH Values

The soil pH in the study area varied from 6.63 to 8.23, with an average value of 7.46. Among the samples, 42 had a pH between 6.5 and 7.5, and eight samples exhibited a pH greater than 7.5, indicating that the topsoil was predominantly neutral. The descriptive statistical indicators for the contents of heavy metals in farmland soils surrounding the mining area are presented in

Table 4. Statistical summary of heavy metal concentrations in soils (mg·kg⁻¹).

Elements		As	Ni	Cu	Zn	Cd	Pb	Hg	Cr	pH
Max		76.2	49.0	45.0	920.0	2.7	464.0	1.2	82.0	8.23
Min		16.3	31.0	22.0	104.0	0.3	28.6	0.1	58.0	6.63
Mean		26.92	39.46	31.18	340.23	1.13	184.61	0.34	71.15	7.46
Standard deviation		10.52	4.02	4.48	235.99	0.81	148.99	0.26	6.24
Cv (%)		39.1	10.2	14.4	69.36	71.7	80.7	75.5	8.8
Background value		12.6	35.2	24.1	69.3	0.12	18.80	0.02	70.2
Standard	6.5 < pH ≤ 7.5	30	100	100	250	0.3	120	2.4	200
	pH > 7.5	25	190	100	300	0.6	170	3.4	250
I/%Excessive Rate I (%)		100	93.4	98.3	100	100	100	100	78.3
II/%Excessive Rate II (%)		15.4	0	0	47.5	76.3	50	0	0

Note: Background value represents soil background values of Gansu Province [42]. The reference standard used is the risk screening value specified in the Soil Environmental Quality Risk Control Standard for Soil Contamination of Agricultural Land (Trial; GB15618–2018; pH > 7.5) [11]. Excessive rate I indicates the proportion of sampling points that surpass the local background value. Excessive rate II denotes the proportion of sampling points that exceed the established standard.

. The mean contents of the eight heavy metals, ranked in descending order, were as follows: Zn > Pb > Cr > Ni > Cu > As > Cd > Hg. When compared to the baseline levels of soils in Gansu Province, the average concentrations of As, Ni, Cu, Zn, Cd, Pb, Hg, and Cr exceeded the baseline levels by factors of 2.13, 1.12, 1.29, 4.91, 9.41, 9.81, 16.98, and 1.01, respectively. The concentrations of five heavy metals—As, Zn, Cd, Pb, and Hg—in all samples surpassed the baseline levels, and, the proportions of samples exceeding the baseline levels for Ni, Cu, and Cr were 89.7, 94.9, and 61.5%, respectively. Compared to the risk screening values outlined in the Standard for the Control and Management of Soil Pollution Risk in Agricultural Land (Trial) of Soil Environmental Quality (GB15618-2018), the mean concentrations of Zn, Cd, and Pb were 1.36, 3.75, and 1.54 times the screening values, respectively. The ratio of samples exceeding the screening values for Zn, Cd, and Pb were 47.5, 76.3, and 50%, respectively. These three elements pose the ecological risk the baseline levels. In contrast, the average concentrations of Ni, Cu, Hg, and Cr did not surpass the screening values, which means there is no ecological risk of heavy metal pollution from these elements. This suggests that Zn, Cd, and Pb are likely the primary heavy metal pollutants in the soil. The coefficient of variation (Cv) serves as an indicator of the degree of spatial dispersion of heavy metals. Generally, a higher Cv indicates greater dispersion, more significant differences in spatial distribution, and a more substantial impact from human activities [41]. The Cv values for heavy metal elements in the soil, arranged in descending order, were: Pb > Hg > Cd > Zn > As > Cu > Ni > Cr. Notably, the Cv values of Zn, Cd, Pb, and Hg exceeded 0.5, indicating high variability. The contents of these four elements exhibited considerable differences in their spatial distribution, suggesting enrichment of heavy metal elements that may be significantly influenced by regional background conditions and human activities.

3.2. Evaluation of Heavy Metal Pollution

3.2.1. Evaluation of the Geo-Accumulation Index

The results of the Igeo calculations in farmland soil within the study area are presented in Figure 2. The average variation trend of Igeo for the eight heavy metals was as follows: Hg (3.27) > Cd (2.31) > Pb (2.24) > Zn (1.47) > As (0.53) > Cu (−0.2) > Ni (−0.44) > Cr (−0.59). The Igeo for Hg ranged from 1.85 to 6.18, with 7.5% of samples exhibiting mild to severe pollution, 37.5% showing moderate to partial pollution, and 10% classified as extremely severely polluted. The Igeo for Cd ranged from 0.59 to 5.15, with 20% of samples indicating mild to severe pollution, 27.5% showing moderate pollution, and 30% classified as severely polluted. The Igeo for Pb ranged from 0.02 to 6.41, with 25% of samples exhibiting mild to moderate pollution and 35% classified as slightly polluted. The Igeo of the other metals was as follows: Zn, 0.02 to 6.41, with 45% of samples indicating mild pollution and 25% showing moderate pollution; As, −0.21 to 3.97, with 85% of samples classified as mildly polluted; Cu, −0.72 to 1.01, with only 15% of samples showing mild contamination. The Igeo values for Ni and Cr were both less than 0, indicating no pollution. The soil of the study area was thus moderately polluted with Hg, and mildly to moderately polluted with Cd, Pb, and Zn.

3.2.2. Evaluation of the Single-Factor Pollution Index and Comprehensive Pollution Index

P_i and P were utilized to assess the heavy metal pollution status of soil in farmland surrounding the Pb and Zn mining area (Figure 3). With respect to P_i, the metals are ranked in the following order: Cd > Pb > Zn > As > Cr > Cu > Ni > Hg. The average P_i for Cd was 2.52, indicating a moderate level of pollution, whereas the average indices for Pb, Zn, and As were 1.27, 1.23, and 1.17, respectively, indicating mild pollution. The average P_i values for Cr, Cu, Ni, and Hg were all below 0.7; thus, we can classify these soils as essentially free of pollution from these particular metals. Among the samples, 33.3% and 15.4% fell into the severe and moderate pollution categories for Cd, respectively. As for Pb and Zn, 26.6 and 15.4% of the samples were classified as moderately polluted, and samples categorized as moderately polluted accounted for 5.1%. P ranged from 0.62 to 4.46, with an average value of 1.93. A total of 94.9% of the samples were found to be polluted, with 56.5% classified as mildly polluted, 12.8% as moderately polluted, and 25.6% as severely polluted.

The P_i for Hg indicated no pollution by this heavy metal. However, the Igeo evaluation results, which took into account the regional background values, indicated moderate to heavy pollution levels. The soil exhibited varying degrees of pollution from heavy metals, such as Cd, Pb, Zn, and As. Additionally, it is important to consider the cumulative pollution risk associated with Hg.

3.3. Assessment of Ecological Risks Posed by Heavy Metals

3.3.1. Potential Ecological Risk Assessment

The evaluation results of the potential ecological risk posed by soil heavy metals are illustrated in Figure 4. The average RI for eight heavy metals in the soil was ranked as follows: Hg > Cd > Pb > As > Cu > Ni > Zn > Cr. The average concentrations of Cu, Ni, Zn, and Cr were all below 40, categorizing them within the slight ecological risk level. In contrast, Hg was classified as posing a very strong ecological risk, with 20.5% of samples exhibiting very strong ecological risks and 79.5% showing strong ecological risks.

The strong and extremely strong ecological risk levels for Cd were 33.4 and 35.9%, respectively. The ecological risk grades for Pb were predominantly slight and strong, comprising 53.8 and 30.8% of the samples, respectively, whereas As was classified solely within the slight ecological risk category, with slight and medium ecological risk grades representing 94.9% and 5.1% of samples, respectively. The RI for heavy metals in the soil ranged from 323.9 to 3009, with an average value of 1050.1, indicating extremely strong ecological risks. The proportions of samples categorized as presenting strong and very strong ecological risk were 33.3 and 67.4%, respectively. The contribution rates of Hg, Cd, Pb, As, Cu, Ni, Zn, and Cr to the RI were 64.7, 26.8, 4.8, 2.0, 0.6, 0.5, and 0.5%, respectively. Substantial contribution rates of Hg and Cd to the RI suggest that these metals present significant risk factors.

Overall, the RI values at the sampling points demonstrated significant spatial heterogeneity, characterized by a distribution pattern with higher values in the northwest and lower values in the southeast. Areas with high RI values (RI > 600) for Cd, As, and Pb were located in the northwestern part of the mining area, which intersects multiple transportation routes and is situated downwind of the prevailing wind direction. The RI values for Hg exhibited a radial attenuation within a 5-km buffer zone surrounding the mining area, with the highest values recorded 2.1 km downwind of the smelter. Low-risk areas (RI < 150) were predominantly found in the southeast, more than 10 km away from the mining site.

The spatial distribution of heavy metal pollution was significantly affected by land use types. Mining, industrial, agricultural, and urban areas are usually the main sources of pollution, and the distribution of pollutants is affected by their emission sources and transmission modes in these areas. Pollution near the mining and industrial areas is high, while agricultural areas are polluted by factors such as chemical fertilizers, pesticides, and irrigation water, while pollution in urban areas is concentrated in traffic intensive and industrial areas. Natural areas such as forests and wetlands are generally less polluted, but may also be affected by pollution sources in surrounding areas. Land use change, such as urbanization and agricultural expansion, changes the transmission path of pollutants, thus affecting the spatial distribution of pollution.

3.3.2. Ecological Risk Early Warning Evaluation

The results of the potential IER for soil heavy metals are presented in Figure 5. The order of the eight heavy metals, ranked by average IER was as follows: Hg > Pb > Cd > Zn > As > Cu > Ni > Cr. The average IER values for Hg, Pb, and Cd exceeded 5, indicating a severe alarm; the average IER of Zn was 3.91, indicating a moderate alarm; the average IER for As was 1.14, indicating a light alarm; and the average values for Ni, Cu, and Cr varied from 0 to 1, indicating an early warning. The overall average IER for the eight heavy metal elements was 38.5, which falls into the category of severe alarm, with a sample proportion of 100%.

In summary, the P and, RI and IER levels in the study area, were primarily influenced by Cd, Pb, Zn, and Hg. This finding is consistent with Igeo values.

3.4. Human Health Risk Assessment

By utilizing the Monte Carlo method, we assessed the cumulative probability of health risks for both adults and children through three exposure pathways: oral intake, inhalation, and dermal contact. Figure 6 illustrates the cumulative probability curves associated with non-carcinogenic health risks in the two age groups.

As shown in Figure 6a–i, the average non-carcinogenic risk index (HQ), total non-carcinogenic risk index (HI), and the 95th percentile values of eight heavy metals in the adult population were are all below 1; thus, the concentration of heavy metals did not pose a non-carcinogenic risk to adults. With respect to the risk to children, although the average HQ and 95th percentile values of seven heavy metals—Ni, Cu, Zn, Cd, Pb, Hg, and Cr—were also below 1, it is noteworthy that the 95th percentile value of As was 1.70, which exceeded the threshold for non-carcinogenic risk control by approximately 9.84%. We conclude that that As in the soil of the study area presents a significant cumulative health risk to children.

Furthermore, the HI and its corresponding 95th percentile value for children were 1.67 and 2.08, respectively; in other words, 94.82% of the HI values exceeded the acceptable risk control value (=1); this finding underscores the risk of heavy metals present in soil within this study area to children, with As identified as the primary pollutant contributing to this risk.

The cumulative probability curve of carcinogenic health risks in the two age groups is shown in Figure 7. In the case of adults, the mean Cr and the 95th percentile values of As, Cd, and the carcinogenic risk (CR) were all within the range of 1.00 × 10⁻⁴ to 1.00 × 10⁻⁶. However, the CR value for As in adults was 4.86%, which exceeds the acceptable carcinogenic risk threshold of 1.00 × 10⁻⁶. This suggests that As poses an acceptable carcinogenic risk for adults. In contrast, the CR and 95th percentile values for Ni and Pb were both below 1.00 × 10⁻⁶, indicating that these two elements did not present a carcinogenic risk for adults. The mean and 95th percentile values of the TCR for adults were also within the range of 1.00 × 10⁻⁴ to 1.00 × 10⁻⁶, showing that the carcinogenic risk from soil heavy metals is within an acceptable range. In the case of children, the mean value of the CR and the 95th percentile value of As were greater than 1.00 × 10⁻⁴, indicating that Cd and Cr presented an acceptable carcinogenic risk for children. However, the CR value for Cd exceeded the carcinogenic risk control threshold by 5.89%. In contrast, the CR and 95th percentile values for Ni and Pb were both below 1.00 × 10⁻⁶, indicating that these two heavy metals pose no carcinogenic risk to children. The mean and 95th percentile TCR values for children were greater than 1.00 × 10⁻⁴, with the TCR being approximately 4.6 times the carcinogenic control value. The proportion exceeding this control value is 100%. This finding suggests that the cumulative carcinogenic risk for children from soil heavy metals was substantial. Overall, As, Cd, and Cr were identified as the primary pollutants contributing to the carcinogenic health risks of children.

4. Discussion

The ecological and health risks of heavy metal-contaminated mining soils have long been a focus of environmental science and regional land use planning. With the ongoing expansion of mineral resource exploitation and smelting activities, a significant amount of heavy metals has accumulated in the soil through aerosol deposition and waste discharge. Heavy metals’ accumulation not only impacts the soil’s physical and chemical nature but also poses a significant risk to ecosystem stability and public health. However, the spatial heterogeneity of heavy metal pollution, along with its multifaceted influences upon ecosystems and human health, presents a major challenge for accurately assessing pollution risks. Even though different approaches have been used to assess the degree of soil heavy metal contamination, the consistency and applicability of the results remain contentious, particularly regarding the relationship between ecological and health risks, and there is a notable lack of systematic research. To address this gap, we analyzed the heavy metal contamination of farmland soils in a mining area by applying different indices: soil accumulation, pollution, and potential ecological risk indices. A health risk assessment was also part of this analysis, to better understand the risks of human exposure. The research findings not only provide a scientific foundation for the development of regional land use planning and pollution control policies, but also enhance the general understanding of the causes and multifaceted effects of pollution in mining areas.

Heavy metal pollution and its potential risks to the environment and human health, especially in mining areas, have become a global concern. We found that the three most prominent heavy metals in the soil of the Xicheng Pb-Zn mining area are Hg, Cd, and Pb. These elements show significant accumulation in the soil, particularly Hg, with an Igeo of 3.27, indicating substantial contamination that far exceeded the regional background values. This result is closely linked to past Pb-Zn smelting activities in the mining area, in which mercury vapor released during the smelting process entered the soil through atmospheric deposition [6,43].

Pollution index analysis revealed that Cd was the element the soil was most severely polluted with, with a pollution intensity of 2.52, which is 3.75 times above the standard value and indicates the strong mobility and bioavailability of Cd in the environment. This finding is consistent with previous studies showing that the high contents of Cd in mining area soils pose a significant ecological threat [44]. Although Pb showed lower pollution intensity in our samples, its presence in the soil remains a potential ecological threat [45].

Hg posed the greatest ecological risk with an Er of 679.13, and approximately 79.5% of the samples displayed moderate to high ecological risks. This indicates that Hg does not only highly accumulate in the environment, but also has significant toxicity, affecting a wide range of organisms. In contrast, the ecological risk indices for Cd and Pb were lower; while these metals still pose ecological threats in heavily polluted areas. Therefore, given the differing risk characteristics of these pollutants, targeted remediation measures are needed to reduce their impacts on ecosystems [46].

Our health risk assessment indicated that As, Cd, and Cr are the primary pollutants affecting children’s health. Particularly As, for which the HI exceeded 1 in 94.82% of the samples, poses a significant health risks for children in the study area. Studies have shown that high exposure to As is linked to its natural sources in the soil [46], and although its concentration may be lower, its toxic effects are more pronounced in children [47,48,49]. We also found in the current study that while the carcinogenic risk of Pb was lower than that of As, its presence in the soil near mining areas still has potential health impacts, especially on children [47,50].

Our comparative analysis of ecological and health risks revealed significant discrepancies in risk identification. While Hg exhibited a high RI and Pb an elevated PI, their associated health risks remained substantially lower than those for Cd and As. For Hg, the pollution in the study area was primarily attributed to metallic Hg, which mainly exists in the form of insoluble mercury sulfide (HgS). This form of Hg has extremely low bioavailability, indicating that it is unlikely to be absorbed by plants or enter the food chain [51]. Additionally, the RfD for Hg is relatively low, further reducing its health risks. Therefore, although Hg poses a significant risk to the ecosystem, its direct threat to human health is relatively small due to its low bioavailability.

Pb mainly exists in the soil in a residual state and is unlikely to be absorbed by crops or inhaled in large quantities [52]. The toxicity risk of Pb is mainly associated with inhalation of dust and direct ingestion of contaminated soil. However, in this study, the carcinogenic risk of Pb was relatively low, especially when compared with other heavy metals such as As and Cd, which have higher bioavailability and toxicity.

We also identified the primary sources of heavy metal pollution in the area: the significant accumulation of Hg was primarily due to metallic mercury used in mineral processing, as well as aerosols emitted by the smelter, which settle in the soil through both atmospheric dry and wet deposition. Cd in the soil is mainly derived from the mining, smelting, and transportation of galena (PbS) and sphalerite (ZnS) in Pb-Zn mines [53]. Soil contamination by Pb may be associated with the high natural background levels of mining soil in the study area; the concentration of Pb in the soil is significantly increased by mineral smelting activities. This source analysis not only confirmed the causes of heavy metal pollution but also provides a scientific foundation for the development of subsequent pollution control measures.

Through an integrated methodological framework, we systematically compared the impacts of ecological risk assessment and health risk evaluation paradigms on ecosystems and human populations, revealing their complementary nature in risk identification. Health risk quantification specifically highlighted pediatric hypersensitivity, with As and Cd being quantitatively validated as predominant toxicological stressors to children’s health. These evidence-based outcomes provide a targeted scientific foundation for environmental policymaking, particularly in formulating child-centered health protection frameworks and strategic priorities for heavy metal remediation in contaminated environments.

There were some limitations in this study. Owing to the limited spatial distribution of soil samples in the study area, their representativeness may impact the results. Future studies should expand the sampling range and increase the sample size. Additionally, the health risk assessment model in this study is derived from standardized reference dose parameters and does not fully account for regional specificity and individual differences. This oversight may result in certain risks being either underestimated or overestimated.

According to the findings of this study, we suggest the implementation of a series of comprehensive policy measures to effectively mitigate heavy metal pollution in agricultural soils, particularly in areas significantly impacted by mining activities.

First, it is essential to strengthen the control of pollution sources. Specifically, the smelting industry should install exhaust gas recovery systems and reduce the concentration of heavy metals in wastewater discharge. Additionally, historical tailing piles should undergo in situ solidification treatment to minimize the migration of heavy metals.

Second, soil remediation should be regarded as a primary strategy. This can be achieved through the application of amendments, such as lime, to neutralize soil pH levels and decrease the bioavailability of heavy metals. Furthermore, promoting the growth of crops with low heavy metal absorption rates and actively conducting phytoremediation projects will help utilize plants for absorbing and stabilizing heavy metals present in soils.

The government should enhance enforcement of soil pollution regulations by reinforcing emission standards for heavy metals within both mining and smelting industries while providing financial incentives that encourage enterprises to adopt green technologies. Establishing special funds dedicated to land restoration efforts will also aid in rehabilitating contaminated lands.

It is further necessary to establish a long-term monitoring network for heavy metals in soil that tracks pollution trends over time to ensure scientific decision-making processes. Moreover, research on the migration and accumulation mechanisms of heavy metals within soils should be intensified alongside developing effective remediation methods.

5. Conclusions

The average concentrations of heavy metals—As, Ni, Cu, Zn, Cd, Pb, Hg, and Cr—in the soil of the study area exceeded the background levels for Gansu Province. Specifically, the levels of Zn, Cd, and Pb surpassed the risk screening values for agricultural soil by factors of 1.36, 3.75, and 1.54, respectively. Zn, Cd, Pb, and Hg were found to be the primary heavy metal pollutants, exhibiting significant variability and a spatial enrichment that was heavily influenced by regional background conditions and human activities.

The results for Igeo indicated that Hg was the most significantly accumulated metal pollutants, followed by Cd and Pb. Pollution index assessment revealed that Cd posed the most serious threat, classified as moderate pollution, whereas Pb, Zn, and As were classified as relatively light pollutants. Cd and Pb were identified as the primary contributors to soil pollution in the study area. Potential ecological risk evaluation indicated that Hg presented a very high ecological risk level, whereas Cd and Pb were classified as medium and low ecological risk levels, respectively. The IER values for Hg, Pb, and Cd all exceeded 5, suggesting that these elements represented potential risk factors in the study area.

Cumulative probability assessment of health risks indicated the presence of both non-carcinogenic and carcinogenic risks associated with heavy metals in the study area. The risk to children was significantly higher than that to adults; the non-carcinogenic risk for adults was not significant; in contrast, the proportion of samples showing a non-carcinogenic risk to children exceeded 94.82%. The carcinogenic risk to adult upon, exposure to heavy metals was considered to be at an acceptable level. In contrast, the carcinogenic risk to children was approximately 4.6 times higher than the control value, placing this age group at a heightened risk for carcinogenic effects.