Geospatial Analysis, Source Apportionment, and Ecological–Health Risks Assessment of Topsoil Heavy Metal(loid)s in a Typical Agricultural Area

Abstract

Soil environmental protection has become a pressing issue for sustainable development. This study collected 153 topsoil samples from a typical agricultural area to evaluate the contamination characteristics of heavy metal(loid)s (HMs), identify their potential sources, and assess the associated ecological and human health risks. The results showed that the mean concentration of HMs was in the order of Zn > Cr > Ni > Pb > Cu > As > Cd > Hg; all HMs were below their background levels, except Cd. The geo-accumulation index (Igeo) and improved Nemerow index (INI) revealed that the overall pollution level was considered as no or slight contamination, while HMs posed low ecological risk according to the ecological hazard factor (E_i) and potential ecological risk index (PERI). In addition, three main sources were identified through the positive matrix factorization (PMF) model: natural source (48.2%; contributed As, Ni, Cu, Pb, and Zn), coal burning and waste disposal (24%; contributed Hg and Cd), and agricultural activities (27.8%; contributed Cr). The human health risk (HHR) assessment model and Monte Carlo simulation were applied to evaluate human health risks, and the results suggested that children faced higher health risks than adults, with 45.83% of samples exceeding the non-carcinogenic acceptable limit. As and Cr were the main contributors to non-carcinogenic and carcinogenic risks, respectively. The findings contributed to the local environmental management and sustainable development of agriculture.

1. Introduction

The soil environment is vital for human survival and sustainable development [1,2,3]. However, soil environmental problems have become a significant issue worldwide due to intense human activities, especially agricultural activities [4,5]. Agricultural processes, including excessive use of fertilizers and pesticides, have accelerated soil degradation, leading to the enrichment of heavy metals (HMs) in the soil. According to previous studies, agricultural soils in many regions are increasingly contaminated with heavy metal(loid)s like Pb, Cd, As, and Hg, which not only deteriorate soil quality but also pose significant health risks [6,7]. The crops containing a high concentration of HMs pose severe hazards to human health, such as cancer, kidney damage, and neurological disorders, and thus make source identification and contamination risk assessments a priority for environmental protection and sustainable agricultural development.

Assessing contamination risk and source identification of soil HMs provide theoretical support for environmental protection policies [8,9]. Several environmental and ecological indicators like the enrichment factor (EF) and geo-accumulation index (Igeo) could be used to measure soil heavy metal contamination and potential ecological risk [10,11,12]. Additionally, various approaches including multivariate statistical analysis, chemical mass balance (CMB), and the positive matrix factorization (PMF) model, are applied for the source apportionment of soil HMs [13,14,15]. The PMF model is widely recognized for providing quantitative source contributions and yielding accurate results [16,17].

The HMs in soil pose a potential threat to human health due to their toxicity, and this needs to be evaluated quantitatively [18]. Excessive intake of HMs will cause severe health issues, including kidney and nerve damage from Pb and Hg and cancer from Cd and As [19]. According to the USEPA, soil HMs pose non-carcinogenic and carcinogenic risks through various exposure pathways (ingestion, inhalation, and dermal contact) [20]. However, deterministic approaches from existing heavy metal levels and exposure parameters might neglect some factors and lead to uncertainty. The Monte Carlo simulation, based on health risk assessment models, provides probabilistic results, complementing deterministic assessments [21].

The study area is located in a typical agricultural area and is a part of the famous Tianfu granary. Agricultural soil in this area may face the abnormal accumulation and potential pollution risk of HMs due to the long-term application of fertilizers and pesticides, restricting the sustainable development of green agriculture. Therefore, it is necessary to estimate their characteristics of content and spatial distribution, ascertain their main sources, and assess ecological and health risks in the study area. In addition, previous research revealed that the topsoil tends to be more exposed to anthropogenic impacts, and thus enriched in HMs, than deep soil [22,23]. Although recent studies have assessed HM pollution in agricultural soils, few have addressed quantitative source identification or conducted a comprehensive risk assessment. Furthermore, probabilistic approaches could provide a more reliable result with a small dataset [24,25]. Therefore, this study integrated multiple approaches including the PMF model, potential ecological risk index, and Monte Carlo simulation to trace the source and evaluate corresponding risks of the HMs in the topsoil of the study area. The study mainly consists of the following objectives: (1) to evaluate the exceeding level and ecological risk of HMs in the topsoil of the study area using multiple environmental indices; (2) to quantitatively identify the sources of HMs in the soil using the PMF model; (3) to assess the health risks caused by HMs by combining deterministic health risks with the Monte Carlo simulation. The outcomes are expected to provide a scientific basis for local soil pollution prevention and management, and contribute to the sustainable development of agriculture.

2. Materials and Methods

2.1. Study Area

The study area is situated in southwestern China. It spans approximately 9 km², with coordinates ranging from 104°34′7″ to 104°37′8″ E and 30°47′15″ to 30°49′6″ N. The area experiences a subtropical monsoon climate. Situated in the central Sichuan Basin, the area features a flat landscape characterized by low hills and plains. The Qingxi River, flowing from northeast to southwest, is the primary waterway. The area’s mineral resources are predominantly sedimentary non-metallic minerals, such as construction sand and gravel deposits, with ore types including quartz, chert, quartzite, granite, and limestone. As a typical agricultural zone, the land cover is mostly cropland, accounting for over 98% of the total area (Figure 1). Geologically, the exposed strata consist of Upper Pleistocene sediments from the Quaternary period, while the lithology is mainly sandstone, conglomerate, and mudstone. The soil types are primarily moist subtropical yellow and purplish soils of the Sichuan Basin, enriched with calcium, phosphorus, and potassium.

2.2. Sample Collecting and Testing

Based on the ‘Specification of Land Quality Geochemical Evaluation (1:50,000)’ [26], a total of 153 topsoil samples were collected from the top 0–20 cm layer of soil, at a density of 16 samples per km² using the grid sampling approach in the study area, during May to June, 2022. All samples were sealed in pollution-free polyethylene plastic bags after removing weeds, gravel, and organic debris, ensuring each sample weighed over 1 kg. Soil samples were preserved and sent to the laboratory in a clean and low-temperature (4 °C) environment; then were dried under natural ventilation and room temperature conditions and filtered using a 100 mesh (0.15 mm) sieve. Finally, the samples obtained were encapsulated and 200 g of each were taken for digestion (HCl and HNO₃, 30 min) and tested.

The experiments were conducted in the Sichuan Experimental Testing Research Center of Natural Resources. The soil pH was tested using potentiometry (ECT 300T, Beiyutech, Shanghai, China); Cr, Cu, Ni, and Zn were determined by inductively coupled plasma optical emission spectrometer (iCAP-7400, Thermo Fisher, Waltham, MA, USA); Cd and Pb were determined by inductively coupled plasma-mass spectrometry (PlasmaMS 300, NCS, Beijing, China); As and Hg were determined by atomic fluorescence spectrometry (AFS-8500, Haiguang, Beijing, China). The detection limits of each PTE are shown in Table S1.

To ensure the accuracy and precision of the measurements, all soil samples were analyzed in triplicate, and the average concentration was calculated. Chinese national standard substances (GBW07558) were added during the testing process to calibrate the instruments, and the results revealed relative standard deviation (RSD) lower than 5%. For quality assurance and control, 5 duplicate samples were set, and the relative deviation was lower than 10%. In addition, 20% parallel samples were inserted during the analytical process, with a relative deviation of 3.45% and a pass rate of 98.21%.

2.3. Pollution and Ecological Risk Assessment

2.3.1. Geo-Accumulation Index

The contamination level of HMs in topsoil was estimated based on the geo-accumulation index (Igeo) and the improved Nemerow index (INI), which could be calculated using the following equations [27]:

$$\mathrm{Igeo}={\log }_2{\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}}}{{1.5\mathrm{B}}_{\mathrm{i}}}}$$

(1)

$$\mathrm{I}\mathrm{N}\mathrm{I}=\sqrt{{\displaystyle \frac{{\left({\mathrm{I}\mathrm{g}\mathrm{e}\mathrm{o}}_{\mathrm{a}\mathrm{v}\mathrm{e}}\right)}^2+{\left({\mathrm{I}\mathrm{g}\mathrm{e}\mathrm{o}}_{\mathrm{m}\mathrm{a}\mathrm{x}}\right)}^2}{2}}}$$

(2)

where C_i represents the HM concentration in the topsoil (mg/kg); B_i indicates the HM soil background level of the study area (mg/kg) [28]; Igeo_ave and Igeo_max are the mean and maximum values of Igeo, respectively. The classifications of Igeo and INI are listed in Table S2.

2.3.2. Potential Ecological Risk Index

The potential ecological risk index (PERI) could be applied to evaluate the ecological risk posed by HMs in topsoil, and it is calculated as follows [29]:

$${\mathrm{E}}_{\mathrm{i}}={\mathrm{T}}_{\mathrm{i}}\times {\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}}}{{\mathrm{B}}_{\mathrm{i}}}}$$

(3)

$$\mathrm{P}\mathrm{E}\mathrm{R}\mathrm{I}=\sum {\mathrm{E}}_{\mathrm{i}}$$

(4)

where E_i is the potential ecological hazard factor of each HM; C_i and B_i are the concentration and background levels of HMs in topsoil (mg/kg); and T_i is the toxicity coefficient (As = 10, Cd = 30, Cr = 2, Hg = 40, Pb = 5, Cu = 5, Ni = 5, and Zn = 1) of each HM [30]. The PERI can be classified into four levels from low to high (Table S2).

2.4. Positive Matrix Factorization Model

The positive matrix factorization (PMF) model is widely conducted in source apportionment, quantifying contributions to soil HMs [31]. It decomposes the original concentration matrix X_ij into the source contribution matrix g_ik, source profile matrix f_ki, and residual error matrix e_ij (Equation (5)). The original matrix is optimized using uncertainty weighting through iterative computation to obtain optimal g_ik and f_ki matrices, thus minimizing the objective function Q (Equation (6)):

$${\mathrm{X}}_{\mathrm{i}\mathrm{j}}=\sum _{\mathrm{k}=1}^{\mathrm{p}}{\mathrm{g}}_{\mathrm{i}\mathrm{k}}{\mathrm{f}}_{\mathrm{k}\mathrm{j}}+{\mathrm{e}}_{\mathrm{i}\mathrm{j}}$$

(5)

$$\mathrm{Q}=\sum _{\mathrm{i}=1}^{\mathrm{n}}\sum _{\mathrm{j}=1}^{\mathrm{m}}{\displaystyle \frac{{\mathrm{X}}_{\mathrm{i}\mathrm{j}}-\sum _{\mathrm{k}=1}^{\mathrm{p}}{\mathrm{g}}_{\mathrm{i}\mathrm{k}}{\mathrm{f}}_{\mathrm{k}\mathrm{j}}}{{\mathsf{\sigma }}_{\mathrm{i}\mathrm{j}}}}$$

(6)

where p, n, and m are the number of factors, samples, and HMs, respectively; and σ_ij is the uncertainty of the j-th element in the i-th samples, which is calculated as follows:

$$\left\{\begin{array}{c}{\mathsf{\sigma }}_{\mathrm{i}\mathrm{j}}=\sqrt{{{\left(\mathrm{E}\mathrm{F}\times {\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)}^2+\left(0.5\times \mathrm{M}\mathrm{D}\mathrm{L}\right)}^2},{\mathrm{X}}_{\mathrm{i}\mathrm{j}}>\mathrm{M}\mathrm{D}\mathrm{L}\\ {\mathsf{\sigma }}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{5}{6}}\times \mathrm{M}\mathrm{D}\mathrm{L} {,\mathrm{X}}_{\mathrm{i}\mathrm{j}}\le \mathrm{M}\mathrm{D}\mathrm{L}\end{array}\right.$$

(7)

where MDL is the method detection limit, x_ij is the HM concentration, and EF is the error fraction.

2.5. Human Health Risk Assessment

2.5.1. Human Health Risk Assessment Model

The impacts of HMs on human health were evaluated using the human health risk (HHR) assessment model [32]. Non-carcinogenic risk and carcinogenic risk were evaluated through ingestion, inhalation, and dermal exposure, with individuals divided into children and adults. The calculation is outlined below:

$$\left\{\begin{array}{c} \mathrm{T}\mathrm{H}\mathrm{I}={\displaystyle \sum {\mathrm{H}\mathrm{Q}}_{\mathrm{i}}}=\sum \left({\displaystyle \frac{{\mathrm{A}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{n}\mathrm{g}}}{{\mathrm{R}\mathrm{f}\mathrm{D}}_{\mathrm{i}\mathrm{n}\mathrm{g}}}}+{\displaystyle \frac{{\mathrm{A}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{n}\mathrm{h}}}{{\mathrm{R}\mathrm{f}\mathrm{D}}_{\mathrm{i}\mathrm{n}\mathrm{h}}}}+{\displaystyle \frac{{\mathrm{A}\mathrm{D}\mathrm{I}}_{\mathrm{d}\mathrm{e}\mathrm{r}}}{{\mathrm{R}\mathrm{f}\mathrm{D}}_{\mathrm{d}\mathrm{e}\mathrm{r}}}}\right)\\ \mathrm{T}\mathrm{C}\mathrm{R}={\displaystyle \sum {\mathrm{C}\mathrm{R}}_{\mathrm{i}}}=\sum {(\mathrm{A}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{n}\mathrm{g}}\times {\mathrm{S}\mathrm{F}}_{\mathrm{i}\mathrm{n}\mathrm{g}}+{\mathrm{A}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{n}\mathrm{h}}\times {\mathrm{S}\mathrm{F}}_{\mathrm{i}\mathrm{n}\mathrm{h}}+{\mathrm{A}\mathrm{D}\mathrm{I}}_{\mathrm{d}\mathrm{e}\mathrm{r}}\times {\mathrm{S}\mathrm{F}}_{\mathrm{d}\mathrm{e}\mathrm{r}})\end{array}\right.$$

(8)

where the THI and TCR are the total non-carcinogenic and carcinogenic risks, respectively, while HQ_i and CR_i are the non-carcinogenic and carcinogenic risks of each HM. RfD and SF are the reference dose and slope factor of HMs, respectively (Table S3). ADI_ing, ADI_inh, and ADI_der are the average daily intakes of HMs through three ways, which can be calculated as follows:

$$\left\{\begin{array}{c}\mathrm{A}\mathrm{D}{\mathrm{I}}_{\mathrm{ing}}=\mathrm{C}\times {\displaystyle \frac{{\mathrm{I}\mathrm{R}}_{\mathrm{ing}}\times \mathrm{E}\mathrm{F}\times \mathrm{E}\mathrm{D}}{\mathrm{B}\mathrm{W}\times \mathrm{A}\mathrm{T}}}\times {10}^{-6}\\ {\mathrm{A}\mathrm{D}\mathrm{I}}_{\mathrm{inh}}=\mathrm{C}\times {\displaystyle \frac{\mathrm{I}{\mathrm{R}}_{\mathrm{inh}}\times \mathrm{E}\mathrm{F}\times \mathrm{E}\mathrm{D}}{\mathrm{P}\mathrm{E}\mathrm{F}\times \mathrm{B}\mathrm{W}\times \mathrm{A}\mathrm{T}}}\\ \mathrm{A}\mathrm{D}{\mathrm{I}}_{\mathrm{der}}=\mathrm{C}\times {\displaystyle \frac{\mathrm{A}\mathrm{F}\times \mathrm{S}\mathrm{A}\times \mathrm{A}\mathrm{F}\times \mathrm{E}\mathrm{F}\times \mathrm{E}\mathrm{D}}{\mathrm{B}\mathrm{W}\times \mathrm{A}\mathrm{T}}}\times {10}^{-6}\end{array}\right.$$

(9)

The reference values for these exposure parameters used in the health risk calculation can be found in Table S4. In addition, a THI or HQ higher than 1.0 is considered an unacceptable non-carcinogenic health risk, while a TCR or CR above 10⁻⁴ represents an unacceptable carcinogenic risk.

2.5.2. Monte Carlo Simulation

The probabilistic health risks are assessed by the Monte Carlo simulation based on the HHR model [33]. Initially, a hypothesis test was conducted to identify the optimal distributions for each HM concentration. Secondly, the distribution of exposure parameters was defined, with their distribution types listed in Table S5. Finally, the simulation was run by setting the number of random seeds to 100 and simulating 10,000 times.

3. Results

3.1. General Characteristics of HMs in Topsoil

The concentration characteristics of HMs are presented in

Table 1. Statistical result of HM concentrations in topsoil (unit: mg/kg).

Parameters	Min	Median	Mean	Max	SD	CV	Background Level [28]
pH	5.300	7.735	7.519	8.680	0.694	0.092	7.700
As	2.102	10.825	10.92	22.299	3.709	0.340	16.200
Cd	0.064	0.232	0.234	0.505	0.064	0.273	0.240
Cr	38.368	63.932	62.963	82.097	8.657	0.137	76.400
Hg	0.020	0.066	0.067	0.195	0.026	0.388	0.084
Pb	6.192	27.868	26.604	41.567	6.492	0.244	29.800
Cu	8.256	23.475	22.848	34.145	4.752	0.208	26.500
Ni	7.526	34.794	33.151	55.589	8.579	0.259	35.900
Zn	24.650	67.514	65.863	125.926	13.501	0.205	94.600

. The pH values varied from 5.300 to 8.680, with a mean value of 7.519, indicating a soil environment that is slightly alkaline. Furthermore, the HM contents (mg/kg) were as follows: As (2.102–22.299), Cd (0.064–0.505), Cr (38.368–82.097), Hg (0.020–0.195), Pb (6.192–41.567), Cu (8.256–34.145), Ni (7.526–55.589), and Zn (24.650–125.926), with the mean values of all HMs lower than their background levels. The coefficient of variation (CV) is used to quantify the relative dispersion of data and can indirectly reflect the impact of anthropogenic activities on natural systems. The CV of Hg concentrations was 0.388 (> 0.36), which reached the high variability, suggesting that it may be influenced by abnormal factors. However, the study area is small with unitary strata and lacks minerals. Therefore, this abnormal perturbation was considered as related to anthropogenic activities. In addition, As, Cd, Cu, Ni, Pb, and Zn exhibited moderate variability (0.36 > CV > 0.16), and Cr showed low variability (CV < 0.16).

As illustrated in Figure 2, the pH values are evenly distributed throughout the study area, with high values observed in the southwest part. In contrast, the high concentration of HMs was predominantly distributed in the northeastern part, except for Cd. Cu, Ni, Zn, and Pb had similar distributions, suggesting their potential common source. Additionally, the high values of Cd and Hg were distributed in a point-like pattern, indicating that they were possibly influenced by localized contamination.

3.2. Contamination Risk Assessment

The Igeo and INI were utilized to estimate the pollution level of HMs, and the results are listed in Table S6. As shown in Figure 3a, the average values of Igeo for HMs were ranked as follows: Cd (−0.68) > Ni (−0.76) > Pb (−0.80) > Cu (−0.84) > Cr (−0.88) > Hg (−1.02) > Zn (−1.14) > As (−1.25). With the exception of Cd, Hg, and Ni, the Igeo for all HMs was below 0, indicating that they had no anomalous accumulation in the study area. In addition, only 3.57%, 1.79%, and 0.60% of the samples reached 0–1 for Cd, Hg, and Ni, which was considered as slight contamination. The INI of HMs varied from 0.47 to 2.43, with a mean value of 0.99. The INI values of 62.50% samples were within 0–1, suggesting that the overall level of HM contamination in the topsoil of the study area was either no or slight contamination. Additionally, 35.71% of samples had slight to moderate contamination. Furthermore, samples with higher contamination levels were concentrated in the southwestern part of the study area (Figure 3c).

The Ei and PERI reveal the potential ecological effects posed by the HM concentration. The mean values of Ei for HMs were in the order of Hg (31.91) > Cd (29.26) > As (6.74) > Ni (4.62) > Pb (4.46) > Cu (4.31) > Cr (1.65) > Zn (0.70). The Ei for all HMs was below 40 except Cd and Hg (Figure 3b), indicating their low ecological risks. Furthermore, the PERI was in the range of 35.10–153.03, with an average value of 83.65. A total of 99.40% of samples were considered as low ecological risk and 0.60% as moderate risk (Table S7). The high values were mainly concentrated in the northern part of the study area and distributed in a point-like pattern (Figure 3d).

4. Discussion

4.1. Source Apportionment of HMs

The Pearson correlation analysis was conducted to estimate the promotion and inhibition of HMs, aiming to reveal the potential common sources for various HMs. The results showed that As, Cu, Ni, Pb, and Zn had significantly positive correlations (>0.66), indicating they probably share the same source or influence each other positively. Additionally, Cr, Cd, and Hg were moderately correlated with other HMs, suggesting that they potentially have multiple sources (Figure 4).

The PMF model was applied to further clarify and quantify the source contribution of soil HMs. The Q value remained stable when the factor number was set to 3 after the model had run 20 times, and the residuals for various HMs ranged from −3 to +3. Except for Cd (0.60), Hg (0.63), and Zn (0.74), the fitted r² values were between 0.83 and 0.85 for the other HMs. These results demonstrated that the model was reasonable for source apportionment in this study.

As shown in Figure 5, Factor 1 contributed 48.2% of the HMs, with higher loadings for As (77.16%), Pb (49.35%), Cu (53.24%), Ni (75.03%), and Zn (51.52%). Their average concentrations were all lower than the soil background levels (Table 1), while the contamination risk assessment showed that these elements were not significantly influenced by anthropogenic factors. Previous studies have confirmed that these elements are associated with soil parent materials [34,35,36,37]. Therefore, Factor 1 was considered a natural source.

Factor 2, contributing 24.0%, was dominated by Cd (65.30%) and Hg (64.92%). The mean content of Cd exceeded the background level, and Hg had the highest CV, while the Igeo revealed that Cd had anomalous accumulation, indicating that Cd and Hg were influenced by human activities. Cd and Hg are commonly observed in industrial discharge, such as in metal processing activities [38,39]. In addition, several studies have shown that Cd and Hg derive from incineration (burning of refuse, coal, and biomass fuel) [40,41]. The study area is a typical agricultural area with no heavy industrial production. Coal burning is an important heating approach and waste disposal is extensive, mainly incineration and landfill. Therefore, these materials indicate that Factor 2 was associated with coal burning or waste disposal.

Factor 3, contributing 27.8%, was characterized by Cr (47.27%), followed by Cu, Pb, Zn, and Ni. These HMs have a certain correlation and are widely distributed in the study area, while the contamination and risk assessment suggested their low pollution and ecological risks. This indicates that they should originate from a wide range of activities within the area rather than specific pollution. In addition, previous studies revealed that Cr, Cu, and Pb originate from agricultural activities [42,43,44], and the use of pesticides and fertilizers in the extensive and long-term agricultural activities in the area provide a potential input pathway for them. Therefore, Factor 3 was considered as the agricultural source.

The source apportionment of HMs could provide reasonable pointers for the management of the local soil environment. For example, taking corresponding measures to address the possible sources of different HMs can effectively reduce their inputs and potential ecological risks, which can contribute to soil environmental governance and sustainable agricultural development in this area.

4.2. Health Risks Assessment of HMs

4.2.1. Deterministic Health Risk Assessment by the HHR Model

The deterministic health risks of HMs were evaluated using the HHR model, and the results are listed in

Table 2. Assessment results of human health risks of HMs.

×	Group	Noncarcinogenic Risks				Carcinogenic Risks
		Min	Mean	Max	>1 (%)	Min	Mean	Max	>10−4 (%)
As	Children	8.62 × 10−2	4.48 × 10−1	9.14 × 10−1	0.00	3.32 × 10−6	1.73 × 10−5	3.53 × 10−5	0.00
	Adults	1.25 × 10−2	6.52 × 10−2	1.33 × 10−1	0.00	1.94 × 10−6	1.01 × 10−5	2.06 × 10−5	0.00
Cd	Children	1.03 × 10−3	3.75 × 10−3	8.09 × 10−3	0.00	4.14 × 10−7	1.51 × 10−6	3.27 × 10−6	0.00
	Adults	2.20 × 10−4	8.03 × 10−4	1.73 × 10−3	0.00	2.44 × 10−7	8.94 × 10−7	1.93 × 10−6	0.00
Cr	Children	2.16 × 10−1	3.55 × 10−1	4.62 × 10−1	0.00	2.62 × 10−5	4.3 × 10−5	5.6 × 10−5	0.00
	Adults	4.87 × 10−2	7.99 × 10−2	1.04 × 10−1	0.00	2.22 × 10−5	3.65 × 10−5	4.76 × 10−5	0.00
Hg	Children	8.96 × 10−4	3.00 × 10−3	8.73 × 10−3	0.00	-	-	-	-
	Adults	1.53 × 10−4	5.11 × 10−4	1.49 × 10−3	0.00	-	-	-	-
Pb	Children	2.60 × 10−2	1.12 × 10−1	1.75 × 10−1	0.00	7.61 × 10−8	3.27 × 10−7	5.11 × 10−7	0.00
	Adults	3.98 × 10−3	1.71 × 10−2	2.67 × 10−2	0.00	6.84 × 10−8	2.94 × 10−7	4.59 × 10−7	0.00
Cu	Children	2.56 × 10−3	7.07 × 10−3	1.06 × 10−2	0.00	-	-	-	-
	Adults	3.77 × 10−4	1.04 × 10−3	1.56 × 10−3	0.00	-	-	-	-
Ni	Children	4.70 × 10−3	2.07 × 10−2	3.47 × 10−2	0.00	-	-	-	-
	Adults	7.09 × 10−4	3.12 × 10−3	5.24 × 10−3	0.00	-	-	-	-
Zn	Children	1.03 × 10−3	2.75 × 10−3	5.26 × 10−3	0.00	-	-	-	-
	Adults	1.56 × 10−4	4.17 × 10−4	7.97 × 10−4	0.00	-	-	-	-
THI or TCR	Children	0.38	0.95	1.53	45.83%	3.44 × 10−5	6.21 × 10−5	8.89 × 10−5	0.00
	Adults	0.08	0.17	0.26	0.00	2.81 × 10−5	4.78 × 10−5	6.53 × 10−5	0.00

. The non-carcinogenic risks of individual HMs were all within acceptable limits for both children and adults. In addition, the total non-carcinogenic health risk (THI) to children and adults were in the range of 0.38–1.53 and 0.08–0.26, with mean values of 0.95 and 0.17, respectively; 45.83% of samples showed the THI to children exceeding 1.0. The average values of carcinogenic health risk followed the order of Cr > As > Cd > Pb, with the total carcinogenic health risk (TCR) lower than the acceptable limit (10⁻⁴) for both children and adults. This suggests that HMs in topsoil pose a potential non-carcinogenic health risk to children and an acceptable carcinogenic health risk to children and adults. Furthermore, ingestion is the most important route leading to health risks (Table S8), followed by inhalation and dermal contact, which is also consistent with similar studies [45,46,47]. Notably, children faced higher non-carcinogenic and carcinogenic health risks than adults, which is associated with the behavioral and physiological differences between the groups [48].

The spatial distribution of health risks for children and adults is depicted in Figure 6. It revealed that soil from the northern and northeastern parts posed higher health risks. These areas are also the main zones of settlement in the study area, and anthropogenic activities such as domestic waste disposal are potential sources of HMs in topsoil, which require stronger regulation and more rational measures to control.

4.2.2. Probabilistic Health Risk Assessment by Monte Carlo Simulation

The deterministic evaluation was based on limited samples and reference values of exposure, the results of which do not represent the real situation well and ignore individual differences in exposure parameters. Therefore, this study employed the Monte Carlo simulation to provide a more comprehensive insight into the health risk assessment.

The THI varied from 0.36 to 1.88 and from 0.05 to 0.54 for children and adults, with mean values of 0.94 and 0.30, respectively. As shown in Figure 7, the THI of adults remained at a low level, while the THI of children exceeded 1.0, starting from the 63.63th percentile. Additionally, the non-carcinogenic health hazard index (HI) values of HMs followed the order: As > Cr > Pb > Ni > Cu > Cd > Hg > Zn, consistent with the deterministic results. Furthermore, the TCR were in the range of 2.21 × 10⁻⁵–1.17 × 10⁻⁴ (mean value = 6.10 × 10⁻⁵) and 2.12 × 10⁻⁵–7.80 × 10⁻⁵ (mean value = 4.72 × 10⁻⁵) for children and adults, with 0.38% of samples showing unacceptable carcinogenic risk to children. Cr contributed the most to the TCR, followed by As (Figure 8). Notably, although some values may exceed acceptable limits, this does not necessarily imply an immediate or imminent risk to the population. Furthermore, a proportion of samples exceeded the threshold, but this does not mean that all individuals are exposed to those levels. The results of the health risk assessment only represent the potential threat to humans posed by these substances under defined conditions, thus providing a reference for environmental management and strategy implementation, and does not denote the actual situation in the study area.

In general, the results of the probabilistic assessment were generally consistent with the deterministic evaluation, but the former provided broader insights into the health risk evaluation [49]. The Monte Carlo simulation not only confirms the rationality of the human health risk (HHR) assessment model but also provides more references for soil health risk assessment.

4.3. Comparison with Similar Studies Worldwide

The HMs in the soil have been increasingly attracting research attention in the agricultural area. Therefore, we selected several similar research studies to compare with the findings of this study. Firstly, this study showed low HM content and pollution levels in agricultural soil, aligning with studies in Thailand and Vietnam [50,51], which have the same geological conditions (sandstone). These areas are less affected by external factors except for agricultural activities, and soil parent materials and natural processes are usually the main sources of HMs in soil. However, agricultural soil near the mining area often shows high HM concentration and pollution levels, and a considerable part of the soil HM is derived from mineral-related processes. For example, studies from Guizhou, Hunan, and the Tibetan Plateau suggest that Pb-Zn mines and Cu mines near the study area make the level of Pb, Zn, and Cu in the soil seriously exceed the standard and can contribute to significant potential ecological risks [52,53,54]. In addition, the HMs in the agricultural soil of developed urban areas are also influenced significantly by industrial activities [55]. Studies from Japan and England revealed that these HMs are associated with livestock manures [56,57]. Notably, most of these studies suggest that Cd and Hg, often originating from anthropogenic activities, are the main contributors to ecological risk, which may be related to their higher toxic coefficient [58,59]. Therefore, it is necessary to take corresponding measures to reduce their inputs for achieving sustainable agricultural development. Furthermore, As poses the most significant effect on human health, some novel methods like machine learning could effectively predict HM content and pollution level [60].

In general, HM in agricultural soil is usually closely related to the parent material, but it needs to be analyzed in combination with background information and field investigation for specific areas. Moreover, analysis using some advanced methods could make this research more interesting.

5. Conclusions

The study employed various methods to analyze the characteristics of HMs, identify their potential sources, and assess the ecological and health risks of topsoil collected from a typical agricultural region. The primary conclusions were as follows:

(1)

The mean concentration order of HMs was in the order of Zn > Cr > Ni > Pb > Cu > As > Cd > Hg, with only Cd exceeding its background values. Hg had the highest variability, and most HMs exhibited higher levels in the northeastern part of the study area.

(2)

The average contamination level of HMs was ranked as follows: Cd > Ni > Pb > Cu > Cr > Hg > Zn > As. Only 3.57%, 1.79%, and 0.60% of Cd, Hg, and Ni in the samples reached slight contamination, and the INI suggests an overall slight contamination (62.50% of samples were within 0–1). In addition, the HMs showed low ecological risk (mean value = 83.65; 99.40% samples considered as low risk).

(3)

Three sources (natural source, coal burning or waste disposal, and agricultural activities) contributed 48.2%, 24%, and 27.8% of HMs, respectively. Whereas the natural source mainly contributed As, Pb, Cu, Ni, and Zn. Cd and Hg were associated with coal burning or waste disposal. In addition, agricultural activities also provided various HMs, especially Cr, followed by Cu, Pb, and Ni.

(4)

Both the total non-carcinogenic and carcinogenic risks for adults were lower than unacceptable limits; children faced higher health risks, and nearly half of the samples (45.83%) showed a non-carcinogenic risk for children greater than 1. As and Cr contributed the most to non-carcinogenic and carcinogenic risks, respectively.

Despite the low contamination level and ecological risk of HMs, it is necessary to take precautionary measures to avoid elevating or expanding pollution. For example, emissions such as garbage in the study area should be treated in an environmentally sound manner and prevented from landfill or burning; the use of pesticides and fertilizers should be regulated in agricultural activities to reduce HM inputs. In addition, protective measures for vulnerable populations, especially children, are necessary to reduce human health risks. In the long term, strengthening publicity and education to improve the public health of the masses and establish environmental protection awareness is the key to eliminating soil pollution and the sustainable development of agriculture in this area.