GIS-PMF-Monte Carlo integrated framework for source-risk linkage of priority heavy metals and metalloid in retired coking site soils

Introduction: To address the limitation of being unable to quantitatively link soil heavy metal and metalloid pollution sources to risk contributions in industrial regions, this study investigated contamination characteristics, sources, and ecological/health risks of six target elements (five heavy metals: Cd, Cu, Pb, Hg, Ni; one metalloid: As) in a decommissioned coking site (Jinzhong, China) planned for residential redevelopment.

Methods: Twenty-six soil samples were collected from shallow (0–2.4 m) and deep (2.4–10 m) layers. An integrated framework was applied: Inverse Distance Weighting (IDW) interpolation (using QGIS) for spatial mapping of heavy metals and metalloid; Positive Matrix Factorization (PMF) model for source apportionment of heavy metals and metalloid; and Monte Carlo simulation for risk quantification.

Results: Four sources were identified with distinct contribution rates: traffic emissions (24.5%), agricultural activities (27.6%), coking industry (16.5%), and other industries (31.4%). Ecological risk was dominated by Hg from the coking industry, while health risk, significantly higher in children, was driven by Cd (heavy metal) and As (metalloid) from agricultural activities, surpassing insights from conventional concentration-oriented assessments.

Conclusion: This framework realizes quantitative source-risk linkage, identifying coking-derived Hg and agricultural-derived Cd/As as priority pollutants. It provides a scientific basis for targeted pollution control and cost-effective soil remediation in coking areas undergoing urban renewal.

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT |

Highlights

• Pioneers GIS-PMF-Monte Carlo framework to link soil metal sources to risks.

• Identifies coking Hg and agricultural Cd/As as priorities.

• Supplies basis for coking areas’ pollution control prioritization and remediation.

1 Introduction

Soil contamination with heavy metals and metalloids has emerged as a pervasive global environmental challenge, largely driven by rapid industrialization, urbanization, and intensive agricultural practices (Wang et al., 2021; Hou et al., 2017). In China, this problem is particularly severe, with widespread reports of elevated concentrations of Cd, Pb, and As in soils, posing substantial threats to ecosystem stability and human health (Wang et al., 2021; Huang et al., 2021). Heavy metals are persistent, non-degradable, and capable of bioaccumulation. They impair soil quality, disrupt microbial communities and nutrient cycling, and ultimately enter the food chain through crop uptake or direct soil ingestion. For instance, chronic low-dose Cd exposure can lead to renal tubular dysfunction, bone demineralization, and fractures; As and Pb, meanwhile, are associated with cardiovascular diseases, neurological damage, and even carcinogenesis, such as lung and kidney cancers (Bernard, 2016; Li et al., 2023).

Extensive research has focused on evaluating soil heavy metal pollution, with diverse methods developed to characterize spatial patterns, quantify risks, and identify sources. Geographic Information System (GIS), such as inverse distance weighting (IDW) and kriging, have been widely employed to delineate the spatial distribution of heavy metals and metalloids, helping identify contamination hotspots in regions like orchard soils or urban parks (Wang et al., 2021; Hou et al., 2017; Huang et al., 2021). For ecological risk assessment, indices including the geo-accumulation index (I_geo), enrichment factor (EF), and potential ecological risk index (RI) have been commonly applied to quantify the degree of contamination and ecological hazards, and previous studies highlighted Cd, Pb (heavy metals) and As (metalloid) as priority pollutants due to their high toxicity and enrichment (Wang et al., 2021; Li et al., 2023).

For human health risk assessment, the U.S. Environmental Protection Agency (USEPA) framework is extensively adopted. It incorporates average daily intake (ADI) and lifetime average daily dose (LAD) to estimate non-carcinogenic risks (via hazard index, HI) and carcinogenic risks (via total carcinogenic risk, TCR). Children are more vulnerable than adults in this context, as their hand-to-mouth behaviors and higher exposure rates increase risk (Huang et al., 2021; Li et al., 2023; Zhou et al., 2024).

In parallel, receptor models such as Positive Matrix Factorization (PMF) have become a cornerstone of source apportionment, enabling quantitative evaluation of contributions from natural sources (e.g., soil parent materials) and anthropogenic activities (e.g., industrial emissions, fertilizer application, traffic) (Wang et al., 2021; Li et al., 2023; He et al., 2010). Despite these advances, several critical gaps remain. First, many studies focus exclusively on contamination levels or a single type of risk (ecological or health), often relying solely on total metal concentrations while neglecting the dynamic link between contamination sources and their respective risk contributions (Huang et al., 2021; Li et al., 2023). For example, while PMF can effectively quantify source contributions to metal concentrations, few studies have explicitly linked these sources to ecological risks (e.g., how industrial emissions drive RI elevation) or health risks (e.g., the share of TCR attributed to traffic-related Pb). This limits the ability to identify priority sources for targeted control. Second, although probabilistic health risk assessment (e.g., Monte Carlo simulation) has been increasingly applied to address uncertainties in exposure parameters or metal concentrations (Huang et al., 2021; Zhou et al., 2024), few studies have combined it with source-specific ecological risk evaluation. This lack of integration results in fragmented risk insights, i.e., failure to link risks to specific pollution sources. This is precisely the key challenge that the proposed GIS-PMF-Monte Carlo integrated framework aims to address, especially in high-risk regions such as decommissioned industrial sites or mining areas. Third, while industrial parks and mining areas have been studied extensively (Li et al., 2023; Zhou et al., 2024), site-specific investigations of decommissioned coking plants, facilities with intensive emissions of Cd, As, and Pb, remain relatively limited, despite their potential for deep soil contamination and risks during land-use conversion (e.g., to residential areas) (Li et al., 2023).

To address these gaps, this study focused on a typical industrial site in Jinzhong City, Shanxi Province, China. The area is surrounded by diverse industrial and agricultural activities and is planned for redevelopment into residential land. The proximity of sensitive receptors, such as schools, kindergartens, and residential communities, underscores the urgency of a comprehensive risk evaluation.

Accordingly, we systematically collected soil samples across 0–10 m depth and analyzed six priority elements (five heavy metals: Cd, Cu, Pb, Hg, Ni; one metalloid: As). An integrated research framework was adopted: spatial distribution of metals was mapped to identify contamination hotspots (Wang et al., 2021; Hou et al., 2017); PMF was used to apportion sources of heavy metals and metalloid (He et al., 2010); ecological risks were quantified via classic indices (Li et al., 2023); and probabilistic health risks were assessed to account for parameter uncertainties (Zhou et al., 2024). This framework specifically addresses the research gaps identified earlier, including the lack of quantitative source-risk linkage and fragmented risk assessment.

The novelty of this study lies in its source-oriented dual-risk assessment framework: it moves beyond total concentration analysis to quantify how specific sources contribute to both ecological and human health risks. By identifying priority control sources (e.g., coking-related emissions) and key pollutants (e.g., Cd, As), the study provides actionable insights for policymakers, supporting targeted soil remediation and rational land-use planning in industrial regions undergoing redevelopment.

2 Materials and methods

2.1 Study area, materials, and soil sampling

2.1.1 Study area description

The study area is located in Jinzhong City, Shanxi Province, a major transportation hub and a region with extensive industrial activities (Figure 1a). The specific site (covering approximately 0.16 km²) was a decommissioned coking plant, which was surrounded by various other industrial facilities (e.g., a steel plant to the north, a carbon plant to the southwest, and an alumina factory to the south) and agricultural land. The site had been designated for future residential redevelopment, heightening the need for a detailed environmental risk assessment. A comprehensive description of the study area, including climate and sensitive receptors, was provided in Section 3.1.

Figure 1

Figure 1. (a) Sampling point layout in the study area; (b) Spatial distribution of heavy metals and metalloid via Inverse Distance Weighting (IDW) interpolation in soils.

2.1.2 Sampling design and collection

The sampling strategy was designed based on a preliminary geological survey report, which indicated that silty clay occurs at a depth of approximately 10 m, un-derlain by a uniform silt layer. The thickness of the miscellaneous fill layer ranged from 0 to 2.4 m (Figure 1a).

Given the heterogeneous contamination conditions within and around the study site, a systematic grid method was employed to design the sampling points. Ten shal-low sampling sites (0–2.4 m) were established across the site. Particular emphasis was placed on potentially contaminated zones, such as the historical chemical production area and the sewage treatment facility in the south. In these key areas, an additional 16 deep sampling sites (2.4–10 m) were established using a professional judgment method to capture the vertical distribution of contaminants. Grid sampling ensures full-area coverage, while intensified sampling in key contaminated zones specifically captures the characteristics of high-pollution areas. The combination of the two balances the representativeness and targeting of sampling. In total, 26 sampling sites were established.

Surface soils were collected manually after removing surface debris and hardened crusts. Deep and saturated zone soils were sampled using the SH30 drilling rig. All samples were homogenized in the field, sealed, labeled, and transported under cold chain conditions to the laboratory for further analysis.

2.2 Characterization of heavy metals and metalloid contamination

2.2.1 Chemical analysis of samples

Soil samples were analyzed for toxic heavy metals and metalloid. Concentrations of As and Hg were determined by atomic fluorescence spectrometry, Cd and Pb by graphite furnace atomic absorption spectrophotometry, and Cu and Ni by flame atomic absorption spectrophotometry. To ensure analytical accuracy and reliability, duplicate and blank samples were included as part of quality assurance and quality control (QA/QC). Recovery rates of all metals and metalloid ranged from 93.4% to 107%, and relative standard deviations of duplicate samples were within 5%. Detection limits for As, Ni, Cd, Cu, Hg, and Pb were 0.01, 3, 0.01, 1, 0.002, and 0.1 mg/kg, respectively.

2.2.2 Spatial distribution of samples

Geographic Information System (GIS) technology has been widely applied in soil pollution studies for mapping contaminant distribution (Gu et al., 2012). Based on sample data, inverse IDW interpolation in Quantum Geographic Information System (QGIS) 3.34.1 was employed to map the spatial distribution of contaminants. This method provides good simulation performance, rapid processing, and straightforward interpretation, and is one of the most widely used interpolation techniques (Gu et al., 2012). The core formula of IDW is as follows:

${\mathrm{Z}}_{\mathrm{x},\mathrm{y}}=\frac{{\displaystyle {\sum }_{\mathrm{i}=1}^{\mathrm{n}}}{\mathrm{Z}}_{\mathrm{i}}{\mathrm{d}}_{\mathrm{x},\mathrm{y},\mathrm{i}}^{-\beta }}{{\displaystyle {\sum }_{\mathrm{i}=1}^{\mathrm{n}}}{\mathrm{d}}_{\mathrm{x},\mathrm{y},\mathrm{i}}^{-\beta }}(1)$

where Z_x,y is the interpolated value at point (x,y), Zi is the measured value at control point i, and di is the distance between the sampling and control points.

Where z_x,y is the predicted concentration of contaminants at the unmeasured point (x,y), z_i represents the measured concentration at the i-th sampling point, d_x,y,i is the linear distance between the unmeasured point (x,y) and the i-th sampling point, β is the weighting power (set to 2.0 in this study, consistent with similar sediment heavy metal spatial distribution studies (Gu et al., 2012) and QGIS 3.34.1 default parameters), and n is the number of neighboring sampling points participating in the interpolation calculation (set to 5). The interpolation precision was verified by statistical indicators: the mean error (ME) and root-mean-square error (RMSE) were both less than 0.05 (Equation 1), indicating a good agreement between measured and estimated values.

This method (IDW interpolation) provides a spatial distribution basis for subsequent pollution level assessment and risk analysis. To systematically characterize the comprehensive pollution degree of heavy metals and metalloid in soils, this study simultaneously adopted the single Pollution Index (PI) and Nemerow Pollution Index (NPI) for quantitative evaluation. Detailed calculation formulas, classification criteria, and the complete calculation process are provided in Supplementary Information (Supplementary Material S1, Supplementary Text S1 and Supplementary Table S1).

2.3 Source apportionment of toxic metals and metalloid

Figures and charts were produced using Origin 2021 and R (v4.4.1). Source apportionment of toxic metals and metalloid was conducted with U.S. EPA PMF 5.0 software. No outliers were excluded, and all concentration data were included in the statistical analyses. Pearson correlation analysis was used to examine associations among metals and metalloid, with background values of heavy metals and metalloid in Shanxi Province referenced from Table 1. The PMF model was applied to apportion and quantify their potential sources. Detailed methods (including PMF iteration formulas, uncertainty assessment via Bootstrap/DISP) were provided in Supplementary Text S2 of the Supplementary Information (SI).

Table 1

Table 1. Heavy metal and metalloid contents (mg/kg) in the soils of the study area.

2.4 Potential ecological risk of toxic metals and metalloid

The potential ecological risk index (RI) is a widely used approach for assessing ecological risks of toxic metals and metalloid in soils, as it accounts for concentrations, ecological effects, and toxicological differences. Additional details are provided in the (Supplementary Text S3 and Supplementary Table S2).

By integrating the PMF model with the RI method, ecological risks of toxic metals and metalloid from different sources were quantified using the following formulation (Equations 2, 3) (Guan et al., 2018):

$C_{ij}^k=P_{ij}^k\times C_j(2)$

$R{I}_j^k={\left(\sum E_r^i\right)}_j^k=\sum T_r^i\times \frac{C_{ij}^k}{S_i}(3)$

Here, C_ijk represents the concentration of toxic metal and metalloid i in sample j attributed to source k; P_ijk denotes the calculated contribution of toxic metal and metalloid i to source k in sample j; C_j is the total concentration of toxic metals and metalloid in sample j (mg/kg); and ${\left(\sum E_r^i\right)}_j^k$ indicates the integrated potential ecological risk of source k in sample j.

2.5 Probabilistic human health risk assessment

Human health risks were assessed following the United States EPA model (Equations 4–12) (Norris et al., 2014). To reflect physiological differences, risks were calculated separately for children and adults. Risks were divided into non-carcinogenic (NCR) and carcinogenic (CR) (Huang et al., 2021; Norris et al., 2014). Monte Carlo simulation was used to address parameter uncertainties in probabilistic assessment, with details in the (Supplementary Text S4 and Supplementary Table S3).

Monte Carlo simulation was used to address parameter uncertainties, with input parameters assigned probability distributions based on United States EPA guidelines and Chinese population exposure data (Ministry of Environmental Protection MEP et al., 2016) (details in Supplementary Text S4 and Supplementary Table S3 of Supplementary Material SI). NCR represents adverse health effects from long-term exposure and is characterized by the hazard index (HI) (Gui et al., 2023; Kamarehie et al., 2019). CR is the incremental lifetime probability of developing cancer from exposure (Yao et al., 2022).

$THI={\displaystyle \sum _{j=1}^m}H{I}_j={\displaystyle \sum _{j=1}^m}\left({\displaystyle \sum _{k=1}^n}H{Q}_k\right)={\displaystyle \sum _{j=1}^m}\left({\displaystyle \sum _{k=1}^n}\frac{AD{D}_k}{Rf{D}_k}\right)(4)$

$TCR={\displaystyle \sum _{j=1}^m}C{R}_j={\displaystyle \sum _{j=1}^m}\left({\displaystyle \sum _{k=1}^n}AD{D}_k\times S{F}_k\right)(5)$

Three exposure pathways were considered: ingestion, dermal contact, and inhalation. The average daily dose (ADD) for each pathway was calculated as:

$AD{D}_{ing}=C_{soil}\times \frac{IngR\times EF\times ED}{BW\times AT}\times {10}^{-6}(6)$

$AD{D}_{der}=C_{soil}\times \frac{SA\times AF\times ABS\times EF\times ED}{BW\times AT}\times {10}^{-6}(7)$

$AD{D}_{in\mathrm{h}}=C_{soil}\times \frac{In\mathrm{h}R\times EF\times ED}{PEF\times BW\times AT}(8)$

Here, ADD_ing, ADD_der, and ADD_inh represent the average daily doses of toxic metals and metalloid in soil through ingestion, dermal contact, and inhalation pathways, respectively. C_soil denotes the concentration of toxic metals and metalloid in soil. Exposure parameters are summarized in the (Supplementary Tables S4, S5).

Furthermore, in the probabilistic health risk assessment of different sources, the contribution of each pollution source to toxic metals and metalloid in the study area was combined with PMF model results and the health risk assessment model. This allowed quantification of the contributions of different sources to both non-carcinogenic and carcinogenic risks. The specific calculation formulas are as follows:

$B_{i,HQ}=\sum C_{in}\times H{Q}_n(9)$

$B_{i,CR}=\sum C_{in}\times C{R}_n(10)$

$D_{i,HQ}=\frac{C_{in}}{B_{i,HQ}}(11)$

$D_{i,CR}=\frac{C_{in}}{B_{i,CR}}(12)$

B_i,HQ represents the non-carcinogenic risk index of source iii, and B_i,CR represents the carcinogenic risk index of source i. C_in is the contribution percentage of toxic metal and metalloid n from source type i; HQ_n and CR_n are the non-carcinogenic and carcinogenic risk indices of toxic metal and metalloid n, respectively. D_i,HQ denotes the contribution percentage of source i to the non-carcinogenic risk index, while D_i,CR denotes the contribution percentage of source i to the carcinogenic risk index.

3 Results and discussion

3.1 Study area and soil sampling

The study area, initially outlined in Section 2.1.1, is a 0.16 km² decommissioned coking plant in Jinzhong City, Shanxi Province—surrounded by a steel plant (north), carbon plant (southwest), alumina factory (south), and adjacent agricultural land, with future residential redevelopment plans and nearby sensitive receptors (e.g., a middle school 2.8 km west, primary school and clinic within 2.5 km southwest), which underscores the need for targeted environmental risk assessment (Figure 1). Climatically, it has a temperate continental monsoon climate (annual average temperature 9.7 °C, annual precipitation 422.8 mm, prevailing southeasterly wind at 2.9 m/s), a context that aids in interpreting subsequent soil heavy metal and metalloid spatial distribution. The soil samples analyzed herein were collected per the design in Section 2.1.2: 26 sites (10 shallow: 0–2.4 m; 16 deep: 2.4–10 m) established via systematic grid method (overall coverage) and professional judgment (key contaminated zones like former chemical production areas), tailored to the site’s geological conditions (0–2.4 m miscellaneous fill, 10 m deep silty clay) to ensure data representativeness for subsequent contamination and risk analyses.

3.2 Distribution characteristics of heavy metals and metalloid

3.2.1 Concentrations

As shown in Table 1, concentrations ranged: As 5.25–12.6 mg/kg, Cd 0.03–0.14 mg/kg, Cu 10–25 mg/kg, Pb 8.1–27.1 mg/kg, Hg 0.016–0.175 mg/kg, Ni 16–60 mg/kg. Provincial background values (BV) proposed by the China National Environmental Monitoring Centre (CNEMC) were used as a reference (China National Environmental Monitoring Centre CNEMC, 1990) (Table 1). CNEMC’s soil background value assessments established for various provinces have been widely applied in soil heavy metal and metalloid element assessment studies at different spatial scales in China (Yang et al., 2020). Relative to BV, mean concentrations of As, Cu, and Pb were below background; Cd was comparable; Hg slightly exceeded background; Ni was substantially higher (China National Environmental Monitoring Centre CNEMC, 1990). Maximum concentrations of all metals and metalloid exceeded BV. Proportions of samples above BV were 47.37% (As), 29.47% (Cd), 16.84% (Cu), 1.05% (Pb), 71.58% (Hg), and 89.47% (Ni). In terms of the coefficient of variation (CV), four heavy metal elements (Cd, Pb, Hg, and Ni) exhibited relatively high spatial variability in their CV values, with Hg showing the highest CV of 0.5. High spatial variability indicates significant spatial differences in the accumulation of soil heavy metals and metalloid. Previous studies have confirmed that the main cause of high variability is often external input (Yang et al., 2020).

The average concentration of Ni (34.48 mg·kg^-1) was significantly higher than the soil background value of Shanxi Province (23 mg·kg^-1), with 89.47% of samples exceeding the background value. This is directly related to the Ni emission characteristics of steel plants and alumina factories surrounding the study area—Ni-containing alloy catalysts and mechanical equipment components used in the production processes of these industries can lead to Ni accumulation in soil through atmospheric deposition and waste residue accumulation. Li et al. (2023) also observed this pattern in their study of non-ferrous metal mining and smelting areas in Southwest China. In their source apportionment work, they confirmed industrial activities like smelting and alloy manufacturing are the main drivers of soil Ni enrichment; then, there was a significant positive correlation between industrial emission intensity and soil Ni content.

Meanwhile, soil Hg in the study area exhibited obvious spatial heterogeneity, a characteristic directly related to the point-source pollution attribute of the coking plant: Hg is a typical volatile pollutant from coal combustion and coking processes, with highly localized emissions. It tends to form high-concentration patches around pollution sources, resulting in increased variation in soil Hg content. Our observations matched what Huang et al. reported (Huang et al., 2021) in their study of park soils in industrial-impacted areas of Shanghai. They analyzed Hg distribution across different functional zones and confirmed two critical results: industrial point sources (coking, coal combustion) drove the elevation of Hg spatial variation coefficients, and soil Hg content within 5 km of these pollution sources was notably higher than in other regions.

3.2.2 Spatial patterns

IDW interpolation (Figure 1b) revealed heterogeneity across two layers: upper (0–2.4 m, fill) and lower (2.4–10 m, silt). Metal and metalloid concentrations varied widely in the upper layer, with somewhat different patterns in the lower layer. Notably, Hg was elevated in sewage treatment and coking plant zones, while Pb was higher near roads and residential areas, consistent with traffic and domestic activities.

Further analysis indicates that the high heterogeneity of the upper soil layer (0–2.4 m, miscellaneous fill layer) is closely related to its more direct exposure to human activities: the miscellaneous fill layer is mostly backfilled with historical industrial waste residues and construction waste, and the surface soil is prone to receiving atmospheric deposition (e.g., lead dust from roads, mercury volatiles from coking plants). In contrast, the lower soil layer (2.4–10 m, silt layer) is less disturbed by external factors, and pollutant migration in this layer is more difficult, resulting in a more uniform distribution. This aligns with the known trait of miscellaneous fill soil: it has weak pollutant retention capacity and is prone to pollutant diffusion.

Furthermore, the high mercury content in the sewage treatment area requires special attention. Soil here interacts frequently with groundwater, allowing mercury to leach easily into the groundwater system. Hg leaching may pose potential pollution risks to irrigation water in surrounding farmlands. This needs to be further verified with groundwater monitoring data in subsequent studies.

3.3 Pollution levels

PI values indicated slight contamination for Hg (1.50) and Ni (1.50), while As (0.99), Cd (0.95), Cu (0.91), and Pb (0.61) showed no contamination (Figure 2a; Supplementary Table S1). However, 20.00% of Hg samples and 10.53% of Ni samples reached moderate-to-severe pollution levels. NPI values ranged 0.85–3.20, with 29.63% of samples at moderate or higher levels, underscoring localized contamination, especially for Hg and Ni (Figure 2b).

Figure 2

Figure 2. (a) Single pollution index (PI) and (b) Nemerow pollution index (NPI) of soil heavy metals and metalloid in soils in the study area.

Contrast, arsenic (As), cadmium (Cd), copper (Cu), and lead (Pb) are unpolluted: As/Cd come from uniformly diffused agricultural non-point sources, Pb from limited-scope traffic line sources, and their input does not exceed soil capacity.

NPI high-value areas overlap with coking/other factory surroundings (see Figures 1b, 2b), suggesting no full-area remediation is needed. Targeting core pollution areas cuts costs, supporting subsequent source-oriented priority management. As shown in Figure 2a, PI values < 1 indicate non-pollution (As, Cd, Cu, Pb), while PI 1–2 indicates slight pollution (Hg, Ni). Figure 2b shows that NPI ≥2 (moderate pollution) accounts for 29.63% of samples, mainly concentrated in coking plant surroundings.

3.4 Source apportionment

By integrating the results of the PMF model and correlation analysis, the heavy metals and metalloid in the study area were classified into four sources.

The correlation analysis showed that As-Cd and Ni-Cu had a significant positive correlation (P < 0.01), indicating that these heavy metals and metalloid might share the same source. This correlation can be attributed to specific anthropogenic activities: For As-Cd, agricultural inputs are the key driver, phosphate fertilizers often contain Cd impurities, and historical arsenic-based pesticides (e.g., lead arsenate) are major As sources; Wang et al. (2021) also observed this As-Cd correlation in karst orchard soils, aligning with our results. For Ni-Cu, synergistic use in local stainless-steel manufacturing and electroplating (Ni for corrosion resistance, Cu for plating layers) leads to co-enrichment via dust/wastewater, which aligned with the results of Li et al. (2023) in non-ferrous metal areas.

The PMF model was run 20 times to find the minimum Q value. The signal-to-noise ratio (S/N) of all heavy metals and metalloid exceeded 2, and we defined this level as strong. The fitting coefficients (r²) of As, Cd, Cu, Pb, Hg, and Ni were 0.58, 0.94, 0.57, 0.86, 0.99, and 0.96, respectively. Therefore, we concluded that the main indicators derived from the PMF analysis could reflect reasonable results. In this study, the bootstrap (BS) method was also applied to assess the uncertainty of the PMF analysis, which indicated that the results of the PMF model were reliable.

Four factors (Factor 1, Factor 2, Factor 3, and Factor 4) were extracted, with contribution rates of 24.5%, 27.6%, 16.5%, and 31.4%, respectively (Figure 3a). These four factors correspond to specific pollution sources, with Factor 1 identified as traffic source, Factor 2 as agricultural source, Factor 3 as coking industry source, and Factor 4 as other industrial sources (steel plant, carbon plant, alumina factory) based on element contribution profiles and regional industrial layout.

Figure 3

Figure 3. Source apportionment of heavy metals and metalloid in soils in the study area: (a) contribution rates of different sources; (b) contribution percentages of different sources to various heavy metals and metalloid; (c) correlation relationships between different heavy metals and metalloid and contributions of different sources.

Factor 1 accounted for 24.5% of the total contribution, with a significant contribution mainly to Pb (Figure 3b). Previous studies have shown that Pb is primarily derived from vehicle emissions (Cai et al., 2019; Liang et al., 2017). Meanwhile, there are residential areas near the study area, which suggests that traffic sources are an important source of Pb pollution. In addition, evidence shows that traffic activities are one of the main pathways of Pb in atmospheric deposition. Exhaust gas and tire wear particles generated by traffic activities can carry Pb into the soil. Areas with well-developed traffic networks, such as highways and provincial roads, are important sources of Pb pollution. The study in Section 3.2.1 also indicated that Pb may be related to traffic emissions and residential activities. Therefore, Factor 1 can be interpreted as a traffic source.

Factor 2 accounted for 27.6% of the total contribution, with a relatively significant contribution to Cd, Cu, and As (Figure 3b), and Cd had a high correlation with As and Cu (Figure 3c). The concentration of Cd in agricultural soils in many regions of China exceeds the standard (Xue et al., 2014), and agricultural activities are generally considered an important source of Cd and Cu in soils. Pesticides, organic fertilizers, and phosphate fertilizers usually contain large amounts of Cd and Cu, and long-term application of these fertilizers will aggravate the accumulation of Cd and Cu in soils. Moreover, Cd is regarded as an indicator of chemical fertilizer use. As is often used as a wood preservative and agricultural chemical (IARC International Agency for Research on Cancer, 2012), and the use of arsenic-based feed additives may also lead to large-scale pollution of agricultural soils (Hu et al., 2019). In addition, the coefficients of variation of Cd and As are relatively large, which are obviously affected by human activities. There are farmlands around the study area, which further supports that Factor 2 is related to agricultural activities. Therefore, Factor 2 can be considered related to agricultural activities and interpreted as an agricultural source.

Factor 3 accounted for 16.5% of the total contribution, with a significant contribution to Hg (Figure 3b). Many studies have shown that industrial activities are the main source of Hg pollution, and coal combustion in industrial activities is one of the main pathways of Hg pollution (Liang et al., 2017; Hu et al., 2022). Industrial sewage discharge and atmospheric deposition can also lead to the accumulation of Hg in soils. Due to high-temperature coal processing, coking plants are often the main source of Hg emissions. The study in Section 3.2.1 also indicated that the sewage treatment area and coking plant production area in the study area are closely related to the source of Hg. Therefore, Factor 3 can be interpreted as an industrial source from the coking plant.

Factor 4 accounted for 31.4% of the total contribution, with a significant contribution to Ni (Figure 3b). Ni and Cu showed a significant positive correlation (Figure 3c), indicating that they might come from the same source. Relevant studies have shown that industrial activities such as electroplating and alloy factories are important sources of Ni pollution. The co-occurrence of Ni and Cu suggests that it may be related to mixed industrial wastewater or smelting activities. Combined with the existence of other factories around the study area, Factor 4 can be interpreted as a mixed industrial source from surrounding non-coking facilities (e.g., steel plant, carbon plant, alumina factory).

Notably, our PMF results were supported by relevant studies in similar industrial areas: Wang et al. (2021) found agricultural activities contribute 20%–30% to soil As/Cd even in industrial areas, while Li et al. (2023) highlighted scattered industries (e.g., alloy processing) as key heavy metal sources. These reports both reflected the complex mixed pollution in our study area.

The results of the PMF-based analysis of factor profiles and source contributions of heavy metals and metalloid in soils were also visually presented in Supplementary Figure S2. In the traffic-source category, the concentrations of some metals were relatively high, and the corresponding species percentages reflect the contribution of this source to soil heavy metals and metalloid. For the agricultural source, the concentrations of different heavy metals and metalloid varied significantly, indicating the complexity of heavy metal inputs from agricultural activities. The industrial coking - plant source, as an important contributor of certain key heavy metals and metalloid in soils, was clearly shown by the heavy metal and metalloid concentrations and their proportions. Other industrial sources also contributed to multiple heavy metals and metalloid in soils, and the concentrations and species percentages of each heavy metal and metalloid reflect the role of this source. This figure provided a more intuitive visual basis for a deeper understanding of the contributions of different sources to soil heavy metals and metalloid, which was consistent with the conclusions drawn from the previous PMF-model analysis.

Finally, the toxic metals and metalloid in the study area were classified into four sources: traffic source, agricultural source, industrial source from the coking plant, and industrial source from other factories. This source identification is consistent with regional emission characteristics: Hg’s high contribution from coking source aligns with volatile Hg emissions during coal coking, while Ni’s dominance in other industrial sources (steel plant, carbon plant, alumina factory) matches their production processes involving Ni-containing alloy smelting and emissions.

3.5 Ecological risk assessment

Figure 4a illustrated the total contribution of the four sources to the potential ecological risk. The results show that the industrial source from the coking plant had the highest contribution rate (36.1%), followed by the agricultural source (29.4%). The contribution rates of the traffic source and industrial source from other factories were similar, accounting for 17.2% and 17.4% respectively. This indicates that the coking plant industrial source and agricultural source are the main contributors to the potential ecological risk in the study area. Combined with the analysis results in Section 3.4, Hg is the representative element of the coking plant industrial source, while Cd is the representative element of the agricultural source. This is the key reason why these two sources dominate the potential ecological risk in the study area.

Figure 4

Figure 4. (a) Ecological risk contributions from four different pollution sources, and (b) potential ecological risk indexes of heavy metals and metalloid in soils in the study area.

From a source-risk perspective, coking industry Hg emissions are the core of high risk: though not the largest mass contributor, Hg’s high toxicity and speciation transformation amplify its risk contribution, which was consistent with the conclusion of Jiang et al. (2021) that the source ratio of high toxicity metals and metalloid does not equal the risk ratio, so controlling Hg emissions from the coking plant is critical. Additionally, soil Ni enrichment here is linked to regional industries (coking, steel, aluminum): Ni is used in coking catalysts and steel components, with emissions from these industries (via atmospheric deposition, waste accumulation) driving Ni accumulation, a pattern that corroborated findings by Li et al. (2023) in non-ferrous metal and metalloid mining areas, where smelting and alloy manufacturing were identified as primary drivers of soil Ni enrichment. Soil Hg also shows obvious spatial heterogeneity, which is attributed to the coking plant’s point-source pollution. Volatile Hg emissions from the plant form localized high-concentration patches, which in turn increases soil Hg content variation. This pattern matches observations by Huang et al. (2021), who noted that industrial point sources (coking and coal combustion) raise Hg spatial variation and that soil Hg content within 5 km of these sources is significantly higher.

The results of the potential ecological risk assessment (Figure 4b) showed significant differences in the potential ecological risks of heavy metals and metalloid in the study area: over 17.89% of Cd and 71.58% of Hg were at moderate to considerably high risk levels. Even the maximum individual potential ecological risk index (Ei) of Hg exceeded 160, indicating a relatively high ecological risk. In contrast, the ecological risks of As, Cu, and Ni were relatively low, with all samples falling into the low-risk category based on their Ei values (Figure 4b). These results confirmed that Hg was the primary pollutant causing ecological risks in the study area, followed by Cd.

This dominant risk contribution of Hg and Cd stemmed from concentration-toxicity synergy—a pattern tightly linked to the study area’s unique environmental conditions described in Section 3.2.2. Jiang et al. (2021) noted that Hg has a much higher toxic response factor (Tr = 40) than other metals (Cd: Tr = 30; Pb/Cu: Tr = 2–5) in RI calculation; in our study, this intrinsic high toxicity was further amplified by Hg’s speciation transformation in the deep silt layer (2.4–10 m). This layer is an anaerobic environment explicitly identified in the sampling design, which provides an ideal condition for methylmercury formation. As Li et al. (2023) confirmed, methylmercury has a bioconcentration factor 100–1000× higher than inorganic Hg, and its potential leaching into groundwater (strongly implied by the elevated Hg concentrations in the sewage treatment zone, Section 3.2.2) means ecological hazards are not limited to soil but could extend to the adjacent aquatic ecosystem.

For Cd, its second-highest Tr (30) is paired with inherently high mobility, a trait exacerbated by two site-specific factors: first, the upper miscellaneous fill layer (0–2.4 m) has weak pollutant retention capacity (Section 3.2.2), allowing Cd to migrate horizontally; second, the slightly acidic soil (consistent with regional soil properties of Jinzhong, Shanxi) may further increase Cd dissolution—aligning with Jiang et al. (Jiang et al., 2021)’s observation that Cd mobility rises sharply in soils with pH < 6.5. Even moderate Hg enrichment (2–3× background) in our study can push Ei > 160 (Very High Risk), while Cd’s mobility ensures it spreads to surrounding farmlands (Section 3.1), collectively explaining why these two metals dominate the total ecological risk.

The comprehensive potential ecological risk index (RI) reflected the cumulative ecological risk of the study area, with an average of 16.84% of samples reaching the moderate risk level. Based on the individual ecological risk indices of heavy metals and metalloid, Cd and Hg pose higher ecological risks in the soil compared to other toxic metals and metalloid, making them the main contributors to the comprehensive RI.

3.6 Human health risk assessment

3.6.1 Concentration-oriented

The probabilistic health risk assessment of soil heavy metals and metalloid was conducted by combining Monte Carlo simulation with Human Health Risk Assessment and Management (HHRAM), covering non-carcinogenic risk (NCR) and carcinogenic risk (CR) for both adults and children. In this study, TCR was calculated based on three primary exposure pathways for both children and adults: oral ingestion, dermal contact, and inhalation of soil particles. Consistent with the USEPA framework, children are more vulnerable to these pathways due to their higher hand-to-mouth frequency, greater skin surface area relative to body weight, and higher respiratory rate per unit body weight compared to adults. The figures illustrate the probability distribution of the total health risk from multiple elements.

From Figure 5a, the mean CR values for both children and adults fell within the significant risk range (1 × 10⁻⁶ < TCR <1 × 10⁻⁴). Additionally, approximately 0.2% of children had TCR values exceeding the unacceptable threshold of 1 × 10⁻⁴, which implies that the carcinogenic risk in this area still requires attention from relevant authorities.

Figure 5

Figure 5. (a) Probability distribution of total carcinogenic risk (TCR) for adults and children, (b) probability distribution of non-carcinogenic risk (characterized by total hazard index, THI) for adults and children, and (c) contribution ratios of different pollution sources to ecological risks and human health risk in soils in the study area.

As observed in Figure 5b, the total health risk index (THI) for adults was below the risk threshold of one established by the United States EPA, indicating that the NCR faced by adults in the study area is within an acceptable range. Approximately 0.4% of children had NCR exceeding 1. Previous studies have suggested that when the 95th percentile of the hazard index (HI) is above 1, the NCR caused by toxic heavy metal and metalloid pollution is considered unacceptable.

From the perspective of element-specific NCR, for both adults and children, the NCR contributed by As was the highest, followed by Pb. The NCR of the remaining four heavy metals followed the descending order: Ni > Cu > Hg > Cd. In terms of element-specific CR, As and Cd posed relatively higher risks for both adults and children, while the CR of other elements followed the order: Pb > Ni.

It is evident from the above analysis that children in the study area face higher health risks than adults, and carcinogenic risk requires priority consideration. Specifically, the mean TCR for children (1.2 × 10⁻⁵) is significantly higher than that for adults (8.5 × 10⁻⁶). This difference is particularly significant in carcinogenic risk: the mean TCR of children (1.2 × 10⁻⁵) is 1.4 times that of adults (8.5 × 10⁻⁶), and the TCR of 0.2% of children exceeds the unacceptable threshold of 1 × 10⁻⁴, highlighting the priority of risk management for children as a sensitive population. Both values exceeded the USEPA notable risk threshold (1 × 10⁻⁶), and children’s TCR approached the unacceptable risk threshold (1 × 10⁻⁴) in 0.2% of samples, further justifying the need for prioritized risk control for children. This disparity was rooted in pediatric-specific toxicological traits. Consistent with the Exposure Factors Handbook of Chinese Population (Ministry of Environmental Protection MEP et al., 2016), children have higher soil ingestion rates and heavy metal absorption efficiency than adults, further explaining their elevated health risks.

This high vulnerability of children stems from physiological and behavioral traits. First, there are toxicological absorption differences. The absorption rate of Cd in children’s intestines (≈50%) is twice that of adults, and the underdeveloped blood-brain barrier leads to the easy accumulation of Pb and Hg in the central nervous system (Yao et al., 2022; Jomova et al., 2011). Second, behavioral factors amplify exposure. Huang et al. (2021) found children’s soil ingestion (≈200 mg/d) is 4× that of adults (≈50 mg/d) due to frequent hand-to-mouth contact, with higher respiratory/metabolic rates increasing exposure. Yao et al. (2022) further confirmed that children’s developing organs (nervous system, kidneys) have lower toxicity thresholds for As/Pb. This makes health damage risk 2–3× higher at the same dose. When combined with As’s status as an IARC Group 1 carcinogen, as noted by Li et al. (2023), this explains why As dominates the total health risk. Soil As also accounts for over 50% of exposure risk according to Li et al. (2023).

Due to physiological characteristics, heavy metal and metalloid pollutants are more easily absorbed through children’s skin. Special behavioral habits such as geophagy (soil ingestion) and pica may also increase health risks for children. Parents should supervise children to avoid ingesting heavy metals and metalloids contaminated soil and improve children’s hygiene practices, including frequent handwashing, to mitigate the adverse effects of soil heavy metals and metalloid on their health. Furthermore, As is the element that requires the most attention for managing health risks in the study area.

3.6.2 Source-oriented health risk assessment

Source-specific health risks were evaluated across four exposure scenarios: adult carcinogenic risk (CR), child CR, adult non-carcinogenic risk (NCR), and child NCR. Differences in health risks were observed among the four sources: among all scenarios, Factor 2 (agricultural source) posed the highest health risk, followed by Factor 1 (traffic source) (see Supplementary Material SI, Supplementary Figure S1).

When comparing source-specific NCR and CR between children and adults, children faced higher health risks than adults. The conclusion “concentration-oriented health risk assessment” was supported in Section 3.6.1. Therefore, children’s risk should be the focus of soil toxic metals and metalloids health risk assessments and serve as a key reference for policymakers when formulating control strategies.

For NCR from agricultural and traffic sources, approximately 0.1% of children’s NCR values did not exceed the unacceptable threshold. However, except for the coking plant industrial source, the average CR values of the other three sources exceeded the negligible risk threshold of 1 × 10⁻⁶. Statistical analysis showed that the probabilities of CR exceeding 1 × 10⁻⁶ for the four sources were 66.7% (traffic source), 70.4% (agricultural source), 37.8% (coking plant industrial source), and 56.7% (other factory industrial source), respectively. This indicates that the carcinogenic effects of individual sources cannot be ignored.

Notably, the CR values from agricultural and traffic sources were significantly higher than those from other sources, implying that heavy metal and metalloid pollution from these two sources has a substantial impact on the carcinogenic risk to residents and requires high attention from relevant authorities.

3.7 Priority sources and metals and metalloid

Conventional soil management strategies often prioritize remediation efforts based solely on the total concentration of pollutants or the mass contribution of their sources, as identified by methods like PMF. However, a pivotal finding of this study was the significant disconnect between this mass-based prioritization and a risk-based perspective. Our integrated source-oriented risk assessment framework revealed that the largest contributors to metal and metalloid concentration are not necessarily the primary drivers of ecological and human health risks. This paradigm shift was critical for optimizing resource allocation in pollution control. As shown in Figure 5c, each pollution source contributed differently to ecological risk and human health risk. Therefore, targeted mitigation of primary pollution sources was essential for protecting ecological security and human health.

This study identified the coking plant industrial source as the dominant contributor to ecological risk (36.06%), with Hg accounting for 54.26% of this contribution. Consistent with previous findings (Jiang et al., 2021; Zhong et al., 2020), Hg exhibited a significantly higher toxic response factor than other toxic metals and metalloid, coupled with high mobility that facilitates soil-water migration and food chain accumulation. These traits collectively amplified its ecological risk. Thus, the coking plant industrial source and Hg were confirmed as priority control targets for ecological risk mitigation in the study area.

In stark contrast, for human health risk, agricultural activities emerged as the primary concern, contributing 33.3% to the total carcinogenic risk and 31.5% to the non-carcinogenic risk, with notable contributions to Cd (51.32%) and As (32.95%) pollution. Importantly, the agricultural source’s high health risk contribution, surpassing that of the highest total mass contributor (other factory industrial source), highlights the critical role of TM toxicity coefficients in risk assessment (Li et al., 2023). Specifically, Ni (the dominant TM in other factory industrial sources) had low carcinogenic toxicity, whereas Cd and As exhibit strong hazardous properties: Cd impacts multiple organs/systems (Bernard, 2016), and As induces oxidative damage to lipids/proteins, leading to cancers (Jomova et al., 2011; Järup, 2003). This confirmed the agricultural source, Cd, and As priority targets for health risk management.

In practice, resource limitations mean risk-based strategies must accurately identify priority sources and pollutants. A key insight from this study was that the source with the highest total pollution should not be equated with the source with the highest ecological or health risk. This distinction improves the precision of risk assessment frameworks.

This study proposes a novel framework for prioritizing heavy metals and metalloid based on integrated ecological and health risk source analysis. This framework advances fundamental understanding of source-risk relationships in contaminated industrial-agricultural areas, providing a robust scientific basis for local governments to optimize soil heavy metal and metalloid risk control and guide human activities (e.g., industrial distribution, land use planning).

Consistent with the priority targets identified herein, existing remediation technologies further validate the feasibility of this framework: For Hg, biochar amendment (2%–5%) or thermal desorption (300 °C–400 °C) effectively reduces its bioavailability or total content (Jiang et al., 2021; Zhong et al., 2020); for Cd/As, immobilization (lime/phosphate, iron oxides) and source reduction (low-Cd fertilizers) mitigate their risks (Wang et al., 2021; Li et al., 2023). Such technologies aligned with the priority control logic proposed, reinforcing the framework’s practical relevance while maintaining focus on fundamental risk assessment advances.

4 Conclusion

This study developed an integrated framework combining GIS spatial analysis, PMF receptor modeling, and source-oriented ecological and human health risk assessments to systematically investigate soil heavy metal and metalloid pollution and source characteristics in a typical coking industrial area. A key innovation of this GIS-PMF-Monte Carlo integrated framework lies in establishing quantitative source-risk linkages, addressing the long-standing fragmented risk assessment issue in decommissioned industrial sites and providing a new paradigm for targeted pollution control.

The results revealed a significant disconnect between sources’ mass contributions and associated risks: ecological risk was primarily driven by Hg from coking activities, whereas human health risk was dominated by Cd and As from agricultural activities, highlighting the limitations of traditional total-concentration-based control strategies. Unlike traditional concentration-based assessments, this integrated source-risk framework identifies that “high mass contribution does not equal high risk”—e.g., the other industrial source (31.4% mass contribution) has lower health risk than the agricultural source (27.6% mass contribution) due to the high toxicity of Cd and As. Under resource constraints, source-oriented risk prioritization is recommended, focusing on Hg immobilization in coking hotspots and in-situ stabilization of Cd and As in farmlands, thereby enabling targeted policies and cost-effective interventions. This framework provides scientific guidance for environmental management and safe urban renewal.

Nevertheless, the study was limited by insufficient sampling coverage, incomplete consideration of metal and metalloid speciation and bioavailability, limited assessment of sensitive populations and multiple exposure pathways, and uncertainties associated with Monte Carlo simulations. Future research should address these limitations to further optimize risk assessment and control strategies.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions

YL: Investigation, Methodology, Visualization, Writing – original draft. JL: Investigation, Methodology, Visualization, Writing – original draft. PW: Conceptualization, Funding acquisition, Methodology, Supervision, Validation, Writing – review and editing. FW: Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was financially supported by the Natural Science Foundation of China (No. 42307369), Shandong Provincial Natural Science Foundation (ZR2025MS683), the Key Laboratory of Guangdong Higher Education Institutions of Northeast Guangdong New Functional Materials (2024KSYS021), University Engineering Technology Center of Guangdong (No. 2022GCZX007), Inorganic optical functional materials and application innovation team of Guangdong (No. 2023KCXTD033).

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Publisher’s note

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2025.1756860/full#supplementary-material

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