Investigation of the Impact of Coal Fires on Soil: A Case Study of the Wugong Coal Fire Area, Xinjiang, China

Abstract

This study focused on the Wugong coal fire area in the Zhunnan coalfield of Xinjiang, analyzing 41 soil samples extending from the fire center outward. The key parameters included pH, soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), available potassium (AK), various ions (Ca²⁺, Na⁺, Mg²⁺, SO₄²⁻, CO₃²⁻, HCO₃⁻, and Cl⁻), and heavy metal concentrations (As, Cr, Hg, Ni, Cd, Cu, Zn, and Pb). The primary objectives were to evaluate heavy metal pollution levels and potential ecological risks using the single factor pollution index (P_i), the Geo-accumulation index (I_Geo), Nemero’s pollution index (P_n), the pollution load index (PLI), and the ecological risk factor (E_rⁱ) and risk index (RI). Spatial distribution analysis indicated higher heavy metal concentrations in the southwestern and central regions. The heavy metals Cr, Ni, Cd, Cu, and Zn reached mild pollution levels, while Hg exhibited high pollution, with P_i, I_Geo, and P_n values of 3.27, 0.61, and 9.68, respectively. Hg (E_rⁱ = 111.07) and Cd (E_rⁱ = 45.91) emerged as the primary ecological risk factors. The overall ecological risk index (RI) of 184.98 indicated a moderate ecological risk. The results demonstrate that soils surrounding the coal fire zone are significantly impacted by coal fire, characterized by severe heavy metal contamination and nutrient deficiency.

1. Introduction

Coal fires occur as an eco-environmental and safety issue induced by coal mining activities [1,2,3,4]. Coal fires lead to loss of coal resources and cause negative impacts like air pollution, vegetation destruction, contamination of both surface and groundwater, land degradation, and other soil contamination [5,6,7]. The coal fires exacerbate the risk of surface subsidence, soil erosion, and land desertification in the surrounding areas [8]. Therefore, an investigation of the coal fire’s impact on soil is a necessary task. The continuous combustion of coal fires promotes the ongoing release of toxic metal elements from the coal, which then enter the surrounding soil of the mining area through processes such as air diffusion and ash deposition [9]. Due to their high toxicity, bio-accumulation potential, and non-degradability [10], the concentration of these heavy metals in the soil continues to rise, posing a threat to environmental quality and human health in the vicinity of coal fire areas [11]. Furthermore, pollutants concentrated in the soil around fire zones can become a source of groundwater contamination due to natural factors like rain, snow, and runoff, thereby increasing their ecological toxicity [12,13,14].

Recent studies mainly revealed that mining and associated activities discharge toxic metals into surrounding soils, leading to contamination of agricultural land, surface water, and riparian zones [15,16,17,18]. Zhang [19] utilized a linear regression model to analyze the pollution characteristics of heavy metals in the Zhundong mining area of Xinjiang, and the results indicated that mercury poses the high ecological risk. Dong [20] accurately depicted the spatial distribution of heavy metals using a semi-variogram model and ordinary kriging interpolation, revealing that the soil in the mining area is severely contaminated by Mn, Cr, and Ni. Lin [21] studied the levels of heavy metal pollution and ecological risks in the soil surrounding the Tongling typical mining area, identifying cadmium and mercury as the primary pollutants. In the study of heavy metals in soils around coal fire areas, Wang and Liang [16,22] measured the concentrations of mercury and other heavy metals in the soil surrounding the Wuda coalfield fire in Inner Mongolia. Zeng [23,24] previously studied the distribution of heavy metals in the soils of the Shuixigou and Daquanhu coalfield fire zones.

Furthermore, the combustion of coal fires significantly raises the temperature of the surrounding soil. High temperatures can lead to the decomposition and loss of soil organic matter [23,25], thereby reducing soil fertility. Additionally, the thermal effects of coal fires can alter the original structure of the soil in the fire zone, resulting in reduced soil moisture [26], deteriorated texture, weakened nutrient cycling, and uneven distribution. Ultimately, this leads to a decrease in the content of soil organic matter and essential nutrients such as nitrogen, phosphorus, and potassium [27,28]. The impact of combustion-induced subsidence could also contribute to the decline in total nitrogen and organic matter content in the soil [29]. However, to date, research on the effects of coal fire combustion on soil chemical properties, as well as its correlation with heavy metal distribution, has no detailed discussion, and detailed descriptions of quantitative methods are relatively scarce [30].

This study investigates the Wugong coal fire area in the Zhunnan coalfield of Xinjiang, where soil environmental quality faces multifaceted threats. The occurrence and development of coal fires are influenced by a variety of factors, including coal seam characteristics, mining technology, ventilation conditions, geological conditions, human activities, and external environment. The extent of soil disturbance from coal fires, the link between heavy metal distribution in surrounding soil and soil chemical properties, and ecological risk trends remain unclear, raising concerns about soil environmental quality. This research aims to clarify the impact of thermal effects from fire zones on soil chemical properties through soil sampling and analysis. We then examine the spatial distribution of heavy metals, explore the correlation between soil chemical properties and heavy metal distribution, and assess the pollution and ecological risk of heavy metals in the fire zone. This study seeks to support soil quality monitoring and ecological risk management of heavy metals in coal fire areas.

2. Materials and Methods

2.1. Study Area

The study area is located in the Wugong coal fire region of the Zhunnan coalfield in Xinjiang, situated at the foothills of the southern edge of the Junggar Basin, which falls within a temperate continental climate zone. The maximum elevation in the area is 1226 meters, while the minimum elevation is 730 m. The dominant wind direction is northwest, with an average wind speed of 2.4 m/s and a peak wind speed of 29 m/s. The coal seams generally trend east–west and dip to the south, exhibiting significant variations in dip angle due to faulting. In the northern section, the dip angle ranges from 60° to 75°, while the southern section is nearly vertical, with angles between 78° and 85° and a maximum combustion depth of 123 m.

2.2. Soil Sample Collection and Treatment

The Wugong coal fire area contains a total of four combustion zones, with the geographic coordinates of the centers of these zones serving as reference points. The sampling area is located at the center of this region, with latitude and longitude ranges of 88.15° to 88.16° E and 44.07° to 44.08° N. A total of 41 surface soil samples (0–20 cm) were collected in the study area, with GPS coordinates recorded for each location, as shown in Figure 1. During sampling, a wooden shovel was used, and each sample was created by mixing five subsamples (500 g each) taken from the four corners and the center of a 10 m × 10 m square. The mixed soil was then reduced using the quartering method to a final weight of 500 g and stored in polyethylene bags. The sampling method adhered to the “Technical Specifications for Soil Environmental Monitoring” (HJ/T 166-2004). All samples were free of plant roots, stones, and other impurities, and were air-dried in the bags. The soil samples were then sieved through 20 and 100 mesh nylon sieves to determine their chemical properties and heavy metal content.

2.3. Sample Analysis

2.3.1. Soil Chemical Properties

Soil pH was assessed using a potentiometric approach with a 5:1 soil-to-water ratio, following the method outlined in ISO 10390:2005 (Leici PHSJ-6, Shanghai Yidian Scientific Instrument Co., Ltd., Shanghai, China). Soil organic carbon (SOC) content was determined via the potassium dichromate heating method, as outlined in Section 6 of “Soil Testing” (NY/T 1121.6-2006). Total nitrogen (TN) was measured using the Kjeldahl method, based on the “Determination of Total Nitrogen in Soil (Semi-Micro Kjeldahl Method)” (NY/T 53-1987). Total phosphorus (TP) was analyzed using the sodium hydroxide fusion–molybdenum antimony colorimetric method, according to NY/T 88-1988. The primary soil ions (available potassium (AK), Ca²⁺, Na⁺, Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and CO₃²⁻) were examined following the “Methods for Agricultural Chemical Analysis of Soils”.

2.3.2. Heavy Metal

Following the “Determination of Copper, Zinc, Lead, Nickel, and Chromium in Soil and Sediments by Flame Atomic Absorption Spectrophotometry” (HJ491-2019), soil samples underwent microwave digestion using a mixed method involving hydrochloric acid, nitric acid, hydrofluoric acid, and hydrogen peroxide to determine the content of chromium (Cr), lead (Pb), nickel (Ni), cadmium (Cd), copper (Cu), and zinc (Zn). Mercury (Hg) and arsenic (As) concentrations were determined using atomic fluorescence spectrometry [31]. All reagents used were of analytical grade, and deionized water was employed throughout the process. To ensure test accuracy and precision, each sample included three replicates. Standard soil samples (ERM-S-510203) were utilized for quality control, maintaining measurement deviations within ±10%.

2.4. Methods for Evaluating Soil Heavy Metal Pollution

2.4.1. Single Factor Pollution Index Method

The single factor pollution index method evaluates pollution by comparing the measured concentration of heavy metals to a standard value. The level of pollution is determined by the magnitude of this ratio. The formula for calculation is as follows:

$$P_i={\displaystyle \frac{c_i}{s_i}}$$

(1)

Here, $P_i$ denotes the pollution index for the individual pollutant [32]; $C_i$ is the measured concentration of the pollutant (mg/kg); and S_i is the standard evaluation value for the pollutant (mg/kg). Referencing the soil background levels of heavy metals in Xinjiang, the pollution classification criteria are detailed in Table 1.

2.4.2. Nemero’s Pollution Index Method

This approach is widely used for evaluating pollution indices, taking into account the impact of both the maximum and average values of the single factor pollution index. It effectively reflects the pollution levels of various heavy metals in the soil environment. The formula is expressed as follows:

$$P_n=\sqrt{{\displaystyle \frac{{P_i^2}_{\max }+{P_i^2}_{avg}}{2}}}$$

(2)

Here, $P_n$ represents Nemero’s pollution index, which is dimensionless [33]; ${P_i^2}_{\max }$ is the highest value of the single factor pollution index, which is also dimensionless. ${P_i^2}_{avg}$ is the mean value of the single factor pollution index, which is dimensionless as well. The pollution classification can be found in Table 1.

Table 1. Specifications of the pollution index applied in pollution and risk assessment.

Index	Classification	Specification	References
Single factor pollution index (Pi )	Pi < 1	Non-pollution (I)	[34,35]
	1 ≤ Pi < 2	Mild pollution (II)
	2 ≤ Pi < 3	Moderate pollution (III)
	3 ≤ Pi < 4	High pollution (IV)
	4 ≤ Pi < 5	Heavy pollution (V)
Geo-accumulation index (IGeo)	Igeo < 0	Non-pollution (I)	[10]
	0 ≤ Igeo < 1	Light to moderate pollution (II)
	1 ≤ Igeo < 2	Moderate pollution (III)
	2 ≤ Igeo < 3	Moderate to heavy pollution (IV)
	3 ≤ Igeo < 4	Heavy pollution (V)
	4 ≤ Igeo < 5	Heavy to strong pollution (VI)
	Igeo ≥ 5	Extreme pollution (VII)
Nemero’s pollution index (Pn)	Pn < 0.7	Non-pollution (I)	[33]
	0.7 ≤ Pn < 1	Mild pollution (II)
	1 ≤ Pn < 2	Moderate Pollution (III)
	2 ≤ Pn < 3	High pollution (IV)
	Pn ≥ 3	Heavy pollution (V)
Pollution load index (PLI)	PLI < 1	Non-pollution (I)	[36,37]
	1 < PLI ≤ 2	Mild pollution (II)
	2 < PLI ≤ 3	Moderate pollution (III)
	PLI > 3	Heavy pollution (IV)
Ecological risk factor (Eri) and risk index (RI)	Eri < 40, RI < 150	Low risk (I)	[34,38,39]
	40 ≤ Eri < 80, 150 ≤ RI < 300	Moderate risk (II)
	80 ≤ Eri < 160, 300 ≤ RI < 600	Considerable risk (III)
	160 ≤ Eri < 320, RI ≥ 600	High risk (IV)
	Eri ≥ 320	Significantly high risk (V)

Table 1. Specifications of the pollution index applied in pollution and risk assessment.

Index	Classification	Specification	References
Single factor pollution index (Pi )	Pi < 1	Non-pollution (I)	[34,35]
	1 ≤ Pi < 2	Mild pollution (II)
	2 ≤ Pi < 3	Moderate pollution (III)
	3 ≤ Pi < 4	High pollution (IV)
	4 ≤ Pi < 5	Heavy pollution (V)
Geo-accumulation index (IGeo)	Igeo < 0	Non-pollution (I)	[10]
	0 ≤ Igeo < 1	Light to moderate pollution (II)
	1 ≤ Igeo < 2	Moderate pollution (III)
	2 ≤ Igeo < 3	Moderate to heavy pollution (IV)
	3 ≤ Igeo < 4	Heavy pollution (V)
	4 ≤ Igeo < 5	Heavy to strong pollution (VI)
	Igeo ≥ 5	Extreme pollution (VII)
Nemero’s pollution index (Pn)	Pn < 0.7	Non-pollution (I)	[33]
	0.7 ≤ Pn < 1	Mild pollution (II)
	1 ≤ Pn < 2	Moderate Pollution (III)
	2 ≤ Pn < 3	High pollution (IV)
	Pn ≥ 3	Heavy pollution (V)
Pollution load index (PLI)	PLI < 1	Non-pollution (I)	[36,37]
	1 < PLI ≤ 2	Mild pollution (II)
	2 < PLI ≤ 3	Moderate pollution (III)
	PLI > 3	Heavy pollution (IV)
Ecological risk factor (Eri) and risk index (RI)	Eri < 40, RI < 150	Low risk (I)	[34,38,39]
	40 ≤ Eri < 80, 150 ≤ RI < 300	Moderate risk (II)
	80 ≤ Eri < 160, 300 ≤ RI < 600	Considerable risk (III)
	160 ≤ Eri < 320, RI ≥ 600	High risk (IV)
	Eri ≥ 320	Significantly high risk (V)

2.4.3. Geo-Accumulation Index Method

The Geo-accumulation index, introduced by Muller in 1969 and often referred to as the Muller index, is extensively utilized by researchers worldwide to assess heavy metal contamination in soil. The formula for its calculation is as follows:

$$I_{Geo}=Lo{g}_2\left({\displaystyle \frac{c_i}{1.5B_i}}\right)$$

(3)

Here, I_Geo denotes the Geo-accumulation index [10]; C_i is the measured concentration of the soil element; and B_i is the background value of the soil element, referencing the heavy metal background levels in Xinjiang. Soil heavy metal pollution is categorized into seven levels based on the Geo-accumulation index, with the classification results detailed in Table 1.

2.4.4. Pollution Load Index

The pollution load index (PLI) is used to assess the degree of contamination from multiple heavy metal elements in the soil of the study area, considering their toxicity. The formula is expressed as follows:

$$PLI=\sqrt[n]{{\displaystyle \frac{c_1}{B_1}}\times {\displaystyle \frac{c_2}{B_2}}\times \cdots \times {\displaystyle \frac{c_n}{B_n}}}$$

(4)

Here, PLI represents the pollution load index at a sample point, which is dimensionless [36,37]; C_i is the measured concentration in mg·kg⁻¹; B_i is the chosen background value in mg·kg^–1; and n is the number of elements. The standards for pollution classification are detailed in Table 1.

2.4.5. Analysis of Potential Ecological Risks

The potential ecological risk index evaluates the ecological risk of specific pollutants by integrating toxicological, environmental, and ecological factors into an index-based grading system. This approach assesses the ecological risks of toxic metals by considering both the individual impacts of each metal and their combined effects. As a result, it offers a comprehensive evaluation of the pollution levels and potential risks of toxic metals in the soil. The formula for calculation is as follows:

$$E_r^i=T_r^i\times P_i$$

(5)

$$RI={\displaystyle {\sum }_{i=1}^n{E}_r^i}$$

(6)

Here, E_rⁱ is the potential ecological risk factor for toxic metal i [34], T_rⁱ is the toxicity response coefficient for toxic metal i [40], Pi denotes the pollution index for the individual pollutant, and RI is the potential ecological risk index [41]. Based on the standardized toxicity coefficients for toxic metals established by Hakansson, the evaluation standards for the ecological risk factor of E_rⁱ and the risk index RI are detailed in Table 1.

SPSS21 was used to calculate descriptive statistics such as mean, median, standard deviation, and variance for all variables. Graphs of the experimental data were created using Origin 2024 and used for data visualization and basic statistical analysis, and Suffer 23 software was used for spatial interpolation and mapping of soil properties.

3. Results and Discussion

3.1. Descriptive Statistics of Soil Chemical Properties

Descriptive statistics are tools used to summarize and describe data. In this study, we applied descriptive statistics to analyze soil pH, SOC, TN, TP, and soil ion composition (including Cl⁻, CO₃²⁻, HCO₃⁻, SO₄²⁻, AK, Ca²⁺, Mg²⁺, and Na⁺) in the study area (

Table 2. Descriptive statistics of the chemical properties of soil.

Index	Minimum	Maximum	Median	Mean (Measured)	Standard Deviation (SD)	Coefficient of Variation (CV, %)
AK	12.30	403.80	29.00	42.32	61.17	1.45
Ca2+	29.90	2472.00	769.10	763.58	679.69	0.89
Na+	22.90	3881.20	216.40	621.75	866.88	1.39
Mg2+	6.00	1105.70	88.00	131.39	183.35	1.40
CO32−	0.00	0.01	0.00	0.00	0.00	3.60
HCO3−	0.13	0.77	0.25	0.28	0.11	0.40
Cl−	0.00	1860.00	54.30	228.05	410.05	1.80
SO42−	67.80	8560.00	2380.00	3168.13	2873.19	0.91
TP	0.16	0.89	0.65	0.63	0.15	0.24
TN	0.15	13.41	2.65	3.88	3.23	0.83
SOC	1.60	199.99	10.53	24.10	37.42	1.55
pH	5.60	8.52	7.33	7.46	0.64	0.09

).

Soil pH impacts vegetation growth and is crucial for soil fertility, especially regarding nutrient availability and microbial activity. Studies show that pH significantly affects the mobility and solubility of toxic metal cations [42,43,44]. In the study area, the average soil pH is 7.46, with a range of 5.60 to 8.52 and a coefficient of variation of 0.09. In contrast, the soil pH in Urumqi is 8.3 [45], and the average soil pH in the Zhundong coalfield is 8.34 ± 0.54 [46]. In general, the soil pH around the fire area is slightly lower. The process of coal fire combustion could introduce acidic substances into the soil, resulting in a decrease in soil pH.

Soil total organic carbon (SOC) is a vital component of soil, enhancing nutrient availability and aiding in the formation of soil aggregates, which improves physical properties. SOC serves as a key indicator for ecological restoration [47]. The SOC levels in the study area are very low, ranging from 1.60 to 199.99 mg/kg, with an average of just 24.10 mg/kg. Based on the nutrient classification standards from the second national soil survey [48], this is classified as the sixth nutrient level (<6 g/kg). In the Zhundong coalfield area, soil organic carbon content ranges from 3.28 to 253.88 g/kg, with an average of 69.5 g/kg [46]. It can be inferred that the thermal effect of coal fires significantly reduces the organic matter content in soil.

Total nitrogen (TN) and total phosphorus (TP) are crucial for plant growth and development, helping to maintain ecological nutrient balance, promote root growth, and enhance soil quality, making them key indicators of soil fertility. In the study area, TN and TP levels are generally very low, averaging 3.88 mg/kg and 0.63 mg/kg, respectively, both classified within the sixth nutrient level (TN < 0.5 g/kg, TP < 0.2 g/kg), indicating very poor conditions. Soil available potassium is another vital indicator, as it is more easily absorbed and utilized by plants. The average available potassium content in the study area is 42.32 mg/kg, categorized as the fifth nutrient level (poor level), with a range from 12.30 to 403.80 mg/kg.

The ionic composition in soil, in particular the cations (Ca²⁺, Mg²⁺, K⁺, and Na⁺) and the anions (Cl⁻, SO₄²⁻, HCO₃⁻, and NO₃⁻), has a vital effect on soil fertility, pH, water retention, and crop growth. The contents of Na⁺, Ca²⁺, and SO₄²⁻ in soil were all higher than 500 mg/kg, which dramatically affects soil secondary salinization [49], especially SO₄²⁻, with an average concentration of 3168.13 mg/kg. The high concentration of SO₄²⁻ in the soil may originate from the decomposition of sulfur-containing minerals in the fire-affected soil. The low levels of SOC, TN, TP, and AK in the mining area soil limit the establishment of vegetation and sustainable productivity, indicating significant soil degradation due to underground coal combustion. Additionally, the coefficient of variation (CV) for all soil physical and chemical indices in the study area is below 10% (Table 2 and Figure 2), suggesting minimal spatial distribution differences and limited disturbance from human activities.

3.2. Characteristics of Heavy Metal Pollution

The chemical analysis of eight heavy metals from 41 soil samples in the study area is summarized in

Table 3. Descriptive statistics of the heavy metals.

Index	Pb	Ni	Cd	Cr	Cu	Zn	As	Hg
Minimum	12.75	22.2	0.1	47.46	23.58	59.64	4.64	0.02
Maximum	28.52	48.64	0.61	85.47	71.4	147.79	15.77	0.22
Median	16.82	27.51	0.16	61.41	31.88	79.43	8.3	0.05
Mean	17.24	29.08	0.18	62.67	34.68	83.38	8.45	0.06
Standard deviation (SD)	3.31	4.7	0.08	8.55	9.4	17.1	2.74	0.04
Coefficient of variation (CV, %)	13.64	27.12	20.51	45.96	19.22	16.15	73.27	32.43
Background value (Xinjiang) [51]	19.4	26.6	0.12	49.3	26.7	68.8	11.2	0.017
Toxic response factor	5	5	30	2	5	1	10	40

. The average concentrations of As, Hg, Cr, Cd, Ni, Pb, Zn, and Cu were 8.45, 0.06, 62.67, 0.18, 29.08, 17.24, 83.38, and 34.68 mg·kg⁻¹, respectively. Compared to the soil background values in Xinjiang, all metals except for Pb and As were slightly below the background levels. In contrast, Ni, Cd, Cr, Cu, Zn, and Hg exceeded the background values by 1.15, 1.50, 1.27, 1.30, 1.21, and 3.00 times, respectively. This suggests an accumulation of these six heavy metals in the soil, indicating a potential pollution risk. The coefficient of variation (CV) for the metals was as follows: As > Cr > Hg > Ni > Cd > Cu > Zn > Pb. Among them, As, Cr, and Hg exhibited moderate variation [50] with CVs of 73.27%, 45.96%, and 32.43%, respectively. This irregular spatial distribution suggests significant external influences.

According to previous studies, the reference values for heavy metals in surface soil are Cr (153 mg/kg) [52], As (22 mg/kg) [53], Ni (287.15 mg/kg) [54], Cu (36 mg/kg) [55], Hg (0.037 mg/kg) [56] Pb (85 mg/kg), Cd (0.15 mg/kg) [56] and Zn (50 mg/kg) [57]. These reference values are based on global standards from various regions. It is clear that background reference values can differ greatly between locations, making it difficult to establish a uniform assessment standard. Therefore, aligning with local environmental regulations is crucial.

The spatial distribution of soil heavy metal concentrations was created using Surfer software V15 (Figure 3). The analysis reveals substantial variations in the high-value areas of different elements. Excluding Pb and As, which are below background levels, Cr, Cu, and Ni exhibit a distinct patchy aggregation effect. Among these, the distributions of Cu and Ni are remarkably similar, with concentrations concentrated in the southwest (Figure 3b,c). Zn, Cd, and Hg also show some similarities in spatial distribution, with their high-value areas concentrated in a point-like distribution in the central parts (Figure 3e–h).

3.3. Correlation Analysis of Soil Chemical Properties and Heavy Metals

Soil nutrients and ions are not independent of one another; rather, they are interconnected and influence each other while following certain intrinsic patterns. The correlation between soil nutrients and the eight major ions is shown in Figure 4a. It is evident that the correlation between Ca²⁺ and SO₄²⁻ is the highest, reaching 0.89. This may be due to the release of sulfur from the coal seam in the form of hydrogen sulfide during coal combustion. When these hydrogen sulfide gases are not completely burned, they can produce sulfur, which can then be oxidized to sulfate ions in the presence of microorganisms, high temperatures, or alkaline conditions, significantly increasing its concentration in the soil. Additionally, in coal fire areas, minerals in the soil (such as gypsum or other calcium-containing minerals) may dissolve due to high temperatures or chemical reactions, releasing both calcium ions and sulfate ions. This phenomenon may lead to a simultaneous increase in the concentrations of both ions. Furthermore, soil pH is negatively correlated with Ca²⁺, Na⁺, Mg²⁺, Cl⁻, and SO₄²⁻ ions, while it demonstrates a strong positive correlation with HCO₃⁻, TN, and SOC content. Moreover, SOC content is significantly positively correlated with HCO₃⁻ and TN. This may be attributed to the thermal effects of coal fires, which cause soil organic matter to release organic acids during decomposition. These organic acids can react with minerals in the soil to form bicarbonate ions.

The soil’s chemical properties can affect the adsorption, migration, and availability of heavy metals [58,59]. To investigate the relationship between heavy metal distribution and soil chemical properties, a correlation analysis was performed. The correlations among the eight heavy metals are primarily positive, with correlation coefficients for As-Cd, Pb-Cd, and Pb-As all exceeding 0.5, indicating a strong positive correlation (p < 0.05) (Figure 4b). This suggests a significant link between these elements during their movement in environmental media, possibly pointing to a common source. Soil pH shows a positive correlation with the concentrations of Cr, Cu, Zn, and Hg in the soil (Figure 4c). Soil pH significantly influences the activity of heavy metals, making it a key factor in soil fertility and plant growth [60,61]. Moreover, soil pH affects the solubility of soil minerals and the adsorption behavior of various ions in both the soil solution and solid phase [61,62]. The activity of Cd and As is primarily affected by soil organic carbon (SOC). SOC can influence metal concentrations through various mechanisms; as its levels rise, it alters the soil colloidal structure and enhances adsorption. Figure 4d shows that the correlation between Cd and available potassium (AK) is 0.77. Additionally, Figure 3d and Figure 4d reveal that their distributions are similar. In areas impacted by fire, plants may be affected by both potassium and cadmium. While absorbing potassium, plants might also take up cadmium, especially when soil cadmium levels are high. This co-absorption could strengthen the correlation between these two elements.

3.4. Risk Assessment

3.4.1. Soil Heavy Metal Pollution Assessment

The single factor pollution index (P_i) results reveal that the average concentrations of heavy metals in the soil, from highest to lowest, are as follows: Hg (3.27) > Cd (1.53) > Cr (1.27) > Cu (1.30) > Zn (1.21) > Ni (1.09) > Pb (0.89) > As (0.75) (Figure 5a). Except for Pb and As, the average exceedance factors for Ni, Cd, Cr, Cu, Zn, and Hg are all above 1 (

Table 4. Heavy metal pollution and risk assessment by pollution indices.

Metal	Pb				Ni				Cd				Hg
Indices	Pi	IGeo	Pn	Eri	Pi	IGeo	Pn	Eri	Pi	IGeo	Pn	Eri	Pi	IGeo	Pn	Eri
Mean	0.89	−0.78	1.22	4.44	1.09	−0.39	1.51	5.77	1.27	−0.05	3.79	45.91	3.27	0.61	9.68	111.07
Median	0.87	−0.79	1.21	4.33	1.03	−0.46	1.49	5.46	1.25	−0.15	3.73	40.50	2.82	0.68	9.49	96.00
Minimum	0.66	−1.19	1.14	3.29	0.83	−0.77	1.42	4.40	0.96	−0.88	3.65	24.50	1.06	−0.74	9.31	36.00
Maximum	1.47	−0.03	1.47	7.35	1.83	0.36	1.83	9.65	1.73	1.77	5.10	153.00	13.12	2.89	13.12	446.00
SD	0.17	0.24	0.07	0.85	0.18	0.21	0.07	0.93	0.17	0.44	0.24	21.10	2.39	0.89	0.68	81.38
Metal	Cr				Cu				Zn				As
Indices	Pi	IGeo	Pn	Eri	Pi	IGeo	Pn	Eri	Pi	IGeo	Pn	Eri	Pi	IGeo	Pn	Eri
Mean	1.53	−0.25	1.52	2.54	1.30	−0.25	2.11	6.49	1.21	−0.33	1.75	1.21	0.75	−1.06	1.14	7.54
Median	1.35	−0.27	1.51	2.49	1.19	−0.33	2.07	5.97	1.15	−0.38	1.73	1.15	0.74	−1.02	1.13	7.41
Minimum	0.82	−0.64	1.40	1.93	0.88	−0.76	1.99	4.41	0.87	−0.79	1.64	0.87	0.41	−1.86	1.04	4.14
Maximum	5.10	0.21	1.73	3.47	2.67	0.83	2.67	13.36	2.15	0.52	2.15	2.15	1.41	−0.09	1.41	14.08
SD	0.70	0.19	0.07	0.35	0.35	0.34	0.13	1.76	0.25	0.27	0.10	0.25	0.24	0.45	0.09	2.45
	Mean			Median			Minimum			Maximum			Standard deviation (SD)
PLI	1.51			1.41			0.79			2.66			1.56
RI	184.97			163.31			79.54			649.06			109.07

), with exceedance rates at sampling locations surpassing 80% (Figure 5b). This indicates a considerable potential for pollution from these six heavy metals. Among them, Hg shows the most severe pollution, with an exceedance rate of 90.20%, surpassing the background value by 10.15 times, indicating high pollution levels. The next are Cd, Cr, Cu, Zn, and Ni, indicating a mild pollution level.

Based on the average Geo-accumulation index (I_geo) values (Figure 5d), the pollution levels of the eight heavy metals, from highest to lowest, are as follows: Hg (0.61) > Cd (−0.05) > Cr (−0.25) = Cu (−0.25) > Zn (−0.33) > Ni (−0.39) > Pb (−0.78) > As (−1.06). Hg is categorized as lightly polluted, with some sampling points showing moderate to heavy pollution, while the average I_geo values for the other seven heavy metals are all below 0, indicating no pollution. This suggests that Hg in the soil of the study area exhibits varying degrees of pollution.

When comparing the two pollution evaluation methods mentioned above, it becomes clear that while the single factor pollution index (P_i) and the Geo-accumulation index (I_geo) provide different results regarding pollution levels, they both consistently identify Hg as the primary contaminant in the soil. This consistency arises because P_i reflects the current pollution status directly, whereas I_geo considers both human impacts and natural factors, which can moderate the pollution assessment, resulting in I_geo values that are slightly lower than those of P_i [63]. Overall, Hg pollution in the study area’s soil is notably severe.

To gain a clearer understanding of the spatial patterns of the primary pollutants, Hg and Cd, in the soil of the study area, spatial distribution maps of their pollution indices (P_i) were generated using Surfer software (Figure 6). The spatial distribution of the two elements shows significant similarity, with the exceedance areas concentrated near the center of the fire zone. This indicates a close relationship between coal combustion activities and the elevated levels of mercury and cadmium. Furthermore, within the boxed area in the figure, the regions of mild pollution for Cd and high pollution for Hg overlap, suggesting a potential common source of contamination for these two elements.

The findings from Nemero’s pollution index (P_n, Figure 5c) reveal that P_n values range from 1.14 to 9.68 (Table 4), with an average of 2.86, indicating an overall high pollution level. Among the sampling locations, 62.5% exhibit moderate pollution, 12.5% show high pollution (Cu), and 25% indicate heavy pollution (Cd, Hg) (Table 4). The pollution load index (PLI) results range from 0.79 to 2.66, with an average of 1.51 (Table 4), suggesting a mild pollution level. Overall, the results from P_n and PLI differ, with the pollution assessment from P_n appearing more severe. This discrepancy arises because P_n emphasizes the concentration of the most significant pollutants (such as heavy metals) in the soil, allowing for a more accurate reflection of their direct impact on the environment. In contrast, PLI is based on the average levels of multiple pollutants, which may dilute the effects of certain severe contaminants.

Additionally, areas with mild or lower pollution levels are mainly concentrated in the upwind or sidewind zones of the fire area. Soil pollution levels in the downwind region are significantly higher than in the upwind region, likely due to the continuous accumulation of heavy metals driven by prevailing winds.

3.4.2. Potential Ecological Risk Assessment

According to the evaluation results of the potential ecological risk factor (E_rⁱ) (Table 4, Figure 7a), the total E_rⁱ scores for As, Cr, Zn, Ni, Pb, and Cu in the study area are all below 40, indicating low ecological risk. In contrast, the E_rⁱ values for Hg and Cd are 111.07 and 45.91, respectively, categorizing them as high ecological risk and moderate ecological risk factors. Among all sampling points, the proportion of sites with mild ecological risk for these two heavy metals is 10% for Hg and 45% for Cd, while the proportions of sites with moderate ecological risk are 27% for Hg and 7% for Cd. The proportion of sampling points with high ecological risk is approximately 48% to 49%. Notably, for Hg (Figure 7b), 12% of the sampling points are categorized as having high ecological risk, while 2% are classified as having significantly high ecological risk. The RI value stands at 184.98; this finding indicates that the ecological risk induced by heavy metals in the soil of the Wugong coal fire area has reached a moderate level.

Overall, Hg and Cd emerge as the primary ecological risk factors in the study area’s soil, with their risk levels reaching high and moderate risks, respectively, which is consistent with previous research [64]. The high ecological toxicity coefficients of Hg and Cd are the primary drivers behind the elevated ecological risk index. Moreover, most soil samples in the study area exhibited Hg and Cd concentrations exceeding background levels, consequently raising the regional risk index to a moderate risk level.

4. Conclusions

This study investigated the nutrient levels, concentrations of eight primary ions, heavy metal content, and their interrelationships in soil samples from the Wugong coal fire area, along with potential pollution and ecological risk assessments. The soil was predominantly alkaline, with extremely low levels of TN, TP, SOC, and AK. The average contents of Cu, Cr, Ni, Hg, Cd, and Zn exceeded the background values for heavy metals in local soils. Soil pH showed positive correlations with Cr, Cu, Zn, and Hg contents, while organic carbon primarily influenced the activity of Cd and As. The eight heavy metals demonstrate overall positive correlations, indicating a common pollution source. Heavy metals exhibited a patchy distribution associated with Cr, Cu, and Ni, with Zn, Cd, and Hg concentrated in specific locations. Pollution assessment revealed contamination of Hg, Cd, Cr, Cu, Zn, and Ni in the coal fire area soils, with Hg pollution being the highest, reaching a severe contamination level. The risk evaluation identified Hg and Cd as the primary ecological risk factors, with risk levels classified as high and moderate, respectively. The risk index (RI) assessment concluded that heavy metal ecological risks in the Wugong coal fire area have reached a moderate level. This research provides valuable insights for future coalfield environmental management, risk control, and ecological restoration.

The findings provide actionable insights for ecological restoration, where the risk hierarchy (Hg > Cd) and spatial contamination patterns enable prioritized remediation zoning. The pH–metal correlations and nutrient stoichiometric imbalances inform targeted strategies for alkaline soil amendment and organic fertilization. However, surface sampling limits understanding of vertical contaminant transport dynamics. Future studies should address deep soil profiling to quantify leaching risks, while extending to heavy metal speciation analysis and isotopic source tracing to optimize remediation protocols in coal fire-impacted area.