Heavy Metal Pollution and Ecological Risk Assessment of Rice Fields on the Northwest Bank of the Lower Yangtze River in HeXian County

Abstract

To investigate the contamination status, sources, spatial distribution, and health risks of heavy metals in paddy soils of Hexian County, 63 surface soil samples were analyzed for eight metals. Multiple pollution indices and multivariate statistical methods were applied to evaluate contamination levels and identify potential sources. Source apportionment was conducted using principal component analysis (PCA) combined with correlation analysis and spatial distribution characteristics. Results showed variable concentrations of heavy metals, with arsenic, copper, and lead exhibiting relatively higher single-factor pollution indices. The Nemerow pollution index (PN) ranged from 0.86 to 3.05 (mean = 1.16), indicating overall slight to moderate pollution, with localized areas showing higher pollution levels. The potential ecological risk index (RI) ranged from 28.06 to 66.45 (mean = 35.66), which was well below the threshold of 150, indicating a low ecological risk. Health risk assessment indicated negligible non-carcinogenic risks for both children and adults. Although carcinogenic risks remained within acceptable limits, children exhibited higher susceptibility, suggesting potential long-term concerns. Overall, these findings provide scientific evidence for targeted pollution control and risk-based agricultural management in Hexian County, and offer practical implications for mitigating heavy metal contamination and protecting agricultural sustainability in regions along the lower Yangtze River.

1. Introduction

Soil is one of the fundamental components of ecosystems and an indispensable resource for human survival and development. With the acceleration of industrialization and urbanization, pollutants enter the soil environment through multiple pathways, and the soil environment directly affects the quality of agricultural products, thereby influencing human well-being. Heavy metal pollution, in particular, has become one of the most widespread and severe environmental problems in China [1,2]. Heavy metals in soil accumulate in plants and cause a series of physiological changes [3]. At the same time, heavy metals in surface soils can easily enter the human body, posing direct threats to human health [4]. Therefore, conducting risk assessments of contaminated soils can reveal the potential impacts of soil pollution on human health and crop safety.

In addition to pollution levels, understanding the sources and occurrence forms of heavy metals in soils is essential for accurate risk assessment. Heavy metals in soils originate from both natural and anthropogenic sources. Natural inputs are primarily derived from parent material weathering, whereas anthropogenic contributions are associated with industrial emissions, agricultural activities (e.g., fertilizers and pesticides), traffic emissions, and atmospheric deposition [5]. In soils, heavy metals occur in multiple geochemical fractions—including exchangeable, carbonate-bound, Fe–Mn oxide-bound, organic matter-bound, and residual forms—which largely determine their mobility and bioavailability. Generally, exchangeable and carbonate-bound fractions are more bioavailable and pose higher environmental risks, whereas residual fractions are relatively stable [6].

Furthermore, the distribution, mobility, and bioavailability of heavy metals are strongly regulated by soil physicochemical properties such as pH, organic matter content, cation exchange capacity, clay minerals, and redox conditions. For example, acidic conditions can enhance metal solubility, while organic matter may either immobilize or mobilize metals depending on complexation mechanisms [7,8]. Therefore, a comprehensive understanding of these factors is critical for accurately interpreting contamination characteristics and associated health risks.

In recent years, numerous studies have investigated soil heavy metal contamination in different regions. For example, Yang Li et al. [9] analyzed and evaluated heavy metal pollution in Chinese farmland soils; Liu Hai et al. [10] assessed the characteristics of heavy metal contamination and health risks in the soil–crop system of the Yangtze River Basin (Anhui section); and Zhang Yi et al. [11] examined heavy metal pollution features and conducted evaluations in industrial zones along the Yangtze River Economic Belt. Previous studies indicate that the threat of heavy metals to China’s cultivated soils has become increasingly severe [12]. Consequently, long-term anthropogenic activities have resulted in sustained accumulation of heavy metals in grain and vegetable cultivation bases, leading to a decline in agricultural product quality [13]. In addition, previous studies have extensively investigated heavy metal contamination in agricultural soils across China, highlighting long-term accumulation and ecological risks [14]. However, integrated investigations of paddy soils remain limited, particularly those addressing pollution levels, spatial distribution, and associated health risks [15,16]. These gaps underscore the necessity of the present study, which provides a comprehensive evaluation of contamination status and associated risks in Hexian County.

The northwest bank of Hexian County in the lower reaches of the Yangtze River is an important rice production base in China, where the environmental quality of paddy soils directly affects the safety and quality of agricultural products [17,18]. In this context, the objectives of this study are threefold: (1) to investigate heavy metal concentrations in paddy soils of Hexian County; (2) to evaluate contamination levels using multiple pollution indices, multivariate statistical methods, and health risk assessment; and (3) to identify potential pollution sources and spatial distribution patterns. To achieve these aims, an integrated assessment framework is applied, combining pollution evaluation, source apportionment, spatial distribution analysis, and human health risk assessment. This approach systematically characterizes contamination levels and associated risks, thereby providing scientific evidence for targeted pollution control and risk-oriented agricultural management in Hexian County and offering broader implications for agricultural sustainability in the lower Yangtze River region. This study differs from previous work by providing an integrated assessment specifically for paddy soils in a major rice-producing region, combining contamination evaluation, spatial distribution analysis, source apportionment, and both non-carcinogenic and carcinogenic health risk assessment within a single framework.

2. Materials and Methods

2.1. Study Area

Hexian County is located in the eastern part of Anhui Province, on the west bank of the lower Yangtze River (118°04′–118°29′ E, 31°22′–32°03′ N). The county has a north–south elongated and east–west narrow terrain, gently sloping from northwest to southeast, with a total area of 1318.6 km². The region belongs to the northern subtropical humid monsoon climate zone, characterized by distinct seasons, abundant precipitation (annual average 1067 mm), and a mean annual temperature of approximately 15.8 °C, providing favorable conditions for agricultural production, particularly rice cultivation.

As part of the alluvial plain of the lower Yangtze River, the study area has experienced long-term fluvial deposition. Such depositional environments are dominated by sediment transport, deposition, and reworking processes, which contribute to the formation of soil parent materials and the redistribution of trace elements. The soils are mainly derived from Quaternary alluvial and lacustrine deposits, which generally reflect low natural background levels of heavy metals due to mixed lithological inputs from upstream catchments.

The region practices crop rotation, with rice cultivated during the summer season. During rice cultivation, irrigation water is abstracted directly from the Yangtze River, and pumping locations may vary spatially and seasonally, potentially introducing variability in the chemical composition of irrigation inputs. In addition to natural geochemical background contributions, long-term agricultural activities, fertilizer and pesticide application, and surrounding industrial and transportation emissions may further contribute to anthropogenic heavy metal inputs into the soils.

2.2. Sample Collection and Testing

The study area is dominated by paddy soils (Hydragric Anthrosols), which are typical anthropogenic soils formed under long-term rice cultivation with alternating flooding and drainage conditions. The soils exhibit a fine-textured profile, with silt accounting for 40–60%, clay for 25–40%, and sand for 10–20%, indicating a silty clay loam texture. Soil pH measured in the field ranges from 5 to 7, reflecting slightly acidic to neutral conditions typical of paddy soils in the lower Yangtze River region. Due to field limitations, organic matter content was not measured. Across the entire sampling region, soils are consistently classified as Hydragric Anthrosols, suggesting a relatively homogeneous soil type distribution within the study area. Therefore, the selected background area can reasonably represent the overall site conditions.

The sampling area covers approximately 0.40 km² (403,200 m²), representing the main rice-growing zone on the northwest bank of Hexian County (Figure 1). A total of 63 surface soil samples (0–20 cm depth) were collected during the summer of 2024. Each sampling site was defined as the center of a 40 m × 40 m plot. Within each plot, 11 subsamples were collected at approximately 8 m intervals using a diagonal sampling strategy. The subsamples were thoroughly mixed to form one composite sample (approximately 1 kg) and stored in polyethylene bags with detailed labeling. During sample preparation, the soil samples were air-dried and plant roots, stones, and anthropogenic debris were removed prior to laboratory analysis.

The samples were then air-dried in a ventilated and dry environment. After complete drying, they were stored in clean sample bags with detailed records. The dried samples were ground and sieved through a 200-mesh nylon sieve. The sieved soil powders were sealed in small dry bags, and pressed into pellets with boric acid backing using a manual tablet press. Each pellet was stored in a new dry bag with corresponding sample information and sent to the laboratory for X-ray fluorescence (XRF) spectrometry analysis to determine heavy metal concentrations.

Analytical accuracy was validated using certified reference materials (CRMs), with recovery rates ranging from 85% to 110%, within the recommended range (80–120%) for environmental geochemistry. Limits of detection (LODs) were calculated as three times the standard deviation of blank measurements (3σ). Precision was assessed by replicate analyses, with relative standard deviations (RSDs) controlled within 5–10%. Standard samples were re-analyzed after every 10–20 measurements to ensure data quality and instrument stability.

2.3. Evaluation Method

2.3.1. Single-Factor Pollution Index Method

Single-Factor Index Method [19,20] is a commonly used approach for environmental quality assessment. The basic principle is to compare the measured concentration of a single pollutant with its corresponding standard value. The advantage of this method lies in its simplicity of calculation, which enables rapid identification of the most prominent pollutant issue. The calculation is performed according to Equation (1).

$$P_{\mathcal{i}}={\displaystyle \frac{C_{\mathcal{i}}}{S_{\mathcal{i}}}}$$

(1)

In the above equation: $P_i$ represents the single-factor pollution index of soil heavy metal $\mathcal{i}$; $C_i$ denotes the measured concentration of the heavy metal (mg·kg⁻¹); and $S_i$ refers to the background value of the soil heavy metal. Evaluation criteria are shown in

Table 1. Classification criteria of the single-factor pollution index.

Level	Pi	Pollution Level
1	Pi ≤ 1	No Pollution
2	1 < Pi ≤ 2	Light Pollution
3	2 < Pi ≤ 3	Moderate Pollution
4	3 < Pi	Heavy Pollution

.

2.3.2. Nemerow Comprehensive Pollution Index Method

The Nemerow comprehensive index [21,22] is a weighted multi-factor environmental quality assessment method that takes into account both the maximum value and the average value. The Nemerow comprehensive pollution index provides an evaluation tool that balances local prominent issues with the overall pollution level. This method is suitable for preliminary screening and large-scale monitoring. It highlights the role of heavy metals with relatively severe pollution and offers a strong descriptive capacity for the pollution status involving multiple factors. The calculation is performed according to Equation (2).

$$P_N=\sqrt{{\displaystyle \frac{{{(P}_{\mathcal{i}max})}^2+{\left(P_{\mathcal{i}ave}\right)}^2}{2}}}$$

(2)

In the above equation: $P_N$ represents the Nemerow comprehensive pollution index; $P_{\mathcal{i}\max }$ denotes the maximum value of the single-factor pollution index; and $P_{\mathcal{i}\mathrm{ave}}$ refers to the average value of the soil single-factor pollution index. The evaluation criteria are shown in

Table 2. Nemerow comprehensive pollution index classification standard.

Level	Nemerow Comprehensive Index	Pollution Level
1	PN ≤ 0.7	Safe
2	0.7 < PN ≤ 1.0	Warning line
3	1.0 < PN ≤ 2.0	Light Pollution
4	2.0 < PN ≤ 3.0	Moderate Pollution
5	3.0 < PN	Heavy Pollution

.

2.3.3. Method of Geo-Accumulation Index

The Method of Geo-Accumulation Index [23], also known as the Muller index, is an environmental assessment method widely used to quantitatively evaluate the degree of heavy metal pollution in soils or sediments. This method not only considers the influence of background values caused by natural geological processes, but also fully accounts for the impact of human activities on heavy metal pollution. Its limitation lies in the relatively insufficient consideration of geographical spatial differences and biological availability. The calculation is performed according to Equation (3).

$$I_{geo}={\log }_2\left({\displaystyle \frac{C_n}{K{B}_n}}\right)$$

(3)

In the above equation: $C_n$ is the content of the element in the sediment; $B_n$ is the background value of the element in the sediment; and $K$ is the constant (taken as 1.5) to account for possible variations in background values caused by lithological differences. See

Table 3. Classification criteria of the method of geo-accumulation index.

Level	Geo-Accumulation Index	Pollution Level
0	Igeo < 0	No Pollution
1	0 ≤ Igeo < 1	No pollution to slight pollution
2	1 ≤ Igeo < 2	Slight pollution
3	2 ≤ Igeo < 3	Slight pollution to Moderate pollution
4	3 ≤ Igeo < 4	Moderate pollution
5	4 ≤ Igeo < 5	Moderate pollution to Strong pollution
6	5 ≤ Igeo	Strong pollution

for evaluation criteria.

2.3.4. Method of Potential Ecological Risk Index

The Method of Potential Ecological Risk Index [24,25], first proposed by Hakanson, comprehensively takes multiple factors into account and is now widely applied in investigations of the ecological risk degree of soil heavy metal pollution. The calculation is performed according to the following equation.

$$E_r^i=T_r^i{C}_f^i=T_r^i{\displaystyle \frac{C_d^i}{C_r^i}}$$

(4)

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

(5)

In the above equation, $E_r^i$ represents the potential ecological risk index of heavy metal i; $T_r^i$ is the toxic-response factor of heavy metal i, with values of 5, 2, 5, 10, 1, 1, 5, and 5 for Cu, Cr, Ni, As, Zn, Mn, Pb, and Co, respectively [26]; $C_f^i$ denotes the pollution coefficient of heavy metal i; $C_d^i$ is the measured concentration of heavy metal i; $C_r^i$ is the background value of heavy metal i; and $RI$ is the comprehensive potential ecological risk index of all heavy metals. Evaluation criteria are shown in

Table 4. Classification criteria of the potential ecological risk index.

${\mathit{E}}_{\mathit{r}}^{\mathit{i}}$	Risk Level Classification	RI	Risk Level Classification
$E_r^i$ ≤ 40	Low Ecological Risk	RI ≤ 150	Low Ecological Risk
40 < $E_r^i$ ≤ 80	Moderate Ecological Risk	150 < RI ≤ 300	Moderate Ecological Risk
80 < $E_r^i$ ≤ 160	Considerable Ecological Risk	300 < RI ≤ 600	Considerable Ecological Risk
160 < $E_r^i$ ≤ 320	High Ecological Risk	600 < RI	High Ecological Risk
320 < $E_r^i$	Extremely High Ecological Risk

.

2.3.5. Health Risk Rssessment Methods

Referring to the U.S. EPA health risk assessment method [27], and taking into account the specific conditions of China, the calculation formula is as follows:

$${ADD}_{oral}={\displaystyle \frac{C\times {IR}_{oral}\times CF\times EF\times ED}{BW\times AT}}$$

(6)

$${ADD}_{inh}={\displaystyle \frac{C\times {IR}_{inh}\times EF\times ED}{PEF\times BW\times AT}}$$

(7)

$${ADD}_{dermal}={\displaystyle \frac{C\times SA\times CF\times AF\times ABS\times EF\times ED}{BW\times AT}}$$

(8)

$$ADD={ADD}_{oral}+{ADD}_{inh}+{ADD}_{dermal}$$

(9)

In the above equation, ADD_oral, ADD_dermal, and ADD_inh represent the estimated average daily doses via hand–mouth ingestion, dermal contact, and inhalation pathways, respectively [mg·(kg·d)⁻¹]. C denotes the concentration of heavy metals in soil (mg·kg⁻¹). To ensure transparency and reproducibility, the definitions and standard values of the exposure parameters (IR, EF, ED, BW, SA, AF, ABS, PEF, etc.) have been explicitly reported in

Table 5. Exposure parameters and elemental absorption factors.

Variable	Adults	Children	Unit	Element	ABS
${IR}_{oral}$	100	200	mg/day	Co	0.001
${IR}_{inh}$	20	10	m3/day	Pb	0.03
EF	350	350	days/year	Cr	0.02
ED	30	6	years	As	0.03
BW	70	15	kg	Cu	0.001
AT (c)	25,550	25,550	days	Zn	0.001
AT (nc)	10,950	2190	days	Ni	0.02
SA	5700	2800	cm2	Mn	0.001
AF	0.07	0.2	mg/cm2
PEF	1.36 × 109	1.36 × 109	m3/kg
CF	1.00 × 10−6	1.00 × 10−6	—

. The parameter values are derived from the EPA Exposure Factors Handbook and the Technical Guidelines for Risk Assessment.

The non-carcinogenic and carcinogenic risk indices of heavy metals to human health are calculated as follows:

$$HI=\sum {HQ}_i=\sum {\displaystyle \frac{{ADD}_{ij}}{{RFD}_{ij}}}$$

(10)

$$TCR=\sum {CR}_i=\sum {ADD}_{ij}\times {SF}_{ij}$$

(11)

In the above equation, i denotes the number of metal elements, and j represents the exposure pathways (soil ingestion, dermal contact and inhalation). RFD refers to the reference dose of each metal element under different exposure pathways [mg·(kg·d)⁻¹], while SF denotes the slope factor of each metal element under different exposure pathways [mg·(kg·d)⁻¹].

3. Results and Discussion

3.1. Heavy Metal Concentrations

3.1.1. Statistical Analysis of Soil Heavy Metal Concentrations

Considering the regional characteristics of the study area, the soil background values of Nanjing City in the lower Yangtze River region were used as the reference baseline, based on Cheng et al. (2014) [28]. Based on

Table 6. Descriptive statistics of soil heavy metal concentrations.

Element	Min	Max	Mean	SD	CV	Soil Background Value
Pb	22.45	25.86	24.35	0.82	3.37%	25
Cr	36.66	99.5	53.18	10.44	19.63%	83
Cu	3.73	75.24	34.01	15.27	44.90%	31
Zn	44.44	90.04	59.29	7.15	12.06%	79
Mn	411.58	742.53	576.81	92.13	16.23%	639
As	11.12	66.15	18.18	7.41	40.76%	15
Co	6.14	11.99	10.13	1.46	14.4%	16
Ni	13.99	50.56	30.98	8.476	27.36%	36

, the average concentrations of eight heavy metals in the study area were obtained. By comparing with the background values, it was found that the average concentrations of Mn, Zn, Co, Ni, and Cr did not exceed the reference background values, whereas Pb, As, and Cu exceeded them, indicating that Pb, As, and Cu are already enriched in the study area.

The coefficient of variation (CV) is an important statistical indicator used to measure the relative dispersion of a dataset, and it is widely applied in soil science to analyze the spatial distribution of heavy metals in soils. A higher CV value indicates greater differences in heavy metal concentrations among soil samples and uneven distribution, usually suggesting significant external inputs or human activities during soil formation, weathering, and leaching processes. Conversely, a lower CV value implies more uniform concentrations, mainly influenced by natural factors [5,29].

In the study area, except for Pb with a CV of only 3.37% (weak variation), the CVs of other heavy metals were all greater than 10%. Specifically, As, Cr, Ni, Cu, Zn, Mn, and Co had CVs of 40.76%, 19.63%, 27.36%, 44.90%, 12.06%, 16.23%, and 14.4%, respectively, all showing moderate variation. This indicates that the fluctuations in soil heavy metal concentrations are not only related to geological structural activities but are also significantly influenced by human activities.

3.1.2. Spatial Distribution Characteristics

The soil heavy metal concentration data of Co, Pb, Cr, As, Ni, Cu, Zn, and Mn were analyzed using Kriging interpolation and mapped with Surfer 11 software. The spatial distribution of soil heavy metal concentrations in the study area is shown in Figure 2.

The spatial distribution of the eight heavy metals in the study area exhibits significant differences, presenting a heterogeneous pattern characterized by localized enrichment coexisting with large-scale low values. High-value areas of As are mainly concentrated in the eastern and central regions, with prominent enrichment features and the most uneven spatial distribution, making As the element with the greatest variability. Cr shows a distinct high-value zone in the southeast, with a continuous gradient decreasing from southeast to northwest, reflecting the influence of geological background. Ni forms a relatively continuous enrichment belt in the central part, with an intact spatial structure, possibly influenced by stratigraphic features or localized human activities. Cu displays patchy high-value zones with discontinuous enrichment, indicating certain anthropogenic inputs. In contrast, Pb, Mn, Zn, and Co exhibit relatively uniform spatial distributions, with overall low concentration levels and no significant high-value zones, showing typical natural background-dominated characteristics and suggesting weaker human influence.

In summary, As is the most enriched and unevenly distributed element among the eight heavy metals. The gradient or localized enrichment of Cr, Ni, and Cu reflects the combined effects of geological processes and human activities, while Pb, Mn, Zn, and Co demonstrate natural distribution patterns controlled by background values.

3.2. Pollution Assessment

3.2.1. Single-Factor and Nemerow Composite Pollution Index

According to Figure 3, the mean values of the eight heavy metals in the study area (Pi) ranked in descending order as follows: As (1.14) > Cu (1.10) > Pb (0.97) > Ni (0.95) > Mn (0.89) > Zn (0.75) > Cr (0.64) > Co (0.63). Among them, As and Cu showed only slight pollution, while Pb, Mn, Zn, Cr, Ni, and Co were all classified as non-polluted. For Cu, 4.76% of sampling points were moderately polluted, 53.38% were slightly polluted, and the remaining 42.86% were non-polluted. For As, 1.59% of sampling points were heavily polluted, 3.17% moderately polluted, 57.14% slightly polluted, and 38.10% non-polluted. Pb, Ni, and Cr were detected only at slight or non-pollution levels, with proportions of Pb (20.63% slight, 79.37% non-polluted), Ni (25.40% slight, 74.60% non-polluted), Cr (1.59% slight, 98.41% non-polluted), Zn (1.59% slight, 98.41% non-polluted), and Mn (23.81% slight, 76.19% non-polluted). Co was entirely non-polluted. Further calculation showed that PN ranged from 0.86 to 3.05, with a mean of 1.16.

The single-factor pollution index results indicate that the overall pollution level of the eight heavy metals in the study area is relatively low, with only As and Cu showing slight pollution characteristics, while the other elements remain in the non-polluted category. The degree of Cu pollution varies spatially, with a few sampling points reaching moderate pollution. Although As generally shows low pollution levels, isolated heavily polluted points exist, suggesting potential localized pollution sources. Pb, Ni, Cr, Zn, Mn, and Co are predominantly non-polluted, with only a few points showing slight pollution, indicating that these elements are mainly controlled by natural background values and are less affected by human activities. The calculated comprehensive pollution index (PN) ranged from 0.86 to 3.05, with a mean of 1.16. The minimum PN value below 1 indicates that some areas are non-polluted, while the maximum value exceeding 3 suggests that certain sites have reached moderate pollution levels, posing potential environmental risks. Although a few sampling points show relatively high PN values, the overall mean suggests that the study area remains at a slight pollution level, with no large-scale significant pollution zones. Overall, the environmental quality of the region is generally good, but localized enrichment of As and Cu requires close attention to prevent further expansion of potential pollution risks.

3.2.2. Geo-Accumulation Index Evaluation

From Figure 4, the distribution of geo-accumulation indices for each element was obtained. The ranking of the eight heavy metals is: As > Pb > Cu > Mn > Ni > Zn > Cr > Co. All eight heavy metals show a geo-accumulation index corresponding to a non-polluted level, indicating that the regional soil has not been significantly affected by external inputs. Further analysis of sampling point distribution shows that 19.05% of Cu sampling points exhibit slight pollution, while the remaining 80.95% are non-polluted; only 1.59% of As sampling points show slight pollution, with the other 98.31% being non-polluted. Except for As and Cu, the other elements (Pb, Ni, Zn, Mn, Cr, and Co) are all at non-polluted levels across all sampling points, with no cases of slight or higher pollution. Overall, the geo-accumulation index evaluation results indicate that the accumulation levels of heavy metals in the study area are generally low, with only minor enrichment observed at a few sites, and the regional environmental quality remains good.

3.2.3. Potential Ecological Risk Index

The calculation results of the potential ecological risk index for individual heavy metals ($E_r^i$) in the study area are shown in Figure 5. The mean values of $E_r^i$ for the eight heavy metals are ranked as follows: As (11.36) > Cu (5.48) > Pb (4.87) > Mn (4.44) > Ni (4.30) > Co (3.16) > Cr (1.28) > Zn (0.75). All elements have $E_r^i$ values far below the slight risk threshold ($E_r^i$ < 40). This indicates that the potential threat of individual heavy metals to the ecosystem is weak, with As contributing the highest risk, though still within the slight risk level.

The comprehensive potential ecological risk index (RI) fluctuates between 28.06 and 66.45, with an average of 35.66. All sampling points fall into the slight ecological risk category, suggesting that the overall ecological risk in the study area is low and no significant ecological hazards are present. Overall, the ecological risk of heavy metals in the study area is mainly influenced by As and Cu, but the risk level remains limited. The ecological risks of the other elements are extremely low, indicating that the regional ecological environment quality is generally safe and stable.

3.2.4. Analysis of Assessment Results

In the study area, the average concentrations of Mn, Zn, Co, Ni, and Cr do not exceed the regional soil background values, whereas the average concentrations of Pb, As, and Cu are higher than the background values, indicating varying degrees of enrichment. The coefficient of variation (CV) shows that, except for Pb (3.37%), which exhibits weak variability, the other elements display moderate variability. The single-factor pollution index (P_i) further reveals that only As and Cu exhibit slight pollution at certain sampling points, while the other elements remain within the non-polluted category. Cu shows considerable spatial variation in pollution levels, with several sampling points reaching moderate pollution, whereas As presents isolated high-value sites, suggesting potential localized pollution sources.

The Nemerow composite pollution index (P_N) ranges from 0.86 to 3.05, with a mean value of 1.16, indicating that the study area is generally at a slight pollution level. Only a few sampling points reach moderate pollution, and no large-scale high-pollution zones are observed. The geo-accumulation index (I_geo) indicates that most heavy metals fall within the non-polluted level (I_geo ≤ 0), while As and Cu show slight accumulation (I_geo > 0) at a few sites, suggesting that the regional soils have not been substantially affected by external heavy-metal inputs.

The potential ecological risk index ($E_r^i$) indicates that the risk values of all elements are far below the threshold for slight ecological risk ($E_r^i$ < 40). Although As contributes the highest individual ecological risk, it still remains within the low-risk range. The comprehensive ecological risk index (RI) ranges from 28.06 to 66.45, with a mean of 35.66, and all sampling points fall within the low ecological risk category according to the Hakanson classification.

Overall, the study area exhibits low levels of heavy-metal pollution and limited ecological risk, suggesting generally stable environmental conditions. However, the localized enrichment of As and Cu warrants continued monitoring to prevent potential expansion of pollution sources.

3.3. Source Analysis

3.3.1. Correlation Analysis of Soil Heavy Metals

Correlation analysis is a widely applied method for evaluating the strength of relationships between two or more correlated variables. Heavy metal elements with significant correlations often share the same or similar sources, which can be used to infer the origins of soil heavy metals [30,31].

Normality of the data was assessed using the Shapiro–Wilk test. The results indicated that Cr, Mn, Zn, As, and Co significantly deviated from normal distribution and were therefore transformed using natural logarithms (ln), whereas Ni, Cu, and Pb were retained in their original form. Subsequently, all variables were standardized using z-scores to eliminate scale differences and ensure the applicability of PCA. The results of the normality test are presented in

Table 7. Results of Shapiro–Wilk normality test and descriptive statistics of soil heavy metals.

Variable	Sample Size	Mean	SD	Skewness	Kurtosis
Ni	63	30.988	30.988	8.544	0.018
Cu	63	34.006	34.006	15.392	0.671
Pb	63	24.353	0.830	−0.217	−0.531
Cr(ln)	63	3.957	0.179	0.946	1.842
Mn(ln)	63	6.329	0.163	0.073	−1.117
Zn(ln)	63	4.076	0.116	0.638	2.256
As(ln)	63	2.852	0.282	1.936	7.586
Co(ln)	63	2.304	0.157	−1.066	0.682

.

Pearson correlation coefficients revealed several significant associations among the studied heavy metals (

Table 8. Pearson correlation coefficients of soil heavy metal concentrations in the study area.

Element	Ni	Cu	Pb	Cr	Mn	Zn	As	Co
Ni	1
Cu	0.544 **	1
Pb	0.175	−0.013	1
Cr	0.271 *	−0.083	0.325 **	1
Mn	0.114	−0.455 **	0.202	0.540 **	1
Zn	0.232	0.087	0.462 **	0.338 **	0.304 *	1
As	−0.074	0.209	0.126	−0.311 *	−0.321 *	−0.149	1
Co	0.429 **	0.112	0.644 **	0.482 **	0.439 **	0.522 **	−0.163	1

* p < 0.05; ** p < 0.01.

). Ni exhibited strong positive correlations with Cu (r = 0.544, p < 0.01) and Co (r = 0.429, p < 0.01), suggesting a common geochemical source or similar anthropogenic inputs. Pb showed a strong correlation with Co (r = 0.644, p < 0.01) and a moderate correlation with Zn (r = 0.462, p < 0.01), indicating possible co-occurrence from industrial activities or traffic emissions. Cr was significantly correlated with Mn (r = 0.540, p < 0.01) and Co (r = 0.482, p < 0.01), suggesting similar geochemical behavior or shared sources. In addition, Co exhibited significant correlations with multiple elements, including Pb (r = 0.644, p < 0.01), Zn (r = 0.522, p < 0.01), Cr (r = 0.482, p < 0.01), and Mn (r = 0.439, p < 0.01), indicating that it may act as an important indicator of mixed pollution sources. In contrast, As showed significant negative correlations with Cr (r = –0.311, p < 0.01) and Mn (r = –0.321, p < 0.05), implying distinct sources or different geochemical pathways. This suggests that As may be more influenced by agricultural inputs or natural background processes, whereas Cr and Mn are more associated with industrial emissions or soil-forming processes.

Overall, most metals showed positive correlations, suggesting the influence of mixed anthropogenic sources such as traffic emissions and industrial activities. These results provide a basis for further source identification using PCA.

3.3.2. Principal Component Analysis

Principal component analysis (PCA) is an important statistical method used to transform multi-indicator problems into fewer comprehensive indicators. Its core idea is to apply a linear transformation to convert multiple potentially highly correlated variables into new, mutually independent variables, namely principal components [32]. By conducting PCA on soil data, several key influencing factors can be identified, providing a basis for evaluating soil heavy metal concentrations.

All variables were standardized using z-scores prior to PCA, ensuring comparability across variables with different units and scales. The applicability of PCA was determined using the KMO measure and Bartlett’s test of sphericity in SPSS 27 software. The KMO value (0.608 > 0.5) and the significance probability of Bartlett’s test (0.000 < 0.05) indicate strong partial correlations among variables, confirming that PCA is suitable. In this study, three principal components were extracted, with a cumulative variance explanation of 73.71%.

As shown in

Table 9. Rotated principal component matrix of soil heavy metals in the study area.

Element	Principal Component
	PC1	PC2	PC3
Ni	0.479	0.605	−0.451
Cu	−0.010	0.900	−0.278
Pb	0.657	0.189	0.598
Cr	0.738	−0.195	−0.198
Mn	0.654	−0.546	−0.076
Zn	0.694	0.127	0.174
As	−0.325	0.446	0.643
Co	0.860	0.178	0.122
Eigenvalue	2.961	1.807	1.069
Variance Explained (%)	37.011	22.430	14.271
Cumulative Variance Explained (%)	37.011	59.441	73.712

, PC1 shows high loadings for Co, Cr, Zn, Pb, and Mn. These elements may originate from a combination of natural geochemical background and anthropogenic inputs. Co, Zn, and Pb are generally associated with human activities such as industrial emissions and traffic-related deposition, while Cr and Mn may be influenced by both lithogenic sources and anthropogenic disturbances. Therefore, PC1 likely represents a mixed source influenced by both natural background and anthropogenic activities related to industrial and traffic emissions.

PC2 is strongly loaded by Cu and Ni. These elements are typically associated with anthropogenic activities, particularly industrial processes and combustion-related emissions. Their co-occurrence indicates a common anthropogenic influence in the study area. Therefore, PC2 likely represents an anthropogenic source associated with combustion-related and traffic-related processes, which commonly contribute Cu and Ni in environmental systems.

PC3 is dominated by As, forming a single-element component. As may be derived from both natural geological sources and anthropogenic activities such as industrial emissions and combustion processes. Therefore, PC3 likely reflects As enrichment influenced by a combination of geogenic background and human activities.

Overall, the PCA results suggest that the studied heavy metals are influenced by both natural and anthropogenic sources. However, PCA alone cannot definitively identify specific emission sources. Further receptor modeling approaches would be required for more accurate source apportionment.

3.3.3. Cluster Analysis of Heavy Metal Elements

All variables were standardized using z-scores prior to R-mode hierarchical cluster analysis to ensure comparability among variables with different units and scales.

As shown in Figure 6, Co, Pb, Zn, Cr, and Mn are grouped into the first cluster, suggesting similar environmental behaviors and possible shared influencing factors. These elements may be affected by a combination of natural geochemical background and anthropogenic inputs, particularly industrial activities and traffic-related emissions.

Cu and Ni form the second cluster, indicating similar distribution patterns and a potential association with anthropogenic activities such as industrial processes and combustion-related emissions.

As forms an independent cluster, suggesting distinct geochemical behavior compared with other elements and possible different influencing factors.

Overall, the clustering results are broadly consistent with the PCA findings, indicating similar grouping patterns among the studied elements. PC1 (Co, Cr, Zn, Pb, Mn) in PCA corresponds well with the first cluster in HCA, suggesting comparable environmental behaviors and potential shared influencing factors. PC2 (Cu, Ni) is also consistent with the second cluster, further supporting their similar distribution characteristics. The independent behavior of As in PC3 aligns with its separate cluster in HCA.

Although Pb is primarily associated with PC1, it also shows minor cross-loading in PCA, suggesting possible mixed influence. However, its clustering behavior is more consistent with the PC1 group, indicating that its dominant influence is similar to Co, Cr, Zn, and Mn.

Considering the spatial characteristics of the study area, the clustered patterns identified by HCA correspond well to the potential pollution sources present in Hexian County. The first cluster (Co, Cr, Zn, Pb, Mn) is associated with industrial and traffic-related activities, the second cluster (Cu, Ni) reflects traffic emissions and river-related inputs, and the independent clustering of As corresponds to agricultural inputs and possible riverfront industrial emissions.

3.3.4. Verification of Pollution Sources

To further verify the potential sources represented by the clustered patterns, the spatial distribution of heavy metals was examined in relation to local industrial, transportation, agricultural, and river-related activities. Given the small spatial extent of the sampling area (0.40 km²) and its immediate surroundings, only nearby sources are expected to exert direct influence, whereas more distant sources mainly contribute to regional background levels. Hexian County is located in eastern Anhui Province on the western bank of the lower Yangtze River, where multiple anthropogenic activities—including industry, transportation, agriculture, and river-related operations—coexist, with their influence on the study area varying substantially depending on distance and transport mechanisms.

Transportation activities represent one of the strongest direct anthropogenic influences. The study area lies within 10 m of a waterproof embankment that serves as the sole access route for vehicles disembarking from the Yangtze River ferry into Hexian County. Frequent acceleration, braking, and idling along this corridor are likely to release Cu, Ni, Pb, and Zn. Spatial distribution patterns show elevated Pb, Zn, and Ni concentrations along this traffic route, consistent with the Cu–Ni association identified in PC2 of the PCA and supported by the Cu–Ni and Pb–Zn correlations in the Pearson analysis. These findings indicate that traffic-related emissions constitute a major direct source affecting the study area.

Agricultural activities also exert a significant direct influence. The study area is dominated by paddy fields subjected to long-term fertilizer and pesticide application. Certain herbicides and historically used pesticides contained As, and irrigation water or agricultural runoff may further introduce As into soils. The elevated As concentrations observed in the eastern part of the study area align with its independent loading in PC3 of the PCA and its separate clustering in the HCA, suggesting that agricultural inputs are a major contributor to As enrichment.

River-related processes exert a direct to moderate influence on soil heavy metal distribution. Irrigation water is directly extracted from the Yangtze River, and temporal variations in pumping locations and seasonal hydrological conditions may introduce Ni, Pb, and As into the soils. In addition, a port and ship-fuel station located approximately 5 km downstream may contribute Ni and Pb through atmospheric deposition and river-mediated transport. These influences correspond to the Cu–Ni association and partially overlap with the As-related component identified in multivariate analyses.

Nearby industrial and commercial facilities exert a moderate influence. Within approximately 5 km of the study area, several facilities—including a food-processing plant, plantations, and recreational botanical gardens—may contribute limited emissions or wastewater inputs. However, their influence is weaker compared with transportation and irrigation-related sources and mainly contributes to background levels of certain heavy metals.

Regional industrial sources represent background-level influences. A large steel plant located on the opposite bank of the Yangtze River may release Pb, Zn, and As through atmospheric transport and flue-gas deposition. However, due to the distance and the presence of the river barrier, its impact on the study area is attenuated and primarily reflected in regional background concentrations rather than direct local contamination.

3.4. Health Risk Assessment

3.4.1. Exposure Characteristics

Based on the established health risk assessment model and the measured concentrations of heavy metals in farmland soils, the average daily doses (ADD) for adults and children through three exposure pathways were calculated. As shown in

Table 10. Average daily exposure (ADD) of soil heavy metals for adults and children in the study area.

Element	ADDinh		ADDoral		ADDdermal		ADD
	Adults	Children	Adults	Children	Adults	Children	Adults	Children
Co	2.04 × 10−9	4.76 × 10−9	1.39 × 10−5	1.29 × 10−4	5.54 × 10−8	3.63 × 10−7	1.39 × 10−5	1.30 × 10−4
Pb	4.91 × 10−9	1.14 × 10−8	3.34 × 10−5	3.11 × 10−4	3.99 × 10−6	2.62 × 10−5	3.74 × 10−5	3.38 × 10−4
Cr	1.07 × 10−8	2.50 × 10−8	7.28 × 10−5	6.80 × 10−4	5.81 × 10−6	3.81 × 10−5	7.87 × 10−5	7.18 × 10−4
Zn	1.19 × 10−8	2.79 × 10−8	8.12 × 10−5	7.58 × 10−4	3.24 × 10−7	2.12 × 10−6	8.16 × 10−5	7.60 × 10−4
Cu	6.85 × 10−9	1.60 × 10−8	4.66 × 10−5	4.35 × 10−4	1.86 × 10−7	1.22 × 10−6	4.68 × 10−5	4.36 × 10−4
Mn	1.14 × 10−7	2.67 × 10−7	7.78 × 10−4	7.26 × 10−3	3.10 × 10−6	2.03 × 10−5	7.81 × 10−4	7.28 × 10−3
As	3.66 × 10−9	8.55 × 10−9	2.49 × 10−5	2.32 × 10−4	2.98 × 10−6	1.95 × 10−5	2.79 × 10−5	2.52 × 10−4
Ni	6.24 × 10−9	1.46 × 10−8	4.24 × 10−5	3.96 × 10−4	3.39 × 10−6	2.22 × 10−5	4.58 × 10−5	4.18 × 10−4
As(c)	1.57 × 10−9	7.33 × 10−10	1.07 × 10−5	1.99 × 10−5	1.28 × 10−6	1.67 × 10−6	1.20 × 10−5	2.16 × 10−5
Cr(c)	4.59 × 10−9	2.14 × 10−9	3.12 × 10−5	5.83 × 10−5	2.49 × 10−6	3.27 × 10−6	3.37 × 10−5	6.16 × 10−5

, substantial differences exist across both exposure pathways and population groups.

Children exhibit significantly higher ADD values than adults for all metals and exposure pathways. In most cases, children’s ADD values are 5–10 times higher than those of adults, reflecting their lower body weight, higher soil ingestion rates, and more frequent hand-to-mouth behavior. This indicates that children are a more sensitive population with respect to soil heavy metal exposure.

Oral ingestion is the dominant exposure pathway for all metals. For example, the Oral ingestion ADD of Zn and Mn in children reaches 10⁻³–10⁻², far exceeding the corresponding inhalation values (10⁻⁹–10⁻⁸) and dermal values (10⁻⁶–10⁻⁵). Inhalation contributes negligibly to total exposure, while dermal contact provides a secondary but non-negligible contribution for elements such as Cr, Ni, and Mn.

The highest exposure levels are observed for Mn and Zn, followed by Cr, Cu, and Ni, while As and Pb show comparatively lower ADD values. These differences are consistent with the measured soil concentrations and the dominance of ingestion exposure. The carcinogenic exposure doses of As and Cr [As(c) and Cr(c)] are also substantially higher in children—approximately 8–10 times those of adults—reflecting the same population-related differences observed for non-carcinogenic exposure. These elevated carcinogenic exposure doses highlight the need for careful evaluation in subsequent carcinogenic risk assessment.

Overall, soil heavy metal exposure in the study area is primarily driven by ingestion, with children experiencing substantially higher exposure than adults. Mn and Zn exhibit the highest ADD values, while Cr, Cu, and Ni show moderate levels. Although As and Pb present lower exposure levels, their toxicological significance warrants continued attention. Carcinogenic exposure of As and Cr also requires further assessment due to elevated levels in children and their relevance to long-term health risk.

According to the classification of carcinogenicity to humans published by the International Agency for Research on Cancer (IARC) [33], the seven heavy metals examined in this study (Cr, Co, Ni, Pb, Hg, Zn, Cu) pose non-carcinogenic risks to humans. In addition, Cr, Ni, and Pb also present carcinogenic risks. Therefore, carcinogenic risk assessment was conducted for Cr, Ni, and Pb, while non-carcinogenic risk assessment was performed for all seven heavy metals. The specific calculation parameters are shown in

Table 11. Reference dose (RfD) and slope factor (SF) of heavy metals via different exposure pathways [34].

Element	RfDoral	RfDinh	RfDdermal	SForal	SFinh	SFdermal
Co	3.00 × 10−2	2.86 × 10−5	3.00 × 10−4	—	—	—
Pb	3.50 × 10−3	3.52 × 10−3	5.25 × 10−4	—	—	—
Cr	3.00 × 10−3	2.86 × 10−5	6.00 × 10−5	8.50 × 10−3	42.00	0
As	3.00 × 10−4	1.23 × 10−4	1.23 × 10−4	1.50	15.10	3.66
Cu	4.00 × 10−2	4.00 × 10−2	1.20 × 10−2	—	—	—
Zn	3.00 × 10−1	3.00 × 10−1	6.00 × 10−2	—	—	—
Ni	2.00 × 10−2	2.06 × 10−2	5.40 × 10−3	—	—	—

.

3.4.2. Non-Carcinogenic Risk

Non-carcinogenic hazard to humans is expressed using the hazard quotient (HQ). In general, when HQ < 1, the exposure pathway is considered unlikely to cause adverse health effects; when HQ ≥ 1, the pathway may pose potential health risks. The hazard index (HI) is used to evaluate the overall non-carcinogenic risk from multiple pollutants or multiple exposure pathways. When HQ or HI > 1, non-carcinogenic health risks are indicated, and the level of hazard increases proportionally with HQ or HI [35].

As shown in

Table 12. Non-carcinogenic health risk assessment of soil heavy metals for adults and children.

Element	HQoral		HQinh		HQdermal		HI
	Adults	Children	Adults	Children	Adults	Children	Adults	Children
Co	4.63 × 10−4	4.30 × 10−3	7.13 × 10−5	1.66 × 10−4	1.85 × 10−4	1.21 × 10−3	7.19 × 10−4	5.68 × 10−3
Pb	9.54 × 10−3	8.89 × 10−2	1.39 × 10−6	3.24 × 10−6	7.60 × 10−3	4.99 × 10−2	1.71 × 10−2	1.39 × 10−1
Cr	2.43 × 10−2	2.27 × 10−1	3.74 × 10−4	8.74 × 10−4	9.68 × 10−2	6.35 × 10−1	1.21 × 10−1	8.63 × 10−1
Zn	2.71 × 10−4	2.53 × 10−3	3.97 × 10−8	9.30 × 10−8	5.40 × 10−6	3.53 × 10−5	2.76 × 10−4	2.56 × 10−3
Cu	1.17 × 10−3	1.09 × 10−2	1.71 × 10−7	4.00 × 10−7	1.55 × 10−5	1.02 × 10−4	1.18 × 10−3	1.10 × 10−2
As	8.30 × 10−2	7.73 × 10−1	9.15 × 10−8	6.95 × 10−5	2.42 × 10−2	1.59 × 10−1	1.07 × 10−1	9.32 × 10−1
Ni	2.12 × 10−3	1.98 × 10−2	3.03 × 10−7	7.09 × 10−7	6.28 × 10−4	4.11 × 10−3	2.75 × 10−3	2.39 × 10−2

, the HI values of the seven heavy metals in the study area are all below 1, indicating that the overall non-carcinogenic risk for both adults and children is within acceptable limits. However, differences among elements and between population groups are evident.

Among all metals, As in children shows the highest HI value (0.932), approaching the threshold of potential concern, suggesting that As may be a relatively important contributor to non-carcinogenic risk. Cr in children also shows a relatively higher HI value (0.863), indicating a comparatively greater contribution to overall risk among the studied metals.

Children exhibit higher HQ values than adults across all exposure pathways. For As, Cr, and Ni, both ingestion (HQ_oral) and dermal exposure (HQ_dermal) contribute to elevated HQ values in children, indicating higher exposure sensitivity in this population. Ingestion is the dominant exposure pathway for all metals. For example, HQ_oral of As in children reaches 7.73 × 10⁻¹, representing the main contribution to its total HI. Dermal exposure also contributes to certain metals, particularly Cr, whose HQ_dermal in children reaches 6.35 × 10⁻¹, indicating a relatively higher contribution compared with other pathways. Inhalation exposure (HQ_inh) remains negligible for all metals.

Overall, As and Cr show relatively higher contributions to non-carcinogenic risk in the study area. In particular, As contributes the most to HI in children, while Cr also represents a notable contributor. Other metals show relatively low HQ and HI values and contribute minimally to overall non-carcinogenic risk.

3.4.3. Carcinogenic Risk

Given that As and Cr are the only heavy metals in the study area soils with carcinogenic slope factors (SF) applicable to resident exposure scenarios, this study conducted carcinogenic risk assessment solely for these two metals. Since Cr lacks a corresponding carcinogenic slope factor for the dermal pathway, its carcinogenic risk was calculated only through oral ingestion and inhalation. Ni was excluded from carcinogenic risk analysis due to the absence of reliable SF values applicable to soil exposure scenarios.

As shown in

Table 13. Carcinogenic health risk assessment of soil heavy metals for adults and children.

Element	CRoral		CRinh		CRdermal		TCR
	Adults	Children	Adults	Children	Adults	Children	Adults	Children
As	1.61 × 10−5	2.99 × 10−5	2.37 × 10−8	1.11 × 10−8	4.68 × 10−6	6.11 × 10−6	2.08 × 10−5	3.60 × 10−5
Cr	2.65 × 10−7	4.96 × 10−7	1.93 × 10−7	8.99 × 10−8	0	0	4.58 × 10−7	5.85 × 10−7
Ni	—	—	—	—	—	—

, the total carcinogenic risks (TCR) of both As and Cr fall within the acceptable range recommended by the U.S. EPA (1 × 10⁻⁶–1 × 10⁻⁴). Among the two metals, As exhibits substantially higher carcinogenic risk levels, with TCR values of 2.08 × 10⁻⁵ for adults and 3.60 × 10⁻⁵ for children. Although these values remain within the acceptable range, the relatively elevated TCR in children indicates a higher level of concern. In contrast, Cr shows much lower TCR values—4.58 × 10⁻⁷ for adults and 5.85 × 10⁻⁷ for children—indicating a comparatively minor contribution to overall carcinogenic risk.

Population differences are evident, as children consistently exhibit higher carcinogenic risk values than adults. For both As and Cr, CR values via oral ingestion and inhalation are higher in children, mainly due to higher exposure levels compared with adults. Among exposure pathways, oral ingestion (CR_oral) is the dominant contributor to total carcinogenic risk. For example, CR_oral values of As are 1.61 × 10⁻⁵ in adults and 2.99 × 10⁻⁵ in children, which are much higher than those associated with inhalation exposure. Inhalation (CR_inh) contributes minimally to the overall carcinogenic risk for both metals. The dermal exposure pathway for Cr was not assessed due to the absence of an established carcinogenic slope factor.

Overall, carcinogenic risks in the study area remain within acceptable limits. However, As is the primary contributor to carcinogenic risk, and children represent the most sensitive population, warranting focused attention in future risk management and soil pollution control efforts.

A number of uncertainties and limitations should be acknowledged in this study. The health risk assessment was based on total metal concentrations in soils, which do not necessarily represent the bioavailable or bioaccessible fractions that determine actual human exposure. Consequently, the estimated risks may be overestimated to some extent. Moreover, the exposure parameters adopted from standard models may not fully capture site-specific characteristics or population variability. Future studies should incorporate metal speciation, bioavailability measurements, and region-specific exposure factors to improve the accuracy and robustness of health risk assessments.

4. Conclusions

(1)

The results indicate that As, Cu, and Pb are enriched in the study area relative to the background values of the lower Yangtze River region, with distinct spatial heterogeneity characterized by localized enrichment and large-scale low-value distributions. Comprehensive assessments based on the single-factor pollution index, the Nemerow composite index, and the geo-accumulation index consistently suggest an overall low level of soil contamination, with slight pollution occurring only for As and Cu at limited sites. The potential ecological risk assessment shows that all elements present low ecological risk, with RI values ranging from 28.06 to 66.45, well below the threshold for slight risk (RI < 150). These findings indicate that the soil environmental quality of the study area remains generally stable and is not significantly impacted by heavy-metal pollution. However, the localized enrichment of As and Cu highlights the need for continued monitoring and targeted management to prevent potential increases in ecological risk in the future.

(2)

Correlation analysis, principal component analysis (PCA), and hierarchical cluster analysis (HCA) demonstrate that soil heavy metals in the study area are mainly controlled by three categories of pollution sources: Co–Zn–Pb–Mn–Cr are influenced by industrial emissions, traffic dust, and regional geological background; Cu–Ni are mainly affected by metal processing, traffic emissions, and fuel combustion; As primarily originates from coal combustion, agricultural inputs, and chemical-related activities. The superimposition of multiple pollution sources leads to varying degrees of heavy metal enrichment in the region.

(3)

Health risk assessment results indicate that the non-carcinogenic risks (HI) of the seven heavy metals are all below 1, suggesting that overall risks remain within acceptable limits. However, As in children shows the highest HI value (0.932) among all elements, indicating that it is a relatively important contributor to non-carcinogenic risk. Carcinogenic risk assessment further shows that the total carcinogenic risk (TCR) of As in children (3.60 × 10⁻⁵) is higher than that in adults and lies at the upper end of the acceptable range, suggesting a relatively higher exposure concern for sensitive populations. In contrast, the carcinogenic risk of Cr remains relatively low for both adults and children.

(4)

Future studies should further investigate temporal variations of heavy metal contamination, expand the scope to broader geographic regions, and integrate advanced modeling approaches to refine ecological and health risk assessments. In addition, research on sustainable remediation strategies, policy interventions, and the transfer of heavy metals within plants will be essential to ensure food safety and ecological security in rice cultivation systems.