Topographical Discrepancy in Heavy Metal Pollution and Risk Assessment from Cornfields in the Licheng District, China

Abstract

Heavy metal pollution refers to the presence of excessive levels of heavy metal elements in soil beyond their natural background concentrations, posing serious threats to human health and ecological systems. Several factors are involved in the contamination disparity in agriculture soils from various terrains, demanding extra care. An examination of the topographical HM dispersions in farmland soils from the Licheng District was conducted to reveal spatial changes in pollution levels and sources and to establish an empirical framework to develop targeted remediation strategies and promote sustainable land management practices. Cd and As had over-standard rates of more than 50% in the low-lying area, whereas the HMs in the high-lying area had over-standard rates of more than 50%. Also, the rates of HMs in high terrain were higher than in low terrain. Using the single-factor pollution index, only low-lying Cu, Ni, Pb, and Hg contamination levels were clean in low-lying and high-lying areas. The overall decline in HM pollution occurred from high to low terrain, triggered by soil physicochemical properties and human interventions. Meanwhile, strong anthropogenic influence fell in high terrain for pollution. Nevertheless, low levels of HM-integrated contamination prevailed in both topographies. Natural and anthropogenic processes gave rise to environmental pollution, such as soil formation, fertilization, metal smelting, and traffic emissions. Overall, the district held a low risk for HMs. The results highlight that strong anthropogenic interventions resulted in increased HM contamination, in addition to natural processes. It is possible to further reduce HM pollution and risk by promoting scientific agricultural techniques, new energy vehicles, and cleaner production.

1. Introduction

As a fundamental component of terrestrial ecosystems, soil integrity is essential for supporting socioeconomic development by safeguarding food security and public health—both of which are key determinants of a nation’s sustainable progress [1]. Heavy metals (HMs) in the soil are poisonous contaminants with multiple properties, such as non-degradability and persistence [2]. Hence, several investigations have been conducted [3,4,5] on these substances selected as priority pollutants [6]. Natural and anthropogenic sources are responsible for releasing them, with anthropogenic activities, including agrochemical application, transportation, industrial waste, and sewage irrigation, constituting the major contributors [7,8]. Subsequently, they are capable of penetrating the soil, giving rise to soil quality deterioration and pollution due to an open and dynamic soil environment [9,10]. According to Bedoya-Perales et al. (2023) [11], HM contamination prevails worldwide. In China, the nationwide soil pollution survey assessed in 2014 found 82.8% of the sites with exceedances due to inorganic pollutants like HMs [12]. As HMs proceed through the food chain and are directly ingested [13], they pose a hazard to human health [14], including kidney and brain damage [15], as well as respiratory issues [16]. Simultaneously, ecosystems also suffer from their influence [17]. In a word, HMs in the soil pose a substantial threat to the ecosystems and the well-being of local inhabitants. It is urgent to conduct further research concerning HMs in soils in order to establish sustainable soil management policies.

It was reported that soil provides over 95% of food for human consumption [18]. Meanwhile, there has been a rise in food demand in response to the growing population worldwide [19]. As of 2017, China produced 661.61 million tons of grain, an increase from 612.23 million tons in 2012 [20]. Nevertheless, anthropogenic activities are prone to harming the soil quality of grain-producing farmland as China’s urbanization and industrialization progress. According to Wang et al. (2023) [21], there are approximately 3.33 million hectares of unsuitable farmland, with the majority polluted by HMs. Agriculture is at risk due to HM accumulation, thereby resulting in the affected growth process of crops, as evidenced by prior cases [22]. Subsequently, the accumulation has adverse effects on food safety and human health through multiple tracks [23]. Therefore, it is essential to focus on the sustainable management of farmland soil, significant for protecting crop security. Licheng District is located in Jinan City of Shandong Province, China. Its GDP reached CNY 96.7 billion in 2017, and its natural population growth rate was 3.94‰ as of the end of 2010. In the meantime, several industries are growing (e.g., tourism, transportation, and agriculture), of which agriculture displays vital contributors to the local economy and population. As a major agricultural crop in the region, corn produces a share of the national yield. While rapid urbanization and industrialization occurred, HM inputs affected the local cornfield soil environment because of several actions. Prior studies [24] focused on metals and polycyclic aromatic hydrocarbons bound to PM2.5 originating from the region. Yet, little is known about HMs in local farmland soils, particularly regarding their levels, potential sources, and ecological risk assessment. Furthermore, the region is located within the capital city of Shandong Province, a place with frequent human activities. In the north, industrial areas are mainly concentrated. Also, studies on HM contamination of farmland soil in middle-latitude diverse topographic regions are limited.

In this study, pH, organic matter, and HMs (Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg) were determined in agrarian soils. The major objectives outlined for the present study include the following: (i) revelation of the soil’s physicochemical and anthropogenic influence on HM-contaminated distribution; (ii) identification of soil HM sources; and (iii) estimation of the ecological risk posed by HM pollution. Our results provide a basis for developing rational policies for sustainable land use.

2. Materials and Methods

2.1. Study Area and Sampling Collection

Licheng District (36°19′–36°53′ N, 116°55′–117°22′ E) is a significant eastern political, economic, and cultural hub of Jinan City, located in Shandong Province, China (Figure 1). It covers an area of approximately 1300 km², intersected by several roads, railways, and rivers. There is high terrain southward and low terrain northward (http://www.gscloud.cn/ (accessed on 24 March 2025)), and its landform types are predominantly mountains, hills, and plains. In terms of climate, it falls within the warm-temperate range, characterized by an annual average temperature and annual average precipitation of 15.5 °C and 615.5 mm (the data are from the Shandong Provincial Bureau of Statistics, China), respectively. High terrain mainly contains cinnamon soil, while low terrain mainly contains Anthrosols.

Considering weather and vegetation coverage, topsoil samples, with sampling depths of 0–20 cm, were collected from the corn-growing region of Licheng District in March 2017 (Figure 1). According to the grid distribution method, each square kilometer was represented by a sampling grid and numbered. In total, there were a total of 90 sampling grids in the region. Subsequently, several means were used to arrange the sub-samples evenly at each grid, including checkerboard and S-shaped distributions, depending on the grid topographic characteristics. Also, the average sampling density was five sites per square kilometer. Following the digging of a 20 cm deep pit with a stainless steel shovel, the soil was removed from the part in contact with the shovel using a bamboo strip at each site. At each grid, equal portions of the subsamples were combined to form a mixed sample of approximately 1 kg. Subsequently, mixtures were placed in sealed bags and transported to the laboratory for air drying. Prior to further chemical analysis, it was necessary to remove soil impurities, including gravel and plant roots, and ground and sieve the soil through agate products and 100 mesh nylon sieves.

2.2. pH and Organic Matter Analysis

The soil sample was treated with distilled water, with carbon dioxide removed, to create a liquid-to-solid ratio of 2.5:1 (v/m) for the water and soil suspension. Next, the suspension experienced shake and rest. Subsequently, the pH value in the suspension was measured using a FE28-FiveEasy pH meter made by METTLER-TOLEDO, Shanghai, China.

The powdered soil sample was mixed with silver sulfate, followed by the addition of 0.4 mol/L potassium dichromate and sulfuric acid. Then, the mixture was heated at 180 °C to bring it to a boil for 5 min. Upon adding the phosphoric acid solution, the mixture was titrated with ferrous sulfate standard solution and developed using phenyl anthranilic acid indicator until a green color appeared. Finally, the organic matter content was calculated.

2.3. HMs Analysis

Each powdery soil sample was digested in the polyethylene container and treated with nitric acid, perchloric acid, hydrofluoric acid, and hydrochloric acid. Simultaneously, detailed descriptions of the digestion process were provided in previous cases [25,26]. Afterward, the digested sample was analyzed using plasma optical emission spectrometry (ICP-OES) (Avio, 220, Waltham, MA, USA) to obtain the Cu, Zn, Cd, Cr, Ni, and Pb contents.

The powdery soil sample was decomposed by the 10 mL hydrochloric acid/nitric acid (1:1, v/v) solution. In addition, the reducing agents thiourea–ascorbic acid and potassium borohydride solution were used. Simultaneously, detailed descriptions of experimental processes were provided in a previous case [25]. Then, the treated sample was measured using an atomic fluorescence spectrometer (AFS, 820, Beijing, China) to determine the As and Hg contents.

To control and assure quality, blank and parallel experiments were conducted. Also, certified standard substances from the China National Standard Reference Material Center were used. Moreover, there were standard curve coefficients greater than 0.999 and measuring errors less than 5%.

2.4. Pollution and Risk Analysis

In the study, an evaluation of the contamination degree and ecological risk posed by HMs in the cornfield soils from Licheng District utilized a variety of approaches to gain a more accurate understanding of the contamination and risk. Below are specific details regarding these methods.

For the assessment of environmental pollution associated with single and comprehensive HM pollution, the single-factor pollution index (P_i) was used, along with the Nemerow comprehensive pollution index (P_N) [27]. The relative methods were indicated in the provided equations [28]:

$$P_i=C_i/S_i$$

(1)

$$P_N=\surd \left(\right(P_{avg}^2+P_{max}^2)/2)$$

(2)

where P_i is the single-factor pollution index; P_N refers to the Nemerow comprehensive pollution index; C_i denotes the ith HM content, mg/kg; S_i represents the background value of the ith HM, mg/kg; P_avg indicates the average of P_i; and P_max represents the maximum of P_i. In the study, the background values of soil HMs in Shandong Province [29] were employed, and the values of Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg are correspondingly 22.6, 63.3, 0.132, 62, 27.1, 23.6, 8.6, and 0.031 mg/kg. Following the relative classification criteria, the contamination grades are further categorized into five levels, as shown in

Table 1. Classification criteria for pollution and risk grade.

Single-Factor Pollution Index a		Nemerow Comprehensive Pollution Index b		Potential Ecological Risk Index c
Pi	Pollution Grade	PN	Pollution Grade	Ei	Risk Grade	RI	Risk Grade
≤1	Clean	≤0.7	Clean	<40	Low risk	<150	Low risk
1–2	Potential pollution	0.7–1	Warning limit	40–80	Moderate risk	150–300	Moderate risk
2–3	Mild pollution	1–2	Slight pollution	80–160	Considerable risk	300–600	High potential risk
3–5	Moderate pollution	2–3	Moderate pollution	160–320	High risk	≥600	Significantly high risk
>5	Heavy pollution	>3	Heavy pollution	≥320	Serious risk	-	-
-	-	-	-	-	-	-	-
-	-	-	-	-	-	-	-

^a Data cited in [30]. ^b Data cited in [28]. ^c Data cited in Yu et al. [31].

.

Based on environmental chemistry, toxicology, and ecology [31], the potential ecological risk index was used to evaluate the ecological risk associated with HMs [32]. Meanwhile, the risk was estimated according to the following equation [33]:

$$E_i=T_i\times P_i$$

(3)

$$RI=\sum E_i$$

(4)

where E_i is the single potential ecological risk index; T_i represents the toxic response coefficient of the ith HM; and RI is the integrated potential ecological risk index. In the study, the response coefficients of Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg are 5, 1, 30, 2, 5, 5, 10, and 40, respectively [34]. Additionally, Table 1 summarizes the relative classification criteria for the risk grade.

The index CF (Contamination Factor) is used. And the equation is listed below:

C_f = C_i/C_b

(5)

where C_i is the mean concentration of individual metal in soil, and C_b is the background concentration.

2.5. Statistical Analyses

ArcGIS 10.2 was utilized to draw the sampling diagram. Additionally, the remaining charts were produced by Origin 2020. The principal component analysis (PCA) with varimax rotation was carried out in SPSS 22 to generate rotated component matrixes. Also, the analysis disclosed the factors experiencing eigenvalues over one.

3. Results

3.1. pH Spatial Distributions

As shown in Figure 2, the pH in the cornfield soils from Licheng District ranged from 5.02 to 8.55, with an average pH of 8.05. In general, the cornfield was characterized by alkaline soil. Depending on soils in the low-lying geographical area, the pH ranged from 7.8 to 8.55, whereas the pH ranged from 5.02 to 8.49 for soils in the elevated geographical region. Also, their averages were 8.24 and 7.95, correspondingly. Accordingly, a decreased pH occurred from south to north throughout the district, where alkaline soils were prevalent. Likewise, the pH in the district was mostly more than the background value of 7.32 [35].

3.2. Organic Matter Spatial Distributions

The organic matter contents in the cornfield soils from Licheng District were in the range of 5.2–65.8 g/kg, with a mean of 17.99 g/kg (Figure 2). According to the second national soil survey, organic matter content, ranging from 10 to 20 g/kg, is used to classify soil fertility into the IV (less absence) level [36,37]. Therefore, the less absence level dominated the soil fertility of the district cornfield. Meanwhile, there was a wide range of organic matter contents in the region with low terrain, ranging from 6.70 to 22.3 g/kg (mean 15.17 g/kg). Similarly, the elevated geographical region revealed a wide range of organic matter contents, varying from 5.20 to 65.80 g/kg (average 19.40 g/kg). These findings showed that lower fertility absence levels occurred on both terrains. Generally, organic matter content was lower in the north relative to the south. Nevertheless, higher contents of organic matter in the regions, when compared with the background value of 13.62 g/kg [35], occupied a substantial proportion.

3.3. HM Spatial Distributions

The Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg contents in the cornfield soils from Licheng District were in the ranges of 11.09–95.88, 39.30–136.24, 0.09–0.38, 47.93–267.70, 17.06–112.59, 12.08–57.93, 4.58–15.27, and 0.02–0.17 mg/kg, correspondingly (

Table 2. HM contents in farmland soils.

HMs		Cu	Zn	Cd	Cr	Ni	Pb	As	Hg
contents		11.09–95.88	39.30–136.24	0.09–0.38	47.93–267.70	17.06–112.59	12.08–57.93	4.58–15.27	0.02–0.17
Low terrain	Mean value	20.17	64.51	0.18	62.36	25.23	21.42	9.36	0.03
	Standard deviation	3.66	9.90	0.05	6.75	3.77	7.28	1.94	0.01
	CF	0.89	1.02	1.34	1.00	0.93	0.91	1.09	0.97
High terrain	Mean value	30.27	77.51	0.18	85.87	37.33	24.79	10.45	0.05
	Standard deviation	11.90	11.84	0.06	42.52	16.66	5.20	2.05	0.03
	CF	1.34	1.22	1.39	1.39	1.38	1.05	1.21	1.68
	t value	8.40	23.78	6.81	4.92	5.14	9.47	10.73	0.30

, Figure 3). Their averages were 26.90, 73.18, 0.18, 78.04, 33.30, 23.67, 10.09, and 0.04 mg/kg, respectively. In the low-lying geographical region, those contents varied from 11.09 to 26.86, 39.30 to 86.99, 0.09 to 0.34, 47.93 to 78.78, 17.06 to 33.92, 16.08 to 57.93, 5.84 to 13.97, and 0.02 to 0.06 mg/kg, respectively, with averages of 20.17, 64.51, 0.18, 62.36, 25.23, 21.42, 9.36, and 0.03 mg/kg, respectively. As for the elevated geographical region, those contents displayed ranges of 19.47–95.88, 58.79–136.24, 0.09–0.38, 52.71–267.70, 24.51–112.59, 12.08–47.27, 4.58–15.27, and 0.02–0.17 mg/kg, respectively, with averages of 30.27, 77.51, 0.18, 85.87, 37.33, 24.79, 10.45, and 0.05 mg/kg. Overall, the southern soil showed higher HM contents in comparison to the northern soil.

3.4. Soil Physicochemical and Anthropogenic Influences on HM Contamination

Based on the results of the CF, there is no significant difference except for the Cu, Ni and Hg contents between low and high terrains (Table 2). But the results of Student’s t-test show that there is significant difference, except for Hg, in the low and high terrains (Table 2).

Using the corresponding background values, the over-standard rates of Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg in the low-lying geographical area were 33.33%, 50.00%, 76.67%, 46.67%, 36.67%, 16.67%, 60.00%, and 33.33%, respectively (Figure 3). The larger over-standard rates, however, were found in the elevated geographical area, with corresponding values of 91.67%, 96.67%, 85.00%, 95.00%, 95.00%, 56.67%, 83.33%, and 78.33%. Therefore, the soils in these regions were contaminated with Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg. A previous case has shown that industrial and traffic sources were responsible for the phenomenon [38]. In addition to industrial and automotive emissions, the irrational application of pesticides and fertilizers can lead to HMs entering and polluting the soil through atmospheric sedimentation and other pathways [39]. Compared to low terrains, high terrains received severe HM pollution, likely associated with differences in anthropogenic intensity.

The mean values of the results of the single-factor pollution index in the low-lying geographical area revealed that indexes of Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg were 0.89, 1.02, 1.34, 1.01, 0.93, 0.91, 1.09, and 0.97, respectively (Figure 4). In spite of the presence of potential Cu, Ni, Pb, and Hg contamination at some sites, the contamination of these metals was generally at clean levels. Moreover, Zn, Cd, Cr, and As were at levels indicating potential pollution. Across the elevated geographical area, the indexes of Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg were correspondingly in the ranges of 0.86–4.24, 0.93–2.15, 0.64–2.87, 0.85–4.32, 0.90–4.15, 0.51–2.00, 0.53–1.78, and 0.52–5.45. The averages of those metals were 1.34, 1.22, 1.39, 1.39, 1.38, 1.05, 1.22, and 1.68, respectively. Accordingly, the region received HMs with potential contamination. Overall, the HM-polluted situation in the area was more severe than that in low terrain, corresponding to the previous finding. The Nemerow comprehensive pollution index in the low and elevated geographical regions had ranges of 0.86–2.04 and 1.02–4.05, with an average of 1.26 and 1.79, respectively. The phenomenon suggested that slight HM pollution was predominant in those regions.

It is important to note that soil pH and organic matter both play critical roles in determining HM behavior and fate in the soil environment [21]. Low-terrain Cu, Zn, and Cd maintained significant negative associations with pH, exceeding the corresponding background value (Figure 2 and Figure 5a). However, a previous study revealed that soil with a low pH level is less prone to accumulating HMs and vice versa [40]. Thus, the connections were probably due to weak anthropogenic inputs of HMs to the area. The remaining HMs, except for Cd, were positively correlated with organic matter. As a whole, there was a higher level of organic matter than its background level (Figure 2). Therefore, high amounts of organic matter contributed to the accumulation of these metals in the soil environment [41]. Additionally, pH was positively correlated with As, and organic matter was positively correlated with Cd and Pb in the high-grade position (Figure 5b). In general, higher pH and organic matter levels than background values were observed in the area (Figure 2). As a result, pH and organic matter facilitated edaphic HM accumulation in the area. Across the district, organic matter contents increased southward, consistent with the trend of HM pollution. On the other hand, pH exhibited the opposite trend of pollution. These findings were partly related to intense fertilization in the high-lying region because of the agricultural area [42]. Aside from providing detailed information on the influence of soil pH and organic matter on heavy metal pollution, the study did not explore other soil physicochemical properties, such as soil texture and cation exchange. This direction will be explored in future studies.

As shown in Figure 6, Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg displayed coefficients of variation (CVs), respectively, of 18.16%, 15.34%, 30.92%, 10.82%, 14.95%, 33.97%, 20.75%, and 34.04% in the low-grade area. It was found that CVs lower than 16%, ranging from 16% to 36%, and exceeding 36% represented low, moderate, and high variations, respectively [43]. In this vein, Zn, Cr, and Ni exhibited low variations, while the remainder showed moderate variations. Compared to the rest, Zn, Cr, and Ni revealed relatively even scatters [44,45]. Aside from that, anthropogenic activities had little impact on those metals but were somewhat effective for the rest [46]. HMs found in high terrain were Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg, which showed CVs of 39.31%, 15.27%, 31.54%, 49.52%, 44.62%, 20.96%, 19.59%, and 64.15%, respectively. Given this, high variations occurred in Cu, Cr, Ni, and Hg, indicative of the intense anthropogenic influence on these [47]. Meanwhile, anthropogenic activities affected Cd, Pb, and As pollution to some extent, as evidenced by their moderate variations in these metals. It was also noteworthy that only Zn displayed low variation in the area where anthropogenic interference on the metal was minimal.

4. Discussion

4.1. Source Apportionment of HM Contamination

The ecological and health risks associated with heavy metal-contaminated soils have long been a central concern in environmental science and regional land use planning. Generally speaking, the main source of heavy metal pollution is industrial pollution, followed by traffic pollution and household waste pollution. However, the spatial heterogeneity of heavy metal pollution, along with its multifaceted influences upon ecosystems and human health, presents a major challenge for accurately assessing pollution risks [1].

It is well known that the content of heavy metals in soils depends on many factors, of which one of the most important is the content of heavy metals in soil-forming rocks. If there is no anthropogenic influence or a weak one, then the content of heavy metals in the soil depends by about 60–70% on the composition of the soil-forming rocks [48]. However, the geological bodies in the Licheng District are mainly composed of intrusive rocks, metamorphic rocks, and carbonites.

The normality and homogeneity of variance were considered before Pearson’s correlation analysis was performed; the results of the normality and homogeneity of variance indicated that the data were suitable for Pearson’s correlation analysis. Pearson’s correlation analysis provides insight into the strength and direction of linear relationships between two variables. As demonstrated by Pearson’s correlations, Cu showed positive correlations with the remaining HMs in the low-lying area (Figure 7a). Furthermore, there were strong positive correlations between Zn and Cd, Cr, Ni, and As. As for Cr, Ni, Pb, As, and Hg, only the correlation of Cr with Cd was significantly positive. However, Ni, Pb, As, and Hg were positive in correlation with Cr. Also, Pb, As, and Hg displayed positive correlations in addition to As and Pb. Therefore, HMs with positive correlation owned homologous sources in the area [49]. In the high-lying region, positive correlations existed between Cu and Zn, Cr, and Ni, whereas a negative correlation occurred between Cu and As (Figure 7b). A previous case verified that the negative correlation indicated divergent origins of HMs [50]. As a result, Cu showed similar sources to Zn, Cr, and Ni but a different source to As. The positive correlations of Zn with Cd, Cr, and Ni and Cd with Pb and Hg indicated that the sources of HMs with an association were similar. Meanwhile, As and Pb produced a positive correlation, whereas Ni and Cr also appeared to correlate positively. The finding certified that As and Pb, as well as Ni and Cr, contained homology. Nevertheless, As and Pb exhibited negative connections with Cr and Ni, implying divergent sources of related HMs.

Further, a PCA for HMs was carried out to provide insight into the specific HM sources (

Table 3. Rotated component matrixes of HMs in farmland soils.

HMs	Low Terrain		High Terrain
	PC1	PC2	PC1	PC2
Cu	0.502	0.765	0.928	0.068
Zn	0.346	0.897	0.703	0.572
Cd	−0.054	0.880	−0.008	0.796
Cr	0.635	0.562	0.949	−0.192
Ni	0.760	0.536	0.936	−0.164
Pb	0.656	0.084	−0.290	0.754
As	0.762	0.441	−0.620	0.428
Hg	0.849	0.036	−0.011	0.543
Variance/%	39	37	45	26

). Two major components explained 76% of the variance in the low-lying geographical region dataset. The first principal component (PC1) weighted 39% of the total variance, where Cr, Ni, Pb, As, and Hg were its primary load elements. Naturally, Cr and Ni were affected by natural processes, such as the soil formation processes and parent materials [51,52]. In light of its lower average, over-standard rate, and CV, Ni was primarily generated from natural sources. Cr retained a higher mean despite its low over-standard rate and CV. It appeared that it was also interrelated with anthropogenic activities, such as metal smelting and plating operations [53], mining, and livestock manure application [54], resulting in contaminated soil with HMs in direct and indirect manners. Although Pb suffered an average below-background value and lower over-standard rate, its CV showed moderate variation. There was evidence that natural sources included lithogenic materials [55]. On the other hand, traffic sources, such as automotive gas emissions and tire and brake wear, were primarily identified as sources of anthropogenic Pb [56]. The longer half-life of environmental Pb from earlier releases has made its persistence possible, even though more gasoline has recently been consumed without Pb [57]. Based on the average, over-standard rate, and CV details, agricultural discharges were the primary source of As invading the soil [58]. It was confirmed that As is present in fertilizers and pesticides, which feed crops and control insects and diseases [26,58]. In nature, natural processes (e.g., soil parent material and rock weathering) are responsible for the presence of Hg in soils [59]. Also, an array of human actions (e.g., mine-related activities, exhaust gas emissions, and fertilized and pesticidal utilization) contribute to the development of Hg [52]. Hence, Hg origins are a function of both natural and anthropogenic behaviors, as illustrated by the related results. Overall, PC1 represented mixed sources of natural and anthropogenic releases. The second principal component (PC2) accounted for 37% of the total variance and was positively dominated by Cu, Zn, and Cd. The Cu with a lower over-standard rate possessed a mean lower than the matching background value, but its medium CV occurred. In addition, Zn was characterized by a high average but low CV. Consequently, their occurrences were caused by the natural and agricultural processes mentioned above [60]. Following the average, above-standard rate, and CV outcomes, agricultural activities (pesticide and fertilizer application) comprised the major sources of Cd [56]. In general, PC2 signified a combined effect involving both natural and agricultural activities. In the elevated geographical area, two components explained the majority of the dataset variance (71%). PC1, contributing 45% of the total variance, was heavily weighted by Cu, Zn, Cr, and Ni. Additionally, they displayed high means and over-standard rates, whereas HMs, except Zn, offered high CVs. Based on this, Cu, Cr, and Ni are largely generated by anthropogenic activities, such as fertilization and metal smelting. Also, Zn production is a result of natural and anthropogenic processes. Overall, PC1 represented mixed sources of natural and anthropogenic releases. The variance explained by PC2 accounted for 26% of the total variance. The Cd, Pb, As, and Hg contributed significant positive loadings to the factor. Due to the higher averages, over-standard rates, and CVs, these metals were primarily from anthropogenic activities. Accordingly, PC2 was concerned with anthropogenic sources.

4.2. Ecological Risk Assessment

Heavy metal pollution and its potential risks to the environment and human health, particularly in areas surrounding urban centers, have become a global concern. Heavy metal accumulation not only impacts the soil’s physical and chemical nature but also poses a significant risk to ecosystem stability and public health [1]. For Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg in the low-lying geographical area, the single potential ecological risk indexes ranged, respectively, from 2.45 to 5.94, 0.62 to 1.37, 20.00 to 76.82, 1.55 to 2.54, 3.15 to 6.26, 3.41 to 12.27, 6.79 to 16.24, and 21.94 to 77.42 (Figure 8). Based on those indexes, the averages were 4.46, 1.02, 40.12, 2.01, 4.66, 4.54, 10.88, and 38.80, respectively. Thus, Cd and Hg at some sites exhibited moderate ecological hazards but presented generally low risks. Furthermore, the rest of the HMs posed low environmental risks. The integrated potential ecological risk of HMs indexes varied between 69.00 and 156.21 (average 106.48), displaying that low integrated risk for MHs prevailed in the area. In the elevated geographical area, there were single potential ecological risk indices for Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg, falling, respectively, from 4.31 to 21.21, 0.93 to 2.15, 19.32 to 86.14, 1.70 to 8.64, 4.52 to 20.77, 2.56 to 10.01, 5.33 to 17.76, and 20.65 to 218.06. Also, the averages of these indexes were 6.70, 1.22, 41.83, 2.77, 6.89, 5.25, 12.16, and 67.20. Additionally, the above indices had means of 6.70, 1.22, 41.83, 2.77, 6.89, 5.25, 12.16, and 67.20, respectively. The environmental risks of exposure to were quite moderate and exacerbated at some points. Nevertheless, the remaining HMs showed minimal risks across all locations. Generally, Cd and Hg HM pollution presented low hazards, consistent with other HM contamination risks. Furthermore, the integrated potential ecological risk indexes fluctuated from 76.86 to 315.03, with a mean of 144.03. Therefore, the area was associated with a low level of comprehensive ecological risk, in keeping with that of partial Iranian farmland soils [61]. However, agricultural soils across the Yellow River Basin and Tangwang Village, China, had higher risks of 253.08 and 640.90 [62,63]. The phenomena were probably due to the diverse intensity of human activities in these areas.

Heavy metals’ accumulation not only impacts the soil’s physical and chemical nature but also poses a significant risk to ecosystem stability and public health. Therefore, a series of comprehensive policy measures should be considered in order to effectively mitigate heavy metal pollution in agricultural soils, such as control of pollution sources and soil remediation [1]. In addition, the government should introduce a series of measures to promote sustainable development including strengthening supervision and law enforcement, improving regulations related to heavy metal pollution, clearly defining emission standards and punishment measures, promoting the use of clean production technologies, and reducing the use of raw materials or processes containing heavy metals.

5. Conclusions

To provide a better understanding of HM pollution discrepancies, farmland in different terrains of Licheng District was investigated to identify edaphic HM contents, sources, and risk assessment. HM contamination was present in the district for Cu, Zn, Cd, Cr, Ni, Pb, As, and Hg. Also, there was severe contamination in high terrain compared with low terrain. However, HM comprehensive contamination in these areas predominated at low levels. Further, HM accumulation was supported by high-lying pH and organic matter, whereas organic matter in low-lying areas aided the process. Anthropogenic interference was less related to Zn in the district, whereas Cd, Pb, and As contents were subject to certain human influence. Meanwhile, Cr and Ni were associated with limited but intense human influence in the low- and high-terrain regions, respectively. In contrast to low terrain, high topography exhibited strong anthropogenic influences for Cu and Hg pollution. Nature- and human-caused discharges of HMs caused HM accumulation in the district, among which were the anthropogenic processes of agricultural, traffic, and industrial activities. Cu and Hg in some sites resulted in increased hazards, while the general risks of all HMs were low in the district. Also, the district held low integrated risks for HMs. Driven by natural and anthropogenic actions, HM pollution showed heterogeneity in farmland soils from the diverse terrains. Compared to the low-lying area, the high-lying area underwent elevated human influence and severe pollution from HMs. Sustainable measures may be required to lower HM emissions, such as promoting scientific agricultural techniques, new energy vehicles, and cleaner production.