Pollution Characteristics and Ecological Risk Assessment of Typical Heavy Metals in the Soil of the Heavy Industrial City Baotou

Abstract

Urban soil samples were collected from the major heavy industrial city of Baotou in Inner Mongolia, China, to investigate the concentration, spatial distribution, and pollution levels of heavy metals. The study employed the geoaccumulation index (Igeo), ecological risk index, and spatial interpolation methods to comprehensively assess urban soil pollution. The results indicated that apart from arsenic (As) and nickel (Ni), the concentrations of heavy metals such as cadmium (Cd), chromium (Cr), manganese (Mn), mercury (Hg), copper (Cu), zinc (Zn), and lead (Pb) were significantly higher than the corresponding background values in the study areas. According to the geoaccumulation index (Igeo), the overall pollution level in the study area ranged from uncontaminated to low pollution. However, variations existed among different urban districts. Among Baotou’s four main urban areas, the soil pollution level in Kundulun District was notably higher compared to the other three urban areas. Mercury (Hg), lead (Pb), zinc (Zn), and copper (Cu) exhibited relatively higher pollution levels across the four district sites. The observed pollution characteristics are closely linked to the distinct industrial attributes of the urban districts: the Kundulun District of Baotou, Inner Mongolia, is renowned for its significant presence of industrial activities such as steel manufacturing, power generation, and coal combustion. These industries play a crucial role in the local economy but also contribute substantially to heavy metal emissions, leading to notable environmental impacts. Similar to the Kundulun District, the Qingshan District of Baotou, Inner Mongolia, is significantly influenced by industrial activities, which have led to elevated concentrations of certain heavy metals and formed higher potential ecological risk index (PERI) hotspots. Implications and Recommendations. The disparity in industrial activities across the four urban districts of Baotou is a principal factor contributing to variations in pollution levels and ecological risks. In conclusion, this research highlights the necessity of aligning industrial zoning with effective environmental management strategies to combat heavy metal pollution in urban soils. By implementing these integrated approaches, Baotou can safeguard its environment and public health, paving the way for a sustainable future.

1. Introduction

Due to the rapid pace of industrialization and urbanization, the accumulation of heavy metals in urban soil has become a major issue. This environmental issue poses a threat to human health and the environment. This accumulation directly impacts human health and contributes to secondary pollution in water bodies, and atmospheric scientists [1,2,3,4,5] worldwide have focused on heavy metal pollution in soil [6,7,8,9,10], examining various aspects such as the sources of heavy metal pollutants, spatial and temporal distributions, chemical forms, migration and transformation, soil environmental background values, environmental capacity, health and environmental standards, bioavailability, risk assessment, pollution control, and remediation. Previous research indicates that urban soil is influenced not only by the climate and parent material but also by human activities. Factors such as the history of urban construction, land use practices, functional zoning, and proximity to pollution sources contribute to the varying degrees and types for soil pollution [11,12,13]. These variations are the primary reason for the significant differences observed in the geochemical characteristics of soils in different cities.

The input of anthropogenic external pollutants is the main cause of environmental geochemical problems in urban soil [14,15,16], which comprise three main urban industrial waste types: coal burning, domestic waste, and motor vehicle exhaust act as the main sources of urban soil pollution [17,18,19]. The geochemical pollution of some urban soils is related to mining activities in the area, while the heavy metal content of some urban soils is related to the soil-forming parent material [20,21,22]. Human activities are primarily responsible for the ecological and geochemical issues observed in urban soils. Consequently, these soils have become the principal repositories for the containment and purification of pollutants in urban environments.

Once urban soil becomes polluted, the pollutants often bind with soil organic matter or minerals, rendering them resistant to degradation and artificial removal, complicating recovery efforts. Heavy metals in soil primarily harm the human body through direct exposure, such as skin contact or inhalation [23,24,25,26,27,28,29,30]. Additionally, these metals can enter the human body indirectly through the food chain, thereby posing a significant threat to human health. Consequently, studying the characteristics and risk assessment of heavy metal pollution in soil is of paramount importance.

Baotou’s special heavy industry system, covering iron and steel, rare earth and other industries, makes the typical heavy metal pollution in its soil unique. The study of heavy metal pollution characteristics in Baotou soil can help to reveal the contamination and accumulation law of heavy metals in soil, the spatial distribution and migration patterns under the multi-industry composite pollution source. The study of Baotou soil can provide key data and case support for the construction of a more comprehensive and precise theoretical system of soil heavy metal pollution and promote the in-depth development of environmental science, soil science and other related disciplines in the direction of heavy metal pollution research.

Therefore, this study focuses on the soil environment of Baotou, a city of heavy industry, and takes typical heavy metals as the objects of study to investigate their pollution characteristics in depth, and applies scientific and rigorous ecological risk assessment methods to comprehensively analyze the potential risks to the ecosystem. By employing Geographic Information System (GIS) technology in conjunction with field sampling data, we have mapped the spatial distribution of heavy metals to elucidate their migration pathways and impact extents. This study aims to fill the gap in this field of research in Baotou, provide accurate and reliable data support and a scientific and reasonable decision-making basis for soil environmental protection, pollution management and ecological restoration in Baotou. It is also expected to provide valuable references and experiences for other cities with similar industrial background and soil pollution problems around the world, and to help promote the in-depth development and technological progress in the field of environmental science in soil heavy metal pollution.

2. Materials and Methods

2.1. Study Area

The study area (109°16′–111°25′) is located in the western part of the Inner Mongolia Autonomous Region (Figure 1). It is the industrial center of Inner Mongolia and the largest city in the autonomous region with an important steel and rare earth production base and an important transportation hub in China. It is located at the southern end of the Mongolian Plateau, bordering the Yellow River in the south and traversed by the Yinshan Mountains, forming three terrain regions: the northern plateau, central mountain, and southern plain. The land area is 27,768 km², the central area is 315 km², and the population in 2023 was 2.762 million. The average altitude of the urban area is 1067 m.

2.2. Sample Collection

Soil samples were mainly taken from the four main urban districts of Baotou City (Kundulun District, Qingshan District, East River District, and Jiuyuan District). Surface soil samples (0–20 cm) were collected. The sampling locations are shown in Figure 1. Soil sampling was carried out in strict accordance with the relevant regulations in the technical specification for soil environmental monitoring (GBT36197-2018) [31]. The sampling density was 4 points/km², 2820 samples were collected, and the sampling area was 705 km². A 2–3 point combined sampling method was used to remove debris in the soil when collecting the samples. The sample mass was generally required to be approximately 500 g.

2.3. Sample Processing and Analysis

The soil samples in this study were pretreated [32] in strict accordance with the standards outlined in the “Technical Specifications for Soil Environmental Monitoring” (HJ/T 166-2004) [33]. This research analyzed the concentrations of nine heavy metals (As, Cd, Cr, Cu, Mn, Ni, Pb, Zn and Hg) in the soil. The soil samples were then analyzed for elements other than mercury (Hg) using inductively coupled plasma mass spectrometry (ICP-MS). The concentration of mercury in the soil was quantified using atomic fluorescence spectrometry (AFS).

2.4. Geoaccumulation Index

The assessment of soil environmental quality typically relies on single pollution indices, where a smaller index indicates lighter pollution and a larger index signifies heavier contamination. When evaluating the overall soil environmental quality of a region for comparison with external regions or historical data, in addition to single pollution indices, composite pollution indices are frequently employed. Given the significant regional background variations in soils, the use of the accumulation index of soil pollution provides a more accurate reflection of anthropogenic contamination levels.

The geoaccumulation index (I_geo), first introduced by the German scholar Müller [34], serves as a critical tool for assessing minor alterations in the background factors of soil environments influenced by natural geological processes. Additionally, it functions as an evaluation index to quantify the contribution of anthropogenic activities to heavy metal pollution in soils [35]. The classification of this index into various grades is detailed in Table 1. The calculation process is illustrated by Equation (1).

(1)${{\rm I}}_{geo}={\log }_2\left(C_i/K{B}_i\right)$

where ${{\rm I}}_{geo}$ is the geoaccumulation index for a specific element;${ C}_i$ is the measured concentration of the heavy metal in the soil sample, expressed in mg/kg; and 1.5 is a coefficient used to account for variations in background values caused by differences in local lithology. This factor adjusts for natural fluctuations and is typically set to 1.5.${ B}_i$ is the background value of the heavy metal in the soil, which reflects the natural concentration of the element in the region. For this study, the background values of nine typical heavy metals were obtained from the benchmark values of the Hetao region and the Inner Mongolia soil environmental background values. The selected background values for the Hetao region are as follows: As (9.61), Cd (100.75), Cr (54.13), Cu (18.88), Mn (506.01), Ni (24.21), Pb (19.67), Zn (52.80), and Hg (16.84).

2.5. Potential Ecological Risk Index

The Hakanson Potential Ecological Risk Model [36] is a widely recognized method for assessing the ecological risk posed by heavy metal pollution in soils and sediments. Proposed by Swedish scientist Lars Hakanson in 1980, this model has become one of the most commonly applied frameworks for evaluating ecological risks associated with heavy metals. The model integrates multiple factors, including total heavy metal concentration, toxicity, mobility, sensitivity, and background values, providing a comprehensive assessment of potential ecological risks.

The toxicity coefficients of Hg, Cd, As, Cu, Pb, Cr, Zn, Ni, and Mn were 40, 30, 10, 5, 5, 2, 1, 5, and 1, respectively [37]. The potential ecological risk coefficient and index evaluation criteria in Hakanson’s original text are shown in Table 2.

The calculation process is shown in Equations (2)–(4).

(2)$E_r^i=T_r^i·C_J^i$

(3)$C_J^i=C_{iD}/C_{iB}$

(4)$RI={\sum }_{\mathit{i\; =\; 1}}^n{E}_r^i$

where RI is the index that provides a comprehensive measure of the overall ecological risk posed by multiple heavy metals in the environment; ${ E}_r^i$ quantifies the ecological risk associated with each specific heavy metal, taking into account both its toxicity and the degree of contamination; ${ T}_r^i$ is the unique toxicity response factor for each heavy metal that reflects its potential harm to the ecosystem; ${ C}_J^i$ compares the measured concentration of a heavy metal in the sample to its background value, highlighting any anthropogenic contribution to pollution; ${ C}_{iD}$ is the measured concentration of the ith heavy metal element; and $C_{iB}$ is the background value of heavy metals in the soil.

2.6. Spatial Differentiation of Heavy Metals in Soil

Soil is a variant of the earth’s surface with a high degree of heterogeneity and a certain temporal and spatial continuity. The occurrence and development of soil are easily affected by many factors. There are two main types of spatial variation in soil heavy metals: random variation and structural variation. Kriging interpolation is the most widely used interpolation method in geostatistics, which is most suitable for both structural and random geographical elements [38]. In this study, universal Kriging was selected to compare and analyze the interpolation simulation results of soil heavy metal content in the study area in order to obtain the optimal spatial distribution interpolation results for each heavy metal element.

The accuracy of spatial prediction results is verified by an independent verification method. The evaluation criteria of Kriging interpolation accuracy are as follows: mean error (ME) and standardized mean error (MSE) are close to 0, standardized root mean square error is close to 1, root mean square error (RMSE) is the smallest, and average standard error is the closest to RMSE. In this section, the ME and RMSE are used as accuracy verification indicators for comparative analysis. The index calculation formula is as follows:

(5)$ME={\displaystyle \frac{1}{n}}{\sum }_{\mathit{i\; =\; 1}}^n\left({\widehat{Z}}_i-Z_i\right)$

(6)$RMSE=\sqrt{{\displaystyle \frac{1}{n}}{\sum }_{\mathit{i\; =\; 1}}^n{\left({\widehat{Z}}_i-Z_i\right)}^2}$

where ${\widehat{Z}}_i$ is the predicted value at the i-th validation point; $Z_i$ is the actual measured value at the i-th validation point; and n is the number of sample points.

Mean error (ME): This metric measures the average bias in the predictions. It indicates whether the model systematically overestimates or underestimates the values. An ME close to 0 suggests that the model’s predictions are unbiased, meaning there is no systematic error. A positive ME indicates a tendency to overestimate, while a negative ME indicates a tendency to underestimate. In studying the spatial distribution of typical heavy metal contamination in the soil of the heavy industrial city of Baotou, it is based on the principle of spatial correlation, which means that things closer together are considered to be more similar to each other than things farther away. The closer the value is to 0, the smaller the error and the better the simulation effect.

Root mean square error (RMSE): This metric provides a measure of the magnitude of prediction errors. It penalizes larger errors more heavily due to the squaring operation, making it sensitive to outliers. A smaller RMSE value indicates better model performance, as it signifies that the predicted values are closer to the actual measurements, and a smaller value indicates a better simulation effect.

2.7. Quality Control and Data Analysis

The accuracy of the total amount of elements was ensured by repeated analysis of GSS-1–GSS-6 standard substances [39]. The logarithmic standard deviation of the calculated measured values and the monitored values of the GSS-1–GSS-6 reference materials were calculated to ensure the precision of the sample analysis. The relative errors of most elements are less than 20%.

3. Results and Discussion

3.1. Trace Metal Concentrations

The characteristics of the soil heavy metal content in the four main urban areas of Baotou City are shown in Table 3 and Figure 2, which can further identify the distribution pattern of heavy metals in urban soil. The distribution of nine kinds of heavy metal elements in the Baotou urban area is extremely uneven, among which the high-anomaly areas of Hg, Cr, Mn, Zn, and Pb appear in the Kundulun District. High-anomaly areas of Hg, Cr, Mn, Ni, Cu, Zn, and Pb were found in the East River District. High-anomaly areas of Hg, Cr, Mn, Zn, and Pb appeared in the Jiuyuan District, while only high-anomaly areas of Hg, Cr, Mn, and Zn appeared in the Qingshan District. The uneven distribution of heavy metals in the soils of these four urban areas is helpful for investigating the relationship between urbanization and its impact on soil composition [36]. The study’s results reveal a significant heterogeneity in the spatial distribution of heavy metal concentrations within the soil of the investigated region. While the mean concentrations of all elements remain below the risk threshold as per GB36600-2018 [40] standards, the average levels of lead (Pb), zinc (Zn), and mercury (Hg) marginally surpass the national mean. Additionally, arsenic (As), mercury (Hg), and zinc (Zn) concentrations exceed the global average.

The comparative results with other typical industrial cities are presented in Table 4. In comparison to Anshan, Baotou exhibits more severe Mn contamination in its surface soil, a phenomenon likely linked to industrial emissions and mining activities in the area. When compared to Tangshan, Baotou’s soil shows generally higher average concentrations of As, Hg, Cr, Ni, Cu, and Zn, indicating that Baotou surpasses Tangshan in terms of heavy metal pollution. Additionally, while the average concentrations of Cd, As, Hg, Cr, Zn, and Pb in Baotou’s soil are slightly higher than those in Chongqing, the difference is not significant. A comprehensive analysis reveals that Wuhan ranks first in heavy metal pollution, followed by Anshan. Baotou and Chongqing exhibit similar levels of pollution, with urban industrial activities being the primary source of soil heavy metal contamination.

3.2. Trace Element Pollution Assessment

Figure 3 visually represents the soil heavy metal pollution levels across the four main urban districts of Baotou, namely Kundulun District, Qingshan District, Jiuyuan District, and another district. The assessment is based on comprehensive sampling and analysis of soil samples from these areas, employing indices such as the geoaccumulation index (I_geo), ecological risk index (ERI), and spatial interpolation methods. The average of each element decreased in the order of Cd (−10.23) < As (−0.93) < Ni (−0.66) < Cu (−0.49) < Mn (−0.47) < Cr (−0.36) < Pb (−0.32) < Zn (−0.24) < Hg (0.68). The average value of Hg in the soil of Baotou City is slightly polluted, and the average value of other elements is not polluted. From the perspective of single elements, it can be determined that there are strong pollution areas of Hg element in the four main urban areas of Baotou City, and other elements reflect different pollution degree characteristics in the four urban areas. The primary cause of this phenomenon is the variation in industrial zones distributed across the four urban districts. In the Kundulun District, Zn and Pb have strong pollution areas, As and Mn have extremely strong pollution areas, Cu has medium–strong pollution areas, and Cr has moderate pollution areas. In the Qingshan District, Cu, Zn, and Pb exist in moderate to strong pollution areas. In the Jiuyuan District, As, Mn, Zn, and Pb have extremely strong pollution areas, Cu has medium to strong pollution areas, and Cr has moderate pollution areas. In the East River District, there is a strong pollution area for Pb and a moderate pollution area for Cr, Cu, and Zn. Highly and extremely polluted areas require targeted interventions and remedial efforts in these areas to address rising pollution levels and ensure the environmental health of these urban areas. The aforementioned pollution characteristics are associated with the unique industrial features of Baotou City, such as the steel and power generation industries, and coal combustion.

In the heavy industrial city of Baotou, the operation of aluminum plants and aluminum slag processing facilities significantly influences the typical patterns of soil heavy metal pollution. The production process at these aluminum plants, particularly during the stages of ore extraction and beneficiation, serves as the initial source of heavy metal contamination. During the extraction and beneficiation of bauxite, various heavy metals such as cadmium (Cd) and chromium (Cr) are present in the ores. These metals are brought out in significant quantities along with the mined materials. Some of these heavy metals enter the tailings during the beneficiation process. If tailings management is inadequate, heavy metals can leach into the surrounding soils through rainwater runoff and dust dispersion, thereby increasing the concentration of corresponding heavy metals in the soil. Improper handling or storage of tailings can lead to environmental issues, including contamination of nearby water bodies and agricultural lands. Dust emissions from exposed tailings piles can also contribute to atmospheric deposition, further exacerbating soil pollution.

In the oxidation–aluminum production phase, high-temperature smelting causes the volatilization of heavy metal impurities from the ores into the atmosphere. Volatile metals like lead (Pb) and mercury (Hg) can escape in gaseous form under these conditions. Subsequently, these metals return to the soil surface via dry and wet deposition, leading to a gradual increase in soil contamination around the plant.

The spatial pattern of soil contamination tends to decrease with distance from the plant due to atmospheric dispersion. Areas downwind of the prevailing winds exhibit higher levels of contamination because of the directional transport of pollutants. For instance, volatile metals released during smelting may settle within a certain radius around the factory, creating a gradient of pollution intensity that diminishes with distance. This phenomenon results in a clear directional pattern of heavy metal accumulation in the soil, with concentrations peaking in the downwind direction.

3.3. Potential Ecological Risk

Potential ecological risk (PERI) was used to assess the potential ecological risk of heavy metal pollutants in soil. In this study, through the analysis shown in Figure 4, it was found that the Kundulun and Jiuyuan Districts have higher ecological risks than the East River District and Qingshan District. Hg, As, and Pb were classified as high risk, Cu and Zn were classified as medium risk, and the remaining elements were classified as low risk. The RI values of the Kundulun District and Jiuyuan District were also higher than those of the East River District and Qingshan District. The RI values of the Kundulun District ranged from 37.92 to 127,138.24, with an average value of 17,525.55. The RI values of the Jiuyuan District ranged from 52.54 to 219,716.63, with an average value of 28,411.45, which was higher than that of the Qingshan District (8875.98) and East River District (128,347.61).

3.4. Spatial Variability Characteristics of Heavy Metals

Analyzing the spatial distribution is a crucial method for pinpointing areas with high concentrations of heavy metal contamination and for tracing potential origins of these metals in soil environments. This approach is particularly useful in urban environments where multiple pollution sources can complicate the identification of specific contributors to soil contamination. Geostatistical analysis, including methods like Kriging interpolation, helps in examining possible pollution sources and provides a detailed understanding of the spatial variability of contaminants. In this research, the Kriging interpolation technique was employed within geostatistical analysis to interpolate the spatial distribution of nine heavy metals [41]. Figure 5 illustrates the spatial distribution map of heavy metal concentrations across nine urban soil samples from Baotou City.

From the perspective of spatial distribution characteristics, the figure shows that there is a large spatial difference in the concentration of trace elements in Baotou urban soil, showing hotspots in the city center [36].

The high-value area of Cd is mainly concentrated in the central and eastern regions of Baotou City, including the Kundulun District, Qingshan District, and East River District. It can be found that the high-value area of Cd is mainly distributed in the industrial waste slag stacking area (Baotou Steel Tailings Dam and Power Plant Ash Storage Pool) and near the Baotou Aluminum Plant in the study area. The distribution is planar, and the accumulation of Cd is closely related to industrial emissions.

The content of As in the soil of Baotou City is higher in the urban area, showing a high background to abnormal distribution. In the middle of the three districts, the suburban area dominated by the Jiuyuan District is distributed from low background to negative anomaly. The abnormal area of As is mainly in the tailings dam of Baotou Steel, and the high content area of the East River District is mainly distributed in the southern area of the Baotou Aluminum Plant. Along the Kundulun River on the south side of the Kundulun District and the Sidao River on the south side of the Qingshan District, two north–east and nearly north–south anomaly zones are formed, respectively. Many factories and enterprises usually select a location near the river.

Hg is banded with high background and abnormal distribution in the urban area of Baotou City from southeast to northwest, and the piedmont alluvial fan in the northwest and northeast of the urban area is the low background area. In the southwestern suburbs of the city is a low background to negative anomaly area.

Cr, Ni, and Cu display similarities in spatial distribution: in the northwest side of the Kundulun District of Baotou City, the piedmont alluvial fan on the northeast side of the Qingshan District, and the suburbs to the east of the East River District show high background and patchy distribution, and local star-shaped scattered mass anomaly areas are formed. Ni and Cu show high background to positive anomalies in the ash storage tank of a power plant. In the tailings dam area of Baotou Steel, Ni, and Cu have a high background, and Cr has a low background. Cr, Ni, and Cu are located in the south of the Qingshan District and the west of the East River District.

One of the core strengths of the Kriging interpolation method lies in its ability to fully account for the spatial autocorrelation of soil heavy metal content data. In the study of Baotou’s soil, the distribution of heavy metals is not random but is influenced by various geographical factors such as industrial layout, wind direction, and water flow, exhibiting a certain spatial structure. Around the Baotou steel industrial area, due to the effects of atmospheric deposition and wastewater discharge, soil heavy metal pollution shows a trend of gradually decreasing from the factory center outward, with a more pronounced pollution belt in the prevailing wind direction. The Kriging interpolation method accurately captures this spatial autocorrelation by calculating the semivariogram between sample points. Thus, when predicting the heavy metal content at unknown points, it not only considers the proximity but also the structural changes in the data’s spatial distribution, making the interpolation results more consistent with the actual spatial distribution patterns. This allows for more precise mapping of the spatial distribution of heavy metal content, aiding in the identification of pollution hotspots and diffusion pathways. The Kriging interpolation method, in contrast, is based on the assumption of stationarity of regionalized variables, meaning it assumes that the statistical characteristics (such as mean and variance) of soil heavy metal content remain stable within a certain range. However, in a heavy industrial city like Baotou, due to long-term industrial development and urban changes, the situation of soil heavy metal pollution may be complex, with local pollution source variations and changes in land use types, leading to non-stationary statistical characteristics of soil heavy metal content. For instance, in an old industrial area of Baotou that has undergone enterprise upgrades and transformations, some regions may experience reduced pollution emissions, resulting in localized trends of change in soil heavy metal content. This could violate the stationarity assumption, thereby affecting the accuracy of the Kriging interpolation method. In such cases, the interpolation results may exhibit deviations in local areas, failing to accurately reflect the actual changes in soil heavy metal content.

4. Conclusions

In this study, the contamination profiles of potentially toxic elements (PTEs) such as cadmium (Cd), arsenic (As), mercury (Hg), chromium (Cr), manganese (Mn), nickel (Ni), copper (Cu), zinc (Zn), and lead (Pb) in soil samples from the Kundulun, Jiuyuan, Qingshan, and East River Districts of Baotou were evaluated. The average concentrations of Cd, Cr, Mn, Hg, Cu, Zn, and Pb in urban soil samples significantly exceeded their respective background levels. Notably, Hg, Pb, Zn, and Cu emerged as the primary contaminants, with their geoaccumulation index (I_geo) values spanning from low to heavily polluted categories. Additionally, the contamination severity across the districts followed the sequence: Kundulun > Jiuyuan > Qingshan > East River. Regarding the assessment of potential ecological risks, mercury (Hg), lead (Pb), and arsenic (As) exhibited a moderate risk, significantly exceeding the risk levels of other potentially toxic elements (PTEs). A notably higher ecological threat was identified in the Kundulun and Jiuyuan Districts, as indicated by the risk index (RI) values. The spatial distribution of PTEs demonstrated a higher concentration and distinct hotspots in Kundulun and Qingshan compared to the other districts. Recommendations for the prevention and remediation of the current pollution status in the study area are proposed as follows: (1) To address the common heavy metal contamination, such as lead and cadmium, in Baotou’s soil, immobilizing agents can be added. For instance, at a vegetable cultivation site in Baotou, the soil contains 200 mg/kg of lead and 2 mg/kg of cadmium. By incorporating 2% lime and 1% apatite as immobilizing agents, the soil’s pH can be increased, causing lead and cadmium to form insoluble compounds, thereby reducing their bioavailability. The phosphate ions in apatite can bind with heavy metal ions to form precipitates. (2) In areas of Baotou with mild to moderate cadmium contamination, planting centipede grass is advisable. Centipede grass has a strong ability to accumulate cadmium, with its above-ground parts containing over 1000 mg/kg of cadmium when the soil concentration is 5 mg/kg. By harvesting the above-ground parts of the centipede grass, heavy metals can be removed from the soil. Typically, 2–3 harvests per year over a period of 3–5 years can significantly reduce soil cadmium levels. (3) Based on the spatial distribution characteristics of heavy metal pollution in Baotou’s soil, land use should be planned rationally. Severely polluted areas should be designated for industrial use or green spaces, avoiding agricultural production or residential development. In a heavy metal pollution hotspot in Baotou, where multiple heavy metal concentrations exceed standards, the area can be planned as an urban park or an industrial park expansion site. By planting landscape plants with high heavy metal tolerance or constructing industrial facilities, the opportunity for residents to come into contact with contaminated soil is minimized, thereby reducing health risks.

Author Contributions

Conceptualization, X.C.; methodology, D.Y. and C.L.; software, Y.R. and Y.H.; validation, Y.S. and R.J. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request, but are not publicly available due to proprietary data.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

Figure 1. Map of the study area and sampling points.

Figure 2. Average concentration of heavy metals in soil in four main urban areas of Baotou City. (a) Mean concentration of heavy metal elements in the soil of the four main urban districts; (b) Standard deviation of heavy metal element concentrations in the soil of the four main urban districts.

Figure 3. Box plots of the geoaccumulation index of heavy metals in the main urban areas of Baotou City (Kundulun District, Qingshan District, Donghe District, Jiuyuan District). The red line in the box plot represents the mean value.

Figure 3. Box plots of the geoaccumulation index of heavy metals in the main urban areas of Baotou City (Kundulun District, Qingshan District, Donghe District, Jiuyuan District). The red line in the box plot represents the mean value.

Figure 4. Potential ecological risk index ([Forumla omitted. See PDF.]) of heavy metals in the main urban areas of Baotou City (Kundulun District, Qingshan District, East River District, and Jiuyuan District): violin diagrams and risk index (RI) box diagram.

Figure 4. Potential ecological risk index ([Forumla omitted. See PDF.]) of heavy metals in the main urban areas of Baotou City (Kundulun District, Qingshan District, East River District, and Jiuyuan District): violin diagrams and risk index (RI) box diagram.

Figure 5. Spatial distribution characteristics of heavy metals in urban soil from Baotou City.

Figure 5. Spatial distribution characteristics of heavy metals in urban soil from Baotou City.

Table 1

Cumulative index classification.

Classification	Clean	Slightly Polluted	Slightly-Moderate Polluted	Moderately Polluted	Moderately-Strongly Polluted	Extremely-Strongly Polluted	Strongly Polluted
${{\rm I}}_{geo}$	<0	[0, 1)	[1, 2)	[2, 3)	[3, 4)	[4, 5)	≥5

Table 2

Potential ecological risk coefficient and index evaluation criteria.

Potential EcologicalRisk Factor (${\mathit{E}}_{\mathit{r}}^{\mathit{i}}$)	Potential EcologicalRisk Index (RI)	Ecological HazardLevel
<40	<150	slight
[40, 80)	[150, 300)	moderate
[80, 160)	[300, 600)	strong
[160, 320)		very strong
≥320	≥600	extremely strong

Table 3

Statistical characteristics of soil heavy metal content in the four districts of Baotou City.

Elements (mg/kg)		Cd	As	Hg	Cr	Mn	Ni	Cu	Zn	Pb
Study area	Mean	0.16	8.26	0.077	65.84	615.51	23.87	21.82	80.77	32.28
	SD	0.42	8.11	0.19	19.73	673.56	6.95	14.02	109.08	69.63
Kundulun	Mean	0.17	9.27	0.07	70.33	857.78	26.65	24.04	109.67	48.43
	MAX	1.45	292.00	2.06	336.50	15,623.00	66.90	339.20	2837.00	1235.40
	MIN	0.04	0.70	0.00	2.30	296.00	9.90	10.90	33.20	4.20
	SD	0.16	14.95	0.134	24.56	80.08	8.14	18.74	188.45	106.72
Jiuyuan	Mean	0.14	7.60	0.07	59.83	501.10	21.12	19.01	66.50	25.33
	MAX	3.63	26.20	3.11	175.00	3200.00	51.30	294.80	985.10	639.50
	MIN	0.04	2.30	0.01	34.00	275.00	12.40	10.30	32.70	6.80
	SD	0.17	2.44	0.192	12.60	156.10	5.40	11.47	61.30	31.50
Qingshan	Mean	0.16	7.42	0.07	67.19	555.98	24.10	21.45	68.10	23.48
	MAX	8.00	26.20	3.53	328.00	1661.00	48.00	180.40	517.90	477.70
	MIN	0.04	2.10	0.01	37.60	328.00	15.20	13.00	38.70	15.60
	SD	0.42	1.99	0.260	18.43	115.75	4.26	10.61	32.24	25.33
East River	Mean	0.20	9.25	0.11	75.65	580.27	27.34	27.32	84.69	32.78
	MAX	19.00	22.10	1.98	330.70	330.70	103.80	128.60	362.10	172.20
	MIN	0.06	0.60	0.01	32.80	32.80	16.40	13.00	42.10	9.60
	SD	0.03	2.63	0.012	8.34	93.62	4.69	5.01	12.01	3.60
Background value of the Hetao area		0.10	9.61	0.02	54.13	506.01	24.21	18.88	52.80	19.67
Risk screening value		0.60	25.00	3.40	250.00		190.00	100.00	300.00	170.00

Table 4

Comparison of soil heavy metal content with other heavy industrial cities.

Elements (mg/kg)	Baotou	Anshan	Tangshan	Wuhan	Chongqing	China	International Standard
Cd	0.158	0.86	0.10	0.86 ± 0.20	0.132	0.68	0.35
As	8.255	3.72	6.79	41.15 ± 24.40	5.80	13.39	6
Hg	0.077		0.065		0.053	0.31	0.06
Cr	65.836	69.93	46.20	58.15 ± 9.05	76.56	63.04
Mn	615.510	291.5				576.17	1000
Ni	23.873	33.48	17.33	158.64 ± 80.86	30.50	26.18	50
Cu	21.825	52.29	20.97	41.80 ± 12.55	23.99	40.78	30
Zn	80.767	213.49	63.38	349.31 ± 151.77	75.58	137.72	50
Pb	32.280	45.05		155.56 ± 179.99	25.55	47.34	35

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