1. Introduction
Heavy metal(loid)s are characterized by their accumulation, persistence, and toxicity. Heavy metal(loid)s are easily absorbed by humans and animals through the food chain, jeopardizing the ecology and human health [
1]. The sources of heavy metal(loid)s in soil are relatively complex and mainly comprise anthropogenic and natural sources [
2]. Anthropogenic sources include mining, smelting, wastewater, exhaust, fertilizers, and pesticides. Natural sources include rock weathering during soil and volcanic eruptions [
3]. Qualitative identification of soil heavy metal(loid) pollution and source analysis are important for controlling soil heavy metal(loid) pollution. Timely determination of soil heavy metal(loid) levels and their source pathways is particularly important for preventing and controlling soil heavy metal(loid) pollution.
Identification of pollution sources is a scientific prerequisite to carry out pollution prevention and control work, and previous researchers have used stable isotope techniques (Pb) [
4,
5], new geochemical tracers (Cd) [
6], and multivariate statistical analyses (PCA and CA) [
7] to qualitatively analyze the sources of heavy metals, whereas the chemical mass balance (CMB) model [
8], the absolute principal component score-multivariate linear regression (APCS-MLR) model [
9], UNMIX model [
10], and Positive Matrix Factorization (PMF) [
11] were used for quantitative analysis, and the accuracy of their results can be significantly affected by the number of samples. T-NSE [
12], which has higher accuracy and flexibility, uses neural networks to derive t-SNE hyperparameter combinations. Semi-supervised learning, which has the advantages of both supervised and unsupervised learning, can be used mainly for datasets with mainly unlabeled data, and few labeled data and automatic clustering of unlabeled data [
13] improves the machine learning prediction performance to a great extent.
With industrialization and urbanization, soil heavy metal(loid) pollution from industrial parks has become increasingly significant. Different concentrations of Cd, Zn, Pb, and Hg pollution have been reported in various industrial parks [
14]. The highly contaminated samples were mainly distributed in enterprise-intensive areas [
15].
The important mineral resource regions in China include Sichuan, Yunnan, and Guizhou. The development of these regional economies has driven heavy metal(loid) pollution by Cd, Zn, Pb, As, and other heavy metal(loid)s [
16,
17]. Heavy metal(loid) pollution of soils is more serious in the Yunnan Province, especially in industrial parks where polluting enterprises are concentrated, owing to the early non-standardized treatment of pollutants, lack of a functional layout, and other reasons. This could result from the extensive accumulation of heavy metal(loid)s in the surrounding soil. However, little is known about heavy metal(loid) pollution in the soil of industrial parks. Furthermore, the unknown characteristics of soil heavy metal(loid) pollution, unclear spatial distribution patterns, and unknown sources make it difficult to provide a basis for controlling soil pollution sources and remediating contaminated land in the area.
This study was conducted in an industrial park in Kunming, Yunnan Province, China. Heavy metal(loid)s in the surface soil (0 to an approximate depth of 20 cm) were studied; evaluation of soil contamination in different land use types using the pollution load index, geoaccumulation index, and Nemero composite index methods; and analysis of heavy metal(loid) sources, which provide theoretical support for the prevention and control of heavy metal(loid) pollution of soil in Kunming City, will inform efforts to enhance regional environmental quality.
2. Materials and Methods
2.1. Overview of the Study Area
The study area is a key industrial park in the northernmost area of Kunming City, Yunnan Province, covering a total area of 1858.79 km2. The study area is divided into two groups. Block 1 is mainly used in chemical industries involving heavy metal(loid) Cu and phosphorus (P). This area focuses on the comprehensive utilization of resources, the processing of ferrous, rare, and precious metals, and the extension and development of new building materials. Block 2 contains advanced manufacturing industries that focus mainly on foodstuffs, light industry, and machinery.
At the end of 2018, there were ferrous metal enterprises, such as the production of ferroalloys, manganese (Mn), Cr, silicon (Si), furnace charge, and steelmaking deoxidizers; nonferrous and rare metal enterprises, such as Cu smelting and sulfuric acid production; P-related chemical enterprises, such as production of feed additives, hazardous chemicals, fertilizer, and other products; machinery manufacturing industries; food and drug processing industries, including pharmaceutical manufacturing, food processing, and others; building materials enterprises, including production of cement, gypsum, mechanized sand, and other products; battery car manufacture; and processing of plastic and other products [
18].
2.2. Sample Collection and Analyses
Based on the sewage discharge of enterprises in the industrial park, the distribution of sensitive points within a certain range in the surrounding area, the original use function of the land parcels, the characteristics of contamination, the soil monitoring network in the industrial park, and the surrounding area were constructed. One hundred samples of differently utilized land surface soil (0–20 cm) were collected; 60 samples were from construction land and 40 from agricultural land. To accurately determine whether the soil was polluted, the pollutant type, and the degree of pollution, sampling points were designed by combining professional judgment and systematic random distribution (
Figure 1).
Soil samples were collected in strict accordance with the China Soil Environmental Quality Risk Control Standards for Soil Pollution of Agricultural Land (GB15618-2018) [
19], Soil Environmental Quality Risk Control Standards for Soil Pollution of Construction Land (GB36600-2018) [
20], Technical Specification for Soil Environmental Monitoring (HJ/T166-2004) [
21], Determination of eight Effective State Elements in Soil (HJ804-2016) [
22], and other relevant technical specifications. The samples were prepared using non-metallic tools and utensils to remove impurities such as plant roots and gravel (at least 2 kg per portion), packed in cloth bags, dried naturally, crushed with a wooden stick, passed through a 2 mm aperture sieve, mixed and weighed, and then sent to the laboratory for analysis. Sample testing was performed at the Testing Center of the China Inspection Body and Laboratory Mandatory Approved by the qualified Kunming Geological Survey Institute of the General Administration of Metallurgical Geology of China. The sample testing process adopted national-level standard substances (GBW series) for quality control. Each batch of the analytical process involved quality control measurements of parallel double samples. Eight elements (As, Pb, Cu, Zn, Cd, Hg, Ni, and Cr) were detected. The Cr content was determined using flame atomic absorption spectroscopy. The As and Hg contents were determined using atomic fluorescence spectrometry. Cd, Pb, Ni, and Cu contents were determined by inductively coupled plasma mass spectrometry. Zn content was determined using inductively coupled plasma emission spectrometry. Finally, pH was determined using a potentiometric method.
2.3. Pollution Load Index (PLI)
The PLI evaluates the integrated pollution levels of multiple heavy metal(loid)s in the soil, visualizes the contribution of pollutants to the polluted area, and determines the spatial and temporal trends of heavy metal(loid)s a site [
23]. The PLI can be used to determine whether pollutants are under the integrated control of natural and anthropogenic sources [
24,
25]. The formulae used are as follows:
where PLI is the pollution load index of a point in the study area, n is the number of polluted heavy metal(loid) elements, CF
i is the pollution coefficient of heavy metal(loid) i in the soil, C
i is the measured content of heavy metal(loid) i in the soil (mg/kg), and C0
i is the evaluation standard of heavy metal(loid) i. The background value of the heavy metal(loid)s in the soil of Kunming was chosen as the standard evaluation value (mg/kg).
The PLI for a given region is calculated as follows.
where P
zone is the regional PLI, n is the number of metal elements, and m is the number of sampling points. The evaluation levels are presented in
Table 1.
2.4. Geoaccumulation Index (Igeo)
This index integrates the effects of anthropogenic factors and soil matrices on the distribution of heavy metal(loid) contents. I
geo is widely used to study soil heavy metal(loid) pollution caused by anthropogenic activities [
26]. The formula is as follows.
where I
geo is the geoaccumulation index, C
n is the measured value of elements in soil (mg/kg), B
n is the background value of heavy metal(loid)s in the soil (mg/kg), and 1.5 reflects the fluctuation of the background value in different areas due to differences in soil matrices. The evaluation levels are listed in
Table 1.
2.5. Nemero Combined Pollution Index
Based on a single factor index, this index synthesizes the pollution level of each heavy metal(loid) and highlights the environmental damage caused by heavy metal(loid)s [
27,
28]. The formula is as follows.
where P
N is the soil heavy metal(loid) Nemero Comprehensive Pollution Index, P
iave is the average value of n heavy metal(loid) single factor pollution index; P
imax is the maximum value of n heavy metal(loid) single factor pollution index; C
i is the measured value of soil heavy metal(loid) i content (mg/kg), and S
i is the evaluation standard for heavy metal(loid) i pollution in soil. Soil Environmental Quality Construction Land Soil Pollution Risk Control Standards (GB15618-2018) and Soil Environmental Quality Agricultural Land Soil Pollution Risk Control Standards (GB36600-2018) [
19,
20] were selected as the heavy metal(loid) control standard values. The evaluation levels are listed in
Table 1.
3. Results
3.1. Statistical Characterization of the Meta-Content of the Selected Heavy Metal(loid)s in Soil Samples
The mean soil pH was 7.8 and 9.02 for construction and agricultural lands, respectively (
Table 2). The average heavy metal(loid) content in the surface soil was in the order of Pb > Zn > Cu > As > Cd > Ni > Hg > Cr for construction land and Zn > Cu > Cr > Pb > Ni > As > Cd > Hg for agricultural land.
The coefficients of variation (Cv) [
29] of As, Hg, Cd, Pb, Cu, and Zn in construction land were all >1, which is an anomalously strong variation, indicating that they are greatly influenced by human activities. Cd content was significantly higher than the background value in Kunming [
30]. The Cv values of the contents of Hg, Cd, and Pb contents in the agricultural soils were >0.5, which also indicated strong variation. Relative to the background values in Kunming and the contents of the upper crust [
31], the study area was significantly enriched in Cd (1023.89 times). All elements exceeded the background values in the agricultural land in Kunming, except for Cr (the data were logarithmically (In) treated to observe subtle changes in heavy metal(loid) content data in a more subtle manner considering that the data differed too much) (
Figure 2).
A comparison of the screening values (
Table 2) revealed certain points in the soil samples where different heavy metal(loid) concentrations were exceeded. In the 20 sample points of the construction land soil, the contents of As, Cd, and Pb exceeded the standards in 14 (70%), five (25%), and eight (40%) samples, respectively. The concentrations of Cd, As, and Cu in the 40 soil sample points on the agricultural land exceeded the standards in 40 (100%), 22 (55%), and 25 (62.5%) samples, respectively. In these soil samples, Zn and Pb exceeded the relevant standards in only 15% and 2.5%, respectively. None of the remaining heavy metal(loid)s exceeded the relevant standards.
3.2. Characterization of Spatial Distribution and Pollution Evaluation
3.2.1. PLI
The PLI was used to evaluate and analyze the pollution characteristics of soil heavy metal(loid)s in the study area. The zone of soil heavy metal(loid)s in the study area was 3.02, indicating heavy soil pollution. The PLI values for each sampling point (
Table 3) showed that the proportions of non-polluted, lightly polluted, moderately polluted, and heavily polluted samples were 1.7, 53.3, 15, and 30%, respectively, with light pollution being predominant. The statistical results of the soil contamination levels of different land use types in the study area showed that the percentage of contaminated sample points on construction land (100%) was higher than that on agricultural land (97.5%).
3.2.2. Geoaccumulation and Nemero Composite Pollution Indices
The I
geo results revealed slight contamination with all heavy metal(loid)s, except for Cr (
Figure 3). The contaminated samples, relative to the total number of samples, were in the following order Cd > As > Zn > Cu = Pb > Hg > Ni > Cr.
The degree of soil heavy metal(loid) pollution is shown in
Table 4. All samples showed heavy pollution (P
N > 3). However, the Nemero Composite Pollution Index of the construction land was higher than that of agricultural land. Construction land was contaminated with As and Pb to a greater extent than agricultural land. Agricultural land was heavily contaminated with As, mildly contaminated with Cd and Pb, and moderately contaminated with Cd.
3.3. Source Analysis of Heavy Metal(loid)s
3.3.1. Spatial Distribution Characteristics
Using the inverse distance-weighted interpolation method in the statistical module of ArcGIS 10.8 software (10.8.12790), the contents of 60 surface soil sample points were interpolated and analyzed to obtain the spatial distribution of the contents of the eight heavy metal(loid)s (
Figure 4) and different distribution characteristics in the region. The soil As content was high in the center of the area and symmetrically distributed in the northwest and southeast. The Ni content is high in the central and southeastern regions. The content distributions of Hg and Pb were very similar, with two highly polluted areas in the center of the area. Areas were highly polluted with Cd and Zn. The Cu content was distributed as an island with a high center and low circumference, and its content gradually decreased along circumference. The distribution pattern of Cr was completely different from that of other heavy metal(loid)s, with the presence of two highly polluted areas in the southeast. In general, the distribution characteristics of the other seven elements are similar. The high pollution content in the center of the area was primarily due to it being an industrial area dominated by heavy metal(loid) smelting, P-related chemical industries, and other relevant industries. It was initially inferred that some heavy metal(loid)s in the study area originated from industrial parks.
3.3.2. Heavy Metal(loid) Correlation Analysis
To investigate the geochemical characterization of heavy metal(loid)s in the soils of construction and agricultural lands in the study area, Pearson correlation was performed for analysis of the contents of heavy metal(loid)s (
Table 5). In the construction land (A), at the
p < 0.01 and
p < 0.05 levels, all the elements, except for Cr, showed highly significant positive correlations between the two elements. Excess As, Cd, and Pb showed highly significant positive correlations with other elements. In the Pearson correlation coefficient table of heavy metal(loid)s in agricultural land soil (B), at
p < 0.01 level, Cr was the only element showing highly significant positive correlation with Ni. At
p < 0.05, Ni was the only element that showed a highly significant positive correlation with Cd, indicating that a portion of the heavy metal(loid)s in the soil (Cr, Ni, and Cd) had other sources. Cd, which exceeded the standard, showed a highly significant positive correlation with the other elements, with the exception of Cr.
3.3.3. Principal Component Analysis of Heavy Metal(loid) Elements
To further analyze the heavy metal(loid) pollution characteristics of construction and agricultural lands, the variables were subjected to principal component analysis. This analysis is one of the most important means for determining the sources of heavy metal(loid)s [
32] (
Table 6A). The three principal component factors obtained by rotational solving contributed approximately 92.39%, and the first principal component factor (PC1) contributed 58.60% of the variance. Six elements (As, Hg, Cd, Pb, Cu, and Zn) exhibited high loadings, indicating similar origins. The second principal component factor (PC2) contributed 18.99%, with high loadings of Ni and Cu. The contribution of the third principal component (PC3) was approximately 14.81%, and Cr had a high loading.
Figure 5A depicts that the elemental components with higher scores in PC1 and PC2 were identical, indicating similar origins. Pearson’s correlation analysis indicates a highly significant correlation between these elements.
The three principal component factors of the rotation solving soil samples from the agricultural land (
Table 6B) contributed approximately 78.43%, whereas PC1 contributed approximately 35.34%, with As, Hg, Cd, and Pb having the highest scores. Ni and Cr had the highest PC2 scores (with approximately 24.75%). PC3 had the highest scores for Cu and Zn, explaining approximately 18.34% of the overall variation. The results of the Pearson correlation analysis are shown in
Figure 5B. Therefore, heavy metal(loid)s have similar sources in construction and agricultural soils.
4. Discussion
4.1. Heavy Metal(loid) Pollution and Risk Assessment
Three evaluation methods were selected to accurately assess the status of soil heavy metal(loid) pollution in the industrial parks, understand the contribution of heavy metal(loid) pollutants to the soil environment in the study area, and perform risk evaluation. The results differed owing to the different evaluation methods used. The three evaluation methods revealed that As, Cd, and Pb pollution in the study area was concerning. In particular, Cd contamination sites were more extensive, consistent with a previous study [
33]. Varying degrees of As (which has a high risk of carcinogenicity), Pb, and, most seriously, Cd contamination were evident in all areas of Yunnan [
34,
35,
36,
37].
4.2. Source Analysis of Heavy Metal(loid) Pollution
Accurate identification of pollution sources is vital for the prevention and control of soil heavy metal(loid) pollution [
38,
39]. Pearson and principal component analyses were used to broadly classify the sources of the eight heavy metal(loid)s contaminating construction land into three categories. During the first stage (As, Cd, Hg, Pb, and Zn), the mean values of these elements significantly exceeded the soil background values in Kunming (
Figure 2). The Cv values of As, Hg, Cd, Pb, and Zn were high, indicating that they were subjected to large anthropogenic factors. Industrial pollution significantly affects As and Hg are greatly affected by industrial pollution [
40,
41]. There are more vehicles in industrial parks, and these enterprises include automobile recycling and dismantling. Cd and Pb may originate from automobile exhaust emissions and tire wear, whereas Pb and Zn may originate from engine wear, braking, or other sources [
42]. Metal smelting is the primary cause of Cd and Pb pollution [
43,
44].
The second category (Ni and Cu) includes elements that occur in metal smelting wastes [
45,
46]. The third category comprises only Cr and is strongly influenced by motor vehicle exhaust [
47], although Cr is released in varying degrees from various parts of the cement production process [
48].
The eight heavy metal(loid)s in the agricultural land were divided into three categories. In the first stage (As, Hg, Cd, and Pb), the average values of the four elements were higher than the background values of the soil in Kunming. The spatial variability of Hg, Cd, and Pb was more obvious. Irrational use of fertilizers and pesticides has led to increased concentrations of As, Cd, and Pb in soil [
49,
50,
51]. Hg contamination is caused by industrial and agricultural pollutants (the latter involves the long-term use of chemical pesticides) [
52,
53]. Cd can reflect a high Cd geological background and transportation source [
54,
55].
The second category (Ni and Cr) is less affected by anthropogenic factors. Ni and Cr are closely related to soil matrix formation [
56,
57]. The third category comprises Cu and Zn, which originate from chicken and pig manure applied during cultivation. Fertilizers containing P or other chemicals can also produce Zn when applied [
58].
4.3. Prevention and Control Measures for Heavy Metal(loid) Pollution
This study identified As, Cd, and Pb as the main heavy metal(loid) pollutants found in industrial parks. Their main sources are industries, such as preparing raw materials for the smelting of Cu and Fe and the smelting process, in which the energy mainly comes from coal combustion [
59,
60,
61]. Second, Pb and Cd originate from transportation and natural sources, respectively. Additionally, exhaust emissions, discharge or leakage of heavy metal(loid)-containing wastewater, storage or improper disposal of waste residues, and steel processing and stacking may result in Cd infiltration into site soil [
62,
63]. Industrial activities, such as hazardous chemical production, pharmaceutical manufacturing, and fertilizer production, can also produce As and Pb pollution [
64,
65]. Pb pollution also results from automobile exhaust, oil leakage, and wear and tear of concrete pavements, rubber tires, brake pads, etc. [
66,
67].
At the national level, regular updates on online environmental pollution monitoring information and data sharing should be jointly promoted. Local governments should properly manage and dispose of waste in accordance with local environmental protection laws, actively promote the upgrading of processes and industries, determine diversified evaluation standards and systems, strictly implement the requirements of air pollutant emission limits, formulate scientifically sound regional soil heavy metal(loid) pollution management, soil utilization and remediation, and balanced governance programs for other ecological elements [
68,
69].
Comprehensive management of industrial parks is performed at the park level, with management projects planned in non-polluted areas to control the generation of new pollutants and a permanent protection system established to ensure park safety. In places affected by heavy metal(loid) pollution, strict control, research, and development of new technologies for heavy metal(loid) pollution remediation should be implemented, such as the study of soil heavy metal(loid) pollution management systems and mechanisms and improved soil heavy metal(loid) pollution management capacity and level. The aim is to achieve sustainable development of the soil environment. Furthermore, enterprises in the affected industrial parks should be supervised to strictly enforce national pollutant emission standards and eliminate the “three industrial wastes.” Relevant companies must formulate technical specifications for the prevention and control of heavy metal(loid)s to enable their supervision and management of heavy metal(loid) emissions. Industrial parks need to establish centralized collection, treatment, and disposal facilities for solid and hazardous wastes and improve measures to prevent seepage, loss, and dispersal of heavy metal(loid) contaminated soil. For the copper smelting industry, strict implementation of measures to curb the emissions of particulate matter and key heavy metal(loid) pollutants is required. The development of clean energy should be encouraged through the development of environmentally friendly raw materials, and the filtration of automobile exhaust pollutants, purification, and prevention capabilities should be strengthened.
As, Cd, Pb, and other heavy metal(loid)s cannot be naturally degraded and are easily enriched in all chains. Acid-based degradation and other methods must also be considered when discharging exhaust gases and treating wastewater [
70,
71].
5. Conclusions
The present findings revealed significant enrichment of As, Hg, Cd, Pb, Ni, Cu, and Zn in construction land soil in Kunming City. The surrounding agricultural land was significantly enriched in As, Hg, Cd, Pb, Cu, and Zn. Cd was the most enriched element in construction and agricultural lands. In the construction land, seven of the heavy metal(loid)s were higher than the background value of Kunming City, except for Cr, and the contents of As, Cd, and Pb in the construction and agricultural land soil samples were all higher than the soil risk screening values of construction and agricultural lands in China.
The PLI results indicated that the study area was heavily polluted. The Igeo values indicated slight pollution by all eight heavy metal(loid)s, with relatively more serious pollution by As, Cd, Cu, Pb, and Zn. The Nemero Integrated Pollution Index showed that the PN for construction and agricultural land exceeded 3. Construction land was more polluted by As and Pb than agricultural land. The As pollution of the construction land was heavy, the Cd and Pb pollution of the construction land was mild, and the Cd pollution of the agricultural land was moderate.
The results of the spatial distribution show that, except for Cr, the distribution characteristics of the other seven elements were relatively similar, and there were noticeably high levels of pollution in the center. Correlation and principal component analyses showed that the sources of pollution were different in general. Arsenic, mercury, cadmium, and lead were primarily derived from industrial and agricultural sources. Traffic sources had a greater impact on Zn, Cd, Pb, and Cr, whereas natural sources were the primary sources of Cr, Ni, Cd, and Ni were also affected by industrial sources. Zn and Cu concentrations were also affected by agricultural sources.
In summary, the surface soil of the study area is severely polluted by As, Cd, and Pb. Cd is a widespread pollutant and should be a priority for the management of soil heavy metal (loid) pollution.
Author Contributions
Conceptualization, Y.Z. and W.L.; methodology, Y.Z. and W.L.; software, W.L.; validation, Y.Z.; formal analysis, W.L. and Y.Z.; investigation, P.W., W.L. and C.S.; resources, Y.Z. and P.W.; data curation, P.W., W.L. and C.S.; writing—original draft preparation, W.L.; writing—review and editing, Y.Z. and W.L.; visualization, W.L.; supervision, Y.Z. and P.W.; project administration, Y.Z. and P.W.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Ten Thousand Talent Program of Yunnan Province (grant no. YNWR-QNBJ-2019-157), the projects of the YM Lab (2011), and the Innovation Team of the Yunnan Province (2008 and 2012).
Data Availability Statement
The dataset used in this study is not publicly available because of data privacy agreements with the Kunming Geological Exploration Institute of the China Metallurgical Geology Bureau. This information can be obtained from the corresponding author upon request.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Fang, B.; Xiao, T.W.; Su, N.N.; Xia, Y.; Shen, Z.G.; Cui, J. Research progress on cadmium uptake and its transport and accumulation among organs in rice. Chin. J. Rice Sci. 2021, 35, 225–237. [Google Scholar]
- Sun, L.; Guo, D.K.; Liu, K.; Meng, H.; Zheng, Y.J.; Yuan, F.Q.; Zhu, G.H. Levels, sources, and spatial distribution of heavy metals in soils from a typical coal industrial city of Tangshan, China. Catena 2019, 175, 101–109. [Google Scholar] [CrossRef]
- Bao, L.R.; Deng, H.; Jia, Z.M.; Li, Y.; Huang, J.X.; Yan, S.M.; Zhang, F.L. Ecological health risk assessment of heavy metals in farmland soils in northwest Xiushan, Chongqing. Geol. China 2020, 47, 1625–1636. [Google Scholar]
- Komárek, M.; Ratié, G.; Vaňková, Z.; Šípková, A.; Chrastný, V. Metal isotope complexation with environmentally relevant surfaces: Opening the isotope fractionation black box. Crit. Rev. Env. Sci. Technol. 2022, 52, 3573–3603. [Google Scholar] [CrossRef]
- Wang, P.; Hu, J.; Liu, T.Y.; Liu, J.K.; Ma, S.R.; Ma, W.M.; Li, J.; Zheng, H.Y.; Lu, R. Advances in the application of metallic isotopes to the identification of contaminant sources in environmental geochemistry. J. Hazard. Mater. 2023, 458, 131913. [Google Scholar] [CrossRef]
- Wiggenhauser, M.; Bigalke, M.; Imseng, M.; Keller, A.; Rehkämper, M.; Wilcke, W.; Frossard, E. Using isotopes to trace freshly applied cadmium through mineral phosphorus fertilization in soil-fertilizer-plant systems. Sci. Total Environ. 2019, 648, 779–786. [Google Scholar] [CrossRef]
- Zhang, Y.X.; Li, T.S.; Guo, Z.H.; Xie, H.M.; Hu, Z.H.; Ran, H.Z.; Li, C.Z.; Jiang, Z.C. Spatial heterogeneity and source apportionment of soil metal(loid)s in an abandoned lead/zinc smelter. J. Environ. Sci. 2023, 127, 519–529. [Google Scholar] [CrossRef]
- Cui, M.; Chen, Y.J.; Yan, C.Q.; Li, J.; Zhang, G. Refined source apportionment of residential and industrial fuel combustion in the Beijing based on real-world source profiles. Sci. Total Environ. 2022, 826, 154101. [Google Scholar] [CrossRef] [PubMed]
- Xie, D.; Li, X.; Zhou, T.T.; Feng, Y.Q. Estimating the contribution of environmental variables to water quality in the postrestoration littoral zones of Taihu Lake using the APCS-MLR model. Sci. Total Environ. 2022, 857, 159678. [Google Scholar] [CrossRef]
- Chen, Z.F.; Ding, Y.F.; Jiang, X.Y.; Duan, H.J.; Ruan, X.L.; Li, Z.H.; Li, Y.P. Combination of UNMIX, PMF model and Pb-Zn-Cu isotopic compositions for quantitative source apportionment of heavy metals in suburban agricultural soils. Ecotox. Environ. Saf. 2022, 234, 113369. [Google Scholar] [CrossRef]
- Anaman, R.; Peng, C.; Jiang, Z.C.; Liu, X.; Zhou, Z.R.; Guo, Z.H.; Xiao, X.Y. Identifying sources and transport routes of heavy metals in soil with different land uses around a smelting site by GIS based PCA and PMF. Sci. Total Environ. 2022, 823, 153759. [Google Scholar] [CrossRef] [PubMed]
- Gove, R.; Cadalzo, L.; Leiby, N.; Singer, J.M.; Zaitzeff, A. New guidance for using t-SNE: Alternative defaults, hyperparameter selection automation, and comparative evaluation. Vis. Inform. 2022, 6, 87–97. [Google Scholar] [CrossRef]
- Kwon, H.; Ali, Z.A.; Wong, B.W. Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs). Environ. Sci. Technol. Let. 2023, 10, 1017–1022. [Google Scholar] [CrossRef] [PubMed]
- Yin, C.J. Inversion of Soil Heavy Metal Pollution in Ganhe Industrial Park, Huangzhong District, Qinghai Province. Master’s Thesis, Chang’an University, Xi’an, China, 2022. [Google Scholar]
- Zhuang, S.K. Environmental Risks and Sources of Heavy Metals and Polycyclic Aromatic Hydrocarbons in Dust of Coal-Related Comprehensive Industrial Park. Master’s Thesis, Shaanxi Normal University, Xi’an, China, 2021. [Google Scholar]
- Chen, Y.L.; Weng, L.P.; Ma, J.; Wu, X.J.; Li, Y.T. Research progress of heavy metal pollution sources in soils in China in recent 10 years. J. Agro-Environ. Sci. 2019, 38, 2219–2238. [Google Scholar]
- Liu, Y.; He, Z.H.; Niu, X.K.; Zhang, D.; Pan, B. Health risk assessment of soil heavy metals in a small watershed of a mining area in Yunnan Province. Environ. Sci. 2022, 43, 936–945. [Google Scholar]
- Dongchuan District People’s Government of Kunming. 14th Five-Year Plan” Industrial High-Quality Development Plan (2021–2025) [EB/OL]; Dongchuan District People’s Government of Kunming: Kunming, China, 2022.
- GB15618-2018; Soil Environmental Quality Construction land Soil Pollution Risk Control Standard. Ministry of Ecology and Environment: Beijing, China, 2018.
- GB36600-2018; Soil Environmental quality Agricultural Land Soil Pollution Risk Control Standard. Ministry of Ecology and Environment: Beijing, China, 2018.
- HJ/T166-2004; The Technical Specification for Soil Environmental Monitoring. General Administration of Environmental Protection of the People’s Republic of China: Beijing, China, 2004.
- HJ804-2016; Soil-Determination of Bioavailable form of Eight Elements. Ministry of Environmental Protection of the People’s Republic of China: Beijing, China, 2016.
- He, R.H.; Xue, Z.J.; Liu, N.; Li, Q.S.; Hu, H.X. Characteristics and evaluation of soil heavy metal pollution under two land use modes. Resour. Environ. Yangtze Basin 2020, 29, 1858–1864. [Google Scholar]
- Rahmanian, M.; Safari, Y. Contamination factor and pollution load index to estimate source apportionment of selected heavy metals in soils around a cement factory, SW Iran. Arch. Agron. Soil Sci. 2022, 68, 903–913. [Google Scholar] [CrossRef]
- Fei, X.F.; Lou, Z.H.; Xiao, R.; Ren, Z.Q.; Lv, X.N. Source analysis and source-oriented risk assessment of heavy metal polution in aricultural soils of diferent cultivated land qualities. J. Clean Prod. 2022, 341, 1786–1879. [Google Scholar] [CrossRef]
- Liu, X.S.; Wang, Y.S.; Yin, D.C.; Wang, X.Q. Distribution characteristics and pollution evaluation of heavy metals in soil of different land use types in Baiyangdian Lake. Chin. J. Soil Sci. 2022, 53, 710–717. [Google Scholar]
- Xiang, M.T.; Li, Y.; Yang, J.Y.; Li, Y.; Li, F.; Hu, B.F.; Cao, Y. Assessment of Heavy Metal Pollution in Soil and Classification of Pollution Risk Management and Control Zones in the Industrial Developed City. Env. Manag. 2020, 66, 1105–1119. [Google Scholar] [CrossRef]
- Chai, L.; Wang, Y.L.; Wang, X.; Ma, L.; Cheng, Z.X.; Su, L.M. Polution characteristics, spatial distributions, and source apportionment of heavy metals in cultivated soil in Lanzho China. Ecol. Indic. 2021, 125, 107507. [Google Scholar] [CrossRef]
- Karimi, N.; Mohammad, T.; Tabatabaii, S.M.; Gholami, A. Geochemical assessment of steel smelter-impacted urban soils, Ahvaz, Iran. J. Geochem. Explor. 2015, 152, 91–109. [Google Scholar] [CrossRef]
- Cheng, H.X.; Li, K.; Li, M.; Yang, K.; Liu, F.; Cheng, X.M. Background and reference values of urban soil chemical elements in China. Earth Sci. Front. 2014, 21, 265–306. [Google Scholar]
- Rudnick, R.L.; Gao, S.L. Composition of the continental trust. In Treatise on Geochemistry; Elsevier: Amsterdam, NY, USA, 2003; Volume 3, pp. 1–64. [Google Scholar]
- Facchinelli, A.; Sacchi, E.; Mallen, L. Multivariate statistical and GIS-based approach to identify heavy metal sources in soils. Environ. Pollut. 2001, 114, 313–324. [Google Scholar] [CrossRef]
- Tang, J.L.; Zhao, K.; Hu, R.X.; Xu, T.; Wang, Y.X.; Yang, Y.; Zhou, B.H. Characteristics, source analysis and pollution assessment of heavy metals in surface soil of Chuzhou. Environ. Sci. 2023, 44, 3562–3572. [Google Scholar]
- Liu, X.Y.; Chen, M.B.; Li, L.Z.; Hu, G.C.; Huang, J.H.; Liu, S.; Zhang, L.J.; Yu, Y.J. Soil heavy metal pollution characteristics and health risk assessment around Huize lead-zinc smelter in Yunnan Province. J. Agric. Resour. Environ. 2016, 33, 221–229. [Google Scholar]
- Ran, J.W.; Ning, P.; Sun, X.; Liang, D.L. Characteristics and potential risks of heavy metal pollution in Gejiu soil crops in Yunnan Province. Environ. Monit. China 2019, 35, 62–68. [Google Scholar]
- Huang, J.X.; Xu, S.C.; Gong, Q.J.; Chen, Z.W.; Huang, Z. Environmental pollution assessment of heavy metals in northeast Huize, Yunnan Province. Acta Geol. Sin. 2022, 43, 93–100. [Google Scholar]
- Liu, Y.; Liu, M.Q.; Wang, L.; Yin, A.J.; Huang, Z.L.; Yao, D.D.; Dai, W.; Wang, N.; Wang, H. Evaluation of heavy metal pollution in farmland around an abandoned silicon plant in Yunnan. J. Agro-Environ. Sci. 2022, 41, 785–793. [Google Scholar]
- Wang, Q.; Xie, Z.Y.; Li, F.B. Using ensemble models to identify and apportion heavy metal pollution sources in agricultural soils on a local scale. Environ. Pollut. 2015, 206, 227–235. [Google Scholar] [CrossRef]
- Zhang, Y.X.; Song, B.; Zhou, Z.Y. Pollution assessment and source apportionment of heavy metals in soil from lead: Zinc mining areas of south China. J. Environ. Chem. Eng. 2023, 11, 109320. [Google Scholar] [CrossRef]
- Wang, H.Z.; Cai, L.M.; Wang, Q.S.; Hu, G.C.; Chen, L.G. A comprehensive exploration of risk assessment and source quantification of potentially toxic elements in road dust: A case study from a large Cu smelter in central China. Catena 2021, 196, 104930. [Google Scholar] [CrossRef]
- Huang, J.L.; Wu, Y.Y.; Sun, J.X.; Li, X.; Geng, X.L.; Zhao, M.L.; Sun, T.; Fan, Z.Q. Health risk assessment of heavy metal(loid)s in park soils of the largest megacity in China by using Monte Carlo simulation coupled with Positive matrix factorization model. J. Hazard. Mater. 2021, 415, 125629. [Google Scholar] [CrossRef] [PubMed]
- Du, X.L.; Zhu, Y.J.; Han, Q.; Yu, Z.Y. The influence of traffic density on heavy metals distribution in urban road runoff in Beijing, China. Environ. Sci. Pollut. Res. 2019, 26, 886–895. [Google Scholar] [CrossRef]
- Liu, L.L.; Liu, Q.Y.; MA, J.; Wu, H.W.; Qu, Y.J.; Gong, Y.W.; Yang, S.H.; An, Y.F.; Zhou, Y.Z. Heavy metal(loid)s in the topsoil of urban parks in Beijing, China: Concentrations, potential sources, and risk assessment. Environ. Pollut. 2020, 260, 114083. [Google Scholar] [CrossRef] [PubMed]
- Qiao, B.W.; Liu, Z.R.; Hu, B.; Liu, J.Y.; Pang, N.N.; Wu, F.K.; Xu, Z.J.; Wang, Y.S. Concentration and source analysis of metal elements in PM2.5 in winter in Beijing. Environ. Sci. 2017, 38, 876–883. [Google Scholar]
- Li, Y.Q.; Zhao, B.W.; Niu, W.J.; Dong, B.; Cai, J.X. Special heavy metal pollution features in atmospheric dust-fall and top soil in main urban districts of Lanzhou. J. Saf. Environ. 2020, 20, 1440–1448. [Google Scholar]
- Shi, X.L.; Zong, Z.; Peng, H.; Zhang, X.J.; Sun, R.; Wang, X.P.; Tian, C.G. Changes of health risks and pollution sources of heavy metals in background atmospheric PM2.5 in North China in recent 10 years. Environ. Sci. 2012, 44, 5335–5343. [Google Scholar]
- Di, Y.A.; Zhou, R.; Yu, Y.; Yan, Y.; Liu, Y.; Ma, Z.Q.; Yang, Y.J. Characteristics and sources of chromium hexavalent in atmospheric particulate matter in urban area of Beijing. Environ. Chem. 2014, 33, 2117–2122. [Google Scholar]
- Zhu, H.P.; Fang, F.M.; Lin, Y.S.; Yao, Y.R.; Wu, M.H. Analysis on the content, sources and potential ecological risks of heavy metals in different media around a cement factory in Digang Town. Environ. Chem. 2017, 36, 2711–2718. [Google Scholar]
- Fei, X.F.; Lou, Z.H.; Xiao, R.; Ren, Z.Q.; Lv, X.N. Contamination assessment and source apportionment of heavy metals in agricultural soil through the synthesis of PMF and GeogDetector models. Sci. Total Environ. 2020, 747, 141293. [Google Scholar] [CrossRef] [PubMed]
- Zhang, X.W.; Wei, S.; Sun, Q.Q.; Wadood, S.A.; Guo, B.L. Source identification and spatial distribution of arsenic and heavy metals in agricultural soil around Hunan industrial estate by positive matrix factorization model, principle components analysis and geo statistical analysis. Ecotox. Environ. Saf. 2018, 159, 354–362. [Google Scholar] [CrossRef]
- Huang, H.W.; Xiao, H.; Wang, D.Q.; Xi, B.D.; Sun, X.J.; Li, J.Y.; Li, X.K. Pollution Characteristics and Health Risk Assessment of Heavy Metals in the Water of Lijiang River Basin. Environ. Sci. 2021, 42, 1714–1723. [Google Scholar]
- Zhang, L.; Zhang, N.M.; Bao, L.; Li, Y.; Zhang, M.; Yang, H.Y.; Lu, H.B. Distribution characteristics and pollution risk assessment of heavy metals in farmland soil in southeast Yunnan. Chin. J. Soil Sci. 2020, 51, 473–480. [Google Scholar]
- Zhang, Y.X.; Wang, M.; Huang, B.; Akhtar, M.S.; Hu, W.Y.; Xie, E.Z. Soil mercury accumulation, spatial distribution and its source identification in an industrial area of the Yangtze Delta, China. Ecotox. Environ. Saf. 2018, 163, 230–237. [Google Scholar] [CrossRef]
- Li, Q.H.; Li, X.X.; Bu, C.J.; Wu, P. Distribution, risk assessment, and source apportionment of heavy metal pollution in cultivated soil of typical mining area in Southwest, China. Environ. Toxicol. Chem. 2023, 42, 888–900. [Google Scholar] [CrossRef]
- Wang, X.K.; Zhang, Y.X.; Huang, B.; Xie, E.Z.; Fan, Y.N.; Hu, W.Y.; Zhao, Y.C.; Qiu, Y.G. Accumulation characteristics and sources of heavy metals in typical urban farmland soils in the Yangtze River Delta. Acta Pedol. Sin. 2021, 58, 82–91. [Google Scholar]
- Jin, Y.L.; O’conno, D.; Ok, Y.S.; Tsang, D.C.W.; Liu, A.; Hou, D.Y. Assessment of sources of heavy metals in soil and dust at children’s playgrounds in Beijing using GIS and multivariate statistical analysis. Environ. Int. 2019, 124, 320–328. [Google Scholar] [CrossRef]
- Jiang, H.H.; Cai, L.M.; Wen, H.H.; Luo, J. Characterizing pollution and source identification of heavy metals in soils using geochemical baseline and PMF approach. Sci. Rep. 2020, 10, 6460. [Google Scholar] [CrossRef]
- Liu, R.L.; Li, S.T.; Wang, X.B.; Wang, M. Status and analysis of heavy metal content in commercial organic fertilizer and organic waste in China. J. Agro-Environ. Sci. 2005, 24, 392–397. [Google Scholar]
- Zhao, D.J.; Wang, X.Q. Heavy metal content, sources and potential ecological risk in floodplain soil of Yunnan, Guizhou and Guangxi. China Environ. Sci. 2020, 40, 1609–1619. [Google Scholar]
- Yang, Y.; Guo, T.T.; Liu, X.L.; Tie, B.Q. Spatial distribution characteristics and pollution assessment of heavy metals in cultivated soil of agricultural small watershed in typical mining areas in Southern China. Environ. Sci. 2023, 44, 1602–1610. [Google Scholar]
- Zeng, J.Q.; Gao, W.Y.; Li, X.; Li, C.X.; Tang, L.; Ke, W.S.; Luo, X.H.; Xue, S.G. Research progress on pollution characteristics and remediation of heavy metals in non-ferrous smelting sites. Chin. J. Non-Ferrous Met. 2023, 33, 3440–3461. [Google Scholar]
- Li, Q.; Cao, Y.; He, L.S.; Wang, Y.F.; Gong, C.; He, S.H. Spatial distribution characteristics and sources of heavy metals in soil of typical smelting industry sites. Environ. Sci. 2021, 42, 5930–5937. [Google Scholar]
- Chen, M.Y.; Zhu, H.H.; She, W.D.; Yin, G.C.; Huang, Z.Z.; Yang, Q.L. Human health risk assessment and source analysis of heavy metals in soil of a legacy shipyard site in the Pearl River Delta. Ecol. Environ. Sci. 2023, 32, 794–804. [Google Scholar]
- Chai, L.; Wang, X.; Ma, L.; Cheng, Z.X.; Su, L.M.; Wang, Y.H. Sources of heavy metals in cultivated soil in Lanzhou based on PMF model. China Environ. Sci. 2020, 40, 3919–3929. [Google Scholar]
- Lu, Y.; Li, Q.; Zhang, Y.; Wang, J.; Yang, L.T.; Ding, Z.Y.; Xu, N.C. Pollution and sources of heavy metals in soil of phosphating plant and surrounding farmland. Environ. Pollut. Control. 2022, 44, 1514–1518+1525. [Google Scholar]
- Dietrich, M.; Huling, J.; Krekeler, M.P.S. Metal pollution investigation of Goldman Park, Middletown Ohio: Evidence for steel and coal pollution in a high child use setting. Sci. Total Environ. 2018, 618, 1350–1362. [Google Scholar] [CrossRef]
- Men, C.; Liu, R.M.; Xu, L.B.; Wang, Q.R.; Guo, L.J.; Miao, Y.X.; Shen, Z.Y. Source-specific ecological risk analysis and critical source identification of heavy metals in road dust in Beijing, China. J. Hazard. Mater. 2020, 388, 121763. [Google Scholar] [CrossRef]
- Ministry of Ecology and Environment of the People’s Republic of China. Opinions on Further Strengthening the Prevention and Control of Heavy Metal Pollution [EB/OL]; Ministry of Ecology and Environment of the People’s Republic of China: Beijing, China, 2022.
- Cai, A.Z. Source Analysis and Pollution Assessment of Heavy Metals in Soil Surrounding Industrial Area of Handan City. Master’s Thesis, Hebei University of Engineering, Handan, China, 2022. [Google Scholar]
- Qi, W.Q.; You, Y.; Lin, Y.C.; Li, T.; Feng, Y.L.; Qiu, L.L. Acceptance monitoring of heavy metal pollution. Labor Prax. 2012, 20–22. [Google Scholar]
- Gan, T.T.; Zhao, N.J.; Yin, G.F.; Ma, M.J.; Meng, D.S.; Fang, L.; Yang, R.F.; Liu, W.Q. Status of heavy metal pollution in agricultural soil in Yangtze River Delta and its prevention and control. Strateg. Study CAE 2021, 23, 174–184. [Google Scholar]
| Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).