Source Apportionment and Ecological-Health Risk Assessments of Potentially Toxic Elements in Topsoil of an Agricultural Region in Southwest China
Abstract
:1. Introduction
- To understand pollution levels and geospatial characteristics of PTE concentrations;
- To accurately identify the main sources and quantify contributions by the PMF model combining multivariate statistical analysis in the CoDa perspective;
- To assess potential ecological risk associated with PTEs through PERI;
- To obtain more realistic assessments of health risks using Monte Carlo simulations.
2. Materials and Methods
2.1. Study Area
2.2. Soil Sampling and Testing
2.3. Improved Nemerow Index
2.4. Compositional Data Analysis
2.5. Ecological Risk Index
2.6. Positive Matrix Factorization Model
2.7. Health Risk Assessment Model
3. Results
3.1. Contamination Characteristics of Potentially Toxic Elements
Index | pH | As | Cd | Cr | Cu | Hg | Ni | Pb | Zn |
---|---|---|---|---|---|---|---|---|---|
Min | 4.26 | 4.43 | 0.10 | 35.16 | 11.19 | 0.02 | 13.02 | 14.00 | 45.67 |
Max | 8.29 | 13.95 | 0.59 | 81.28 | 50.82 | 0.40 | 46.25 | 38.48 | 99.90 |
Mean | 6.31 | 8.36 | 0.26 | 59.16 | 23.80 | 0.07 | 28.47 | 25.24 | 70.46 |
CVs | 19.92% | 21.46% | 33.08% | 17.01% | 35.23% | 59.41% | 24.17% | 18.44% | 16.69% |
RSVs | --- | 20.00 | 0.30 | 150.00 | 50.00 | 0.50 | 60.00 | 70.00 | 200.00 |
TVs | --- | 10.4 | 0.079 | 79.00 | 31.10 | 0.06 | 32.60 | 30.90 | 86.50 |
3.2. Geospatial Distributions of Potentially Toxic Elements
4. Discussion
4.1. Soil Acidity and Its Implications for Agriculture
4.2. Source Apportionment of Potentially Toxic Elements
4.2.1. Compositional Associations of the PTEs
4.2.2. Source Identification Based on the PMF Model
4.3. Potential Ecological Risk Assessment
4.4. Human Health Risk Assessment
4.4.1. Deterministic Human Health Risk Assessment
4.4.2. Probabilistic Human Health Risk Assessment
4.4.3. Sensitivity Analysis
4.5. Comparison with Some Agricultural Regions
4.6. Limitations and Future Scope
5. Conclusions
- Elevated elemental concentrations of Cd (mean: 0.26 mg/kg; CV: 33.08%) and Hg (mean: 0.07 mg/kg; CV: 59.41%) were observed, exceeding their respective threshold values (Cd: 0.079 mg/kg; Hg: 0.06 mg/kg). Spatial analysis revealed distinct distribution patterns, with Cd enrichment predominantly occurring in GFGP implementation areas, while Hg accumulation was concentrated in urbanized areas.
- Source apportionment identified four primary contributors to PTE concentrations: soil parent materials (32.48%), traffic emissions (28.31%), atmospheric deposition (21.48%), and agricultural activities (17.86%). Cd, Cu, and Hg are mainly sourced from legacy agricultural effects, soil parent materials, and atmospheric deposition, respectively. Zn, Cr, and Ni exhibited mixed sources (soil parent materials and traffic emissions). The elements As and Pb were primarily associated with traffic emissions, with Pb showing secondary influences from soil parent materials and legacy agricultural effects.
- In total, 60.51% of the sample sites exhibited PERI values (mean > 150) exceeding the threshold for low ecological risk, falling in legacy agricultural regions.
- The PTEs in soils were found to pose a potential non-carcinogenic health risk exclusively to children, while carcinogenic risk assessment results indicated negligible risks for both children and adults. Notably, the risk levels for children consistently exceeded those for adults, highlighting their greater vulnerability to PTE exposure. Among the elements analyzed, As was identified as the most hazardous PTE. Geospatial analysis revealed that the relatively high-risk areas were predominantly concentrated in the GFGP region.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Classification | Description | Classification | Description |
---|---|---|---|
Igeo < 0 | No contamination | INI ≤ 0.5 | No pollution |
0 ≤ Igeo < 1 | Slight to moderate contamination | 0.5 < INI ≤ 1 | Warning limit |
1 ≤ Igeo < 2 | Moderate contamination | 1 < INI ≤ 2 | Slight pollution |
2 ≤ Igeo < 3 | Moderate to high contamination | 2 < INI ≤ 3 | Moderate pollution |
3 ≤ Igeo < 4 | High contamination | INI > 3 | High pollution |
4 ≤ Igeo < 5 | High to extreme contamination | ||
Igeo ≥ 5 | Extremely serious contamination |
Parameter | Value | Unit | References | |
---|---|---|---|---|
Ingestion rate (Ring) | children | 200 | mg/day | [123] |
adults | 100 | |||
Inhalation rate (Rinh) | children | 7.5 | m3/day | [123] |
adults | 14.5 | |||
Exposure frequency (EF) | - | 350 | day/a | [124] |
Exposure duration (ED) | children | 6 | a | [124] |
adults | 24 | |||
Exposed skin area (SA) | children | 2448 | cm2 | [124] |
adults | 5075 | |||
Skin adherence factor (AF) | children | 0.2 | mg/m2·day | [123] |
adults | 0.07 | |||
Dermal absorption factor (ABS) | - | 0.001 | unitless | [123] |
Particle emission factor (PEF) | - | 1.36 × 109 | m3/kg | [123] |
Average exposure time (AT) | carcinogens | 70 × 365 | days | [125] |
non-carcinogens | ED × 365 | |||
Average body weight (BW) | children | 15.9 | kg | [124] |
adults | 56.8 |
PTE | RfD (mg/kg/day) | SF (per mg/kg/day) | ||||
---|---|---|---|---|---|---|
Ingestion | Inhalation | Dermal | Ingestion | Inhalation | Dermal | |
Cr | 3.00 × 103 | 2.86 × 105 | 6.00 × 105 | − | − | − |
Hg | 3.00 × 104 | 7.04 × 105 | 2.10 × 105 | − | − | − |
Ni | 2.00 × 102 | 9.00 × 105 | 5.40 × 103 | − | − | − |
Cu | 4.00 × 102 | 4.00 × 102 | 1.20 × 102 | − | − | − |
Zn | 3.00 × 101 | 3.00 × 101 | 6.00 × 102 | − | − | − |
As | 3.00 × 104 | 1.23 × 104 | 1.20 × 104 | 1.5 | 15.1 | 3.66 |
Cd | 1.00 × 103 | 2.86 × 106 | 2.50 × 105 | 6.1 | 6.3 | 20 |
Pb | 3.00 × 103 | 3.52 × 103 | 5.25 × 104 | 8.50 × 103 | 4.20 × 102 | 4.25 × 101 |
Parameters | Probabilistic Distribution | Reference |
---|---|---|
Ingestion rate (Ring) | Lognormal | [127] |
Inhalation rate (Rinh) | Lognormal | [127] |
Exposure frequency (EF) | Triangular | [128] |
Exposure duration (ED) | Uniform | [129] |
Exposed skin area (SA) | lognormal | [129] |
Skin adherence factor (AF) | Beta | [125] |
Dermal absorption factor (ABS) | Point | [125] |
Particle emission factor (PEF) | Point | [129] |
Average exposure time (AT) | Point | [127] |
Average body weight (BW) | Normal | [127] |
References
- Gomiero, T. Soil Degradation, Land Scarcity and Food Security: Reviewing a Complex Challenge. Sustainability 2016, 8, 281. [Google Scholar] [CrossRef]
- Greiner, L.; Keller, A.; Grêt-Regamey, A.; Papritz, A. Soil function assessment: Review of methods for quantifying the contributions of soils to ecosystem services. Land Use Policy 2017, 69, 224–237. [Google Scholar] [CrossRef]
- Lal, R.; Bouma, J.; Brevik, E.; Dawson, L.; Field, D.J.; Glaser, B.; Hatano, R.; Hartemink, A.E.; Kosaki, T.; Lascelles, B.; et al. Soils and sustainable development goals of the United Nations: An International Union of Soil Sciences perspective. Geoderma Reg. 2021, 25, e00398. [Google Scholar] [CrossRef]
- Christensen, M. Sustainable Agriculture; Springer Nature: Dordrecht, The Netherlands, 2011; Volume 2. [Google Scholar] [CrossRef]
- Agyeman, P.C.; Ahado, S.K.; Borůvka, L.; Biney, J.K.M.; Sarkodie, V.Y.O.; Kebonye, N.M.; Kingsley, J. Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/sediments: A bibliometric review. Environ. Geochem. Health 2021, 43, 1715–1739. [Google Scholar] [CrossRef]
- Pobi, K.K.; Nayek, S.; Gope, M.; Rai, A.K.; Saha, R. Sources evaluation, ecological and health risk assessment of potential toxic metals (PTMs) in surface soils of an industrial area, India. Environ. Geochem. Health 2020, 42, 4159–4180. [Google Scholar] [CrossRef]
- Yadav, M.; George, N.; Dwibedi, V. Emergence of toxic trace elements in plant environment: Insights into potential of silica nanoparticles for mitigation of metal toxicity in plants. Environ. Pollut. 2023, 333, 122112. [Google Scholar] [CrossRef]
- Galhardi, J.A.; de Mello, J.W.; Wilkinson, K.J. Bioaccumulation of potentially toxic elements from the soils surrounding a legacy uranium mine in Brazil. Chemosphere 2020, 261, 127679. [Google Scholar] [CrossRef]
- Naimi, N.; Pilevar, Z.; Ranaei, V.; Mahmudiono, T.; Fakhri, Y.; Paseban, A.; Atamaleki, A.; Janghorban, F.; Khaneghah, A.M. The concentration of potentially toxic elements (PTEs) in apple fruit: A global systematic review, meta-analysis, and health risk assessment. Environ. Sci. Pollut. Res. 2022, 29, 54013–54024. [Google Scholar] [CrossRef] [PubMed]
- Zhang, H.; Pu, M.; Li, H.; Lu, B.; Zhang, X.; Li, S.; Zhao, C.; Pu, W.; Liu, R.; Guo, K.; et al. Progress and prospects for remediation of soil potentially toxic elements pollution: A state-of-the-art review. Environ. Technol. Innov. 2024, 35, 103703. [Google Scholar] [CrossRef]
- Jiang, F.; Ren, B.; Hursthouse, A.; Deng, R.; Wang, Z. Distribution, source identification, and ecological-health risks of potentially toxic elements (PTEs) in soil of thallium mine area (southwestern Guizhou, China). Environ. Sci. Pollut. Res. 2019, 26, 16556–16567. [Google Scholar] [CrossRef]
- Luo, X.; Ren, B.; Hursthouse, A.S.; Jiang, F.; Deng, R.-J. Potentially toxic elements (PTEs) in crops, soil, and water near Xiangtan manganese mine, China: Potential risk to health in the food chain. Environ. Geochem. Health 2020, 42, 1965–1976. [Google Scholar] [CrossRef] [PubMed]
- Terzano, R.; Rascio, I.; Allegretta, I.; Porfido, C.; Spagnuolo, M.; Khanghahi, M.Y.; Crecchio, C.; Sakellariadou, F.; Gattullo, C.E. Fire effects on the distribution and bioavailability of potentially toxic elements (PTEs) in agricultural soils. Chemosphere 2021, 281, 130752. [Google Scholar] [CrossRef] [PubMed]
- More, S.; Dhakate, R. Geogenic and anthropogenic sources of heavy metals in soil: An ecological and health risk assessment in the granitic terrain of South India. CATENA 2025, 254, 108960. [Google Scholar] [CrossRef]
- Safadoust, A.; Khaleghi, S.; Kolahchi, Z. Environmental risks of heavy metals in railway soils: Challenges to ecosystem management. Sci. Total. Environ. 2025, 974, 179217. [Google Scholar] [CrossRef]
- Wang, P.; Yu, F.; Lv, H.; Wu, L.; Zhou, H. Potential risk of heavy metals release in sediments and soils of the Yellow River Basin (Henan section): A perspective on bioavailability and bioaccessibility. Ecotoxicol. Environ. Saf. 2025, 291, 117799. [Google Scholar] [CrossRef]
- Sharafi, S.; Salehi, F. Comprehensive assessment of heavy metal (HMs) contamination and associated health risks in agricultural soils and groundwater proximal to industrial sites. Sci. Rep. 2025, 15, 7518. [Google Scholar] [CrossRef]
- Mondal, S.; Sen, A. Deciphering the consequences of heavy metals and metalloid hazard in agricultural soil of West Bengal: A comprehensive soil to health risk analysis. Sci. Total. Environ. 2025, 970, 178976. [Google Scholar] [CrossRef]
- Chen, X.; Luo, Z.; Wang, Z.; Zhang, W.; Wang, T.; Su, X.; Zeng, C.; Li, Z. Trade-offs between grain supply and soil conservation in the Grain for Green Program under changing climate: A case study in the Three Gorges Reservoir region. Sci. Total. Environ. 2024, 945, 173786. [Google Scholar] [CrossRef]
- Lei, Q.; Qi, C.; Ye, C.; Fang, G. Health shock, the Green for Grain Program and medical expenses: Empirical Evidence on the well-being of Chinese Farmers. Econ. Anal. Policy 2023, 78, 406–418. [Google Scholar] [CrossRef]
- Chen, L.; Beiyuan, J.; Hu, W.; Zhang, Z.; Duan, C.; Cui, Q.; Zhu, X.; He, H.; Huang, X.; Fang, L. Phytoremediation of potentially toxic elements (PTEs) contaminated soils using alfalfa (Medicago sativa L.): A comprehensive review. Chemosphere 2022, 293, 133577. [Google Scholar] [CrossRef]
- Peco, J.D.; Higueras, P.; Campos, J.A.; Esbrí, J.M.; Moreno, M.M.; Battaglia-Brunet, F.; Sandalio, L.M. Abandoned Mine Lands Reclamation by Plant Remediation Technologies. Sustainability 2021, 13, 6555. [Google Scholar] [CrossRef]
- Liu, L.; Wang, J.; Zhai, J.; Yan, D.; Lin, Z. Regional disparities and technological approaches in heavy metal remediation: A comprehensive analysis of soil contamination in Asia. Chemosphere 2024, 366, 143485. [Google Scholar] [CrossRef] [PubMed]
- Cheng, J.; Ding, C.; Li, X.; Zhang, T.; Wang, X. Heavy metals in navel orange orchards of Xinfeng County and their transfer from soils to navel oranges. Ecotoxicol. Environ. Saf. 2015, 122, 153–158. [Google Scholar] [CrossRef] [PubMed]
- Nowack, B.; Schulin, R.; Luster, J. Metal fractionation in a contaminated soil after reforestation: Temporal changes versus spatial variability. Environ. Pollut. 2010, 158, 3272–3278. [Google Scholar] [CrossRef]
- Römkens, P.; Bouwman, L.; Boon, G. Effect of plant growth on copper solubility and speciation in soil solution samples. Environ. Pollut. 1999, 106, 315–321. [Google Scholar] [CrossRef]
- Chen, H.; Tian, Y.; Cai, Y.; Liu, Q.; Ma, J.; Wei, Y.; Yang, A. A 50-year systemic review of bioavailability application in Soil environmental criteria and risk assessment. Environ. Pollut. 2023, 335, 122272. [Google Scholar] [CrossRef]
- Shi, B.; Meng, J.; Wang, T.; Li, Q.; Zhang, Q.; Su, G. The main strategies for soil pollution apportionment: A review of the numerical methods. J. Environ. Sci. 2024, 136, 95–109. [Google Scholar] [CrossRef]
- Wei, D.; Yang, S.; Zou, L.; Torres-Martínez, J.A.; Zheng, Y.; Hu, Q.; Zhang, Y. Appraisal of potential toxic elements pollution, sources apportionment, and health risks in groundwater from a coastal area of SE China. J. Environ. Manag. 2025, 377, 124691. [Google Scholar] [CrossRef]
- Xie, Z.; Liu, W.; Chen, S.; Yao, R.; Yang, C.; Zhang, X.; Li, J.; Wang, Y.; Zhang, Y. Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis. J. Hydrol. Reg. Stud. 2025, 58, 102227. [Google Scholar] [CrossRef]
- Zhao, P.; Wang, Y.; Xie, Y.; Uddin, G.; Xu, Z.; Chang, X.; Zhang, Y. Landslide susceptibility assessment using information quantity and machine learning integrated models: A case study of Sichuan province, southwestern China. Earth Sci. Informatics 2025, 18, 190. [Google Scholar] [CrossRef]
- Razum, I.; Pavlaković, S.M.; Rubinić, V.; Durn, G. New soil weathering index based on compositional data analyses of silt to sand sized parent mineral assemblages of terra rossa soils. J. Geochem. Explor. 2024, 263, 107513. [Google Scholar] [CrossRef]
- Tepanosyan, G.; Poghosyan, Z.; Sahakyan, L. Identification of spatial clusters of potentially toxic elements in different soil types using unsupervised machine learning and compositional data analysis. Soil Environ. Health 2024, 2, 100085. [Google Scholar] [CrossRef]
- Wang, Y.; Yuan, X.; Zhang, Y.; Zhang, X.; Xiao, Y.; Duo, J.; Huang, X.; Sun, M.; Lv, G. Hydrochemical, D–O–Sr isotopic and electromagnetic characteristics of geothermal waters from the Erdaoqiao area, SW China: Insights into genetic mechanism and scaling potential. Ore Geol. Rev. 2023, 158, 105486. [Google Scholar] [CrossRef]
- Deng, L.; Shangguan, Z.-P.; Li, R. Effects of the grain-for-green program on soil erosion in China. Int. J. Sediment Res. 2012, 27, 120–127. [Google Scholar] [CrossRef]
- Nde, S.C.; Palamuleni, L.G.; Aruwajoye, G.S.; Massoukou, R.Y.M.; Richard, G.; Felicite, O.M.; Bett, S.K. Human health risk assessment of potentially toxic elements in soils and rice grains (Oryza sativa) using a combination of probabilistic indices and carcinogenic risk modelling. J. Hazard. Mater. Adv. 2025, 18, 100664. [Google Scholar] [CrossRef]
- Siddig, M.M.; Brevik, E.C.; Sauer, D. Human health risk assessment from potentially toxic elements in the soils of Sudan: A meta-analysis. Sci. Total Environ. 2025, 958, 178196. [Google Scholar] [CrossRef]
- Moriarity, R.J.; Wilton, M.J.; Tsuji, L.J.; Sarkar, A.; Liberda, E.N. Evaluating human health risks from exposure to agricultural soil contaminants using one- and two-dimensional Monte Carlo simulations. Environ. Res. 2025, 265, 120391. [Google Scholar] [CrossRef]
- Shen, H.; Li, X.; Dong, J.; Zheng, X.; Jiang, Y.; Jin, P.; Kui, X.; Liu, H.; Zhang, X.; Yan, X. Risk assessment based on Cr, Mn, Co, Ni, Cu, Zn, Ba, Pb, and Sc contents in soils and blood Pb levels in children: Seasonable variations and Monte Carlo simulations. Soil Environ. Health 2025, 3, 100131. [Google Scholar] [CrossRef]
- Čakmak, D.; Perović, V.; Pavlović, D.; Matić, M.; Jakšić, D.; Tanirbergenov, S.; Pavlović, P. Development of optimisation methods to identify sources of pollution and assess potential health risks in the vicinity of antimony mines. Env. Geochem. Health 2025, 47, 73. [Google Scholar] [CrossRef]
- Khan, M.U.; Rai, N.; Shahwaar, M.; Musahib, M.; Rahman, A. Understanding arsenic and manganese enrichment in the aquifers of the Ghaghara river basin, Middle Gangetic Plain (MGP), India: A multivariate statistical, compositional data analysis (CoDA), and receptor model approach. J. Geochem. Explor. 2024, 264, 107532. [Google Scholar] [CrossRef]
- Ali, W.; Muhammad, S. Compositional data analysis of heavy metal contamination and eco-environmental risks in Himalayan agricultural soils, northern Pakistan. J. Geochem. Explor. 2023, 255, 107323. [Google Scholar] [CrossRef]
- Tepanosyan, G.; Sahakyan, L.; Maghakyan, N.; Saghatelyan, A. Combination of compositional data analysis and machine learning approaches to identify sources and geochemical associations of potentially toxic elements in soil and assess the associated human health risk in a mining city. Environ. Pollut. 2020, 261, 114210. [Google Scholar] [CrossRef] [PubMed]
- Liu, Z.; Wang, L.; Yan, M.; Ma, B.; Cao, R. Source apportionment of soil heavy metals based on multivariate statistical analysis and the PMF model: A case study of the Nanyang Basin, China. Environ. Technol. Innov. 2024, 33, 103537. [Google Scholar] [CrossRef]
- Zhang, Y.; Yan, Y.; Yao, R.; Wei, D.; Huang, X.; Luo, M.; Wei, C.; Chen, S.; Yang, C. Natural background levels, source apportionment and health risks of potentially toxic elements in groundwater of highly urbanized area. Sci. Total Environ. 2024, 935, 173276. [Google Scholar] [CrossRef]
- Mai, X.; Tang, J.; Tang, J.; Zhu, X.; Yang, Z.; Liu, X.; Zhuang, X.; Feng, G.; Tang, L. Research progress on the environmental risk assessment and remediation technologies of heavy metal pollution in agricultural soil. J. Environ. Sci. 2025, 149, 1–20. [Google Scholar] [CrossRef]
- Xiang, Q.; Yu, H.; Chu, H.; Hu, M.; Xu, T.; Xu, X.; He, Z. The potential ecological risk assessment of soil heavy metals using self-organizing map. Sci. Total Environ. 2022, 843, 156978. [Google Scholar] [CrossRef]
- Korkanç, S.Y.; Korkanç, M.; Amiri, A.F. Effects of land use/cover change on heavy metal distribution of soils in wetlands and ecological risk assessment. Sci. Total. Environ. 2024, 923, 171603. [Google Scholar] [CrossRef] [PubMed]
- Fei, X.; Lou, Z.; Sheng, M.; Xiaonan, L.; Ren, Z.; Xiao, R. Source-oriented stochastic health risk assessment of toxic metals in soil via a hybrid model and Monte Carlo simulation. Ecotoxicol. Environ. Saf. 2024, 286, 117209. [Google Scholar] [CrossRef]
- Panqing, Y.; Abliz, A.; Xiaoli, S.; Aisaiduli, H. Human health-risk assessment of heavy metal–contaminated soil based on Monte Carlo simulation. Sci. Rep. 2023, 13, 7033. [Google Scholar] [CrossRef]
- Zhang, Y.; Dai, Y.; Wang, Y.; Huang, X.; Xiao, Y.; Pei, Q. Hydrochemistry, quality and potential health risk appraisal of nitrate enriched groundwater in the Nanchong area, southwestern China. Sci. Total. Environ. 2021, 784, 147186. [Google Scholar] [CrossRef]
- Kravchenko, E.; Minkina, T.; Mandzhieva, S.; Bauer, T.; Lacynnik, E.; Wong, M.H.; Nazarenko, O. Ecological and health risk assessments of heavy metal contamination in soils surrounding a coal power plant. J. Hazard. Mater. 2025, 484, 136751. [Google Scholar] [CrossRef]
- Ma, J.; Ren, W.; Dai, S.; Wang, H.; Chen, S.; Song, J.; Jia, J.; Chen, H.; Tan, C.; Sui, Y.; et al. Spatial distribution and ecological-health risks associated with herbicides in soils and crop kernels of the black soil region in China. Sci. Total. Environ. 2024, 908, 168439. [Google Scholar] [CrossRef]
- Li, R.; Yan, Y.; Xu, J.; Yang, C.; Chen, S.; Wang, Y.; Zhang, Y. Evaluate the groundwater quality and human health risks for sustainable drinking and irrigation purposes in mountainous region of Chongqing, Southwest China. J. Contam. Hydrol. 2024, 264, 104344. [Google Scholar] [CrossRef] [PubMed]
- Guo, G.; Wang, Y.; Zhang, D.; Lei, M. Source-specific ecological and health risks of potentially toxic elements in agricultural soils in Southern Yunnan Province and associated uncertainty analysis. J. Hazard. Mater. 2021, 417, 126144. [Google Scholar] [CrossRef]
- Environmental Biotechnology: For Sustainable Future; Springer: Singapore, 2019. [CrossRef]
- Chen, H.; Teng, Y.; Lu, S.; Wang, Y.; Wang, J. Contamination features and health risk of soil heavy metals in China. Sci. Total Environ. 2015, 512–513, 143–153. [Google Scholar] [CrossRef] [PubMed]
- Guo, G.; Wu, F.; Xie, F.; Zhang, R. Spatial distribution and pollution assessment of heavy metals in urban soils from southwest China. J. Environ. Sci. 2012, 24, 410–418. [Google Scholar] [CrossRef] [PubMed]
- DZ/T 0258-2014; Specification for Multi-Purpose Regional Geochemical Survey (1:250,000). Ministry of Land and Resources of the People’s Republic of China: Beijing, China, 2014.
- Rao, L.; Zheng, C.; Chen, J.-B.; Cai, J.-Z.; Yang, Z.-B.; Xu, X.-X.; Lv, G.-C.; Xu, C.-L.; Wang, G.-Y.; Man, Y.-B.; et al. Ecological and human health hazards of soil heavy metals after wildfire: A case study of Liangshan Yi autonomous prefecture, China. Chemosphere 2024, 352, 141506. [Google Scholar] [CrossRef]
- Yang, Y.; Lu, X.; Yu, B.; Wang, Z.; Wang, L.; Lei, K.; Zuo, L.; Fan, P.; Liang, T. Exploring the environmental risks and seasonal variations of potentially toxic elements (PTEs) in fine road dust in resource-based cities based on Monte Carlo simulation, geo-detector and random forest model. J. Hazard. Mater. 2024, 473, 134708. [Google Scholar] [CrossRef]
- Luo, J.; Feng, S.; Ning, W.; Liu, Q.; Cao, M. Integrated source analysis and network ecological risk assessment of soil heavy metals in Qinghai–Tibet plateau pastoral regions. J. Hazard. Mater. 2025, 490, 137780. [Google Scholar] [CrossRef]
- Muller, G. Index of Geoaccumulation in Sediments of the Rhine River. GeoJournal 1969, 2, 108–118. [Google Scholar]
- Hakanson, L. An ecological risk index for aquatic pollution control. A sedimentological approach. Water Res. 1980, 14, 975–1001. [Google Scholar] [CrossRef]
- Nematollahi, M.J.; Keshavarzi, B.; Zaremoaiedi, F.; Rajabzadeh, M.A.; Moore, F. Ecological-health risk assessment and bioavailability of potentially toxic elements (PTEs) in soil and plant around a copper smelter. Environ. Monit. Assess. 2020, 192, 639. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Q.; Wang, H.; Xu, Z.; Li, G.; Yang, M.; Liu, J. Quantitative identification of groundwater contamination sources by combining isotope tracer technique with PMF model in an arid area of northwestern China. J. Environ. Manag. 2023, 325, 116588. [Google Scholar] [CrossRef]
- US EPA. Exposure Factors Handbook; U.S. Environmental Protection Agency: Washington, DC, USA, 1997.
- McKinney, W. Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, Austin, TX, USA, 28 June–3 July 2010; Volume 445, pp. 51–56. [Google Scholar] [CrossRef]
- Harris, C.R.; Millman, K.J.; van der Walt, S.J.; Gommers, R.; Virtanen, P.; Cournapeau, D.; Wieser, E.; Taylor, J.; Berg, S.; Smith, N.J.; et al. Array programming with NumPy. Nature 2020, 585, 357–362. [Google Scholar] [CrossRef]
- Virtanen, P.; Gommers, R.; Oliphant, T.E.; Haberland, M.; Reddy, T.; Cournapeau, D.; Burovski, E.; Peterson, P.; Weckesser, W.; Bright, J.; et al. SciPy 1.0 Contributors. SciPy 1.0 Fundamental algorithms for scientific computing in Python. Nat. Methods 2020, 17, 261–272. [Google Scholar] [CrossRef] [PubMed]
- Hunter, J.D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 2007, 9, 90–95. [Google Scholar] [CrossRef]
- Wang, J.; Yuan, J.; Hou, Q.; Yang, Z.; You, Y.; Yu, T.; Ji, J.; Dou, L.; Ha, X.; Sheng, W.; et al. Distribution of potentially toxic elements in soils and sediments in Pearl River Delta, China: Natural versus anthropogenic source discrimination. Sci. Total. Environ. 2023, 903, 166573. [Google Scholar] [CrossRef]
- He, L.; Wu, C.; Zeng, D.; Chen, X.; Sun, B. Distribution of Heavy Metals and Ecological Risk of Soils in the Typical Geological Background Region of Southwest China. Rock Miner. Anal. 2021, 40, 384–396. [Google Scholar]
- Wang, Y.; Zhang, Z.; Li, Y.; Liang, C.; Huang, H.; Wang, S. Available heavy metals concentrations in agricultural soils: Relationship with soil properties and total heavy metals concentrations in different industries. J. Hazard. Mater. 2024, 471, 134410. [Google Scholar] [CrossRef]
- Sharma, U.C.; Datta, M.; Sharma, V. Soil Acidity: Management Options for Higher Crop Productivity; Springer Nature: Cham, Switzerland, 2025. [Google Scholar] [CrossRef]
- Xiang, M.; Li, Y.; Yang, J.; Lei, K.; Li, Y.; Li, F.; Zheng, D.; Fang, X.; Cao, Y. Heavy metal contamination risk assessment and correlation analysis of heavy metal contents in soil and crops. Environ. Pollut. 2021, 278, 116911. [Google Scholar] [CrossRef]
- Bonifacio, E.; Falsone, G.; Piazza, S. Linking Ni and Cr concentrations to soil mineralogy: Does it help to assess metal contamination when the natural background is high? J. Soils Sediments 2010, 10, 1475–1486. [Google Scholar] [CrossRef]
- Hu, Z.; Wu, Z.; Luo, W.; Liu, S.; Tu, C. Spatial distribution, risk assessment, and source apportionment of soil heavy metals in a karst county based on grid survey. Sci. Total. Environ. 2024, 953, 176049. [Google Scholar] [CrossRef]
- Kelepertzis, E.; Galanos, E.; Mitsis, I. Origin, mineral speciation and geochemical baseline mapping of Ni and Cr in agricultural topsoils of Thiva Valley (central Greece). J. Geochem. Explor. 2013, 125, 56–68. [Google Scholar] [CrossRef]
- Liu, H.; Zhang, Y.; Yang, J.; Wang, H.; Li, Y.; Shi, Y.; Li, D.; Holm, P.E.; Ou, Q.; Hu, W. Quantitative source apportionment, risk assessment and distribution of heavy metals in agricultural soils from southern Shandong Peninsula of China. Sci. Total Environ. 2021, 767, 144879. [Google Scholar] [CrossRef] [PubMed]
- Zhu, Y.; Hou, K.; Liu, J.; Zhang, L.; Yang, K.; Li, Y.; Yuan, B.; Li, R.; Xue, Y.; Li, H.; et al. Multimodel-based quantitative source apportionment and risk assessment of soil heavy metals: A reliable method to achieve regional pollution traceability and management. Sci. Total Environ. 2024, 956, 177368. [Google Scholar] [CrossRef]
- Zhou, W.; Yu, R.; Guo, F.; Shen, C.; Liu, Y.; Huang, Y. Source apportionment and risk assessment of soil heavy metals in the Huangshui River Basin using a hybrid model. Ecol. Indic. 2024, 160, 111906. [Google Scholar] [CrossRef]
- Qiao, P.; Wang, S.; Lei, M.; Guo, G.; Yang, J.; Wei, Y.; Gou, Y.; Li, P.; Zhang, Z. Influencing factors identification and the nested structure analysis of heavy metals in soils in entire city and surrounding the multiple pollution sources. J. Hazard. Mater. 2023, 449, 130961. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Jiang, B.; Gao, Z.; Wang, M.; Feng, J.; Xia, L.; Liu, J. Health risk assessment of soil heavy metals in a typical mining town in north China based on Monte Carlo simulation coupled with Positive matrix factorization model. Environ. Res. 2024, 251, 118696. [Google Scholar] [CrossRef]
- Łukaszek-Chmielewska, A.; Rakowska, J.; Rachwał, M.; Stawarz, O. Assessment of forest soil contamination by heavy metals in the Polish National Park near Warsaw. Sci. Rep. 2025, 15, 4099. [Google Scholar] [CrossRef]
- Zhang, Y.; Shang, P.; Wang, J.; Norris, P.; Romero, C.E.; Pan, W.-P. Trace element (Hg, As, Cr, Cd, Pb) distribution and speciation in coal-fired power plants. Fuel 2017, 208, 647–654. [Google Scholar] [CrossRef]
- Gałuszka, A. Different Approaches in Using and Understanding the Term “Geochemical Background”–Practical Implications for Environmental Studies. Pol. J. Environ. Stud. 2006, 16, 3. [Google Scholar]
- Prasad, S.; Yadav, K.K.; Kumar, S.; Gupta, N.; Cabral-Pinto, M.M.; Rezania, S.; Radwan, N.; Alam, J. Chromium contamination and effect on environmental health and its remediation: A sustainable approaches. J. Environ. Manag. 2021, 285, 112174. [Google Scholar] [CrossRef] [PubMed]
- Liu, S.; Wang, X.; Guo, G.; Yan, Z. Status and environmental management of soil mercury pollution in China: A review. J. Environ. Manag. 2021, 277, 111442. [Google Scholar] [CrossRef]
- Zhao, B.; O’Connor, D.; Zhang, H.; Jin, Y.; Wang, Y.; Yang, X.; Hou, R.; Hou, D. Assessing mercury pollution at a primary ore site with both ancient and industrial mining and smelting activities. Environ. Pollut. 2023, 336, 122413. [Google Scholar] [CrossRef] [PubMed]
- Yang, J.; Ma, H.; Zhang, R.; Ji, W. Effects of “Grain for Green” program on soil hydraulic properties: A meta-analysis. Geoderma 2025, 453, 117130. [Google Scholar] [CrossRef]
- Khan, A.; Khan, M.S.; Hadi, F.; Khan, Q.; Ali, K.; Saddiq, G. Risk assessment and soil heavy metal contamination near marble processing plants (MPPs) in district Malakand, Pakistan. Sci. Rep. 2024, 14, 21533. [Google Scholar] [CrossRef]
- Man, Y.B.; Sun, X.L.; Zhao, Y.G.; Lopez, B.N.; Chung, S.S.; Wu, S.C.; Cheung, K.C.; Wong, M.H. Health risk assessment of abandoned agricultural soils based on heavy metal contents in Hong Kong, the world’s most populated city. Environ. Int. 2010, 36, 570–576. [Google Scholar] [CrossRef]
- Olawoyin, R.; Oyewole, S.A.; Grayson, R.L. Potential risk effect from elevated levels of soil heavy metals on human health in the Niger delta. Ecotoxicol. Environ. Saf. 2012, 85, 120–130. [Google Scholar] [CrossRef]
- Zhang, Y.; Jiang, B.; Gao, Z.; Liu, J. Source-specific probabilistic health risk judgement of soil heavy metals in a typical resource-based town in North China. Ecol. Indic. 2024, 169, 112854. [Google Scholar] [CrossRef]
- Cai, L.-M.; Wang, Q.-S.; Luo, J.; Chen, L.-G.; Zhu, R.-L.; Wang, S.; Tang, C.-H. Heavy metal contamination and health risk assessment for children near a large Cu-smelter in central China. Sci. Total Environ. 2019, 650, 725–733. [Google Scholar] [CrossRef]
- de Carvalho, M.R.; de Almeida, T.A.; Van Opbergen, G.A.Z.; Bispo, F.H.A.; Botelho, L.; de Lima, A.B.; Marchiori, P.E.R.; Guilherme, L.R.G. Arsenic, cadmium, and chromium concentrations in contrasting phosphate fertilizers and their bioaccumulation by crops: Towards a green label? Environ. Res. 2024, 263, 120171. [Google Scholar] [CrossRef] [PubMed]
- Guo, G.; Chen, S.; Lei, M.; Wang, L.; Yang, J.; Qiao, P. Spatiotemporal distribution characteristics of potentially toxic elements in agricultural soils across China and associated health risks and driving mechanism. Sci. Total Environ. 2023, 887, 163897. [Google Scholar] [CrossRef]
- Suciu, N.A.; De Vivo, R.; Rizzati, N.; Capri, E. Cd content in phosphate fertilizer: Which potential risk for the environment and human health? Curr. Opin. Environ. Sci. Health 2022, 30, 100392. [Google Scholar] [CrossRef]
- Jiao, W.; Chen, W.; Chang, A.C.; Page, A.L. Environmental risks of trace elements associated with long-term phosphate fertilizers applications: A review. Environ. Pollut. 2012, 168, 44–53. [Google Scholar] [CrossRef]
- Nziguheba, G.; Smolders, E. Inputs of trace elements in agricultural soils via phosphate fertilizers in European countries. Sci. Total Environ. 2008, 390, 53–57. [Google Scholar] [CrossRef] [PubMed]
- Gao, L.; Wang, Z.; Li, S.; Chen, J. Bioavailability and toxicity of trace metals (Cd, Cr, Cu, Ni, and Zn) in sediment cores from the Shima River, South China. Chemosphere 2018, 192, 31–42. [Google Scholar] [CrossRef]
- Li, Y.; Rahman, S.U.; Qiu, Z.; Shahzad, S.M.; Nawaz, M.F.; Huang, J.; Naveed, S.; Li, L.; Wang, X.; Cheng, H. Toxic effects of cadmium on the physiological and biochemical attributes of plants, and phytoremediation strategies: A review. Environ. Pollut. 2023, 325, 121433. [Google Scholar] [CrossRef] [PubMed]
- Nour, H.; Ramadan, F.; Wahed, N.A.; Rakha, A. Spatial distribution and contamination of specific heavy metals in the sediment of Bahr Mouse, Egypt. Egypt. J. Chem. 2024, 67, 99–109. [Google Scholar] [CrossRef]
- Deng, W.; Hao, G.; Liu, W. Source-specific risks apportionment and critical sources identification of potentially harmful elements in urban road dust combining positive matrix factorization model with multiple attribute decision making method. Ecol. Indic. 2022, 144, 109449. [Google Scholar] [CrossRef]
- Jin, Z.; Lv, J. Evaluating source-oriented human health risk of potentially toxic elements: A new exploration of multiple age groups division. Sci. Total Environ. 2021, 787, 147502. [Google Scholar] [CrossRef]
- Rocha, F.; Ratola, N.; Homem, V. Heavy metal(loid)s and nutrients in sewage sludge in Portugal–Suitability for use in agricultural soils and assessment of potential risks. Sci. Total Environ. 2025, 964, 178595. [Google Scholar] [CrossRef] [PubMed]
- Wu, Z.-Q.; Li, H.-Y.; Lu, L.-Y.; Liang, G.-J.; Wu, T.-T.; Zhu, J.-X. Distributions and risk assessment of heavy metals in solid waste in lead-zinc mining areas and across the soil water body sediment agricultural product ecosystem in their surrounding areas. China Geol. 2024, 7, 92–106. [Google Scholar] [CrossRef]
- Yang, J.; Han, Z.; Yan, Y.; Guo, G.; Wang, L.; Shi, H.; Liao, X. Neglected pathways of heavy metal input into agricultural soil: Water–land migration of heavy metals due to flooding events. Water Res. 2024, 267, 122469. [Google Scholar] [CrossRef]
- Castañeda, M.; Avila, B.S.; Ríos, S.E.G.; Peñuela, G.A. Levels of heavy metals in tropical fruits and soils from agricultural crops in Antioquia, Colombia. A probabilistic assessment of health risk associated with their consumption. Food Humanit. 2025, 4, 100503. [Google Scholar] [CrossRef]
- Eliyan, C.; McConville, J.; Zurbrügg, C.; Koottatep, T.; Sothea, K.; Vinnerås, B. Heavy metal contamination of faecal sludge for agricultural production in Phnom Penh, Cambodia. J. Environ. Manag. 2024, 349, 119436. [Google Scholar] [CrossRef]
- Suh, G.C.; Afahnwie, N.A.; Tiabou, A.F.; Djibril, K.N.G.; Meniemoh, A.R.; Yiika, L.P. Source apportionment, ecological and toxicological risk assessment of trace metals in agricultural soils of Wabane, South West Region, Cameroon. J. Trace Elements Miner. 2025, 12, 100218. [Google Scholar] [CrossRef]
- Khodrani, N.E.; Omrania, S.; Zouahri, A.; Douaik, A.; Iaaich, H.; Yahyaoui, A.; Fekhaoui, M. Spatial Distribution and Mapping of Heavy Metals in Agricultural Soils of the Sfafaa region (Gharb, Morocco). Mater. Today Proc. 2019, 13, 832–840. [Google Scholar] [CrossRef]
- Barsova, N.; Yakimenko, O.; Tolpeshta, I.; Motuzova, G. Current state and dynamics of heavy metal soil pollution in Russian Federation—A review. Environ. Pollut. 2019, 249, 200–207. [Google Scholar] [CrossRef]
- Suska-Malawska, M.; Vyrakhamanova, A.; Ibraeva, M.; Poshanov, M.; Sulwiński, M.; Toderich, K.; Mętrak, M. Spatial and In-Depth Distribution of Soil Salinity and Heavy Metals (Pb, Zn, Cd, Ni, Cu) in Arable Irrigated Soils in Southern Kazakhstan. Agronomy 2022, 12, 1207. [Google Scholar] [CrossRef]
- Ramazanova, E.; Lee, S.H.; Lee, W. Stochastic risk assessment of urban soils contaminated by heavy metals in Kazakhstan. Sci. Total. Environ. 2021, 750, 141535. [Google Scholar] [CrossRef]
- Obada, M.C.C.; Oliveira, M.E.F.Z.; Elguera, N.Y.M.; Pulcha, S.E.S.; Banda, A.A.P.; Ortiz, J.A.Q.; Murillo, V.S.; Pacheco, H.G.J. Assessment of heavy metal contamination in surface sediments: Seasonal influence in the Majes-Camaná basin of the Arequipa region, Peru. Results Eng. 2025, 25, 103673. [Google Scholar] [CrossRef]
- Alharbi, T.; Nour, H.E.; El-Sorogy, A.S.; Al-Kahtany, K.; Giacobbe, S.; Alarifi, S.S. Evaluation of health risks and heavy metals toxicity in agricultural soils in Central Saudi Arabia. Environ. Monit. Assess. 2025, 197, 419. [Google Scholar] [CrossRef] [PubMed]
- Qi, C.; Hu, T.; Zheng, Y.; Wu, M.; Tang, F.H.M.; Liu, M.; Zhang, B.; Derrible, S.; Chen, Q.; Hu, G.; et al. Global and regional patterns of soil metal(loid) mobility and associated risks. Nat. Commun. 2025, 16, 2947. [Google Scholar] [CrossRef] [PubMed]
- Ma, Y.; Huang, L.; Li, J.; Cao, W.; Cai, Y. Carbon potential of China’s Grain to Green Program and its contribution to the carbon target. Resour. Conserv. Recycl. 2024, 200, 107272. [Google Scholar] [CrossRef]
- Persson, M.; Moberg, J.; Ostwald, M.; Xu, J. The Chinese Grain for Green Programme: Assessing the carbon sequestered via land reform. J. Environ. Manag. 2013, 126, 142–146. [Google Scholar] [CrossRef]
- Zhang, L. Understanding the new quality productive forces in the energy sector. Energy Nexus 2024, 16, 100352. [Google Scholar] [CrossRef]
- US EPA. Soil Screening Guidance: Technical Background Document; Superfund US EPA: Washington, DC, USA, 1996. [Google Scholar]
- HJ 25.3-2014; Technical Guidelines for Risk Assessment of Contaminated Sites. Ministry of Environmental Protection of the People’s Republic of China (MEPPRC): Beijing, China, 2014. (In Chinese)
- EPA/600/R-09/052F; Exposure Factors Handbook: 2011 Edition. National Center for Environmental Assessment Office of Research and Development U.S. Environmental Protection Agency (US EPA): Washington, DC, USA, 2011.
- US EPA. Region IX. Regional Screening Levels (Formerly PRGs); US EPA: San Francisco, CA, USA, 2013. Available online: http://www.epa.gov/region9/superfund/prg/ (accessed on 2 May 2025).
- Duan, X.L.; Zhao, X.G.; Wang, B.B.; Chen, Y.T.; Cao, S.Z. Highlight of the Chinese Exposure Factors Handbook (Adults); China Environmental Science Press: Beijing, China, 2014. [Google Scholar]
- Chen, G.; Wang, X.; Wang, R.; Liu, G. Health risk assessment of potentially harmful elements in subsidence water bodies using a Monte Carlo approach: An example from the Huainan coal mining area, China. Ecotoxicol. Environ. Saf. 2019, 171, 737–745. [Google Scholar] [CrossRef]
- OSWER 9355.4-24; Supplemental Guidance for Developing Soil Screening Levels for Superfund Sites. Office of Solid Waste and Emergency Response: Washington, DC, USA, 2001.
Ei | PERI | Classification |
---|---|---|
<40 | <150 | Low risk |
40–80 | 150–300 | Moderate risk |
80–160 | 300–600 | Considerable risk |
160–320 | 600–1200 | High risk |
≥320 | ≥1200 | Extremely high risk |
PTEs | Population | Non-Carcinogenic Risk | Carcinogenic Risk | ||||
---|---|---|---|---|---|---|---|
Min | Max | Mean | Min | Max | Mean | ||
As | Children | 0.18 | 0.56 | 0.34 | 6.92 × 10−6 | 2.18 × 10−5 | 1.3 × 10−5 |
Adults | 2.52 × 10−2 | 7.92 × 10−2 | 4.75 × 10−2 | 3.89 × 10−6 | 1.22 × 10−5 | 7.33 × 10−6 | |
Cd | Children | 1.31 × 10−3 | 7.84 × 10−3 | 3.52 × 10−3 | 6.23 × 10−7 | 3.73 × 10−6 | 1.67 × 10−6 |
Adults | 1.95 × 10−4 | 1.17 × 10−3 | 5.24 × 10−4 | 3.5 × 10−7 | 2.1 × 10−6 | 9.41 × 10−7 | |
Cr | Children | 0.16 | 0.37 | 0.27 | --- | --- | --- |
Adults | 2.35 × 10−2 | 5.44 × 10−2 | 3.96 × 10−2 | --- | --- | --- | |
Cu | Children | 3.40 × 10−3 | 1.54 × 10−2 | 7.24 × 10−3 | --- | --- | --- |
Adults | 4.78 × 10−4 | 2.17 × 10−3 | 1.02 × 10−3 | --- | --- | --- | |
Hg | Children | 8.74 × 10−4 | 1.65 × 10−2 | 2.73 × 10−3 | --- | --- | --- |
Adults | 1.24 × 10−4 | 2.34 × 10−3 | 3.87 × 10−4 | --- | --- | --- | |
Ni | Children | 7.97 × 10−3 | 2.65 × 10−2 | 1.74 × 10−2 | --- | --- | --- |
Adults | 1.14 × 10−3 | 3.79 × 10−3 | 2.49 × 10−3 | --- | --- | --- | |
Pb | Children | 5.71 × 10−2 | 0.16 | 0.10 | 1.38 × 10−7 | 3.8 × 10−7 | 2.49 × 10−7 |
Adults | 8.04 × 10−3 | 2.21 × 10−2 | 1.45 × 10−2 | 8.11 × 10−8 | 2.23 × 10−7 | 1.46 × 10−7 | |
Zn | Children | 1.86 × 10−3 | 4.07 × 10−4 | 2.87 × 10−3 | --- | --- | --- |
Adults | 2.62 × 10−4 | 5.72 × 10−4 | 4.04 × 10−4 | --- | --- | --- | |
Total | Children | 0.53 | 1.09 | 0.74 | 8.48 × 10−6 | 2.35 × 10−5 | 1.5 × 10−5 |
Adults | 0.07 | 0.16 | 0.11 | 4.77 × 10−6 | 1.32 × 10−5 | 8.42 × 10−6 |
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. |
© 2025 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/).
Share and Cite
Wang, Y.; Yang, S.; Wei, D.; Li, H.; Luo, M.; Zhao, X.; Zhang, Y.; Wang, Y. Source Apportionment and Ecological-Health Risk Assessments of Potentially Toxic Elements in Topsoil of an Agricultural Region in Southwest China. Land 2025, 14, 1192. https://doi.org/10.3390/land14061192
Wang Y, Yang S, Wei D, Li H, Luo M, Zhao X, Zhang Y, Wang Y. Source Apportionment and Ecological-Health Risk Assessments of Potentially Toxic Elements in Topsoil of an Agricultural Region in Southwest China. Land. 2025; 14(6):1192. https://doi.org/10.3390/land14061192
Chicago/Turabian StyleWang, Yangshuang, Shiming Yang, Denghui Wei, Haidong Li, Ming Luo, Xiaoyan Zhao, Yunhui Zhang, and Ying Wang. 2025. "Source Apportionment and Ecological-Health Risk Assessments of Potentially Toxic Elements in Topsoil of an Agricultural Region in Southwest China" Land 14, no. 6: 1192. https://doi.org/10.3390/land14061192
APA StyleWang, Y., Yang, S., Wei, D., Li, H., Luo, M., Zhao, X., Zhang, Y., & Wang, Y. (2025). Source Apportionment and Ecological-Health Risk Assessments of Potentially Toxic Elements in Topsoil of an Agricultural Region in Southwest China. Land, 14(6), 1192. https://doi.org/10.3390/land14061192