Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils
Abstract The accumulation of heavy metal(loid)s (HMs) in the soils of urban parks in industrial cities has raised global concerns because of their environmental and health impacts. However, traditional deterministic assessments commonly overlook uncertainties in pollution evaluation, failing to accu...
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Nature Portfolio
2025-05-01
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| Online Access: | https://doi.org/10.1038/s41598-025-02307-1 |
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| author | Jun Li Xu Li Xi-Sheng Tai Xin-Ying Tuo Fa-Yuan Zhou Yi-Jing Rong Fei Zang |
| author_facet | Jun Li Xu Li Xi-Sheng Tai Xin-Ying Tuo Fa-Yuan Zhou Yi-Jing Rong Fei Zang |
| author_sort | Jun Li |
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| description | Abstract The accumulation of heavy metal(loid)s (HMs) in the soils of urban parks in industrial cities has raised global concerns because of their environmental and health impacts. However, traditional deterministic assessments commonly overlook uncertainties in pollution evaluation, failing to accurately quantify source-specific contributions and associated risks. This study combines multivariate statistical methods, machine learning techniques, and positive matrix factorization (PMF) with Monte Carlo simulation to investigate HM sources, probabilistic pollution levels, source-based ecological risks, and population-specific health hazards in seven urban parks in a nickel-based mining city in China. Results showed that average concentrations of Cd (0.53 mg/kg), Cr (77.72 mg/kg), Cu (171.15 mg/kg), Hg (0.03 mg/kg), Ni (125.42 mg/kg), Pb (27.13 mg/kg), and Zn (81.97 mg/kg) exceeded their background values, except for As (11.85 mg/kg), particularly for Cd, Cu, and Ni, with exceedance rates of 98.46%, 100.00%, and 100.00%, respectively. Probabilistic assessments revealed that pollution levels were particularly high due to Cd, Cu, and Ni. Source apportionment using PMF, correlation analysis, hierarchical cluster analysis, and super-clustering of self-organizing maps identified fertilizers and pesticides (19.33%), industrial atmospheric deposition (21.13%), mining and agrochemicals (16.41%), and mining and transport activities (43.13%) as the major pollution sources. Probabilistic ecological risk assessments showed significant risks from Cd, Hg, and Cu. Non-carcinogenic risks were negligible, while carcinogenic risks were cautionary, especially for children. Mining and transportation activities were the main contributors to ecological risks, while fertilizers, pesticides, and Ni were the primary health risk factors. This study provides a robust framework to improve the accuracy of risk evaluation and offers valuable guidance for targeted interventions and sustainable management of urban soils. |
| format | Article |
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| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-dc8bf0777f244cb1bc8899740f954a3e2025-08-20T03:08:25ZengNature PortfolioScientific Reports2045-23222025-05-0115111510.1038/s41598-025-02307-1Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soilsJun Li0Xu Li1Xi-Sheng Tai2Xin-Ying Tuo3Fa-Yuan Zhou4Yi-Jing Rong5Fei Zang6School of Environment and Urban Construction, Lanzhou City UniversitySchool of Environment and Urban Construction, Lanzhou City UniversitySchool of Environment and Urban Construction, Lanzhou City UniversitySchool of Environment and Urban Construction, Lanzhou City UniversitySchool of Environment and Urban Construction, Lanzhou City UniversitySchool of Environment and Urban Construction, Lanzhou City UniversityCollege of Pastoral Agriculture Science and Technology, Lanzhou UniversityAbstract The accumulation of heavy metal(loid)s (HMs) in the soils of urban parks in industrial cities has raised global concerns because of their environmental and health impacts. However, traditional deterministic assessments commonly overlook uncertainties in pollution evaluation, failing to accurately quantify source-specific contributions and associated risks. This study combines multivariate statistical methods, machine learning techniques, and positive matrix factorization (PMF) with Monte Carlo simulation to investigate HM sources, probabilistic pollution levels, source-based ecological risks, and population-specific health hazards in seven urban parks in a nickel-based mining city in China. Results showed that average concentrations of Cd (0.53 mg/kg), Cr (77.72 mg/kg), Cu (171.15 mg/kg), Hg (0.03 mg/kg), Ni (125.42 mg/kg), Pb (27.13 mg/kg), and Zn (81.97 mg/kg) exceeded their background values, except for As (11.85 mg/kg), particularly for Cd, Cu, and Ni, with exceedance rates of 98.46%, 100.00%, and 100.00%, respectively. Probabilistic assessments revealed that pollution levels were particularly high due to Cd, Cu, and Ni. Source apportionment using PMF, correlation analysis, hierarchical cluster analysis, and super-clustering of self-organizing maps identified fertilizers and pesticides (19.33%), industrial atmospheric deposition (21.13%), mining and agrochemicals (16.41%), and mining and transport activities (43.13%) as the major pollution sources. Probabilistic ecological risk assessments showed significant risks from Cd, Hg, and Cu. Non-carcinogenic risks were negligible, while carcinogenic risks were cautionary, especially for children. Mining and transportation activities were the main contributors to ecological risks, while fertilizers, pesticides, and Ni were the primary health risk factors. This study provides a robust framework to improve the accuracy of risk evaluation and offers valuable guidance for targeted interventions and sustainable management of urban soils.https://doi.org/10.1038/s41598-025-02307-1Green spacesHeavy metal(loid)s pollutionEcological-health riskSource apportionmentMonte Carlo simulation |
| spellingShingle | Jun Li Xu Li Xi-Sheng Tai Xin-Ying Tuo Fa-Yuan Zhou Yi-Jing Rong Fei Zang Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils Scientific Reports Green spaces Heavy metal(loid)s pollution Ecological-health risk Source apportionment Monte Carlo simulation |
| title | Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils |
| title_full | Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils |
| title_fullStr | Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils |
| title_full_unstemmed | Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils |
| title_short | Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils |
| title_sort | machine learning assisted source identification and probabilistic ecological health risk assessment of heavy metal loid s in urban park soils |
| topic | Green spaces Heavy metal(loid)s pollution Ecological-health risk Source apportionment Monte Carlo simulation |
| url | https://doi.org/10.1038/s41598-025-02307-1 |
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