A novel ecological risk assessment method of potentially toxic elements based on soil nematode communities
Potentially toxic elements (PTEs) in soil near coal mines threaten soil biota, ecosystem stability, and human health. Soil nematodes, which quickly respond to environmental changes, are reliable biological indicators of PTEs contamination. However, research on establishing a systematic ecological ri...
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| Language: | English |
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Elsevier
2025-08-01
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| Series: | Geoderma |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0016706125002411 |
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| author | Xiujuan Yang Li Cao Bijun Cheng Huirong Duan Zixuan Fu Xiaofang Xu Qianying Xiang Shuhan Wang Xiaoqing Yan Zhihong Zhang Hongmei Zhang |
| author_facet | Xiujuan Yang Li Cao Bijun Cheng Huirong Duan Zixuan Fu Xiaofang Xu Qianying Xiang Shuhan Wang Xiaoqing Yan Zhihong Zhang Hongmei Zhang |
| author_sort | Xiujuan Yang |
| collection | DOAJ |
| description | Potentially toxic elements (PTEs) in soil near coal mines threaten soil biota, ecosystem stability, and human health. Soil nematodes, which quickly respond to environmental changes, are reliable biological indicators of PTEs contamination. However, research on establishing a systematic ecological risk assessment model for PTEs contamination using general community indices and nematode-based indices (NBIs) are limited. To address the research gap, we selected 7 cities in Shanxi Province, China, where coal mining is actively conducted. Bayesian kernel machine regression (BKMR) was used to analyze dose–response relationships of PTEs, general community indices, and NBIs. Additionally, based on the general community indices and NBIs, the study developed ecological risk assessment models of PTEs using machine learning techniques. The results showed moderate pollution with significant spatial and seasonal variations, and PTEs such as Pb, Hg, Mn, and Zn concentrations significantly exceeded (0.2 to 6.35 times) than background values. Structure index (SI), nematode channel ratio (NCR), and maturity index (MI) showed negative linear dose–response relationships with PTEs concentration. The ridge regression (Ridge) model performed the best for the nemerow synthetic pollution index (NSPI) and potential ecological risk index (RI) of comprehensive PTEs, while the random forest (RF) model performed the best for the pollution load index (PLI). NCR, MI, and Shannon-Weaver diversity index (H) were the most important factors in determining NSPI (NCR = 21.08 %, MI = 20.78 %, and H = 18.48 %) and RI (NCR = 20.90 %, MI = 20.90 %, and H = 19.50 %). The results highlight that PTEs contamination near coal mine areas was severe, leading to significant disturbances in nematode community structure. Applying general community indices and NBIs, Ridge and RF models can effectively predict the ecological risks of PTEs. |
| format | Article |
| id | doaj-art-d886b81d9d0440ddb0ee120ce78f79fb |
| institution | DOAJ |
| issn | 1872-6259 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoderma |
| spelling | doaj-art-d886b81d9d0440ddb0ee120ce78f79fb2025-08-20T03:23:34ZengElsevierGeoderma1872-62592025-08-0146011740310.1016/j.geoderma.2025.117403A novel ecological risk assessment method of potentially toxic elements based on soil nematode communitiesXiujuan Yang0Li Cao1Bijun Cheng2Huirong Duan3Zixuan Fu4Xiaofang Xu5Qianying Xiang6Shuhan Wang7Xiaoqing Yan8Zhihong Zhang9Hongmei Zhang10Key Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China; Department of Public Health Laboratory Sciences, School of Public Health, Shanxi Medical University, Taiyuan 030001, China; Academic Affairs Office, Shanxi Medical University, Taiyuan 030001, China; Shanxi Key Laboratory of Functional Proteins, Shanxi Medical University, Taiyuan 030001, China; Corresponding authors at: Key Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China.Key Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China; Department of Public Health Laboratory Sciences, School of Public Health, Shanxi Medical University, Taiyuan 030001, China; Academy of Medical Science, Shanxi Medical University, Taiyuan 030001, China; Department of Environmental Health, Shanxi Medical University, Taiyuan 030001, ChinaKey Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China; Department of Public Health Laboratory Sciences, School of Public Health, Shanxi Medical University, Taiyuan 030001, ChinaKey Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China; Department of Public Health Laboratory Sciences, School of Public Health, Shanxi Medical University, Taiyuan 030001, ChinaKey Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China; School of Management, Shanxi Medical University, Taiyuan 030001, ChinaKey Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China; School of Management, Shanxi Medical University, Taiyuan 030001, ChinaKey Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China; Department of Public Health Laboratory Sciences, School of Public Health, Shanxi Medical University, Taiyuan 030001, ChinaKey Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China; Department of Public Health Laboratory Sciences, School of Public Health, Shanxi Medical University, Taiyuan 030001, ChinaKey Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China; Department of Public Health Laboratory Sciences, School of Public Health, Shanxi Medical University, Taiyuan 030001, ChinaKey Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China; Department of Environmental Health, Shanxi Medical University, Taiyuan 030001, ChinaKey Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China; Department of Environmental Health, Shanxi Medical University, Taiyuan 030001, China; Corresponding authors at: Key Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Shanxi Medical University, Taiyuan 030001, China.Potentially toxic elements (PTEs) in soil near coal mines threaten soil biota, ecosystem stability, and human health. Soil nematodes, which quickly respond to environmental changes, are reliable biological indicators of PTEs contamination. However, research on establishing a systematic ecological risk assessment model for PTEs contamination using general community indices and nematode-based indices (NBIs) are limited. To address the research gap, we selected 7 cities in Shanxi Province, China, where coal mining is actively conducted. Bayesian kernel machine regression (BKMR) was used to analyze dose–response relationships of PTEs, general community indices, and NBIs. Additionally, based on the general community indices and NBIs, the study developed ecological risk assessment models of PTEs using machine learning techniques. The results showed moderate pollution with significant spatial and seasonal variations, and PTEs such as Pb, Hg, Mn, and Zn concentrations significantly exceeded (0.2 to 6.35 times) than background values. Structure index (SI), nematode channel ratio (NCR), and maturity index (MI) showed negative linear dose–response relationships with PTEs concentration. The ridge regression (Ridge) model performed the best for the nemerow synthetic pollution index (NSPI) and potential ecological risk index (RI) of comprehensive PTEs, while the random forest (RF) model performed the best for the pollution load index (PLI). NCR, MI, and Shannon-Weaver diversity index (H) were the most important factors in determining NSPI (NCR = 21.08 %, MI = 20.78 %, and H = 18.48 %) and RI (NCR = 20.90 %, MI = 20.90 %, and H = 19.50 %). The results highlight that PTEs contamination near coal mine areas was severe, leading to significant disturbances in nematode community structure. Applying general community indices and NBIs, Ridge and RF models can effectively predict the ecological risks of PTEs.http://www.sciencedirect.com/science/article/pii/S0016706125002411Heavy metal(loid)sEcological riskNematode-based indices (NBIs)Bayesian kernel machine regression (BKMR)Random forest (RF)Ridge regression (Ridge) |
| spellingShingle | Xiujuan Yang Li Cao Bijun Cheng Huirong Duan Zixuan Fu Xiaofang Xu Qianying Xiang Shuhan Wang Xiaoqing Yan Zhihong Zhang Hongmei Zhang A novel ecological risk assessment method of potentially toxic elements based on soil nematode communities Geoderma Heavy metal(loid)s Ecological risk Nematode-based indices (NBIs) Bayesian kernel machine regression (BKMR) Random forest (RF) Ridge regression (Ridge) |
| title | A novel ecological risk assessment method of potentially toxic elements based on soil nematode communities |
| title_full | A novel ecological risk assessment method of potentially toxic elements based on soil nematode communities |
| title_fullStr | A novel ecological risk assessment method of potentially toxic elements based on soil nematode communities |
| title_full_unstemmed | A novel ecological risk assessment method of potentially toxic elements based on soil nematode communities |
| title_short | A novel ecological risk assessment method of potentially toxic elements based on soil nematode communities |
| title_sort | novel ecological risk assessment method of potentially toxic elements based on soil nematode communities |
| topic | Heavy metal(loid)s Ecological risk Nematode-based indices (NBIs) Bayesian kernel machine regression (BKMR) Random forest (RF) Ridge regression (Ridge) |
| url | http://www.sciencedirect.com/science/article/pii/S0016706125002411 |
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