Advanced matter-element extension model and machine learning for source-specific probabilistic health risk assessment of heavy metals/metaloids in soil-rose systems in Kushui, Northwest China
Heavy metals/metaloids (HMs) contamination in soil-agricultural systems is a global environmental concern, with significant implications for food safety and public health. However, research focusing on accurate contamination assessment and precise source identification with source-specific probabili...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24014742 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832576523889016832 |
|---|---|
| author | Jun Li Li-Bang Ma Jun-Zhuo Liu Xu Li Yun-Qin Yang Xi-Sheng Tai Fa-Yuan Zhou Fei Zang |
| author_facet | Jun Li Li-Bang Ma Jun-Zhuo Liu Xu Li Yun-Qin Yang Xi-Sheng Tai Fa-Yuan Zhou Fei Zang |
| author_sort | Jun Li |
| collection | DOAJ |
| description | Heavy metals/metaloids (HMs) contamination in soil-agricultural systems is a global environmental concern, with significant implications for food safety and public health. However, research focusing on accurate contamination assessment and precise source identification with source-specific probabilistic health risk in soil-rose systems, particularly in semi-arid regions, is scarce. This study introduces a comprehensive framework utilizing the contamination factor (CF), geo-accumulation index (Igeo), pollution loading index (PLI), and improved matter-element extension model (IMEM) for enhanced contamination evaluation, combines correlation analysis (CA), self-organizing map (SOM), and positive matrix factorization (PMF), for precise source analysis, and applies source-specific probabilistic health risk assessment in the soil-rose system. The results showed that approximately 13.40 %, 63.92 %, 84.54 %, 89.69 %, 89.69 %, 92.78 %, 94.85 %, and 65.88 % of Hg, Cr, Cd, Ni, Pb, As, Zn, and Cu in soils exceeded their background values. Despite low bioconcentration in roses, Hg levels in 5.62 % of samples surpassed safety standards. Soils displayed varying levels of HM contamination, especially from As, Cd, Cu, and Zn, largely attributed to household coal burning, industrial emissions, fertilization and pesticide application, and road traffic based on multiple approaches. Source-specific probabilistic risk assessment indicated soil HMs posed carcinogenic threats to adults and children, while roses were considered safe for consumption. Road emissions were identified as the principal contributors to health risks, with Cr and Ni identified as priority control elements. This framework refines contamination assessment, enhances source apportionment, and provides a scalable model for global agricultural and environmental risk management. |
| format | Article |
| id | doaj-art-0679455af69b41f19c48217f7d3661fc |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-0679455af69b41f19c48217f7d3661fc2025-01-31T05:10:30ZengElsevierEcological Indicators1470-160X2025-01-01170113017Advanced matter-element extension model and machine learning for source-specific probabilistic health risk assessment of heavy metals/metaloids in soil-rose systems in Kushui, Northwest ChinaJun Li0Li-Bang Ma1Jun-Zhuo Liu2Xu Li3Yun-Qin Yang4Xi-Sheng Tai5Fa-Yuan Zhou6Fei Zang7School of Environment and Urban Construction, Lanzhou City University, Lanzhou 730070, China; College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China; Corresponding author at: No. 11, Jiefang Road, Anning District, School of Environment and Urban Construction, Lanzhou City University, Lanzhou 730070, China.College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, ChinaState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, ChinaSchool of Environment and Urban Construction, Lanzhou City University, Lanzhou 730070, ChinaSchool of Environment and Urban Construction, Lanzhou City University, Lanzhou 730070, ChinaSchool of Environment and Urban Construction, Lanzhou City University, Lanzhou 730070, ChinaSchool of Environment and Urban Construction, Lanzhou City University, Lanzhou 730070, ChinaCollege of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, ChinaHeavy metals/metaloids (HMs) contamination in soil-agricultural systems is a global environmental concern, with significant implications for food safety and public health. However, research focusing on accurate contamination assessment and precise source identification with source-specific probabilistic health risk in soil-rose systems, particularly in semi-arid regions, is scarce. This study introduces a comprehensive framework utilizing the contamination factor (CF), geo-accumulation index (Igeo), pollution loading index (PLI), and improved matter-element extension model (IMEM) for enhanced contamination evaluation, combines correlation analysis (CA), self-organizing map (SOM), and positive matrix factorization (PMF), for precise source analysis, and applies source-specific probabilistic health risk assessment in the soil-rose system. The results showed that approximately 13.40 %, 63.92 %, 84.54 %, 89.69 %, 89.69 %, 92.78 %, 94.85 %, and 65.88 % of Hg, Cr, Cd, Ni, Pb, As, Zn, and Cu in soils exceeded their background values. Despite low bioconcentration in roses, Hg levels in 5.62 % of samples surpassed safety standards. Soils displayed varying levels of HM contamination, especially from As, Cd, Cu, and Zn, largely attributed to household coal burning, industrial emissions, fertilization and pesticide application, and road traffic based on multiple approaches. Source-specific probabilistic risk assessment indicated soil HMs posed carcinogenic threats to adults and children, while roses were considered safe for consumption. Road emissions were identified as the principal contributors to health risks, with Cr and Ni identified as priority control elements. This framework refines contamination assessment, enhances source apportionment, and provides a scalable model for global agricultural and environmental risk management.http://www.sciencedirect.com/science/article/pii/S1470160X24014742Metal pollutionMonte Carlo simulationPositive matrix factorizationRisk assessmentSoil-rose systemSource apportionment |
| spellingShingle | Jun Li Li-Bang Ma Jun-Zhuo Liu Xu Li Yun-Qin Yang Xi-Sheng Tai Fa-Yuan Zhou Fei Zang Advanced matter-element extension model and machine learning for source-specific probabilistic health risk assessment of heavy metals/metaloids in soil-rose systems in Kushui, Northwest China Ecological Indicators Metal pollution Monte Carlo simulation Positive matrix factorization Risk assessment Soil-rose system Source apportionment |
| title | Advanced matter-element extension model and machine learning for source-specific probabilistic health risk assessment of heavy metals/metaloids in soil-rose systems in Kushui, Northwest China |
| title_full | Advanced matter-element extension model and machine learning for source-specific probabilistic health risk assessment of heavy metals/metaloids in soil-rose systems in Kushui, Northwest China |
| title_fullStr | Advanced matter-element extension model and machine learning for source-specific probabilistic health risk assessment of heavy metals/metaloids in soil-rose systems in Kushui, Northwest China |
| title_full_unstemmed | Advanced matter-element extension model and machine learning for source-specific probabilistic health risk assessment of heavy metals/metaloids in soil-rose systems in Kushui, Northwest China |
| title_short | Advanced matter-element extension model and machine learning for source-specific probabilistic health risk assessment of heavy metals/metaloids in soil-rose systems in Kushui, Northwest China |
| title_sort | advanced matter element extension model and machine learning for source specific probabilistic health risk assessment of heavy metals metaloids in soil rose systems in kushui northwest china |
| topic | Metal pollution Monte Carlo simulation Positive matrix factorization Risk assessment Soil-rose system Source apportionment |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24014742 |
| work_keys_str_mv | AT junli advancedmatterelementextensionmodelandmachinelearningforsourcespecificprobabilistichealthriskassessmentofheavymetalsmetaloidsinsoilrosesystemsinkushuinorthwestchina AT libangma advancedmatterelementextensionmodelandmachinelearningforsourcespecificprobabilistichealthriskassessmentofheavymetalsmetaloidsinsoilrosesystemsinkushuinorthwestchina AT junzhuoliu advancedmatterelementextensionmodelandmachinelearningforsourcespecificprobabilistichealthriskassessmentofheavymetalsmetaloidsinsoilrosesystemsinkushuinorthwestchina AT xuli advancedmatterelementextensionmodelandmachinelearningforsourcespecificprobabilistichealthriskassessmentofheavymetalsmetaloidsinsoilrosesystemsinkushuinorthwestchina AT yunqinyang advancedmatterelementextensionmodelandmachinelearningforsourcespecificprobabilistichealthriskassessmentofheavymetalsmetaloidsinsoilrosesystemsinkushuinorthwestchina AT xishengtai advancedmatterelementextensionmodelandmachinelearningforsourcespecificprobabilistichealthriskassessmentofheavymetalsmetaloidsinsoilrosesystemsinkushuinorthwestchina AT fayuanzhou advancedmatterelementextensionmodelandmachinelearningforsourcespecificprobabilistichealthriskassessmentofheavymetalsmetaloidsinsoilrosesystemsinkushuinorthwestchina AT feizang advancedmatterelementextensionmodelandmachinelearningforsourcespecificprobabilistichealthriskassessmentofheavymetalsmetaloidsinsoilrosesystemsinkushuinorthwestchina |