Machine learning-driven optimization of arsenic phytoextraction using amendments
文献类型：期刊论文
| 作者 | Shi, Huading2,3; Yan, Yunxian1,3; Han, Zhaoyang1,3; Wang, Liang1,3; Guo, Guanghui1,3; Yang, Jun1,3 |
| 刊名 | ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
 |
| 出版日期 | 2025-09-01 |
| 卷号 | 302页码:118705 |
| 关键词 | Enhanced phytoremediation Hyperaccumulator Sustainability Random forest Main factor Economic cost |
| ISSN号 | 0147-6513 |
| DOI | 10.1016/j.ecoenv.2025.118705 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Exogenous amendments are crucial for enhancing the remediation efficiency of arsenic-contaminated soils by Pteris vittata. However, their effectiveness is unstable due to various factors, and neglecting their economic costs hinder broader application. In this study, we analyzed 2299 data points from 121 published datasets and used machine learning to predict and optimize the performance of amendments to enhance the phytoextraction efficiency. Using a random forest model, we predicted changes in As accumulation in P. vittata in response to specific amendments, considering 18 parameters across four categories: changes in P. vittata, amendments, soil properties, and cultivation conditions. The model achieved an R2 value of 0.846. Using %IncMSE to quantify parameter contribution, we found that the biomass of P. vittata had a greater influence than the As concentration. Additionally, amendment type, application time, cultivation duration, and soil-available As were key factors in enhancing As accumulation in P. vittata. Regarding economic cost, different amendments required an investment ranging from 0.57 to 3903.86 CNY to enhance 1 g of As accumulation in P. vittata. Among these, phosphate fertilizers had the lowest cost, whereas calcium acetate, ethylenediamine-N,N '-disuccinic acid, and glutathione did not have economic advantages as amendments. This study offers guidance on the development of amendments, providing an important reference for the practical application of phytoextraction in As-contaminated soils. |
| URL标识 | 查看原文 |
| WOS关键词 | PTERIS-VITTATA L. ; CONTAMINATED-SOILS ; REDUCTIVE EXTRACTION ; POLLUTED SOILS ; REMEDIATION ; PHYTOREMEDIATION ; ACCUMULATION ; COMBINATION ; DITHIONITE ; PHOSPHATE |
| WOS研究方向 | Environmental Sciences & Ecology ; Toxicology |
| 语种 | 英语 |
| WOS记录号 | WOS:001554608000004 |
| 出版者 | ACADEMIC PRESS INC ELSEVIER SCIENCE |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215587]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Yan, Yunxian; Yang, Jun |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Minist Ecol & Environm, Tech Ctr Soil Agr & Rural Ecol & Environm, Beijing 100012, Peoples R China; 3.Chinese Acad Sci, Key Lab Resource Utilizat & Environm Remediat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Shi, Huading,Yan, Yunxian,Han, Zhaoyang,et al. Machine learning-driven optimization of arsenic phytoextraction using amendments[J]. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY,2025,302:118705. |
| APA | Shi, Huading,Yan, Yunxian,Han, Zhaoyang,Wang, Liang,Guo, Guanghui,&Yang, Jun.(2025).Machine learning-driven optimization of arsenic phytoextraction using amendments.ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY,302,118705. |
| MLA | Shi, Huading,et al."Machine learning-driven optimization of arsenic phytoextraction using amendments".ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 302(2025):118705. |
入库方式： OAI收割
来源：地理科学与资源研究所
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