Machine learning-driven optimization of arsenic phytoextraction using amendments
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...
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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Ecotoxicology and Environmental Safety |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651325010504 |
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| author | Huading Shi Yunxian Yan Zhaoyang Han Liang Wang Guanghui Guo Jun Yang |
| author_facet | Huading Shi Yunxian Yan Zhaoyang Han Liang Wang Guanghui Guo Jun Yang |
| author_sort | Huading Shi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bbb893c1bae3490e8468fbe96e014d14 |
| institution | DOAJ |
| issn | 0147-6513 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-bbb893c1bae3490e8468fbe96e014d142025-08-20T03:03:53ZengElsevierEcotoxicology and Environmental Safety0147-65132025-09-0130211870510.1016/j.ecoenv.2025.118705Machine learning-driven optimization of arsenic phytoextraction using amendmentsHuading Shi0Yunxian Yan1Zhaoyang Han2Liang Wang3Guanghui Guo4Jun Yang5Key Laboratory of Resource Utilization and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Technical Centre for Soil, Agricultural and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, ChinaKey Laboratory of Resource Utilization and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding authors at: Key Laboratory of Resource Utilization and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.Key Laboratory of Resource Utilization and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaKey Laboratory of Resource Utilization and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaKey Laboratory of Resource Utilization and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaKey Laboratory of Resource Utilization and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding authors at: Key Laboratory of Resource Utilization and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.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.http://www.sciencedirect.com/science/article/pii/S0147651325010504Enhanced phytoremediationHyperaccumulatorSustainabilityRandom forestMain factorEconomic cost |
| spellingShingle | Huading Shi Yunxian Yan Zhaoyang Han Liang Wang Guanghui Guo Jun Yang Machine learning-driven optimization of arsenic phytoextraction using amendments Ecotoxicology and Environmental Safety Enhanced phytoremediation Hyperaccumulator Sustainability Random forest Main factor Economic cost |
| title | Machine learning-driven optimization of arsenic phytoextraction using amendments |
| title_full | Machine learning-driven optimization of arsenic phytoextraction using amendments |
| title_fullStr | Machine learning-driven optimization of arsenic phytoextraction using amendments |
| title_full_unstemmed | Machine learning-driven optimization of arsenic phytoextraction using amendments |
| title_short | Machine learning-driven optimization of arsenic phytoextraction using amendments |
| title_sort | machine learning driven optimization of arsenic phytoextraction using amendments |
| topic | Enhanced phytoremediation Hyperaccumulator Sustainability Random forest Main factor Economic cost |
| url | http://www.sciencedirect.com/science/article/pii/S0147651325010504 |
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