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Modeling phytoremediation of heavy metal contaminated soils through machine learning

Authors
Shi, LiangLi, JiePalansooriya, Kumuduni NiroshikaChen, YahuaHou, DeyiMeers, ErikTsang, Daniel C. W.Wang, XiaonanOk, Yong Sik
Issue Date
5-1월-2023
Publisher
ELSEVIER
Keywords
Heavy metal; Hyperaccumulator; Machine learning; Phytoextraction; Soil remediation
Citation
JOURNAL OF HAZARDOUS MATERIALS, v.441
Indexed
SCOPUS
Journal Title
JOURNAL OF HAZARDOUS MATERIALS
Volume
441
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/146455
DOI
10.1016/j.jhazmat.2022.129904
ISSN
0304-3894
Abstract
As an important subtopic within phytoremediation, hyperaccumulators have garnered significant attention due to their ability of super-enriching heavy metals. Identifying the factors that affecting phytoextraction efficiency has important application value in guiding the efficient remediation of heavy metal contaminated soil. However, it is challenging to identify the critical factors that affect the phytoextraction of heavy metals in soil-hyperaccumulator ecosystems because the current projections on phytoremediation extrapolations are rudimentary at best using simple linear models. Here, machine learning (ML) approaches were used to predict the important factors that affecting phytoextraction efficiency of hyperaccumulators. ML analysis was based on 173 data points with consideration of soil properties, experimental conditions, plant families, low-molecular -weight organic acids from plants, plant genes, and heavy metal properties. Heavy metal properties, especially the metal ion radius, were the most important factors that affect heavy metal accumulation in shoots, and the plant family was the most important factor that affect the bioconcentration factor, metal extraction ratio, and remediation time. Furthermore, the Crassulaceae family had the highest potential as hyperaccumulators for phytoremediation, which was related to the expression of genes encoding heavy metal transporting ATPase (HMA), Metallothioneins (MTL), and natural resistance associated macrophage protein (NRAMP), and also the secretion of malate and threonine. New insights into the effects of plant characteristics, experimental conditions, soil characteristics, and heavy metal properties on phytoextraction efficiency from ML model interpretation could guide the efficient phytoremediation by identifying the best hyperaccumulators and resolving its efficient remediation mechanisms.
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College of Life Sciences and Biotechnology > Division of Environmental Science and Ecological Engineering > 1. Journal Articles

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