Modeling phytoremediation of heavy metal contaminated soils through machine learning

Liang Shi, Jie Li, Kumuduni Niroshika Palansooriya, Yahua Chen, Deyi Hou, Erik Meers, Daniel C.W. Tsang, Xiaonan Wang, Yong Sik Ok

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

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.

Original languageEnglish
Article number129904
JournalJournal of hazardous materials
Volume441
DOIs
Publication statusPublished - 2023 Jan 5

Bibliographical note

Funding Information:
This work was carried out with the support of the Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01475801 ) from Rural Development Administration, the Republic of Korea. This work was also supported by the International Postdoctoral Exchange Program Fellowship ( PC2020041 ). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C2011734 ). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( NRF-2021R1A6A1A10045235 ).

Publisher Copyright:
© 2022

Keywords

  • Heavy metal
  • Hyperaccumulator
  • Machine learning
  • Phytoextraction
  • Soil remediation

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution
  • Health, Toxicology and Mutagenesis

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