한빛사논문
- 조회484
Liang Shi a,b,1, Jie Li c,d,1, Kumuduni Niroshika Palansooriya a,e, Yahua Chen b, Deyi Hou f, Erik Meers g, Daniel C.W. Tsang h, Xiaonan Wang i, Yong Sik Ok a
aKorea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South Korea
bCollege of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
cDepartment of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
dCAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
eState Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China
fSchool of Environment, Tsinghua University, Beijing 100084, China
gDepartment of Green Chemistry & Technology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgiu
hDepartment of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
iDepartment of Chemical Engineering, Tsinghua University, Beijing 100084, China
1These authors contributed equally to this work.
Corresponding authors: Xiaonan Wang, Yong Sik Ok
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.
논문정보
- Research article
- 2023년 01월 (BRIC 등록일 2023-07-26)
- 국내(교신)+국외 연구진
- 바이오·의료융합 > 바이오소재
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