Modelling bioaccumulation of heavy metals in soil-crop ecosystems and identifying its controlling factors using machine learning
2020
Hu, Bifeng | Xue, Jie | Zhou, Yin | Shao, Shuai | Fu, Zhiyi | Li, Yan | Chen, Songchao | Qi, Lin | Shi, Zhou | Unité de Science du Sol (Orléans) (URSols) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences ; Zhejiang University [Hangzhou, China] | Institute of Land Science and Property, School of Public Affairs ; Zhejiang University [Hangzhou, China] | School of Earth Sciences [Hangzhou] ; Zhejiang University [Hangzhou, China] | InfoSol (InfoSol) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Ningbo Planting Management Station ; Ningbo University (NBU)
The prediction and identification of the factors controlling heavy metal transfer in soil-crop ecosystems are of critical importance. In this study, random forest (RF), gradient boosted machine (GBM), and generalised linear (GLM) models were compared after being used to model and identify prior factors that affect the transfer of heavy metals (HMs) in soil-crop systems in the Yangtze River Delta, China, based on 13 covariates with 1822 pairs of soil-crop samples. The mean bioaccumulation factors (BAFs) for all crops followed the order Cd > Zn > As > Cu > Ni > Hg > Cr > Pb. The RF model showed the best prediction ability for the BAFs of HMs in soil-crop ecosystems, followed by GBM and GLM. The R2 values of the RF models for the BAFs of Zn, Cu, Cr, Ni, Hg, Cd, As, and Pb were 0.84, 0.66, 0.59, 0.58, 0.58, 0.51, 0.30, and 0.17, respectively. The primary controlling factor in soil-to-crop transfer of all HMs under study was plant type, followed by soil heavy metal content and soil organic materials. The model used herein could be used to assist the prediction of heavy metal contents in crops based on heavy metal contents in soil and other covariates, and can significantly reduce the cost, labour, and time requirements involved with laboratory analysis. It can also be used to quantify the importance of variables and identify potential control factors in heavy metal bioaccumulation in soil-crop ecosystems.
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