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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.
Afficher plus [+] Moins [-]Nitrogen balances and leaching in four agricultural catchments in southeastern Norway
1998
Bechmann, M. | Eggestad, H.O. | Vagstad, N. (Jordforsk, Centre for Soil and Environmental Research, N-1432 As (Norway))
Soil pollution and land contamination.. ed. 3
1996
Alloway, B.J. (Department of Soil Science, University of Reading, London Road, Reading RG1 5AQ (United Kingdom))
Temporal and Spatial Monitoring of the pH and Heavy Metals in a Soil Polluted by Mine Spill. Post Cleaning Effects
2007
Ordóñez Fernandez, R. | Giráldez Cervera, J.V. | Vanderlinden, K. | Carbonell Bojollo, R. | González Fernández, P.
The bursting of the mining dam of Aznalcollar (Seville, Spain) triggered an increase in the concentration of heavy metals in the soils of the river Guadiamar valley as a result of the leaching of the pyritic sludge deposited on them. After the cleaning operations which included, as well as mechanical clearing, the addition of different amendments, some areas with residual sludge remained, from which some heavy metals are being mobilized by the cyclical recharge and discharge processes of water in the profiles. This paper analyzes the effect of the soil recovery operations and the climatology on the concentration of metals and their distribution in the soil profile in an area affected by the toxic spill. Fourteen points have been selected in a plot in which acidity persists, there is no vegetation, and residual sludge stains can be seen at a glance. The temporal and spatial evolution of the extractable metals: Fe, Cu, Mn and Zn, the pH and the oxidable fraction has been measured in-depth. The results obtained up to now indicate a leaching of the pollutant towards deeper horizons, finding, at a depth of 757 cm, pH values of 3.5 and very high Fe and Mn concentrations available, especially in the profiles with large sized pores, with a big fraction of sand. On the surface, seasonally, there are low pH values of around 2.5 and extractable Fe contents of over 4000 ppm, which might have an influence on the quality of surface runoff or underground water.
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