Use of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils

Quantitative estimations of sources and spatial distribution of soil heavy metals (HMs) is essential for strategizing policies for soil protection and remediation. As a special soil ecosystem, the intensified human activities on coastal reclaimed lands generally causes soil contamination with HMs. T...

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Autores principales:	Huan Zhang, Aijing Yin, Xiaohui Yang, Manman Fan, Shuangshuang Shao, Jingtao Wu, Pengbao Wu, Ming Zhang, Chao Gao
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Source apportionment Spatial prediction Heavy metal Random forest Positive matrix factorization Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/bad4a150f7874042954c5138c598f811
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spelling	oai:doaj.org-article:bad4a150f7874042954c5138c598f8112021-12-01T04:39:49ZUse of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils1470-160X10.1016/j.ecolind.2020.107233https://doaj.org/article/bad4a150f7874042954c5138c598f8112021-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X20311729https://doaj.org/toc/1470-160XQuantitative estimations of sources and spatial distribution of soil heavy metals (HMs) is essential for strategizing policies for soil protection and remediation. As a special soil ecosystem, the intensified human activities on coastal reclaimed lands generally causes soil contamination with HMs. This study aimed to apportion sources of HMs and predict their spatial distributions on coastal reclaimed lands. A total of 241 surface (0–20 cm) soil and sediment samples were collected from a reclamation zone following intensive agricultural use of eastern China. The concentrations of soil and sediment As, Cr, Cu, Ni, Pb, Zn, Cd, and Hg were measured along with organic carbon, nitrogen, phosphorus, pH, Cl, clay, silt, sand, CaO, Fe2O3, Al2O3, and SiO2. The potential sources of HMs were identified and apportioned using random forest (RF) and positive matrix factorization (PMF) models. According to the models, natural and a portion of anthropogenic sources, agricultural activities, and human emission from solar power generation and vehicle exhaust contributed 42.9%, 28.9%, and 28.2% of the total HMs, respectively. Separately, 65.0% of As, 36.6% of Cr, 49.1% of Cu, 46.4% of Ni, 39.5% of Pb, and 44.0% of Zn were originated from natural and some anthropogenic sources. Agricultural activities contributed 54.9% of Cd and 46.4% of Hg to the reclaimed soils. Emissions from solar power generation and vehicle exhaust had significant influences on Cr and Pb, with contributions of 39.0% and 28.0%, respectively. Furthermore, the RF model yielded satisfying results in predicting HM distributions based on the measurement of soil variables. When only considering independent variables, the RF model revealed slightly lower but still satisfactory abilities in HMs prediction. In reclaimed soils, the temporal increase and close relationship between soil Cd and phosphorus signified the potential threats of Cd contamination in coastal reclaimed soils. Therefore, the applications of Cd-rich phosphoric fertilizers should be considered with high concern.Huan ZhangAijing YinXiaohui YangManman FanShuangshuang ShaoJingtao WuPengbao WuMing ZhangChao GaoElsevierarticleSource apportionmentSpatial predictionHeavy metalRandom forestPositive matrix factorizationEcologyQH540-549.5ENEcological Indicators, Vol 122, Iss , Pp 107233- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Source apportionment Spatial prediction Heavy metal Random forest Positive matrix factorization Ecology QH540-549.5
spellingShingle	Source apportionment Spatial prediction Heavy metal Random forest Positive matrix factorization Ecology QH540-549.5 Huan Zhang Aijing Yin Xiaohui Yang Manman Fan Shuangshuang Shao Jingtao Wu Pengbao Wu Ming Zhang Chao Gao Use of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils
description	Quantitative estimations of sources and spatial distribution of soil heavy metals (HMs) is essential for strategizing policies for soil protection and remediation. As a special soil ecosystem, the intensified human activities on coastal reclaimed lands generally causes soil contamination with HMs. This study aimed to apportion sources of HMs and predict their spatial distributions on coastal reclaimed lands. A total of 241 surface (0–20 cm) soil and sediment samples were collected from a reclamation zone following intensive agricultural use of eastern China. The concentrations of soil and sediment As, Cr, Cu, Ni, Pb, Zn, Cd, and Hg were measured along with organic carbon, nitrogen, phosphorus, pH, Cl, clay, silt, sand, CaO, Fe2O3, Al2O3, and SiO2. The potential sources of HMs were identified and apportioned using random forest (RF) and positive matrix factorization (PMF) models. According to the models, natural and a portion of anthropogenic sources, agricultural activities, and human emission from solar power generation and vehicle exhaust contributed 42.9%, 28.9%, and 28.2% of the total HMs, respectively. Separately, 65.0% of As, 36.6% of Cr, 49.1% of Cu, 46.4% of Ni, 39.5% of Pb, and 44.0% of Zn were originated from natural and some anthropogenic sources. Agricultural activities contributed 54.9% of Cd and 46.4% of Hg to the reclaimed soils. Emissions from solar power generation and vehicle exhaust had significant influences on Cr and Pb, with contributions of 39.0% and 28.0%, respectively. Furthermore, the RF model yielded satisfying results in predicting HM distributions based on the measurement of soil variables. When only considering independent variables, the RF model revealed slightly lower but still satisfactory abilities in HMs prediction. In reclaimed soils, the temporal increase and close relationship between soil Cd and phosphorus signified the potential threats of Cd contamination in coastal reclaimed soils. Therefore, the applications of Cd-rich phosphoric fertilizers should be considered with high concern.
format	article
author	Huan Zhang Aijing Yin Xiaohui Yang Manman Fan Shuangshuang Shao Jingtao Wu Pengbao Wu Ming Zhang Chao Gao
author_facet	Huan Zhang Aijing Yin Xiaohui Yang Manman Fan Shuangshuang Shao Jingtao Wu Pengbao Wu Ming Zhang Chao Gao
author_sort	Huan Zhang
title	Use of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils
title_short	Use of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils
title_full	Use of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils
title_fullStr	Use of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils
title_full_unstemmed	Use of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils
title_sort	use of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/bad4a150f7874042954c5138c598f811
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Use of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils

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