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

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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