Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Investigating the source apportionment of heavy metals in soil surrounding reservoir using partial least-squares regression model
The assessment of heavy metal pollution is crucial for water conservation. This study determined the contents of heavy metals (Cd, Cr, Cu, Ni, Pb, Zn and As) from 39 soil samples surrounding a reservoir, and analyzed the corresponding source and enrichment using enrichment factors and partial least-squares regression. The concentration of Cr (54.06 mg/kg) was lower than the background value of the reservoir area, while the Cd concentration was higher (0.96 mg/kg). Moreover, Cd, Cr, Ni, Pb, Zn and As concentrations in the south exceeded those of the northeast in the Nanwan lake reservoir (NLR). Cd and As were the dominant contaminated elements in the NLR. The Cd enrichment factor value was 11.25, areas with moderate and higher levels of pollution of Cd occupied 89.0% of the total area, while As occupied 18.4%. The dominant sources of Zn, Ni, Cu, Pb and Cr were identified as natural inputs, those of As were agricultural production activities, and those of Cd were industrial production activities. This study provides insight into the heavy metal pollution and key factors of land-use types in watersheds with tea trees as the dominant vegetation cover, and aids in the planning of water pollution prevention and ecological protection. HIGHLIGHTS A novel technology partial least-squares regression model.; The principle sources were identified by calculating the variable importance for the projection (VIP).; The inherent defects of traditional regression algorithms in handling multicollinear and noisy data were overcome.;
Investigating the source apportionment of heavy metals in soil surrounding reservoir using partial least-squares regression model
The assessment of heavy metal pollution is crucial for water conservation. This study determined the contents of heavy metals (Cd, Cr, Cu, Ni, Pb, Zn and As) from 39 soil samples surrounding a reservoir, and analyzed the corresponding source and enrichment using enrichment factors and partial least-squares regression. The concentration of Cr (54.06 mg/kg) was lower than the background value of the reservoir area, while the Cd concentration was higher (0.96 mg/kg). Moreover, Cd, Cr, Ni, Pb, Zn and As concentrations in the south exceeded those of the northeast in the Nanwan lake reservoir (NLR). Cd and As were the dominant contaminated elements in the NLR. The Cd enrichment factor value was 11.25, areas with moderate and higher levels of pollution of Cd occupied 89.0% of the total area, while As occupied 18.4%. The dominant sources of Zn, Ni, Cu, Pb and Cr were identified as natural inputs, those of As were agricultural production activities, and those of Cd were industrial production activities. This study provides insight into the heavy metal pollution and key factors of land-use types in watersheds with tea trees as the dominant vegetation cover, and aids in the planning of water pollution prevention and ecological protection. HIGHLIGHTS A novel technology partial least-squares regression model.; The principle sources were identified by calculating the variable importance for the projection (VIP).; The inherent defects of traditional regression algorithms in handling multicollinear and noisy data were overcome.;
Investigating the source apportionment of heavy metals in soil surrounding reservoir using partial least-squares regression model
Xu-dong Huang (Autor:in) / Pei-pei Han (Autor:in) / Mei-jing Ma (Autor:in) / Qiong Cao (Autor:in) / Wei-zhuo Li (Autor:in) / Fang Wan (Autor:in) / Xiao-li Zhang (Autor:in) / Qi-hui Chai (Autor:in) / Ling Zhong (Autor:in) / Bao-jian Li (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
The Universal Partial Least Squares, UNIPALS, Algorithm for Partial Least Squares, PLS, Regression
| British Library Conference Proceedings | 1993
Estimating flow data in urban drainage using partial least squares regression
| Taylor & Francis Verlag | 2017
Estimating flow data in urban drainage using partial least squares regression
| Online Contents | 2016
Spatial Distribution and Source Apportionment of Soil Heavy Metals in Pearl River Delta, China
| DOAJ | 2021
Application of Partial Least-Squares Regression in Seasonal Streamflow Forecasting
| British Library Online Contents | 2010