Assessment of heavy metals and nutrients availability in oil palm plantation effected by bauxite mining using geostatistical and multivariate analyses
The study aimed at evaluation of soil heavy metals in oil palm plantation and selected nutrients availability effected by mining. It was investigated using multivariate and geostatistical analyses followed by assessment using environmental indices. Samples were collected from both mining and o...
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Main Authors: | , , , , , |
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格式: | Conference or Workshop Item |
语言: | English English |
出版: |
IOP Publishing
2022
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在线阅读: | http://irep.iium.edu.my/99784/2/99784_Assessment%20of%20heavy%20metals%20and%20nutrients.pdf http://irep.iium.edu.my/99784/3/99784_Assessment%20of%20heavy%20metals%20and%20nutrients_Scopus.pdf http://irep.iium.edu.my/99784/ https://doi.org/10.1088/1755-1315/1064/1/012002 |
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总结: | The study aimed at evaluation of soil heavy metals in oil palm plantation
and selected nutrients availability effected by mining. It was investigated using
multivariate and geostatistical analyses followed by assessment using environmental
indices. Samples were collected from both mining and oil palm cultivated area,
prepared, and analyzed using ICPMS. Semi-variogram and kriging were done by
using GS+
and ArcGIS 10.8, respectively. Content of Cu, Cr and Zn were higher
compared to the Dutch target values and the 95% ‘Investigation Levels’ determined
for Malaysia soil, while Pb showed a lower value. Analysis of Principal Component
suggested that the heavy metals were from one source of contamination, particularly
the mining activities and long-term agricultural practices. Geostatistics analyses
revealed that Zn, Cu, Pb and Fe confront to a strong spatial dependence structure and
in line with multivariate and statistical analysis, except for Cr, which had a moderate
spatial dependence. Geoaccumulation Index demonstrated contamination occurred in
the order of Cr>Cu>Fe>Zn>Ni>Pb. The heavy metals contamination has impacted the
micronutrients contents as lower K, Ca and Mg were observed. These findings
highlight that combining multivariate and geostatistical analysis can be valuable tools
for assessing environmental contamination. |
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