Geostatistical Insights into Trace Metal Dynamics: Techniques for Spatial Mapping, Variability Analysis, and Source Characterization in the Environment

Ligeiaziba Sylva1

Sylvester Chibueze Izah2,Email

Milan Hait3

1Department of Mathematics, Faculty of Science, Bayelsa Medical University, Yenagoa 569101, Bayelsa state, Nigeria.
2Department of Microbiology, Faculty of Science, Bayelsa Medical University, Yenagoa 569101, Bayelsa state, Nigeria.
3Department of Chemistry, Dr. C. V. Raman University, Bilaspur 495113, Chhattisgarh, India.

Abstract

The study provides an overview of the geostatistical techniques applied in the investigation of trace metals in the environment. Geostatistical methods such as Kriging, Spatial Autocorrelation Analysis, Spatial Scan Statistics (SSS), Local Indicators of Spatial Association (LISA), Principal Component Analysis (PCA), and Sequential Gaussian Simulation (SGS) can be used for the understanding of the spatial distribution, variability, and sources of trace metals in the environmental matrices. Kriging is a widely used spatial interpolation technique for creating accurate concentration maps, filling data gaps, and supporting risk assessments in environmental science. Spatial autocorrelation analysis can be used to predict trace metal concentrations in unsampled areas and identify crucial geographical patterns for effective pollution control. Spatial scan statistics can be used to identify hotspots or clusters of trace metal concentrations, which can aid in the development of targeted environmental management programs. LISA can explore localized links between sites and trace metal concentrations and offers insights into spatial distribution and pollution causes. PCA, a dimensionality reduction technique, can be used to distinguish between anthropogenic and natural sources of metal concentrations. SGS can be used to generate realistic geographic distributions of trace metals, particularly useful when data is sparse. Despite the effectiveness of these geostatistical techniques, they face challenges, including assumptions of stationarity, data quality issues, and the requirement for advanced statistical and computing skills. Therefore, overcoming these challenges is imperative to fully leveraging geostatistical approaches in trace metals analysis for environmental management and protection.

Geostatistical Insights into Trace Metal Dynamics: Techniques for Spatial Mapping, Variability Analysis, and Source Characterization in the Environment