Heavy metal dataset of Visakhapatnam TPP
Abstract
This study hypothesizes that heavy metal concentrations in soils surrounding a coal-fired thermal power plant in the Visakhapatnam region, India, are influenced by both geogenic (natural geological background) and anthropogenic (industrial and human-induced) sources. It proposes that metals such as arsenic (As), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn) exhibit spatial variability, with elevated concentrations reflecting contamination from coal combustion, fly ash deposition, industrial emissions, and ash pond leaching in addition to natural geological contributions. Data Overview This dataset comprises heavy metal concentration data from 43 soil samples collected around a coal-fired thermal power plant in the Visakhapatnam region, India. The analyzed trace elements include As, Cr, Cu, Hg, Ni, Pb, and Zn. Concentrations are reported in mg/kg and were determined using High-Resolution Inductively Coupled Plasma Mass Spectrometry (HR-ICPMS), ensuring high analytical precision and reliability. The dataset includes: Sample identification numbers Measured concentrations of trace metals (mg/kg) The dataset is organized in tabular format, with rows representing individual samples and columns representing metal concentrations. Interpretation and Use of the Data This dataset provides comprehensive insight into heavy metal contamination patterns and associated health risks in soils affected by coal-fired thermal power plant activities. It supports environmental monitoring, pollution source identification, spatial risk mapping, and public health assessment. The data can be used by researchers, environmental agencies, and policymakers to evaluate contamination dynamics, design mitigation strategies, and develop sustainable land management practices in industrial regions.
Fields of Study & Subjects
Heavy metals in environment
Coal and Its By-products
Water Quality and Pollution Assessment
Publication details
- Journal / source
- Mendeley Data
- Publication date
- 15 February 2026
- Language
- EN
- Document type
- Dataset