Using Sentinel-2 data to estimate the concentration of heavy metals caused by industrial activities in Ust-Kamenogorsk, Northeastern Kazakhstan

Felegari S. Sharifi A. Khosravi M. Sabanov S. Tariq A. Karuppannan S.
November 2023 Elsevier Ltd

Heliyon
2023 #9 Issue 11

This study aims to investigate the change in heavy metal concentration and evaluate pollution intensity using Sentinel-2 data. Sixty samples collected from the surface soil in the area were used to determine the concentration of lead, copper, and zinc using atomic absorption spectroscopy. Then, the step-by-step regression method was used in ArcGIS software to determine the relationship between the concentration of heavy metals and the ranking of the influential spectral bands of Sentinel-2 to monitor heavy metals in the relevant sampling points. According to the results, lead monitoring was effective through the blue channel, the ratio of green to near infrared-IV channels, and the ratio of short-wave infrared-III to near infrared-II channels. At the same time, copper was monitored through reflectance values in the red channel, the ratios of green to near infrared-IV channels, and the ratio of short-wave infrared-III to near infrared-II channels. The blue channel and the ratio of green to near infrared-IV channels the ratio of near infrared-II to near infrared-IV channels were efficient for zinc monitoring. Pollution Load Indices (PLI) and Geographical Accumulation Index (Igeo) were calculated to classify the contaminated soils of the region. The efficiency of each relationship obtained was evaluated using the root mean square error (RMSE) and Pearsons correlation coefficient (R). In summary, the copper, lead, and zinc equations had RMSE values of 1.8, 2.5, and 1.60 mg/kg, respectively. The Pearson correlation coefficients (R) for copper, lead, and zinc were 0.80, 0.76, and 0.72, respectively, which indicated good agreement between measured and estimated values.

Heavy metals , Soil contamination , Spatial distribution , Statistical analysis pollution load indices

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Department of Civil Engineering, Montana State University, Bozeman, 59717, MT, United States
Department of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
School of Minning and Geoscience, Nazarbayev University, Astana, Kazakhstan
Department of Wildlife, Fisheries, and Aquaculture, College of Forest Resources, Mississippi State University, Mississippi State, 39762-9690, MS, United States
Department of Applied Geology, School of Applied Nartural Sciences, Adama Science and Technology University, P.O. Box 1888, Adama, Ethiopia

Department of Civil Engineering
Department of Surveying Engineering
School of Minning and Geoscience
Department of Wildlife
Department of Applied Geology

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