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

Shilan Felegari; Alireza Sharifi; Mohammad Khosravi; Sergei Sabanov; Aqil Tariq; Shankar Karuppannan

doi:10.1016/j.heliyon.2023.e21908

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

Heliyon. 2023 Nov 6;9(11):e21908. doi: 10.1016/j.heliyon.2023.e21908. eCollection 2023 Nov.

Authors

Shilan Felegari¹, Alireza Sharifi², Mohammad Khosravi¹, Sergei Sabanov³, Aqil Tariq⁴, Shankar Karuppannan⁵

Affiliations

¹ Department of Civil Engineering, Montana State University, Bozeman, MT, 59717, USA.
² Department of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
³ School of Minning and Geoscience, Nazarbayev University, Astana, Kazakhstan.
⁴ Department of Wildlife, Fisheries, and Aquaculture, College of Forest Resources, Mississippi State University, Mississippi State, 39762-9690, MS, USA.
⁵ Department of Applied Geology, School of Applied Nartural Sciences, Adama Science and Technology University, Adama P.O. Box 1888, Ethiopia.

Abstract

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 Pearson's 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.

Keywords: Heavy metals; Soil contamination; Spatial distribution; Statistical analysis pollution load indices.