Geochemical transformation of soil cover and vegetation in a drained floodplain lake affected by long-term discharge of effluents from rayon industry plants, lower Don River Basin, Southern Russia.
Elizaveta Yu KonstantinovaMarina BurachevskayaSaglara S MandzhievaTatiana V BauerTatiana M MinkinaVictor ChaplyginInna ZamulinaAlexandr KonstantinovSvetlana SushkovaPublished in: Environmental geochemistry and health (2020)
Lake Atamanskoye is one of the most polluted aquatic environments in the South of Russia. This water body was affected by long-term pollution by effluent from industrial rayon plants located in the city of Kamensk-Shakhtinsky. Accumulation of pollutants resulted in the degradation of Lake Atamanskoye, which is currently drained. This research focused on the geochemical transformation of soils and vegetation within the territory of the former water body and its surroundings. Methods of study included the evaluation of potentially toxic elements (PTEs) in soils and plants by X-ray fluorescence, as well as the contents of their forms by sequential extraction and statistical processing of the data. The results revealed that Spolic Technosols and Fluvisols represent the most widespread soils within Lake Atamanskoye. The concentration of metals found in the soils of the lakebed is several orders of magnitude higher than the regional geochemical background and world soil baseline values due to long-term industrial pollution. The natural and technogenic soils were subdivided into two groups according to pH. Alkaline soils in the presence of carbonates were characterised by high levels of PTEs, while acidic soils with higher proportions of exchangeable fractions and higher potential for metal accumulation in adjacent plants had lower levels of PTEs.
Keyphrases
- heavy metals
- risk assessment
- health risk assessment
- health risk
- human health
- climate change
- wastewater treatment
- high resolution
- magnetic resonance
- big data
- magnetic resonance imaging
- computed tomography
- electronic health record
- single molecule
- machine learning
- air pollution
- mass spectrometry
- particulate matter
- drinking water
- deep learning