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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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  9. Air Pollution Modelling And Control
  11. Soil Type And Content Of Macro-elements Determine Hotspots Of Cu And Ni Accumulation In Soils Of Subarctic Industrial Barren: Inference From A Cascade Machine Learning

Soil type and content of macro-elements determine hotspots of Cu and Ni accumulation in soils of subarctic industrial barren: inference from a cascade machine learning

Yury Dvornikov1, Marina Slukovskaya2, Artem Gurinov3

  • 1Smart Urban Nature laboratory, Peoples' Friendship University of Russia, Miklukho-Maklaya, 8/2, Moscow, 117198, Russia; Laboratory of Carbon Monitoring in Terrestrial Ecosystems, Institute of Physicochemical and Biological Problems of Soil Science of the Russian Academy of Sciences, Institutskaya str., 2, Pushchino, 142290, Russia.

Environmental Pollution (Barking, Essex : 1987)
|May 15, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning accurately maps heavy metal pollution hotspots in the Russian Arctic. This study identifies soil types and macro-elements as key factors for understanding copper and nickel distribution in degraded industrial areas.

Area of Science:

  • Environmental Science
  • Geochemistry
  • Machine Learning Applications

Background:

  • Industrial activities, specifically ferrous and non-ferrous metallurgy, cause significant aerial technogenic pollution.
  • The Russian Arctic faces environmental degradation in vulnerable ecosystems due to this pollution.
  • Monchegorsk's industrial zone, impacted by a copper-nickel smelter since the 1950s, exhibits soil heterogeneity, vegetation loss, and erosion.

Purpose of the Study:

  • To quantitatively describe spatial redistribution patterns of aerial depositions of copper (Cu) and nickel (Ni).
  • To assess the effectiveness of machine learning in mapping heavy metal contamination in degraded environments.
  • To identify key predictors for understanding heavy metal distribution and hotspots.

Main Methods:

  • Application of cascade machine learning (gradient boosting machines).
Keywords:
Aerial pollutionCuDigital soil mappingGradient boosting machines

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  • Extensive soil sampling campaign (n=506) over 343 hectares.
  • Analysis of soil types, macro-elements (Ca, Fe), topography, hydrology, and spectral properties.

    • Main Results:

    • Extremely high levels of Cu and Ni contamination were detected (max concentrations 29.87 g/kg and 30.12 g/kg, respectively).
    • Soil types and macro-element content (Ca, Fe) significantly improved the accuracy of mapping Cu and Ni spatial variability and hotspots.
    • Models incorporating interactions between macro-elements and heavy metals showed higher accuracy.

    Conclusions:

    • Cascade machine learning is a promising tool for mapping heavy metal distribution in eroded, degraded, and highly polluted areas.
    • The findings support land reclamation planning and the allocation of bioremediation measures.
    • Understanding soil properties and macro-element interactions is crucial for predicting heavy metal contamination patterns.
    Heavy metals
    Ni
    Smelter impact