Optimizing water purification with Shungite: a metric approach for characterizing its efficiency in removing contaminants

Abstract Shungite is a Precambrian carbon-rich metamorphic rock from the Lake Onega region (Karelia, Russia), consisting of fullerene-like polyhedral carbon within a silicate matrix. Its high carbon content and extended conjugation confer notable sorptive and catalytic behavior. We evaluated its multifunctional potential for water purification targeting organic, inorganic, and microbial contaminants. Two grades (Type I, Type III) were characterized by X-ray diffraction, Scanning Electron Microscopy combined with Energy Dispersive X-ray Spectroscopy, and Raman spectroscopy, and tested under batch conditions within a metric framework combining removal efficiency and distribution coefficient. After 7 days (Type I), removals were: 32.5% for Mercury, 14.9% for Lead, 20.8% for Cadmium, 32.7% for Arsenic, 13.6% for Zinc, and 21.0% for Aluminium. Microbial counts decreased by up to $$0.24 \log _{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.24</mml:mn> <mml:msub> <mml:mo>log</mml:mo> <mml:mn>10</mml:mn> </mml:msub> </mml:mrow> </mml:math> for total coliforms and 0.18 log $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mn>10</mml:mn> <mml:mrow/> </mml:mmultiscripts> </mml:math> for Escherichia coli and Enterococcus . Corresponding distribution coefficient values for Type I ranged from 15 to 1300 L/kg, consistently exceeding Type III for cationic metals (notably Mercury), while Arsenic(V) responses depended on pH/speciation. Organic-load indicators (Chemical Oxygen Demand, Biochemical Oxygen Demand (5 days), Total Organic Carbon) increased by 10–16%, consistent with Chemical Oxygen Demand leaching; mitigation (e.g., pre-washing or hybridization with activated carbon) is discussed. Overall, results support Shungite as a versatile sorbent/reactive medium, particularly for cationic metals and microbial indicators. The proposed metrics enable direct comparison with conventional carbons and mineral adsorbents under similar conditions.

Authors

Institutions

Publication Details

Journal
International Journal of Environmental Science and Technology
Published
2025-12-04
DOI
https://doi.org/10.1007/s13762-025-06837-z
Primary Topic
Environmental remediation with nanomaterials
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimizing water purification with Shungite: a metric approach for characterizing its efficiency in removing contaminants

A. Lay-Ekuakille, M. Palmisano, Diana Di Luccio, S. Maggi et al.
International Journal of Environmental Science and Technology
Environmental remediation with nanomaterials
article

Optimizing water purification with Shungite: a metric approach for characterizing its efficiency in removing contaminants

A. Lay-Ekuakille, M. Palmisano, Diana Di Luccio, S. Maggi, J. P. Rico Mora, Néstor Hernando Campos, F. Fragnito
article en

Abstract

Abstract Shungite is a Precambrian carbon-rich metamorphic rock from the Lake Onega region (Karelia, Russia), consisting of fullerene-like polyhedral carbon within a silicate matrix. Its high carbon content and extended conjugation confer notable sorptive and catalytic behavior. We evaluated its multifunctional potential for water purification targeting organic, inorganic, and microbial contaminants. Two grades (Type I, Type III) were characterized by X-ray diffraction, Scanning Electron Microscopy combined with Energy Dispersive X-ray Spectroscopy, and Raman spectroscopy, and tested under batch conditions within a metric framework combining removal efficiency and distribution coefficient. After 7 days (Type I), removals were: 32.5% for Mercury, 14.9% for Lead, 20.8% for Cadmium, 32.7% for Arsenic, 13.6% for Zinc, and 21.0% for Aluminium. Microbial counts decreased by up to $$0.24 \log _{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.24</mml:mn> <mml:msub> <mml:mo>log</mml:mo> <mml:mn>10</mml:mn> </mml:msub> </mml:mrow> </mml:math> for total coliforms and 0.18 log $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mn>10</mml:mn> <mml:mrow/> </mml:mmultiscripts> </mml:math> for Escherichia coli and Enterococcus . Corresponding distribution coefficient values for Type I ranged from 15 to 1300 L/kg, consistently exceeding Type III for cationic metals (notably Mercury), while Arsenic(V) responses depended on pH/speciation. Organic-load indicators (Chemical Oxygen Demand, Biochemical Oxygen Demand (5 days), Total Organic Carbon) increased by 10–16%, consistent with Chemical Oxygen Demand leaching; mitigation (e.g., pre-washing or hybridization with activated carbon) is discussed. Overall, results support Shungite as a versatile sorbent/reactive medium, particularly for cationic metals and microbial indicators. The proposed metrics enable direct comparison with conventional carbons and mineral adsorbents under similar conditions.

International Journal of Environmental Science and TechnologyVol. 23(2)
University of Salento (IT), Parthenope University of Naples (IT), Innovation Engineering (Italy) (IT), Istituto per il Sistema Produzione Animale in Ambiente Mediterraneo (IT), National Research Council (IT), Institute of Atmospheric Pollution Research (IT), PanAmerican Bioinformatics Institute (CO)
Università degli Studi di Napoli Federico II, Al-Farabi Kazakh National University
Openalex Percentile: Top 62%
Environmental remediation with nanomaterials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.