Pulse Brain · Growing Health Evidence Index
Tier 3 — Observational / field trialPeer-reviewed

Synergistic biochar and Serratia marcescens tackle toxic metal contamination: A multifaceted machine learning approach

Hamid Rehman, Aqib Hassan Ali Khan, Tayyab Ashfaq Butt, Muhammad Toqeer, Muhammad Bilal, Mahtab Ahmad, Ahmed A. Alawi Al-Naghi, Enamur R. Latifee, Omar Algassem, Mazhar Iqbal

Journal of Environmental Management · 2024

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Summary

This 2024 study investigated the synergistic potential of biochar and the bacterium Serratia marcescens as a bioremediation strategy for soils contaminated with toxic metals. The research incorporated machine learning approaches, likely to identify optimal conditions or predict remediation efficacy. Whilst the title suggests promising results, the full extent of findings regarding metal immobilisation and practical applicability remains to be confirmed from the full text.

UK applicability

The findings could be relevant to UK soil remediation programmes, particularly where legacy industrial contamination is present. However, applicability depends on whether the study addressed temperate soil conditions and UK regulatory frameworks for soil quality standards.

Key measures

As suggested by the title: toxic metal concentrations or bioavailability; possibly bacterial abundance or activity; machine learning model performance metrics

Outcomes reported

The study evaluated the efficacy of biochar combined with Serratia marcescens bacteria in reducing toxic metal bioavailability and mobility in contaminated soil, likely using machine learning to model or predict remediation outcomes.

Theme
Farming systems, soils & land use
Subject
Pesticides, contaminants & food safety
Study type
Research
Study design
Laboratory / in vitro
Source type
Peer-reviewed study
Status
Published
System type
Other
DOI
10.1016/j.jenvman.2024.122575
Catalogue ID
SNmp4zks93-jzpz01

Topic tags

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