ArticleEnvironmental science & technology2022
Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning.
Article in Environmental science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Critical review of adsorption technologies for heavy metal removal: mechanistic, computational, and policy perspectives.RSC advances · 2026Review
- Review
- Atmospheric Particulate Matter as a Carrier of Pb, Cd, and Ni: From Environmental Transfer and Bioaccessibility to Molecular Toxicity and Predictive Modeling.Journal of xenobiotics · 2026Review
- Microstructural and mechanical effects of biochar particle size on clay compressibility and lead immobilization.Scientific reports · 2026Article
- Article
- Development of a soil cadmium bioaccessibility prediction model for human health risk assessment.iScience · 2026Article
- Bacillus subtilis immobilization on biochars produced from different feedstocks and pyrolysis temperatures: performance, mechanisms, and salt tolerance.Bioresources and bioprocessing · 2026Article
- Pilot-scale remediation, resource utilization, and long-term stabilization of cadmium, lead and arsenic co-contaminated soil.Scientific reports · 2026Article
- Article
- Interpretable machine learning models to predict cadmium in wheat for safe production and soil management.Fundamental research · 2026Article
- Biotechnological potential of Ganoderma species in mycoremediation: a bibliometric analysis.Folia microbiologica · 2026Review
- Machine learning prediction and calibration of cellulose-based solid-phase extraction performance for pharmaceuticals across aqueous matrices.RSC advances · 2026Review
- Article
- Article
- Geochemical fate of lead in contaminated residential soils following application of amendments for lead immobilization.Frontiers in chemistry · 2026Article
- Unveiling the role of soil microorganisms in indicating paddy soil health via metagenomics combined with machine learning.ISME communications · 2026Article
- Soil bacterial and fungal communities respond differently to the biochar amendment in a cadmium-contaminated paddy field.Scientific reports · 2025Article
- Heterogeneity, nonlinearity, and multifactor interactions of polycyclic aromatic hydrocarbons in steelworks soils.Environmental science and ecotechnology · 2025Article
- Photocatalytic degradation of single and binary mixture of malachite green and rhodamine B dyes by biochar-capped iron oxide nanocomposites.Environmental science and pollution research international · 2025Article
- Spatial Differentiation of Heavy Metals/Metalloids, Microbial Risk Genes and Soil Microbiota in a Sulfur-Contaminated Landscape.Microorganisms · 2025Article
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Authors and funding
11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Biochar application is a promising strategy for the remediation of contaminated soil, while ensuring sustainable waste management. Biochar remediation of heavy metal (HM)-contaminated soil primarily depends on the properties of the soil, biochar, and HM. The optimum conditions for HM immobilization in biochar-amended soils are site-specific and vary among studies. Therefore, a generalized approach to predict HM immobilization efficiency in biochar-amended soils is required. This study employs machine learning (ML) approaches to predict the HM immobilization efficiency of biochar in biochar-amended soils. The nitrogen content in the biochar (0.3-25.9%) and biochar application rate (0.5-10%) were the two most significant features affecting HM immobilization. Causal analysis showed that the empirical categories for HM immobilization efficiency, in the order of importance, were biochar properties > experimental conditions > soil properties > HM properties. Therefore, this study presents new insights into the effects of biochar properties and soil properties on HM immobilization. This approach can help determine the optimum conditions for enhanced HM immobilization in biochar-amended soils.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.