The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning
Open accessAbstract
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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Self-citationMetal contamination and bioremediation of agricultural soils for food safety and sustainability
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Self-citationBiochar reduces the bioavailability and phytotoxicity of heavy metals
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Response of microbial communities to biochar-amended soils: a critical review
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Modeling phytoremediation of heavy metal contaminated soils through machine learning
Shi, L., Li, J., Palansooriya, K. N., Chen, Y., Hou, D., Meers, E., Tsang, D. C. W., Wang, X., & Ok, Y. S. (2023) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2022.129904 · Cited by 152
Self-citationUnveiling microbial nitrogen metabolism in rivers using a machine learning approach
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Xu, K., Zhu, Z., Yu, H., & Zou, X. (2025) · Algal Research · doi:10.1016/j.algal.2025.103985 · Cited by 15
Fu, Y., Zhang, Q., Tan, Z., Yu, S., & Zhang, Y. (2025) · Journal of Environmental Chemical Engineering · doi:10.1016/j.jece.2025.115406 · Cited by 14
Metadata
- Authors
- Palansooriya, K. N., Li, J., Dissanayake, P. D., Suvarna, M., Li, L., Yuan, X., Sarkar, B., Tsang, D. C. W., Rinklebe, J., Wang, X., & Ok, Y. S.
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-03-15
- Volume
- 56
- Issue
- 7
- Pages
- 4187-4198
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- OpenAlex
- W4221059529
- Cited by
- 518
- References
- 75
- FWCI
- 20.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Palansooriya, K. N., Li, J., Dissanayake, P. D., Suvarna, M., Li, L., Yuan, X., Sarkar, B., Tsang, D. C. W., Rinklebe, J., Wang, X., & Ok, Y. S. (2022). Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning. Environmental Science & Technology. https://doi.org/10.1021/acs.est.1c08302
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