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Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning. / Palansooriya, Kumuduni N.; Li, Jie; Dissanayake, Pavani D. et al.
In: Environmental Science and Technology, Vol. 56, No. 7, 30.04.2022, p. 4187-4198.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Palansooriya, KN, Li, J, Dissanayake, PD, Suvarna, M, Li, L, Yuan, X, Sarkar, B, Tsang, DCW, Rinklebe, J, Wang, X & Ok, YS 2022, 'Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning', Environmental Science and Technology, vol. 56, no. 7, pp. 4187-4198. https://doi.org/10.1021/acs.est.1c08302

APA

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 and Technology, 56(7), 4187-4198. https://doi.org/10.1021/acs.est.1c08302

Vancouver

Palansooriya KN, Li J, Dissanayake PD, Suvarna M, Li L, Yuan X et al. Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning. Environmental Science and Technology. 2022 Apr 30;56(7):4187-4198. Epub 2022 Mar 15. doi: 10.1021/acs.est.1c08302

Author

Palansooriya, Kumuduni N. ; Li, Jie ; Dissanayake, Pavani D. et al. / Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning. In: Environmental Science and Technology. 2022 ; Vol. 56, No. 7. pp. 4187-4198.

Bibtex

@article{7072ca9a116440e2b676a82d232ac0d6,
title = "Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning",
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.",
keywords = "Environmental Chemistry, General Chemistry",
author = "Palansooriya, {Kumuduni N.} and Jie Li and Dissanayake, {Pavani D.} and Manu Suvarna and Lanyu Li and Xiangzhou Yuan and Binoy Sarkar and Tsang, {Daniel C. W.} and J{\"o}rg Rinklebe and Xiaonan Wang and Ok, {Yong Sik}",
year = "2022",
month = apr,
day = "30",
doi = "10.1021/acs.est.1c08302",
language = "English",
volume = "56",
pages = "4187--4198",
journal = "Environmental Science and Technology",
issn = "0013-936X",
publisher = "American Chemical Society",
number = "7",

}

RIS

TY - JOUR

T1 - Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning

AU - Palansooriya, Kumuduni N.

AU - Li, Jie

AU - Dissanayake, Pavani D.

AU - Suvarna, Manu

AU - Li, Lanyu

AU - Yuan, Xiangzhou

AU - Sarkar, Binoy

AU - Tsang, Daniel C. W.

AU - Rinklebe, Jörg

AU - Wang, Xiaonan

AU - Ok, Yong Sik

PY - 2022/4/30

Y1 - 2022/4/30

N2 - 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.

AB - 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.

KW - Environmental Chemistry

KW - General Chemistry

U2 - 10.1021/acs.est.1c08302

DO - 10.1021/acs.est.1c08302

M3 - Journal article

VL - 56

SP - 4187

EP - 4198

JO - Environmental Science and Technology

JF - Environmental Science and Technology

SN - 0013-936X

IS - 7

ER -