Machine learning based prediction and experimental validation of arsenite and arsenate sorption on biochars

Wei Zhang, Waqar Muhammad Ashraf, Sachini Supunsala Senadheera, Daniel S. Alessi, Filip M.G. Tack, Yong Sik Ok

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Arsenic (As) contamination in water is a significant environmental concern with profound implications for human health. Accurate prediction of the adsorption capacity of arsenite [As(III)] and arsenate [As(V)] on biochar is vital for the reclamation and recycling of polluted water resources. However, comprehending the intricate mechanisms that govern arsenic accumulation on biochar remains a formidable challenge. Data from the literature on As adsorption to biochar was compiled and fed into machine learning (ML) based modelling algorithms, including AdaBoost, LGBoost, and XGBoost, in order to build models to predict the adsorption efficiency of As(III) and As(V) to biochar, based on the compositional and structural properties. The XGBoost model showed superior accuracy and performance for prediction of As adsorption efficiency (for As(III): coefficient of determination (R2) = 0.93 and root mean square error (RMSE) = 1.29; for As(V), R2 = 0.99, RMSE = 0.62). The initial concentrations of As(III) and As(V) as well as the dosage of the adsorbent were the most significant factors influencing adsorption, explaining 48 % and 66 % of the variability for As(III) and As(V), respectively. The structural properties and composition of the biochar explained 12 % and 40 %, respectively, of the variability of As(III) adsorption, and 13 % and 21 % of that of As(V). The XGBoost models were validated using experimental data. R2 values were 0.9 and 0.84, and RMSE values 6.5 and 8.90 for As(III) and As(V), respectively. The ML approach can be a valuable tool for improving the treatment of inorganic As in aqueous environments as it can help estimate the optimal adsorption conditions of As in biochar-amended water, and serve as an early warning for As-contaminated water.

Original languageEnglish
Article number166678
JournalScience of the Total Environment
Volume904
DOIs
Publication statusPublished - 2023 Dec 15

Bibliographical note

Publisher Copyright:
© 2023

Keywords

  • Aesenic
  • Biochar
  • Clean water and sanitation
  • Green and sustainable remediation
  • Machine learning
  • Sustainable development goals

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution

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