Mechanistic insights into the removal of As(III) and As(V) by iron modified carbon based materials with the aid of machine learning

Is Cr(III) re-oxidation occurring in Cr-contaminated soils after remediation: Meta-analysis and machine learning prediction

Whether Cr(III) in Cr(III)-containing sites formed after Cr(VI) reduction and stabilization remediation are re-oxidized and pose toxicity risks again has been a growing concern. In this study, 1030 data were collected to perform a meta-analysis to clarify the effects of various factors (oxidant type, soil and Cr(III) solid compound properties, aging conditions, and testing methods) on Cr(III) oxidation. We observed that the soil properties of clay, pH ≥ 8, the lower CEC capacity, easily reducible Mn content, and Cr(III) content, and the higher Eh value and Fe content can promote the re-oxidation of Cr(III). Publication bias and sensitivity analyses confirmed the stability and reliability of the meta-analysis. Subsequently, we used five machine learning algorithms to construct and optimize the models. The prediction results of the RF model (RMSE <1.36, R2 >0.71) with good algorithm performance showed that after ten years of remediation, the extractable Cr(VI) concentration in the soil was 0.0087 mg/L, indicating a negligible secondary pollution risk of Cr(III) re-oxidation. This study provides theoretical support for subsequent risk management and control after Cr(VI) soil remediation and provides a solution for the quantitative prediction of Cr(III) re-oxidation.

Machine learning applications for biochar studies: A mini-review

Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.

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

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.

Unraveling the roles of microporous and micro-mesoporous structures of carbon supports on iron oxide properties and As (V) removal performance in contaminated water

This study investigates the impact of microporous (SP-C) and micro-mesoporous carbon (DP-C) supports on the dispersion and phase transformation of iron oxides and their arsenic (V) removal efficiency. The research demonstrates that carbon-supported iron oxide sorbents exhibit superior As(V) uptake capacity compared to unsupported Fe2O3, attributed to reduced iron oxide crystallite sizes and As(V) adsorption on carbon supports. Maximum As(V) uptake capacities of 23.8 mg/g and 18.9 mg/g were achieved for Fe/SP-C and Fe/DP-C at 30 wt% and 50 wt% iron loading, respectively. The study reveals a nonlinear relationship between As(V) sorption capacity and iron oxide crystallite size after excluding As(V) adsorption capacity on carbon supports, suggesting the iron oxide phase (Fe3O4) plays a role in determining adsorption capacity. Iron oxide-loaded DP-C sorbents exhibit faster adsorption rates at low As(V) concentrations (5 mg/L) than SP-C sorbents due to their bimodal pore structure. Adsorption behavior varies at higher As(V) concentrations (45 mg/L), with Fe/DP-C reaching maximum capacity more slowly due to limited available adsorptive sites. All adsorbents maintained near-complete As(V) removal efficiency over five cycles. The findings provide insights for designing more efficient adsorbents for As(V) removal from contaminated water sources.