Evaluation of self-oxidation and selectivity of iron-based reductant in anaerobic pentachlorophenol contaminated soil

Characteristic study of biological CaCO3-supported nanoscale zero-valent iron: stability and migration performance

nZVI has attracted much attention in the remediation of contaminated soil and groundwater, but the application is limited due to its aggregation, poor stability, and weak migration performance. The biological CaCO3 was used as the carrier material to support nZVI and solved the nZVI agglomeration, which had the advantages of biological carbon fixation and green environmental protection. Meanwhile, the distribution of nZVI was characterised by SEM-EDS and TEM carefully. Subsequently, the dispersion stability of bare nZVI and CaCO3@nZVI composite was studied by the settlement experiment and Zeta potential. Sand column and elution experiments were conducted to study the migration performance of different materials in porous media, and the adhesion coefficient and maximum migration distances of different materials in sand columns were explored. SEM-EDS and TEM results showed that nZVI could be uniformly distributed on the surface of biological CaCO3. Compared with bare nZVI, CaCO3@nZVI composite suspension had better stability and higher absolute value of Zeta potential. The migration performance of nZVI was poor, while CaCO3@nZVI composite could penetrate the sand column and have good migration performance. What's more, the elution rates of bare nZVI and CaCO3@nZVI composite in quartz sand columns were 5.8% and 51.6%, and the maximum migration distances were 0.193 and 0.885 m, respectively. In summary, this paper studies the stability and migration performance of bare nZVI and CaCO3@nZVI composite, providing the experimental and theoretical support for the application of CaCO3@nZVI composite, which is conducive to promoting the development of green remediation functional materials.

Utilizing machine learning for reactive material selection and width design in permeable reactive barrier (PRB)

Permeable reactive barrier (PRB) is an important groundwater treatment technology. However, selecting the optimal reactive material and estimating the width remain critical and challenging problems in PRB design. Machine learning (ML) has advantages in predicting evolution and tracing contaminants in temporal and spatial distribution. In this study, ML was developed to design PRB, and its feasibility was validated through experiments and a case study. ML algorithm showed a good prediction about the Freundlich equilibrium parameter (R2 0.94 for KF, R2 0.96 for n). After SHapley Additive exPlanation (SHAP) analysis, redefining the range of the significant impact factors (initial concentration and pH) can further improve the prediction accuracy (R2 0.99 for KF, R2 0.99 for n). To mitigate model bias and ensure comprehensiveness, evaluation index with expert opinions was used to determine the optimal material from candidate materials. Meanwhile, the ML algorithm was also applied to predict the width of the mass transport zone in the adsorption column. This procedure showed excellent accuracy with R2 and root-mean-square-error (RMSE) of 0.98 and 1.2, respectively. Compared with the traditional width design methodology, ML can enhance design efficiency and save experiment time. The novel approach is based on traditional design principles, and the limitations and challenges are highlighted. After further expanding the data set and optimizing the algorithm, the accuracy of ML can make up for the existing limitations and obtain wider applications.

Zero-valent iron based materials selection for permeable reactive barrier using machine learning

The zero-valent iron (ZVI) based reactive materials are potential remediation reagents in permeable reactive barriers (PRB). Considering that reactive materials is the essential to determining the long-term stability of PRB and the emergence of a large number of new iron-based materials. Here, we present a new approach using machine learning to screen PRB reactive materials, which proposes to improve the efficiency and practicality of selection of ZVI-based materials. To compensate for the insufficient amount of existing machine learning source data and the real-world implementation, machine learning combines evaluation index (EI) and reactive material experimental evaluations. XGboost model is applied to estimate the kinetic data and SHAP is used to improve the accuracy of model. Batch and column tests were conducted to investigate the geochemical characteristics of groundwater. The study find that specific surface area is a fundamental factor correlated with the kinetic constants of ZVI-based materials, according to SHAP analysis. Reclassifying the data with specific surface area significantly improved prediction accuracy (reducing RMSE from 1.84 to 0.6). Experimental evaluation results showed that ZVI had 3.2 times higher anaerobic corrosion reaction kinetic constants and 3.8 times lower selectivity than AC-ZVI. Mechanistic studies revealed the transformation pathways and endpoint products of iron compounds. Overall, this study is a successful initial attempt to use machine learning for selecting reactive materials.