Application of urease-producing microbial community in seawater to dust suppression in desert

Microscopic deposition-property relationships in microbial-induced consolidation of coal dusts

In recent years, the preparation of new microbial dust suppressants based on microbial induced carbonate precipitation (MICP) technology through enriched urease-producing microbial communities has become a new topic in the field of coal dust control. The deposition of CaCO3 was the key to suppress coal dust. However, deposition characteristics in the field is not sufficient and the relationship between deposition characteristics and erosion resistance is not clear, which hinders the development of engineering application of new microbial dust suppressant. Therefore, based on X-CT technology, this paper observed and quantified micro-deposition of bio-consolidated coal dust with different calcium sources. Furthermore, a conceptual framework for deposition was proposed and its correlation with erosion resistance was revealed. The results showed that CaCO3 induced by calcium chloride and calcium lactate was aggregate deposited. Aggregate deposited CaCO3 was small in volume, showed the distribution of aggregation in the central area and loose outside, and mosaiced pores. CaCO3 induced by calcium nitrate was surface deposition due to attached biomass. Surface deposition was mostly large volume CaCO3 expanding from the inside out, which could cover coal dust to a high degree and completely cemented pores. In addition, the threshold detachment velocity of coal dust cemented by surface deposition was increased by 17.6-19.1% compared to aggregate deposition. This depended on the abundance and strength of CaCO3 bonding between coal dust particles under different deposition. The two-factor model based on porosity and CaCO3 coverage can well express relationship between erosion resistance and depositional characteristics. Those results will help the engineering application of MICP technology in coal dust suppression.

State-of-the-art review of soil erosion control by MICP and EICP techniques: Problems, applications, and prospects

In recent years, the application of microbially induced calcite precipitation (MICP) and enzyme-induced carbonate precipitation (EICP) techniques have been extensively studied to mitigate soil erosion, yielding substantial achievements in this regard. This paper presents a comprehensive review of the recent progress in erosion control by MICP and EICP techniques. To further discuss the effectiveness of erosion mitigation in-depth, the estimation methods and characterization of erosion resistance were initially compiled. Moreover, factors affecting the erosion resistance of MICP/EICP-treated soil were expounded, spanning from soil properties to treatment protocols and environmental conditions. The development of optimization and upscaling in erosion mitigation via MICP/EICP was also included in this review. In addition, this review discussed the limitations and correspondingly proposed prospective applications of erosion control via the MICP/EICP approach. The current review presents up-to-date information on the research activities for improving erosion resistance by MICP/EICP, aiming at providing insights for interdisciplinary researchers and guidance for promoting this method to further applications in erosion mitigation.

Bentonite addition enhances the biodegradation of petroleum pollutants and bacterial community succession during the aerobic co-composting of waste heavy oil with agricultural wastes

Soil contamination with petroleum significantly threatens the ecological equilibrium and human health. In this context, aerobic co-composting of waste heavy oil with agricultural wastes was performed in the present study to remediate petroleum pollutants through four treatments: CK (control), T1 (petroleum pollutant), T2 (petroleum pollutant with bentonite), and T3 (petroleum pollutant with humic acid-modified bentonite). Comprehensive analyses were conducted to determine the physicochemical parameters, enzymatic activities, removal of petroleum pollutants, microbial community structure, and water-extractable organic matter in different composting systems. Structural equation modeling was employed to identify the key factors influencing the removal of petroleum pollutants. According to the results, petroleum pollutant removal percentages of 44.94%, 79.09%, and 79.67% could be achieved with T1, T2, and T3, respectively. In addition, the activities of polyphenol oxidase (51.21 U/g) and catalase (367.91 U/g), which are the enzymes related to petroleum hydrocarbon degradation, were the highest in T3. Moreover, bentonite addition to the treatment increased the nitrate nitrogen storage in the compost from 10.95 mg/kg in T1 to 18.63 and 17.41 mg/kg in T2 and T3, respectively. Humic acid-modified bentonite could enhance the degree of compost humification, thereby leading to a higher-quality compost product. Collectively, these findings established bentonite addition as an efficient approach to enhance the compost remediation of petroleum pollutants.

Synergistic interactions of microbial fuel cell and microbially induced carbonate precipitation technology with molasses as the substrate

The application of microbially induced carbonate precipitation (MICP) technology is critical, but many challenges remain. In this paper, a microbial fuel cell (MFC) is used to treat molasses wastewater, and the effluent is used as the substrate to promote the growth of urease-producing bacteria. The results showed that the maximum voltage of MFC was 500 mV, and the maximum power density was 169.86 mW/m². The mineralization rate reached 100% on the 15^th day, and the mineralized product was calcite CaCO₃. According to the microbial community analysis, the unclassified_Comamondaceae, Arcobacter, and Aeromonas, which could improve the OH^-, signal molecular transmission and small molecular nutrients to promote the urease activity of urease-producing bacteria. The above conclusions provide a new way to reuse molasses wastewater efficiently and to apply MICP technology in dust suppression.

Optimization of biocementation responses by artificial neural network and random forest in comparison to response surface methodology

In this article, the optimization of the specific urease activity (SUA) and the calcium carbonate (CaCO₃) using microbially induced calcite precipitation (MICP) was compared to optimization using three algorithms based on machine learning: random forest regressor, artificial neural networks (ANNs), and multivariate linear regression. This study applied the techniques in two existing response surface method (RSM) experiments involving MICP technique. Random forest-based models and artificial neural network-based models were submitted through the optimization of hyperparameters via cross-validation technique and grid search, to select the best-optimized model. For this study, the random forest-based algorithm is aimed at having the best performance of 0.9381 and 0.9463 in comparison to the original r² of 0.9021 and 0.8530, respectively. This study is aimed at exploring the capability of using machine learning-based models in small datasets for the purpose of optimization of experimental variables in MICP technique and the meaningfulness of the models by their specificities in the small experimental datasets applied to experimental designs. This study is aimed at exploring the capability of using machine learning-based models in small datasets for experimental variable optimization in MICP technique. The use of these techniques can create prerogatives to scale and mitigate costs in future experiments associated to the field.