Influence of humic acid on microbial induced carbonate precipitation for organic soil improvement

Synergistic mechanisms of humic acid and biomineralization in cadmium remediation using Lysinibacillus fusiformis

Heavy metal pollution, particularly cadmium, poses severe environmental and health risks due to its high toxicity and mobility, necessitating effective remediation strategies. While both microbially induced carbonate precipitation (MICP) and humic acid adsorption are promising methods for heavy metal mitigation, their combined effects, particularly the influence of humic acid on the MICP process, have not been thoroughly investigated. This study explores the interaction between humic acid and MICP, revealing that humic acid significantly inhibits the MICP process by reducing urease activity, with the 10% humic acid treatment resulting in a 23.8% reduction in urease activity compared to the control. Additionally, while higher concentrations of humic acid did not significantly reduce cadmium ion concentrations, they did result in a slight increase in organically bound cadmium, indicating an interaction that could alter metal speciation in the soil. These findings provide important insights into the mechanisms by which humic acid affects MICP, offering a foundation for optimizing combined remediation approaches. Future research should aim to fine-tune the balance between MICP and humic acid to enhance the overall efficiency of cadmium remediation strategies. This study contributes to the development of more effective and sustainable methods for addressing cadmium contamination.

Optimized kernel extreme learning machine using Sine Cosine Algorithm for prediction of unconfined compression strength of MICP cemented soil

Microbially induced calcite precipitation (MICP) is an eco-friendly bio-remediation technology. The unconfined compressive strength (UCS) of MICP cemented soil is an important indicator of repair effectiveness. This study proposes a machine learning technique utilizing the Sine Cosine Algorithm (SCA) to optimize the regularization coefficient C and kernel width γ of the kernel extreme learning machine (KELM) to predict the UCS of MICP cemented soil. To evaluate the performance of the proposed models, a dataset containing 180 groups of the UCS of MICP cemented soil was obtained. The results obtained by SCA-KELM were compared with those obtained by the Random Forest algorithm (RF), Support Vector Machine (SVM), and KELM. The performance of these models was evaluated by the scores of MAE, RMSE, and R2. The results indicate that the SCA-KELM algorithm exhibits optimal prediction performance (Total score: 21). After optimizing KELM with SCA, the total score improved by 110%, suggesting that SCA significantly enhances the KELM performance. After model development, the optimal population size for SCA-KELM was determined to be 50. Based on the mutual information test, an innovative method was developed for categorizing factor sensitivity by employing importance scores as the partitioning criterion. This method categorizes the influencing factors into three tiers: high (importance score: 8.03-11.14%), medium (importance score: 5.93-7.25%), and low (importance score: 3.23-5.18%). These results suggest that the proposed SCA-KELM algorithm can be regarded as a powerful tool for predicting the UCS of MICP cemented soil.

Enhanced Sorption Performance of Natural Zeolites Modified with pH-Fractionated Humic Acids for the Removal of Methylene Blue from Water

This work explores the effect of humic acids (HA) fractionation on the sorption ability of a natural zeolite (NYT)-HA adduct. HA were extracted from compost, fractionated via the pH fractionation method, and characterized via UV-Vis spectroscopy and gel permeation chromatography. The HA samples were immobilized onto NYT via thermal treatment. The resulting adducts (NYT-HA) were tested for their ability to remove methylene blue (MB) from an aqueous solution. It was found that the sorption performance of NYT-HA strongly depends on the chemical characteristics of humic acids. Sorption capacity increased with the molecular weight and hydrophobicity degree of the HA fractions. Hydrophobic and π-π interactions are likely the primary mechanisms by which MB interacts with HA. The sorption kinetic data conform to the pseudo-second-order model. The Freundlich isotherm model adequately described the sorption equilibrium and revealed that the uptake of MB onto NYT-HA is endothermic in nature.

Preliminary Study on the Preparation of Conductive Nanosized Calcium Carbonate Utilizing Biogas Slurry by a Synchronous Double Decomposition Coating Method

Nanosized calcium carbonate (NCC) plays a vital role in the rubber and plastic fields as a filler, but it cannot resolve the electrostatic problem. Humic-acid-based NCC (HA-NCC) was accidentally discovered in the reaction between biogas slurry and calcium chloride (CaCl₂), based on nutrient recovery and gradient treatment technology to solve the biogas slurry problem. A preliminary study on the preparation of conductive nanosized calcium carbonate (CNCC) from the HA-NCC was implemented. Meanwhile, a synchronous double decomposition coating method was proposed to properly explain the formation of HA-NCC in the biogas slurry. The CNCC was further obtained through drying and carbonizing the HA-NCC sample. The morphology of CNCC was a square shape with aggregation, and its crystals were calcite. The C content of CNCC was 5% higher than that of the normal CaCO₃, implying a synchronous coating effect of soluble HA in biogas slurry on NCC. The weight loss of CNCC was about 2.5% at 630 °C, explaining why the HA-NCC remained black at 550 °C for 4 h. The CNCC was partly ordered and graphitized. The resistivity of the CNCC reached 2.62 × 10⁶ Ω·cm. It could be used as a conductive powder. In view of the favorable characteristics described above, CNCC would be expected to be a filler and antistatic agent for plastics and rubbers to enhance the tensile and bending resistance of polymer materials, while eliminating electrostatic hazards. The results are also of great significance for developing high-end products to realize resource utilization of biogas slurry.