Cadmium leachability and microbial population dynamics in a stabilized and bioaugmented model contaminated soil

This article presents a laboratory study on the consequences of the application of combined soil stabilization and bioaugmentation in the remediation of a model contaminated soil. Stabilization and bioaugmentation are two techniques commonly applied independently for the remediation of heavy metal and organic contamination respectively. However, for a cocktail of contaminants combined treatments are currently being considered. The model soil was contaminated with a cocktail of organics and heavy metals based on the soil and contaminant conditions in a real contaminated site. The soil stabilization treatment was applied using either zeolite or green waste compost as additives and a commercially available hydrocarbon degrading microbial consortium was used for the bioaugmentation treatment. The effects of stabilization with or without bioaugmentation on the leachability of cadmium and copper was observed using an EU batch leaching test procedure and a flow-through column leaching test, both using deionized water at a pH of 5.6. In addition, the population of hydrocarbon degrading microorganisms was monitored using a modified plate count procedure in cases where bioaugmentation was applied. It was found that while the stabilization treatment reduced the metal leachability by up to 60%, the bioaugmentation treatment increased it by up to 100% Microbial survival was also higher in the stabilized soil samples.

Carbonating magnesia for soil stabilization

This paper investigates the potential for carbonating reactive magnesia (MgO) to serve as a more sustainable soil stabilization method by providing rapid and significant strength development of the stabilized soil through absorbing substantial quantities of CO2. Gaseous CO2 was forced through laboratory-prepared reactive MgO-treated soil samples in a triaxial cell set-up, and their resulting mechanical and microstructural properties were investigated using unconfined compressive strength, X-ray diffraction, and scanning electron microscopy. The results showed that adequately carbonated MgO-treated soils could, in a few hours, reach a similar strength range to corresponding 28 day Portland cement (PC)-stabilized soils. Hydrated magnesium carbonates, namely nesquehonite and hydromagnesite-dypingite, were the main products of the carbonated MgO in the soil, and were responsible for the significant strength development.

The influence of injection conditions and soil types on soil improvement by microbial functions

Research has begun on Microbial Carbonate Precipitation (MCP), which shows promise as a soil improvement method because of its low carbon dioxide emission compared to cement stabilized agents. MCP produces calcium carbonate from carbonates and calcium in soil voids through ureolysis by "Bacillus Pasteurii". This study focuses on how the amount of calcium carbonate precipitation is affected by the injection conditions of the microorganism and nutrient salt, such as the number of injections and the soil type. Experiments were conducted to simulate soil improvement by bio-grouting soil in a syringe. The results indicate that the amount of precipitation is affected by injection conditions and soil type, suggesting that, in order for soil improvement by MCP to be effective, it is necessary to set injection conditions that are in accordance with the soil conditions. © 2011 ASCE.

Relating monolithic and granular leaching from contaminated soil treated with different cementitious binders.

This work employed a clayey, silty, sandy gravel contaminated with a mixture of metals (Cd, Cu, Pb, Ni and Zn) and diesel. The contaminated soil was treated with 5 and 10% dosages of different cementitious binders. The binders include Portland cement, cement-fly ash, cement-slag and lime-slag mixtures. Monolithic leaching from the treated soils was evaluated over a 64-day period alongside granular leachability of 49- and 84-day old samples. Surface wash-off was the predominant leaching mechanism for monolithic samples. In this condition, with data from different binders and curing ages combined, granular leachability as a function of monolithic leaching generally followed degrees 4 and 6 polynomial functions. The only exception was for Cu, which followed the multistage dose-response model. The relationship between both leaching tests varied with the type of metal, curing age/residence time of monolithic samples in the leachant, and binder formulation. The results provide useful design information on the relationship between leachability of metals from monolithic forms of S/S treated soils and the ultimate leachability in the eventual breakdown of the stabilized/solidified soil.

Preliminary model development for predicting strength and stiffness of cement-stabilized soils using artificial neural networks

This paper presents ongoing work on data collection and collation from a large number of laboratory cement-stabilization projects worldwide. The aim is to employ Artificial Neural Networks (ANN) to establish relationships between variables, which define the properties of cement-stabilized soils, and the two parameters determined by the Unconfined Compression Test, the Unconfined Compressive Strength (UCS), and stiffness, using E50 calculated from UCS results. Bayesian predictive neural network models are developed to predict the UCS values of cement-stabilized inorganic clays/silts, as well as sands as a function of selected soil mix variables, such as grain size distribution, water content, cement content and curing time. A model which can predict the stiffness values of cement-stabilized clays/silts is also developed and compared to the UCS model. The UCS model results emulate known trends better and provide more accurate estimates than the results from the E50 stiffness model. © 2013 American Society of Civil Engineers.

Development of predictive models for cement stabilized soils

Factors that affect the engineering properties of cement stabilized soils such as strength are discussed in this paper using data on these factors. The selected factors studied in this paper are initial soil water content, grain size distribution, organic matter content, binder dosage, age and curing temperature, which has been collated from a number of international deep mixing projects. Some resulting correlations from this data are discussed and presented. The concept of Artificial Neural Networks and its applicability in developing predictive models for deep mixed soils is presented and discussed using a subset of the collated data. The results from the neural network model were found to emulate the known trends and reasonable estimates of strength as a function of the selected variables were obtained. © 2012 American Society of Civil Engineers.

Properties of two model soils stabilized with different blends and contents of GGBS, MgO, lime, and PC

This paper addresses the use of ground granulated blast furnace slag (GGBS) and reactive magnesia (MgO) blends for soil stabilization, comparing them with GGBS-lime blends and Portland cement (PC) for enhanced technical performance. A range of tests were conducted to investigate the properties of stabilized soils, including unconfined compressive strength (UCS), permeability, and microstructural analyses by using X-ray diffraction (XRD) and scanning electron microscopy (SEM). The influence of GGBS:MgO ratio, binder content, soil type, and curing period were addressed. The UCS results revealed that GGBS-MgO was more efficient than GGBS-lime as a binder for soil stabilization, with an optimum MgO content in the range of 5-20% of the blends content, varying with binder content and curing age. The 28-day UCS values of the optimum GGBS-MgO mixes were up to almost four times higher than that of corresponding PC mixes. The microstructural analyses showed the hydrotalcite was produced during the GGBS hydration activated by MgO, although the main hydration products of the GGBS-MgO stabilized soils were similar to those of PC. © 2014 American Society of Civil Engineers.