Remediation of lead-contaminated groundwater by oyster shell powder-peanut shell biochar mixture.

Groundwater pollution caused by lead ions has become a widespread issue worldwide due to the ever-increasing development of industrial activities. Such pollution poses significant threats to both humans and the environment. Oyster shell powder-peanut shell biochar mixture (OSP-PSB mixture) was used for lead-contaminated groundwater treatment by permeable reactive barrier (PRB) technology. Basic characteristics of materials proved that OSP-PSB mixture has good adsorption properties; OSP with particle sizes ranging from 0.85 to 1.18 mm was used in this research; according to engineering and adsorption characteristics, OSP-PSB mixture (5:1) showed excellent permeability (4.35 × 10-4 cm/s) and lead adsorption capacity(27 mg/g); long-term permeability of the OSP-PSB mixture slightly decreased over time and met the permeability requirements for PRB; the removal mechanisms of lead ions by OSP-PSB mixture include precipitation, surface complexation, ion exchange, and physical adsorption. The experiment results showed that the OSP-PSB mixture fulfills the actual project requirements of PRB.

Predicting the hydraulic conductivity of compacted soil barriers in landfills using machine learning techniques.

Machine learning models (MLMs) were developed to predict saturated hydraulic conductivity of compacted soil barriers and help to identify appropriate soils for the construction of landfill liners and covers. Data from hydraulic conductivity tests on compacted soil barriers were collected from the literature and compiled into a database for MLM construction. The database contains 329 records of hydraulic conductivity tests associated with 12 selected impact factors covering physical properties, compaction efforts, and hydration and mineralogy behaviors of compacted soil barriers. Three machine learning algorithms (random forest, gradient boosting decision tree, and neural network) were used to develop MLMs, and a statistical technique (multiple linear regression) was used to compare the precision of predictions with the MLMs. Results from this study showed that the random forest model provided the best prediction of the hydraulic conductivity of compacted soil barriers, with 100% of predicted hydraulic conductivity within 100-time differences to measured hydraulic conductivity and 93% within 10-time differences. Feature importance analysis showed that void ratio after compaction, fines content, specific gravity, degree of saturation after compaction, and plasticity index of soils are the top-five factors (in descending order) that influence the hydraulic conductivity of compacted soil barriers and are recommended for a precise prediction. Three predictive MLMs were created for industries as simple tools to screen the soils prior to the construction of compacted soil barriers in landfill liners and covers.

Physico-mechanical and microstructural behaviour of high-water content zinc-contaminated dredged sediment treated with integrated approach PHDVPSS.

This paper uses a new integrated method, namely PHDVPSS, which utilizes vacuum pressure (VP) coupled with prefabricated horizontal drain along with solidification/stabilization (SS) for the effective treatment of high-water content dredged contaminated sediment (DCS). This study sought to evaluate the physico-mechanical and microstructural behaviour of high-water content DCS treated with MgO-GGBS (MG) and Portland cement (PC) as PHDVPSS binders and compared to the traditional Portland cement solidification/stabilization (SS-PC) method. Physico-mechanical and microstructural characteristics of the DCS treated with the PHDVPSS method were evaluated by performing a number of tests such as unconfined compressive strength (UCS), toxicity characteristics of the leaching process (TCLP), pH, X-ray diffraction (XRD) and scanning electron microscopy (SEM) combined with energy-dispersive X-ray spectroscopy (EDS). Treatment results showed that the DCS treated with the MG binder in the PHDVPSS method showed superior performance in terms of a significant reduction in the water content and leachability of zinc (Zn) along with higher mechanical strength and dry density of the samples compared to the traditional SS-PC method. After 56-day curing time, VP-MG cases showed 17.6 % and 50 % higher dry density values, resulting in 2.5 and 17.3 times higher UCS values than VP-PC and SS-PC cases, respectively. In contrast, VP-MG cases showed lower pH values than those of VP-PC and SS-PC cases. Moreover, VP-MG cases exhibited 37.5 % and 44.3 % lower leached Zn concentration during a TCLP test than VP-PC cases and SS-PC cases, respectively. XRD and SEM-EDS tests showed that more voluminous hydration products were produced in the VP-MG cases, which in turn produced a dense stabilized matrix and significantly reduced the leachability of zinc.