Removal of trichloroethylene (TCE) contaminated soil using a two-stage anaerobic-aerobic composting technique

Burning question: Rethinking organohalide degradation strategy for bioremediation applications

Organohalides are widespread pollutants that pose significant environmental hazards due to their high degree of halogenation and elevated redox potentials, making them resistant to natural attenuation. Traditional bioremediation approaches, primarily relying on bioaugmentation and biostimulation, often fall short of achieving complete detoxification. Furthermore, the emergence of complex halogenated pollutants, such as per- and polyfluoroalkyl substances (PFASs), further complicates remediation efforts. Therefore, there is a pressing need to reconsider novel approaches for more efficient remediation of these recalcitrant pollutants. This review proposes novel redox-potential-mediated hybrid bioprocesses, tailored to the physicochemical properties of pollutants and their environmental contexts, to achieve complete detoxification of organohalides. The possible scenarios for the proposed bioremediation approaches are further discussed. In anaerobic environments, such as sediment and groundwater, microbial reductive dehalogenation coupled with fermentation and methanogenesis can convert organohalides into carbon dioxide and methane. In environments with anaerobic-aerobic alternation, such as paddy soil and wetlands, a synergistic process involving reduction and oxidation can facilitate the complete mineralization of highly halogenated organic compounds. Future research should focus on in-depth exploration of microbial consortia, the application of ecological principles-guided strategies, and the development of bioinspired-designed techniques. This paper contributes to the academic discourse by proposing innovative remediation strategies tailored to the complexities of organohalide pollution.

Characteristics of PVP-stabilised NZVI and application to dechlorination of soil-sorbed TCE with ionic surfactant

The characteristics of polyvinylpyrrolidone (PVP)-stabilised nano-zero-valent iron (PVP-NZVI) and its application, combined with surfactant, to trichloroethylene (TCE)-contaminated soil were investigated. Two surfactants (cetyltrimethylammonium bromide [CTAB] and sodium dodecyl sulphate [SDS]) were tested for their ability to enhance the remedial activity of PVP-NZVI in 3 h batch experiments. The prepared PVP-NZVI formed nanoparticles ∼70 nm in diameter. The isoelectric point of PVP-NZVI was about 8.51, similar to the initial pH. X-ray diffraction and X-ray photoelectron spectroscopy analyses revealed that ZVI was the main active component of PVP-NZVI, and carbonised products of the target were observed. The TCE dechlorination efficiency by PVP-NZVI was about 84.73%; the efficiency by PVP-NZVI was about 20% higher when combined with SDS than with CTAB. Therefore, application of PVP-NZVI with SDS represents a potential remediation approach for TCE-contaminated soil.

Experimental studies and neural network modeling of the removal of trichloroethylene vapor in a biofilter

In this study, bamboo carrier based lab scale compost biofilter was evaluated to treat synthetic waste air containing trichloroethylene (TCE) under continuous operation mode. The effect of inlet TCE concentration and gas flow rate and its removal was investigated. Maximum TCE removal efficiency was found to be 89% under optimum conditions of inlet 0.986 g/m³ TCE concentration corresponding to a loading rate of 43 g/m³ h and 0.042 m³/h gas flow rate at empty bed residence time (EBRT) of 2 min. For the first time, Artificial Neural Network (ANN) was applied to predict the performance of the compost biofilter in terms of TCE removal. The ANN model used a three layer feed forward based Levenberg-Marquardt algorithm, and its topology consisted of 3-25-1 as the optimum number for the three layers (input, hidden and output). An excellent match between the experimental and ANN predicted the value of TCE removal was obtained with a coefficient of determination (R²) value greater than 0.99 during the model training, validation, testing and overall. Furthermore, statistical analysis of the ANN model performance mediated its prediction accuracy of the bioreactor to treat TCE contaminated systems.