An approach for estimating microbial growth and chlorinated ethenes biotransformation in groundwater based on discrete observations.

The bioremediation of chlorinated ethenes (CEs) contaminated groundwater is attracting increasingly attention in practical remediation projects. However, modelling of microbial metabolic processes under the constraints of substrate and environmental factors is inadequate. This study developed a new kinetic model, which incorporated the logistic model and Dual-Monod kinetic to represent the interaction between the controlled microbial growth and the bioavailable substrates in CE-contaminated groundwater.

Machine Learning Modeling Based on Microbial Community for Prediction of Natural Attenuation in Groundwater.

Natural attenuation is widely adopted as a remediation strategy, and the attenuation potential is crucial to evaluate whether remediation goals can be achieved within the specified time. In this work, long-term monitoring of indigenous microbial communities as well as benzene, toluene, ethylbenzene, and xylene (BTEX) and chlorinated aliphatic hydrocarbons (CAHs) in groundwater was conducted at a historic pesticide manufacturing site. A machine learning approach for natural attenuation prediction was developed with random forest classification (RFC) followed by either random forest regression (RFR) or artificial neural networks (ANNs), utilizing microbiological information and contaminant attenuation rates for model training and cross-validation.

Modeling and optimization study on degradation of organic contaminants using nZVI activated persulfate based on response surface methodology and artificial neural network: a case study of benzene as the model pollutant.

Due to the complicated transport and reactive behavior of organic contamination in groundwater, the development of mathematical models to aid field remediation planning and implementation attracts increasing attentions. In this study, the approach coupling response surface methodology (RSM), artificial neural networks (ANN), and kinetic models was implemented to model the degradation effects of nano-zero-valent iron (nZVI) activated persulfate (PS) systems on benzene, a common organic pollutant in groundwater. The proposed model was applied to optimize the process parameters in order to help predict the effects of multiple factors on benzene degradation rate.

Bioremediation of chlorinated ethenes contaminated groundwater and the reactive transport modeling - A review.

Improper disposal of chlorinated ethenes (CEs), a class of widely used solvents in chemical manufacturing and cleaning industries, often leads to severe groundwater contamination. In situ bioremediation of CE-contaminated groundwater has received continuous attention in recent years. The reactive transport simulation is a valuable tool for planning and designing in situ bioremediation systems.

Photocatalytic oxidation degradation of inhibitory fatty acids for aged Chlorella vulgaris cultivation medium recycling.

The medium used for Chlorella vulgaris cultivation exerted obvious inhibitory effects on the growth of C. vulgaris after several culture-harvest cycles. The accumulated fatty acids secreted by C.

Field study of microbial community structure and dechlorination activity in a multi-solvents co-contaminated site undergoing natural attenuation.

BTEX and chlorinated aliphatic hydrocarbons (CAHs) are the common pollutants found at contaminated sites, and natural attenuation (NA) of CAHs was widely observed where they coexist. In this work, the groundwater in a site co-contaminated with BTEX and CAHs was monitored for 1 year. The compositions and activities of the microfloras, especially dechlorinators and their relationships with the contaminants, geochemical properties, seasons and depth were evaluated.