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. The proposed model was based on discrete observations to simulate microbial growth under the constraints of substrate and environmental conditions, reducing the amount of observational data required for the model. Meanwhile, the proposed model introduced two new kinetic parameters, the effective specific growth rate µeff and the real self-limiting coefficient of microbial growth keff,sl, to simplified the number of independent parameters. A parameter estimation method based on the quasi-Newton's algorithm for the proposed model was also developed. The model was validated based on the hypothetical data, experimental results, and a published dataset, demonstrated the successful simulation of microbial growth and the sequential biodegradation of PCE in groundwater systems (*E < 0.3). The monitoring duration and the sampling schedule have significant impacts on estimating the biological parameters, and large errors would be induced when the data from the periods of extremely low substrate concentration or microbial growth decline were involved in parameter estimation. The research suggested that the proposed kinetic model provided a new insight to express the limitation of microbial population growth due to the available substrates and environmental factors, and is hoping to be applied in actual CE-contaminated sites.

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. Results showed that the RFC could accurately predict the feasibility of natural attenuation for both BTEX and CAHs, and it could successfully identify the key genera. The RFR model was sufficient for the BTEX natural attenuation rate prediction but unreliable for CAHs. The ANN model showed better performance in the prediction of the attenuation rates for both BTEX and CAHs. Based on the assessments, a composite modeling method of RFC and ANN was proposed, which could reduce the mean absolute percentage errors. This study reveals that the combined machine learning approach under the synergistic use of field microbial data has promising potential for predicting natural attenuation.

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. Meanwhile, the chemical oxidation kinetics was developed based on batch experiments under the optimized reaction conditions to predict the temporal degradation of benzene. The results indicated that benzene (0.25 mmol) would be theoretically completely oxidized in 1.45 mM PS with the PS/nZVI molar ratio of 4:1 at pH 3.9°C and 21.9 C. The RSM model predicted well the effects of the four factors on benzene degradation rate (R2 = 0.948), and the ANN with a hidden layer structure of [8-8] performed better compared to the RSM (R2 = 0.980). In addition, the involved benzene degradation systems fit well with the Type-2 and Type-3 pseudo-second order (PSO) kinetic models with R2 > 0.999. It suggested that the proposed statistical and kinetic-based modeling approach is promising support for predicting the chemical oxidation performance of organic contaminants in groundwater under the influence of multiple factors.

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. vulgaris during their growth process were expected to be the cell inhibition components. In this work, the ultraviolet-driven photocatalytic oxidation technique was applied for the degradation of microalgae cell growth inhibition components in the aged cultivation medium, and the reaction parameters were optimized. The results indicated that the photocatalytic oxidation processes using 0.5 g/L [Formula: see text] NPs as the catalyst under the aeration condition showed as high as 74.61 ± 4.60% FA degradation efficiency after 20 min illumination, and the contents of -COOH, [Formula: see text] (α) and -COO-R functional groups in the aged C. vulgaris medium were significantly reduced. In addition, the modification of the photocatalyst further improved the ability of the degradation of FA. When the modified [Formula: see text]/AC and [Formula: see text]/Ag catalysts were applied, the FA degradation rates reached as high as 92.46 ± 0.37% and 93.91 ± 1.37%, respectively. In the recycled medium treated with [Formula: see text]/AC, the cell density in the stable phase reached 96.33 ± 1.83% of that in the fresh medium as the control. In summary, the photocatalytic oxidation with the modified [Formula: see text]/AC catalyst was proposed as the efficient strategy to realize the recycling of the aged C. vulgaris cultivation medium via the degradation of the FA as the cell growth inhibitors.

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. The results are consistent with the well-known NA conceptual model where CAHs are not able to stimulate the enrichment of dechlorinators alone, but BTEX does promote dechlorination. The higher temperature, rather than ORP in the deeper groundwater of the wet season became a key factor to promote the abundance of dechlorinators, but only when BTEX was available, indicating that the substrates from the BTEX biodegradation played an important role in the dechlorinator enrichment. The elevated ORP in the shallower groundwater exceeded the optimum conditions for reductive dechlorination and no significant seasonal variation of dechlorinators was found. The co-occurrence network revealed the cooperative interactions among the functional microfloras in which dechlorinators, BTEX degraders, and fermentative bacteria jointly promoted the dechlorination. These findings provided us a further understanding of the NA processes in a commingled plume.