Model-based evidence for the relevance of microbial community variability to the efficiency of the anaerobic reductive dechlorination of TCE.

The composition of mixed dechlorinating communities varies considerably in field and laboratory conditions. Dechlorinators thrive alongside with distinctive populations that help or hinder dechlorination. The variability of the composition of dechlorinating communities inevitably precludes a firm consensus regarding the optimal strategies for biostimulation. This lack of consensus motivated a model-based approach for the investigation of how the variability of the composition of a microbial community impacts the electron donor supply strategies for accelerating chloroethene removal. To this end, a kinetic model accounting for dechlorination in conjunction with cooperative and competing processes was developed. Model parameters were estimated using a multi-experiment, multi-start algorithm and data from research previously performed with two generations of a methane-producing, Dehalococcoides mccartyi-dominated consortium. The two generations of the consortium functioned comparably under maintenance conditions but performed divergently under high electron donor surpluses. The multi-experiment, multi-start algorithm overcame the hurdles of poor parameter identifiability and offered a probable cause for the different behaviors exhibited by each of the two generations of the chloroethene-degrading consortium: modest differences in the make-up of non-dechlorinators, which were minority populations, significantly influenced the fate of the offered electron donor.

Reductive dechlorination of trichloroethene under different sulfate-reducing and electron donor conditions.

The effect of sulfate presence on reductive dechlorination of chlorinated ethenes has been a matter of conflict among the limited reports found in literature. This paper aims to clarify the misconceptions regarding the performance of trichloroethene biotransformation under sulfate reducing conditions by evaluating the effect of different sulfate concentrations on reductive dechlorination and to assess the influence of electron donor dose on dechlorination rate. To this end, batch experiments containing different sulfate and butyrate concentrations were conducted using trichloroethene-dechlorinating and sulfate-reducing parent cultures. Results demonstrated that if sufficient time and electron donor is provided, complete dechlorination can be achieved, even at up to 400 mg/L initial sulfate concentration. However, the rate of dichloroethene and vinyl chloride degradation is reduced as sulfide concentration increases. Moreover, the excess electron donor dose induced a slightly slower dechlorination rate. The findings of this paper present an explanatory framework for the dechlorination of TCE under sulfate reducing conditions and can contribute to the state-of-art bioremediation of contaminated sites.

DNAPL distribution in the source zone: effect of soil structure and uncertainty reduction with increased sampling density.

This paper focuses on parameters describing the distribution of dense nonaqueous phase liquid (DNAPL) contaminants and investigates the variability of these parameters that results from soil heterogeneity. In addition, it quantifies the uncertainty reduction that can be achieved with increased density of soil sampling. Numerical simulations of DNAPL releases were performed using stochastic realizations of hydraulic conductivity fields generated with the same geostatistical parameters and conditioning data at two sampling densities, thus generating two simulation ensembles of low and high density (three-fold increase) of soil sampling. The results showed that DNAPL plumes in aquifers identical in a statistical sense exhibit qualitatively different patterns, ranging from compact to finger-like. The corresponding quantitative differences were expressed by defining several alternative measures that describe the DNAPL plume and computing these measures for each simulation of the two ensembles. The uncertainty in the plume features under study was affected to different degrees by the variability of the soil, with coefficients of variation ranging from about 20% to 90%, for the low-density sampling. Meanwhile, the increased soil sampling frequency resulted in reductions of uncertainty varying from 7% to 69%, for low- and high-uncertainty variables, respectively. In view of the varying uncertainty in the characteristics of a DNAPL plume, remedial designs that require estimates of the less uncertain features of the plume may be preferred over others that need a more detailed characterization of the source zone architecture.

Dechlorinating ability of TCE-fed microcosms with different electron donors.

The main objective of the work presented herein is to assess the effect of different electron donors (butyric acid and methanol) on the dechlorinating activity of two microbial cultures where active methanogenic populations are present, in an effort to evaluate the importance of the electron donor selection process. The ability of each anaerobic culture to dechlorinate TCE, when enriched with either butyric acid or methanol, was verified based on the results of gas chromatography. In addition, the fluorescent in situ hybridization (FISH) and the polymerase chain reaction (PCR) methods gave positive results for the presence of Dehalococcoides spp. According to results of the batch tests conducted in this study, it appears that the selection of the electron donor for stimulating TCE dechlorination depends on microbial culture composition; therefore, the decision on the appropriate electron donor should be based on site-specific microcosm studies.

Embedding expert knowledge in a decision model: evaluating natural attenuation at TCE sites.

This paper describes a generalized methodology that enables the translation of expert knowledge about any complex process involved in a remedial decision into easy-to-use decision tools. The methodology is applied to evaluate reductive dechlorination as a remedial possibility at sites contaminated with trichloroethene (TCE), building on an existing protocol/scoring system put forth by the US Air Force and the US EPA. An alternate scoring system is proposed, which has two major advantages, namely that it: (i) attributes relative weights to findings based on expert beliefs; and (ii) systematically includes negative weights for negative findings. The ability of the proposed scoring system to assess the bioattenuation potential of TCE is demonstrated using data from extensively studied sites.

Site-specific updating and aggregation of Bayesian belief network models for multiple experts.

A method for combining multiple expert opinions that are encoded in a Bayesian Belief Network (BBN) model is presented and applied to a problem involving the cleanup of hazardous chemicals at a site with contaminated groundwater. The method uses Bayes Rule to update each expert model with the observed evidence, then uses it again to compute posterior probability weights for each model. The weights reflect the consistency of each model with the observed evidence, allowing the aggregate model to be tailored to the particular conditions observed in the site-specific application of the risk model. The Bayesian update is easy to implement, since the likelihood for the set of evidence (observations for selected nodes of the BBN model) is readily computed by sequential execution of the BBN model. The method is demonstrated using a simple pedagogical example and subsequently applied to a groundwater contamination problem using an expert-knowledge BBN model. The BBN model in this application predicts the probability that reductive dechlorination of the contaminant trichlorethene (TCE) is occurring at a site--a critical step in the demonstration of the feasibility of monitored natural attenuation for site cleanup--given information on 14 measurable antecedent and descendant conditions. The predictions for the BBN models for 21 experts are weighted and aggregated using examples of hypothetical and actual site data. The method allows more weight for those expert models that are more reflective of the site conditions, and is shown to yield an aggregate prediction that differs from that of simple model averaging in a potentially significant manner.