Activity of toluene-degrading Pseudomonas putida in the early growth phase of a biofilm for waste gas treatment

Pedersen, Anne Rathmann; Møller, Søren; Molin, Søren; Arvin, Erik

doi:10.1002/(sici)1097-0290(19970420)54:2<131::aid-bit5>3.0.co;2-m

Cited by 71 publications

(19 citation statements)

References 23 publications

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“…Alonso et al [11] state that a method for the control of biomass accumulation (e.g., backwashing) is essential to sustain high levels of performance. Using an apparatus similar to the one of the present research and P. putida, Pedersen et al [12] called the attention upon the need for investigations of the bio®lm growth in the ®lters, in order to prevent clogging and to optimize and control the puri®cation process.…”

Section: Degrading Microorganismsmentioning

confidence: 99%

Biodegradation of toluene in a trickling filter

Peixoto

Мота

1998

Bioprocess Engineering

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A trickling ®lter packed with PVC 16 mm Raschig rings was used to study the degradation of toluene in a polluted air stream, by means of a bacterial bio®lm of Pseudomonas putida ATCC 17484. A polluted stream was simulated by blending air with a controlled amount of toluene. The mixing was accomplished in a special mixing chamber designed for that purpose. Induction of the enzymes of the toluene degradative pathway and adaptation of the inoculum were done in batch cultures with minimum mineral media and phenol. The continuous experiments were monitored by mass spectrometry for the quanti®cation of the various gases and of toluene removal. A 94% toluene removal was achieved with contacting times above one minute and toluene concentrations up to 400 ppm.

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Section: Degrading Microorganismsmentioning

confidence: 99%

Biodegradation of toluene in a trickling filter

Peixoto

Мота

1998

Bioprocess Engineering

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“…The application of molecular techniques facilitates analyses of environmental samples by the profiling of phylogenetic diversity based on 16S rDNA to assess community structure and succession (14,21,28) or the wholecell targeting of specific taxa or individual organisms by fluorescent in situ hybridization (FISH) (12). These culture-independent molecular studies can provide a more complete understanding of microbial community composition than pure culture studies in that they can demonstrate the in situ pres-ence (and potentially physiological status) of key taxa by ribosome counting (29) and may direct isolation efforts toward these key groups for further laboratory characterization. Ultimately, the aim of a combined molecular characterization of in situ communities and subsequent targeted isolation strategies is the examination of in situ distribution of organisms, their specific physiological function, and their potential for manipulation to optimize community processes.…”

mentioning

confidence: 99%

Bacterial Community Structure and Physiological State within an Industrial Phenol Bioremediation System

Whiteley

Bailey

2000

Appl Environ Microbiol

175

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The structure of bacterial populations in specific compartments of an operational industrial phenol remediation system was assessed to examine bacterial community diversity, distribution, and physiological state with respect to the remediation of phenolic polluted wastewater. Rapid community fingerprinting by PCRbased denaturing gradient gel electrophoresis (DGGE) of 16S rDNA indicated highly structured bacterial communities residing in all nine compartments of the treatment plant and not exclusively within the Vitox biological reactor. Whole-cell targeting by fluorescent in situ hybridization with specific oligonucleotides (directed to the ␣, ␤ and ␥ subclasses of the class Proteobacteria [␣-, ␤-, and ␥-Proteobacteria, respectively], the Cytophaga-Flavobacterium group, and the Pseudomonas group) tended to mirror gross changes in bacterial community composition when compared with DGGE community fingerprinting. At the whole-cell level, the treatment compartments were numerically dominated by cells assigned to the Cytophaga-Flavobacterium group and to the ␥-Proteobacteria. The ␣ subclass Proteobacteria were of low relative abundance throughout the treatment system whilst the ␤ subclass of the Proteobacteria exhibited local dominance in several of the processing compartments. Quantitative image analyses of cellular fluorescence was used as an indicator of physiological state within the populations probed with rDNA. For cells hybridized with EUB338, the mean fluorescence per cell decreased with increasing phenolic concentration, indicating the strong influence of the primary pollutant upon cellular rRNA content. The ␥ subclass of the Proteobacteria had a ribosome content which correlated positively with total phenolics and thiocyanate. While members of the Cytophaga-Flavobacterium group were numerically dominant in the processing system, their abundance and ribosome content data for individual populations did not correlate with any of the measured chemical parameters. The potential importance of the ␥-Proteobacteria and the Cytophaga-Flavobacteria during this bioremediation process was highlighted.

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“…The lowest published K S values we have found for any organic compound are for toluene (C Sat = 5 mM) degradation under aerobic conditions: 0.64 lM for Pseudomonas fluorescens K3-2 (Shreve and Vogel, 1993) and 1 lM for Ps. putida R1 (Pedersen et al, 1997). The compound with the highest K S relative to C Sat that we could find is for aerobic biodegradation of 1-methylnaphthalene (C Sat = 200 lM, K S = 37 lM) (Knightes and Peters, 2003); the authors were unable to fit the Monod model to any of the higher molecular weight PAHs they studied.…”

Section: Krumins and Fennellmentioning

confidence: 75%

Identifying the Correct Biotransformation Model from Polychlorinated Biphenyl and Dioxin Dechlorination Batch Studies

Krumins

Fennell

2014

Environmental Engineering Science

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We performed Monte Carlo simulations of batch transformations of hydrophobic compounds using typical numbers of data points, extent of reaction, and measurement error, to identify the most appropriate biotransformation model to describe such data under different conditions. Highly hydrophobic compounds such as polychlorinated biphenyls (PCBs) and dioxins present special challenges for parameterization due to low environmental concentrations and slow biotransformation rates, which result in high sample variability, few samples, and limited substrate concentration range. Four models of varying complexity (zero-order, first-order, Monod, and Best) were fit to simulated data. Various combinations of initial concentration (S 0 ), half saturation concentration (K S ), maximum substrate utilization rate (q max ), measurement error, number of data points per batch run, and extent of biotransformation were simulated. One thousand Monte-Carlo runs were performed for each parameter combination, and AIC c (Akaike's information criterion corrected for small numbers of data points) was used to determine the most appropriate model. Neither the Best model nor the zero-order model ever produced the lowest AIC c for a majority of simulations under any combination of test conditions. With 10% measurement error, the first-order model always outperformed the others. In the case of 1% measurement error with 10 evenly-spaced data points, the Monod model was the better choice when S 0 > K S and the system was not mass transfer limited (k$S 0 > 1 5 q max ); otherwise, the first-order model was indicated. S 0 is constrained by the compound's aqueous solubility; therefore, for highly hydrophobic compounds such as PCBs or polychlorinated dibenzo-p-dioxins and dibenzofurans, a first-order model is likely to fit batch biotransformation data as well or better than a more complicated model.

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Activity of toluene-degrading Pseudomonas putida in the early growth phase of a biofilm for waste gas treatment

Cited by 71 publications

References 23 publications

Biodegradation of toluene in a trickling filter

Biodegradation of toluene in a trickling filter

Bacterial Community Structure and Physiological State within an Industrial Phenol Bioremediation System

Identifying the Correct Biotransformation Model from Polychlorinated Biphenyl and Dioxin Dechlorination Batch Studies

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