Showing papers by "Pedro J. J. Alvarez published in 2005"
TL;DR: In this paper, a plant-microbe interaction affecting polycyclic aromatic hydrocarbon phytoremediation was demonstrated with Sphingomonas yanoikuyae JAR02 that utilized plant root extracts and exudates as primary substrates.
191 citations
Book•
07 Dec 2005
TL;DR: In this paper, the basic concepts of Ground Water Flow and Contaminant Transport Processes are presented as well as analytical models for Natural Attenuation, and a performance assessment and demonstration of Bioremediation and natural attenuation is presented.
Abstract: Preface. 1. Introduction to Bioremediation. 2. Geochemical Attenuation Mechanisms. 3. Biodegradation Principles. 4. Fundamentals of Ground Water Flow and Contaminant Transport Processes. 5. Fate and Transport Equations and Analytical Models for Natural Attenuation. 6. Numerical Modeling of Contaminant Transport, Transformation, and Degradation Processes. 7. Field and Laboratory Techniques to Determine Site-Specific Parameters for Modeling the Fate and Transport of Groundwater Pollutants. 8. Bioremediation Technologies. 9. Performance Assessment and Demonstration of Bioremediation and Natural Attenuation. Appendix A: Chemical Properties of Various Compounds. Appendix B: Free Energy and Thermodynamic Feasibility of Chemical and Biochemical Reactions. Appendix C: Commonly Used Numerical Groundwater Flow and Solute Transport Codes (Modified after Wiedemeier et al., 1999). Appendix D: Nonparametric Statistical Tests for Determining the Effectiveness of Natural Attenuation (after Wisconsin Department of Natural Resources). Appendix E: Critical Values of the Student t-Distribution. Glossary. Index.
172 citations
TL;DR: The addition of anaerobic electron acceptors could enhance BTEX biodegradation not only by facilitating their anaer aerobic biodegrading but also by accelerating the mineralization of ethanol or other substrates that are labile under anaerilic conditions.
Abstract: Flow-through aquifer columns were used to investigate the feasibility of adding sulfate, EDTA-Fe(III) or nitrate to enhance the biodegradation of BTEX and ethanol mixtures. The rapid biodegradation of ethanol near the inlet depleted the influent dissolved oxygen (8 mg l(-1)), stimulated methanogenesis, and decreased BTEX biodegradation efficiencies from > 99% in the absence of ethanol to an average of 32% for benzene, 49% for toluene, 77% for ethylbenzene, and about 30% for xylenes. The addition of sulfate, EDTA-Fe(III) or nitrate suppressed methanogenesis and significantly increased BTEX biodegradation efficiencies. Nevertheless, occasional clogging was experienced by the column augmented with EDTA-Fe(III) due to iron precipitation. Enhanced benzene biodegradation (> 70% in all biostimulated columns) is noteworthy because benzene is often recalcitrant under anaerobic conditions. Influent dissolved oxygen apparently played a critical role because no significant benzene biotransformation was observed after oxygen was purged out of the influent media. The addition of anaerobic electron acceptors could enhance BTEX biodegradation not only by facilitating their anaerobic biodegradation but also by accelerating the mineralization of ethanol or other substrates that are labile under anaerobic conditions. This would alleviate the biochemical oxygen demand (BOD) and increase the likelihood that entraining oxygen would be used for the biotransformation of residual BTEX.
64 citations
05 Dec 2005
56 citations
TL;DR: Batch experiments with a homoacetogenic pure culture of Acetobacterium paludosum showed that anaerobic RDX degradation is the fastest when auxiliary growth substrates and nitrogen sources (ammonium) are not added, suggesting that the absence of easily assimilated nitrogen sources, such as ammonium, enhancesRDX degradation.
Abstract: Substrates and nutrients are often added to contaminated soil or groundwater to enhance bioremediation. Nevertheless, this practice may be counterproductive in some cases where nutrient addition might relieve selective pressure for pollutant biodegradation. Batch experiments with a homoacetogenic pure culture of Acetobacterium paludosum showed that anaerobic RDX degradation is the fastest when auxiliary growth substrates (yeast extract plus fructose) and nitrogen sources (ammonium) are not added. This bacterium degraded RDX faster under autotrophic (H2-fed) than under heterotrophic conditions, even though heterotrophic growth was faster. The inhibitory effect of ammonium is postulated to be due to the repression of enzymes that initiate RDX degradation by reducing its nitro groups, based on the known fact that ammonia represses nitrate and nitrite reductases. This observation suggests that the absence of easily assimilated nitrogen sources, such as ammonium, enhances RDX degradation. Although specific end products of RDX degradation were not determined, the production of nitrous oxide (N2O) suggests that A. paludosum cleaved the triazine ring.
56 citations
TL;DR: How microbes capable of degrading them can be isolated, characterised and their relevant enzymes engineered for this purpose and ways to avoid potential side-effects are discussed.
36 citations
TL;DR: Microcosm data were used to develop a deterministic model to describe how rhizodeposition affects the fate of phenanthrene in aged contaminated soil, and model simulations suggested that the system was approaching a stable end-point after 201 days of simulated rhizoremediation, and corroborated that microorganisms have a significant impact on the fate.
Abstract: Microcosm data were used to develop a deterministic model to describe how rhizodeposition affects the fate of phenanthrene in aged contaminated soil. Microbial mineralization and soil sequestration of 14C-phenanthrene were compared in microcosms amended weekly with phenolic-rich mulberry root extracts versus unamended controls. Mineralization was higher in the amended soils simulating the rhizosphere (57.7 +/- 0.9%) than in controls simulating bulk (unplanted) soils (53.2 +/- 0.7%) after 201 days (p < 0.05). Humin was the main soil sink for the residual 14C-label. Whereas the total 14C-label associated with humin remained constant in biologically active soils (at about 30%), it increased up to 80% after 201 days in sterile controls. The initial phenanthrene extraction with n-butanol (commonly used to assess bioavailability) slightly underestimated the fraction thatwas mineralized (assessed by 14CO2 recovery). Changes in the unextractable fraction (determined by combustion in a biological oxidizer) suggested the presence of two soil sequestration domains: (1) irreversibly bound residue, and (2) an intermediate transition phase that is unextractable by solvents at a given point in time but could become bioavailable due to physicochemical or biological transformations of the binding matrix. The fate of phenanthrene was accurately modeled by considering the transfer of the 14C label between different soil compartments as first-order kinetic processes. Model simulations suggested that the system was approaching a stable end-point after 201 days of simulated rhizoremediation, and corroborated that microorganisms have a significant impact on the fate of phenanthrene in soil.
25 citations
TL;DR: Results show that high RDX removal efficiency by ZVI-PRBs is achievable and sustainable and that the efficacy and start-up of ZVI -PRBs might be enhanced by bioaugmentation, and shorter acclimation periods associated withBioaugmented PRBs may be desirable for rapid RDX mineralization, thereby preventing breakthrough of potentially undesirable byproducts.
Abstract: Flow-through columns packed with "aged" zero-valent iron (ZVI) between layers of soil and sand were constructed to mimic a one-dimensional permeable reactive iron barrier (PRB). The columns were continuously fed RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine, ca. 18 mg l−1) for over one year. Two columns were bioaugmented with dissimilatory iron reducing bacteria (DIRB) Shewanella algae BrY or Geobacter metallireducens GS-15 to investigate their potential to enhance the reactivity of aged iron by reductive dissolution of passivating iron oxides or via production of biogenic reactive minerals. A third column was not bioaugmented to evaluate colonization by indigenous soil microorganisms. [14C]-RDX was completely removed in all columns at the start of the iron layer, and concentration profiles showed rapid and sustainable RDX removal over one year; however, a phylogenetic profile conducted after one year using DGGE analysis of recovered DNA did not detect S. algae BrY or G. metallireducens in their respective...
15 citations
05 Dec 2005
8 citations
TL;DR: This special issue of Environment International, a number of selected papers presented at the Second European Bioremediation Conference are published, and future trends and directions for the restoration of contaminated sites using environmental biotechnology-based technologies are discussed.
4 citations
05 Dec 2005
05 Dec 2005
05 Dec 2005
05 Dec 2005
TL;DR: The Mann-Whitney U Test as mentioned in this paper is applicable to data that may or may not exhibit seasonal behavior, but the test requires S consecutive rounds of quarterly or semi-annual sampling results.
Abstract: Two nonparametric statistical tests are described here: the Mann--Kendall (S) and Mann Whitney (U) statistical tests. These tests can be used to show whether groundwater contaminant concentrations in a monitoring well are increasing, stable or decreasing. However, neither test is able to determine the rate in which the concentrations are changing over time. The Mann—Kendall Test can be. used with a minimum of 4 rounds of sampling results; however, the Mann—Kendall Test is not valid for data that exhibit seasonal behavior. The Mann—Whitney U Test is applicable to data that may or may not exhibit seasonal behavior, but the test requires S consecutive rounds of quarterly or semi—annual sampling results. To demonstrate that natural attenuation is effective, the chosen statistical test must show decreasing contaminant concentrations at an appropriate confidence level, given in the test methodologies that follow. Mann—Kendall Test 1. Assemble well data for at least 4 sampling events for each contaminant in the order in which the data Was collected, Include all contaminants that have exceeded the ES at one or more monitoring wells. Include data from: a. One or more contaminated monitoring wells near the downgradient plume margin, which may include piezometers, b. A monitoring well near the source zone, and c. At least one monitoring well along a flow line between the source zone well and plume margin well. 2. For purposes of the Mann—Kendall test, all non—detect data values should be assigned a single value that is less than the detection limit, even if the detection limit varies over time. 3. Tests for Seasonality in Data. For seasonally affected data, either remove the seasonality in the data (e.g., by only testing data from the seasons with the highest contaminant concentrations) or use a statistical test that is unaffected by seasonality, such as the Mann—Whitney U Test. To test for data 'seasonality: a. Determine if groundwater flow direction changes with season by comparing a water table map from each season that the contaminant concentrations are measured. If the flow diction changes from one sampling period to another and shifts the plume away from the wells being used in the statistical test, then data from those seasons that are shifted away from the centerline monitoring wells can not be used in the Mann—Kendall Test. b. Determine if groundwater elevation and contaminant concentration change seasonally. Plot contaminant concentration versus groundwater level for each well to be assessed by the Mann—Kendall Test. If groundwater concentrations change as water level changes, then the data is seasonally affected. The seasons with the highest contaminant concentrations should be included in the Mann—Kendall Test. 4. Calculate the Mann—Kendall Statistic (S) using a manual method or a DNR supplied spreadsheet. Assess all contaminants in the plume for the selected wells being assessed with the Mann—Kendall Test. Enter data for each contaminant in the order it was collected. a. Manual Method to Calculate Mann—Kendall Statistic. Compare data sequentially, comparing sampling event 1 to sampling events 2 through n, then sampling event 2 to sampling events 3 through n, etc. Each row is filled in with a 1, 0 or —1, as follows: Along row 2, if: Concentration of event xi > event 1: Enter +1 Concentration of event x i = event 1: Enter 0 Concentration of event xi C event 1: Enter —1 Where: n = total number of sampling events xi = value of given sample event, with i = 2 to n