Naresh Singhal

Sorption and biodegradation of organic micropollutants during river bank filtration: A laboratory column study

This study investigated sorption and biodegradation behaviour of 14 organic micropollutants (OMP) in soil columns representative of the first metre (oxic conditions) of the river bank filtration (RBF) process. Breakthrough curves were modelled to differentiate between OMP sorption and biodegradation. The main objective of this study was to investigate if the OMP biodegradation rate could be related to the physico-chemical properties (charge, hydrophobicity and molecular weight) or functional groups of the OMPs. Although trends were observed between charge or hydrophobicity and the biodegradation rate for charged compounds, a statistically significant linear relationship for the complete OMP mixture could not be obtained using these physico-chemical properties. However, a statistically significant relationship was obtained between biological degradation rates and the OMP functional groups. The presence of ethers and carbonyl groups will increase biodegradability, while the presence of amines, ring structures, aliphatic ethers and sulphur will decrease biodegradability. This predictive model based on functional groups can be used by drinking water companies to make a first estimate whether a newly detected compound will be biodegraded during the first metre of RBF or that additional treatment is required. In addition, the influence of active and inactive biomass (biosorption), sand grains and the water matrix on OMP sorption was found to be negligible under the conditions investigated in this study. Retardation factors for most compounds were close to 1, indicating mobile behaviour of these compounds during soil passage. Adaptation of the biomass towards the dosed OMPs was not observed for a 6 month period, implying that new developed RBF sites might not be able to biodegrade compounds such as atrazine and sulfamethoxazole in the first few months of operation.

Biodegradation of agricultural herbicides in sequencing batch reactors under aerobic or anaerobic conditions

This study investigated the biodegradability of the herbicides isoproturon and 2,4-dichlorophenoxyacetic acid (2,4-D) in sequencing batch reactors (SBRs). Two laboratory-scale (2L liquid volume) SBRs were employed: one reactor performing under aerobic and the other under anaerobic conditions. The aerobic SBR was operated at an ambient temperature (22+/-2 degrees C), while the anaerobic SBR was run in the lower mesophilic range (30+/-2 degrees C). Each bioreactor was seeded with a 3:1 mixture (by weight) of fresh sludge and biomass that had been previously exposed to both herbicides. The effect of herbicide concentration on either treatment process was explored at a hydraulic retention time (HRT) of 48 h, using glucose as a supplemental carbon substrate. Although no isoproturon degradation was observed in either system during the study, complete 2,4-D removal occurred after an acclimation period of approximately 30 d (aerobic SBR) and 70 d (anaerobic SBR). The aerobic reactor achieved complete 2,4-D utilization at feed concentrations up to 500 mg/L. A further increase to 700 mg/L, however, proved to be inhibitory since 2,4-D biodegradation was negligible. On the other hand, the anaerobic SBR was able to degrade 120 mg/L of 2,4-D, which corresponds to 40% of the maximum feed concentration applied. Moreover, glucose was consumed first throughout the experiment in a sequential utilization pattern relating to 2,4-D, with biodegradation of both substrates following closely first-order kinetics.

Modeling Biogeochemical Processes in Leachate-Contaminated Soils: A Review

During subsurface transport, reactive solutes are subject to a variety of hydrological, physical and biochemical processes. The major hydrological and physical processes include advection, diffusion and hydrodynamic dispersion, and key biochemical processes are aqueous complexation, precipitation/dissolution, adsorption/desorption, microbial reactions, and redox transformations. The addition of strongly reduced landfill leachate to an aquifer may lead to the development of different redox environments depending on factors such as the redox capacities and reactivities of the reduced and oxidised compounds in the leachate and the aquifer. The prevailing redox environment is key to understanding the fate of pollutants in the aquifer. The local hydrogeologic conditions such as hydraulic conductivity, ion exchange capacity, and buffering capacity of the soil are also important in assessing the potential for groundwater pollution. Attenuating processes such as bacterial growth and metal precipitation, which alter soil characteristics, must be considered to correctly assess environmental impact. A multicomponent reactive solute transport model coupled to kinetic biodegradation and precipitation/dissolution model, and geochemical equilibrium model can be used to assess the impact of contaminants leaking from landfills on groundwater quality. The fluid flow model can also be coupled to the transport model to simulate the clogging of soils using a relationship between permeability and change in soil porosity. This paper discusses the different biogeochemical processes occurring in leachate-contaminated soils and the modeling of the transport and fate of organic and inorganic contaminants under such conditions.

AMENDMENTS FOR ENHANCING COPPER UPTAKE BY BRASSICA JUNCEA AND LOLIUM PERENNE FROM SOLUTION

Phytoextraction of metals is frequently limited by contaminant bioavailability and plant uptake rates. Chemical amendments can be added to increase the uptake and translocation of metals to aerial biomass. A range of amendments of various types was tested for increasing the copper uptake with the test species Indian mustard and ryegrass. These included citric acid (an organic acid); histidine (an amino acid); ethylenediaminetriacetic acid (EDTA), nitrilotriacetic acid (NTA), and ethelynediaminedisuccinic acid (EDDS) (aminopolycarboxylic acids); rhamnolipid (a biosurfactant); and Triton X-100 (a synthetic surfactant). EDTA was the most effective amendment for enhancing copper uptake and translocation into the shoots of Indian mustard and ryegrass, with respective shoot tissue copper levels of 1230 and 1360 μg-Cu/g-dry weight after 10 d compared to 90 and 220 μg-Cu/g-dry weight, respectively, in the unamended treatments. However, the EDTA application resulted in symptoms of toxicity in both Indian mustard and ryegrass, leading to drastic decreases in biomass yield. The application of high levels (300 mg/L) of the biodegradable chelator EDDS was found to be effective for improving translocation of copper in both species. The NTA addition provided benefits to root and shoot growth, with increased copper translocation to shoot tissue. Tests with biosurfactants and synthetic surfactants indicated detrimental effects on copper uptake, biomass yield, and the translocation of copper from roots to shoots in both plant species.

Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems.

We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can be used to analyze complex “omics” data and to infer and optimize metabolic processes. Thereby, SMN models are suitable to capitalize on advances in high-throughput molecular and metabolic data generation. SMN models are starting to be applied to describe microbial interactions during wastewater treatment, in-situ bioremediation, microalgae blooms methanogenic fermentation, and bioplastic production. Despite their current challenges, we envisage that SMN models have future potential for the design and development of novel growth media, biochemical pathways and synthetic microbial associations.

Effects of surfactants on cell surface tension parameters and hydrophobicity of Pseudomonas putida 852 and Rhodococcus erythropolis 3586.

The interaction between bacteria and surfaces is central to many environmental, industrial and medical applications. Surfactants are commonly used in these applications and can potentially influence the bacterium/surface interaction. The effect of surfactants upon bacterial cell surface thermodynamic properties was examined using a combination of contact angle measurements and LW-AB surface free energy calculation. Two bacterial strains, hydrophilic Pseudomonas putida 852 and hydrophobic Rhodococcus erythropolis 3586, and two surfactant types, the anionic biosurfactant rhamnolipid and the non-ionic chemical surfactant tergitol, were examined. The study demonstrated that surfactant treatment could modify cell surface tension parameters including Lifshitz-van der Waals (γ(LW)), electron-donor (γ(-)) and electron-acceptor (γ(+)) and thereby the bacterial cell hydrophobicity, depending on the surfactant type and concentration and the bacterial surface characteristics. Rhamnolipid and tergitol were found to increase P. putida 852 hydrophobicity, but decrease the hydrophobicity of R. erythropolis 3586. The extent of change was dependent on surfactant concentration. Among the three surface tension parameters, γ(-) was found to be the most important in predicting bacterial cell hydrophobicity.

Fenton degradation of tetrachloroethene and hexachloroethane in Fe(II) catalyzed systems

The degradation of tetrachlorothene (PCE) and hexachloroethane (HCA) using Fe(II) and Fe(II)-citrate at different H(2)O(2) concentrations was studied to clarify the role of oxidation and reduction pathways in Fenton chemistry. The interactions between oxidative and reductive radicals, and the cyclic nature of the Fe(II)-Fe(III) ions make for a complex system that displays a suppression or enhancement of PCE or HCA degradation as the experimental conditions are varied. PCE degradation decreased, while HCA degradation increased, for larger H(2)O(2) concentration. The degradations of PCE and HCA were lower in vials where they were individually present compared to vials with the PCE-HCA mixture. Using Fe(II)-citrate instead of Fe(II) resulted in slower PCE and insignificant HCA degradation. These observations indicate that degradation efficiency losses arise from interactions between the oxidant and reductant radical moieties, and that the production of reduction radicals is only significant when the hydroxyl radical (OH) production is rapid.

Development of an ANN-based air pollution forecasting system with explicit knowledge through sensitivity analysis

Abstract Little attention is given to applying the artificial neural network (ANN) modeling technique to understand site–specific air pollution dispersion mechanisms, the order of importance of meteorological variables in determining concentrations as well as the important time scales that influence emission patterns. In this paper, we propose a methodology for extracting the key information from routinely–available meteorological parameters and the emission pattern of sources present throughout the year (e.g. traffic emissions) to build a reliable and physically–based ANN air pollution forecasting tool. The methodology is tested by modeling NO 2 concentrations at a site near a major highway in Auckland, New Zealand. The basic model consists of an ANN model for predicting NO 2 concentrations using eight predictor variables: wind speed, wind direction, solar radiation, temperature, relative humidity, as well as “hour of the day”, “day of the week” and “month of the year” representing the time variations in emissions according to their corresponding time scales. Of the three input optimization techniques explored in this study, namely a genetic algorithm, forward selection, and backward elimination, the genetic algorithm technique gave predictions resulting in the smallest mean absolute error. The nature of the internal nonlinear function of the trained genetically–optimized neural network model was then extracted based on the response of the model to perturbations to individual predictor variables through sensitivity analyses. A simplified model, based on the successive removal of the least significant meteorological predictor variables, was then developed until subsequent removal resulted in a significant decrease in model performance. The developed ANN model was found to outperform a linear regression model based on the same input parameters. The proposed approach illustrates how the ANN modeling technique can be used to identify the key meteorological variables required to adequately capture the temporal variability in air pollution concentrations for a specific scenario.

Catalytic oxidative degradation of 17α-ethinylestradiol by FeIII-TAML/H2O2: Estrogenicities of the products of partial, and extensive oxidation

The oxidative degradation of the oral contraceptive 17α-ethinylestradiol (EE(2)) in water by a new advanced catalytic oxidation process was investigated. The oxidant employed was hydrogen peroxide in aqueous solution and the catalyst was the iron tetra-amido macrocyclic ligand (Fe(III)-TAML) complex that has been designated Na[Fe(H(2)O)(B*)] (Fe(III)-B*). EE(2) (10 μM) was oxidised rapidly by the Fe(III)-B*/H(2)O(2) (5 nM/4 mM) catalytic oxidation system at 25 °C, and for reactions at pH 8.40-11.00, no unchanged EE2 was detected in the reaction mixtures after 60 min. No oxidation of EE(2) was detected in blank reactions using either H(2)O(2) or Fe(III)-B* alone. The maximum rate of EE(2) loss occurred at pH 10.21. At this pH the half-life of EE(2) was 2.1 min and the oxidised products showed around 30% estrogenicity removal, as determined by the yeast estrogen screen (YES) bioassay. At pH 11.00, partial oxidation of EE(2) by Fe(III)-B*/H(2)O(2) (5 nM/4 mM) was studied (half-life of EE(2) was 14.5 min) and in this case the initial intermediates formed were a mixture of the epimers 17α-ethynyl-1,4-estradiene-10α,17β-diol-3-one (1a) and 17α-ethynyl-1,4-estradiene-10β,17β-diol-3-one (1b) (identified by LC-ToF-MS and (1)H NMR spectroscopy). Significantly, this product mixture displayed a slightly higher estrogenicity than EE(2) itself, as determined by the YES bioassay. Upon the addition of further aliquots of Fe(III)-B* (to give a Fe(III)-B* concentration of 500 nM) and H(2)O(2) (to bring the concentration up to 4 mM assuming the final concentration had dropped to zero) to this reaction mixture the amounts of 1a and 1b slowly decreased to zero over a 60 min period as they were oxidised to unidentified products that showed no estrogenicity. Thus, partial oxidation of EE(2) gave products that have slightly increased estrogenicity, whereas more extensive oxidation by the advanced catalytic oxidation system completely removed all estrogenicity. These results underscore the importance of controlling the level of oxidation during the removal of EE(2) from water by oxidative processes.

Drainage mechanism of microbubble dispersion and factors influencing its stability

Microbubble dispersion stability is a desirable characteristic in applications such as separation processes and in-situ bioremediation. This study investigates the effects of surfactant concentration, pH and ionic strength on the stability of dispersions of rhamnolipid, a common anionic biosurfactant. Microbubble dispersions of rhamnolipid and the non-ionic synthetic surfactant tergitol 15-S-12 were prepared by intensive stirring at 8000 rpm with solutions of 500-4000 mg l(-1) surfactant concentration at pH 6-8. The ionic strength tests were performed with 1000-3000 mg l(-1) sodium chloride. Dispersion stability increases for higher surfactant concentrations, but decreases with rising pH and increasing salt concentration. However, increasing the pH in the co-presence of salt enhances dispersion stability. A modified model showing improved fits to liquid drainage from the dispersions is presented and it is shown that liquid drainage occurs in three distinct phases, instead of two phases as previously assumed in the literature.