Shankararaman Chellam

Role of extracellular polymeric substances in bioflocculation of activated sludge microorganisms under glucose-controlled conditions

Extracellular polymeric substances (EPS) secreted by suspended cultures of microorganisms from an activated sludge plant in the presence of glucose were characterized in detail using colorimetry, X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared (FTIR) spectroscopy. EPS produced by the multi-species community were similar to literature reports of pure cultures in terms of functionalities with respect to C and O but differed subtly in terms of N and P. Hence, it appears that EPS produced by different microorganisms maybe homologous in major chemical constituents but may differ in minor components such as lipids and phosphodiesters. The role of specific EPS constituents on microbial aggregation was also determined. The weak tendency of microorganisms to bioflocculate during the exponential growth phase was attributed to electrostatic repulsion when EPS concentration was low and acidic in nature (higher fraction of uronic acids to total EPS) as well as reduced polymer bridging. However, during the stationary phase, polymeric interactions overwhelmed electrostatic interactions (lower fraction of uronic acids to total EPS) resulting in improved bioflocculation. More specifically, microorganisms appeared to aggregate in the presence of protein secondary structures including aggregated strands, beta-sheets, alpha- and 3-turn helical structures. Bioflocculation was also favored by increasing O-acetylated carbohydrates and overall C-(O,N) and O=C-OH+O=C-OR functionalities.

Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks

A robust artificial neural network (ANN) model requiring minimal training, closely predicted membrane fouling during nanofiltration (NF) of ground and surface water. Neural networks accurately simulated the total resistance to water permeation across NF membranes during bench-scale experiments with flat membrane sheets, tests with single spiral-wound elements, as well as pilot- and full-scale tests with multiple spiral-wound elements arranged in two stages. ANN inputs included physically meaningful and independent variables including flow rates and feed water quality parameters (pH, UV254, and total dissolved solids (TDS)) that are commonly monitored during water treatment thereby facilitating their implementation. Therefore, under the experimental conditions investigated, colloidal fouling and biofouling appeared to be negligible because accurate ANN predictions were possible without using feed water turbidity and bacteria concentrations as inputs. One emphasis during this work was to minimize the data employed for ANN training while simultaneously performing simulations in purely predictive mode for entire cycles (experiments). Cumulatively, using only 10% of experimental data for ANN training allowed prediction of 93% of them with <5% absolute relative error. Hence, simple to implement ANNs are capable of capturing changes in feed water quality, flux, and recovery and can successfully overcome difficulties associated with mechanistic models to accurately predict long-term fouling during municipal drinking water nanofiltration.

Evaluation of crossflow filtration models based on shear-induced diffusion and particle adhesion: Complications induced by feed suspension polydispersivity

Abstract Specific flux data were obtained during the transient period of flux decline in laminar crossflow filtration. Effects of hydrodynamics on cake parameters such as specific resistance, mass and particle size distribution were studied experimentally. An evaluation of crossflow filtration models suggests that a model based on shear-induced diffusion [1] is a better predictor of specific flux decline than a particle adhesion model [2]. Even for relatively narrowly distributed suspensions, polydispersivity complicates analyses in a manner that is not adequately addressed by these models. Changes in experimental specific cake resistances with module hydrodynamics coupled to the inadequacy of these models for accurately predicting time-dependent specific flux profiles, cake specific resistances, and mass suggests that cake morphology is a key variable that needs to be incorporated in future modeling efforts.

Peer Reviewed: The Promise of Membrane Technology

An expanded understanding of membrane technology is fostering new environmental applications.

Simplified Analysis of Contaminant Rejection During Ground- and Surface Water Nanofiltration Under the Information Collection Rule

A simple, closed-form analytical expression based on the homogenous solution diffusion model is derived for contaminant removal during nanofiltration (NF) of ground and surface water. Solute permeation and back-diffusion coefficients were used as fitting parameters to model rejection characteristics of four thin-film composite NF membranes under conditions typical of drinking water NF. Nonlinear fits of the model to experimental data suggests that the United States Environmental Protection Agencys (USEPA)s Information Collection Rule protocol for bench-scale studies could be improved to obtain greater precision of the mass transfer coefficients. The model was found to fit rejection data for several water treatment contaminants including total organic carbon, precursors to total organic halide, four trihalomethanes and nine haloacetic acids containing chlorine and bromine, calcium and total hardness, alkalinity and conductivity. The simplified approach to mass transfer calculations from multisolute systems suggests that feed water recovery has a stronger influence on contaminant rejection than permeate flux. Evidence for coupled transport of divalent inorganic ions is also presented. Even though the model developed does not account for ion coupling and cannot be applied in a purely predictive mode, it can assist in the better design and interpretation of data obtained from site-specific pilot-scale water treatment NF studies conducted in support of plant design.

Fluid mechanics and fractal aggregates

Abstract The disturbances in uniform creeping flow in the presence of an isolated porous floc are investigated theoretically. Using the Carman-Kozeny equation, the floc permeability is related to its fractal dimension, D . Fluid streamlines, drag coefficient and the fluid collection efficiency of porous aggregates are expressed in terms of D . As D increases, for a fixed packing factor and ratio of primary particle radius to floc radius, the permeability is found to decrease and the fluid mechanics resembles more closely that of an isolated impermeable sphere. As a simplification, it is suggested that a rectilinear model for flow up to an impervious sphere may be a reasonable approximation for aggregate-aggregate and particle-aggregate interactions if D ≲ 2 . Curvilinear models for flow up to an impervious sphere may be accurate approximations for interactions involving aggregates with higher fractal dimensions D ≳ 2.3 .

Emissions of Organic Compounds and Trace Metals in Fine Particulate Matter from Motor Vehicles: A Tunnel Study in Houston, Texas

Abstract Fine particulate matter (PM) samples collected in a highway tunnel in Houston, TX, were analyzed to quantify the concentrations of 14 n-alkanes, 12 polycyclic aromatic hydrocarbons, and nine petroleum biomarkers, as well as 21 metals, with the ultimate aim of identifying appropriate tracers for diesel engines. First, an exploratory multivariate dimensionality reduction technique called principal component analysis (PCA) was employed to identify all potential candidates for tracers. Next, emission indices were calculated to interpret PCA results physically. Emission indices of n-heneicosane, n-docosane, n-tricosane, n-tetracosane, n-pentacosane, fluoranthene, and pyrene were correlated highly and increased strongly with percentage carbon present in the tunnel emanating from diesel vehicles. This suggests that these organic compounds are useful molecular markers to separate emissions from diesel and gasoline engines. Additionally, the results are the first quantification of the metal composition of PM with aerodynamic diameters smaller than 2.5 [H9262]m (PM2.5) emissions from mobile sources in Houston. PCA of trace metal concentrations followed by emission index calculations revealed that barium in fine airborne particles can be linked quantitatively to diesel engine emissions, demonstrating its role as an elemental tracer for heavy-duty trucks.

Spectroscopic characterization of extracellular polymeric substances from Escherichia coli and Serratia marcescens: suppression using sub-inhibitory concentrations of bismuth thiols.

Free and bound (or capsular) EPS produced by suspended cultures of Escherichia coli and Serratia marcescens were characterized in detail using colorimetric analysis of total proteins and polysaccharides, Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and Auger electron spectroscopy (AES) in the presence and absence of bismuth-based antifouling agents. Subtle differences in the chemical composition of free and bound EPS were observed for both bacteria in the absence of bismuth. Total polysaccharides and proteins in free and bound EPS decreased upon treatment with subinhibitory concentrations of lipophilic bismuth thiols (bismuth dimercaptopropanol, BisBAL; bismuth ethanedithiol, BisEDT; and bismuth pyrithione, BisPYR), with BisBAL being most effective. Bismuth thiols also influenced acetylation and carboxylation of polysaccharides in EPS from S. marcescens. Extensive homology between EPS samples in the presence and absence of bismuth was observed with proteins, polysaccharides, and nucleic acids varying predominantly only in the total amount produced. Second derivative analysis of the amide I region of FTIR spectra revealed decreases in protein secondary structures in the presence of bismuth thiols. Hence, antifouling properties of bismuth thiols appear to originate in their ability to suppress O-acetylation and protein secondary structure formation in addition to free and bound EPS secretion.

Predicting contaminant removal during municipal drinking water nanofiltration using artificial neural networks

Abstract An artificial neural network model for steady-state contaminant removal during nanofiltration of ground and surface waters under conditions typical of drinking water treatment is derived and validated. Operating conditions such as flux, feed water recovery, and element recovery (surrogate for cross-flow velocity), and feed water quality parameters including pH, total dissolved solids concentration (surrogate for ionic strength), target contaminant concentration, and where possible the diffusion coefficient were used as inputs to predict the ratio of permeate to feed concentration of the target contaminant. Contaminants reported herein include dissolved organic carbon, precursors to total organic halide, four trihalomethanes and nine haloacetic acids containing chlorine and bromine, hardness, alkalinity, and total dissolved solids. Additionally, source waters from seven different locations and two commercial thin-film composite membranes operating in a wide range of permeate fluxes and feed water recoveries were considered. Deterministic and pseudostochastic simulations showed that artificial neural networks closely predicted permeate concentrations of each one of these organic and inorganic contaminants. Therefore, neural networks can be used to circumvent difficulties associated with formulating and solving the highly non-linear Nernst–Planck equation to calculate solute removal from multi-component solutions at high recovery. Moreover, neural networks can predict the transport of heterogeneous and difficult to characterize water treatment contaminants such as natural organic matter and disinfection by-product precursors, whose physicochemical properties are unknown. Such models can be used to screen membranes prior to conducting expensive large-scale tests as well as in the better design and interpretation of data obtained from site-specific water treatment nanofiltration studies conducted in support of plant design.

Bacteriophage inactivation by UV-A illuminated fullerenes: role of nanoparticle-virus association and biological targets.

Inactivation rates of the MS2 bacteriophage and (1)O(2) generation rates by four different photosensitized aqueous fullerene suspensions were in the same order: aqu-nC(60) < C(60)(OH)(6) ≈ C(60)(OH)(24) < C(60)(NH(2))(6). Alterations to capsid protein secondary structures and protein oxidation were inferred by detecting changes in infrared vibrational frequencies and carbonyl groups respectively. MS2 inactivation appears to be the result of loss of capsid structural integrity (localized deformation) and the reduced ability to eject genomic RNA into its bacterial host. Evidence is also presented for possible capsid rupture in MS2 exposed to UV-A illuminated C(60)(NH(2))(6) through TEM imagery and detection of RNA infrared fingerprints in ATR-FTIR spectra. Fullerene-virus mixtures were also directly visualized in the aqueous phase using a novel enhanced darkfield transmission optical microscope fitted with a hyperspectral imaging (HSI) spectrometer. Perturbations in intermolecular extended chains, HSI, and electrostatic interactions suggest that inactivation is a function of the relative proximity between nanoparticles and viruses and (1)O(2) generation rate. MS2 log survival ratios were linearly related to CT (product of (1)O(2) concentration C and exposure time T) demonstrating the applicability of classical Chick-Watson kinetics for all fullerenes employed in this study. Results suggest that antiviral properties of fullerenes can be increased by adjusting the type of surface functionalization and extent of cage derivatization thereby increasing the (1)O(2) generation rate and facilitating closer association with biological targets.