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FEATURE pubs.acs.org/est

Greening Coal: Breakthroughs and Challenges in Carbon Capture and Storage Philip H. Stauffer,*,† Gordon N. Keating,† Richard S. Middleton,† Hari S. Viswanathan,† Kathryn A. Berchtold,‡ Rajinder P. Singh,‡ Rajesh J. Pawar,† and Anthony Mancino§ †

Earth and Environmental Sciences Division (EES), Los Alamos. National Laboratory (LANL) Materials Chemistry Division at LANL § International Research, Analysis, and Technical Development Division at LANL ‡

’ INTRODUCTION Carbon capture and storage (CCS) is a critical technology for reducing emissions of greenhouse gases (GHGs) to the atmosphere. CCS is being considered as one piece of a strategy for stabilizing atmospheric CO2 concentrations.1 This plan requires that globally, billions of tonnes of carbon dioxide (GtCO2) each year must be captured, concentrated, and stored to keep it out of the atmosphere for hundreds to thousands of years. The nearterm approach is to capture and compress CO2 from stationary industrial sources (e.g., coal and natural gas burning power plants) and transport it through pipelines for injection and long-term storage in geologic reservoirs (e.g., depleted oil/gas fields and deep saline aquifers). In 2009, U.S. coal power plants generated 307 gigawatts of electricity (GWe) and produced 2.4 GtCO2 out of total U.S. emissions of 6 GtCO2.2 The existing fleet of coal-fired power plants will continue to be a major source of electricity for the next 20 years, with estimated production capacity increasing to 400 GWe.3 In addition, electrical generation in China has expanded rapidly in recent years, nearing the size of the U.S. fleet,4 and three-quarters of China’s power plants burn r 2011 American Chemical Society

coal.5 Given the persistence of this global capacity for coal combustion for the next few decades, CCS represents a bridging technology that will allow us to continue to generate electricity in existing power plants while we transition to a low-carbon energy future. CCS technology must be deployed at a massive scale to have a meaningful impact on reducing industrial CO2 emissions to the atmosphere. This could require the U.S. to capture on the order of 1 GtCO2/yr from hundreds of typical coal-burning power plants and to construct dedicated pipelines to handle a CO2 volume 25% greater than the U.S.’ 2009 daily oil consumption.6 Additionally, this volume of CO2 will require finding extensive geologic formations in low risk environments to store between 1 - 3 km3 of supercritical CO2 each year. This paper explores the science and technology related to CO2 capture, geologic storage, and system-wide integration. We emphasize strategies and technologies suitable for making CCS a reality in the near future, with particular focus on retrofitting existing coal-fired power plants to capture and compress CO2 for geologic storage. Projects involving coal combustion retrofits currently represent an area of particular focus for implementing CCS in the power industry in the next decade. Examples include turbine retrofit and capture of 1 million tonnes per year (MtCO2/yr) at the coal-fired Boundary Dam plant (SaskPower) in Saskatchewan,7 oxy-fuel combustion retrofit and capture of 1.3 MtCO2/yr in the FutureGen 2.0 plant (Ameren) in Illinois,8 and postcombustion capture of 3,000 tCO2/yr at the Gaobeidian power plant in Shanghai.9

’ CO2 CAPTURE Power plants are responsible for greater than one-third of the CO2 emissions worldwide and are a prime focus of global CCS efforts.10 Capturing CO2 from the mixed-gas streams produced during power generation is a first and critical step for CCS. Three strategies for incorporating capture into power generation scenarios are of primary focus today: post-, pre- and oxy-combustion capture (Figure 1). Postcombustion capture systems are designed to separate CO2 from pulverized coal (PC) derived flue gas. PC flue gas contains 10 13% CO2 with a balance of N2, steam, and other impurities (SOx, NOx, heavy metals).11 Oxy-combustion power plants are modified versions of conventional PC plants using Published: September 09, 2011 8597

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Figure 1. Post-, oxy-, and precombustion concepts and separation system integration into power plants.

oxygen (O2) diluted with recycled flue gas instead of air to combust coal into steam and high purity CO2. Precombustion systems are designed to separate CO 2 from synthesis gas (“syngas”) prior to electricity/hydrogen (H2) production and are applicable to new, more efficient, integrated gasification combined cycle (IGCC) plants. Syngas from the coal gasifier is primarily comprised of H2and carbon monoxide (CO). A watergas-shift reactor is added in the capture process to convert CO to CO2, thus facilitating capture while producing additional hydrogen. Power generation with capture using oxy- and precombustion processes has 10 37% higher net efficiency than that of a new air-fired PC plant without CO2 capture and allows more flexibility for future improvements in design.12 The U.S. Department of Energy (DOE) CCS target is to achieve 90% CO2 capture while limiting the increase in cost of electricity (COE) to 35 and 10%, respectively, for plants implementing postcombustion and pre/oxy-combustion capture. 13 The energy consumption and losses associated with CO2 capture using today’s technologies represent an unacceptably high proportion (>75%) of the total cost of CCS (capture/compression, transport, storage). The DOE cost targets require significant improvements in large scale deployable capture technologies. Although all of these technologies will

be vital in the long term, current U.S. and global power production industries are dominated by PC-based plants. Therefore, postcombustion capture is the most likely to have the largest impact on total CO2 emissions reductions over the next few decades. The U.S. Energy Information Administration estimates that, in 2030, 78% of the CO 2 emissions resulting from U.S. electricity generation will still be derived from the current fleet of PC plants.14 Thus, to effect change in the near term, we believe that these PC emissions must be addressed to a large extent through postcombustion separation and capture retrofits to those plants. CO2 separation technologies are readily available and have been used industrially for nearly 60 years. These technologies are based on chemical solvents (e.g., monoethanolamine, MEA) and physical solvents (e.g., glycol or methanol). A regeneration step is used to reclaim the solvent for reuse. Although technically suited for CO2 capture applications, implementation is hindered by exorbitant operating costs due to high energy penalties for solvent regeneration and material and environmental costs due to solvent attrition. In one study, the estimated cost of implementing these existing capture technologies in the operational U.S. power plants will increase the COE by 330%.13 8598

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Environmental Science & Technology The development of new and innovative capture systems for PC application is imperative. Fortunately, increasing research budgets have enabled scientists and engineers worldwide to make progress in this burgeoning field. Emerging CO2 separation technologies under various stages of development include solvents, sorbents, membranes, and biologically mediated separation systems. A brief summary describing the important aspects of these emerging technologies related to CO2 capture from coalderived power production is provided below. Technology development goals for improved solvents include the realization of low-cost, noncorrosive, stable, low-toxicity materials with high CO2 capacity, rapid mass transfer kinetics, low regeneration energy, and high impurity tolerance.12,13,15 17 Aqueous ammonia (AA) and ionic liquid (IL) based materials are forerunners in current solvent research, development, and demonstration (RD&D) efforts. Systems such as these provide an opportunity for use of less corrosive, more stable solvents with chemically tunable mass transfer rates and capacities, thereby addressing some of the limitations of the more conventional amine-based materials. AA technologies are under pilot-plant and midscale demonstration while IL based solvents are currently at the laboratory development stage. Solid sorbents work by adsorbing gaseous CO2 onto a surface, followed by temperature or pressure driven desorption. CO2 interacts with the sorbent chemically (e.g., immobilized amines and carbonates) or physically (e.g., high surface area metal organic frameworks and zeolites).12,13,15 17 Since these CO2sorbent interactions are weaker than those between CO2 and chemical solvents, less heat is typically required for regeneration and CO2 release.15 Preliminary cost analyses indicate 15% improvement in COE using sorbents as compared to MEA capture.18 Process and materials optimization remain challenges for this technology. As with sorbents, materials cost, stability, CO2 capacity and mass transfer optimization are critical to commercial viability. Additionally, movement of large volumes of solids (e.g., fluidized beds), and the mechanical and thermal robustness required for such process schemes, provide additional process, and materials challenges. Membranes are currently top contenders among new postcombustion CO2 separation technologies due to their low energy consumption, lack of moving parts, and modular design opportunities. However, they incur extra cost due to flue gas compression required to create the driving force for transport, post cleaning, and/or multistage operation; membranes must also be stable in the presence of flue gas contaminants and high temperatures. Both organic and inorganic membrane approaches are being pursued. Development is currently focused on achieving high CO2 throughput with adequate selectivity using lowcost materials. The RD&D performance of organic membranes has the potential to reduce capture costs to as low as $23/tCO2, significantly lower than the $54/tCO2 cost using existing industrial amine based separation technologies.19,20 Further development and pilot-scale efforts are ongoing to fully quantify and increase the estimated cost saving of using membranes for CO2 capture. New efforts employing mechanically robust roomtemperature IL based membranes hold promise for realizing unprecedented CO2 throughputs and capture costs of > = <
y þ y I 1 2 2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi p p ð4Þ erf ¼ þ erf I0 2> 2 ðDcy Þeff x=vf > ; : 2 ðDcy Þeff x=vf where I0 is the peak fluorescent intensity. Experimentally obtained images were analyzed for the fluorescent intensity distribution at cross sections 4.9, 9.8, 19.6, 29.4, and 36.75 mm downstream from the FITC
dextran inlet point into the porous bottom channel using an image processing program (Image J) developed by the National Institute of Health. For the sake of consistency, the intensity profiles obtained at each cross section were normalized with respect to the corresponding peak intensity value, as opposed to the peak intensity at the inlet. It was not possible to measure the maximum intensity (I0) near the tracer inlet (Figure 1) due to light interference from the tracer inlet channel. To obtain the effective transverse dispersion coefficients, normalized steady state transverse intensity profiles were fitted to the analytical solution given by eq 4. Due to symmetry of the device, the intensity profiles can be cut in half by a vertical plane at y = 0, and therefore, only half of the porous channel region (y g 0) was chosen for modeling the fluorescent intensity profiles. The tracer plume width, h, was measured immediately downstream (x = 1.05 mm) from the point of injection of the tracer into the porous bottom channel and was assumed to be constant at that cross section (x = 1.05 mm) due to continuous injection of FITC
dextran (I[x e 1.05 mm,
h/2 e y e h/2, t g tequib] = I0), where tequib is the time required to attain equilibrium flow conditions. The total width of the device (6.3 mm) was approximately three times the maximum plume width observed during the experiments, and therefore, channel boundaries were approximated as infinite distance, zero concentration boundary conditions. 8783

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Figure 4. Markers represent FITC
dextran normalized intensity profiles in MFD-I at a cross section 29.2 mm downstream from the inlet in experiments with motile bacteria (left plot), immobilized bacteria (middle plot), and no bacteria (right plot). Each marker represents pixel intensity averaged over 10 pixels, and the curves represent corresponding modeled profiles for the respective effective transverse dispersion coefficients obtained as the global average of triplicate experiments.

2.6.2. Apparent Transverse Dispersivity. The effective transverse dispersion coefficient in porous media is given by the following: ðDcy Þeff ¼ ðD0 Þeff þ αapp vf

ð5Þ

where (D0)eff is the effective diffusion coefficient and αapp is the apparent transverse dispersivity of the porous medium. Both (D0)eff and αapp are lumped parameters that account for the effect of the presence of bacteria and porous media in the system. The effective diffusion coefficient of the tracer can be given by the following:38
40 ε ðD0 Þeff ¼ D0 ð6Þ τ where ε and τ are porosity and tortuosity of the porous medium and D0 is the molecular diffusion coefficient of the tracer. The porosity (ε) values for different porous media are reported in Table 1. The tortuosity term incorporates the effects of the available pore space and the transport mechanism of the tracers 39 and an assumed value of 2 will be used in this study based on values used in similar groundwater studies.2,27 The molecular diffusion coefficient of the tracer FITC
dextran is D0 = 2.3  10
5 mm2/s.41

3. RESULTS 3.1. Comparison of FITC
Dextran Intensity Profiles. A fluorescent plume along the length of the device was apparent as FITC
dextran entered and flowed through the porous bottom channel. At steady-state flow conditions, images of the plume were recorded at the predefined cross sections (Figure 1). Figure 3 shows representative images at cross sections 9.8 and 29.4 mm downstream from the FITC
dextran inlet in MFD-I under different experimental conditions. Transverse dispersion of the plume is evident from the increase in the width of fluorescent intensity profiles from 9.8 mm (row 1, Figure 3) to 29.4 mm (row 2, Figure 3) in all three experimental conditions (columns 1
3). Comparison of concentration profiles under different experimental conditions (across different columns) shows wider profile widths in experiments with motile bacteria (column 1, Figure 3) than in experiments with no bacteria (column 2, Figure 3) at respective cross sections. To further verify that the effect was due to bacterial motility, control experiments were performed in which the motile bacteria were replaced by similar concentrations of nonmotile bacteria in a third set of experiments. The widths of the fluorescent intensity

Figure 5. Variation of effective transverse dispersion coefficient, (Dcv)eff, with Darcy velocity in MFD-I under three different experimental conditions. Error bars represent standard error values of nine best fit profiles at three cross sections of the device in triplicate experiments. The lines represent results of the dispersion model (eq 6) with the best-fit model parameter values given in Table 1.

profiles in experiments with nonmotile bacteria (column 3, Figure 3) are not significantly different from the corresponding profile widths in experiments with no bacteria (column 2, Figure 3). These observations indicate that the wider profile widths in experiments with motile bacteria are the result of the random motility of E. coli HBC33 in porous media. 3.2. Effective Transverse Dispersion Coefficients. Fluorescent intensity distributions were analyzed at cross sections 4.9, 9.8, 19.6, 29.4, and 36.75 mm downstream from the tracer inlet point in the porous bottom channel of the MFDs. The experimentally obtained representative normalized intensity profiles for MFD-I 29.4 mm downstream from the inlet under various experimental conditions are shown in Figure 4. The best-fit dispersion coefficient values at each cross section of the device were obtained by fitting the model (eq 4) to the corresponding experimentally obtained normalized intensity profiles using leastsquares regression analysis. Average transverse dispersion coefficients (ATDC) for each cross section were calculated by averaging the best-fit values of triplicate experiments. The average best-fit ATDC values for two upstream cross sections (4.9 and 9.8 mm) were abnormally high (due to reasons explained in the Supporting Information) and therefore were not included in the effective transverse dispersion coefficients, (Dcy)eff, calculations. (Dcy)eff for the device under different experimental conditions was calculated by averaging the ATDC values at cross sections 8784

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Environmental Science & Technology 19.6, 29.4, and 36.75 mm downstream from the inlet, where these values were nearly constant (Figure S1 of the Supporting Information). The model-predicted (eq 4) profiles for the effective transverse dispersion coefficients for MFD-I at a cross section 29.4 mm downstream from the inlet under various experimental conditions are shown in Figure 4 along with the corresponding experimentally obtained profiles. The best-fit curves to the experimentally obtained effective transverse dispersion coefficient data for MFD-I are presented in Figure 5 under three experimental conditions. 3.3. Apparent Transverse Dispersivities. Effective diffusion coefficients for three porous media geometries were calculated (eq 6), and the values obtained are reported in Table 1. Apparent transverse dispersivity values under each experimental condition in each MFD were calculated using eq 5 given the effective diffusion coefficient values along with the best-fit effective transverse dispersion coefficients, (Dcy)eff, obtained in the previous section. Least-squares regression analysis was used to determine the best-fit values of the effective diffusion coefficient, (D0)eff, and the apparent dispersivity, αapp, for triplicate experiments. Apparent dispersivity values for MFD-I under three experimental conditions are reported in Table 1. Compared with experiments run with no bacteria, it can be observed that the presence of motile bacteria results in a 2.5-fold increase in the apparent transverse dispersivity of the device. The presence of nonmotile bacteria also resulted in a marginal (1.3 times) increase in the apparent transverse dispersivity of the device (Table 1). 3.4. Effect of Pore Geometry on Average and Effective Transverse Dispersion Coefficients. The ATDC values for MFD-II at Darcy velocities 0.11 and 0.22 mm/s at various cross sections are shown in Figure S1 of the Supporting Information. Similar to MFD-I, ATDC values for MFD-II at cross sections closer to the inlet (at 4.9 and 9.8 mm) are significantly higher than the values for the remaining three downstream cross sections, where ATDC values were nearly constant. Similar observations were made for MFD-III as well, and effective transverse dispersion coefficient values for MFD-II and MFDIII were calculated by averaging ATDC values at three downstream cross sections, as in the case of MFD-I. Apparent transverse dispersivities and effective diffusion coefficient values were also calculated for MFD-II and MFD-III as described for MFD-I, and the resulting values are presented in Table 1. A 3-fold increase in the apparent transverse dispersivity was observed in experiments with motile bacteria as opposed to no bacteria in MFD-II, while no such increase was observed in MFD-III.

4. DISCUSSION Hydrodynamic dispersion results in an increase in the variance of a tracer in porous media as it moves downstream from its injection point. This phenomenon is responsible for the spreading of fluorescent intensity profiles from cross sections 9.8 mm (column 1, Figure 3) to 29.4 mm (column 2, Figure 3) downstream of the inlet in all three experimental conditions. Wider profile widths were observed at each cross section in the presence of motile E. coli HCB33 (row 1, Figure 3) as compared to experiments with no bacteria (row 2, Figure 3). However, the profile widths at each cross section in the experiments with nonmotile bacteria (row 3, Figure 3) were similar to those in experiments with no bacteria. Best-fit effective dispersion coefficient values display a similar trend, with largest values for the

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Figure 6. Effect of flow velocity on mixing enhancement due to bacterial random motility in different MFDs. Error bars indicate standard errors.

experiments with motile bacteria and approximately similar values for experiments with nonmotile bacteria and no bacteria (Figure 6). These two observations indicate that random motility of E. coli HCB33 does enhance mixing of FITC
dextran in the porous MFDs. The marginal differences in the effective dispersion coefficient values in experiments with nonmotile bacteria and no bacteria (Figure 5) may be attributed to the following factors. Even though the nonmotile bacteria may not have contributed in mixing enhancement due to their motility, frequent collision with the porous media structures may have resulted in their deflection across the flow lines and thereby resulted in flow disturbances. The other reasons could be the pore space occupied by the nonmotile bacteria (bacterial concentration used in this study accounts for approximately ∼0.36% of the total available pore space (calculated based on the values reported by Kim and Breuer19 in similar studies) or the non-Fickian (or anomalous) diffusion of the tracer due to bacterial swimming in porous media. In general, the best-fit models for the effective dispersion coefficients capture the shapes of experimentally obtained profiles well (Figure 4). However, the minor disparity between model predictions and experimentally obtained values at the lower tails of the curves (Figure 4) may be attributed to nonFickian diffusion of tracer due to bacterial motility42,43 or to experimental limitations, including nonideal inlet conditions and/or pump fluctuations. Another potential source of error between experimental data and modeled predictions may have been introduced when intensity profiles at downstream cross sections were normalized with respect to their local maximum intensity rather than the peak intensity at the inlet. Although the peak intensity values at all cross sections were analyzed and no significant change was observed among the various cross sections, it is still possible that the normalization process was a source of error. While dispersivity is generally considered an intrinsic property of the porous medium, different longitudinal44,45 and transverse27 dispersivity values have been reported for the same porous medium based on the dispersant used. In their colloidal transport study in micromodels, Auset and Keller24 have emphasized the dependence of dispersivity on the size of the dispersant in addition to properties of the porous medium. Results from this study also show three different values of apparent transverse dispersivity for similar porous media under three different experimental conditions (with bacteria, with nonmotile bacteria, and without bacteria) when the same 8785

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dispersant (FITC
dextran) was used. Therefore, we suggest that dispersivity may be considered as a system-specific parameter, incorporating effects of the porous medium and dispersant and characteristics of the transporting fluid. 4.1. Mixing Enhancement Index. A mixing enhancement index (MEI) was defined to quantify the extent of mixing enhancement and was quantified in terms of the ratio of the effective transverse dispersion coefficient values in experiments with and without motile bacteria: Mixing enhancement index ðMEIÞ ¼

ððDcy Þeff Þwith bacteria ððDcy Þeff Þno bacteria

ð7Þ

The effect of flow velocity on the MEI is shown in Figure 6. For MFD-I and MFD-II, MEI shows an increasing trend initially and finally decreases at the highest flow velocity used in this study. Mixing enhancement in the presence of motile bacteria in these devices may be attributed to the free swimming of bacteria across flow streamlines creating flow disturbances in their surrounding microenvironment. A common understanding may suggest that increasing Darcy velocity should adversely affect bacterial random motility and thereby the contaminant mixing index. This belief would be further strengthened by the fact that higher flow velocities will cause greater shear rates on motile bacteria in porous MFDs. However, Marcos46 showed in his study of the effect of shear flow on the motility of the marine bacterium Pseudomonas haloplanktis, a bacterium with similar shape, size, and swimming properties to E. coli HCB 33, that bacteria are capable of overcoming vortices of moderate strength. Experimental results of Marcos46 showed bacteria crossing flow streamlines under moderate shear flow conditions as well as aligning themselves across the flow stream lines when vortices were switched from low to moderate strength. Lanning et al.26 have also reported similar transverse movement of E. coli HCB1 across flow stream lines at Darcy velocities greater than those used in this study (∼1 mm/s) as a result of chemotactic migration. Thus, it can be argued that the flow velocity range of 0.11
0.44 mm/s used in this study may cause low to moderate shear flow conditions and thereby result in an increase in MEI (Figure 6). The decrease in the MEI values in MFD-I and MFDII at the highest Darcy velocity (0.88 mm/s) suggests that, at stronger shear flow rates, bacteria not only follow the flow streamline but also align their body axis with the flow lines.46 In contrast to the other two devices, the observed MEI in MFD-III remained approximately constant for all Darcy velocities tested. The reason for this difference may be attributed to the pore throat spacing of MFD-III, because all other features of this device are similar to MFD-I. Typical mean bacterial run lengths for E. coli HCB1, a bacteria similar to E. coli HCB33 used in this study, are ∼28 μm (calculated based on the values reported by Dong et al.44), which is comparable to the pore throat size in MFD-III (32 μm). However, the pore throat size in MFD-I (50 μm) is significantly larger and would cause less interfere with bacterial free swimming paths as compared to MFD-III. Therefore, we believe that the ratio of pore throat spacing to the bacterial run length may be a critical parameter for enhanced mixing, though this speculation should be verified with single cell experiments. Comparing across the three devices, MFD-II resulted in the highest MEIs for almost all flow velocities, which may be attributed to its specific pore geometry; the smaller

grain in the middle of the pore, which was not present in the other two pore geometries, may have acted as an additional agent of mixing in this device. Results from this study contribute to a better understanding of mass transfer mechanisms in porous media and provide a framework for incorporating mixing enhancement due to bacterial motility in bioremediation models. In most bioremediation studies at the laboratory and field scales, contaminant transport is predicted based on tracer tests as a surrogate for contaminants that do not involve bacteria.27 Such contaminant transport predictions may be erroneous as results from this study show that motile bacteria may provide significant contaminant mixing in porous media. Incorporating contaminant mixing due to bacterial motility in field scale bioremediation strategies such as monitored natural attenuation may be useful for more accurate predictions of remediation time frames. Further research may look beyond the effect of bacterial random motility on contaminant mixing in porous media to include other motility mechanisms such as chemotaxis, which is more likely to prevail and may dominate mixing enhancement in certain bioremediation scenarios.

’ ASSOCIATED CONTENT

bS

Supporting Information. Supporting Information is available detailing the microfabrication procedure used in this study and providing further explanation of the effective transverse dispersivity calculations. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Phone: (215) 895-2987; fax: (215) 895-1363; e-mail: mira.s. [email protected].

’ ACKNOWLEDGMENT The authors gratefully acknowledge funding for this research, which was provided by the National Science Foundation through award EAR 0911429. We also thank three anonymous reviewers whose insightful comments helped to greatly improve this manuscript. ’ REFERENCES (1) Ajdari, A.; Bontoux, N.; Stone, H. A. Hydrodynamic dispersion in shallow microchannels: the effect of cross-sectional shape. Anal. Chem. 2005, 78 (2), 387–392. (2) Acharya, R. C.; Valocchi, A. J.; Werth, C. J.; Willingham, T. W. Pore-scale simulation of dispersion and reaction along a transverse mixing zone in two-dimensional porous media. Water Resour. Res. 2007, 43, (10). (3) Cirpka, O. A.; Frind, E. O.; Helmig, R. Numerical simulation of biodegradation controlled by transverse mixing. J. Contam. Hydrol. 1999, 40 (2), 159–182. (4) Cirpka, O. A.; Olsson, A.; Ju, Q. S.; Rahman, M. A.; Grathwohl, P. Determination of transverse dispersion coefficients from reactive plume lengths. Ground Water 2006, 44 (2), 212–221. (5) Huang, W. E.; Oswald, S. E.; Lerner, D. N.; Smith, C. C.; Zheng, C. M. Dissolved oxygen imaging in a porous medium to investigate biodegradation in a plume with limited electron acceptor supply. Environ. Sci. Technol. 2003, 37 (9), 1905–1911. 8786

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Environmental Science & Technology (6) Knutson, C.; Valocchi, A.; Werth, C. Comparison of continuum and pore-scale models of nutrient biodegradation under transverse mixing conditions. Adv. Water Resour. 2007, 30 (6
7), 1421–1431. (7) Rahman, M. A.; Jose, S. C.; Nowak, W.; Cirpka, O. A. Experiments on vertical transverse mixing in a large-scale heterogeneous model aquifer. J. Contam. Hydrol. 2005, 80 (3
4), 130–148. (8) Werth, C. J.; Cirpka, O. A.; Grathwohl, P. Enhanced mixing and reaction through flow focusing in heterogeneous porous media. Water Resour. Res. 2006, 42, 12. (9) Willingham, T. W.; Werth, C. J.; Valocchi, A. J. Evaluation of the effects of porous media structure on mixing-controlled reactions using pore-scale modeling and micromodel experiments. Environ. Sci. Technol. 2008, 42 (9), 3185–3193. (10) Singh, R.; Olson, M. S. Kinetics of trichloroethylene and toluene toxicity to Pseudomonas putida F1. Environ. Toxicol. Chem. 2010, 29 (1), 56–63. (11) Abuhamed, T.; Bayraktar, E.; Mehmetoglu, T.; Mehmetoglu, U. Kinetics model for growth of Pseudomonas putida F1 during benzene, toluene and phenol biodegradation. Process Biochem. 2004, 39 (8), 983–988. (12) Kim, D. J.; Choi, J. W.; Choi, N. C.; Mahendran, B.; Lee, C. E. Modeling of growth kinetics for Pseudomonas spp. during benzene degradation. Appl. Microbiol. Biotechnol. 2005, 69 (4), 456–462. (13) Ray, S.; Peters, C. A. Changes in microbiological metabolism under chemical stress. Chemosphere 2008, 71 (3), 474–483. (14) Ford, R. M.; Harvey, R. W. Role of chemotaxis in the transport of bacteria through saturated porous media. Adv. Water Resour. 2007, 30 (6
7), 1608–1617. (15) Turner, L.; Ryu, W. S.; Berg, H. C. Real-time imaging of fluorescent flagellar filaments. J. Bacteriol. 2000, 182 (10), 2793–2801. (16) Kim, M. J.; Breuer, K. S. Use of bacterial carpets to enhance mixing in microfluidic systems. J. Fluids Eng. 2007, 129 (3), 319–324. (17) Al-Fandi, M.; Jaradat, M. A. K.; Fandi, K.; Beech, J. P.; Tegenfeldt, J. O.; Yih, T. C. Nano-engineered living bacterial motors for active microfluidic mixing. IET Nanobiotechnol. 2010, 4 (3), 61–71. (18) Darnton, N.; Turner, L.; Breuer, K.; Berg, H. C. Moving fluid with bacterial carpets. Biophys. J. 2004, 86 (3), 1863–1870. (19) Kim, M. J.; Breuer, K. S. Enhanced diffusion due to motile bacteria. Phys. Fluids 2004, 16 (9), L78–L81. (20) Kim, M. J.; Breuer, K. S. Controlled mixing in microfluidic systems using bacterial chemotaxis. Anal. Chem. 2007, 79 (3), 955–959. (21) Sokolov, A.; Goldstein, R. E.; Feldchtein, F. I.; Aranson, I. S. Enhanced mixing and spatial instability in concentrated bacterial suspensions. Phys. Rev. E 2009, 80, (3). (22) Wu, X. L.; Libchaber, A. Particle diffusion in a quasi-twodimensional bacterial bath. Phys. Rev. Lett. 2000, 84 (13), 3017–3020. (23) Stone, H. A.; Stroock, A. D.; Ajdari, A. Engineering flows in small devices: microfluidics toward a lab-on-a-chip. Annu. Rev. Fluid Mech. 2004, 36, 381–411. (24) Auset, M.; Keller, A. A. Pore-scale processes that control dispersion of colloids in saturated porous media. Water Resour. Res. 2004, 40, (3). (25) Auset, M.; Keller, A. A.; Brissaud, F.; Lazarova, V. Intermittent filtration of bacteria and colloids in porous media. Water Resour. Res. 2005, 41, (9). (26) Lanning, L. M.; Ford, R. M.; Long, T. Bacterial chemotaxis transverse to axial flow in a microfluidic channel. Biotechnol. Bioeng. 2008, 100 (4), 653–663. (27) Long, T.; Ford, R. M. Enhanced transverse migration of bacteria by chemotaxis in a porous T-sensor. Environ. Sci. Technol. 2009, 43 (5), 1546–1552. (28) Wan, J. M.; Wilson, J. L. Visualization of the role of the gaswater interface on the fate and transport of colloids in porous-media. Water Resour. Res. 1994, 30 (1), 11–23. (29) Smith, J.; Gao, B.; Funabashi, H.; Tran, T. N.; Luo, D.; Ahner, B. A.; Steenhuis, T. S.; Hay, A. G.; Walter, M. T. Pore-scale quantification of colloid transport in saturated porous media. Environ. Sci. Technol. 2008, 42 (2), 517–523.

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(30) Lanning, L. M.; Ford, R. M. Glass micromodel study of bacterial dispersion in spatially periodic porous networks. Biotechnol. Bioeng. 2002, 78 (5), 556–566. (31) Dupin, H. J.; McCarty, P. L. Impact of colony morphologies and disinfection on biological clogging in porous media. Environ. Sci. Technol. 2000, 34 (8), 1513–1520. (32) Nambi, I. M.; Werth, C. J.; Sanford, R. A.; Valocchi, A. J. Porescale analysis of anaerobic halorespiring bacterial growth along the transverse mixing zone of an etched silicon pore network. Environ. Sci. Technol. 2003, 37 (24), 5617–5624. (33) Stewart, T. L.; Fogler, H. S. Biomass plug development and propagation in porous media. Biotechnol. Bioeng. 2001, 72 (3), 353–363. (34) Whitesides, G. M.; Stroock, A. D. Flexible methods for microfluidics. Phys. Today 2001, 54 (6), 42–48. (35) Duffy, D. C.; McDonald, J. C.; Schueller, O. J. A.; Whitesides, G. M. Rapid prototyping of microfluidic systems in poly(dimethylsiloxane). Anal. Chem. 1998, 70 (23), 4974–4984. (36) McCabe, W. L.; Smith, J. C.; Harriot, P. Unit Operations of Chemical Engineering, 7th ed.; McGraw-Hill: New York, 2005. (37) Crank, J. The Mathematics of Diffusion, 2nd ed.; Clarendon Press: Oxford, U.K., 1975. (38) Sherwood, J. L.; Sung, J. C.; Ford, R. M.; Fernandez, E. J.; Maneval, J. E.; Smith, J. A. Analysis of bacterial random motility in a porous medium using magnetic resonance imaging and immunomagnetic labeling. Environ. Sci. Technol. 2003, 37 (4), 781–785. (39) Olson, M. S.; Ford, R. M.; Smith, J. A.; Fernandez, E. J. Analysis of column tortuosity for MnCl2 and bacterial diffusion using magnetic resonance imaging. Environ. Sci. Technol. 2005, 39 (1), 149–154. (40) Geankoplis, C. J. Transport Processes and Unit Operations, 3rd ed.; PTR Prentice Hall: Engelwood Cliffs, N.J., 1993; p xiii, p. 921. (41) Periasamy, N.; Verkman, A. S. Analysis of fluorophore diffusion by continuous distributions of diffusion coefficients: application to photobleaching measurements of multicomponent and anomalous diffusion. Biophys. J. 1998, 75 (1), 557–67. (42) Berkowitz, B.; Cortis, A.; Dentz, M.; Scher, H. Modeling nonFickian transport in geological formations as a continuous time random walk. Rev. Geophys. 2006, 44 (2), RG2003. (43) Bijeljic, B.; Rubin, S.; Scher, H.; Berkowitz, B. Non-Fickian transport in porous media with bimodal structural heterogeneity. J. Contam. Hydrol. 2011, 120
121, 213–221. (44) Dong, H.; Rothmel, R.; Onstott, T. C.; Fuller, M. E.; DeFlaun, M. F.; Streger, S. H.; Dunlap, R.; Fletcher, M. Simultaneous transport of two bacterial strains in intact cores from Oyster, Virginia: biological effects and numerical modeling. Appl. Environ. Microbiol. 2002, 68 (5), 2120–2132. (45) Hornberger, G. M.; Mills, A. L.; Herman, J. S. Bacterial transport in porous media: evaluation of a model using laboratory observations. Water Resour. Res. 1992, 28 (3), 915–923. (46) Marcos, R. S. Microorganisms in vortices: a microfluidic setup. Limnol. Oceanogr.: Methods 2006, 4, 392–398.

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Floc Volume Effects in Suspensions and Its Relevance for Wastewater Engineering Jochen Henkel,* Barbara Siembida-L€osch, and Martin Wagner Technische Universit€at Darmstadt, Institut IWAR, Section for Wastewater Technologies, Petersenstrasse 13, 64287 Darmstadt, Germany ABSTRACT: The aim of this paper is to better understand oxygen transfer reduction caused by floc suspensions. We demonstrate that the overall floc volume significantly influences oxygen transfer depletion. Submerged fine bubble and coarse bubble diffusers are affected in the same way by this phenomenon. The mixed liquor suspended solids concentration (MLSS concentration) is not an appropriate parameter for describing or relating phenomena that are caused by the overall floc volume in activated sludge (e.g., oxygen transfer depression and sludge sedimentation characteristics). A better correlation is achieved by using the mixed liquor volatile suspended solids concentration (MLVSS concentration). To characterize the effects of the overall floc volume in suspensions whose MLVSS concentration cannot be determined (e.g., inorganic iron hydroxide flocs), a new method—the hydrostatic floc volume (HFV)—that approximates the overall floc volume in floc suspensions is introduced. Application of this method demonstrates that oxygen transfer depression caused by iron hydroxide flocs and activated sludge flocs is similar.

’ INTRODUCTION The sedimentation behavior of activated sludge continues to be an active research area.1
4 The main driver for these investigations is to understand and optimize the settling performance of activated sludge in the clarifier, which is essential for reliable operation of activated sludge plants. The standard procedure applied, in practice, to judge the sedimentation behavior is the integrated sludge volume index (SVI). It relates the sludge volume determined after 30 min to the MLSS concentration. Variations of this method, mainly to overcome the bridging problems with filamentous sludge or at higher sludge concentrations, have been developed, including the dilution (DSVI) or the stirred method (SSVI). Bridging is caused by the interaction of the flocs. Generally, steric interactions, which hinder separation of the free water from the bound water of the flocs, occur more frequently with increasing floc number.5,6 Recently, our group was able to demonstrate that the overall floc volume influences oxygen transfer in floc suspensions.7 This was achieved using iron hydroxide flocs and relating oxygen transfer depression (α-factor) to the overall floc volume. In contrast to the SVI concept, the purpose of the floc volume concept is not to estimate the sedimentation behavior of activated sludge but to use a simple method for estimating the overall volume of all flocs in the system. In an earlier study we hypothesized that the better correlation of oxygen transfer depression and the MLVSS concentration, compared to the MLSS concentration of different activated sludge sources, was obtained because the MLVSS concentration regulates the free water content and better correlates with the overall floc volume in activated sludge.8 One explanation is that r 2011 American Chemical Society

the major part of the activated sludge floc consists of water (>90%), which is bound to organic matter as extracellular polymeric substances (EPS), which again contribute to more than 50% of the organic dried matter of sludge.9
12 Consequently, it is the amount of bounded water that governs the mass and volume of the entire floc. The MLSS concentration, in contrast to the MLVSS concentration, also includes inorganic materials, such as silt, clay, and sand, which escape removal in the primary settling tank and which, compared to the organic matter, have low water binding capabilities but contribute significantly to the dry solid content (∼15
25%). This inorganic content varies, depending on the wastewater influent composition and the primary settling tank performance, which led to the wide range of values for α-factors in the past, if the values were related to the MLSS concentration of different wastewater sources.8 We also hypothesized that oxygen transfer depression caused by iron hydroxide flocs is similar to the mechanism by which activated sludge flocs influence oxygen transfer.7 However, at that time, because a methodology for approximating the overall floc volume of activated sludge was lacking, we were not able to verify this hypothesis. This paper aims to fill these knowledge gaps to better understand oxygen transfer reduction caused by activated sludge flocs and by iron hydroxide flocs. A new method is introduced called Received: May 24, 2011 Accepted: August 18, 2011 Revised: August 14, 2011 Published: September 14, 2011 8788

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the hydrostatic floc volume (HFV) that approximates the overall floc volume in activated sludge and is able to compare results obtained in floc suspensions. Furthermore, we show that the MLSS concentration is not an appropriate parameter to correlate and compare phenomena that are caused by the floc volume.

’ MATERIALS AND METHODS Generally, the experiments in this study are a continuation of the experiments described in previous studies.7,8 In the current work, two pilot-scale membrane bioreactors fed with municipal wastewater were used for activated sludge sampling. Oxygen transfer measurements with iron hydroxide flocs and wastewater sludge were performed in a separate lab-scale column. Hydrostatic Floc Volume (HFV). In a mixture of rigid particles and a liquid, the total volume of the particles, in terms of the total volume of the suspension (solid holdup), can be easily determined by the bulk volume of the particles. However, this relationship cannot be used for elastic flocs that incorporate a great deal of water, as is the case for iron hydroxide flocs and activated sludge flocs. The only method which generates an estimate of floc volume is the sludge volume index (SVI, APHA 2710 D).13 It determines the sludge volume after 30 min of sedimentation and relates it to the suspended solids concentration. It is used to characterize the sedimentation characteristics of activated sludge in the clarifier. However, because sedimentation still continues after 30 min, it is not an appropriate parameter to determine the overall floc volume. Consequently, a method had to be developed that enables approximation of the overall floc volume in these suspensions. The following procedure was applied. A 1 L sample of activated sludge or iron hydroxide suspension was taken and stored in a settling column (7 cm diameter). The sedimentation process of the sample continued until the volume of the settled flocs remained constant. In the case of activated sludge, the flocs started to float because of microbial gas production, and therefore, the sludge respiration had to be suppressed. This was achieved using cyanide, as described by Dobbs et al.,14 at a dose of 0.1 g of cyanide per 1 g of biomass dry content. The final volume of the suspension is called the hydrostatic floc volume (HFV). An example of activated sludge sedimentation and definition of the HFV is summarized in Table 1. It can be seen that after approximately 45 h the suspension reached its terminal volume, which is the HFV. Oxygen Transfer Measurements and α-Factor Calculation. The α-factor is the relationship between the oxygen transfer

coefficient (kLa) in the investigated medium (in our case activated sludge and iron hydroxide suspension) and the oxygen transfer coefficient in clean water (eq 1). α-factor ¼

kL amedium kL acleanw

ð1Þ

The α-factor describes how much better or worse oxygen diffuses into the medium compared to clean water and plays an important role in estimating the required standard oxygen transfer rate (SOTR) in activated sludge, which is the key parameter in submerged diffused aeration systems. To determine oxygen transfer coefficients, the desorption method according to Wagner et al.15 was applied (see also Henkel et al.7,8). Four oxygen sensors (iRAS automation GmbH, Bad Klosterlausnitz, Germany) recorded the change in oxygen concentration in the suspension at constant air flow rate and

Table 1. Example of Floc Sedimentation (MLSS = 7.2 g/L; MLVSS = 6 g/L) elapsed time

volume ratio (mL/L)

0h

1000

2 h 15 min 3 h 15 min

380 340

20 h 20 min

260

44 h 55 min

250 (HFV)

141 h

250

Figure 1. Volumetric mass transfer coefficient during iron hydroxide experiments (fine bubble aeration).

solid concentration. The air flow rate, which was measured with a thermal flow sensor TA10 (Hoentzsch GmbH, Waiblingen, Germany), was then changed and the procedure repeated at the same solid concentration. Three air flow rates were chosen for each test (1, 2, 3 m3/h). Subsequently, the three average kLa values were plotted against the superficial gas velocity (SGV). If applicable, a linear trend line was plotted and calculated. This procedure was repeated for every solid concentration and the clean water test. An example is given in Figure 1. Finally, the α-factor was calculated by dividing the trend line equation at a specific solid concentration and specific air flow rate (2 m3air/(diffuser 3 h)) by the equation obtained during the clean water test. This procedure was applied because it was impossible to repeat exactly the same air flow rate for each test series. Humidity, air pressure, air temperature, and the air blower itself are subject to daily variations that influence the air flow rate, which has to be transformed to standard conditions. Lab-Scale Experiments. The bubble column used in this experiment was 1.30 m high and 0.43 m in diameter. The total water volume in each test was 100 L. In contrast to the iron hydroxide experiments performed by Henkel et al.,7 this time 4.5 kg of ferric chloride was mixed with 250 L of potable water in a separate vessel and bentonite was not added. The pH was then adjusted to 8 with 1 M sodium hydroxide, which substantially increased the salt concentration. As the salt content also influences oxygen transfer, the supernatant of the iron hydroxide flocs was replaced by potable water several times until the salt content reached a concentration equivalent to that of potable water. The final salt content varied between 600 and 850 mg/L. After sedimentation of the iron hydroxide flocs, 150 L of the supernatant was stored separately. 8789

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Table 2. Hydrodynamic Specifications during Lab-Scale Oxygen Transfer Experiments diffuser

number of

riser surface,

superficial gas velocity

specific slit aeration

specific diffuser aeration rate,

type

orifices, n

m2

(SGV), cm3Air/(cm2Surf. 3 s)

rate, cm3Air/(slit 3 s)

m3Air/(diffuser 3 h)

disc

8000

0.15

0.15
0.55

0.03
0.10

1
3

tube

10

0.15

0.15
0.55

25
85

1
3

Figure 2. Relationship between sludge volumes (dilution method) and MLSS concentration.

Figure 3. Relationship between sludge volumes and MLVSS concentration.

The oxygen transfer experiments started at the highest iron hydroxide concentration (26.1 g/L). After each experiment, a fixed amount of iron hydroxide flocs was removed from the system and replaced with the stored supernatant. kLa and the α-factor were determined as described in section. A fine bubble and a coarse bubble aeration device were tested to investigate whether iron hydroxide flocs show the same behavior as observed previously with activated sludge.8 The specifications of the setup are listed in Table 2. Finally, the same experiments were performed with activated sludge and the fine bubble aeration device. The activated sludge was taken from a membrane bioreactor fed with real wastewater from a municipal wastewater plant (DarmstadtEberstadt, Germany). To achieve a range of sludge concentrations, the sludge was diluted with permeate from the membrane bioreactor. The highest sludge concentration tested was 17.5 g/L. Sedimentation Experiments. Once the hydrostatic floc volume procedure was established, it was applied during operation of two pilot-scale membrane bioreactors. The membrane bioreactors had a water volume of 1 m3 (1.7 m water depth), and the wastewater flow was 100 L/h. The organic load was 0.9 kg of COD/d. The F/M ratio depended on the actual MLVSS concentration in the tank but ranged from 0.45 to 0.02 kg BOD5/ (kg MLVSS 3 d). Additionally, the sludge volume was determined after 30 min without dilution to compare the results.13

different kinds of sludges showing different settling behavior at the same MLSS concentration could show similar settling behavior at the same MLVSS concentration if the overall floc volume is the main reason for bridging. Figure 2 depicts the sludge volume, determined with the dilution method, as a function of the MLSS concentration of three sludges. The data for the greywater sludge was generated during the 2 year operation of two membrane bioreactors fed with synthetic greywater.7,8 The data for the wastewater sludges were generated during the operation of two separate membrane bioreactors fed with real wastewater. A linear relationship can be observed in all three cases, although the variation of the sludge volume at a certain MLSS concentration was quite large. Whereas the wastewater sludges showed a similar development with increasing MLSS concentration, the greywater sludge volume increased more slowly with increasing MLSS concentration. Calculation of the SVI indicates that the sedimentation characteristic of membrane bioreactor wastewater sludge at a specific MLSS concentration was worse (for example, SVI 83 mL/g at MLSS of 12 g/L) than that of the greywater sludge (SVI ≈ 33 mL/g at MLSS of 12 g/L). Microscopic investigation of the sludges revealed that neither filamentous bacteria nor bulking sludge were present in the MBR greywater sludge and the MBR wastewater sludge 2010. During colder periods, especially in January and February, the MBR wastewater sludge 2009 did contain filamentous bacteria. The major difference between sludges was the organic content, which was around 38% and 82% for the greywater sludge and the wastewater sludge, respectively. The nontypical low ignition loss of greywater sludge was caused by the presence of a large amount of abrasive materials, used in toothpaste, and zeolites, used in washing agents, in the greywater influent. If the same data are plotted against MLVSS concentration (Figure 3), each type of sludge showed a much closer relationship than before. We concluded that the overall floc volumes of greywater and wastewater sludge at a certain MLVSS concentration were

’ RESULTS AND DISCUSSION Sludge Volume Index. The sludge volume index provides a rough estimate of how well sludge settles in the clarifier. It is calculated from the ratio of sludge volume after 30 min to MLSS concentration. As noted, the dilution method and/or stirring method have been applied to determine sludge volume after 30 min, when sludge settling properties are hindered by bridging between flocs.5,16 We hypothesized that the MLVSS concentration better correlates to the overall floc volume in activated sludge. One conjecture generated by this hypothesis was that

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Figure 4. Sludge volume after 30 min of sedimentation (blue triangles) and hydrostatic floc volume (yellow rectangles).

similar, which resulted in similar sedimentation characteristic, as observed in this study, and similar oxygen transfer behaviors of different kinds of sludges observed in our previous study.8 This is another indicator for the close relationship of MLVSS concentration to the overall floc volume in activated sludge. Hydrostatic Floc Volume. As noted in the former section, the sludge volume method, especially at high suspended solids concentrations, delivers a relatively wide range of sludge volumes at a certain MLSS or MLVSS concentration. Additionally, sludge volumes larger than 1000 mL/L are an artifact of the dilution method, caused by multiplication of the dilution applied to the sludge, rather than an approximation of the overall floc volume present in the sludge. Because the MLVSS concentration may only be used to approximate the overall floc volume in biological floc aggregates and we wanted to compare activated sludge flocs with iron hydroxide flocs (inorganic flocs), we developed a new method (see Materials and Methods) to approximate the overall floc volume in floc suspensions that does not destroy the floc structure and is easy to apply. We emphasize that it is not an alternative to the SVI method, which aims at characterizing activated sludge sedimentation behavior in the clarifier. Figure 4 shows the results of sludge volume determination according to the APHA method,13 without stirring or dilution, and the HFV method, related to the MLVSS concentration. The volume ratio increased with increasing MLVSS concentration in both cases. A linear correlation was observed for the HFV method up to a floc volume of about 500 mL/L at an MLVSS concentration of 11 g/L. The trend line follows the equation HFV ¼ 43:85 3 MLVSSð( 22Þ

ð2Þ

Compared to the sludge volume method with dilution presented in the section Sludge Volume Index, the HFV method still delivers reasonable values at elevated MLVSS concentrations and, additionally, shows smaller deviations. The values achieved with the sludge volume method without dilution demonstrate the effect of bridging that occurs at sludge volumes higher than 300 mL/L. The steric interferences of the flocs hinder sedimentation and result in a sharp increase in sludge volumes. It seems as if the HFV method resolves this bridging effect and correlates well with the MLVSS concentration. It indicates that this method may be used to correlate the overall floc volume of activated sludge. Oxygen Transfer at Various Floc Concentrations. In a previous study, we demonstrated that the MLVSS concentration affects oxygen transfer depression (α-factor) of submerged fine

Figure 5. α-Factor of iron hydroxide flocs and activated sludge flocs related to the concentration of suspended solids.

Figure 6. α-Factor of iron hydroxide flocs and activated sludge flocs related to the hydrostatic floc volume.

bubble and coarse bubble aeration systems in the same way.8 To test the hypothesis that the mechanism of oxygen transfer depression caused by activated sludge flocs and iron hydroxide flocs is similar, we exposed iron hydroxide flocs to fine and coarse bubble aeration systems. Figure 5 illustrates the results of this experiment. Additionally, one set of tests was performed with activated sludge in the same column to compare the results. Figure 5 shows that the α-factor in the experiments with iron hydroxide decreased linearly with increasing MLSS concentration, irrespective of the aeration system (coarse bubble aeration or fine bubble aeration). This result is identical to the data derived from activated sludge in the earlier studies. However, even though the decrease is also linear for activated sludge flocs, the slope, compared to the iron hydroxide flocs, is different. Hence, it could be concluded that activated sludge flocs reduce oxygen transfer more strongly than iron hydroxide flocs. As mentioned above, one reason why we developed the HFV method was the theory that the MLSS concentration did not correlate with the overall floc volume of various suspensions. In Figure 6 we compare the α-factors from Figure 5 to the HFV. In contrast to the results shown in Figure 5, all suspensions now show similar trends. This result is remarkable because hydroxide flocs and activated sludge flocs are very different in 8791

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Environmental Science & Technology their water binding capacities and densities.17,18 The results support the hypothesis that the overall floc volume is the actual driver for oxygen transfer depression in suspensions that contain flocs and that the MLSS concentration insufficiently correlates with the overall floc volume. These results also suggest that the HFV is a useful parameter for comparing phenomena that can be attributed to the floc volume, such as gas transfer. In oxygen transfer studies activated sludge has been considered to be a pseudohomogeneous medium with non-Newtonian pseudoplastic fluid properties.19 One way of characterizing these fluids is to measure their apparent viscosity. Although the apparent viscosity of activated sludge has often been assumed to be responsible for the decrease in the α-factor, supporting systematic studies are scarce. Some authors correlated the apparent viscosity to the α-factor of different sludges but only with limited success, especially if the results of these studies are compared with each other.20,21 It is debatable whether apparent viscosity is a meaningful parameter in terms of oxygen transfer in floc suspensions. A promising solution to this dilemma would be to replace the idea of a pseudohomogeneous medium with the new concept of a suspension consisting of flocs that interact with the air bubble. Interpretations of how the floc influences oxygen transfer into the free water fraction could then incorporate results that come from investigations of solid suspensions in the field of chemical engineering. A parameter used to compare oxygen transfer in solid suspensions is the solid holdup, which relates the overall volume occupied by the solids to the overall volume of the suspension. With increasing solid holdup (εS), a decrease in oxygen transfer is commonly observed.22
24 Mena et al.25 summarize eight ways in which the gas
liquid system can be affected by solids. Transferring these observations to activated sludge leads to the proposition that the following phenomena may influence oxygen transfer. 1 The increased floc volume decreases the interfacial area between the bubble and the liquid phase, since it makes contact with the bubble surface and hinders transfer to the liquid phase. 2 The tendency of bubbles to coalesce is favored by attachment of small, hydrophobic flocs, which leads to larger bubbles with a lower specific surface area. 3 Turbulence in the bubble wake is diminished due to accumulation of flocs in the wake area. 4 With increasing floc number, the possibility of collision during bubble formation at the orifices increases, which reduces the bubble formation frequency and leads to larger bubbles at the orifice. 5 The decrease in the free water content leads to an increase in gas holdup, related to the liquid phase at the same air flow rate. This shifts the critical superficial gas velocity, which is responsible for the change from the homogeneous flow regime to the heterogeneous flow regime, to lower values. 6 The increased floc volume also increases the probability of collision between the flocs themselves and increases the apparent viscosity. This again leads to a decrease in the liquid velocity at the same air flow rate, which results in formation of larger bubbles at the orifice and enhances the probability of bubble coalescence. All these effects would lead to a decrease of the volumetric oxygen transfer coefficient kLa in activated sludge. Since suppression of the α-factor for fine bubble and coarse bubble aeration systems was similar and bubble formation and bubble rise

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characteristics of coarse bubbles are not affected by the liquid properties,26 only two phenomena remain that could explain the similar behavior. The first is the reduction of turbulence in the bubble wake area; the second describes suppression of the oxygen transfer from the bubble to the liquid phase. However, further investigations are required to determine which mechanism is responsible for this phenomenon.

’ AUTHOR INFORMATION Corresponding Author

*Phone: +1 303 273 3871, fax: +1 303 273 3413; e-mail: [email protected].

’ ACKNOWLEDGMENT The authors wish to thank Prof. Cornel, our department head, for his time and inspiring discussions, the German Federal Ministry of Education & Research, and the Faudi Foundation for their financial support. Ms. Anita Curt, our laboratory assistant at the wastewater treatment plant in Eberstadt, deserves special thanks for the enormous number of samples she processed during this work. ’ REFERENCES (1) Chen, Y.; Yang, H.; Gu, G. Effect of acid and surfactant treatment on activated sludge dewatering and settling. Water Res. 2001, 35 (11), 2615–2620. (2) Li, X. Y.; Yang, S. F. Influence of loosely bound extracellular polymeric substances (EPS) on the flocculation, sedimentation and dewaterability of activated sludge. Water Res. 2007, 41 (5), 1022–1030. (3) Schuler, A. J.; Jang, H. Causes of variable biomass density and its effects on settleability in full-scale biological wastewater treatment systems. Environ. Sci. Technol. 2007, 41 (5), 1675–1681. (4) Wilen, B.-M.; Lumley, D.; Mattsson, A.; Mino, T. Relationship between floc composition and flocculation and settling properties studied at a full scale activated sludge plant. Water Res. 2008, 42 (16), 4404–4418. (5) Dick, R. I.; Vesilind, P. A. The Sludge Volume Index: What Is It? Water Pollut. Control Fed. 1969, 41 (7), 1285–1291. (6) Bye, C. M.; Dold, P. L. Sludge volume index settleability measures: Effect of solids characteristics and test parameters. Water Environ. Res. 1998, 70 (1), 87–93. (7) Henkel, J.; Cornel, P.; Wagner, M. Free Water Content and Sludge Retention Time: Impact on Oxygen Transfer in Activated Sludge. Environ. Sci. Technol. 2009, 43 (22), 8561–8565. (8) Henkel, J.; Lemac, M.; Wagner, M.; Cornel, P. Oxygen transfer in membrane bioreactors treating synthetic greywater. Water Res. 2009, 43 (6), 1711–1719. (9) Vaxelaire, J.; Cezac, P. Moisture distribution in activated sludges: a review. Water Res. 2004, 38 (9), 2215–2230. (10) Smollen, M. Moisture retention characteristics and volume reduction of municipal sludges. Water SA 1988, 14 (1), 25–28. (11) Frolund, B.; Palmgren, R.; Keiding, K.; Nielsen, P. H. Extraction of extracellular polymers from activated sludge using a cation exchange resin. Water Res. 1996, 30 (8), 1749–1758. (12) Wilen, B. M.; Jin, B.; Lant, P. The influence of key chemical constituents in activated sludge on surface and flocculating properties. Water Res. 2003, 37 (9), 2127–2139. (13) APHA, Standard methods for the examination of water and wastewater; American Public Health Association, American Water Works Association, Water Pollution Control Federation: Washington DC, 2005. 8792

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(14) Dobbs, R. A.; Shan, Y. G.; Wang, L. P.; Govind, R. Sorption on Wastewater solids - Elimination of biological activity. Water Environ. Res. 1995, 67 (3), 327–329. (15) Wagner, M. R.; Johannes P€opel, H.; Kalte, P. Pure oxygen desorption method - A new and cost-effective method for the determination of oxygen transfer rates in clean water. Water Sci. Technol. 1998, 38 (3), 103–109. € (16) Stobbe, G. Uber das Verhalten von belebtem Schlamm in aufsteigender Wasserbewegung; Ver€offentlichungen des Institutes f€ur Siedlungswasserwirtschaft der Technischen Hochschule Hannover: Hannover, Germany, 1964; Vol. 18. (17) Colin, F.; Gazbar, S. Distribution of water in sludges in relation to their mechanical dewatering. Water Res. 1995, 29 (8), 2000–2005. (18) Wu, C. C.; Huang, C.; Lee, D. J. Bound water content and water binding strength on sludge flocs. Water Res. 1998, 32 (3), 900–904. (19) Yang, G. Q.; Du, B.; Fan, L. S. Bubble formation and dynamics in gas-liquid-solid fluidization- A review. Chem. Eng. Sci. 2007, 62 (1
2), 2–27. (20) Krampe, J.; Krauth, K. Oxygen transfer into activated sludge with high MLSS concentrations. Water Sci. Technol. 2003, 47 (11), 297–303. (21) Krause, S. Untersuchungen zum Energiebedarf von Membranbelebungsanlagen. Dissertation; Technische Universit€at Darmstadt, Darmstadt, 2005. (22) Schumpe, A.; Fang, L. K.; Deckwer, W.-D. Stoff€ubergang Gas/ Fl€ussigkeit in Suspensions-Blasens€aulen. Chem. Ing. Tech. 1984, 56 (12), 924–926. (23) Krishna, R.; deSwart, J. W. A.; Ellenberger, J.; Martina, G. B.; Maretto, C. Gas holdup in slurry bubble columns: Effect of column diameter and slurry concentrations. AIChE J. 1997, 43 (2), 311–316. (24) Freitas, C.; Teixeira, J. A. Oxygen mass transfer in a high solids loading three-phase internal-loop airlift reactor. Chem. Eng. J. 2001, 84 (1), 57–61. (25) Mena, P. C.; Ruzicka, M. C.; Rocha, F. A.; Teixeira, J. A.; Drahos, J. Effect of solids on homogeneous-heterogeneous flow regime transition in bubble columns. Chem. Eng. Sci. 2005, 60 (22), 6013–6026. (26) Kumar, R.; Kuloor, N. R., The formation of bubbles and drops. In Advances in chemical engineering; Drew, T., Cokelet, G., Hoopes, J., Vermeulen, T., Eds. Academic Press: New York, 1970; Vol. 8, pp 255
368.

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In Situ Profiling of Microbial Communities in Full-Scale Aerobic Sequencing Batch Reactors Treating Winery Waste in Australia Simon J. McIlroy, Lachlan B. M. Speirs, Joseph Tucci, and Robert J. Seviour* Biotechnology Research Centre, Department of Pharmacy and Applied Science, La Trobe University, Bendigo, Victoria 3552, Australia ABSTRACT: On-site aerobic sequencing batch reactor (SBR) treatment plants are implemented in many Australian wineries to treat the large volumes of associated wastewater they generate. Yet very little is known about their microbiology. This paper represents the first attempt to analyze the communities of three such systems sampled during both vintage and nonvintage operational periods using molecular methods. Alphaproteobacterial tetrad forming organisms (TFO) related to members of the genus Defluviicoccus and Amaricoccus dominated all three systems in both operational periods. Candidatus ‘Alysiosphaera europaea’ and Zoogloea were codominant in two communities. Production of high levels of exocellular capsular material by Zoogloea and Amaricoccus is thought to explain the poor settleability of solids in one of these plants. The dominance of these organisms is thought to result from the high COD to N/P ratios that characterize winery wastes, and it is suggested that manipulating this ratio with nutrient dosing may help control the problems they cause.

’ INTRODUCTION The wine industry is a major global industry with over 25 billion liters of wine produced in 2008 alone (www.oiv.org). It has been estimated that for each liter of wine 1
2.5 L of wastewater is produced,1,2 although this can be as high as 14 L.3 Consequently, large amounts of wastewater are generated at various stages during the wine production period (vintage) as well as from bottling and cleaning of equipment and the maintenance of cellar hygiene in nonvintage periods.1,4,5 A wide range of treatment technologies has been applied around the world for dealing with this wastewater, involving both aerobic and anaerobic plant configurations, where one of the main selection criteria used is their operating costs (reviewed by refs 4 and 5). Of the aerobic configurations, which are more efficient for treatment, sequencing batch reactors are used in many Australian wineries (J. Constable, personal communication) because of their simplicity and suitability for coping with the high seasonal variations in winery waste composition and generated loads.1,6 The chemical nature of winery wastewater differs substantially from that of domestic sewage. In particular, it possesses high BOD/ COD:N/P ratios, which, while fluctuating considerably over the course of the year, for COD:P ratios are reported to be in excess of 100:1.1,6 Its pH is also generally lower than domestic sewage being below 4.5.1 These marked differences reflect the high levels of sugars and organic acids in winery wastewaters, and in many plants P and N supplementation of the wastewater is undertaken to facilitate its biological treatment (J. Constable, personal communication). The high carbon load poses serious potential environmental threats, and so again driven by cost considerations, many of the larger wineries treat their own wastes on site instead of discharging them into domestic sewerage systems. Molecular rRNA-based methods have allowed us to begin to resolve the biodiversity of the microbial communities in plants of r 2011 American Chemical Society

many different configurations treating wastewater from domestic and industrial sources.7 Furthermore, they have provided the tools to reveal the in situ functions of many of these populations, especially those involved in P and N removal.8
10 It is quite clear that the microbial community that develops in a treatment process reflects the selective pressures imposed by both the plant operational conditions and the nature of the wastewater being treated.11 As mentioned above, winery wastewaters are highly distinctive in their nature, which poses challenges for their successful biological treatment. Despite this, very few attempts have been made to understand the microbiology involved, and these have been restricted largely to recognizing dominant morphotypes from microscopic examination of the biomass (e.g., ref 12) or from studies using culture dependent methods (e.g., refs 2 and 13). Both are restricted in the levels of information they can provide on community biodiversity. This situation is surprising, because many of the aerobic SBR treatment plants in Australia suffer from operational problems like poor sludge settleability, which are likely to be microbiological in origin. Consequently, we applied molecular techniques to profile communities of three aerobic SBR systems treating the waste in different wine-producing regions in Australia. The data presented here show for the first time that these are each dominated by bacterial populations reported elsewhere in habitats largely characterized by high COD:P ratios, and we propose that these bacteria may cause the operational problems encountered there. Received: May 31, 2011 Accepted: August 29, 2011 Revised: August 29, 2011 Published: August 29, 2011 8794

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Table 1. Plant Operating Parameters Plant A item

a

nonvintage

typical crush (Tonne)

N/A

SBR volume (ML)

1.1

Plant B vintage

30 000

nonvintage N/A

Plant C vintage

20 000

4.6

nonvintage N/A

vintage 1000

0.2

daily flow (kL)

30

280

350

550

4

20

total cycle time (h)

4

12

12

12

12

12

aerate (min)a

30

560

500

400

600

565

settle (min) decant (min)

60 10

60 60

60 40

60 70

105 7

60 40

feed (min)

10

60

130

190

8

55

MLSS (g L
1)

5000

9000
11 000

6000

4480

2400

5600

SRT (days)

Not dewatered

20
30

40
60

20
30

15
20

8
10

COD

1000

8000
10 000

5000
7000

5000
7000

10 400

16 000

BOD

500

6000
8000

3000
4000

4000
6000

6700

10 000

DO (end of aeration)

1
2

1
2

3
4

3
4

7.1

5.8

pH, feed pH, SBR

5
7 8

4
5 7.5

4
5 6.7
7.3

4
5 6.8
7.3

5.7 7.1

4.0 8.1

temp. (°C)

12
14

30
35

12
14

28
30

11

24.6

nutrient dosage (kg week
1)

none

urea, 10; DAPb 5

none

urea, 140

urea, 10; DAP,b 2

urea, 20; DAP,b 5

To reduce operating costs, these reactors are not always aerated during the feed steps or periods of the aeration stages. b Diammonium phosphate.

’ EXPERIMENTAL SECTION Samples. Samples were taken from three full-scale sequencing batch reactors (SBR) treating winery waste in New South Wales (plant A) in nonvintage and South Australia (plant B) and Tasmania (plant C) in both vintage (during wine production) (April 2011) and nonvintage (July 2010) operating periods. Operating parameters for each plant are given in Table 1. Typical influent total P values were 5
10 mg/L for plants A and B and 5
30 mg/L for plant C. Fluorescence in Situ Hybridization (FISH). FISH was carried out using PFA (paraformaldehyde) fixed biomass samples as described by Daims et al.14 The FISH probes listed in Table 2 were used and hybridizations performed at 46 °C for 1.5 h unless otherwise stated. All probes were purchased from ProOligo (Sigma-Aldrich, Australia). Slide wells were mounted in Vectashield (Vector Laboratories) and viewed on a Nikon E800 epifluorescence microscope. To increase cell wall permeability to some of the FISH probes, pretreatments with lysozyme, achromopeptidase, and mild acid hydrolysis as detailed in the protocol of Kragelund et al.15 were applied to biomass samples. Appropriate controls for biomass autofluorescence using the non-EUB probes were included in all analyses. Histochemical Staining. Intracellular polyphosphate granules were detected by applying 4,6-diamidino-2-phenylindole (DAPI) solution (50 μg mL
1) for 10 min to air-dried biomass smears as described by Kawaharasaki et al.46 Biomass was also stained for the presence of intracellular polyhydroxyalkanoate (PHA) granules with Nile Blue A (100 mg L
1 in ethanol) at 55 °C for 10 min, based on the method of Ostle and Holt47 as described in Ahn et al.48 The India ink negative capsule and Neisser staining methods were carried out as described by Jenkins et al.49 16S rRNA Gene Clone Library Preparation from Plant A Biomass. Biomass DNA was extracted using three different methods in attempts to minimize possible extraction biases

associated with each: the sodium trichloroacetate-based method of McIlroy et al.,50 the potassium xanthogenate-based method of Tillett and Neilan,51 and the FastDNA SPIN Kit (Qbiogene, Melbourne, Australia). For each of the DNA extracts, 16S rRNA genes were PCR amplified in quadruplicate with the 27f/1492r and 27f/1525r primer sets,52 with annealing temperatures of 42 and 52 °C, respectively, and the resulting products pooled and stored at
70 °C until required. All other PCR, cloning, and sequencing details are those described by Nittami et al.29 Partial sequence reads (>500 bp) were organized into OTUs based on shared 99% sequence similarities and a ‘complete’ 16S rRNA sequence obtained for a representative from each OTU of interest. 16S rRNA Sequencing of Cultured Isolates. Attempts were made to culture some of the dominant morphotypes from plant C by streaking out the biomass onto solid media. Both GS and R2A agar were used as described by Maszenan et al.53 Colonies were screened microscopically, and those with the desired morphotype (see Results) were subcultured repeatedly until Gram staining suggested they were pure. A single colony was then used for 16S rRNA sequencing as above, except the DNA was extracted from cells as described by McIlroy et al.,54 and PCR products were sequenced directly by AGRF (Brisbane, Australia).

’ RESULTS Application of the EUBmix FISH probe set indicated that the majority of the organisms present in all three winery wastewater treatment plant biomass samples taken during both vintage and nonvintage operational periods were Bacteria. Large yeast-like cells, fungal hyphae, and Protozoa were seen too, particularly in the plant C waste sample, but always in low abundance. Almost all bacterial cells stained strongly for PHA inclusions, while a smaller number (approximately 50% of biovolume); +++ = very abundant (approx. 25
50% of biovolume); ++ = common (approx. 5
25% of biovolume); + = some to few (approx. 97% similar to Candidatus ‘A. europaea’ (W-OTU1 and W-OTU2) (Figure 3). These sequences had only a single terminal mismatch to that of

the Noli-644 probe target (Table 4), suggesting this FISH probe is the one binding to this filament in situ. They also contained a potential binding site for the DF988 probe, with 3 terminal mismatches and a missing internal base (Table 4). The positions of the terminal mismatches were similar to those allowing nonspecific binding of the DF988 FISH probe to the same position in the 16S rRNA sequence of cluster III Defluviicoccus members29 as shown in Table 4 and have a lower theoretical ΔΔG°overall (calculated for the perfectly matched sequence with mathFISH software57). The ability of FISH probes to bind to target sites despite there being missing bases in the target site and forming a bulge over these missing bases has been demonstrated previously.58 However improbable the DF988 probe binding to Candidatus ‘A. europaea’ despite four mismatches in its target site might seem, inclusion in the FISH protocol of an unlabeled competitor probe with the perfect match to this putative probe target sequence, as shown in Table 4, eliminated all the above-background level fluorescence (data not shown). FISH data also suggested that cluster I TFO members of the alphaproteobacterial Defluviicoccus vanus-related group were 8797

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Figure 1. Micrographs of SBR-activated sludge biomass from (a
d) plant A and (e and f) plant B. (a) Neisser stain of the dominant filament in plant A; (b) phase contrast images with corresponding composite FISH image with the EUBmix (Fluos = green) and DF988 (CY3 = red) probe sets (green + red = yellow); (c) phase contrast image with corresponding fluorescence image after Nile Blue A staining (PHA granules fluoresce red); (d) phase contrast image with corresponding composite FISH image with the EUBmix (green) and DF218 (red) probe sets; (e) phase contrast image with corresponding composite FISH image with the ALF968 (green) and DF218 (red) probe sets; (f) phase contrast image with corresponding composite FISH image with the EUBmix (green) and DF988 (red) probe sets. Scale bars indicate 10 μm.

codominant with this filament in the plant A community, being present mainly as tight spherical clustered cells within the flocs (Figure 1d). These populations were detected in the clone library (W-OTU3) (Figure 3). Members of cluster II Defluviicoccus TFO were also present in the flocs but at much lower abundances. Unfortunately, analysis of the corresponding bacterial community

during the vintage period was not possible as the plant was decommissioned at the end of the nonvintage period. FISH Analysis of Plant B. The microbial community composition of the biomass taken from this plant in both vintage and nonvintage was very similar, with both being dominated overwhelmingly by TFO cells responding to the alphaproteobacterial 8798

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Figure 2. Micrographs of SBR-activated sludge biomass from plant C: (a) phase contrast image with the corresponding composite FISH image with the ALF968 (Fluos = green) and AMAR 839 (CY3 = red) probes (green + red = yellow); (b) phase contrast image with the corresponding fluorescence image after Nile Blue A staining (PHA granules fluoresce red); (c) phase contrast image with the corresponding fluorescence image after DAPI staining (PolyP granules fluoresce light yellow); (d) phase contrast image with India ink capsule staining; (e) phase contrast images with the corresponding composite FISH image with the EUBmix (green), ZRA23a (red), and Beta42a (CY5 = blue) probe sets (green + red + blue = white). Scale bars indicate 10 μm.

probe ALF968. The vast majority of these were identified as members of clusters I and II of Defluviicoccus-related organisms. Cluster I members, the more dominant of the two, occurred in large irregular clusters at times exceeding 200 μm in diameter (Figure 1e). Cluster II members were present as smaller clusters of TFO scattered throughout the biomass (Figure 1f). Thin Neisser-negative, PHA-positive filaments with a morphology similar to that of Haliscomenobacter hydrossis but not hybridizing with the HHY probe were seen commonly protruding from the flocs. These hybridized with the Beta42a probe but were too thin to be Sphaerotilus natans, a betaproteobacterial filament implicated in bulking in activated sludge systems, and they also failed to respond to the SNA probe designed to target it. Filamentous Chloroflexi were also present, although in much lower abundances, and some of these responded to the FISH probes targeting the Eikelboom morphotypes type 080341 and type 1851,37 with the former the more abundant of the two. Little

difference in the community composition was observed between samples taken during the nonvintage and vintage periods, except that cluster I Defluviicoccus cell clusters were generally smaller and more loosely associated in the vintage community. FISH Analysis of Plant C Community. This community from the nonvintage period was again dominated by alphaproteobacterial TFO arranged in large sheets (Figure 2a
d). However, these did not respond to any of the Defluviicoccus subgroup FISH probes but instead fluoresced with the AMAR839 probe targeting members of the genus Amaricoccus (Figure 2a). The intensity of the fluorescence signal with this probe varied substantially between individual cells and was improved considerably after a lysozyme prehybridization treatment. These AMAR839-positive cells also stained brightly for PHA inclusions (Figure 2b), and DAPI staining indicated that many possessed small polyP inclusion bodies (Figure 2c). Clusters were surrounded by substantial capsular material (Figure 2d). This community was codominated 8799

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Environmental Science & Technology by betaproteobacterial coccibacilli arranged in large microcolonies. These were identified after FISH with the ZRA23a probe as members of the genus Zoogloea (Figure 2e), and staining showed the cells were again heavily capsulated (Figure 2d). A small number of Chloroflexi filaments, most of which responded to the probe targeting type 0803 (T0803
0654), and H. hydrossis were also detected by FISH, but filamentous bacteria were generally rare. In the plant sample taken from the vintage period, both Amaricoccus TFO and Zoogloea were still present but now in much lower abundance (estimated visually to be about a 50% reduction in both). During the nonvintage period plant C suffered from poor solids settleability or bulking. Yet the biomass possessed insufficient numbers of bacterial filaments to explain this, and interfloc bridging was absent.49 Furthermore, the few fungal filaments seen (Figure 2a) were not associated with the flocs but suspended in the mixed liquor. Instead, the flocs contained heavily capsulated Amaricoccus TFO and Zoogloea (Figure 2d), and it seems much

Figure 3. Maximum likelihood tree of 16S rRNA gene sequences obtained in this study (represented in bold) (all sequences were at least 1200 bp long). The JF957136 sequence represents isolates W-TFO1, W-TFO2, and W-TFO3. Parsimony bootstrap values were calculated as percentages of 1000 analysis and are only indicated for values g 75%: (O) bootstrap value g 75%; (b) bootstrap value g 95%. Scale bar corresponds to 0.1 substitutions per nucleotide position. Brackets define Defluviicoccus-related clusters. AS = Activated sludge. OTU = Operational taxonomic unit.

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more likely that these two heavily capsulated populations were both responsible for this episode of nonfilamentous ‘viscous’ bulking.49 Identification of Isolates from Plant C Biomass. Four of the 100 isolates cultured onto GS medium from plant C were Gramnegative tetrads. The 16S rRNA gene sequence of the first three of these isolates (designated W-TFO1, W-TFO2, and W-TFO3: Acc. No. JF957136) were identical and 97.3% similar to Amaricoccus kaplicensis (Figure 3), also isolated from activated sludge.53 The 16S rRNA sequence of the fourth of these isolates (W-TFO4: Acc. No. JF957137) was 98.8% similarity to the other three and 98.0% similar to A. kaplicensis (Figure 3). All isolates were pleomorphic, with large variation in their cell sizes, typical of Amaricoccus.59,60 These also stained positively for aerobic polyphosphate and PHA storage and produced an extensive capsule. Unlike other Amaricoccus isolates,53 these did not grow on R2A or PYC agar. Their ability to accumulate polyphosphate is also different to that reported for the other Amaricoccus isolates.53

’ DISCUSSION This paper describes for the first time the community composition of winery wastewater treatment plants using cultureindependent rRNA-based methods. It also provides an explanation for the dominance of certain populations and a possible solution to the operational problems they appear to cause. The FISH data from three SBRs treating winery wastewater unexpectedly showed each community was dominated by members of the Alphaproteobacteria. The dominating TFO populations were identified as members of clusters I and II Defluviicoccus-related organisms and possibly new Amaricoccus spp., Candidatus ‘A. europaea’ and Zoogloea were also codominant in the biomass from plants A and C, respectively. Organisms with a similar TFO morphology to those seen in these plants occur commonly in activated sludge communities analyzed by microscopy.61 TFO have also been reported to dominate the biomass treating wastes from wineries,12 although these TFO were not identified nor were they in the study of Liu et al.,62 where they dominated overwhelmingly the community of an EBPR laboratory reactor supplied with a high C:P feed. Bacteria growing as tetrads and representing a wide phylogenetic diversity are known to occur in activated sludge communities.61 However, the TFO of Liu et al.62 are considered likely to be members of the genus Defluviicoccus, since the alphaproteobacterial TFO in the community of a reactor set up to operate in exactly the same way were identified as cluster I members of this genus.58,63 Furthermore, bearing in mind the earlier reports

Table 4. Mismatches in Target Sites Between FISH Probes and Selected 16S rRNA Sequences

*

E. coli position numbering. Base mismatches to the Noli-644 and DF988 probes are highlighted and italicized. 8800

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Environmental Science & Technology concerning TFO distribution, it is probably more than a coincidence that the only cultured member of this genus, Defluviicoccus vanus, was isolated from an activated sludge plant treating high carbohydrate brewery wastes.60 In fact, members of this genus are seen frequently in large numbers in anaerobic:aerobic EBPR plant communities operating with low P removal capacity. They are thought to outcompete the polyphosphate-accumulating organisms (PAO) under P-limiting conditions, producing extensive capsular material and storing instead glycogen, thus earning the description of glycogen-accumulating organisms (GAO).11,64 Yet they are not restricted to plants of this configuration, and have been detected in high abundance in communities of plants dealing with paper mill waste, which is also characterized by its high COD:P ratio.30,65
67 Furthermore, extremely high Defluviicoccus enrichments (up to 95%) have been achieved in laboratoryscale reactor communities fed high COD:P feeds,68
70 adding further evidence to support the view that these conditions, so typical of these winery wastes, support their excessive proliferation. The ecology of Amaricoccus spp. is less clear than that of Defluviicoccus, yet they seem to share several similar ecological traits. Originally isolated from a laboratory-scale EBPR reactor fed glucose and not acetate and showing low EBPR capacity,71 these TFO ‘G-Bacteria’ were identified as members of a new genus Amaricoccus.53 Four species have been described so far with Amaricoccus tamworthensis, like D. vanus, being isolated from an activated sludge plant treating carbohydrate-rich brewery malting wastes. Amaricoccus spp. were also detected by FISH in high abundance in the communities of plants treating paper mill wastes.72 Yet their impact on EBPR capacity is likely to be less detrimental than that of Defluviicoccus. Pure cultures of A. kaplicensis have shown no ability to assimilate substrates under anaerobic conditions,59 meaning that they should not pose a threat to the polyphosphate-accumulating organisms (PAO) in the anaerobic zones of EBPR plants. Why Candidatus ‘A. europaea’ should dominate the community of plant A is unclear, since little is known of its ecophysiology.55 However, Levantesi et al.56 reported its presence in large amounts especially in activated sludge plants treating paper mill and potato wastes, where again the raw plant influent in both would be distinguished by its very high COD:N/P ratio. All the Alphaproteobacteria in these winery wastes appear to store considerable intracellular PHA stores in these winery wastes, and Defluviicoccus, Amaricoccus, and Zoogloea (especially the latter two) also synthesize substantial exocellular capsular material (Figure 2d). These metabolic features are typical responses of bacterial populations to conditions of unbalanced growth73 which would arise from high COD:P/N ratio feeds. Zoogloea populations are commonly seen in activated sludge plants,22 and pure culture studies74 suggest exocellular polysaccharide production by them is encouraged under N-limiting conditions. Therefore, it seems likely that Defluviicoccus, Amaricoccus, ‘Alysiosphaera’, and Zoogloea share a similar ecological niche in that they proliferate under nutrient-limiting high C:N/P feed conditions, and this operational feature probably explains their dominance in these winery wastewater treatment systems. The high relative abundances of floc-associated Amaricoccus and Zoogloea aggregates (Figure 2) may also explain the operational solids separation problems experienced by plant C. The nonfilamentous viscous bulking49 experienced by the plant during the nonvintage period of operation is considered to be caused by them, encouraged to proliferate excessively by the high COD:P/N

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ratio of the feed. Heavily encapsulated TFO bacteria were also thought to be responsible for nonfilamentous bulking in a plant treating winery waste in Hungary.12 This solids separation problem observed in plant C did not occur in vintage, where the Amaricoccus population decreased substantially. The reason for this decline in relative abundance of Amaricoccus is unclear. Clearly more studies of this kind are needed with winery plants to see if the microbiological trends recorded here occur globally. The data from this study suggest that winery wastewater treatment plants and others treating carbohydrate-rich waste may contain a rich reservoir of further biodiversity of Defluviicoccus and Amaricoccus members. They also suggest that episodes of nonfilamentous viscous bulking, invariably linked in the past to Zoogloea, may in fact be caused by other bacterial populations also favored by N- and/or P-limiting conditions like Amaricoccus. If so, bulking control in these plants may be controlled readily by careful P and N supplementation of the influent wastes to allow balanced growth of the communities and to discourage the excessive proliferation of these potentially troublesome bacterial populations.

’ AUTHOR INFORMATION Corresponding Author

*Phone: +61 3 5444 7459. Fax: +61 3 5444 7476. E-mail: r.seviour@ latrobe.edu.au.

’ ACKNOWLEDGMENT The authors would like to thank the plant operators, Robert Morris, Andrew Johns, Grant Kohlhagen, and Nichola Seymour, for supplying samples and plant operation information. Thanks also go to John Constable (JJC Engineering Pty. Ltd) for his helpful advice and information. S.M. and L.S. were supported by a Post Graduate Writing Up Award from La Trobe University and APA Ph.D. scholarship, respectively. Photograph of a stream used in the TOC art was taken by Kristy McIlroy and used with permission. ’ REFERENCES (1) Brito, A. G.; Peixoto, J.; Oliveira, J. M.; Oliveira, J. A.; Costa, C.; Nogueira, R.; Rodrigues, A. Brewery and winery wastewater treatment: some focal points of design and operation. In Utilization of by-products and treatment of waste in the food industry; Oreopoulou, V., Russ, W., Eds.; Springer: New York, 2007; Vol. 3, pp 109
131. (2) Eus
ebio, A.; Petruccioli, M.; Lageiro, M.; Federici, F.; Duarte, J. C. Microbial characterisation of activated sludge in jet-loop bioreactors treating winery wastewaters. J. Ind. Microbiol. Biotechnol. 2004, 31 (1), 29–34. (3) Eusebi, A. L.; Nardelli, P.; Gatti, G.; Battistoni, P.; Cecchi, F. From conventional activated sludge to alternate oxic/anoxic process: the optimization of winery wastewater treatment. Water Sci. Technol. 2009, 60 (4), 1041–1048. (4) Arvanitoyannis, I. S.; Ladas, D.; Mavromatis, A. Wine waste treatment methodology. Int. J. Food Sci. Technol. 2006, 41 (10), 1117–1151. (5) Frost, P.; Kumar, A.; Correll, R.; Quayle, W.; Kookana, R.; Christen, E.; Oemcke, D. Current practices for winery wastewater management and its reuse: an Australian industry survey. Wine Ind. J. 2007, 22 (1), 40–46. (6) Torrijos, M.; Moletta, R. Winery wastewater depollution by sequencing batch reactor. Water Sci. Technol. 1997, 35 (1), 249–257. (7) Seviour, R. J.; Nielsen, P. H. Microbial Ecology of Activated Sludge; IWA Publishing: London, 2010. 8801

dx.doi.org/10.1021/es2018576 |Environ. Sci. Technol. 2011, 45, 8794–8803

Environmental Science & Technology (8) He, S.; McMahon, K. D. Microbiology of Candidatus Accumulibacter in activated sludge. Microb. Biotechnol. In press, doi:10.1111/ j.1751-7915.2011.00248.x. (9) McMahon, K. D.; He, S.; Oehmen, A. The microbiology of phosphorus removal. In Microbial ecology of activated sludge; Seviour, R. J., Nielsen, P. H., Eds.; IWA Publishing: London, 2010; pp 281
319. (10) Daims, H.; Wagner, M. The microbiology of nitrogen removal. In Microbial ecology of activated sludge; Seviour, R. J., Nielsen, P. H., Eds.; IWA Publishing: London, 2010; pp 259
280. (11) Oehmen, A.; Lemos, P. C.; Carvalho, G.; Yuan, Z.; Keller, J.; Blackall, L. L.; Reis, M. A. M. Advances in enhanced biological phosphorus removal: from micro to macro scale. Water Res. 2007, 41 (11), 2271–2300. (12) Jobb
agy, A.; Liter
athy, B.; Tardy, G. Implementation of glycogen accumulating bacteria in treating nutrient-deficient wastewater. Water Sci. Technol. 2002, 46 (1
2), 185–190. (13) Gonzalez, J. M.; Jurado, V.; Laiz, L.; Zimmermann, J.; Hermosin, B.; Saiz-Jimenez, C. Pectinatus portalensis non. sp., a relatively fastgrowing, coccoidal, novel Pectinatus species isolated from a wastewater treatment plant. Antonie van Leeuwenhoek 2004, 86 (3), 241–248. (14) Daims, H.; Stoecker, K.; Wagner, M. Fluorescence in situ hybridization for the detection of prokaryotes. In Molecular Microbial Ecology; Osborn, A. M., Smith, C. J., Eds.; Taylor & Francis: New York, 2005; pp 213
239. (15) Kragelund, C.; Remesova, Z.; Nielsen, J.; Thomsen, T.; Eales, K.; Seviour, R.; Wanner, J.; Nielsen, P. Ecophysiology of mycolic acidcontaining Actinobacteria (Mycolata) in activated sludge foams. FEMS Microbiol. Ecol. 2007, 61 (1), 174–184. (16) Amann, R. I.; Binder, B. J.; Olson, R. J.; Chisolm, S. W.; Devereux, R.; Stahl, D. A. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 1990, 56 (6), 1919–1925. (17) Daims, H.; Br€uhl, A.; Amann, R.; Schleifer, K.; Wagner, M. The domain-specific probe EUB338 is insufficient for the detection of all Bacteria: development and evaluation of a more comprehensive probe set. Syst. Appl. Microbiol. 1999, 22 (3), 434–444. (18) Wallner, G.; Amann, R.; Beisker, W. Optimizing fluorescent in situ hybridization with rRNA-targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry 1993, 14 (2), 136–143. (19) Manz, W.; Amann, R.; Ludwig, W.; Wagner, M.; Scheifer, K.-H. Phylogenetic oligodeoxynucleotide probes for the major subclasses of Proteobacteria: problems and solutions. Syst. Appl. Microbiol. 1992, 15 (4), 593–600. (20) Zilles, J.; Peccia, J.; Kim, M.; Hung, C.; Noguera, D. Involvement of Rhodocyclus-related organisms in phosphorus removal in fullscale wastewater treatment plants. Appl. Environ. Microbiol. 2002, 68 (6), 2763–2769. (21) Crocetti, G. R.; Hugenholtz, P.; Bond, P. L.; Schuler, A.; Keller, J.; Jenkins, D.; Blackall, L. L. Identification of polyphosphate-accumulating organisms and design of 16S rRNA-directed probes for their detection and quantitation. Appl. Environ. Microbiol. 2000, 66 (3), 1175–1182. (22) Rossello-Mora, R. A.; Wagner, M. The abundance of Zoogloea ramigera in sewage treatment plants. Appl. Environ. Microbiol. 1995, 61 (2), 702–707. (23) Hess, A.; Zarda, B.; Hahn, D.; Haner, A.; Stax, D.; Hohener, P.; Zeyer, J. In situ analysis of denitrifying toluene- and m-xylene-degrading bacteria in a diesel fuel-contaminated laboratory aquifer column. Appl. Environ. Microbiol. 1997, 63 (6), 2136–41. (24) Wagner, M.; Amann, R.; K€ampfer, P.; Assmus, B.; Hartmann, A.; Hutzler, P.; Springer, N.; Schleifer, K.-H. Identification and in situ detection of gram-negative filamentous bacteria in activated sludge. Syst. Appl. Microbiol. 1994, 17 (3), 405–417. (25) Kong, Y.; Ong, S. L.; Ng, W. J.; Liu, W.-T. Diversity and distribution of a deeply branched novel proteobacterial group found in anaerobic-aerobic activated sludge processes. Environ. Microbiol. 2002, 4 (11), 753–757. (26) Neef, A.; Witzenberger, R.; K€ampfer, P. Detection of sphingomonads and in situ identification in activated sludge using 16S rRNA-

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targeted oligonucleotide probes. J. Ind. Microbiol. Biotechnol. 1999, 23 (4
5), 261–267. (27) Wong, M.-T.; Tan, F. M.; Ng, W. J.; Liu, W.-T. Identification and occurrence of tetrad-forming Alphaproteobacteria in anaerobic-aerobic activated sludge processes. Microbiology 2004, 150 (11), 3741–3748. (28) Meyer, R. L.; Saunders, A. M.; Blackall, L. L. Putative glycogenaccumulating organisms belonging to the Alphaproteobacteria identified through rRNA-based stable isotope probing. Microbiology 2006, 152 (2), 419–429. (29) Nittami, T.; McIlroy, S.; Seviour, E. M.; Schroeder, S.; Seviour, R. J. Candidatus Monilibacter spp., common bulking filaments in activated sludge, are members of Cluster III Defluviicoccus. Syst. Appl. Microbiol. 2009, 32 (7), 480–489. (30) McIlroy, S.; Seviour, R. J. Elucidating further phylogenetic diversity among the Defluviicoccus-related glycogen-accumulating organisms in activated sludge. Environ. Microbiol. Rep. 2009, 1 (6), 563–568. (31) Maszenan, A.-M.; Seviour, R. J.; Patel, B. K.; Wanner, J. A fluorescently-labelled r-RNA targeted oligonucleotide probe for the in situ detection of G-bacteria of the genus Amaricoccus in activated sludge. J. Appl. Microbiol. 2000, 88 (5), 826–835. (32) Snaidr, I.; Beimfohr, C.; Levantesi, C.; Rossetti, S.; van der Waarde, J.; Geurkink, B.; Eikelboom, D.; Lemaitre, M.; Tandoi, V. Phylogenetic analysis and in situ identification of “Nostocoida limicola”like filamentous bacteria in activated sludge from industrial wastewater treatment plants. Water Sci. Technol. 2002, 46 (1
2), 99–104. (33) Roller, C.; Wagner, M.; Amann, R.; Ludwig, W.; Schleifer, K.-H. In situ probing of Gram-positive bacteria with high DNA G + C content using 23S rRNA-targeted oligonucleotides. Microbiology 1994, 140 (10), 2849–2858. (34) Meier, H.; Amann, R.; Ludwig, W.; Schleifer, K. Specific oligonucleotide probes for in situ detection of a major group of Grampositive bacteria with low DNA G + C content. Syst. Appl. Microbiol. 1999, 22 (3), 186–196. (35) Bj€ornsson, L.; Hugenholtz, P.; Tyson, G. W.; Blackall, L. L. Filamentous Chloroflexi (green non-sulfur bacteria) are abundant in wastewater treatment processes with biological nutrient removal. Microbiology 2002, 148 (8), 2309–2318. (36) Gich, F.; Garcia-Gil, J.; Overmann, J. Previously unknown and phylogenetically diverse members of the green nonsulfur bacteria are indigenous to freshwater lakes. Arch. Microbiol. 2001, 177 (1), 1–10. (37) Beer, M.; Seviour, E. M.; Kong, Y.; Cunningham, M.; Blackall, L. L.; Seviour, R. J. Phylogeny of the filamentous bacterium Eikelboom Type 1851, and design and application of a 16S rRNA targeted oligonucleotide probe for its fluorescence in situ identification in activated sludge. FEMS Microbiol. Lett. 2002, 207 (2), 179–183. (38) Kragelund, C.; Levantesi, C.; Borger, A.; Thelen, K.; Eikelboom, D.; Tandoi, V.; Kong, Y.; van der Waarde, J.; Krooneman, J.; Rossetti, S.; Thomsen, T.; Nielsen, P. Identity, abundance and ecophysiology of filamentous Chloroflexi species present in activated sludge treatment plants. FEMS Microbiol Ecol 2007, 59 (3), 671–682. (39) Speirs, L.; Nittami, T.; McIlroy, S.; Schroeder, S.; Seviour, R. J. Filamentous bacterium Eikelboom type 0092 in activated sludge plants in Australia is a member of the phylum Chloroflexi. Appl. Environ. Microbiol. 2009, 75 (8), 2446–52. (40) Speirs, L. B. M.; McIlroy, S. J.; Petrovski, S.; Seviour, R. J. The activated sludge bulking filament Eikelboom morphotype 0914 is a member of the Chloroflexi. Environ. Microbiol. Rep. 2011, 3 (2), 159–165. (41) Kragelund, C.; Thomsen, T. R.; Mielczarek, A. T.; Nielsen, P. H. Eikelboom’s morphotype 0803 in activated sludge belongs to the genus Caldilinea in the phylum Chloroflexi. FEMS Microbiol. Ecol. 2011, 76 (3), 451–62. (42) Neef, A.; Amann, R.; Schlesner, H.; Schleifer, K.-H. Monitoring a widespread bacterial group: in situ detection of Planctomycetes with 16S rRNA-targeted probes. Microbiology 1998, 144 (12), 3257–3266. (43) Manz, W.; Amann, R.; Ludwig, W.; Vancanneyt, M.; Schleifer, K. Application of a suite of 16S rRNA-specific oligonucleotide probes designed to investigate bacteria of the phylum Cytophaga-Flavobacter-Bacteroides in the natural environment. Microbiology 1996, 142 (5), 1097–1106. 8802

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Environmental Science & Technology (44) Weller, R.; Gl€ockner, F.; Amann, R. 16S rRNA-targeted oligonucleotide probes for the in situ detection of members of the phylum Cytophaga-Flavobacterium-Bacteroides. Syst. Appl. Microbiol. 2000, 23 (1), 107–114. (45) Hugenholtz, P.; Tyson, G. W.; Webb, R. I.; Wagner, A. M.; Blackall, L. L. Investigation of candidate division TM7, a recently recognized major lineage of the domain bacteria with no known pureculture representatives. Appl. Environ. Microbiol. 2001, 67 (1), 411–419. (46) Kawaharasakia, M.; Tanakab, H.; Kanagawaa, T.; Nakamuraa, K. In situ identification of polyphosphate-accumulating bacteria in activated sludge by dual staining with rRNA-targeted oligonucleotide probes and 40 ,6-diamidino-2-phenylindol (DAPI) at a polyphosphateprobing concentration. Water Res. 1999, 33 (1), 257–265. (47) Ostle, A.; Holt, J. Nile blue A as a fluorescent stain for poly-betahydroxybutyrate. Appl. Environ. Microbiol. 1982, 44 (1), 238–241. (48) Ahn, J.; Schroeder, S.; Beer, M.; McIlroy, S.; Bayly, R. C.; May, J. W.; Vasiliadis, G.; Seviour, R. J. Ecology of the microbial community removing phosphate from wastewater under continuously aerobic conditions in a sequencing batch reactor. Appl. Environ. Microbiol. 2007, 73 (7), 2257–2270. (49) Jenkins, D.; Richard, M. G.; Daigger, G. T. Manual on the causes and control of activated sludge bulking, foaming and other solids separation problems, 3rd ed.; CRC Press LLC: London, England, 2004. (50) McIlroy, S.; Porter, K.; Seviour, R. J.; Tillett, D. Simple and safe method for simultaneous isolation of microbial RNA and DNA from problematic populations. Appl. Environ. Microbiol. 2008, 74 (21), 6806–6807. (51) Tillett, D.; Neilan, B. A. Xanthogenate nucleic acid isolation from cultured and environmental Cyanobacteria. J. Phycol. 2000, 36 (1), 251–258. (52) Lane, D. 16S/23S rRNA sequencing. In Modern microbial methods: Nucleic acid techniques in bacterial systematics; Stackebrandt, E., Goodfellow, M., Eds.; John Wiley & Sons: England, 1991; pp 115
175. (53) Maszenan, A.; Seviour, R.; Patel, B.; Rees, G.; McDougall, B. Amaricoccus gen. nov., a Gram-negative coccus occurring in regular packages or tetrads, isolated from activated sludge biomass, and descriptions of Amaricoccus veronensis sp. nov., Amaricoccus tamworthensis sp. nov., Amaricoccus macauensis sp. nov., and Amaricoccus kaplicensis sp. nov. Int. J. Syst. Bacteriol. 1997, 47 (3), 727–734. (54) McIlroy, S.; Hoefel, D.; Schroeder, S.; Ahn, J.; Tillett, D.; Saint, C.; Seviour, R. FACS enrichment and identification of floc-associated alphaproteobacterial tetrad-forming organisms in an activated sludge community. FEMS Microbiol. Lett. 2008, 285 (1), 130–135. (55) Kragelund, C.; Kong, Y.; van der Waarde, J.; Thelen, K.; Eikelboom, D.; Tandoi, V.; Thomsen, T.; Nielsen, P. Ecophysiology of different filamentous Alphaproteobacteria in industrial wastewater treatment plants. Microbiology 2006, 152 (10), 3003–3012. (56) Levantesi, C.; Beimfohr, C.; Geurkink, B.; Rossetti, S.; Thelen, K.; Krooneman, J.; Snaidr, J.; van der Waarde, J.; Tandoi, V. Filamentous Alphaproteobacteria associated with bulking in industrial wastewater treatment plants. Syst. Appl. Microbiol. 2004, 27 (6), 716–727. (57) Yilmaz, L. S.; Parnerkar, S.; Noguera, D. R. mathFISH, a web tool that uses thermodynamics-based mathematical models for in silico evaluation of oligonucleotide probes for fluorescence in situ hybridization. Appl. Environ. Microbiol. 2011, 77 (3), 1118–22. (58) McIlroy, S. J.; Tillett, D.; Petrovski, S.; Seviour, R. J. Non-target sites with single nucleotide insertions or deletions are frequently found in 16S rRNA sequences and can lead to false positives in fluorescence in situ hybridization (FISH). Environ. Microbiol. 2011, 13 (1), 38–47. (59) Falvo, A.; Levantesi, C.; Rossetti, S.; Seviour, R.; Tandoi, V. Synthesis of intracellular storage polymers by Amaricoccus kaplicensis, a tetrad forming bacterium present in activated sludge. J. Appl. Microbiol. 2001, 91 (2), 299–305. (60) Maszenan, A.; Seviour, R.; Patel, B.; Janssen, P.; Wanner, J. Defluvicoccus vanus gen. nov., sp. nov., a novel Gram-negative coccus/ coccobacillus in the Alphaproteobacteria from activated sludge. Int. J. Syst. Evol. Microbiol. 2005, 55 (5), 2105–2111. (61) Seviour, R. J.; Maszenan, A. M.; Soddell, J. A.; Tandoi, V.; Patel, B. K. C.; Kong, Y.; Schumann, P. Microbiology of the ’G-bacteria’ in activated sludge. Environ. Microbiol. 2000, 2 (6), 581–593.

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(62) Liu, W. T.; Mino, T.; Nakamura, K.; Matsuo, T. Glycogen accumulating population and its anaerobic substrate uptake in anaerobic-aerobic activated sludge without biological phosphorus removal. Water Res. 1996, 30 (1), 75–82. (63) Kong, Y. H.; Beer, M.; Rees, G. N.; Seviour, R. J. Functional analysis of microbial communities in aerobic-anaerobic sequencing batch reactors fed with different phosphorus/carbon (P/C) ratios. Microbiology 2002, 148 (8), 2299–2307. (64) Mino, T.; Liu, W.-T.; Kurisu, F.; Matsuo, T. Modelling glycogen storage and denitrification capability of microorganisms in enhanced biological phosphate removal processes. Water Sci. Technol. 1995, 31 (2), 25–34. (65) McIlroy, S. J.; Nittami, T.; Seviour, E. M.; Seviour, R. J. Filamentous members of cluster III Defluviicoccus have the in situ phenotype expected of a glycogen accumulating organism in activated sludge. FEMS Microbiol. Ecol. 2010, 74 (1), 248–256. (66) Pisco, A. R.; Bengtsson, S.; Werker, A.; Reis, M. A. M.; Lemos, P. C. Community structure evolution and enrichment of glycogenaccumulating organisms producing polyhydroxyalkanoates from fermented molasses. Appl. Environ. Microbiol. 2009, 75 (14), 4676–86. (67) Bengtsson, S.; Werker, A.; Welander, T. Production of polyhydroxyalkanoates by glycogen accumulating organisms treating a paper mill wastewater. Water Sci. Technol. 2008, 58 (2), 323–330. (68) Burow, L. C.; Mabbett, A. N.; Borras, L.; Blackall, L. L. Anaerobic central metabolic pathways active during polyhydroxyalkanoate production in uncultured cluster 1 Defluviicoccus enriched in activated sludge communities. FEMS Microbiol. Lett. 2009, 298 (1), 79–84. (69) Oehmen, A.; Zeng, R.; Saunders, A.; Blackall, L.; Keller, J.; Yuan, Z. Anaerobic and aerobic metabolism of glycogen-accumulating organisms selected with propionate as the sole carbon source. Microbiology 2006, 152 (9), 2767–2778. (70) Dai, Y.; Yuan, Z.; Wang, X.; Oehmen, A.; Keller, J. Anaerobic metabolism of Defluviicoccus vanus related glycogen accumulating organisms (GAOs) with acetate and propionate as carbon sources. Water Res. 2007, 41 (9), 1885–1896. (71) Cech, J. S.; Hartman, P. Competition between polyphosphate and polysaccharide accumulating bacteria in enhanced biological phosphate removal systems. Water Res. 1993, 27 (7), 1219–1225. (72) McIlroy, S. J. Phylogenetic diversity and ecophysiology of alphaproteobacterial glycogen accumulating organisms in enhanced biological phosphorus removal activated sludge systems. Ph.D. Thesis, La Trobe University, Bendigo, 2010. (73) Kessler, B.; Witholt, B. Factors involved in the regulatory network of polyhydroxyalkanoate metabolism. J. Biotechnol. 2001, 86 (2), 97–104. (74) Norberg, A. B.; Enfors, S.-O. Production of extracellular polysaccharide by Zoogloea ramigera. Appl. Environ. Microbiol. 1982, 44 (5), 1231–1237.

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Large Shift in Source of Fine Sediment in the Upper Mississippi River Patrick Belmont,*,†,‡ Karen B. Gran,‡,§ Shawn P. Schottler,|| Peter R. Wilcock,‡,^ Stephanie S. Day,‡,# Carrie Jennings,#,r J. Wesley Lauer,O Enrica Viparelli,‡,[ Jane K. Willenbring,‡,z,+ Daniel R. Engstrom,|| and Gary Parker‡,[ †

Department of Watershed Science, Utah State University, Logan, Utah 84332, United States National Center for Earth-surface Dynamics, University of Minnesota, Minneapolis, Minnesota 55414, United States § Department of Geological Sciences, University of Minnesota-Duluth, Duluth, Minnesota 55812, United States St. Croix Watershed Research Station, Science Museum of Minnesota, Marine on St. Croix, Minnesota 55047, United States ^ Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States # Geology and Geophysics, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States r Minnesota Geological Survey, St. Paul, Minnesota 55114, United States O Department of Civil and Environmental Engineering, Seattle University, Seattle, Washington 98122, United States [ Department of Civil & Environmental Engineering and Department of Geology, University of Illinois, Urbana, Illinois 61801, United States z Department of Earth and Environmental Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States + Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Section 3.4, Earth Surface Geochemistry, D-14473 Potsdam, Germany

)

‡

bS Supporting Information ABSTRACT: Although sediment is a natural constituent of rivers, excess loading to rivers and streams is a leading cause of impairment and biodiversity loss. Remedial actions require identification of the sources and mechanisms of sediment supply. This task is complicated by the scale and complexity of large watersheds as well as changes in climate and land use that alter the drivers of sediment supply. Previous studies in Lake Pepin, a natural lake on the Mississippi River, indicate that sediment supply to the lake has increased 10-fold over the past 150 years. Herein we combine geochemical fingerprinting and a suite of geomorphic change detection techniques with a sediment mass balance for a tributary watershed to demonstrate that, although the sediment loading remains very large, the dominant source of sediment has shifted from agricultural soil erosion to accelerated erosion of stream banks and bluffs, driven by increased river discharge. Such hydrologic amplification of natural erosion processes calls for a new approach to watershed sediment modeling that explicitly accounts for channel and floodplain dynamics that amplify or dampen landscape processes. Further, this finding illustrates a new challenge in remediating nonpoint sediment pollution and indicates that management efforts must expand from soil erosion to factors contributing to increased water runoff.

’ INTRODUCTION Sediment and turbidity are leading causes of impairment in U.S. rivers and streams1,2 and remedial action requires identification of the sources and mechanisms of sediment supply. Despite extraordinary efforts, sediment remains one of the most difficult nonpoint-source pollutants to quantify for several reasons.37 Erosion is typically episodic and highly localized. Erosion mechanisms are strongly nonlinear, and their rates are contingent on multiple factors including climate, geology, and land use history.8,9 Eroded sediment may exit the watershed quickly or be stored for very long periods.10,11 Finally, the accuracy of current methods for estimating sediment r 2011 American Chemical Society

yield from agricultural watersheds has been challenged4,8,12 because most estimates are based on empirical models of soil erosion and require a scalar reduction factor to estimate sediment yield as a fraction of erosion. Few studies provide evidence to constrain this reduction factor, and available observations indicate diverse and highly nonlinear scaling with drainage area.7,8 Received: June 4, 2011 Accepted: August 31, 2011 Revised: August 30, 2011 Published: August 31, 2011 8804

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Environmental Science & Technology

Figure 1. Inset A shows the Lake Pepin (LP) watershed within the upper Midwest, composed of the Minnesota (MNR), upper Mississippi (MISS), and St. Croix River (SC) watersheds. Inset B shows lidar topography data for the incised portion of the Le Sueur watershed (LS), including the locations of gaging stations on all three main branches.

Accurate identification of sediment sources and erosion rates are needed to understand and manage the landscape sediment routing system13 and related biogeochemical processes.14 The reliability of sediment source estimates can be improved by using multiple, overlapping methods of measurement within the strong constraint of a mass balance, or sediment budget.15 A sediment budget is a useful tool for evaluating landscape change and sediment yield.10,1619 The scope and accuracy of sediment budgets depend strongly on the availability of information for earlier conditions in a watershed. For example, historical information such as photos, maps, and field studies have been used to provide reliable information on previous conditions in order to close a sediment budget over a time period long enough to average over stochastic temporal variability. This approach is strengthened by a suite of new research tools that allow precise dating of land surfaces, geochemical identification of sediment provenance, and high-resolution measurement of topography using airborne and terrestrial lidar.20,21 The waters of the Upper Mississippi River (UMR) and its major tributaries have been listed as impaired for turbidity by the U.S. Environmental Protection Agency. Turbidity, eutrophication, and sedimentation have been identified as urgent problems for Lake Pepin, a natural lake on the Mississippi River of exceptional recreational and popular importance (Figure 1). Coring records examining the past 500 years indicate that sedimentation rates in Lake Pepin may have increased by as much as an order of magnitude over the past 150 years.22 Of the sediment delivered to Lake Pepin, past and present, 80% to 90% derives from the Minnesota River Basin (MRB), despite the fact that the MRB comprises only a third of the drainage area.22,23 The relatively high sediment yield of the MRB stems from a combination of Quaternary landscape history and human land and water management. Land cover in the basin has shifted from poorly drained tall-grass prairie and wetlands24 to 78% row crop agriculture25 over the 150 yr period of increased sedimentation, suggesting that the change in land use underlies the increase.26 The 2880 km2 Le Sueur River watershed produces the highest sediment yield (73.5 Mg/km2) of any Minnesota River tributary, accounting for as much as 30% of the Minnesota River sediment load.26 The Le Sueur landscape is naturally primed for rapid geomorphic change and large sediment supply. The geologic substrate is a 60 m thick package of semiconsolidated, but soft, fine-grained (67% silt and clay, 33% sand, PBBs > PBEB > PBT for both sites. This BFR profile was similar to the atmospheric deposition profile observed at the same sites in our previous study.9 SI Figure S3 depicts the congener profiles of the atmospheric PBDEs at the two sites. BDE209 was the most dominant congener in almost all of the samples. It contributed 47 ( 17% and 59 ( 16% of the total PBDEs at the e-waste and rural sites, respectively. This is consistent with the fact that the deca-BDE mixture is one of the most frequently used flame retardants across the world, especially in electronic/electric products.3,18 On the other hand, it is surprising to observe high contributions of diand tri-BDE congeners in the air (especially of the e-waste site) because these congeners are not present, or present only in minor amounts, in the technical PBDE mixtures.19 Highest geometric mean concentrations of di- and tri-BDEs of 4120 and 3000 pg/m3, respectively, were found at the e-waste site and were comparable to the concentrations of tetra-, penta-, and nona-BDEs (Table 1). The atmospheric PBDE congener profiles in this study were quite different from the profiles that have typically been observed in the air from other locations, in which di- and tri-BDEs accounted for very small proportions of the PBDEs.20
23 In fact, tetra-, penta-, and hexa-BDEs, which are mainly derived from technical penta-BDE mixtures, were also present in significant proportions in the studied area compared to those in the urban air in southern China from an early study.11 This finding provides evidence that e-waste recycling is a significant emission source of less brominated PBDEs (di- through hexa-BDEs) to the environment. These PBDEs (in particular those absent from the technical PBDE products) could originate from photolytical degradation of highly brominated BDEs in the environment. However, they are more likely to originate from pyrolytic degradation during the recycling processes (e-waste burning) due to the shielding effects of carbonaceous aerosols to photodegradation indicated in a previous study.24 Additionally, high ambient temperatures facilitate the volatilization of less brominated congeners (with relatively high vapor pressures) from contaminated environmental compartments, massive e-waste, and recycled e-waste remains stacked in the fields. The total PBDE concentrations (∑36PBDEs) at the e-waste site in this study were lower than those detected in the air from another e-waste site (Guiyu) in China, with average concentrations of 21 500 pg/m3 in 2004 (∑22PBDEs without BDE209) and 8760 pg/m3 in 2005 (∑11PBDEs).16,17 These differences could

Figure 1. Seasonal variations (from July 2007 to June 2008) of the major BFRs in the gaseous phase at the two sites in southern China. E-waste site: A (sum of di-, tri-, and tetra-BDEs, PBT, PEEB, and HBB) and B (sum of penta- and hexa-BDEs). Rural site: C (sum of di- and triBDEs, PBT, PEEB, and HBB) and D (sum of tetra-, penta-, and hexaBDEs).

be ascribed to the distance from the sampling sites to the e-waste recycling facilities and/or the recycling manners. There has been a substantial decrease in the activities of open burning e-waste because of the increasingly stringent restrictions. For instance, the highest concentrations in Guiyu were measured right at a building where there are open burning operations.16 It has been shown in previous studies that the average PBDE concentrations in urban air were on an order of 50
150 pg/m3, and those in rural/remote air were generally in the range of 5
15 pg/m3 in North America, Europe, and Asia.1,20,23,25,26 A few studies have reported higher average concentrations in China (2450 pg/m3 in urban air and 220 pg/m3 in rural air)11,27 and in Ontario, Canada (300 pg/m3 in rural air)28 as well as lower concentrations in the Arctic (1
8 pg/m3).22,29 These levels are generally lower than the average concentrations of ∑16PBDEs (that have been typically reported in the literature) at the e-waste site (2220 pg/m3) and rural site (192 pg/m3) in the present study (SI Table S2). Atmospheric measurements of non-PBDE BFRs in other studies are very limited. The average concentrations of BTBPE in rural air were 3.4 pg/m3 in the east-central U.S. and 0.5
1.2 pg/m3 near the Great Lakes (U.S.), which were comparable to that in this study.21,25 The average concentrations of DBDPE in the air near the Great Lakes were 1
22 pg/m3, much lower than the concentrations in the present study.25 PBEB, with a relatively high concentration of 550 pg/m3, was measured near the Great Lakes.25 Seasonal Variations. The seasonal variations of the total air BFR concentrations during the sampling year (July 2007
June 2008) at the e-waste and rural sites are shown in SI Figure S4. The monthly air BFR concentrations (five day averaged concentration) varied from 912 to 10 300 pg/m3 at the e-waste site. Seasonal variations of the total BFR concentrations in the gaseous and particle phases were generally similar (r = 0.85, p = 0.001). The monthly air BFR concentrations at the rural site ranged from 144 to 1160 pg/m3. Different seasonal variations were observed for the gaseous and particle-bound BFRs. 8821

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Figure 2. Seasonal variations (from July 2007 to June 2008) of the major BFRs in the particle phase at the two sites in southern China. E-waste site: A (sum of di-, tri-, and tetra-BDEs, PBT, PEEB, and HBB), B (sum of penta-, hexa-, and hepta-BDEs), and C (sum of octa-, nona-, and deca-, BTBPE, DBDPE). Rural site: D (sum of tri-, tetra-, and pentaBDEs), E (sum of hexa-, hepta-, and octa-BDEs), and F (nona- and decaBDEs and DBDPE).

Seasonal variations of the major gaseous and particle-bound BFRs (BFR concentrations with close variations were combined) were compared in Figures 1 and 2. At both the sites, the concentrations of more volatile BFRs (di-, tri, and tetra-BDEs, PBT, PBEB, and HBB) in the gaseous phase were relatively high in the summer months. Gaseous penta- and hexa-BDEs showed a similar seasonal variation to the more volatile BFRs at the e-waste site; whereas variation of gaseous tetra-, penta-, and hexa-BDEs differed from the more volatile BFRs at the rural site (Figure 1). The particle-bound BFR concentrations showed different seasonal variations at both the sites (Figure 2). It is interesting to find that, at the e-waste site, penta-, hexa-, and hepta-BDEs exhibited variations similar to the less volatile BFRs (octa-, nona-, and decaBDEs, DBDPE, and BTBPE) in warm season (April to September) and to the more volatile BFRs in colder season (October to March). This may suggest an influence of gas-particle partitioning behavior of these BFRs (e.g., lower temperature favors deposition of gaseous BFRs to particles). At the rural site, particle-bound hexa-, hepta-, and octa-BDEs and BTBPE showed a seasonal variation similar to more volatile tri-, tetra-, and penta-BDEs rather than less volatile nonaand deca-BDEs and DBDPE. These less volatile compounds are the dominant ingredients of technical deca-BDE and DBDPE products that are used in large quantities the nearby urban region; while trithrough octa-BDEs and BTBPE are more important pollutants at the e-waste site than in the urban region.9 The results suggest differences in the source and/or behavior of the BFRs in the atmosphere at the two sites.

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The seasonal variations are believed to be affected by the meteorological conditions. However, significant relationships were not found between the air concentrations (individual or total BFRs) and wind speed or relative humidity. Temperature is one of the major factors controlling seasonality of SOCs in air. The influence of ambient temperature on BFRs at the sites is discussed below. Influences of Ambient Temperature. The CC equation was applied to the major gaseous BFRs at the two sites, and the regression results are summarized in SI Table S3. The CC plots show apparent differences in temperature dependence of the BFR concentrations at high and low temperatures at the e-waste site (Figure 3). At high temperatures (19
30 °C, average daily temperature), the gaseous concentrations (except for hepta-BDEs) were positively correlated with temperatures (p < 0.001), which explained approximately 44%
78% (r2) of the variations. The mean slope of the CC plots in the present study was
20 840 ( 3250, which are steeper than those for PBDEs reported from Europe (
9110 ( 3940) and North America (
6400 ( 600 for BDE47 and
5300 ( 960 for BDE99).25 To date, the temperature dependence of air concentrations associated with e-waste has not been examined. This finding suggests that gaseous concentrations of BFRs in high-temperature seasons at the e-waste site are strongly controlled by temperature-driven evaporation from contaminated surfaces (e-waste, soils, and recycled e-waste remains as mentioned above) in the local surroundings of this site.14,15 This result also has important implications for the global transport from warm climates to colder climates of these chemicals resulting from e-waste recycling (grass-hopper effect). At low temperatures (8
19 °C) during the winter, many BFRs showed increasing concentrations with declining temperatures, although the linear relationships were not all statistically significant (SI Table S3). The differences in temperature relationships for SOCs in winter and other seasons have been reported in early studies, although the winter temperatures were quite different.14,15,30 These studies attributed the lack of a temperature dependence of SOCs at lower temperatures to LRAT being the dominant sources of the air concentrations. Despite the correlations with temperature for many BFRs in winter in the present study, we think temperature may explain a small fraction at best of the variability of these BFRs in the air. Their occurrence was also unlikely to be a result of LRAT because of the obvious presence of local sources. Instead, we speculated that the elevated concentrations were due to the increasing e-waste recycling activities in winter of the sampling year, which resulted in an increased emission of pollutants. Similar CC plots for PBDEs have also been observed in Chilton, UK, where the high concentrations in winter resulted from seasonal combustion sources.20 At the rural site, BFR concentrations had significant temperature dependence for di-, tri, and tetra-BDEs and PBT but were weak or lacking in temperature dependence for other BFRs (SI Table S3 and Figure S5). The slopes of the CC plots (
7185 ( 2500) were significantly flatter than those observed at the e-waste site. A clear decline in slope with increasing distance from the suspected sources have been observed for PCBs.14 A flat slope or low temperature dependence indicates that regional or longrange atmospheric transport controls atmospheric levels at a sampling site.14,15 The e-waste and urban areas are the main sources of BFRs in the rural air as indicated in our recent finding.9 Gas-Particle Partitioning. Gas-particle partitioning is an important process governing the atmospheric fate of SOCs. 8822

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Figure 3. Clausius
Clapeyron (CC) plots of BFRs at the e-waste site. Data of all samples from September 2007 and a sample from February 2008 were excluded because of the exceptionally low and high concentrations, respectively. The open circles represent data from winter.

The partitioning is usually defined by the partition coefficient (KP) KP ¼ ðF=TSPÞ=A

ð2Þ

where F and A are the particle-bound and gaseous concentrations (pg/m3), respectively, and TSP is the concentration of total suspended particles (μg/m3). Two mechanisms (adsorption onto the particle surface and absorption into the organic matter of particles) have been used to interpret the partitioning, both of which lead to a linear relationship between log KP and log PoL: log KP ¼ mlog PLo þ b

ð3Þ

where PoL is subcooled liquid vapor pressure.31,32 Although it has been suggested that the slope m should be near
1 for true equilibrium partitioning for both adsorption and absorption processes, deviations of m for field data do not necessarily indicate nonequilibrium conditions. Instead, the slopes for a given set of compounds rely largely on sorption properties of particles, ambient temperature, and relative humidity.33 The relationships between log KP and temperature-corrected log PoL were investigated separately for PBDEs from different sampling events at the two sites (Table 2 and SI Figures S6
7). The slopes of Junge-Pankow plots ranged widely from
0.59 to
1.29 at the e-waste site. The influence of temperature and

relative humidity on the varied slopes was evaluated by investigating the correlations between them. The slopes correlated positively with temperature (p = 0.04) (slopes tended to be flat with increasing temperature) but not with relative humidity (SI Figure S8). While possible blow-off sampling artifact could cause this relationship with temperature,34 a likely interpretation for this is that more BFRs with a high vapor pressure would volatilize into the air as ambient temperature increased, and their subsequent partitioning onto/into particles would make the slopes flatter. This interpretation also implies a nonequilibrium of the partitioning. However, temperatures explained only ∼36% of the total variation of the slopes. Another factor was probably attributed to different sorptive properties of the atmospheric particles as a result of their various origins at this site. The particles could originate from e-waste shredding and burning, resuspension of soil and dust, and secondary aerosols formed in the atmosphere. The observed slopes at the e-waste site were not able to indicate adsorption or absorption mechanisms.33 Pankow calculated that intercept values between
7.3 and
8.9 would specify absorptive partitioning. 31 The intercepts b (
4.42 to
7.72) were mostly outside this range suggesting a primary adsorption mechanism. The slopes (
0.23 to
0.80) for data from the rural site clearly deviated from
1 (except for those with no linear relationships for three events), indicating that absorption into 8823

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Table 2. Slopes (m), Intercepts (b), r2, p-Values, and Number of Data Points (n) of the Linear Regression of Log KP (m3/μg) versus Log PoL (Pa) of PBDEs in Different Sampling Eventsa sampling event

e-waste site m ( SE

b

b ( SE

rural site 2

r

p-value

n

m ( SE

b ( SE

r2

p-value

n

July 2007


0.83 ( 0.05


5.80 ( 0.15

0.82