Long-term analysis of a full-scale activated sludge wastewater treatment system exhibiting seasonal biological foaming

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Long-term analysis of a full-scale activated sludge wastewater treatment system exhibiting seasonal biological foaming Dominic Frigona,1, R. Michael Guthrieb, G. Timothy Bachmanb, James Royerb, Barbara Baileyc, Lutgarde Raskina,,2 a

Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, USA Urbana-Champaign Sanitary District, 1100 East University Ave., Urbana, IL 61801, USA c Department of Statistics, University of Illinois at Urbana-Champaign, 101 Illini Hall, 725 South Wright St. Champaign, IL 61820, USA b

ar t ic l e i n f o

A B S T R A C T

Article history:

The seasonal accumulation of biological foam on the activated sludge system of the

Received 3 June 2005

Urbana-Champaign Sanitary District Northeast (UCSD-NE) wastewater treatment plant

Received in revised form

was investigated over an 8-year period by statistical analyses including path analysis,

7 December 2005

multivariate regression, and principal component analysis. Results of these analyses

Accepted 8 December 2005

suggested that variation in the activated sludge reactor temperature and the use of a

Available online 3 February 2006

stream bypassing the primary clarifier were the two main factors determining the observed

Keywords:

temporal foam profile. Characterization of the primary clarifier influent and effluent

Foaming

suggested the involvement of high lipid loading rates from the bypass stream in foam

Gordonia amarae

accumulation. In light of these results, it is hypothesized that increasing temperatures and

Nocardia

lipid loading rates are responsible for foam formation through the same mechanism: the

Lipids

foam-forming microbial population is specialized in consuming lipids, substrates classified

Fat

as slowly degradable. When the temperature increases, the rate of lipid hydrolysis becomes

Operational temperature

sufficiently high for this population to become abundant, accumulate on the surfaces of the aeration basins, and cause biological foaming. & 2006 Elsevier Ltd. All rights reserved.

1.

Introduction

Biological foaming in activated sludge wastewater treatment systems can be described as the formation of a scum layer on the surfaces of aeration basins and secondary clarifiers due to the presence of large quantities of hydrophobic filamentous (Jenkins et al., 1993; Soddell, 1999) and possibly non-filamentous microorganisms (Davenport and Curtis, 2002; Klein, 2003;

Soddell, 1999). This problem is widespread around the world, and 20–60% of wastewater treatment plants experience biological foaming from time to time (Pitt and Jenkins, 1990; Pujol et al., 1991; Seviour et al., 1990, 1994). Several mycolic acid containing actinomycetes (hereafter referred to as mycolata) have been found in biological foam (Goodfellow et al., 1996) and they are believed to be the main group of microorganisms responsible for stable foam accumulation

Corresponding author. Tel.: +1 734 647 6920; fax: +1 734 763 2275.

E-mail addresses: [email protected] (D. Frigon), [email protected] (L. Raskin). 1 Current address: Department of Microbiology and Immunology, University of British Columbia, 1365 - 2350 Health Sciences Mall, Vancouver, BC, Canada V6T 1Z3. 2 Current address: Department of Civil and Environmental Engineering, University of Michigan, 107 EWRE Bldg, 1351 Beal Ave., Ann Arbor, MI 48109-2125, USA. 0043-1354/$ - see front matter & 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2005.12.015

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991

Nomenclature

x¯ y;x¯ ; x¯ y;s vectors of yearly means calculated with the x¯ w

Bold characters vector of data over time Italics characters name of a variable n number of observations in a vector vector of weekly means calculated with the raw x¯ w

sy;x¯ sy;s vectors of yearly standard deviation calculated

and sw vectors, respectively

sw

data vector of weekly standard deviations calculated with the raw data

with the x¯ w and sw vectors, respectively

Miny;x¯ ; Maxy;x¯ vectors of yearly minima and maxima, respectively, calculated with the x¯ w vector

x¯ all;x¯ ; x¯ all;s overall means calculated with the x¯ w and sw vectors, respectively.

sall;x¯ ; sall;s overall standard deviations calculated with the x¯ w and sw vectors, respectively.

(Soddell, 1999). Within this group, Gordonia amarae, Skermania piniformis, and Rhodococcus rhodochrous are the main organisms previously identified in foaming microbial communities. Despite major improvements in our ability to identify and quantify foam-causing microorganisms due to the development of molecular biology tools (Goodfellow et al., 1996; Soddell, 1999), the underlying mechanisms leading to the formation of biological foam remain ambiguous and illusive. As a result, treatment plant operators often discover that their systems are prone to the accumulation of biological foam only after startup. Recently, it was demonstrated that the sole presence of mycolata biomass above a certain threshold is sufficient to render activated sludge conducive to produce biological foam (Davenport et al., 2000; de los Reyes III and Raskin, 2002). This explains why anaerobic digesters that stabilize waste activated sludge from foaming activated sludge systems often foam themselves (de los Reyes et al., 1998). However, the underlying mechanisms determining the level of mycolata in activated sludge remain to be clarified. In the literature, two observations dominate the debate. First, temperature apparently determines to some degree the propensity of a system to accumulate biological foam, since it was observed that the accumulation of foam due to the overabundance of mycolata occurs more frequently during the summer and in warmer climates (Pipes, 1978; Pitt and Jenkins, 1990; Soddell and Seviour, 1994; Soddell, 1999; Oerther et al., 2001; de los Reyes III and Raskin, 2002). Second, activated sludge systems receiving high concentrations of hydrophobic substrates appear to be more likely to accumulate biological foam (Pipes, 1978; Forster, 1992; Franz and Matsche´, 1994). These field observations are consistent with the demonstrated ability of mycolata to grow well on lipids (Kurane et al., 1986; Blackall et al., 1991; Khan and Forster, 1991; Soddell and Seviour, 1996) and their possible competitive advantage in gaining access to hydrophobic substrates (Lemmer, 1986; Soddell, 1999). Thus, it is likely that elevated temperatures and high lipid loading rates provide environments conducive for the accumulation of substantial levels of mycolata. The current study presents the history of biological foaming at the Urbana-Champaign Sanitary District Northeast (UCSDNE) wastewater treatment plant (Urbana, IL) and its relationship to plant operation. This treatment plant contains an activated sludge system that exhibits biological foam accumulation mainly due to G. amarae during the summer season (Oerther et al., 2001; de los Reyes III and Raskin, 2002). The goal of this study was to identify the factors contributing to

the seasonal appearance of the biological foam. A three-step strategy was used to reach this goal. In Step 1, a series of hypotheses that could explain the seasonal foaming phenomenon were formulated. In Step 2, a data set describing the foam intensity and the operation of the wastewater treatment system over a period of 8 years was constructed. The relative merit of each hypothesis was evaluated by statistically analyzing the data set following three approaches: path analysis, multivariate regression analysis using a temporal scale consisting of weekly intervals, and correlation analysis using a temporal scale divided in yearly intervals. Finally, the important operational parameters were further characterized to determine their specific impact on the system. In Step 3, the conclusions from Step 2 were confirmed by operating laboratory-scale reactors.

2.

Materials and methods

2.1.

Plant description and operation

2.1.1.

Plant description

A schematic of the UCSD-NE wastewater treatment plant (Urbana, IL) in normal operation is presented in Fig. 1. This plant treats wastewater from municipal and industrial (mainly food industries) sources. The two types of wastewater are treated separately throughout the preliminary and primary treatment steps. Then, the primary effluent from the industrial wastewater treatment train is treated by a trickling filter, the effluent of which is combined with the primary effluent from the municipal wastewater treatment train. The resulting mixture is fed to an activated sludge reactor. During periods of high flow (e.g., during rainfall events) or low loading rates (e.g., during the summer when the majority of the University of Illinois students are absent), a portion of the flow from the municipal collector is being diverted to bypass the municipal primary clarifier. The activated sludge reactor is usually operated in contactstabilization configuration (Fig. 1). However, for part of the summer, the reactor is operated as a conventional (plug-flow) system using three (or four, during the transition periods) of the four aeration basins (while one is drained for maintenance purposes). These changes greatly affect the distribution and the concentration of solids in the system. A description of the other treatment units at this plant was provided by Oerther et al. (2001).

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Secondary Clarifier

Aeration 3

Aeration 2

Primary Clarifier

Aeration 1

Municipal

Reaeration

Activated Sludge System

Bypass

Industrial

Primary Clarifier

Trickling Filter

Fig. 1 – Schematic of the primary and secondary wastewater treatment system (tertiary wastewater treatment and sludge treatment are not shown) at the Northeast Plant of the Urbana-Champaign Sanitary District (Urbana, IL). The schematic is shown for operation at normal flow conditions and the activated sludge system is shown in contactstabilization mode.

2.1.2.

Operators’ observations foaming index

The UCSD-NE wastewater treatment plant maintains a written weekly record of visual observations of the surface area of the aeration tanks and secondary clarifiers. For the period 1993–2000, these descriptive comments were transformed into an operators’ observations foaming index (OOFI). Typical comments and the corresponding OOFI values were: no foam (0), little foam (1), foam in re-aeration tank of contactstabilization configuration (2), foam throughout aeration tanks (3), floating material on secondary clarifiers (4). The levels of mixed liquor suspended solids (MLSS) in the re-aeration tank are almost four times higher than in the other aeration tanks. As a result, the foam usually appears first on the re-aeration tank, which justifies the use of a lower OOFI when foam is only present in the re-aeration tank. Because it was assumed that the foaming situation did not change rapidly, comments such as ‘‘better than last week’’ or ‘‘worse than last week’’ were incorporated by subtracting or adding 0.25, respectively, to the previous OOFI value. However, the OOFI was not allowed to become negative. Finally, the OOFI could not be derived for 1997 because the operator’s journal had been lost for this year. Fortunately, the timing of the foaming event was given by (Oerther et al., 2001) and the OOFI values were arbitrarily set to 3 during the foaming event and set to 0 during the rest of 1997.

2.1.3.

Data set describing the wastewater treatment plant

Performance and operational conditions of the plant were described by 58 parameters (Table 1), which were measured several times per week according to Standard Methods (American Public Health Association, 1992), or were computed from these measurements as follows: 1. In order to describe the activated sludge configuration in a quantitative way (contact-stabilization vs. conventional,

and four tanks vs. three tanks), a reactor configuration variable, representing the proportion of the solids inventory in the first aeration tank, was calculated. A simple flow balance analysis of the system considering the return activated sludge flow, the activated sludge reactor influent flow, and the reactor configuration was used to determine the distribution of the solids inventory through the reactor. 2. The measured MLSS concentration (MLSSMEASURED) in the aeration tanks is a function of the solids inventory and the activated sludge system configuration. The effect of the configuration on the measured MLSS level in the first aeration tank (Fig. 1) was removed as follows: i. Linear regression: MLSSMEASURED,i ¼ a0+a1
configurationi+ei where a’s are regression parameters, ei is the residual term, i refers to the ith observation. ii. New variable: MLSSCORRECTED,i ¼ MLSSMEASURED,ia1
configurationi. 3. The oxygen uptake rate (OUR) was obtained by performing the following mass balance on the COD fed to the activated sludge (AS) system in 1 day. OUR ¼ biodegradable influent COD=day  biodegradable effluent COD=day  new sludge units ðCODÞ=day þ inert particulate influent COD=day: Since COD was not measured by the UCSD personnel, BOD5 and TSS measurements were used as surrogate measurements. The conversion factors proposed by Grady et al. (1999) were used to convert the measurements to COD units: OUR ¼ AS influent BOD5 loading rate
1:71  AS effluent BOD5 loading rate
1:71  new sludge units ðmassÞ
1:22 þ AS influent TSS loading rate
0:56.

b

Activated sludge effluent NH+4 (as N)

Activated sludge effluent TSS

Activated sludge influent TSS

11 clarifier effluents TSSb

Plant Influent TSS

Activated sludge effluent BOD5

Activated sludge influent BOD5

11 clarifier effluent BOD5b

Influent and effluent concentrations (mg/L) Plant influent BOD5b

Activated sludge influent

Trickling filter (TF) effluent

x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw

x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw

415 414 415 409 415 414 411 404 415 414 415 408 415 412 415 408 415 409

415 415 415 415 415 415 415 415 412 408 415 415

na

170.3 28.5 98.6 11.3 61.9 9.3 4.3 0.9 225.2 65.2 55.4 7.5 70.8 12.5 8.5 2.0 8.0 1.4

57909 7170 25300 2736 29791 4041 3388 2285 19137 3519 49932 4305

x¯ all

56.6 26.2 25.3 6.9 17.4 5.7 1.6 1.0 75.9 54.3 8.8 5.2 23.1 12.7 3.6 1.9 3.6 0.8

16178 7749 8553 2168 9193 2775 6536 3716 6869 2476 8594 2698

sall

Precipitation (mm/day)

Minimum daily atmospheric temperature (1C)

Municipal 11 clarifier bypass usage (Closed:0; Open:1) Weather conditions Maximum daily atmospheric temperature (1C)

Activated sludge effluent TSS

Activated sludge influent TSS

Municipal 11 clarifier bypass TSS

TF effluent TSSc

Municipal 11 clarifier effluent TSS

Plant influent TSS

Activated sludge effluent BOD5

Activated sludge influent BOD5

Municipal 11 clarifier bypass BOD5

TF effluent BOD5c

Municipal 11 clarifier effluent BOD5

Influent and effluent loading rates (metric tons/day) Plant influent BOD5

Process parameter description (unit)

x¯ w sw x¯ w sw x¯ w sw

sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw

x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w

16.4 4 5.7 3.4 3.2 5.3

4.03 1.69 0.29 1.24 0.46 0.61 0.49 3.58 0.71 0.43 0.11 0.40

414 415 407 414 406 415 415 415 412 415 408 415

415 415 415 415 415 414

9.38 1.69 2.91 0.38 0.19 0.48 0.43 0.3 3.06 0.52 0.22 0.05 12.68

x¯ all

415 414 415 408 414 406 415 415 415 414 411 404 415

n

10.9 1.8 9.6 1.6 5.7 5.8

2.93 0.52 0.21 0.73 0.61 1.18 0.82 1.27 0.74 0.19 0.11 0.49

2.76 1.37 0.84 0.26 0.83 0.41 0.84 0.49 0.85 0.31 0.09 0.06 4.21

sall

WAT E R R E S E A R C H

Municipal 11 clarifier bypass

Municipal 11 clarifier effluent

Industrial 11 clarifier effluent

Influent and effluent flow rates (m /day) Plant influent

3

Process parameter description (unit)

Table 1 – Performance and operational conditions of the UCSD-NE wastewater treatment plant during 1993–2000

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993

4681 269 1057 112 797 112 75.3 1.4 18.5 0.6 7.44 0.04 4.07 0.25 0.32 0.06 17657 1143 2.26 0.16 1.78 0.13 0.15 0.01 0.26 0.02 0.47 0.25

946 198 227 60 206 60 5.0 3.4 3.9 0.4 0.16 0.02 0.69 0.17 0.15 0.04 4441 985 0.40 0.09 0.29 0.07 0.05 0.01 0.03 0.02 0.24 0.17

sall

Configuration (proportion of sludge inventory)

New sludge units (as COD; metric tons/day)

Oxygen supply efficiency in aeration tank 3 (metric tons/day/mg O2/L)

Oxygen supply efficiency in aeration tank 2 (metric tons/day/mg O2/L)

Oxygen supply efficiency in aeration tank 1 (metric tons/day/mg O2/L)

Oxygen supply efficiency in reaeration tank (metric tons/day/mg O2/L)

Dissolved oxygen in aeration tank 3 (mg/L)

Dissolved oxygen in aeration tank 2 (mg/L)

Dissolved oxygen in aeration tank 1 (mg/L)

Dissolved oxygen in reaeration tank (mg/L)

Oxygen uptake rate (OUR, metric tons/day)

Total Air (103 m3/day)

Air/BOD5 (m3/kg/day)

Process parameter description (unit)

x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw

415 413 415 414 410 401 395 390 410 407 399 397 396 392 386 320 402 336 391 324 387 327 415 415 415 415

n

118 21 370 22 3.14 1.53 0.85 0.29 1.57 0.64 2.33 0.83 3.42 0.94 7.24 4.91 5.37 3.95 2.09 1.61 1.47 1.24 3.81 1.12 0.183 0.01

x¯ all

37 13 324 19 1.78 1.03 0.83 0.45 1.17 0.49 1.02 0.5 1.3 0.55 7.45 5.08 8.41 5.81 2.21 2.02 1.87 2.76 0.96 0.61 0.069 0.013

sall

Parameters are grouped as influent and effluent flows, concentrations, and loading rates, weather conditions, and activated sludge operating conditions. a n: number of observations b The industrial and municipal streams were combined according to the flow ratio prior to the measurements. c The BOD5 and TSS concentrations in the trickling filter (TF) effluent stream fed to the activated sludge reactor were not measured, but were estimated by mass balances. Note that the results from the mass balances were allowed to become negative.

Biomass yield (kg COD/kg COD)

RAS flow/[RAS flow+activated sludge influent flow]

Sludge volume index (SVI; mL/g)

328 327 415 415 415 415 376 248 415 415 415 414 414 415 415 413 415 414 415 414 415 415 415 414 415 415 410 401

x¯ all

WAT E R R E S E A R C H

Hydraulic retention time (HRT): aeration tanks (hour)

Hydraulic retention time (HRT): aeration and clarifiers (hour)

Return activates sludge (RAS) flow (m3/day)

x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw x¯ w sw

na

994

F:M Ratio (g BOD5 / g MLSS)

Dynamic Sludge Age (day)

pH

Reactor temperature (1C)

Volatile suspended solids (VSS, %)

MLSSCORRECTED in aeration tanks (mg/L)

MLSSMEASURED in aeration tanks (mg/L)

Activated sludge Reactor operating conditions MLSSMEASURED in reaeration tanks (mg/L)

Process parameter description (unit)

Table 1 (continued )

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4. Palm et al. (1980) showed that low dissolved oxygen (DO) bulking was not only determined by the DO concentration, but also by the ratio DO:OUR. Hereafter, we refer to this ratio as the oxygen supply efficiency. It was calculated for each tank from the daily average DO concentration and the OUR (above). 5. The biomass yield was calculated from the quantities defined above: Biomass yield ¼ 1  OUR=biodegradable influent COD: 6. The Municipal 11 clarifier bypass usage (bypass usage for short) was a binary coded variable calculated from the BOD5 loading rates to the activated sludge reactor and from the bypass stream. If the weekly mean ðx¯ w Þ of the BOD5 loading rate from the bypass stream was higher than 4% of the BOD5 loading rate to the activated sludge reactor, the variable was coded ‘1’, else, it was coded ‘0’. In order to match the temporal scale of the operational data with the scale of the OOFI observations, the vectors of operational data were reduced to vectors of weekly means ðx¯ w Þ and vectors of weekly standard deviations (sw). Thus, 115 weekly variables with a maximum of 415 observations (the first and last weeks were not complete and were not included in the analysis) were generated from the 58 original parameters (note that the bypass usage was already a weekly parameter, so the sw is not available for this parameter). For the vectors of weekly observations of each variable, outliers were arbitrarily defined as values being further from the overall vector average ðx¯ all Þ by more than four times the overall vector standard deviation (sall), or as being more than ten times the overall vector average ðx¯ all Þ. Outliers were removed from the vectors. The remaining weekly observations formed the basic data set.

2.1.4. Wastewater composition and primary clarifier efficiency Grab samples from the municipal primary clarifier influent and effluent were obtained on the same day approximately 90 min apart (approximating the mean hydraulic retention time (HRT) in the primary clarifier at the time of sampling). Soluble fractions were prepared by filtering the samples through a 0.45-mm filter. Total and soluble COD values were determined according to the COD reflux method (American Public Health Association, 1992). Total and soluble reactive sugars concentrations were determined using the anthrone method (Daniels et al., 1994). Soluble proteins were determined using the Biorad DC protein assay kit (Biorad, Hercules, CA) according to the manufacturer’s instructions for low protein concentration microplate assays. For the particulate protein fraction, particulate material was harvested by centrifugation of a 1-mL sample at 16,000g for 5 min. The pellet was resuspended in 0.5 mL of Reagent A’ of the Biorad DC protein assay kit and incubated at 60 1C for 2 h. The protein concentration in this final mixture was determined by transferring a 38.5-mL aliquot to 250 mL of Reagent B (Biorad DC protein assay kit) and following the manufacturer’s instructions for microplate assays. Bovine serum albumin was used as protein standard.

995

Lipids were extracted three times from 300-mL sample using 10 mL of extraction solvent (hexane:methyl-tertiarybutyl ether; 80:20, v:v) according to the standard protocol for fats, oils and greases analysis (American Public Health Association, 1992). The solvent from the pooled extracts was evaporated and the fatty acids in the lipids were esterified using alkaline borontrifluoride (BF3)-methanol in test tube reflux according to (American Oil Chemists’ Society, 1998). Fatty acid methyl esters thus prepared were recovered in 2 mL of heptane and analyzed using a gas chromatograph (model 5890, Agilent Technology, Palo Alto, CA) equipped with a 50-m (0.25-mm internal diameter) CP-SIL 88 column (Varian, Palo Alto, CA) and a flame ionization detector. The carrier gas was helium. The flow rate was kept constant at 27 cm/s through each run while the temperature was increased according to the following program: 110 1C for 4 min, +10 1C/min until 160 1C, +3 1C/min until 185 1C, +8 1C/min until 225 1C, and 225 1C for 5 min. Trinonadecanoin (Nu-Check Prep Inc., Elysian, MN) was used as internal standard for quantification.

2.2.

Laboratory-scale reactors

2.2.1.

Reactor operation

Sequencing batch reactors (SBR) consisting of 250-mL Erlenmeyer flasks filled with 100-mL of activated sludge mixed liquor were maintained at 25 1C on a shaking table operated at 250 rpm. The mixed liquor was settled twice per day in a graduated cylinder for 1 h and 80 mL of supernatant was withdrawn. After settling, the remaining 20 mL of sludge was returned to its corresponding flask and 80 mL of fresh feed was added to the flask. Once per day, 25 mL of mixed liquor was wasted. This mode of operation provided an HRT of 15 h and a nominal solids retention time (SRT) (VReactor/QWaste) of 4 days. The feed for each flask either consisted of a sub-sample of a primary influent grab sample, a primary effluent grab sample, or a 50/50 (v/v) mixture of the two grab samples. In a fourth set, the flasks were fed Stanier’s mineral medium (KH2PO4 2.84 g/L, Na2HPO4 2.72 g/L, (NH4)2)SO4 1 g/L, Huntner mineral base [containing: Mg, Ca, Mo, Fe, Zn, Mn, Cu, Co, B] 20 mL; Stanier et al., 1966) containing tripalmitin (glyceryl tripalmitate; 0.41 g/L) as the sole carbon and energy source. Three independent flasks were operated for each conditions.

2.2.2.

RNA extraction and membrane hybridization

RNA was extracted from the laboratory-scale reactors’ biomass by a low-pH hot-phenol bead beating procedure (Raskin et al., 1995; Stahl et al., 1988) with phenol equilibrated to pH 5.1 (Sigma, St-Louis, MO), two bead-beating periods of 2 min each, and two periods of 10 min incubation at 60 1C after each bead-beating step. After extraction, RNA was precipitated from the resulting aqueous phase by using ammonium acetate and ethanol. The RNA pellet was resuspended in distilled deionized water and kept at 80 1C. RNA samples for membrane hybridization were slot blotted (100 ng of RNA per slot) onto a Magnacharge membrane (Osmonics Inc., Minnetonka, MN) after a 10 min denaturation at room temperature with 1.5% (final concentration) glutaraldehyde (Raskin et al., 1994). Hybridization was performed at 40 1C following the protocol of (Raskin et al., 1994) except for the use of the Perfect HybTM hybridization buffer (Sigma,

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St-Louis, MO). A wash buffer containing 1
saline sodium citrate buffer (Sambrook et al., 1989) and 1% sodium dodecyl sulfate was used in two non-stringent washes of 1 h each at 40 1C and a stringent wash of 30 min. The probes were used to target members of (1) the genus Gordonia (S-G-Gor-0596-a-A22, TGCAGAATTTCACAGACGACGC, stringent wash: 54 1C; de los Reyes et al., 1997), (2) the mycolata (S-*-Myb-0736-b-A-22, CAGCGTCAGTTACTN5CCCAGAG, stringent wash: 51 1C; de los Reyes et al., 1997), and all organisms (S-*-Univ-1390-a-A-18, GACGGGCGGTGTGTACAA, stringent wash: 44 1C; Zheng et al., 1996).

2.3.

Statistical analysis

2.3.1.

Path analysis

The goal of path analysis is to decompose the observed matrix of correlations in direct effects, indirect effects, and fortuitous correlations (Legendre and Legendre, 1998). Here, only the variables identified to describe the hypotheses (below) were used. At the start of the analysis, a network of direct effects relating the operational variables to each other and to the OOFI was assumed. This assumed network was then used for the decomposition of the correlation matrix. Possible reaction delays (lags) of the OOFI were considered by adjusting the temporal alignment of each variable to the OOFI such that their correlation was maximized. The path analysis was performed using the CALIS procedure of SAS/STAT v.8.2 (SAS Institute Inc., 2001). Missing values in the basic data set (less than 5% of observation for any variable) were filled by linear interpolation prior to analysis. The statistical significance of a calculated direct effect was tested by the t-test and the validity of the entire network was tested by a w2-test comparing the expected correlations and the observed correlations.

2.3.2.

2.3.4.

ANOVA

Analysis of variance of the laboratory-scale reactor data was performed with the GLM procedure of SAS/STAT v.8.2 (SAS Institute Inc., 2001).

Multivariate analysis

In order to identify important parameters affecting the OOFI without a priori hypotheses, the OOFI observations were regressed over a subset of selected operational variables. In order to account for possible delays (lags) in the response of the OOFI, changes in operational variables were considered in the model at lag 0, 1, 2, and 3 weeks. The regression analysis was performed using the REG procedure of SAS/STAT v.8.2 (SAS Institute Inc., 2001) and the variables were selected using the MINR algorithm (step-wise algorithm sequentially entering the variable that produce the smallest increases in R2 and testing at each step if switching variables already included in the model could also increase R2) until the Schwartz–Bayesian criterion (SBC) reached a minimum. The underlying structure (interactions between independent variables to fit the dependent variable) of the linear regression model was visualized by principal component analysis (PCA; Legendre and Legendre, 1998) performed on the correlation matrix using the PRINCOMP procedure of SAS/STAT v.8.2 (SAS Institute Inc., 2001). For all these analyses, missing values in the basic data set were filled by linear interpolation.

2.3.3.

standard deviations (sw) such that 230 (115 weekly variables
2 operators; operators: x¯ and s) variables with seven observations were obtained. Note that the year 1997 was not included in the analysis since the OOFI was not available. Correlations were calculated between these 230 variables and the OOFI using the CORR procedure of SAS/STAT v.8.2 (SAS Institute Inc., 2001). A few variables identified by this procedure could be linked by linear regression modeling to the use of the bypass stream. The regression model tested the effect of the bypass usage variable (independent variable) on an identified variable (dependent variable) by considering the possible effects of reactor temperature (independent variable) and autocorrelation of the residuals. The autocorrelation structure of the ‘‘bypass usage+reactor temperature’’ regression residuals was determined according to the Box–Jenkins method (Box et al., 1994) using the ARIMA procedure of SAS/ETS v.8.2 (SAS Institute Inc., 2001). The bypass usage and reactor temperature effects were then tested using the AUTOREG procedure of SAS/ETS v.8.2 (SAS Institute Inc., 2001). The identified variables that were significantly affected by the bypass usage were further investigated by determining the contribution of their respective upstream variables. For example, the sw of the activated sludge influent BOD5 loading rates was modeled using the sw of (1) Municipal 11 clarifier bypass BOD5 loading rates and (2) Municipal 11 clarifier effluent BOD5 loading rates , and (3) trickling filter effluent BOD5 loading rates. The contributions of upstream variables were determined by linear regression without an intercept. The regression models also considered the autocorrelation structure and were evaluated as before.

Yearly averages trend analysis

Vectors of yearly means ðx¯ y Þ and standard deviations (sy) were calculated from the vectors of weekly means ðx¯ w Þ and

2.4.

Hypotheses

Several mechanisms can be hypothesized to explain the seasonal biological foaming events at the UCSD-NE plant. Only those hypotheses directly evaluated in this study are presented below and are summarized in Table 2, allowing them to be empirically tested. Note that additional hypotheses complementing the ones presented here were presented elsewhere (Frigon, 2005), but they were rejected after statistical analyses. Hypothesis 1. Changes in activated sludge system configuration determine the OOFI profile. For maintenance purposes, the activated sludge system configuration is changed from a contact-stabilization to a conventional configuration for some time during each summer. Because the solids concentration increases in the aeration tanks when the activated sludge system configuration is changed, the increase in foam coverage during the summer as indicated by an increase in the OOFI may be the result of an increase in the concentration of foam-forming microorganisms to above the foam-formation threshold level (de los Reyes III and Raskin, 2002) in every aeration tank. Furthermore, the activated sludge system configuration may have an effect on the growth of

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Table 2 – Hypotheses and respective main effect variables to explain seasonal biological foaming at the UCSD-NE wastewater treatment plant during 1993–2000 Hypothesis

Variable determining OOFI

(1) Activated sludge system configuration

x¯ w of configuration

(2) Influent composition and concentration

x¯ w of activated sludge influent BOD5 loading rates x¯ w of Mun. 11 clarifier bypass BOD5 loading rates x¯ w of biomass yield

(3) MLSS concentration

x¯ w of MLSSCORRECTED x¯ w of dynamic sludge age x¯ w of F:M ratio

(4) Winter temperature lower than minimum growth temperature of foamformers

x¯ w of reactor temperature Miny;x¯ of reactor temperature

(5) Oxygen transfer limitations x¯ w of dissolved oxygen in aeration tank 1 x¯ w of oxygen supply efficiency in aeration tank 1 (6) Foam-formers are SDS consumers / SDS hydrolysis rate determines foamformer level

foam-forming microorganisms. Hypothesis 1 predicts that the reactor configuration variable (Table 1) determines the OOFI profile. Hypothesis 2. Changes in the types and concentrations of organic compounds in the activated sludge system influent over the year determine the OOFI profile. The total concentration of organic compounds can be estimated from the BOD5 measurements. However, direct information on the types of organic compounds is not available, and is difficult to obtain a posteriori. Two strategies can be used to gain partial information on the activated sludge influent composition. First, tracking the amount of BOD5 and TSS contributed by the different streams entering the activated sludge system can be used as a surrogate measure for the influent composition since the basic composition of the various streams is presumably different due to different levels of ‘‘pretreatment’’ (raw wastewater, primary clarifier effluent, trickling filter effluent). Because (1) very little BOD5 is contributed by the trickling filter effluent and (2) the BOD5 entering the activated sludge reactor comes principally from the primary clarifier effluent (Table 1), only the variables activated sludge influent BOD5 loading rates and municipal 11 clarifier bypass BOD5 loading rates were considered. Second, variation in influent composition can be detected by monitoring the biomass yield. The microbial consumption of different organic compounds results in different biomass yields. By extension, wastewaters of different composition produce different biomass yields. Hypothesis 2 predicts that either the contributions of the different streams entering the activated sludge system or the biomass yield variable determines the OOFI profile. Hypothesis 3. Changes in MLSS concentration determine the OOFI profile. As mentioned before, the foam-forming population needs to be present in the mixed liquor above a certain

x¯ w of reactor temperature

threshold before foam starts accumulating on the surfaces of activated sludge reactors (de los Reyes III and Raskin, 2002). Due to the frequent occurrence of strong thunderstorms during the summer, the UCSD personnel aims to maintain a higher MLSS level during the summer to prevent negative effects associated with higher flow rates during storms. Higher MLSS concentrations increase the probability that mycolata levels reach the foam-formation threshold. The practice of maintaining a higher MLSS level may increase the SRT in the summer because the sludge yield is lower (higher hydrolysis and biomass decay rates). Changes in SRT and food to microorganisms (F:M) ratio (as it relates to SRT) are well known to change the selection of microorganisms growing in activated sludge (Jenkins et al., 1993). Thus, Hypothesis 3 predicts that OOFI changes in relation to either (1) the MLSSCORRECTED, (2) the dynamic sludge age (the variable measured by the UCSD personnel instead of SRT), or (3) the F:M ratio. Hypothesis 4. The winter temperature is outside the growth range of the foam-causing microorganisms. The minimum growth temperatures for several Gordonia spp. (the main foam-forming population at the UCSD-NE plant; Oerther et al., 2000; de los Reyes III and Raskin, 2002) were determined to be between 10 and 15 1C (Soddell and Seviour, 1995). Thus, cold weather may prohibit the growth of the Gordonia population. This hypothesis predicts that the reactor temperature is the main variable affecting the OOFI. Hypothesis 5. Oxygen transfer limitations at higher temperatures are responsible for a selective advantage of the foam forming population in the summer. As the temperature increases, oxygen supply becomes diffusion limited. The hydrophobic foam-formers then partition to the air bubble interface gaining primary access to the diffusing oxygen

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providing them with a selective advantage. This hypothesis predicts that episodes of high OOFI should be closely related to periods of low DO or low oxygen supply efficiency, both of which limit biomass activity. Hypothesis 6. Changes in the lipid hydrolysis rate determine the OOFI profile. Foaming has been associated with high fat loading rates (Lemmer, 1986; Khan and Forster, 1991; Forster, 1992). Furthermore, the ability of the foam-forming bacteria to consume hydrophobic substrates has been demonstrated in the laboratory (Iwahori et al., 1995; Soddell and Seviour, 1996) and by modeling exercises (Kappeler and Gujer, 1994). Thus, we hypothesize that the foam-forming population is specialized in consuming lipids. As lipids are slowly degradable substrates (SDSs) (Christ et al., 2000), their degradation can be modeled using a hydrolysis function (Kappeler and Gujer, 1994; Gujer et al., 1999). Following these modeling concepts, heterogeneous hydrolysis (encompassing true enzymatic hydrolysis and simple mass transfer) would be the step determining the abundance of the microbial population. Consequently, an increase in temperature during the summer months would lead to an increase in the rate of hydrolysis and, ultimately, in the abundance of the foam-formers. Unfortunately, there are no direct parameters available that can be used to characterize the factors consistent with this hypothesis. Thus, this hypothesis predicts that reactor temperature directly determines the OOFI profile.

2.4.1.

Temperature effect

Comparing the OOFI and the reactor temperature profiles (Fig. 2a and c) intuitively suggests a link between these variables. While each one of the six hypotheses discussed above is indeed related to temperature, the hypotheses provide more than a simple association; they try to determine the underlying mechanism of this association. As discussed above, the association between OOFI and reactor temperature is either due to changes in the activated sludge system operation (Hypotheses 1–3), or due to temperature driven physicochemical effects or kinetics (Hypotheses 4–6). The goal of this study was to evaluate the relative merit of each hypothesis in order to identify the factors contributing to the appearance of seasonal biological foaming. In a first step, it needs to be ascertained whether the temperature effect is direct (arguing for Hypotheses 4 and 6) or indirect (via one of the variables characterizing Hypotheses 1–3 and 5). In the scenario of temperature showing an important direct effect on the OOFI, Hypotheses 4 and 6 could probably be differentiated by the their respective temporal behavior. Hypothesis 6 describes an immediate response of the foam-forming population to changes in temperature. On the other hand, Hypothesis 4 predicts that foaming events end with temperature falling below the minimum growth temperature of the foam-formers, but the prediction for the beginning of a foaming event is not as clear. However, it could be argued that a mild winter would lead to the earlier appearance of foam the next summer. It follows that Hypothesis 4 suggests that the yearly OOFI mean (x¯ y;x¯ of OOFI) would be positively correlated with the yearly minimum reactor temperatures means (Miny;x¯ of reactor temperature).

3.

Results

The OOFI profile of the UCSD-NE wastewater treatment plant between 1993 and 2000 (Fig. 2a) has a number of characteristics useful to decipher the underlying mechanisms responsible for biological foaming. First, the profile exhibits strong seasonality. Every year between 1993 and 1999, foam appeared during mid to late summer and disappeared at the beginning of the winter. Second, two unusual periods with respect to the behavior of the foam profile occurred: (1) The operators observed accumulation of foam in the spring of 1995. (2) No foaming event occurred in the year 2000. These three characteristics should be kept in mind when evaluating the value of the various hypotheses.

3.1.

Path analysis

The goal of this study is to directly test hypotheses that can explain the observed features of the OOFI profiles. The operational variable that seems to correspond to foaming events most of the time is the reactor temperature (Fig. 2a and c). In fact, if a lag (delay in the response) of 3 weeks is considered, the correlation level between the OOFI and reactor temperature is 0.46, the highest of all operational variables. This correlation level quantifies the seasonality of foaming events. However, it does not provide an underlying mechanism as argued in the Hypothesis section. As described above, the hypotheses explaining foam seasonality all suggest some level of correlation between OOFI and reactor temperature. In order to discriminate between these hypotheses we identified intermediate operational variables for each hypothesis and measured the correlation among all these variables. A measured correlation is the sum of three components: direct effect of one variable onto the other (or an interrelationship), indirect effect via an intermediate variable, or a fortuitous correlation. In path analysis, one first describes the possible direct effects of variables on each other. These are represented graphically in Fig. 3 by direct arrows. This description is then used to decompose the correlation matrix and quantify the contribution of direct effects, indirect effects, and fortuitous correlations to the measured correlations. The magnitude of the direct effects are reported in Fig. 3 as coefficients besides the arrow. Finally, an indirect effect is defined by a path of two or more arrows going from one variable to another one. The magnitude of an indirect effect is obtained by multiplying the coefficients found in this path (e.g., the indirect effect of reactor temperature on OOFI via reactor configuration only is 0.073 (0.419
0.174); Fig. 3). The path diagram of Fig. 3 does not contain the variables biomass yield, dynamic sluge age, and F:M ratio even though they were identified as possible intermediate variables in Hypotheses 2 and 3 (Table 2). These variables were excluded from the analysis since the coefficients of their respective direct effects on OOFI were found to be not significant (Po0:05). For the set of remaining variables, the path diagram models the correlation matrix relatively well (w2-test, P40:25). As expected, path analysis (Fig. 3) suggests that reactor temperature had a significant (Po0.05) direct effect on the six

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Fig. 2 – Temporal profiles of the operators’ observations foaming index (OOFI), empirical model predictions, and important operational variables: (a) OOFI (gray bars) and empirical model predictions (line) based on 18 important variables (Table 3). The bars for 1997 are light gray because the operational journal for 1997 was unavailable. The position of the foaming event was determined based on Oerther et al. (2001) and the amplitude (OOFI ¼ 3) is arbitrary. (b) OOFI model predictions when only fitted with the Temperature-cluster variables (T-variables, see Table 3 for variables names, black line, 8 variables) or with the bypass-cluster variables (BP-variables, see Table 3 for names, gray line, 10 variables). C-h) Important operational variables ðx¯ w Þ. The black circles represent parameters on the left y-axis and the gray triangles indicate parameters on the right y-axis.

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Reactor Temperature (Lag: 3 weeks) 0.419 (0.045)

Configuration (Lag: 0 week)

0.105 (0.049) Bypass: 100%

0.8805 (0.061)

MLSSCORRECTED

(Lag: 0 week)

-0.119 (0.060)

DO: 90% DO/OUR: 10%

0.8509 (0.059)

-0.198 (0.044)

0.3086 (0.0476) DO: 60% DO/OUR: 40%

0.9081 (0.063)

0.158 (0.054) DO: 80 % DO/OUR: 20 %

Oxygen Transfer Limitation DO tank 1 (Lag: 1 week) DO/OUR tank1 (Lag: 1 week) DO: 15%

0.174 (0.043)

0.455 (0.04391) Bypass: 47% AS: 53%

0.368 (0.073) DO: 90% DO/OUR: 10%

DO/OUR: 85%

0.188 (0.044)

BOD 5 Loading Rates AS Influent (Lag: 1 week) Municipal 1° Clarif. Bypass (Lags: 1 and 2 weeks) AS: 14%

0.341 (0.047)

0.846 (0.061)

Bypass: 86%

0.142 (0.043)

0.219 (0.044)

0.772 (0.054)

OOFI

Fig. 3 – Path diagram obtained from the analysis of the correlation matrix of the variables identified as possibly determining the OOFI temporal profile. The diagram was obtained by analyzing seven variables adjusted at the lag maximizing their correlation with the OOFI. For simplicity, variables represented a single hypothesis were grouped in the same box. Stand-alone numbers are the magnitude of direct effects. Numbers in brackets are the associated standard errors. Numbers in bubbles are the magnitude of direct effects of the non-defined exogenous variables. Percentages represent the approximate proportion of the direction of an effect when it is going toward or coming from boxes combining several variables. They were calculated from the proportion of the square of the coefficients going to the initial variables. DO/OUR: oxygen transfer efficiency.

remaining intermediate variables. The positive direct effect of reactor temperature on oxygen transfer limitation and MLSSCORRECTED is probably due to the direct effect of temperature on the rates of biochemical reactions. For oxygen transfer limitation (Fig. 2f), a rise in temperature increases the biomass respiration rate, and reduces the oxygen transfer efficiency by decreasing the saturation DO concentration, resulting in an increase in oxygen transfer limitation (Fig. 3). The negative direct effect of reactor temperature on MLSSCORRECTED (Fig. 2g) makes sense since a rise in temperature increases the hydrolysis and decay rates (ASM3; Gujer et al., 1999), resulting in a decrease in sludge yield Fig. 2h). For a constant BOD5 loading rate and a constant SRT, a lower sludge yield leads to a lower sludge inventory and a lower MLSS level. The positive direct effects of temperature on the configuration (Fig. 2c) and the municipal 11 clarifier bypass BOD5 loading rates (Fig. 2d) are mainly due to the seasonality of plant operation. The reactor configuration is changed during the summer for maintenance purposes and the primary clarifier bypass is mostly opened during the summer to maintain the total BOD

loading rate to the activated sludge reactor when University of Illinois students are away. Finally, a comparison of the magnitudes of the direct and indirect effects on OOFI (Fig. 3) indicates that the direct effect of reactor temperature is the largest, suggesting that it contains the highest explanatory power among the variables included in the analysis.

3.2.

Multivariate analysis

The six hypotheses described above were related to nine operational variables and path analysis was used to quantify in the linear regression framework their possible contribution to OOFI. However, one should consider that other operational variables could be more important in determining the OOFI than the ones identified a priori. Such variables could be related to mechanisms not included in the hypotheses. In order to address this limitation, the entire data set was screened by regression analysis using the MINR algorithm to identify the group of operational variables most successful at

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Table 3 – Linear regression model fitting the OOFI profile Model description

Variables

Standardized coefficienta

Short name

Lag 0

Lag 1

Lag 2

Lag 3

2

Total R ¼ 0.67 Temperature cluster Partial R2 ¼ 0.39 Average Lagb ¼ 2.3

Bypass cluster Partial R2 ¼ 0.27 Average Lag ¼ 1.8

x¯ w x¯ w x¯ w x¯ w x¯ w x¯ w x¯ w x¯ w

of of of of of of of of

reactor temperature configuration F:M ratio hydraulic retention time activated sludge effluent NH+4 dynamic sludge age dissolved oxygen in reaeration tank minimum daily atmospheric temperature

x¯ w of Mun. 11 claifier bypass BOD5 loading rates x¯ w of municipal 11 clarifier bypass usage x¯ w of MLSSCORRECTED sw of biomass yield sw of activated sludge effluent NH+4 sw of activated sludge influent BOD5 concentration sw of activated sludge influent BOD5 loading rates sw of trickling filter effluent TSS concentration x¯ w of VSS sw of plant influent TSS concentration

T1 T2 T3 T4 T5 T6 T7 T8

BP1 BP2 BP3 BP4 BP5 BP6 BP7 BP8 BP9 BP10

0.412 0.814 0.173 0.249 0.198 0.142 0.257 0.317

0.197 0.150 0.163 0.117 0.113

0.103

0.145

0.272

0.083 0.179 0.183

0.106

a

Standardized coefficients: regression coefficients calculated once all variables in the model were centered (mean ¼ 0) and standardized (SD ¼ 1). b Average lag ¼ Si(lagi
standardized coefficienti2)/Si standardized coefficienti2.

empirically modeling the OOFI profile. The final model contained 18 operational variables (Table 3). The ultimate goal of the multivariate linear regression modeling was not to predict or forecast foaming events, but to interpret the foaming dynamics in order to identify the underlying mechanisms. In this context, it is still difficult to interpret in mechanistic terms the structure of the empirical model because the 18 variables cooperatively work to fit the OOFI temporal profile. Therefore, the mechanistic interpretation must be done in the multivariate context (Legendre and Legendre, 1998). With this in mind, we performed PCA on the correlation matrix of the 20 variables in the system (18 operational variables, OOFI, and predicted OOFI). The results for the first two principal components (PC) are presented in Fig. 4. Only the first two PCs are used for interpretation because only those contained a larger proportion of the total variance than what is expected for a system of variables decomposed at random (reference values according to the broken stick model; Legendre and Legendre, 1998). PCA was used to allow the visualization of all the variables in reduced space (Legendre and Legendre, 1998). One can consider a system of m variables as vectors oriented in an mdimensional hyperspace. PCA is used to locate a twodimensional plane through this hyperspace onto which the variables (vectors) can be projected and visualized (Fig. 4). The quality of the projection of each variable can be assessed with the equilibrium circle (Fig. 4). Under the hypothesis of equal representation of each variable in all PCs, the length of all

vectors would be equal to the radius of the equilibrium circle. Thus, vectors with apex falling close to or beyond the equilibrium circle are variables well represented in the reduced space visualized. In PCA projection, the correlation between variables is equal to the inverse cosine (cos1) of the angle between them (Legendre and Legendre, 1998). Accordingly, the vectors of variables with correlations close to 1 or 1 are projected with angles close to 01 or 1801, respectively. Alternatively, the vectors of variables with correlations close to 0 are projected at a right angle. The projection of the OOFI in the space of the first two PCs is along PC1 (Fig. 4). Consequently, the OOFI vector separates the 18 variables in two groups. The first eight variables (first group; Table 3) are projected in quadrants 2 and 4 (Cluster 1), while the next ten variables (second group; Table 3) are projected in quadrants 1 and 3 (Cluster 2). In order to interpret the meaning of the two clusters, the OOFI was regressed independently on each variable cluster (Table 3). The predictions of these regressions are presented in Fig. 2b and projected in the PC space of Fig. 4. This last analysis showed that different characteristics of the OOFI profile are associated with the two variable clusters (Fig. 2b). The empirical model built with the Cluster 1 variables seems to exhibit a stronger seasonality than the model built with the Cluster 2 variables. On the other hand, the Cluster 2 model fits better the unusual foaming episode in early 1995 and the low OOFI values throughout 2000. By projecting these two new vectors of regression predictions in the space of the PCA, it was found

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Fig. 4 – Principal component analysis biplot projecting the variables involved in the linear regression model (Table 3) describing the OOFI in the space of the first two principal components (PC). The vectors representing operational variables are in black and the vectors representing OOFI predictions of linear regression models are in gray. Percentages in parentheses are the portion of the variance of the correlation matrix captured by the respective PC.

that the Cluster 1 predictions projected close to the reactor temperature (T1, Fig. 4). Thus, it appears that the Cluster 1 variables cooperatively fit the reactor temperature profile to the OOFI. Consequently, Cluster 1 was interpreted as the temperature (T) cluster. On the other hand, the regression prediction vector produced with the Cluster 2 variables projected close to the Municipal 11 clarifier bypass BOD5 loading rates (BP1, Fig. 4). Similarly to the previous interpretation, Cluster 2 was interpreted as the 11 clarifier bypass (BP) cluster. In light of this interpretation, we can see that the seasonality of the foaming events at the UCSD-NE plant is driven by the reactor temperature (Fig. 2a–c) in agreement with the path analysis. The lack of a foaming event in 2000, the unusual foaming event in the spring of 1995, and the early OOFI peak in 1998 can all be explained by the use of the municipal primary clarifier bypass stream (Fig. 2a, b and d).

3.3.

Yearly averages trend analysis

It was observed that the degree of foaming per year (yearly sum of OOFI) varied throughout the 8 years included in this study (Fig. 2a). This variation was analyzed by correlating it to the yearly means (x¯ y;x¯ ; x¯ y;s ) and yearly standard deviations (sy;x¯ , sy,s) of each variable. Only the variables with significant (Po0:05) correlations are reported in Table 4. In agreement with the previous section, bypass usage is one of the seven variables detected.

When interpreting this result mechanistically, one should ask whether the variables represent different mechanisms or if they are all related to a single mechanism. In fact, five of the seven variables identified in this analysis had a significantly different average when the bypass stream was open compared to when the bypass was closed (Table 4). This conclusion was verified even when the test controlled for reactor temperature and temporal autocorrelation as possible confounding factors. The effect was most pronounced for sw of dynamic sludge age and x¯ w of biomass yield (Table 4) with respective changes of 19% and 21%. The source of these variations in sw of dynamic sludge age and x¯ w of biomass yield were traced back to the characteristics of the bypass stream by detailed linear regression modeling (results not shown). Thus, the changes in these last variables were not occurring fortuitously at the time of primary clarifier bypass stream usage. In conclusion, all but one of the identified variables (sy;x¯ of reactor temperature) suggest that the yearly variation in OOFI is due to variation in the usage of the bypass stream, in agreement with the previous section.

3.4.

Wastewater composition

The multivariate regression analysis of weekly data (x¯ w , sw) and the correlation analysis of yearly data ðx¯ y;x¯ ; x¯ y;s ; sy;x¯ ; sy;s Þ implicated the use of the municipal primary clarifier bypass

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Table 4 – Correlation between the yearly sum of OOFI and the yearly average or yearly standard deviation of operational variables for the period 1993–2000 (excluding 1997) Variable

Units

x¯ y;x¯ of MLSSMEASURED in aeration tanks x¯ y;x¯ of dynamic sludge age

1038723d

NSe

47716

4.5%

NS

0.04470.018

18.9%

0.01370.003

0.50970.110

0.10470.023

21.0%

e f g

0.89

NAg

Miny;x¯

0.60

NA

Maxy;x¯

0.31

NA

x¯ y;x¯ of Mun. 11 clarifier bypass usage

Changec

0.23370.011

0.86

x¯ y;x¯ of AS influent BOD5 loading rates x¯ y;x¯ of MLSSCORRECTED in aeration tanks

Open bypass

0.90

(1C)

d

Temperatureb

0.94

(COD/COD)

c

Intercept

(day)

Reactor temperaturef sy;x¯

b

Linear regression model at weekly scale

(mg/L)

x¯ y;x¯ of biomass yield

a

Yearly correlationa

(% week)

0.83

(m. tons/day)

0.77

28917190

NA NS

379759

13.1%

(mg/L)

0.75

923789

154786

68716

8.8%

The Pearson correlation coefficient. Only significant (Po0:05) values are reported. Temperature effect values reported are for 18.5 1C (average temperature). Change (%) in variable when bypass was open. Bypass closed value: intercept+temperature. Value7standard error. Total degrees of freedom: 414. NS: not significant (P40:1). Tests took autocorrelation into account. Correlation analysis identified the sy;x¯ vector. The others are presented to understand the nature of the correlation. NA: not applicable.

Table 5 – Average composition of municipal primary clarifier influent and effluent streams between January and April 2003 Wastewater component

Proteins Carbohydrates Lipids Sum a b

COD concentrationa (mg/L)

Removal (mg COD/L)

Removal (%)

Influent

Effluent

Mean7SE

Average rankb

Mean7SE

Average rank

68 64 1059 1192

40 22 80 141

2873.9 4378.6 9807490 10507498

2.8 2.3 1.0

41.173.2 64.475.6 80.578.7 76.779.2

3.0 2.0 1.0

Average of four monthly samples obtained between January and April 2003. Removal levels were ranked from 1 (highest removal) to 3 (lowest removal) for each of the four sampling dates and the averages computed.

stream in the accumulation of biological foam at the UCSDNE wastewater treatment plant. Since previous studies showed that the concentration of foam-forming bacteria is high during foaming events at this site (Oerther et al., 2001; de los Reyes III and Raskin, 2002), the simplest interpretation for the bypass result is that the clarifier bypass stream is supplementing the activated sludge influent with compounds that promote the growth of the foam-forming bacteria. Under normal operation, these compounds would be efficiently removed in the clarifier. In order to identify these compounds, grab samples of the municipal primary clarifier influent and effluent were obtained once a month between January and April 2003. The concentrations of proteins, sugars, and lipids in the municipal primary clarifier influent and effluent were determined and are presented in Table 5. This analysis showed that the removal rate by the primary clarifier was highest for the lipids. Note that the high level of lipids observed in the influent samples may be due to the choice of

sampling the surface of the influent stream, since lipid-rich materials are more likely to float. This choice was justified because the primary bypass stream is operated by a dropdown gate, a system more likely to direct the surface of the stream directly towards the activated sludge system.

3.5.

Laboratory-scale reactors

To evaluate if lipids from the bypass stream promoted the growth of foam-forming bacteria, four sets of laboratory-scale SBR reactors were fed primary clarifier effluent, bypass wastewater (i.e., wastewater only treated by preliminary treatment), a 50:50 mixture of these two streams, and a minimal medium containing tripalmitin. After 2 weeks of operation, the reactors fed primary clarifier effluent did not show a significant (P40:1) enrichment of mycolata (Fig. 5). While the reactors fed the 50:50 mixture showed a trend towards the enrichment of mycolata (Fig. 5), the enrichment

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Fig. 5 – Relative abundance of mycolata rRNA in SBR reactors fed different wastewaters or minimal medium containing tripalmitin. The left bar of each duplet is the initial abundance of mycolata, and the right bar is the final abundance after 2 weeks of operating the SBRs. Same color bars in a duplet indicate non-significant enrichment, hashed bars indicate significant enrichment level at a ¼ 0:1, black bars indicate significant enrichment at a ¼ 0:01. Error bars indicate standard error (n ¼ 3).

could not be substantiated statistically (P ¼ 0:11). However, the reactors fed bypass stream wastewater or a minimal medium with only tripalmitin (fat) as the sole source of carbon and energy showed significant (Po0:1) enrichment of the mycolata population (Fig. 5).

4.

Discussion

4.1.

Evaluating the relative merits of the hypotheses

The study presented here attempts to quantify the relative merits of six hypotheses that may explain the mechanism of seasonal biological foaming at the UCSD-NE wastewater treatment plant. Three statistical techniques were used to evaluate the predictive power of each hypothesis. Path analysis showed that the suspected effect of reactor temperature on the OOFI was direct and not mediated by any of the intermediate variables identified by the hypotheses. Thus, path analysis lends support to Hypotheses 4 and 6. Multivariate linear regression was used to test the six hypotheses without a priori biases. Indeed, this analysis could have refuted the six hypotheses by finding important operational variables unrelated to them. Since the final 18variable regression model captured the three important features of the OOFI profiles, it suggests that the operational data are sufficient to explain the behavior of foam coverage. Further analysis of the 18 variables by PCA suggested that they could be separated into two clusters: Cluster 1 was related to the reactor temperature, and Cluster 2 was related to use of the municipal primary clarifier bypass stream.

Therefore, the multivariate regression analysis supported Hypotheses 2, 4, and 6. Correlation of yearly operational data ðx¯ y;x¯ ; x¯ y;s ; sy;x¯ ; sy;s Þ to the yearly sum of OOFI (Fig. 2) identified variables related to Hypotheses 2 (biomass yield, bypass usage, activated sludge influent BOD5 loading rates), 3 (MLSSCORRECTED, dynamic sludge age), and 4 (Miny;x¯ of reactor temperature; Table 4). Further analysis of these variables, however, showed that the use of the primary bypass stream changed all of them significantly, except for reactor temperature. This finding suggests that effect of the identified variables is due to a single mechanism: the use of the municipal primary bypass stream (Hypothesis 2). On the other hand, the bypass usage changed the MLSSMEASURED (the most correlated variable; Table 4), by only 4.5% (the smallest relative change; Table 4). In addition, removing the effect of the reactor configuration from the MLSSMEASURED (transforming the MLSSMEASURED to MLSSCORRECTED) decreased the correlation with the OOFI, but increased the relative effect of the bypass usage (Table 4). Therefore, the MLSS concentration itself seems to play a role in the foaming process. In conclusion, the correlation analysis can be seen as strongly supporting Hypothesis 2, and weekly supporting Hypothesis 3. The variable identified by the correlation analysis and associated to Hypothesis 4 was the sy;x¯ , of reactor temperature. This variable is related to the range (MaxMin) of temperatures during a year. Hypothesis 4 predicts that the foam intensity for 1 year should be positively correlated to the minimum temperature observed during the year. A closer look at the yearly maximum and minimum temperatures showed that, during the study period, the minimum temperature was negatively correlated to the OOFI while the maximum temperature was positively correlated (Table 4). In

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addition, an average year has only 8 weeks below 14 1C, only 3 weeks below 13 1C (the minimum growth temperature of Gordonia spp. is 10–15 1C; Soddell and Seviour, 1995), and often these weeks are not consecutive. Finally, the temperature never fell below 10 1C. Therefore, the correlation analysis of the yearly operational data argues against Hypothesis 4. Consequently, the high correlation of the yearly temperature variance of the OOFI should be seen as fortuitous. Can the relative merit of the hypotheses be evaluated in order to reach a global conclusion with respect to the mechanism underlying seasonal biological foam accumulation at the UCSD-NE plant? Combining the results from the three statistical analyses leads us to reject Hypotheses 1, 4, and 5 because they were either not supported or the data were contrary to the predictions of the hypothesis. Visual comparisons of the configuration profile (Hypothesis 1; Fig. 2f), the DO profile (Hypothesis 5; Fig. 2c), and oxygen supply efficiency profile (Hypothesis 5; Fig. 2c) with the OOFI profile agree with this conclusion. Hypothesis 3 was only weekly supported by the correlation analysis (above). This hypothesis was formulated according to the rules of operation stating that the MLSS concentration in the aeration tanks must be kept above a minimum level during the summer. Following this operational rule, the solids inventory or the SRT would have increased. In fact, the solids inventory (as seen from the MLSSCORRECTED; Fig. 2g) and the dynamic sludge age (surrogate for SRT; Fig. 2g) went down during the summer while the OOFI went up. Therefore, the seasonal OOFI profile does not appear to be related to the mechanism stated in Hypothesis 3. However, the MLSS may still play a role as pointed out above. A higher MLSS concentration in the activated sludge reactor probably makes it more sensitive to produce biological foam or to produce foaming events with a higher intensity. Consequently, the successes in controlling biological foaming by reducing the SRT reported in the literature (Jenkins et al., 1993) could in fact be due to a lowering of the MLSS concentration. At this point, Hypotheses 2 and 6 cannot be rejected. However, reading the hypotheses in the context of the data from the UCSD-NE plant, it can be concluded that these hypotheses are not mutually exclusive. In fact, the high lipid loading rate found to promote foam accumulation at the UCSDNE plant is consistent with both hypotheses. Nevertheless, Hypothesis 6 is slightly superior to Hypothesis 2 because it accounts for the importance of temperature. Therefore, we conclude that the data support Hypothesis 6 most strongly. Finally, we experimentally confirmed that the foam-forming population had a competitive advantage when growing on lipids as the only carbon and energy source as suggested by Hypothesis 6. Since previous work had established that the mycolata were responsible for foaming at the UCSD-NE plant (de los Reyes et al., 1997; de los Reyes et al., 1998; de los Reyes III and Raskin, 2002; Oerther et al., 2001), we tested the enrichment of this population in laboratory scale reactor fed a bypass stream sample and a minimal medium containing tripalmitin (Fig. 5). The results showed an enrichment of the mycolata under these conditions, substantiating the interpretation that the foam-forming mycolata are specialized in consuming lipids and that the lipids are fed to the activated sludge reactor via the primary clarifier bypass stream.

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4.2. Temperature and lipid loading rate: a single mechanism The two factors identified in this study that promoted the accumulation of biological foam at the UCSD-NE plant were also found to promote foam accumulation in other systems. For example, high lipid loading rates were found to promote foaming in full-scale (Pipes, 1978; Forster, 1992; Franz and Matsche´, 1994) and pilot-scale (Bendt et al., 1989) wastewater treatment plants. These findings are consistent with the physiology of representative foam-forming microorganisms, which are able to consume hydrophobic substrates (Kurane et al., 1986; Blackall et al., 1991; Khan and Forster, 1991; Soddell and Seviour, 1996). Furthermore, it was argued that the hydrophobic cell wall of the foam-forming microorganisms would provide them with a competitive advantage in consuming hydrophobic substrates. The competitive advantage of foam-forming bacteria growing on lipids was substantiated in the current study by operating laboratory-scale reactors fed only lipids. In the literature, this advantage was rationalized by first pointing out that foam-forming bacteria would have the tendency to adsorb to the surface of lipids bringing them in close contact with this food source (Lemmer, 1986). It was also suggested that the hydrophobic cell wall of these bacteria would scavenge free hydrophobic molecules effectively reducing their half-saturation constants (KS) for hydrophobic substrates (Soddell, 1999). The role of temperature in the occurrence of biological foaming events is also well documented. A higher frequency of foaming plants has been reported for warmer climates (Pipes, 1978; Pitt and Jenkins, 1990; Soddell and Seviour, 1994; Soddell, 1999). It was suggested that these observations are due to the relatively high minimum (10–15 1C; Soddell and Seviour, 1995) and optimum (28 1C; Lemmer and Poop, 1982) growth temperatures of Gordonia spp. In addition, Lemmer and Poop (1982) suggested that winter foaming occurrences involving mycolata are due to Rhodococcus spp., and not Gordonia spp. The suggestion agrees with the minimum growth temperatures of Rhodococcus spp. being lower than the ones of Gordonia spp. (Soddell and Seviour, 1995), but it still awaits further testing. Although the current study does not provide such a test, it does not contradict Lemmer and Poop’s (1982) suggestion since previous work at the UCSD-NE plant attributed summer foaming events to high levels of Gordonia spp. (Oerther et al., 2000; de los Reyes III and Raskin, 2002). The few direct studies of foaming events in 1997 (Oerther et al., 2000) and 1999 (de los Reyes III and Raskin, 2002; de los Reyes et al., 2002) at the UCSD-NE plant effectively identified Gordonia spp. as the foam-forming population. A 1995 study at this site also found a high percentage of Gordonia spp. in the mycolata population (de los Reyes et al., 1998). The abundance of Microthrix parvicella in foam and mixed liquor samples obtained during the 1999 foaming event was also measured. In all samples, the abundance was low indicating the foaming event was not due to M. parvicella. Together, these studies suggest that Gordonia spp. remained the foamforming population in this plant between 1993 and 2000. Finally, the 1999 study also suggested the implication of a population of g-Proteobacteria in foam formation because of

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the enrichment of this group of organisms in the foam layer compared to the underlying mixed liquor (de los Reyes et al., 2002). Although, this population was much less abundant than the Gordonia population. A subsequent cloning study found this population to be related to the genus Alkanindiges (Klein et al., in preparation). This finding is relevant to the understanding of the mechanism leading to the occurrence of foaming events at the UCSD-NE plant as all the cultured isolates belonging to this genus require long-chain alkanes or fatty acids for growth (Bogan et al., 2003; Woo et al., 2005). These findings can now be interpreted in light of the current long-term analysis that shows how lipid loading rate and temperature interact very strongly to determine the biological foam accumulation at the UCSD-NE plant. Because the same microbial population was affected by the same parameters over an extended period of time, these studies suggest that these two factors are parts of a singles mechanism. We propose that this mechanism is the one described by Hypothesis 6, and can be summarized as follows. The presence of biological foam on the surface of an activated sludge reactor is only a function of the level of the Gordonia biomass (de los Reyes III and Raskin, 2002). Gordonia spp. are microorganisms specialized in consuming hydrophobic substrates (mainly lipids in municipal wastewaters). Assuming that hydrophobic substrates are SDSs requiring some sort of ‘‘hydrolysis’’ (true hydrolysis by lipases or simple mass transfer limited mechanisms), the level of the Gordonia biomass in the activated sludge community is determined by the COD flux through the ‘‘hydrolysis’’ reaction. The data point to two mechanisms capable of increasing this flux: increase in the ‘‘hydrolysis’’ reaction rate by temperature, and increase in the COD loading rate of hydrophobic SDSs. Therefore, the association of biological foaming events to high temperatures and to high lipid loading rates may indeed represent a single mechanism. Other lines of evidence further support the development and the verification of the model proposed in Hypothesis 6. For example, Hypothesis 6 was developed with the help of a sensitivity analysis of the biomass increase between 12 and 25 1C. Previous field work at the UCSD-NE plant showed that the Gordonia population increased approximately five fold between regular operation (low temperature) and periods of foam accumulation (high temperature; Oerther et al., 2000; de los Reyes III and Raskin, 2002). We developed a model that conceptualized the activated sludge community as a collection of non-competing populations that can be parameterized as the ASM3 model (Gujer et al., 1999), and we analyzed a spectrum of populations from SDS specialists (i.e., only consuming SDS) to readily degradable substrate (RDS) specialists (i.e., only consuming RDS; Frigon, 2005). It was found that the hydrolysis constant of a SDS specialist population was the most sensitive model parameter. It was twice as sensitive as the growth rate of SDS specialists, the second most sensitive parameter. The other model parameters were nearly insensitive. This result suggests that the hydrolysis reaction is the factor determining the seasonal variation in the abundance of the foam-forming population. A second line of evidence relates to the analysis of diurnal rRNA dynamics. The previous model was used to predict the diurnal dynamics of rRNA in activated sludge submitted to

diurnal variations in COD loading rates typically observed in municipal activated sludge systems (Frigon et al. 2002b). The model predicted that the rRNA level of a population of RDS specialists would vary following the COD loading rates. Conversely, the rRNA level of a population of SDS specialists would stay constant. The rRNA diurnal dynamics predicted for an SDS consuming populations was observed for the Gordonia population growing in the activated sludge reactor of the UCSD-NE plant (Frigon et al., 2002a, b). Clearly, experiments such as microautoradiography-fluorescence in situ hybridization (MAR-FISH) would be helpful to further test Hypothesis 6. In the mean time, this model seems to account for a major part of the data presented here and in the literature concerning foaming events caused by Gordonia spp.

5.

Conclusions

(1) Two operational parameters are responsible for biological foaming events at the UCSD-NE wastewater treatment plant: temperature and the use of the primary clarifier bypass stream. (2) The importance of the primary clarifier bypass stream is due to the high lipid level it contributes to the influent of the activated sludge reactor. (3) The relationship between the occurrence of foam and (1) higher temperature and (2) higher lipid loading rates may represent a single mechanism if the foam-forming population is assumed to be specialized in consuming lipids, substrates classified as slowly degradable.

Acknowledgments Hala Jawlakh and John Shwartz are thanked for evaluating the OOFI. Thanks are also extended to Eberhard Morgenroth, Robert Sanford, and Craig Criddle for stimulating discussions. This work was supported by a grant from the National Science Foundation (BES 97-33826), a scholarship to DF from the Fond Que´be´cois de la Recherche sur la Nature et les Technologies, a University of Illinois dissertation completion fellowship to DF, and the Paul L. Bush Award from the Water Environment Research Federation. R E F E R E N C E S

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