20120905_Forrester Thesis-1200dpi.pdf

COMPARISON OF EFFECTIVENESS OF FOUR NOCARDIOFORM ENUMERATION
METHODS IN PREDICTING ACTIVATED SLUDGE FOAMING DURING
WASTEWATER TREATMENT
A Thesis
Presented to the faculty of the Department of Biological Sciences
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF SCIENCE
in
Biological Sciences
by
Shannon E. Forrester
SUMMER
2012

COMPARISON OF EFFECTIVENESS OF FOUR NOCARDIOFORM ENUMERATION
Approved by:
METHODS IN PREDICTING ACTIVATED SLUDGE FOAMING DURING
WASTEWATER TREATMENT
A Thesis
by
Shannon E. Forrester
__________________________________, Committee Chair
Enid Gonzalez, Ph.D
__________________________________, Second Reader
Susanne Lindgren, Ph.D
__________________________________, Third Reader
Eugene Dammel, Ph.D
____________________________
Date
ii

Student: Shannon E. Forrester
I certify that this student has met the requirements for format contained in the University format
manual, and that this thesis is suitable for shelving in the Library and credit is to be awarded for
the thesis.
__________________________, Graduate Coordinator ___________________
Ronald Coleman, Ph.D Date
Department of Biological Sciences
iii

Abstract
of
COMPARISON OF EFFECTIVENESS OF FOUR NOCARDIOFORM ENUMERATION
METHODS IN PREDICTING ACTIVATED SLUDGE FOAMING DURING
WASTEWATER TREATMENT
by
Shannon E. Forrester
Foaming of activated sludge during wastewater treatment can compromise the quality of
treated wastewater (effluent) and create operational difficulties. The presence of high
concentrations of mycolic acid-containing bacteria (mycolata) has been linked to foam
production. Specifically, the nocardioform group of mycolata is often used as an indicator of
bacterial foaming potential during secondary treatment in the activated sludge process, due to
their role in foaming and their ease of enumeration. One major goal of nocardioform
enumeration is to discover the threshold at which higher nocardioform concentrations will likely
result in foaming. Such threshold values can potentially provide an important tool for
wastewater treatment plant operators to predict and manage foaming.
The purpose of my study was to help treatment plant staff control activated sludge foaming
in the most efficient manner possible by comparing the accuracy and precision of four
nocardioform counting methods to determine which method best predicts foaming. The second
part of my study was to determine the nocardioform concentration threshold for foaming of
activated sludge at Sacramento Regional Wastewater Treatment Plant (SRWTP). Four types of
nocardioform counts were performed: total and dispersed filament intersection counts and total
iv

and dispersed filament length counts. These counts were performed on Gram-stained slides
under 1000X magnification using an eyepiece reticule (grid) of a light microscope. The
accuracy of each counting method in predicting foaming was assessed through correlation with
mixed liquor foam measures. Two independent measures of foam severity were used during
this study: (1) foam potential tests conducted in the laboratory and (2) observations of foam
coverage over one secondary sedimentation tank. Laboratory foaming tests involved the
bubbling of air through samples of activated sludge and measuring the resulting foam height
and percent collapse of foam (in the absence of aeration). Precision of the nocardioform
counting methods was compared by calculating the relative standard deviation (RSD) of four
pseudoreplicate strip counts per method each day, followed by Friedman’s test of RSD mean
ranks to test for statistically significant differences in the means. The nocardioform
concentration threshold for foaming was calculated for total filament intersections/g volatile
suspended solids (VSS). In order to confirm and explain the nocardioform count results,
isolation and identification of nocardioforms from samples of activated sludge used for
enumeration were attempted during both years of my study via culturing and 16s rDNA
sequencing.
The majority of my study was conducted at SRWTP over the course of two nocardioform
blooms (late summer and fall of 2009 and 2010). All four counting methods showed relatively
weak correlations with the mixed liquor foaming measures used during my study, likely due to
the contributions to foaming made by non-nocardioform mycolata and possibly surfactants
during that time. The accidental isolation of Mycobacterium on two separate occasions during
my study supports the notion of non-nocardioform mycolata contributing to activated sludge
foaming at SRWTP. When foam stability was used as the criterion for defining the foaming
v

threshold of mixed liquor, the corresponding nocardioform abundance fell into the range
historically associated with foaming at SRWTP (1 x 10 6 total intersections/g VSS). The results
of my study indicate that total nocardioform intersection counts most accurately predict
activated sludge foaming, compared to the other three counting methods examined.
Nocardioform length counts have better precision, but are less accurate at predicting foaming
compared to total intersection counts.
_______________________, Committee Chair
Enid Gonzalez, Ph.D
_______________________
Date
vi

ACKNOWLEDGEMENTS
I would like to thank SRWTP for the use of facilities, data, and supportive staff. I am
especially grateful for the technical advice offered by the Laboratory Director Lucy Boehm, the
Biology Section supervisor Don Schwartz, Biologist Dr. Gisela Cluster, and SRWTP O&M
engineers Josh Nurmi and Jeremy Boyce. A special thank you is in order for Mick Berklich,
O&M Manager, for giving valuable advice and obtaining operator logbooks for use in my study.
I am also grateful for the support and advice offered by members of my committee at CSU
Sacramento: Dr. Lindgren, Dr. Dammel, and especially Dr. Gonzalez. Their patience and
understanding of my difficulties helped tremendously. My family’s patience with my hectic
schedule juggling work and classes, in addition to my husband’s assistance with the figures in
my thesis, were valuable to me. Finally, I am indebted to my brother for finding time to
proofread my thesis and help me with its formatting. This project never would have been
completed without the assistance of so many knowledgeable people.
vii

TABLE OF CONTENTS
viii
Page
Acknowledgements ..................................................................................................................... vii
List of Tables ............................................................................................................................... ix
List of Figures ............................................................................................................................... x
INTRODUCTION ........................................................................................................................ 1
MATERIALS AND METHODS ................................................................................................ 13
Sample Information ....................................................................................................... 13
Nocardioform Enumeration Techniques ........................................................................ 14
Foam Estimation Techniques ......................................................................................... 17
Nocardioform Isolation and Identification ..................................................................... 20
RESULTS ................................................................................................................................... 23
DISCUSSION ............................................................................................................................. 40
Appendix A. List of Abbreviations ............................................................................................ 57
Appendix B. Operator Logbook Entries for X04 (CO Tanks and SSTs) and
X08 (Digesters) during 2009 and 2010. ............................................................... 58
Appendix C. Summary of study findings, recommendations, and alternative
methods of monitoring activated sludge foaming. ............................................... 59
Literature Cited ........................................................................................................................... 61

LIST OF TABLES
Tables Page
1. Comparison of the correlations between each nocardioform counting
method and laboratory foaming test and SST foam observation results
using both nonparametric Spearman's rho and linear regression
analyses. ......................................................................................................................... 29
2. SPSS output for nonparametric Friedman test comparing the relative
standard deviations of all four nocardioform counting methods. .................................. 32
ix

LIST OF FIGURES
Figures Page
1. Bound and dispersed nocardioform filament intersection counts using
light microscopy. ........................................................................................................... 16
2. Bound and dispersed nocardioform filament length counts using light
microscopy. .................................................................................................................... 18
3. Summary graph depicting trends in all four nocardioform counts,
laboratory foaming test results, and SST foam percent observations
during both 2009 and 2010.. .......................................................................................... 25
4. Linear regression scatterplots of each type of nocardioform filament
count vs. percent foam of SRWTP ML 3 for 2009 and 2010: Total
Intersection Counts, Dispersed Intersection Counts, Total Length
Counts, and Dispersed Length Counts. .......................................................................... 27
5. Scatterplot showing the results of the laboratory foaming tests
performed in 2010. ......................................................................................................... 35
6. Scatterplot showing the relationship between activated sludge foaming
episodes causing disturbances in secondary treatment at SRWTP and
total nocardioform intersection counts associated with the
disturbances.. ................................................................................................................. 37
7. Temporal trends in SRWTP secondary treatment parameters associated
with activated sludge foaming compared to total nocardioform
intersection counts during both 2009 and 2010.. ........................................................... 50
x

INTRODUCTION
Foaming of activated sludge during wastewater treatment often results in costly equipment
damage, safety hazards for treatment plant operators, and discharge of poor-quality effluent
(treated wastewater). Effluent quality is of particular importance locally, as our waterways
struggle to support threatened fish species, fisheries and recreational activities. Biological
foaming during wastewater treatment is caused primarily by nocardioforms, a group of
filamentous bacteria found in activated sludge. Control of nocardioform foam by treatment
plant operators requires accurate enumeration of these bacteria, in order to predict and quantify
foaming episodes and adjust operational parameters accordingly.
Wastewater treatment in Sacramento County is primarily performed by the Sacramento
Regional Wastewater Treatment Plant (SRWTP). SRWTP, located in Elk Grove, California,
has been operating since 1982 and serves over one million residents and businesses. A
wastewater collection system, incorporating more than 140 miles of interceptor pipe, conveys
wastewater to SRWTP from cities throughout Sacramento County. Once at the treatment plant,
an average of 150 million gallons of wastewater per day undergo an extensive treatment process
before being discharged into the Sacramento River (SRCSD 2008).
The wastewater treatment process at SRWTP involves three stages: primary treatment,
secondary treatment, and disinfection. The primary process begins with incoming wastewater
(influent) flowing through bar screens to remove the largest debris and ends with primary
sedimentation of grit and heavier solids. The secondary process is primarily responsible for
removal of soluble organic material from wastewater. A carefully-balanced microbial
community oxidizes organic compounds in wastewater in the presence of oxygen in large tanks
called “reactors” or “CO tanks”. The quality of treatment plant effluent is strongly affected by
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the amount of time wastewater is allowed to spend in the reactors, which is limited by
overgrowth of filamentous bacteria. After spending a designated amount of time in the reactors,
reactor microbes settle out of the suspension in a set of non-aerated tanks called “secondary
clarifiers” or “secondary sedimentation tanks” (SSTs). The suspension of microbes and
wastewater is called “mixed liquor” while in reactors, and is also called “activated sludge”
throughout the entire secondary process. Secondary clarifier supernatant is then chlorinated and
sulfur dioxide is added to neutralize any residual chlorine before the treatment plant effluent is
discharged into the Sacramento River. Some of the settled microbes in the SSTs, referred to as
“Return Activated Sludge” (RAS), are returned to the reactors to seed the mixed liquor, while
the rest (“Waste Activated Sludge” or WAS) are eventually sent to anaerobic digesters for
further bacterial metabolization and pathogen reduction (SRCSD 2008).
A critical step in wastewater treatment is the activated sludge process, in which
microorganisms in oxygenated reactors metabolize harmful organic compounds in the mixed
liquor and are separated from mixed liquor supernatant in secondary clarifiers. This process is
responsible for removing harmful compounds and solid material from treated wastewater that
would be detrimental to local waterways. Activated sludge is primarily composed of bacteria
and protozoa, but often contains nematodes, rotifers and fungi. Two types of bacteria are
necessary for the activated sludge process: filamentous and non-filamentous (floc-forming
bacteria). In order for proper settling of solids to occur there must be a balance between these
types of bacteria, with filamentous bacteria forming a solid skeletal network to which the floc-
formers can adhere. An overgrowth of filamentous bacteria causes poor settling of solids
(bulking), while an overgrowth of floc-forming bacteria creates a turbid supernatant full of
bacteria. This can be a major problem requiring increased disinfection before discharging such
wastewater (Office of Water Programs 2007).
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One way to better understand the activated sludge process is to study the microbial
composition of the sludge. Hydrolytic bacteria break up large organic polymer molecules such
as polysaccharides, proteins and lipids. Organic compounds are also oxidized by organotropic
microbes under oxic or anoxic conditions. Chemolithotrophic microorganisms require oxic
conditions in order to oxidize ammonia nitrogen. Finally, polyphosphate accumulating bacteria
(poly-P bacteria) are responsible for phosphorus removal from wastewater, as they are able to
store polyphosphate under aerobic conditions and later release phosphate under anaerobic
conditions in order to provide energy for nutrient uptake and storage in an anaerobic
environment (Wanner 1994). Removal of harmful compounds and excess nutrients from
wastewater primarily occurs during the activated sludge process, further necessitating
maintenance of microbial balance during this process.
Microbial ecology of activated sludge is currently a popular area of study. Yi et al. (2012)
extracted phospholipid fatty acids (PLFAs) from activated sludge samples from four full-scale
wastewater treatment plants. They found that different treatment plants utilizing similar
secondary treatment process parameters shared similar activated sludge PLFA profiles,
regardless of seasonal fluctuations in nutrient and substrate concentrations. So, microbial
community composition may be influenced more by wastewater treatment processes than by
wastewater composition. However, Yi et al. (2012) also found that influent wastewater quality
will have an effect on microbial community structure, just as this microbe community later
affects effluent water quality. This could explain why these researchers found a decline in
microbial community diversity in the summer and increases in diversity in autumn months (Yi
et al. 2012).
Another recent study of activated sludge microbial ecology involved the construction of 16S
rRNA gene clone libraries from activated sludge samples in order to discover the phylogenetic
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diversity of the microbial communities present. Yang et al. (2011) found Proteobacteria to be
the numerically dominant group, while members of Bacteroidetes and Firmicutes were less
abundant in all samples. At lower taxonomic levels, the four genera with the highest abundance
were Thauera, Dechloromonas, Nitrosomonas, and Bacillus. Additionally, many unclassified
sequences were found in the gene clone library, indicating the presence of novel species in the
activated sludge samples analyzed during this study. As with the Yi et al. (2012) study, Yang et
al. (2011) also found that bacterial community structure was similar among samples taken from
different wastewater treatment plants. Finally, Yang et al. (2011) discovered genes responsible
for nitrification and denitrification in the DNA extracted from their activated sludge samples.
They concluded that it is possible to attribute certain functions of activated sludge to specific
genera of microbes, and that the genes responsible can be routinely monitored in order to gauge
activated sludge performance (Yang et al. 2011).
Microbial balance in activated sludge results from various selective forces, both controlled
and uncontrolled. Reactor conditions under the control of treatment plant operators include
oxygen concentration, balance of nutrients vs. microorganisms (F/M ratio), and amount of time
mixed liquor biomass resides in the reactors and secondary clarifiers (Mean Cell Residence
Time or MCRT). Uncontrolled influent wastewater properties include pH, temperature, and
composition (chemical or nutrient) (Wanner 1993). Finally, selective predation by protozoa and
metazoa on preferred bacterial species can significantly impact competition among activated
sludge bacteria (Wanner 1994).
One common reason treatment plant operators must control microbial balance is to
discourage overgrowth of nocardioforms and other bacteria responsible for causing problematic
foaming of activated sludge. Foam is formed when gas is trapped within a liquid, and can be
stabilized when solid material is present. Two major instigators of activated sludge foam are
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detergents and filamentous bacteria. Detergent foam is white, unstable, lacks suspended solids
and filamentous bacteria, and is caused by high surfactant concentration in plant influent.
However, now that most detergents are biodegradable, detergent foams are generally rare in
activated sludge. Filamentous bacterial foam is brown, viscous, stable, and contains many
solids. This type of foam is called “biological foam” because its solid content contains a high
abundance of filamentous bacteria, particularly nocardioforms (Tipping 1995). Biological foam
is stable due to (1) filamentous bacterial production of extracellular compounds possessing
biosurfactant properties and (2) hydrophobic cell walls of these bacteria (Wanner 1998). One
other contributor to biological foam stability is the sludge trapped in the foam; sludge solids act
as dams preventing escape of liquid from the foam (Tipping 1995).
Biological foaming during the activated sludge process is caused by certain groups of
hydrophobic bacteria; in the Sacramento region these are primarily nocardioforms. Members of
the nocardioform group are all actinomycetes/mycolata; they all contain mycolic acids in their
cell wall lipids, causing their cell surfaces to be hydrophobic (Iwahori et al. 2001).
Nocardioform filaments are Gram-positive, display characteristic branching of their hyphae, and
typically have a length of 5-30 µm and a diameter of 0.5-1.0 µm. The nocardioforms Gordonia
amarae, Skermania piniformis and Rhodococcus sp. are widely linked to severe foaming
episodes (Tipping 1995). Nocardioforms have been found to be the dominant filamentous
bacteria in foams in Danish, Czech and Swedish treatment plants, but are much less
concentrated in the underlying mixed liquor (Kragelund et al. 2007).
One nocardioform species in particular, Gordonia amarae, is known to cause activated
sludge foaming both worldwide and at our local SRWTP (Boyce and Dial 2009). In fact, Tsang
et al. (2008) boldly stated that G. amarae was “the major causal microorganism” in activated
sludge foaming (Tsang et al. 2008). G. amarae was first isolated from sewage foam by
5

Lechevalier and Lechevalier in 1974 and assigned the name Nocardia amarae (Lechevalier and
Lechevalier 1974). Subsequently, in 1994, Goodfellow et al. suggested that N. amarae be
transferred to the Gordonia genus due to character consistency with the members of this genus
and conflict with those of the Nocardia genus (Goodfellow et al. 1994). Members of the genus
Gordonia have been isolated from a range of native biotopes, including soil, wastewater
treatment reactors and biofilters, diseased humans, and soil contaminated with hydrocarbons
(Arenskötter et al. 2004). It should be noted that although G. amarae shows a strong propensity
for foaming, it has also been found to reside in treatment plants without foaming problems. The
fact that this bacterial species can exist as part of non-foaming activated sludge suggests that a
threshold concentration of G. amarae must be required for foaming problems to occur (Pitt and
Jenkins 1990).
The reasons for nocardioform foaming of activated sludge are not entirely understood, but
these foaming episodes have commonly been linked to a few conditions. The three most
common conditions seem to be: (1) high wastewater temperature (de los Reyes and Raskin
2002; Pitt and Jenkins 1990), (2) Low food: microorganism ratio (F:M ratio) in reactors, and (3)
lengthy mean cell residence times (MCRTs) (Pitt and Jenkins 1990). MCRT describes the
average time a microorganism spends in the activated sludge process (reactors and secondary
clarifiers). The MCRT of activated sludge can be calculated using the following equation:
MCRT, days = (activated sludge TSS, kg)/(TSS removed from process, kg/day), where TSS is
the Total Suspended Solids of a sample of mixed liquor (Office of Water Programs 2007). All
three of these conditions allow nocardioforms to outcompete other mixed liquor bacteria,
disturbing the delicate microbial balance and causing nocardioform numbers to increase to
nuisance levels.
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An understanding of nocardioform foaming cannot be complete without an overview of the
mechanism of foam production by nocardioforms, especially by G. amarae. One important
aspect of foam production concerns the relative importance of nocardioform cell surface
hydrophobicity (CSH) vs. extracellular biosurfactants secreted into mixed liquor supernatant by
these bacteria. Iwahori et al. (2001) found that G. amarae culture supernatant was able to foam
and to emulsify n-hexadecane, demonstrating the importance of biosurfactants secreted by G.
amarae cells in foam formation. A second finding of this study was a relatively high affinity to
hexadecane shown by G. amarae cells, evidence of hydrophobic cell surfaces. This surface
hydrophobicity was hypothesized by Iwahori et al. to cause attachment of G. amarae cells to air
bubbles in mixed liquor, resulting in stable foam (Iwahori et al. 2001).
Because wastewater treatment plant operators can manage activated sludge foaming if
alerted early about rising nocardioform numbers, it is important to quantify these bacteria in
activated sludge regularly. One popular method of enumeration is to use light microscopy to
count nocardioform cells along transects of Gram-stained slides (1000X). Two types of counts
have been proposed for this method: (1) Total Filament Intersection Counts (Pitt and Jenkins
1990) and (2) Total and Dispersed Filament Length Counts (Narayanan et al. 2003). These
involve counting either the intersection of each nocardioform filament with the point of a
microscope’s ocular grid along a slide transect (Pitt and Jenkins 1990), or tallying the lengths of
all nocardioform cells within the grid along a slide transect (Narayanan et al. 2003). The
second major method of nocardioform enumeration is by fluorescent in situ hybridization
(FISH). Small-subunit rRNA genes are first sequenced, allowing oligonucleotide probes to be
designed, which will hybridize to bacterial membranes (de los Reyes et al. 1998). Due to lower
costs associated with nocardioform intersection and length counts, these are currently the
methods performed at SRWTP.
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Narayanan et al. (2003) compared the effectiveness of counting bound vs. dispersed
nocardioform filament lengths in predicting activated sludge foaming. Bound filaments are
encased in attached bacteria, usually both filamentous and single-celled, while dispersed
filaments are free from any attached growth. The rationale for comparing bound and dispersed
nocardioform filaments is that the cell surface hydrophobicity of nocardioform filaments is
believed to cause filament attachment to air bubbles, thereby forming stable foam. If floc-
forming bacteria or other solids adhere to the filaments, their hydrophobic surfaces can no
longer make contact with liquid-air interfaces to create foam. These floc-bound nocardioforms
settle out of solution in the same manner as other non-foaming filamentous bacteria, and
therefore should not be included in nocardioform counts when the purpose of the count is a
correlation with activated sludge foaming. Narayanan et al. (2003) performed nocardioform
filament length counts for both bound and dispersed filaments and measured foam height of a
bench-scale aeration basin. This study dealt exclusively with a bench-scale activated sludge
system and the results were not verified using wastewater treatment plant activated sludge
samples. This experiment resulted in dispersed nocardioform filament length counts having a
stronger association with activated sludge foaming than did bound nocardioform length counts
(Narayanan et al. 2003).
Based on this study, SRWTP conducted a RAS Polymer Addition Study (2009), comparing
the Narayanan et al. (2003) bound and dispersed nocardioform length count methods to the
intersection count method already in use at SRWTP for predicting foaming of activated sludge.
All three types of counts were performed on daily samples of SRWTP mixed liquor by
laboratory staff, while foam observations of secondary sedimentation tanks (SSTs) were
recorded by treatment plant operators. SRWTP engineers also concluded that dispersed
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nocardioform length counts were better indicators of foaming than were the other two counting
methods (Boyce and Dial 2009).
The results of the Narayanan et al. (2003) study and the SRWTP RAS Polymer Addition
Study (2009) both show that nocardioforms can exist in activated sludge without causing
appreciable foaming to occur. Once nocardioforms reach a certain abundance, activated sludge
foaming will be initiated (Narayanan et al. 2003; Boyce and Dial 2009). The nocardioform
abundance corresponding to foam initiation is called the foam threshold of activated sludge.
One practical application of nocardioform enumeration is to calculate this threshold, above
which foaming will likely occur. It seems that no universal nocardioform concentration has
been linked to foaming episodes among treatment plants, because other periodic instigators of
foam such as non-nocardioform mycolata and surfactants in wastewater may also contribute to
foam formation (Petrovski et al. 2011).
Activated sludge foaming threshold determination is currently a popular area of study, due to
its potential usefulness in foam control. De los Reyes and Raskin (2002) used light microscopy
to measure nocardioform filament lengths, obtaining the same nocardioform foaming threshold
concentration of 2 x 10 8 µm/ml in batch tests and in a full-scale activated sludge plant (de los
Reyes and Raskin 2002). In 2003 Narayanan et al. refined this enumeration method to include
only measurements of dispersed nocardioform filaments, in order to better correlate
nocardioform counts with activated sludge foaming. The foaming threshold obtained during
this study was approximately 5 x 10 7 µm/g TSS (Narayanan et al. 2003), while the same
dispersed nocardioform filament length enumeration method resulted in an activated sludge
foaming threshold of 1.4 x 10 7 µm/g TSS during the SRWTP RAS Polymer Addition Study.
The SRWTP foaming threshold was based on the nocardioform level at which foaming became
9

problematic in the anaerobic digesters during the Polymer Addition study (Boyce and Dial
2009).
Davenport et al. (2000) used quantitative FISH to enumerate mycolata in mixed liquor in
order to obtain their foaming threshold concentration. Foaming was found to occur when
mycolata numbers exceeded 2 x 10 6 cells/ml or 4 x 10 12 cells/m 2 in the mixed liquor. Unlike the
de los Reyes and Raskin (2002) study in which only the nocardioform groups of mycolata were
counted, Davenport et al. included all mycolata in their threshold determination. In fact, most
of the mycolata counted in the Davenport et al. study were rods or cocci, necessitating the use
of a molecular enumeration method such as quantitative FISH (Davenport et al. 2000). The
comparison of these two studies highlights some difficulties in trying to find a universal
mycolata/nocardioform threshold for foaming; differences in species counted, method accuracy,
and mycolata concentration units make it difficult or impossible to compare the threshold values
from different studies.
Foaming threshold values can even differ among similar studies conducted by the same
researcher. In 2008, Davenport et al. repeated their 2000 study of mycolata threshold values
and found that mycolata concentrations in mixed liquor could exceed the threshold of 2 x 10 6
cells/ml without foam production. They investigated this phenomenon by specifically
enumerating members of the mycolata genera Corynebacterium and Dietzia, known to be less
hydrophobic than other mycolata. As expected, members of these two genera were numerically
dominant in mixed liquor samples from non-foaming wastewater treatment plants exceeding the
mycolata threshold. Davenport et al. (2008) mitigated this threshold violation by limiting the
applicability of mycolata threshold values to only strongly hydrophobic groups of mycolata
(Davenport et al. 2008). Such a large qualification of threshold values further adds to the
difficulty of finding a universal threshold for mycolata foaming.
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Based on the Narayanan et al. (2003) and Boyce and Dial (2009) studies above, I conducted
my own investigation of nocardioform counts vs. foaming of activated sludge. I studied the
relationship between nocardioform counts and activated sludge foaming during the
nocardioform blooms that occurred at SRWTP in Fall of 2009 and 2010. I compared the three
counting methods studied by Boyce and Dial (2009) and added a fourth method of my own
invention: counts of dispersed nocardioform intersections. Accuracy of each counting method
in predicting mixed liquor foaming was assessed through correlation with foam measures. I
used a foam potential test conducted in the laboratory along with secondary sedimentation tank
observations to gauge foam severity. Finally, I recorded logbook entries by SRWTP operators
documenting equipment damage caused by activated sludge foaming during the time periods
corresponding to my data collection. In order to validate my nocardioform counts, isolation and
identification of nocardioforms were attempted during both years of my study. The following
hypotheses and objectives summarize my study:
Hypotheses
(1.) Dispersed nocardioform intersection counts should predict foam production in SRWTP
activated sludge as well as, or better than, the other three types of counts (Total
Intersection, Total Length, and Dispersed Length counts) due to cell surface
hydrophobicity of nocardioform filaments and larger potential for enumeration error of
length counts.
a. Null Hypothesis: The four counting methods should show no difference in
their ability to predict activated sludge foam production at SRWTP.
(2.) The initiation of foam (“foaming threshold”) in SRWTP activated sludge should
correspond to a nocardioform total intersection count of low 10 6 intersections/gram
volatile suspended solids (VSS).
11

Objectives
a. Null Hypothesis: SRWTP activated sludge foaming threshold does not
correspond to a nocardioform total intersection count of low 10 6
intersections/gram VSS.
(1.) Determine which nocardioform counting method best predicts activated sludge
foaming. Accuracy can be gauged through statistical analyses such as regression and/or
correlation applied to each nocardioform count vs. foam dataset to test their strengths in
predicting mixed liquor foaming. Relative standard deviation (RSD) of four strip
counts per counting method (pseudoreplicates) each day, followed by 2 Factor ANOVA
calculations on RSD values to test for statistical significance, should sufficiently gauge
relative precision of the nocardioform enumeration techniques compared.
(2.) Determine a nocardioform concentration threshold for foaming of mixed liquor at
SRWTP for total intersections/g VSS counts.
(3.) Isolate and identify a nocardioform from a sample of mixed liquor used for
nocardioform enumeration during both years of study via culturing and 16s rDNA
sequencing, respectively, to confirm and explain nocardioform counts and growth
trends observed during the study.
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Sample Information
MATERIALS AND METHODS
Activated sludge samples were taken from Mixed Liquor Channel 3 at SRWTP over two
nocardioform blooms (29 June 2009 to 23 November 2009 and 13 September 2010 to 29
November 2010). This channel was chosen for my study because it is the routine sampling
location for nocardioform counts at SRWTP, allowing me to perform most of the counts during
working hours. Samples were collected into 4L plastic sample containers by lowering a dipper
into an underground dedicated sampling port located between the Channel 3 reactors and
secondary clarifiers (SSTs). Foam was removed from the dipper before the samples were
transferred into their containers. Because mixed liquor samples were taken upon exiting the
reactors, peak nocardioform abundances should have been captured. At that point,
nocardioforms had been given oxygen, nutrients, and time to grow and reproduce before being
washed out of the reactors into the SSTs, so their numbers should have been higher than in other
parts of the secondary process (Office of Water Programs 2007).
Samples were collected by treatment plant operators between 10:45 AM and 12:00 PM each
Monday and Tuesday in 2009 and each Monday and Thursday in 2010. Thursday nocardioform
counts were performed for the 2010 study in order to maximize data collection, after a delay in
the nocardioform bloom that year. SRWTP MCRT was under 3 days during my study, so a
nocardioform population sampled on a Monday should have left this portion of the secondary
process by Thursday, and therefore not be sampled twice during my 2010 study.
In 2009, nocardioform counts were performed on samples collected on Mondays, while
corresponding foaming tests were conducted the following day. This schedule had been
decided by SRWTP staff long before my study began. Although both tests should have been
13

conducted on the same sample aliquot, it can be argued that samples collected only one day
apart were actually subsamples of the larger bacterial population residing in the reactors and
SSTs due to the MCRT of approximately two days during 2009. The CO tank (reactor) solids
retention time was less than one day for most of my 2009 study, indicating that the majority of
the organisms in the mixed liquor spent less than a day in the CO tanks/reactors before entering
the SSTs. However, a portion of the mixed liquor in the SSTs is routinely concentrated three to
four times and returned to seed the reactors with beneficial microbes, called Return Activated
Sludge (RAS) (Office of Water Programs 2007), at a flow rate of approximately 27% of
incoming wastewater flow. RAS comprises approximately 93% of the solids entering the
reactors (Josh Nurmi, Assoc. Civil Engineer SRWTP, personal communication, 10 January
2012), so samples of mixed liquor collected one day apart should contain a large portion of the
same nocardioform population.
Nocardioform Enumeration Techniques
(1.) Intersection Counts: Total and Dispersed
A 4L sample of Mixed Liquor Channel 3 obtained by SRWTP operators was homogenized
by vigorous shaking a few times before ~200 mL was poured into a clean plastic cup to be used
for nocardioform counts. TSS and VSS analyses needed to calculate nocardioform abundance
were performed on a mixed liquor aliquot from the same 4L container. The mixed liquor
sample in the cup was mixed by pouring back and forth into a clean cup three times (to avoid
over-mixing and to prevent floc disturbance). A 50 µl aliquot of this suspension was placed
onto a slide, taking care to spread the solids evenly across the surface of the slide. This was
performed in triplicate. Once slides were completely dried, they were Gram stained using the
Hücker modification (Jenkins et al. 1987), and viewed under 1000x magnification using a light
microscope with a reticular grid. On each slide three equally-spaced transects/strips (strip =
14

entire narrow length of the slide) were scanned under the microscope, using the tip of the
reticular grid to count nocardioform filaments (Fig. 1A). Each time the tip of the grid passed
over a filament, one intersection was counted. All nocardioform filament intersections were
included for total intersection counts. For dispersed intersection counts, only the intersections
not touching any floc or filaments were counted (Fig. 1B). A total of three strips per slide were
counted and each experiment was conducted in triplicate. For the three slides counted, the
average number of intersections/g VSS of ML Channel 3 was calculated for both total and
dispersed counts. VSS (Volatile Suspended Solids) values were calculated daily by lab staff on
the same sample used for enumeration. The following calculation was used to determine both
total and dispersed nocardioform intersection counts/g VSS (Cluster 2009: Method SRWTP):
(1.) Calculate the average number of intersections (ŷ) per slide:
a. Add the counts of the three strips for each of the three slides.
b. Average the results of the three slides.
c. Multiply by the sample dilution factor.
(2.) Calculate the g VSS for each slide:
a. g VSS = (TSS mg/L) (VSS%/100) (1L/1000ml) ( 50µl/slide) (1ml/1000µl)
(1g/1000mg)
(3.) Nocardioform filament count [intersections/g VSS] = average number of intersections
(ŷ)/g VSS
(2.) Nocardioform Filament Length Counts: Total and Dispersed
Two of the slides made for nocardioform intersection counts were selected for filament
length counts. Once again, 1000X magnification and a calibrated reticular grid were used to
scan two vertical strips on each slide, on/near the outermost two strips counted during
15

B
A
Figure 1. Bound and dispersed nocardioform filament intersection
counts using light microscopy. Figure 1A depicts a microscope slide
with a frosted edge showing three strips and reticular grid point used
to enumerate filament intersections with this point. Figure 1B shows
bound and dispersed nocardioform filaments being counted with the
point of a reticular grid under 1000x magnification.
16

intersection enumeration (Fig. 2A). The entire lengths of all nocardioform cells within the grid
were measured using gridlines 5 µm apart, so filaments of shorter lengths were estimated (Fig.
2B). All of the filament lengths from all grids counted within each strip were then tallied and
recorded for the total length count. Nocardioform filament length free from bacterial floc and
other filaments was counted separately for the dispersed length count (Fig. 2B). Two strips each
were counted on duplicate slides. Fewer strips were counted due to the increased time required
for this method, compared to the nocardioform intersection counting methods. When
nocardioform concentrations were high low sample dilutions were necessary, as well as an
estimation of filament lengths when crowding disallowed accurate length measurement. For the
four strips counted, average nocardioform filament length (µm)/g TSS of ML Channel 3 was
calculated for both total and dispersed counts. TSS (Total Suspended Solids) values were
calculated daily by lab staff on the same sample used for enumeration. The following
calculation was used to determine both total and dispersed nocardioform filament length (µm)/g
TSS (Cluster 2009: Total and dispersed nocardioform count):
Nocardioform filament length (µm)/g TSS = [Average Filament Length (µm)/50 µL] (1,000
µL/mL) (1,000 mL/L) [1/TSS (mg/L)] (1,000 mg/g).
Foam Estimation Techniques
(1.) Laboratory Foaming Test
The Mixed Liquor Channel 3 sample used for nocardioform counts was also used for the
Foaming Test during the 2010 part of my study. During 2009, I utilized Foaming Test data
collected by SRWTP laboratory staff, so most of my Foaming Tests were performed on ML
Channel 3 samples collected at the same time the following day (Tuesdays). Because the
MCRT was approximately 2 days during that time, a sample collected the next day still
contained a large percentage of the nocardioform population counted the prior day.
17

B
A
Figure 2. Bound and dispersed nocardioform filament length counts
using light microscopy. Figure 2A depicts a microscope slide with a
frosted edge showing two strips and reticular grid area used to
enumerate filament lengths within this grid for each strip. Figure 2B
shows bound and dispersed nocardioform filament lengths counted
using the gridlines within a reticular grid under 1000x magnification.
The length of the entire grid is 100 µm and distance between adjacent
gridlines is 5 µm.
18

The first step in the Foaming Test was to pour a 1,000 mL sample aliquot into a 2 L
graduated cylinder and then to record the sample temperature. A meter connected between an
air supply and an airstone at the bottom of the graduated cylinder ensured that 5 L/min of air
was delivered into the sample for ten minutes. Foam volume in the graduated cylinder was
recorded immediately before shutting off and disconnecting the air supply. After ten more
minutes the volume of uncollapsed foam remaining in the graduated cylinder was recorded and
the % Foam (aerated) and % Collapse were calculated for the sample using the following two
equations (SRCSD Environmental Laboratory 2009):
(1.) % Foam (aerated) = (maximum foam volume) x 100
(initial sample volume)
(2.) % Collapse = (maximum foam volume – foam volume after 10 min.) x 100
(maximum foam volume)
(2.) Field Foam Observation
Each Monday and Thursday during the 2010 nocardioform bloom I measured the percent of
foam coverage of the northwest quadrant of the inner well of Secondary Sedimentation Tank
#17 adjacent to the ML 3 sample point used for nocardioform enumeration (Boyce and Dial
2009). Field observations of activated sludge foam severity must be conducted at the SSTs for
the mixed liquor channel of interest because most of the activated sludge process is
underground.
(3.) SRWTP Operator Logbook Entries
The third way I attempted to gauge nocardioform foam severity was to search through
SRWTP operator logbooks for entries documenting equipment failures due to activated sludge
foaming during the time of my study. I found logbooks for areas X04 (CO Tanks and SSTs)
and X08 (Digesters) for both 2009 and 2010. Because much of the equipment damage caused
19

y foam reportedly occurs in the digesters (Boyce and Dial 2009), I included this data in my
analysis. I recorded logbook entries documenting equipment damage related to activated sludge
foam in the table in Appendix B. Equipment damage was linked to total nocardioform
intersection counts based on the dates on which both occurred. It takes less than six minutes for
mixed liquor to travel from the CO tanks to the ML Channel 3 sampling point, so damage in the
CO tanks must occur on the same day as the nocardioform count to be included in the analysis.
Mixed liquor nocardioforms can take between 3 to 32 hours to travel to the digesters from the
sample point, depending on (1) the mode of travel, either via sludge or wasted surface scum,
and (2) the thickening process used. So, damage to the digesters can occur on either the same
day as nocardioform counts, or on the following day, in order to be included in the logbook
analysis (Josh Nurmi, Assoc. Civil Engineer SRWTP, personal communication, 27 October
2011). This correlation of equipment damage with nocardioform abundance will be used to
determine a secondary nocardioform concentration threshold for foaming of activated sludge.
Laboratory foaming test data can only show the threshold between little/no foam and significant
foam under controlled conditions, but logbook data can potentially show the nocardioform
threshold at which foaming disrupts secondary process functioning. This functional foaming
threshold would correspond to the lowest nocardioform abundance causing significant foaming-
related equipment problems.
Nocardioform Isolation and Identification
(1.) Overview of Isolation Procedure Completed Over the Summer and Fall of 2009:
The Mixed Liquor Channel 3 sample from 29 August 2009 was homogenized by shaking
before 1.0 L was poured into a clean graduated cylinder. Air was bubbled through the sample
for 5 minutes in order to create foam. Approximately 33 g of this foam was collected from the
sample surface and added to 404 mL deionized (DI) water. This suspension of ML Channel 3
20

foam and DI water was quickly sonicated (3 pulses of 1 second each at 8000 no load min -1 ) to
detach competing bacteria while avoiding damaging nocardioform filaments. In order to
remove small non-nocardioform bacteria, 5 mL of this suspension was filtered through 1 µm
filter paper using a vacuum apparatus, and the bacteria remaining on the filter were plated by
briefly placing the surface of the filter paper on one side of a TYEG agar w/NaCl plate and
streaking for isolation. Unfiltered diluted foam and three DI water control replicates were also
plated on TYEG agar w/NaCl. [TYEG agar w/NaCl, pH 7.1: 1% tryptone, 0.25% yeast extract,
0.2% glucose, 1.5% agar, and 0.7% NaCl in DI water (Reyes and Raskin 2002).] Finally, the
plates were incubated at 35°C for 8 days. Three suspected nocardioform colonies were each
inoculated in parallel into liquid TYEG media and re-streaked onto solid TYEG agar plates in
order to study whether bacterial growth and cell morphology differ between liquid and solid
substrates. Isolated nocardioform colonies were stored in liquid TYEG containing 20%
glycerol (volume/volume) at -20°C until needed for identification.
(2.) Overview of Isolation Procedure Completed Over the Fall of 2010:
There was a high abundance of free-floating filamentous bacteria in the mixed liquor during
2010, so I did not expect the filtration method to work. Instead, I added roughly 20 mL of
mixed liquor foam to ~70 mL of sterile phosphate buffer and shook the suspension vigorously.
I waited almost an hour for most floc particles to settle, after which time I used a sterile needle
to dab at solid particles (clusters of bacteria, including nocardioforms) at the surface of the
suspension and streak them onto Standard Methods agar plates. [CRITERION TM Standard
Methods agar, purchased from Hardy Diagnostics, is composed of 0.5% pancreatic digest of
casein, 0.24% yeast extract, 0.1% glucose, and 1.5% agar (weight/volume), pH 7.0±0.2 at
25⁰C.] I used Standard Methods agar during 2010 because TYEG agar did not seem to be very
effective at nocardioform isolation, in addition to the fact that I was able to acquire the Standard
21

Methods agar at no cost. Incubation time and temperature were the same as for 2009. This
isolation procedure was used on ML Channel 3 samples collected on two different dates: 28
September 2010 and 29 October 2010. As in 2009, two suspect nocardioform colonies from
each ML Channel 3 sample were inoculated in parallel into both Tryptic Soy Broth and onto
fresh Standard Methods agar (each colony into both media types) to compare cell morphologies
between liquid and solid media. Isolated nocardioform colonies were stored in a solution of
TSB and 11% glycerol (volume/volume) at -20°C until needed for identification.
(3.) Overview of Nocardioform Molecular Identification Procedure:
On 12 March 2010, total genomic DNA was extracted from an isolated nocardioform colony
from the ML Channel 3 sample collected on 29 August 2009 (grown on a freshly-prepared
TYEG agar plate). This was followed by PCR amplification of the DNA fragment encoding
bacterial 16S rRNA using the following primers: U1510R (5’-GGTTACCTTGTTACGACTT)
(Baker et al. 2003; Reysenbach and Pace 1995) and E8F (5’-AGAGTTTGATCCTGGCTCAG)
(Baker et al. 2003; Reysenbach et al. 1994). Agarose gel electrophoresis was run on the
resultant PCR product to make sure that the DNA extraction was successful. Unfortunately, the
gel showed only DNA from the positive control; the absence of DNA from the extracted
isolates indicates that the extraction procedure failed. In order to ensure successful
identification of my nocardioform isolates, I sent them to a commercial laboratory for PCR and
identification. Three frozen nocardioform isolates from ML Channel 3 collected on 17
September 2009, one from 28 September 2010, and two from 29 October 2010 were streaked
onto Standard Methods agar plates. After approximately one week of incubation at 35°C, the
plates were shipped overnight to Microbial ID, Inc. in Newark, Delaware. I requested a
polyphasic analysis, incorporating fatty acid analysis (FAME) with DNA sequencing of each
isolate, in order to ensure correct identifications.
22

RESULTS
Because nocardioforms are believed to cause activated sludge foaming, linear regression was
selected as an appropriate model for data analysis. Once my data collection was complete I first
tested the residuals from this model for normality and homoscedasticity in order to determine if
model assumptions were met and linear regression analysis could indeed be used. My data
consisted of twelve nocardioform count vs. activated sludge foam analyses: four counting
methods correlated to laboratory foaming tests for 2009 and 2010, in addition to correlations of
each count to SST foam coverage observations during 2010. Four of the twelve residuals failed
the normality assumption, having Shapiro-Wilk p-values less than 0.05. Homoscedasticity was
tested graphically, plotting residuals of each count vs. foam measure against the raw counts
used to calculate those residuals. If there was equal variance, the datapoints would be spread
evenly above and below a horizontal line at which residual values would be zero. The residual
plots for all 12 analyses seemed roughly homoscedastic, with more clustering of points in the
2009 datasets (probably due to fewer points than for 2010). For most residuals, log10
transformations increased normality and slightly increased homoscedasticity. Therefore, linear
regression could be performed on log10 transformed foam data for all twelve count vs. foam
analyses. However, test assumptions for linear regression were not perfectly met, so non-
parametric correlation analysis such as Spearman’s Rho should also be performed to determine
the strength of the relationship between each counting method and foam production in ML for
both 2009 and 2010 datasets (hypothesis 1). The most accurate counting method will be the one
with the highest r 2 and Spearman’s rho correlation coefficient for both 2009 and 2010
nocardioform count/foam test correlations and also for the 2010 nocardioform count/SST foam
observation correlations.
23

The precision of each nocardioform enumeration method (Objective 1) can be measured
sufficiently through relative standard deviation (RSD) calculations, commonly used in
environmental laboratories and by the Environmental Protection Agency. Because each
counting method requires counts of several strips (slide transects), four strips were used as
pseudoreplicates each day for the purpose of calculating a mean relative standard deviation for
each counting method over the whole study. The nocardioform enumeration method with the
highest precision would have the lowest mean RSD. Finally, statistical analysis of RSD means
was performed using non-parametric Friedman’s test due to departures from normality of the
data, and also due to the repeated-measures test design (performing all four counting methods
on the same sample slides each day).
Before statistical analysis was performed, a graphical comparison of the four nocardioform
counting methods and activated sludge foam measures was constructed as a means of
simplifying and summarizing the results of this study. Figure 3 below shows not only trends in
nocardioform count values over time, but also the relationship of each count with each other and
with two foam measures. It is common practice in wastewater treatment to plot trends of many
parameters of interest over time, in order to look for patterns indicating problems or potential
future problems. In this case, the trends show that there is indeed some correlation between all
four nocardioform counts and both foam measures during the time periods studied. The most
noticeable divergence from common trends is the laboratory foaming test Foam Percent dataset,
with four stray peaks. This was likely due to other components of the mixed liquor unrelated to
nocardioforms, such as surfactants or other types of foaming filamentous bacteria discussed
earlier. Also, it seems that nocardioform counts and related foam measures were all lower in
2009 than in 2010, the possible reasons to be discussed later. The last observation is that the
2009 dataset spanned a longer period of time than the 2010 one, yet had fewer datapoints. The
24

Figure 3. Summary graph depicting trends in all four nocardioform counts, laboratory foaming test results, and
SST foam percent observations during both 2009 and 2010. (The dates are listed as month/day/year.)
25

eason for this was that I chose to perform nocardioform counts twice weekly in 2010, instead
of one time per week as in 2009, due to a late nocardioform bloom in 2010.
Figure 4 shows linear regression scatterplots for all four nocardioform counting methods
during both 2009 and 2010. Nocardioform counts were compared to activated sludge foam
production using two different foam estimation techniques: laboratory foaming tests and SST
foam coverage observations. Laboratory foaming tests were conducted during both 2009 and
2010, while SST observations were only made in 2010 as a secondary means of gauging
activated sludge foaming. Both types of foam measures were log10 transformed in order to
correct for departure from normality of the residuals of most nocardioform count/foam
regressions.
The linear regression scatterplot comparison in Figure 4 shows that most of the r 2 values
obtained during 2009 were lower than those obtained during 2010, indicating weaker
correlations between nocardioform counts and activated sludge foaming during that year. The
most likely cause of this was the timing of sample collection during 2009. Because foaming
test samples were collected one day after those for nocardioform counts, a proportion of the
nocardioform population differed between the two samples and therefore the foam tests did not
accurately measure activated sludge foaming by the nocardioform populations counted. During
2010 both nocardioform counts and laboratory foaming tests were performed on the same
sample, so foaming tests more accurately measured nocardioform foaming. The second
sampling issue possibly contributing to lower r 2 numbers during 2009 may have been the
temporal variability of sample collection during 2009 vs. that of 2010. During 2010, samples
were collected twice each week for approximately 2.5 months, while most 2009 samples were
collected weekly (excluding some weeks) for almost 5 months. This may have contributed to
higher probability of encountering extraneous sources of foam, such as surfactants or blooms of
26

2009
2010
2010 SST Observation
Log₁₀ Percent Foam (Lab Foam Test)
Log₁₀ Percent Foam (Lab Foam Test)
Log₁₀ Foam % (SST Foam Coverage)
Nocardioform Filament Count Method
Total Intersections/g VSS Dispersed Intersections/g VSS Total Filament Length/g TSS Dispersed Filament Length/g TSS
Figure 4. Linear regression scatterplots of each type of nocardioform filament count vs. percent foam of SRWTP ML 3 for 2009 and 2010: Total Intersection Counts, Dispersed Intersection Counts, Total Length
Counts, and Dispersed Length Counts. All four counts were performed on the same sample each Monday during the study. Laboratory foaming tests were conducted the following day during 2009 and the same
day as the nocardioform counts in 2010. The MCRT averaged ~2 days during the study. Observations of percent foam coverage over the surface of one SST were made the same afternoons as the nocardioform
counts and foaming tests.
27

non-nocardioform foaming bacteria in the activated sludge during the extended period of study
in 2009. Despite the longer period of sample collection during 2009, fewer samples were
collected that year compared to 2010, possibly contributing to the differences in r 2 values
between the two years.
In addition to differences in r 2 trends between the two years of study, there were also r 2 trend
differences among the four nocardioform counting methods and between the two measures of
activated sludge foaming. Figure 4 shows that total nocardioform intersection counts had the
highest r 2 values compared to those of the other three counting methods during both 2009 and
2010, including the analyses of SST foam observations during 2010. These consistently higher
r 2 values indicate that total intersection counts more strongly correlate with activated sludge
foam production than do any of the other three counting methods. This finding does not support
my hypothesis that dispersed nocardioform intersection counts would best predict activated
sludge foaming. Lastly, Figure 4 also shows that linear regression analyses of all four
nocardioform counting methods vs. SST percent foam coverage have higher r 2 values than any
of the regression analyses involving laboratory foaming tests for either 2009 or 2010. It seems
that activated sludge foaming can be measured better by observing foam coverage over one SST
than by performing foaming potential tests of mixed liquor in the laboratory. Perhaps this was
the reason why SRWTP staff decided to discontinue laboratory foaming tests at the end of 2009.
Another way to compare nocardioform count/activated sludge foaming correlations is in a
simplified tabular form seen in Table 1. Because the assumptions of normality and possibly
homoscedasticity required for linear regression analysis were not achieved by the residuals of
every nocardioform count vs. foam comparison even after log10 transformation, nonparametric
Spearman’s rho was used alongside linear regression calculations. Combining the power of
linear regression analysis with the robustness of Spearman’s rho will better support the
28

Table 1. Comparison of the correlations between each nocardioform counting method and laboratory foaming test and SST foam observation results
using both nonparametric Spearman's rho and linear regression analyses.
Nocardioform
Count Method
Total
Intersections/g
VSS
Dispersed
Intersections/g
VSS
Total Length
(μm)/g TSS
Dispersed
Length (μm)/g
TSS
2009 Foam Test
Spearman's rho
nonparametric
correlation
Correlation
Correlation
0.494 0.713**
0.808**
Coefficient
Coefficient
r² 0.470
r² 0.508
Sig. (1-tailed) 0.026 Sig. (1-tailed) 0.000 Sig. (1-tailed) 0.000
*
Correlation
Coefficient
N 16 N 22 N 22
N 16 N 22 N 22
N 16 N 22 N 22
Correlation
Correlation
Correlation
0.503 0.698**
0.753**
Coefficient
Coefficient
Coefficient
r² 0.340
r² 0.410
Sig. (1-tailed) 0.024 Sig. (1-tailed) 0.000 Sig. (1-tailed) 0.000
*
N 16 N 22 N 22
** Correlation is significant at the 0.01 level (1-tailed).
* Correlation is significant at the 0.05 level (1-tailed).
Linear
regression
2010 Foam Test
Spearman's rho
nonparametric
correlation
Linear
regression
2010 SST Observations
Spearman's rho
nonparametric
correlation
Correlation
Correlation
Correlation
0.618 0.615**
0.732**
Coefficient
Coefficient
Coefficient
r² 0.459
r² 0.389
Sig. (1-tailed) 0.005 Sig. (1-tailed) 0.001 Sig. (1-tailed) 0.000
**
Correlation
Correlation
Correlation
0.484 0.696**
0.804**
Coefficient
Coefficient
Coefficient
r² 0.373
r² 0.429
Sig. (1-tailed) 0.029 Sig. (1-tailed) 0.000 Sig. (1-tailed) 0.000
*
Linear
regression
r²
r²
r²
r²
0.674
0.529
0.465
0.425
29

conclusion that total nocardioform intersection counts most accurately predict activated sludge
foaming. Transformed foam test and SST observation values were used for regression but not
for correlation analyses in order to provide a separate, independent means of analysis.
Spearman’s rho correlation coefficients showed significance at p

counting methods was performed on each of the four replicate slide strips. Relative standard
deviation (RSD) was calculated for each set of four pseudoreplicate nocardioform strip counts
for each nocardioform counting method every day of the study. Precision of the four counting
methods was assessed through analysis of the mean RSD of each nocardioform counting
method during the entire study. Statistical analysis of these RSD means required a non-
parametric repeated-measures test due to departures from normality and unequal means among
the RSD values of the four nocardioform counting methods performed on the same sample slide
strips (related measurements). Friedman’s test is able to analyze datasets having the
aforementioned criteria, so this test was chosen for the RSD comparison among the
nocardioform counting methods.
Table 2 shows the SPSS (PASW Statistics 18.0.0, July 2009) output for Friedman’s test of
mean ranks of the RSD data among all four nocardioform counting methods. According to this
test there is a significant difference among the mean RSD ranks of the four nocardioform
counting methods compared (x 2 =31.106, df=3, p

Table 2. SPSS output for nonparametric Friedman test comparing the relative standard deviations of all four nocardioform
counting methods.
RSD Comparison of all 4 Nocardioform Counting Methods
Descriptive Statistics
Count Method
N Mean Std. Deviation Minimum Maximum
RSD Total Intersection 38 88.5834 55.69333 .00 200.00
RSD Dispersed Intersection 38 94.2334 64.91792 .00 200.00
RSD Total Length 38 57.9887 25.79935 17.89 124.95
RSD Dispersed Length 38 65.4611 28.46418 22.26 135.57
Ranks
Nonparametric ANOVA: Friedman Test
Test Statistics a
Count Method
Mean Rank
N 38
RSD Total Intersection 2.83 Chi-square 31.106
RSD Dispersed Intersection 3.22 df 3
RSD Total Length 1.68 Asymp. Sig. .000
RSD Dispersed Length 2.26 a. Friedman Test
32

standard deviation. Even though relative standard deviation calculations account for different
measurement scales through division of averages of each set of replicates, RSD is greatly
increased when strip counts of zero are included. Because very low numbers of dispersed
nocardioform filaments were detected during my study, a difference of only a few intersections
among replicate strips created disproportionally large RSD values compared to length counts
with a few µm difference among replicates. Also, sample dilutions were chosen in order to
maximize the efficiency of nocardioform length counts, so dilutions were always too high for
precise dispersed nocardioform intersection counts.
Once accuracy and precision were compared among nocardioform counting methods, it was
time to address my second hypothesis concerning the foaming threshold of activated sludge at
SRWTP. I predicted the onset of activated sludge foaming would correspond to a total
nocardioform intersection count of low 10 6 intersections/g VSS. Three independent measures of
activated sludge foaming were employed during my study: laboratory foaming potential tests,
SST foam observations, and logbook entries by SRWTP operators documenting any secondary
treatment process disturbances caused by foaming. Linear regression was used to predict
foaming thresholds based on laboratory foaming tests and SST observations, while logistic
regression was intended to predict a threshold based on operator logbook data.
Before it was possible to calculate the nocardioform level corresponding to the onset of
activated sludge foaming it was necessary to better define the onset of foaming. I gathered
foam test results for fourteen tests just prior to my 2009 study, all of which had corresponding
total nocardioform intersection counts less than the detection limit (none observed). When
nocardioforms were not observed, the foam test results ranged from 4% to 10% aerated foam.
Even a deionized water blank produced 4% foam when under aeration. Similarly, the y-
intercept of the linear regression equation for the 2010 Total Nocardioform Intersection Count
33

vs. Laboratory Foaming Test Percent Aerated Foam was 12.898 (t=2.917, p

Figure 5. Scatterplot showing the results of the laboratory foaming tests performed in 2010. The percent
of mixed liquor foam measured under aeration was compared to the percent of this foam which collapsed
after 10 minutes post-aeration. Foam remaining after such an extended period of time likely indicates
elevated nocardioform numbers, and can therefore be used to quantify a threshold for activated sludge
foaming due to nocardioforms.
35

filamentous bacteria. Consequently, linear regression was used to assess the relationship
between activated sludge foaming under aeration and total nocardioform intersection counts.
Using linear regression coefficients for slope and y-intercept, it was possible to calculate a total
nocardioform intersection count of 1.0x10 6 intersections/g VSS when the lower end of the foam
threshold range, or 27% aerated foam, was reached. Interestingly, this same nocardioform
concentration, corresponding to 27% activated sludge foam, was calculated using both
laboratory foam test and SST foam observation data from 2010. The high end of the foam
threshold range, 30% aerated foam, was reached when total nocardioform intersection counts
were 1.2x10 6 intersections/g VSS. These findings support my hypothesis that the SRWTP
activated sludge foaming threshold would be reached when nocardioform total intersection
counts climbed to low 10 6 intersections/g VSS.
In order to support and strengthen my findings, I also attempted to use a secondary means of
calculating a nocardioform total intersection value corresponding to an activated sludge
foaming threshold, using SRWTP operator logbooks documenting activated sludge foaming
problems during secondary treatment. Operator logbook entries could only be analyzed
statistically through logistic regression because process disturbance is a dichotomous variable.
For each date of total intersection count a nominal result of “present” or “absent” process
disturbance was recorded. In order to analyze this data through logistic regression, the values 1
and 0 were used to represent presence and absence of process disturbances, respectively.
Logistic regression analysis was performed on this data to determine the nocardioform total
intersection count at which process disturbances due to activated sludge foaming first occurred
(foaming threshold). Figure 6 shows a scatterplot of process disturbance vs. total intersection
count. This figure illustrates two obvious reasons for the failure of logistic regression to
determine the foaming threshold: (1) too few process disturbances were listed in the operator
36

Figure 6. Scatterplot showing the relationship between activated sludge foaming episodes causing
disturbances in secondary treatment at SRWTP and total nocardioform intersection counts associated
with the disturbances. For each date of nocardioform intersection count, process disturbances are listed
as either present (1) or absent (0).
37

logbooks and (2) process disturbances occurred nearly throughout the entire range of
nocardioform concentrations. In addition, when an omnibus test of model coefficients was run
in SPSS to assess the logistic regression model used, the chi-square value was not significant,
indicating that the overall model was not statistically significant. Consequently, operator
logbook data cannot be used for foam threshold analysis, therefore activated sludge foaming
threshold concentration of nocardioforms can only be calculated using the linear regression
models described earlier.
The final portion of my project involved the isolation and identification of nocardioforms
from mixed liquor samples analyzed during both 2009 and 2010. Ten different isolation
attempts were made, three of which resulted in bacterial identifications (two of the three
attempts produced multiple isolates). Mixed liquor samples were aerated to form foam, which
was then diluted before either (1) sonication and filtration (performed in 2009) or (2) skimming
surface solids (as in 2010) and streaking onto agar plates. Two different types of agar media
were used during my study due to availability of media and apparent lack of selective
capabilities of TYEG media during 2009.
Each nocardioform isolation attempt entailed viewing several different types of colonies on
each agar plate under 400x magnification with a light microscope. The only colonies whose
members resembled nocardioforms were white/crème colored, round, and had smooth edges. In
2009 the isolated bacteria appeared to have typical nocardioform branched morphology, but
were always accompanied by Gram positive cocci. In 2010 nocardioform branching
morphology was observed most often when grown in liquid tryptic soy broth, compared to
growth on Standard Methods agar. No coccoid forms were observed with the 2010 isolates.
Not surprisingly, three isolates from 2009 were all determined to be non-nocardioform bacteria
belonging to the genus Microbacterium, while three isolates from 2010 were matched on the
38

species level to Mycobacterium mucogenicum by Midi Labs (a commercial laboratory in
Newark, DE).
39

DISCUSSION
The purpose of this study was to assess the relative accuracy and precision of four
nocardioform filament counting methods: total and dispersed intersection counts and total and
dispersed length counts. My first hypothesis, that dispersed nocardioform intersection counts
should most accurately predict foam production in SRWTP activated sludge, was not supported
by the results of this study. Instead, total intersection counts showed the strongest correlation
with activated sludge foaming measures. The most likely explanation is that too few dispersed
nocardioform filaments were encountered during the dispersed intersection counts. This is also
the reason for the poor precision of the dispersed intersection counting method compared to the
other three methods. Conversely, total nocardioform length counts showed the highest
precision due to large numbers of filaments encountered. Length counts would be expected to
have better precision than intersection counts because of the larger sample area counted:
microscope slide transect widths of 100 µm are counted for length counts, while intersection
counts focus on transects only 1 µm in width. It is surprising that the extra information gained
by counting entire filament lengths did not produce superior accuracy in predicting activated
sludge foaming by the total nocardioform length count method. The reason for the relative
inaccuracy of nocardioform length counts may have been a higher rate of counting error due to
eye fatigue and filament area estimations when nocardioform filaments were crowded together.
My second hypothesis predicted that the initiation of foam (“foaming threshold”) in SRWTP
activated sludge would correspond to a nocardioform total intersection count of low 10 6
intersections/gram volatile suspended solids (VSS). The results of this study support this
hypothesis, because the foaming threshold based on laboratory foaming tests and SST foam
observations ranged from 1.0x10 6 to 1.2x10 6 total nocardioform filament intersections/g VSS.
40

The total intersection value of 1.0x10 6 intersections/g VSS is the level at which SRWTP
personnel begin increased nocardioform monitoring, including doubling the frequency of
filament counts. It is reassuring that my activated sludge foaming threshold calculations
support a historical value based on years of observations by SRWTP staff.
Originally, I had planned on calculating an activated sludge foaming threshold based on
three sources: (1) laboratory foaming tests, (2) SST foam observations, and (3) SRWTP
operator logbook entries documenting process disturbances resulting from foaming issues. I
correlated dates of secondary treatment process problems caused by foam with nocardioform
total intersection counts corresponding to those dates. My intent for these operator logbook
entries was to better define the nocardioform level at which foaming becomes problematic,
calculating a foaming threshold with a practical basis. Unfortunately, I was unable to use the
logbook data for any calculations. Not only were there too few entries related to foaming
during my two years of study, but the process disturbances listed occurred over a very wide
range of nocardioform numbers. It is possible that nocardioform numbers were not high enough
to cause foaming problems during my study. The foaming issues occurring at the lowest
nocardioform concentrations may have been due to non-nocardioform sources of foam. The
other possibility is that SRWTP operators were inconsistent in their descriptions of reasons for
equipment repairs. Only logbook entries listing foaming as a reason for equipment failure were
included in my study, so any entries omitting reasons for repairs were not included in my
analysis. In fact, most entries did not include any reasons for equipment repairs, and those that
did seemed to be written by the same operators. These inconsistencies precluded the use of
operator logbook data in calculating nocardioform threshold for foaming in the secondary
process at SRWTP. Luckily, the other two measures of activated sludge foaming were
sufficient to estimate a rough foaming threshold range.
41

Another area of my study in which I encountered difficulties was the nocardioform isolation
from mixed liquor samples. During 2009 I spent considerable time and money on measures to
maximize nocardioform growth and to restrict growth of competing bacteria. Some of these
efforts included blending and sonicating mixed liquor samples in order to free bound
nocardioforms from attached floc, filtering mixed liquor supernatant to concentrate
nocardioforms on the filter, and preparing TYEG media to encourage growth of nocardioforms.
The end result was isolation of a member of the genus Microbacterium instead of a
nocardioform. Many Microbacterium strains are Gram-positive rods, some forming V shapes
but lacking primary branching (Takeuchi and Hatano 1998). I must have mistaken clusters of
these V-shaped cells for branched filaments when I examined the colonies microscopically.
Also, each isolated colony I examined seemed to contain Gram positive cocci alongside the bent
rods, but according to Takeuchi and Hatano (1998), members of Microbacterium do not
generally have a rod-coccus growth cycle. There must have been two species of bacteria
coexisting within each colony.
During 2010 nocardioform concentrations in mixed liquor were generally higher than in
2009, so I tried less cumbersome means of nocardioform isolation. I simply aerated mixed
liquor samples to bring nocardioforms to the surface before diluting surface foam with sterile DI
water or phosphate buffer. Then, I skimmed the surface of the diluted foam sample with a loop
and streaked the foam onto either TYEG agar or Standard Methods agar. I even tried to streak
undiluted mixed liquor foam onto agar plates, but ended up with overgrowth of competing
bacteria, yeast, and fungus. After only a few failed attempts at nocardioform isolation, I
discovered what appeared to be gram-positive branched filamentous bacteria. However, the
results of a polyphasic analysis incorporating both DNA sequencing and FAME indicated that
three isolates from two different samples taken in 2010 were all Mycobacterium mucogenicum.
42

These gram-positive curved bacilli do not have branched morphology (Springer et al. 1995), so
it is puzzling that I mistook this type of bacteria for a nocardioform. Once again, perhaps
clusters of curved bacilli are similar in appearance to longer branched filaments.
Interestingly, although not itself a nocardioform, Mycobacterium mucogenicum have cell
walls containing mycolic acids like nocardioforms (Springer et al. 1995). Because mycolic
acids confer hydrophobicity to bacterial cells, M. mucogenicum likely concentrate in mixed
liquor foam along with any nocardioforms. This would explain why two separate nocardioform
isolation attempts resulted in M. mucogenicum instead. Also, this group grows relatively
quickly (2-4 days) under aerobic conditions in the same temperature range as many
nocardioforms (28-37°C) (Springer et al.1995), so it is likely that they outcompeted
nocardioforms during my 2010 isolation attempts.
There is one major problem with using microscopy to tentatively identify and enumerate
nocardioforms. Some filamentous bacteria, including the nocardioform Gordonia amarae, can
experience morphological shifts from filamentous to coccoidal and rod-shaped cells when
grown under stressful environmental conditions, such as on solid agar (Ramothokang et al.
2006). If this is a common occurrence, then it seems impossible to isolate such nocardioforms
using microscopy to examine colonies grown on agar; without the typical nocardioform
filament branched morphology to use for preliminary selection of isolates, one would have to
perform DNA sequencing of dozens of bacterial colonies just to find a single nocardioform.
Instead, it might be more efficient to try a metagenomic approach to nocardioform
identification. This would allow for detection of uncultivable and possibly novel nocardioforms
in samples of mixed liquor.
Metagenomic analysis of activated sludge could also be used to detect all groups of mycolic
acid-containing actinomycetes (mycolata), instead of focusing on nocardioforms. According to
43

Davenport and Curtis (2002), many groups of mycolata play a role in activated sludge foaming
due to their shared hydrophobicity. In fact, these researchers found a strong correlation between
mixed liquor foaming onset and increases in rod and coccoid morphotypes of mycolata. These
morphotypes were also found to comprise over 79% of the mycolata population in all of the
mixed liquor samples studied. In light of these results, Davenport and Curtis (2002) cautioned
that enumeration of filamentous mycolata such as nocardioforms using conventional
microscopy may be misleading. If it is true that nocardioforms act more as indicators of
mycolata populations in mixed liquor, rather than as the major cause of foaming, then the range
of relatively weak correlations between foaming and nocardioform counts encountered in my
own study would be expected.
Another study demonstrating the unpredictable relationship between concentrations of
filamentous foam-forming bacteria and activated sludge foaming was conducted by Hug et al. in
2005. These researchers measured foam coverage over activated sludge basins and abundance
of nocardioforms and M. parvicella in samples of the activated sludge in a Swiss treatment
plant. They found that the foaming was variable throughout the study and only partially
correlated with nocardioform concentration. Contrary to previous studies, M. parvicella
showed no correlation with foaming events at the treatment plant monitored in the Hug et al.
study. The conclusion drawn by these researchers was that high numbers of M. Parvicella and
nocardioforms are “not sufficient” to cause foaming problems (Hug et al. 2005).
A study by Narayanan et al. (2003) highlights possible reasons for the unpredictability of
nocardioform abundance correlations to activated sludge foaming. Mixed liquor and foam
samples collected from the City of San Francisco’s Southeast Water Pollution Control Plant
were tested for total suspended solids (TSS) and for total and dispersed nocardioform
abundance. Mixed liquor was found to contain approximately 53% of the TSS, compared to
44

47% in the surface foam. This foam contained 67% of all nocardioforms, with only 33%
remaining in the underlying mixed liquor. Finally, 74% of the nocardioforms in the foam layer
were dispersed, while 26% were floc-bound (Narayanan et al. 2003).
A few conclusions can be drawn from these results. First, it is unlikely that all of the TSS in
the surface foam was composed of nocardioforms, so this mixed liquor foam must have
contained a significant proportion of other bacteria. Because 26% of the nocardioforms in the
foam were floc-bound, there must have been at least floc-forming bacteria in the foam along
with nocardioforms. If these floc-forming bacteria and the others contributing to the high foam
TSS happened to be mycolata, then it is not clear how large of a contribution to mixed liquor
foaming was made by nocardioforms at the City of San Francisco’s Southeast Water Pollution
Control Plant.
The Narayanan et al. (2003) study results also highlight a potential problem with all
nocardioform counting methods relying on microscopy. These methods are based on the
assumption that the microscope slides counted accurately represent the nocardioform population
in the activated sludge sample analyzed. Sample slides are prepared by gently mixing samples
before pipetting 50 µl onto each slide (Cluster 2009). Samples must be gently mixed in order to
avoid removing attached bacteria from bound nocardioforms, so it seems impossible to evenly
incorporate surface foam back into the liquid portion of the sample drawn for sample slides.
Also, a pipette is used to transfer sample aliquots onto slides, so any foam floating on the
surface will be missed. If Narayanan et al. (2003) are correct in their estimation that 67% of all
aeration basin mixed liquor nocardioforms reside in surface foam, then the inability to
incorporate foam during sample slide preparation can cause inaccurate nocardioform counts.
During my own study I used the same sample slides for all four of the nocardioform counting
methods compared, so any difficulties in slide preparation would affect all methods. This may
45

have also contributed to the relatively poor correlations of my four counting methods with
activated sludge foaming.
In light of the potential inaccuracies involved in conventional nocardioform enumeration
techniques using microscopy, it is surprising that the nocardioform foaming threshold range
calculated from the results of my study closely matched the nocardioform level historically
associated with activated sludge foaming at SRWTP (1.0x10 6 -1.2x10 6 total intersections/g
VSS). However, the threshold for foaming calculated during the SRWTP RAS Polymer
Addition Study was approximately 2.7x10 6 total nocardioform intersections/g VSS (Boyce and
Dial 2009). Although these threshold values differ, the magnitude of difference is much smaller
than one would expect given the number of uncontrolled variables occurring during wastewater
treatment during different time periods.
The complexity of wastewater as a medium for bacterial growth and the variable nature of
wastewater treatment parameters over time may cause nocardioform concentration thresholds
for activated sludge foam onset to also vary with changing conditions. For example, a certain
MCRT or F/M ratio may cause non-nocardioform mycolata to outcompete the nocardioforms in
activated sludge for a period of time. If these mycolata form stable foams, then any foaming
threshold calculation based on nocardioform numbers during this time would be misleading and
would differ from a previously calculated threshold. This situation again highlights the
importance of determining the magnitude of nocardioform contribution to activated sludge
foaming. Do nocardioforms themselves play the major role in foam formation, or are they a
visible indicator of a larger mycolata population primarily responsible for the foaming?
Petrovski et al. (2011) investigated some of the mechanisms of foam formation in activated
sludge. The two major factors found to influence foaming were: (1) the types of mycolata
present in activated sludge and (2) the presence of surfactants in the wastewater. Sixty five
46

mycolata strains underwent laboratory foaming tests similar to the foaming tests conducted
during my own study. Petrovski et al. (2011) found that the majority of the mycolata strains
produced foams, but the amount and stability of foam varied among the strains. Cell surface
hydrophobicity (CSH) of each mycolata strain was then tested in order to explore possible
reasons for the foaming differences among the strains. Not surprisingly, all mycolata strains
were found to be hydrophobic, although CSH values varied considerably among the strains.
The differences in foaming properties and cell surface hydrophobicity values among the
mycolata strains were further supported by differences in foaming threshold values among
them, although surfactant production by these mycolata made it difficult to directly link
foaming capacity, cell surface hydrophobicity, and foaming threshold values. Stable foams
required high mycolata cell numbers, ranging from 2.5x10 6 cfu/ml (Gordonia sputi) to 1.5x10 9
cfu/ml (Gordonia terrae). G. amarae reached its foaming threshold at a cell abundance of
1.5x10 8 cfu/ml, so relatively more cells of this nocardioform are required to produce stable
foam compared to other mycolata. Similar foaming threshold values were obtained from pure
cultures of mycolata grown in artificial media or in mixed liquor (Petrovski et al. 2011).
Because G. amarae seems to be the most common nocardioform at SRWTP, increases in
non-nocardioform mycolata may weaken the correlation between nocardioform counts and
activated sludge foaming. In addition, the ratio of G. amarae to other mycolata likely changes
with fluctuations in secondary treatment parameters, so the accuracy of nocardioform counts
would also be variable. This seems the most likely explanation for the relatively weak
correlations between nocardioform concentration and activated sludge foaming encountered
during my study. Also, the randomness and nonlinearity of data points in my scatterplots
depicting this relationship (Figure 4) would be expected if non-nocardioform mycolata were
periodically the dominant foam-formers during my study. Two separate nocardioform isolation
47

attempts during 2010 resulted in isolation of Mycobacterium from SRWTP mixed liquor foam.
This confirms the presence of at least one type of non-nocardioform mycolata during my study.
Also, the fact that these mycolata were present in high enough numbers to be isolated from two
different samples collected one month apart during 2010 seems evidence of non-nocardioform
mycolata contributing to activated sludge foaming at SRWTP during my study. The only
unknown factor was the relative contribution to foaming made by the mycolata species
Mycobacterium mucogenicum, compared to G. amarae. According to Davenport et al. (2008),
both Gordonia and Mycobacterium are among the most hydrophobic genera of mycolata, so it
seems that their foaming propensities should be comparable. In addition, de los Reyes et al.
(1998) quantified both G. amarae and mycolata in a sample of RAS collected from SRWTP
during an episode of activated sludge foaming. The mycolata group made up 1.8% of the total
small-subunit rRNA genes in the sample, while G. amarae ranged from 0.2% to 0.4%. The
conclusion drawn from this study was that a significant proportion of the mycolata in SRWTP
activated sludge was composed of members of genera other than Gordonia. The authors went
on to state that “This further supported the observation that G. amarae was not contributing to
foaming problems in the Oakland and Sacramento plants” (de los Reyes et al. 1998).
Activated sludge foaming can be unpredictable even if nocardioforms are the major group of
mycolata present. Periodic surfactant addition to wastewater can have a large effect on
activated sludge foaming and foaming thresholds. Petrovski et al. (2011) added Triton-X 100
surfactant to pure cultures of non-foaming mycolata strains, resulting in stable foam formation
by all of these strains. This surfactant also caused at least a 10 fold decrease in foaming
threshold values for all mycolata strains tested, meaning that fewer mycolata cells were required
to produce stable foam in the presence of surfactant (Petrovski et al. 2011). Most detergents are
now biodegradable, so they are no longer a significant source of surfactant addition to
48

wastewater systems (Tipping 1995). It may still be possible for high concentrations of
industrial surfactants to enter the wastewater treatment process through accidental discharges.
One mechanism by which surfactants cause activated sludge foaming may be through
deflocculation of floc-bound nocardioforms, exposing their hydrophobic cell walls for
attachment to air bubbles (Narayanan et al. 2003). Surprisingly, another source of surfactant in
activated sludge may be certain groups of bacteria which produce it. Petrovski et al. (2011)
found that Bacillus subtilis produces a surfactant called “surfactin” in activated sludge, leading
to severe foaming. In fact, foaming thresholds for pure cultures of mycolata were reduced 10
fold to 100 fold when B. subtilis culture supernatant (containing surfactin) was added (Petrovski
et al. 2011).
Activated sludge foaming propensity can also be affected by secondary treatment parameters
during the wastewater treatment process. The most commonly cited treatment parameters
associated with increased nocardioform foaming are long MCRTs (mean cell residence times in
the activated sludge process), low F:M ratios (the ratio of food to microorganisms in activated
sludge), and high wastewater temperatures (Pitt and Jenkins 1990). Figure 7 below shows all
three parameters and total nocardioform intersection count results plotted throughout both 2009
and 2010 at SRWTP. For simplicity, secondary treatment parameters are only shown for the
dates on which nocardioform counts were performed. Also, 2009 and 2010 study data are
shown on the same graph in order to highlight differences between the two years. Temperature
of wastewater influent entering SRWTP was stable during the entire study, with gradual
decreases corresponding to seasonal temperature declines, beginning in the fall of both years.
MCRT and CO tank SRT (Solids Retention Time) both measure the amount of time
microorganisms spend in the activated sludge process, but CO tank SRT specifically measures
the time microorganisms spend in CO tanks before entering SSTs. During my study both
49

Figure 7. Temporal trends in SRWTP secondary treatment parameters associated with activated sludge foaming
compared to total nocardioform intersection counts during both 2009 and 2010. (The dates are listed as
month/day/year.) These comparisons were made in order to discover possible causes of nocardioform foaming
during both years of this study.
50

parameters followed similar trends over time, showing more variability in 2010 compared to
2009. Increasing MCRTs and CO tank SRTs were often accompanied by increases in
nocardioform numbers, although the association appears rather loose in my limited dataset.
More data points would likely clarify this relationship. Finally, trends in F:M ratios were
generally opposite of nocardioform abundance trends; lower F:M ratios were associated with
higher nocardioform counts. In 2010, F:M ratios were lower than those in 2009, while
nocardioform counts were higher in 2010 and lower in 2009. This same correlation was found
by Boyce and Dial (2009) during the SRWTP RAS Polymer Addition Study. They found better
correlations between F:M ratios and nocardioform counts compared to those between MCRT
and nocardioform counts. Also, wastewater temperature and pH did not significantly affect
nocardioform abundance in the activated sludge during the Polymer Addition study (Boyce and
Dial 2009).
Variations in secondary treatment parameters can explain fluctuations in nocardioform
numbers in activated sludge over time. Longer MCRTs and SRTs favor the growth of
nocardioforms due to their slower growth rates compared to floc-formers. However, this cannot
be the only cause of foaming, because foaming can occur in activated sludge plants operating
MCRTs of only 2 days or less (Tipping 1995). SRWTP experienced activated sludge foaming
during my study at MCRTs of only 1.5 to 2.5 days. Dissolved oxygen (DO) content of the
mixed liquor can also affect foam formation, depending on the species of filamentous bacteria
responsible for the foam. Two major groups of filamentous bacteria known to cause foaming
problems, nocardioforms and Microthrix parvicella, have different DO requirements.
Nocardioforms are strict aerobes, while Microthrix can grow under anoxic conditions (Tipping
1995). High influent concentrations of hydrophobic material such as lipids can also increase
activated sludge foaming by providing foaming filamentous bacteria with their preferred
51

substrate. This can also explain why higher reactor temperatures are often associated with an
increase in foaming; accelerated lipid hydrolysis occurs at higher temperatures, providing
foaming filamentous bacteria with extra nutrients with which to outcompete other activated
sludge bacteria (Frigon et al. 2006). However, as with DO concentration, the optimal
temperature for foaming depends on the dominant foaming bacteria. For example, the
nocardioform group includes members grown in pure culture at temperatures ranging from 5°C
to 40°C or higher (Soddell and Seviour 1995).
An understanding of the secondary treatment parameters associated with activated sludge
foaming can allow treatment plant operators to effectively control foam. One common method
of nocardioform foam control used locally at SRWTP is the reduction of the MCRT once
nocardioform counts indicate need for foam control. Reduction of MCRT controls
nocardioform numbers in mixed liquor by washing them out of the reactors before they have
time to overgrow, because their growth rate is slower than that of other activated sludge
bacteria. An MCRT of 6 days or less is commonly sufficient to control nocardioform foaming
(Pitt and Jenkins 1990). The effective MCRT for a treatment plant depends on influent
temperature and foam disposal methods, as well as other factors. High temperatures require
lower MCRTs to control foam because many nocardioforms show increased growth at such
temperatures. Also, treatment plants disposing of activated sludge nocardioform foam into
plant influent constantly re-seed their mixed liquor with nocardioforms, so a lower MCRT is
often required. However, MCRT control is not a perfect solution to nocardioform foaming
problems because some treatment processes occurring in the reactors (such as nitrification)
require longer MCRTs (Tipping 1995). Sometimes there is a tradeoff between process control
and effluent quality.
52

Chlorination of RAS is another popular means of controlling activated sludge biological
foaming. Excessive proliferation of filamentous bacteria is responsible for both bulking and
foaming problems. Addition of chlorine to RAS acts to destroy exposed filaments bridging
bacterial flocs, allowing separated flocs to settle out of solution. It also removes nocardioforms
from solution but not from inside flocs. Filaments inside flocs are protected from exposure to
chlorine, so once chlorination is ceased they quickly extend from the flocs and grow to nuisance
levels (Ovez et al. 2006). And, to complicate matters, chlorine doses must be low enough to
avoid killing activated sludge bacteria responsible for removal of harmful compounds from
wastewater.
A third popular method of suppressing nocardioform foam in activated sludge is through the
addition of cationic polymer to the secondary clarifiers. Cationic polymer binds to exposed
nocardioform filaments, preventing contact of hydrophobic filament surfaces with any liquid-air
interfaces and promoting settling of the filaments as they bind to bacterial floc (Shao et al.
1997). As with chlorination, a disadvantage of polymer addition to activated sludge is the rapid
re-growth of filamentous bacteria soon after the treatment is discontinued (Juang 2005).
Controlling nocardioform foaming in activated sludge plants has proven to be a difficult
task. There seem to be no universal methods that can be successfully used in every treatment
plant due to differences in process control parameters, influent composition, and even ambient
temperature. The purpose of my study was to help SRWTP operators control activated sludge
foaming in the most cost-efficient manner possible by comparing the accuracy and precision of
the three nocardioform counting methods currently used, in addition to testing a new method of
nocardioform enumeration (dispersed intersection counts). All four counting methods showed
relatively weak correlations with mixed liquor foaming measures used during my study, likely
due to the contributions to foaming made by non-nocardioform mycolata and possibly
53

surfactants during that time (Petrovski et al. 2011). The accidental isolation of Mycobacterium
on two separate occasions during my study supports the notion of non-nocardioform mycolata
contributing to activated sludge foaming at SRWTP. Although the relative contributions of
nocardioforms vs. non-nocardioform mycolata to activated sludge foaming cannot be
determined using conventional microscopic examinations, there is evidence that nocardioforms
can reliably be linked with biological foaming. When foam stability is used as the criterion for
defining the foaming threshold of mixed liquor, the corresponding nocardioform abundance
falls into the range historically associated with foaming at SRWTP. The results of my study
indicate that total nocardioform intersection counts most accurately predict activated sludge
foaming, compared to the other three counting methods examined. Nocardioform length counts
have better precision, but are less accurate at predicting foaming compared to total intersection
counts. Also, length counts require much more time to complete compared to intersection
counts, so it would be more cost-efficient to predict activated sludge foaming using total
intersection counts.
If cost were not an issue, it would be possible to better refine nocardioform enumeration
techniques and to explore the contributions to activated sludge foaming made by non-
nocardioform mycolata compared to nocardioforms. It may be possible to increase accuracy
and decrease labor costs of nocardioform length counts by investing in microscopy imaging
software. For example, SPOT MetaMorph software can perform automated counting of
bacterial cells (www.spotimaging.com/software), allowing for quicker, more accurate
nocardioform length counts. Also, fluorescence in situ hybridization (FISH) analysis of mixed
liquor could be used to compare the relative proportions of nocardioforms vs. non-nocardioform
mycolata over time. Probes designed to target the 16S rRNA of members of several common
nocardioform groups can be used on the same mixed liquor sample as a mycolata-specific
54

probe, allowing cell abundances to be calculated for each group. This would be an extension of
the de los Reyes et al. (1998) study comparing the abundance of G. amarae strains to that of the
general mycolata group in RAS at SRWTP. This analysis could be performed periodically in
order to assess the accuracy of nocardioform counts in predicting activated sludge foaming at
different times of the year. It would be helpful to know whether other groups of mycolata
periodically outcompete nocardioforms and weaken the correlation between nocardioform
abundance and activated sludge foaming. Quantitative real-time PCR (q PCR) can also be used
to determine relative proportions of nocardioforms and mycolata in samples of activated sludge.
Marrengane et al. (2011) used this method to quantify Gordonia abundance in samples of mixed
liquor from three activated sludge plants, targeting bacterial 16S rRNA genes for enumeration.
During q PCR, primers were used to amplify the genes of interest, enabling accurate counts of
the amplicons (Marrengane et al. 2011).
In addition to genetic analysis of activated sludge samples, alternative methods can also be
used to monitor foaming. Fatty acid methyl ester (FAME) analysis was used by Cha et al.
(1999) to quantify G. amarae in mixed liquor samples. FAMEs in G. amarae cellular
membranes were extracted from the samples and analyzed by a gas-liquid chromatograph in
order to identify and quantify these bacteria (Cha et al. 1999). Petrovski et al. (2011) monitored
activated sludge foaming propensity without microbial identification through microbial
adherence to hydrocarbon (MATH) assays (Rosenberg et al. 1980), followed by measurements
of surface tension and surfactin concentrations. MATH assays measure cell surface
hydrophobicity of bacterial cells by comparing absorbance measurements of cell suspensions
before and after addition of hydrocarbons such as n-hexadecane (Rosenberg et al. 1980). The
surfactant levels of mixed liquor samples can be monitored through surface tension
55

measurements of mixed liquor supernatant and by high performance liquid chromatography
(HPLC) analysis of the samples for surfactin concentrations (Petrovski et al. 2011).
Many methods are currently used by researchers and wastewater treatment plant staff to
predict and measure activated sludge foaming propensity. It is not clear from the literature
whether any method shows consistent accuracy across different treatment plants over time. For
economic reasons, treatment plants require the most accurate method with the smallest costs, for
both materials and labor. The scope of my study only included comparison of four
nocardioform enumeration methods using light microscopy, so further studies would be
required to compare these methods with genetic analyses or cell surface hydrophobicity and
surfactant measurements of activated sludge to determine which technique best predicts
activated sludge foaming.
56

APPENDIX A
List of Abbreviations
CO tank Carbonaceous Oxidation tank
F/M ratio Food/Microorganism ratio
MCRT Mean Cell Residence Time
ML Mixed Liquor
RAS Return Activated Sludge
RSD Relative Standard Deviation
SRT Solids Retention Time
SST Secondary Sedimentation Tank
TSS Total Suspended Solids
TYEG Tryptone Yeast Extract Glucose
VSS Volatile Suspended Solids
WAS Waste Activated Sludge
57

APPENDIX B
Operator Logbook Entries for X04 (CO Tanks and SSTs) and X08 (Digesters) during 2009 and 2010. (The dates are
listed as month/day/year.)
58
Date
Process Disruption due to Foaming
2009
6/10/09 Digesters # 10 and # 11 are foaming.
6/13/09 Digester # 7 foam overflowing.
6/29/09 Digester # 7 foaming.
7/26/09 Turned on foam suppression sprays to Digester #'s 7, 10, and 11.
7/31/09 Foaming in Digester # 7 standpipes.
8/8/09 Digester # 5 and # 7 levels slowly rising. Possible foam issue.
8/14/09 Digester # 7 foaming over overflow. Appears to be foam issue.
9/18/09 Turned on foam sprays to Digester # 11 due to high level.
9/19/09 Turned on sprays to Digester # 10 due to foam level.
9/28/09 Foam buildup at South Clss selector sump.
10/27/09
Nocardia foam build-up in South Classifying selector. Brown film in scum rings of SSTs. High level Batt.
3 mixed liquor scum sump.
2010
9/16/10 Lots of foam N. Deck, 1st stage tanks.
9/19/10
Lots of foam in RAS Clss. Selector sump and N. Deck 1st stage. Foam in SST scum rings. North oxygen
tanks all full of foam -- stage 4 overflowing with it.
9/20/10 Tank 11 stage 1 contained heavy foam. Walkways covered with foam.
9/21/10 Lots of foaming in the secondaries.
9/22/10 Nocardia in all of the SSTs in service.
9/23/10
Alarm: high level Digester # 10; turned on foam control spray. Digester # 6 also high level due to
foaming.
9/26/10 Digesters # 8 and # 10 foam problems.
10/9/10 Digester # 8: high level alarm. Sprays used to clear.
10/14/10 Tank 11 stage 1 contained too much foam to collect sample.
11/21/10 DAFT 4 sump foaming. Sprays turned on.

APPENDIX C
Summary of study findings, recommendations, and alternative methods of monitoring activated
sludge foaming.
• Study objectives:
o Compare accuracy and precision of four nocardioform counting methods in
their prediction of activated sludge foam production
o Calculate the nocardioform concentration threshold for foaming of activated
sludge at SRWTP
• Results of study:
o All four nocardioform counting methods showed relatively weak correlations
with mixed liquor foaming measures
non-nocardioform mycolata and surfactants may have contributed to
foaming
Mycobacterium isolated twice during my study
• non-nocardioform mycolata
o The foaming threshold of mixed liquor calculated from my data matches the
nocardioform abundance historically associated with foaming at SRWTP
(1x10 6 total intersections/g VSS)
o Total nocardioform intersection counts most accurately predict activated sludge
foaming
o Nocardioform filament length counts have the best precision but inferior
accuracy compared to intersection counts
also more time-consuming than intersection counts
• Recommendations for monitoring activated sludge foaming based on study results:
o Continue total nocardioform intersection counts for weekly routine monitoring
o Perform laboratory foaming tests on the same samples used for nocardioform
counts
to monitor the correlation between nocardioform abundance and
activated sludge foaming over time
o Research feasibility of alternative methods of monitoring activated sludge
foaming
because all four nocardioform counting methods compared in my study
showed relatively weak correlations with foaming
• Alternative methods of monitoring activated sludge foaming:
o Microscopy imaging software
may increase accuracy and decrease labor costs of nocardioform length
counts
performs automated counting of bacterial cells
SPOT MetaMorph software
o Fluorescence in situ hybridization (FISH) analysis of mixed liquor
can be used to compare the relative proportions of nocardioforms vs.
non-nocardioform mycolata
probes designed to target the 16S rRNA of members of common
nocardioform groups can be used on the same sample as a mycolataspecific
probe
• cell abundances can be calculated for each group
59

• similar to the de los Reyes et al. (1998) study comparing the
abundance of G. amarae strains to that of the general mycolata
group in RAS at SRWTP
o Quantitative real-time PCR (q PCR)
can also be used to determine relative proportions of nocardioforms and
mycolata in samples of mixed liquor
Marrengane et al. (2011) used this method to quantify Gordonia
abundance in samples of mixed liquor
bacterial 16S rRNA genes are targeted for enumeration
primers help to amplify the genes of interest, enabling accurate counts
of the amplicons
o Fatty acid methyl ester (FAME) analysis
used by Cha et al. (1999) to quantify G. amarae in mixed liquor
samples
FAMEs in G. amarae cellular membranes were extracted from the
samples and analyzed by a gas-liquid chromatograph
to identify and quantify bacteria
o Microbial adherence to hydrocarbon (MATH) assays
measure cell surface hydrophobicity (CSH) of bacterial cells
compare absorbance measurements of cell suspensions before and after
addition of hydrocarbons such as n-hexadecane
Rosenberg et al. (1980)
o Surface tension measurements of mixed liquor supernatant
to monitor surfactant levels of activated sludge
Petrovski et al. (2011) used this method to monitor activated sludge
foaming propensity
o High performance liquid chromatography (HPLC) analysis
tests for surfactin concentrations
also used by Petrovski et al. (2011) to monitor the surfactant levels of
activated sludge
o Further studies are required to determine which technique best predicts
activated sludge foaming
should show consistent accuracy across different treatment plants over
time
60

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