PhD_MOHAMMED_BABU_e.
EFFECT OF ALGAL BIOFILM
AND OPERATIONAL
conditions on nitrogen
removal in wastewater
stabilization PONDs
mohammed babu
EFFECT OF ALGAL BIOFILM AND
OPERATIONAL
CONDITIONS
ON
NITROGEN
REMOVAL
IN
WASTEWATER STABILIZATION PONDS
MOHAMMED BABU
Thesis committee
Thesis supervisor
Prof. dr. H.J. Gijzen
Professor of Environmental Biotechnology
UNESCO-IHE Institute for Water Education
Delft, The Netherlands
Thesis co-supervisors
Dr. ir. N.P van der Steen
Senior Lecturer in Sanitary Engineering
UNESCO-IHE Institute for Water Education
Delft, The Netherlands
Dr. ir. C.M. Hooijmans
Senior Lecturer in Sanitary Engineering
UNESCO-IHE Institute for Water Education
Delft, The Netherlands
Other members
Prof. dr. ir. P.N.L. Lens
UNESCO-IHE Institute for Water Education
Delft, The Netherlands
Prof. dr. ir. H.H.M. Rijnaarts
Wageningen University
Wageningen, The Netherlands
Prof. dr. R. Haberl
University of Natural Resources and Life Sciences
Vienna, Austria
Prof. dr. F. Kansiime
Makerere University Institute of Environment and Natural Resources
Kampala, Uganda
This research was conducted under the auspices of the Wageningen University Institute
for Environment and Climate Research (WIMEK)
Effect of Algal Biofilm and Operational
Conditions on Nitrogen Removal in
Wastewater Stabilization Ponds
Thesis
Submitted in fulfilment of the requirements of
the Academic Board of Wageningen University and
the Academic Board of the UNESCO-IHE Institute of Water Education
for the degree of doctor
to be defended in public
on Friday 28 January 2011 at 10.00 a.m.
in Delft, the Netherlands
by
MOHAMMED BABU
Born in Mbale, Uganda
CRC Press/Balkema is an imprint of the Taylor & Francis Group, an informal business
© 2011, Mohammed Babu
All rights reserved. No part of this publication or the information contained herein
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ISBN 978-0-415-66946-7 (Taylor & Francis Group)
ISBN 978-90-8585-849-2 (Wageningen University)
Dedication
This thesis is dedicated to my late uncle Shantilal .A. Vyas
May God bless and rest your soul in peace
Acknowledgements
The author is extremely grateful to the Netherlands Government for providing financial
assistance through the Netherlands Fellowship Program. I would also wish to extend my
sincere thanks to the EU-SWITCH project for the financial assistance of my research
project.
,¶P YHU\ JUDWHIXO WR P\ SURPRWHU 3URIHVVRU +XXE *LM]HQ IRU KLV NLQGQHVV JXLGDQFH
valuable discussions and comments during the writing of this dissertation. I greatly
appreciate his trips to Uganda and invaluable input and support in this study.
,¶P SDUticularly indebted to my co-promoters Dr. Peter van der Steen and Dr. Tineke
Hooijmans for constructive ideas during the research period. I would greatly thank them
for accepting to come to Uganda and providing invaluable guidance. I extend my
heartfelt thanks to them for allocating time for meetings and critically reviewing the
manuscripts. Special thanks go to Dr. Henk Lubberding who mentored me during my
MSc study, kept in touch with me, notified and supported me when the PhD opportunity
became available.
Thanks to the Managing Director and management of National Water and Sewerage
Corporation for giving me permission and opportunity to use their facilities at the
%XJROREL6HZDJH7UHDWPHQW:RUNV ,¶PVSHFLILFDOO\ JUDWHIXO WR Eng. Kiwanuka Sonko,
Dr. Kaggwa Rose and Mr. Kanyesige Christopher for their assistance and link to National
water.
I would wish to extend my appreciation to the Rector and Vice rector of Islamic
University in Uganda, Dr. A.K. Ssengendo and Dr. M. Mpeza for giving me the
opportunity and supporting me when pursuing this cause. Thanks to Dr. P.S.N.A
Ssekimpi for being parental and providing guidance throughout my under and
postgraduate studies. Thanks to my colleagues Dr. S. Nachuha and Mr. S. Okurut who
gave me courage and support during this period.
The laboratory staff of IHE Fred, Frank, Peter, Lyzette, Don and the rest; you did a great
job for me while I was in Delft. Thanks to Edwin Hes and Mushi .M. for their assistance
during experimental work while in Delft. I extend my appreciation to the laboratory staff
of BSTW especially Juliet, Nyombi .J. Mutyaba, C., Arra, K, Wetaga, H and Saazi Job
for supporting me in the lab. Not forgetting, my cousin Rama for accepting to do the dirty
work of pumping of wastewater daily into the anaerobic tank; thanks a lot dear! Thanks
to my PhD colleagues Kittiwet, K., Heyddy, L., Barreto C., Uwamariya V., Sekomo, C.,
Bagoth S; Ansa E and rest who made me feel at home while in Delft.
Special thanks to my beloved family; my mother, father and sisters who gave me the
courage and strength to move on. Great and heartfelt appreciation to my lovely wife
Samiha and her family, my sons Fesal and Irfan; I deeply appreciate your patience.
Thanks for tolerating my long hours in the lab and my absence from home for many
months. Finally, I acknowledge all those who helped me but whose names have not
appeared on this page. May God bless you all!
List of abbreviations
Ammonium
NH4+
Ammonium nitrogen
NH4+ -N
et al
and others
AT
Anaerobic Tank
Biofilm nitrification rate
R bio
Biochemical Oxygen Demand (5days)
BOD5
BNR
Biological Nitrogen Removal
BSTW
Bugolobi Sewage Treatment Works
Bulk water nitrification
R bulk
cm
Centimeters
COD
Chemical Oxygen Demand
CFD
Computational Fluid Dynamics
per day
d-1
DO
Dissolved Oxygen
d
Dispersion number
Eff
Effluent
EU
European Union
EPS
Extra Polymeric Substance
FP
Facultative ponds
Q
Flow rate
g
Grams
hrs
Hours
HRT
Hydraulic Retention time
Inf
Influent
KjN
Kjeldahl nitrogen
MP
Maturation ponds
m
Meters
μm
Micro meters
mg
Milligrams
Milligrams per liter
mg l-1
Nitrate
NO3
Nitrate nitrogen
NO3-N
Nitrite
NO2
Nitrite nitrogen
NO2-N
N2
Nitrogen gas
Nitrogen Oxides
N2O
Org-N
Organic nitrogen
N
Reactors in series
Pe
Peclet number
pH
Potentiometric hydrogen ion concentration
s
Seconds
SPSS
Statistical Package for Social Sciences
spp
Species
T
Temperature (oC)
TSS
Total suspended Solids
UNEP
US$
WSP
Greek
ȕ
μE
Į
ો2
United Nations Environmental Program
US dollars
Wastewater stabilization ponds
Standard coefficient
Micro-(LQVWHLQ¶V
Index of short circuiting
Variance
Table of Contents
Chapter 1
Introduction ................................................................................................... 1
Chapter 2
The effect of baffles on algal-bacterial biofilm structure and composition of
zooplankton in wastewater stabilization ponds........................................... 21
Chapter 3
Comparison of hydraulic flow patterns of four pilot scale baffled
wastewater stabilization ponds ................................................................... 49
Chapter 4
Nitrification in bulk water and biofilms of algae wastewater stabilization
ponds ........................................................................................................... 62
Chapter 5
Nitrification rates of algal-bacterial biofilms in wastewater stabilization
ponds under light and dark conditions ........................................................ 74
Chapter 6
Effect of operational conditions on the nitrogen removal in a pilot scale
baffled wastewater stabilization ponds under tropical conditions .............. 87
Chapter 7
Nitrogen mass balances for pilot scale biofilm stabilization ponds under
tropical conditions ..................................................................................... 100
Summary ......................................................................................................................... 119
Samenvatting................................................................................................................... 124
Curriculum vitae
Chapter 1
Introduction
1
Chapter 1
Introduction
Water pollution as a result of nitrogen contamination is a worldwide problem. Nitrogen is
known to be detrimental to public health and the environment (Gijzen and Mulder, 2001).
For instance, ammonia can cause eutrophication and oxygen depletion in the receiving
water; this may result in proliferation of aquatic weeds and massive fish kills. Other effects
of nitrogen pollution include underground water pollution (Laegreid et al., 1999; Zhu et al.,
2005), blue-baby syndrome in infants (Bulger et al., 1989) and the emission of gasses
contributing to the greenhouse effect (Takaya et al., 2003).
There are various sources of nitrogen in the environment; the major ones include
agricultural runoff (Cang et al., 2004; Sheng wei et al., 2009), domestic and industrial
wastewater among others. Control of non-point sources is a challenge although proper
agricultural and land management practices could abate this problem (Amans and Slangen,
1994; Sheng wei et al., 2009). For point sources like domestic and industrial wastewater,
pollution can be reduced through wastewater treatment.
The approach of minimizing nitrogen pollution through wastewater treatment differs
between developed and developing countries. In industrialized nations, high environmental
standards and stringent regulations have been set (Metcalf and Eddy, 2003) and this
requires advanced wastewater treatment (Jorgensen and Williams, 2001). Activated sludge
with biological nitrogen removal (BNR) is an example of advanced wastewater treatment
commonly used by developed nations. This is effective in wastewater treatment but has
disadvantages of high capital investments, maintenance costs and requires skilled
manpower (Veenstra and Alaerts, 1996). Other systems for nutrient removal based on
chemical treatment of wastewater are chemical precipitation, ammonia stripping and ion
exchange. Although they are efficient, use of chemicals is undesirable and expensive
(UNEP, 1999).
Developing countries cannot afford advanced wastewater treatment systems due to
prohibitive costs involved in construction and maintenance (Veenstra and Alaerts, 1996).
Those in tropical regions opt for natural systems which are driven by solar energy. The
major systems used include constructed wetlands and the conventional wastewater
stabilization ponds.
Constructed wetlands mainly use aquatic plants that have root systems which provide
attachment sites for bacterial growth and activity. Alaerts et al., (1996); Bonomo et al.,
(1997); Gijzen and Khondker, (1997); Zimmo et al., (2000) Korner et al., (2003) and
Caicedo et al., (2005) have used duckweed in wastewater treatment. The major advantages
of duckweed systems are low operation costs, low energy requirements, they can withstand
loading shocks and they are effective in reducing odour and TSS. They also have a
potential for resource recovery (Caicedo et al., 2005). The major disadvantages include
relatively large area for construction, less efficient in pathogen removal and limited
nitrification. Papyrus (Okia, 2000; Kansiime and van Bruggen, 2001) and Phragmites
2
(Green and Upton, 1995; Okia, 2000) have also been used in constructed wetlands. Like in
duckweed systems, nitrification is also limited in these systems due to limitation of oxygen.
The wetlands also demand periodic harvesting of plant biomass for effective nutrient
removal. Furthermore, the efficiency of wetlands may be reduced over time due to
sedimentation.
Wastewater stabilization ponds (WSP) are the most common wastewater treatment
technologies used in developing nations, especially in tropical regions. This is due to costeffectiveness in construction and maintenance. According to UNEP (1999), wastewater
stabilization ponds are still the cheapest treatment technology. They are effective in
removal of organic matter (Mara and Pearson, 1998) and pathogens (Van der Steen et al.,
1999; Zimmo et al., 2002).
The major disadvantage of wastewater stabilization ponds is the requirement of relatively
large areas for construction. Large area requirement is still a big challenge and critical in
application of WSP even in developing countries where land may not be expensive
(Pearson, 1996). Future demographic projections indicate that by 2017, the developing
world is likely to become more urban than rural in character (United Nations, 2004). Rapid
urban growth of cities and towns in developing world is outstripping their capacities to
provide adequate services (Cohen, 2006). This implies that as urbanization and population
increases, the demand for adequate services like wastewater treatment will be higher. More
space for expansion of wastewater treatment plants to achieve effective treatment will be
necessary. Usually urban and population growth are associated with increased demand for
land hence the cost of land is expected to become high as well (Yu et al., 1997). This may
be a future bottle neck to application WSP in the developing world unless research on more
compact WSP systems is successful.
The other disadvantages of WSP are bad odour, mosquito breeding, high total suspended
solids (TSS) in the effluent (Mara, 2004); short-circuiting (Shilton et al., 2000); narrow
zone for nitrification, since the aerobic zone is limited to the upper 50 cm (Baskran et al.,
1992), long hydraulic retention time and low nitrifier biomass in the water column
(McLean et al., 2000; Zimmo et al., 2000).
The aim of this study was to enhance nitrogen removal in WSP. It has been proposed that
introduction of attachment surfaces in wastewater stabilization ponds can improve the
process of nitrogen removal (Pearson, 2005). Attachment surfaces for nitrifiers in
wastewater stabilization ponds have been tried in Australia and have shown potential for
application (Baskran et al., 1992; Craggs et al., 2000; McLean et al., 2000). In this study,
baffles were used as the attachment surfaces; baffles as biofilm surfaces have been tested
but limited to laboratory scale studies (Kilani and Ognurombi, 1984; Muttamara and
Puetpaiboon, 1997). Studies at pilot and full scale under tropical conditions were yet to be
done. The effect of different arrangement of baffles on hydraulic performance and nutrient
removal in wastewater stabilization ponds under tropical conditions was not known. This
was one of the major issues that this research addressed. The other aspects that this research
addressed include: effect of baffles on algal-bacterial biofilm structure and composition,
3
nitrification in bulk water and biofilms; and biofilm nitrification rates under different
oxygen and pH conditions.
However, before baffles are used as intervention in improving nitrogen removal in WSP (as
in this research), there is need for a deeper understanding of the problems associated to
nitrogen removal in WSP. The factors that affect biofilm formation should be clearly
understood. Different nitrogen transformation processes in wastewater stabilization ponds
were reviewed in light of the current study.
Problems associated with algae ponds
Studies by Van der Steen et al., (2000a, 2000b), Zimmo et al., (2000) and many others
using algae in nitrogen and pathogen removal is promising. Despite the promising results,
there are still a number of problems associated with wastewater stabilization ponds. These
problems were categorized into two, i.e. limitation of biological processes and shortcircuiting.
1. Limitation of the biological process
This includes (a) insufficient surface area for microbial attachment, (b) thermal
stratification and (c) transport of wastewater from aerobic to anaerobic zone for bacterial
activity.
(a) Insufficient attachment surface
Lack of surface area for nitrifiers and denitrifiers is a limitation for nitrogen removal in
algae ponds (Zimmo et al., 2000). Algae lack roots (as compared to other aquatic plants
with long and extensive roots) hence provide a smaller surface area for microbial
attachment and activity. Suspended algae and other materials in sewage can provide
attachment to bacteria in the water column (McLean et al., 2000). However, this is
temporary attachment since suspended matter settles at the bottom of the ponds with time.
Suspended algae can also be washed out of the ponds taking with them the attached
bacteria. Hammer and Knight (1994) report that nitrifying bacteria live as layered
outgrowths on attachment sites and rarely do they live as free floating. In this context,
provision of artificial substrata becomes a good option of improving nitrifier attachment
hence creating favorable conditions for their growth. The biggest challenge however is
balance between nitrifiers and heterotrophic bacteria; the former are known to be slow
growers and in the event of high organic loading, they can be easily out competed
(Loosdrecht et al., 2000).
Studies incorporating attachment surfaces in WSP have shown positive results (Baskaran et
al., 1992; Zhao and Wang, 1996; McLean et al., 2000; Schumacher and Sekoulov, 2002).
Previous work using polyvinyl acetate as the artificial attachment media has also shown
improved removal efficiencies of 96%, 76% and 90% for NH4-N, COD and BOD
respectively (Zhao and Wang, 1996). McLean et al., (2000) used geotextile-polyfelt TS
1600 as the carrier material and reports NH4-N reduction from 40 mg l-1 in the influent to
8.7 mg l-1 in the effluent of algae ponds with biofilm support. It was observed that nitrifier
populations in lagoons without biofilm support could only achieve high nitrification rates if
4
algal biomass was high. The algae acted as attachment substrata most especially during
seasons of low wash out.
Prior to introducing biofilm surfaces for nitrifier attachment in algae ponds, deeper
understanding of factors that affect biofilm development is necessary. Development of
attached microbial growth is a very complex process involving many variables. The major
ones include oxygen, pH, temperature, nutrients, cations, substrata, extracellular polymeric
substance (EPS) and flow velocity (Characklis et al., 1990; Esterl et al., 2003).
Oxygen is vital in respiration for all aerobic living organisms. It serves as the final electron
acceptor in the electron carrier system during provision of energy. For any metabolic
activity to be sustained, energy requirement must be fulfilled. In practical terms, bulk water
dissolved oxygen concentration of 2.0 to 3.0 mg l-1 is satisfactory for aerobic suspended
nitrifier growth (Grady et al., 1999; Metcalf and Eddy, 2003; Arceivala and Asolekar,
2008). For attached growth systems, this may be higher especially with mass transfer
limitations (Loosdrecht et al., 2000). Oxygen limitation is one of the main factors
responsible for biofilm sloughing. During biofilm development, thicker slime layers are
laid on the existing ones. Substrate and oxygen is consumed from the surrounding
wastewater before penetrating deeper. The bacteria in the deeper region undergo
endogenous respiratory state and lose the ability to cling on the surface (Metcalf and Eddy,
2003). This results into biofilm sloughing.
Wastewater has a wide range of pH variations making it suitable for diverse growth of
microorganisms. Nitrifiers and denitrifiers have been reported to grow well in pH ranges
from 7.2 to 9.0 and 7.0 to 8.0 respectively (Metcalf and Eddy, 2003). Very high or very low
pH values are detrimental to physiology of microorganisms. Schumacher and Sekoulov
(2002) observed increased pond pH by algal biofilms (due to consumption of carbon
dioxide by the photosynthesis). The resulting high pH values caused a decrease in nitrogen
removal (Table 1.1). This is an indication that probably the process of denitrification was
affected; which was to the disadvantage of the treatment process. Previous studies (Caicedo
et al., 2005) have shown that combination of algae and duckweed systems can counteract
the effect of high pH. In duckweed ponds, light penetration is limited hence algal
productivity is virtually absent. Stable pH of 6.8 to 7.0 and relatively low oxygen levels has
been reported in these ponds. Contrary to Schumacher and Sekoulov (2002), it is thought
that biofilms provide a variety of microenvironments that could favor treatment processes.
For instance, the outer aerobic zones of the algal biofilms are ideal for nitrifiers while the
deeper anaerobic zones could be favorable to denitrifiers.
Table 1.1 Effect of bulk water pH on nitrogen removal rates by nitrification-denitrification (Schumacher and
Sekoulov, 2002)
pH
Removal rates (g-N m-2hr-1)
7.0-8.0
0.36
8.0-9.0
0.19
9.0-10.0
0.16
10.0-11.0
0.097
5
Temperature is a driving force for many metabolic reactions. Very high or low
temperatures tend to affect enzyme activity thus limiting metabolic reactions. Studies by
Donlan et al., (1994) on effects of seasonal variations on biofilm formation in drinking
water caste iron pipes indicated increased growth with increased temperature. In their work,
all the study sites sampled under warm conditions (15 to 250C) had higher rates of biofilm
formation compared to those under cold conditions (4 to 150C). This is important for
tropical and subtropical regions where temperature is normally high. It can be expected that
biofilm formation in these regions will be higher and probably provide diverse
microenvironments for effective wastewater treatment.
Nutrients in wastewater are abundant in the bulk water; hence limitation mostly occurs in
biofilms. Different ratios of nutrients available determine the type of biofilm that develops
(Loosdrecht et al., 2000). Higher concentration of biodegradable organic substances usually
favors the growth of heterotrophs. Microorganisms consume nutrients in their vicinity
creating a nutrient gradient. The gradient causes nutrient replenishment, which is
advantageous to the fast growers normally found on biofilm surfaces. The slow growers are
usually relegated to the base (Loosdrecht et al., 2000) and due to mass transfer limitations;
they may enter endogenous respiratory state and be sloughed (Lewandoski and Beyenal,
2003). Increase in nutrients results in increased growth (Cowan et al., 1991) as long as
other factors do not become limiting.
Cations are a vital component in biofilm development. They increase bacteria attachment to
surfaces by either physiology-dependant mechanisms or by reducing negative repulsive
forces between bacteria and surfaces. Electrolytes such as calcium and magnesium are
important cellular cations and cofactors for enzymatic reactions. These play a role of
enhancing attachment indirectly. Alternatively, cations improve bacterial attachment by
reducing repulsive forces between negatively charged bacterial cells and glass surfaces
through neutralizing the charges. Fletcher, (1988) was able to demonstrate this
phenomenon in experiments on the effects of Sodium, Calcium, Lanthanum and Iron (III)
on attachment of Pseudomonas fluorescens to glass. It was found that the cations reduced
repulsive forces between any two groups of adhesive polymers found in slimes.
One of the most important factors for attached growth is the surface characteristic. Rough
and porous surfaces have been found to be suitable for microbial growth. These provide
increased surface area for attachment and protection against hydraulic shear forces
(Oliveira et al., 2003). A diversity of microorganisms will colonize rough and porous
surfaces more rapidly due to the variety of microenvironments created (Characklis et al.,
1990).
Substrata with higher hydrophobicity or wettability are known to favor microbial
attachment. Hydrophilic substances attract water, apparently bringing the cells closer to the
substratum. Hydrophilic interactions can also prevail between the cells and the substratum
thereby reducing repulsive forces (Oliveira et al., 2003). Non-polar substances like Teflon
and plastics attach microorganisms more rapidly than glass and metals.
6
Attachment cannot be possible without a matrix upon which cells are deposited. This is
provided by a substance known as extracellular polymeric substance (EPS) made by the
bacteria. EPS is composed of nucleic acids, proteins and other organic matter (85-90%). Its
development depends on the nutritional status of the surrounding media. EPS are highly
hydrated substances due to the hydrogen bond formation with water. This property enables
them to prevent cells from desiccation. EPS are also known to protect microorganisms
against toxic substances and anti-biotics (Bishop, 2003).
EPS have unique properties of possessing negatively charged groups on their surface. This
permits binding to cations such as calcium and magnesium, which are known to form crosslinks with polymer strands providing greater binding force in biofilms (Fletcher, 1988;
Bishop, 2003). EPS are essential in wastewater treatment, variations in its biological,
chemical and physical properties make treatment technologies like activated sludge,
trickling filters, rotating biological contactors, fluidized or submerged fixed-bed reactors
depend on them (Bryers and Characklis 1990; Metcalf and Eddy, 2003).
For organisms to attach, the rate of attachment should be greater than the washout rate.
These two processes are greatly influenced by velocity. The zone adjacent to the
substratum-liquid interface is termed as the hydrodynamic boundary layer. Its thickness
depends on linear velocity; the higher the velocity, the thinner it becomes. Increasing flow
velocity exerts mechanical stress on the biofilm thus wearing it out (Esterl et al., 2003).
Although beyond a certain threshold it may erode and abraise the biofilm (Morgenroth,
2003), some degree of velocity may have a positive effect. Rijnaarts et al., (1993) and
Donlan et al., (1994) have shown that fluid movement aids transportation of cells to the
substratum for deposition.
(b) Thermal stratification
Temperature differences can greatly influence effluent quality. Thermal stratification leads
to formation of water layers, each with different characteristics of temperature, oxygen, pH
and redox potential. This effects denitrification since movement of nitrates from upper
aerobic to deeper anaerobic water layers may not occur. Stratification can also strongly
influence removal of pathogens from wastewater through photo oxidation (Curtis et al.,
1992, 1994). Prevention of mixing more or less limits photo oxidation to top layers. Several
authors (Pescod and Almansi 1996; Van der Steen et al., 2000a) have observed this effect
in wastewater stabilization ponds. Shilton and Harrison (2003) recommend overcoming this
problem by adequate mixing of the influent into the main stream.
(c) Transport of wastewater from aerobic to anaerobic zones
In open water, aerobic zones are known to exist in the upper layers where there is sufficient
light and contact with atmospheric oxygen. These zones are mostly created by
photosynthetic evolution of oxygen by the algae; this is documented to be the basis of a
7
successful wastewater treatment in oxidation ponds (Mara et al., 1992; Fruend et al., 1993).
The aerobic zones tend to favor the process of nitrification. Caicedo, (2005) observed
nitrification in upper water layers of duckweed ponds in which open aerobic zones were
created. The deeper water layers are usually anaerobic due to insufficient light to promote
photosynthesis. The oxygen produced in the open upper layers is usually consumed within
the vicinity and little of it diffuses to the deeper water layers. The deeper zones are thus
anoxic providing the right conditions for denitrification. In horizontal flow systems, the
nitrates formed in the aerobic zones are transported to lower anoxic zones (where
denitrification occurs) by diffusion. This movement may not be effective especially in the
event of thermal stratification. For effective nitrification, a mechanism of movement of
nitrates from the aerobic to anaerobic zone may be necessary. Nitrates can be moved to the
deeper zones by the alternating upward and downward flow patterns induced by the vertical
baffles.
2. Short- circuiting
Short-circuiting can cause significant loss of treatment efficiency. A small deviation from
the anticipated hydraulic retention time (HRT) can lead to poor effluent quality (Shilton et
al., 2000). According to Shilton and Harrison, (2003) and Barbagallo et al., (2003), the
major causes of short-circuiting in wastewater stabilization ponds are:
(a) Inlet momentum
(b) Wind effects
(c) Temperature effects
(a) In-let momentum
This is related to inlet types. Shilton and Harrison (2003) have analyzed different inlet
types and have pointed out the shortcomings of each of them. Table 1.2 summarizes this
information.
Table 1.2 Different types of inlets and their shortcoming (Shilton and Harrison, 2003)
Inlet type
Remarks
Horizontal inlet
Jetting effect drives the bulk volume into circulation
(Horizontal pipe submerged in the water)
patterns with higher velocity than inflow. Influent moves
faster to outlet
Large horizontal inlet (Larger pipes)
Same effect as horizontal inlet but only delays shortcircuiting
Vertical inlet
Fluid moves into two plumes along the two adjacent
walls.
Vertical inlet with stub baffles
Improved flow but limitation of overloading in the inlet
zone due to the stubs
Diffuse inlet (Manifold)
Improves flow, expensive to install and maintain,
recommended for pre-treated water
Has the same effect as the horizontal inlet despite the
Inflow dropping from horizontal pipe
(Horizontal pipe discharging at top of water fact that water drops vertical into the bulk.
surface)
8
The inlet type depends on the pond type. For instance in ponds that receive high organic or
solid loading, a horizontal inlet pipe ensures good mixing of the influent into the pond
mass. However, to prevent the influent from swirling quickly to the outlet, use of stub
baffles is recommended. For ponds that receive pre-treated influent (e.g. maturation ponds),
diffuse inlets are recommended; although these are expensive to install in full scale systems
(Shilton and Harrison, 2003).
(b) Wind effects
Wind effect is mainly experienced in areas or seasons where wind velocity is high. The
wind currents force the influent to move rapidly to the outlet resulting into short- circuiting.
Although there are suggestions that wind currents add oxygen to the water, its contribution
has been found insignificant. Algae play a more important role in oxygenation than wind
(Shilton and Harrison, 2003).
(c) Temperature effects
Temperature differences cause thermal stratification that result in vertical density
boundaries. This prevents vertical mixing thus inflow can short-cut at the top layer of the
stratified pond. This may significantly affect treatment. Therefore a system designed to
break up stratification is desired.
Microbiology
Nitrogen transformations in wastewater treatment
They are various forms of nitrogen in the aquatic environment. The major nitrogen
transformation routes in wastewater stabilization ponds are shown in figure 1.1. The
transformations include ammonification, nitrification, denitrification, ammonia
volatilization, assimilation, sedimentation, fixation and anaerobic ammonia oxidation
(ANNAMOX).
Ammonification
This is the biological transformation of organic nitrogen to ammonia. It is a microbial
mediated process which is carried out by the ammonifying bacteria through mineralization
of amino acids, urea and uric acid to ammonia. During this process, energy is released
which the bacteria utilize for metabolic activities. Ammonifying bacteria can also use
ammonia directly in building biomass. Transformation of organic nitrogen to ammonia
normally proceeds at a faster rate under sufficient oxygen concentrations. It is usually faster
than nitrification and if the latter is affected, ammonia accumulation occurs. This happens
mostly during a rapid change from aerobic to anaerobic conditions. The optimum
temperature and pH for ammonification range from 40oC to 60oC and 6.5 to 8.5,
respectively. These temperature ranges rarely occur in biological treatment systems such as
algae ponds and wetlands (Kadlec and Knight, 1996). In conventional treatment systems,
ammonification is usually considered a first order kinetic reaction. For wetland systems,
organic nitrogen decreases with time in agreement with first order kinetics (Kadlec and
Knight, 1996).
9
N2
NH3
Volatilization
Denitrification
Ammonification
Org- Nitrogen
Nitrification
NH4-N
NO3-N
NO2-N
Nitrogen in
Nitrogen out
Algae
Bacteria
Decomposition
Sediment
Sedimentation
Figure 1.1: Major nitrogen transformation routes in algae ponds
Nitrification
This is an important biological process in wastewater treatment which occurs by two-step
oxidation of ammonia. There are a number of autotrophic nitrifying bacteria that perform
nitrification but the most important genera are Nitrosomonas and Nitrobacter (Metcalf and
Eddy, 2003). Ammonia oxidation is an aerobic process that requires oxygen. In
Nitrosomonas spp for instance, oxygen is used by the enzyme ammonium oxygenase that
initiates the ammonia oxidation pathway (Tiedje, 1988). The two-step reaction starts with
oxidation of ammonia to nitrite by Nitrosomonas spp followed by conversion of the nitrites
to nitrates by Nitrobacter spp.
2NH4+ + 3O2
2NO2 - + O2
2NO2 - + 4H+ +2H2O
2NO3 ±
(1)
(2)
The nitrates formed can be assimilated by other organisms or can be denitrified to
dinitrogen gas. Apart from denitrification, nitrates can also be transformed to other
nitrogen forms through two other processes namely; assimilatory nitrate reduction and
dissimilatory nitrate reduction. Assimilatory nitrate reduction involves the reduction of
nitrate to ammonia by bacteria (Metcalf and Eddy, 2003). The ammonia produced is
usually used up in cell synthesis especially in absence of NH4+ ions in the growth medium.
Attached algal biofilms have also been reported to carry out assimilatory nitrate reduction
on their cell surfaces under high pH (>10) and oxygen concentrations of 9 mg l-1
(Schumacher and Sekoulov, 2002). Assimilatory nitrate reduction is mainly regulated by
ammonium and carbon. On the other hand, dissimilatory nitrate reduction also involves the
reduction of nitrate to ammonia but the process is regulated by oxygen and it is unaffected
by ammonium. This pathway is well suited for anaerobic environments (Tiedje, 1988). In
dissimilatory nitrate reduction, nitrate reduction to ammonia is for the purpose of energy
yield rather than for biomass development. This implies that the ammonium produced
would accumulate in the surrounding medium. Bulger et al., (1989) found this mechanism
to account for higher ammonium concentration in ground water polluted with nitrate rich
10
leachate from a land fill and seepage from wastewater stabilization ponds. The oxidizing
ground water from the land fill provided the source of nitrates while the seepage from the
waste stabilization ponds provided organic matter and reducing conditions. This favored
dissimilatory nitrate reduction hence increasing the ammonium concentrations in the
ground water to levels higher than that found in the lagoons.
For nitrification to occur, sufficient oxygen must be present. Metcalf and Eddy (2003)
reported 4.57g O2 and 7.14g of alkalinity (calcium carbonate) as a requirement for complete
oxidation of 1g of NH4+ - N. Dissolved oxygen less than 0.50 mg l-1 is thought to limit
nitrification in suspended growth under laboratory conditions. Other factors that affect
nitrification include temperature, pH, BOD, toxic compounds and high concentration of
other forms of nitrogen (Arceivala and Asolekar, 2008). For pure bacterial cultures,
temperature range from 250C to 350C has been found to be optimum for nitrification
(Kadlec and Knight, 1996). The optimum pH values required in suspended growth range
from 7.2 to 9.0 (Metcalf and Eddy, 1991). Lower pH values of 6.5 have also been reported
for pure cultures of ammonia oxidizers (Princic et al., 1998). High BOD levels are known
to favor growth of heterotrophic bacteria. Since nitrifiers are slow growers, they are usually
out-competed by heterotrophic bacteria under conditions of high BOD. Other forms of
nitrogen that inhibit nitrification include free ammonia, nitrous acid and nitrites. It has been
found that concentrations of 100 mg l-1 of NH4+ - N and 20 mg l-1 of NO2 -N at pH 7.0 and
temperature of 200C inhibits ammonia and nitrite oxidation (Metcalf and Eddy, 2003).
The kinetics of nitrification follows the Monod equation. It is an aerobic process, which can
be controlled by dissolved oxygen flux from the atmosphere. Usually, oxygen flux into
wetlands and treatment systems is by mass transfer from the atmospheric sources and this is
a first order process (Kadlec and Knight, 1996).
Denitrification
This is the biological reduction of nitrate to nitric oxide, nitrous oxide, and nitrogen gas by
microorganisms. It is a vital process in wastewater treatment where prevention of
eutrophication and NO3-N pollution of ground water is required (Metcalf and Eddy, 2003).
In nature, denitrification is an important process because it closes the loop of the nitrogen
cycle. Without this process, atmospheric nitrogen would be depleted (Kadlec and Knight,
1996).
Denitrification is favored in absence of oxygen (anoxic or anaerobic) although most
denitrifiers are facultative. In absence of dissolved oxygen, the denitrifying bacteria use the
oxygen bound to the nitrate or nitrite as the final electron acceptor (Kadlec and Knight,
1996). Under high oxygen levels, most common denitrifiers use oxygen as the final
electron acceptor in preference to nitrate; this is due to the high energy yields. Furthermore,
the denitrifiers require high amounts of energy for splitting the nitrogen-oxygen strong
bonds in nitrate; they usually tend to avoid this path. Denitrification in the bulk water will
cease under high oxygen conditions but the process still continues to occur in the
microscopic anoxic zones of biofilms.
11
Another important factor known to limit denitrification is COD since absence of carbon
inhibits denitrification. It is estimated that 2.86g of COD is required to denitrify 1 g of
NO3-N (Oostrom, 1995). Utilization of carbon by denitrifiers is coupled with production of
alkalinity. For instance, for every 1g of NO3-N reduced with methanol, 3.0 g of bicarbonate
as CaCO3 is produced (Kadlec and Knight, 1996). Increased production of alkalinity causes
increase in pH and high values are detrimental to many microorganisms. The pH and
temperature ranges suitable for denitrifier growth is from 7 to 8 and 50C to 250C
respectively.
Denitrification is not limited to only anaerobic conditions; recent studies have shown that
aerobic denitrifiers such as Paracoccus denitrificans (formerly Thiosphaera pantotropa),
Microvirgula aerodenitrificans and Thaurea mechernichesis exist. Paracoccus
denitrificans is known to reduce nitrates even at oxygen saturation levels (Loyd et al.,
1987; Robertson and Kuenen, 1990; Takaya et al., 2003).
Although denitrification is a key nitrogen removal mechanism in wastewater (Zimmo et al.,
2000; Metcalf and Eddy, 2003; Caicedo, 2005), denitrifiers (both aerobic and anaerobic)
produce more nitrous oxide than nitrogen gas (Takaya et al., 2003). Investigations on the
activities of Pseudomonas stutzeri and Paracoccus denitrificans under different oxygen
concentrations (anoxic, hyperoxic and oxic) have been done (Takaya et al., 2003). Results
indicated that Pseudomonas stutzeri in particular, does not produce nitrogen gas under
anoxic conditions. It is argued that complete anoxic conditions barely exist or exist to a
small extent in treatment systems that use aerobic nitrification followed by anaerobic
denitrification. Under such conditions, these bacteria have a high potential of nitrous oxide
production compared to nitrogen gas production. Same studies have also shown that the
typical aerobic denitrifiers, Paracoccus denitrificans produce more N20 than nitrogen gas
under oxic conditions. Two strains of aerobic denitrifiers, Pseudomonas stutzeri TR2 and
Pseudomonas sp. strain K50 are able to produce more nitrogen gas and less N20 under
aerobic conditions (Takaya et al., 2003).
Ammonia Volatilization
This is the loss of un-ionized ammonia to the atmosphere. Ammonia is volatile and can be
lost to the atmosphere through diffusion at conditions of high pH and temperature. In water,
ammonia exists as un-ionized (NH3) and ionized (NH4+) forms, and existence of the two
species is pH and temperature dependent. At high pH and temperature, the ammonia
fraction dominates over ammonium. The percentage of un-ionized ammonia in relation to
pH and temperature can be determined using the equation proposed by Emerson et al.,
(1975) and Pano and Middlebrooks (1982):
% Un-ionized NH3 = 100/ (1+10(pKa ±pH))
pKa = 0.09108 + (2729.92/ 273.2 + T)
Where T = temperature oC
12
(3)
Although algae ponds are known to have elevated pH values, ammonia volatilization has
been found to be an insignificant removal mechanism in these ponds (Zimmo et al., 2003).
Nitrogen fixation
This is a biological process where atmospheric nitrogen is reduced to ammonia nitrogen by
bacteria and cyanobacteria. It is an adaptive process for organisms living in nitrogen
deficient conditions. It rarely occurs in nitrogen rich environments such as wastewater
(Kadlec and Knight, 1996). This is due to repression of the process by presence of
ammonia under such conditions. This transformation is negligible in wastewater treatment
systems.
Assimilation and decomposition
This is the conversion of inorganic nitrogen into organic forms for cell and tissue synthesis
in living organisms. In plants, ammonia nitrogen is much preferred to nitrate nitrogen.
Nitrate uptake may be favored in ammonia-deficient, nitrate-rich conditions. In wastewater
stabilization ponds, death and decomposition of phytoplankton may partly release the
assimilated nitrogen into the water. This provides a mechanism of internal cycling of
nutrients.
ANNAMOX
ANNAMOX is a process discovered by Mulder et al., (1995). It is an anaerobic biological
process in which ammonia is converted to nitrogen gas with nitrite as the electron acceptor.
Autotrophic bacteria of the order Planctomycetes are known to carry out this process. It
does not require a carbon source as compared to denitrification (Dongen et al., 2001) and
requires low oxygen concentration. Thus, it may be a substituting mechanism for
denitrification under conditions of limited availability of organic matter.
Scope of this Thesis
This thesis presents work done on nitrogen removal both in the laboratory and in pilot scale
wastewater stabilization ponds. Laboratory work included studying biofilm and bulk water
nitrification rates under different environmental conditions. The rates were then used in the
nitrogen mass balances of the pilot scale wastewater stabilization ponds. The ponds were
constructed and operated under tropical conditions in Kampala - Uganda. There were four
pilot scale ponds; with three of them fitted with baffles. It is known that nitrogen removal
in wastewater stabilization ponds is limited by nitrification process which in turn is limited
by lack of attachment surface for nitrifiers. The baffles in this case provided more surface
area for biofilm attachment required for nitrifier growth. The pilot scale system was
operated under two operational conditions i.e. under low ammonia loading (Period 1) and
high ammonia loading (Period 2). The results presented in this thesis are divided according
to the two operational conditions. This research was limited to only nitrogen removal in
wastewater stabilization ponds. Other aspects such as BOD, COD, TSS, pH, oxygen,
13
temperature etc. were considered only as monitoring parameters and were used in
explaining nitrogen removal processes.
The first chapter of this thesis is the introduction which discusses various aspects of
wastewater stabilization ponds with respect to nitrogen removal. Different paths of nitrogen
transformation in wastewater stabilization ponds have been addressed here.
Chapter 2 mainly focused on biofilm characteristics i.e. biofilm growth rate, dry and wet
biomass as well as algal and zooplankton species composition in wastewater stabilization
ponds. These are important factors that could influence processes in wastewater
stabilization ponds. In chapter 3, results of tracer studies are presented. The effect of baffles
on pond hydraulic characteristics was investigated. Chapter 4 analyzed nitrification rates in
bulk water and biofilm. Laboratory activity tests were performed to discriminate between
bulk water and biofilm nitrification rates; their relative importance in nitrogen removal was
discussed.
The effect of light, dark, oxygen and pH on biofilm nitrification rates are described in
chapter 5. Chapter 6 presents the results of the general performance of the four ponds. The
effects of operational conditions and baffles on nitrogen removal in the ponds were studied
here. Chapter 7 focused on nitrogen mass balances; bulk water and biofilm nitrification
rates of the pilot scale ponds were determined. These were fitted in the Kjeldahl nitrogen
mass balance equation and used to predict effluent Kjeldahl nitrogen of the ponds. Total
nitrogen mass balances were also performed in chapter 7. Finally, the thesis ends with a
general summary of the study and outlook.
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20
Chapter 2
The effect of baffles on algal-bacterial biofilm structure and composition of
zooplankton in wastewater stabilization ponds
21
Chapter 2
The effect of baffles on algal-bacterial biofilm structure and composition of
zooplankton in wastewater stabilization ponds
Abstract
Nitrifier biomass in the water column of wastewater stabilization ponds is low and this
reduces nitrification rates. For this study, baffles were installed as attachment surface for
nitrifiers. Four pilot scale wastewater stabilization ponds were constructed in Kampala,
Uganda; pond 1 was control while ponds 2, 3 and 4 had baffles of the same total surface
area but different configurations. The ponds were operated for two periods i.e. under an
influent BOD of 72±45 mg l-1 and ammonia of 34±7 mg l-1 (period 1) and an influent BOD
of 29±9 and ammonia of 51±4 mg l-1 (period 2, achieved by covering facultative pond). The
objective of this study was to investigate biofilm biomass and, distribution and composition
of algae on the baffles. The diversity and biomass of zooplankton in the pond water column
was also investigated. Dry weight of biofilm was determined gravimetrically while the wet
weight of the algae in the biofilm were determined using the bio volume method. The
results showed that the algal-bacterial biofilms grew rapidly within 2-3 weeks and after
that, the thickness did not increase. The algal-bacterial biofilm dry weights at 5cm depth of
all ponds during period 1 were higher than those of period 2. This is probably due to the
contribution of influent algae and heterotrophic biomass. As the facultative pond during
period 1 was not covered, more algae entered the maturation ponds forming thick biofilms
on the baffles, the algae in the deeper layers died forming a layer of detritus material.
Additionally, the higher influent BOD during period 1 might have favored growth of more
heterotrophic bacteria. The dry weight measurements included all components of the
biofilm such as algae, heterotrophic organisms, midge larvae and detritus while the wet
weight specific to only algae, was measured by the bio volume method. This involved
identifying algae under the microscope and calculating their wet weight using geometric
formula. It was found that the total wet weight of the algal biofilm during period 2 were
higher than in period 1, which was opposite to the dry weight results. Probably the dry
weight during period 1 consisted to a large extent of dead algae material, which is excluded
from the wet algae weight. The advantage of bio volume method is that wet weight of only
living algae is determined. The reason why the total wet weights of biofilm algae were
higher during period 2 can be attributed to covering of the facultative pond. During period
2, no algae entered the maturation ponds therefore the TSS in the water column was
significantly lower than in period 1. This permitted more light penetration into the deeper
parts of the ponds thus increasing the area for attached algal growth. Additionally, the
influent ammonia during period 2 was higher; this coupled with no algae entering from the
covered facultative pond could have reduced competition. More algal biomass during
period 2 led to improved oxygen conditions in the ponds. The DO in all ponds at 45 and
70cm depth were significantly higher during period 2; this is important for ammonia
oxidation. Algal species were identified and generally, there was a shift in the dominant
group from period 1 to period 2 indicating a change in pond behavior. The zooplanktons
were also diverse with young stages of copepod (Nauplius larvae) being dominant in all the
four ponds. The distribution of algae and zooplankton in the four ponds showed that the
22
baffles had an effect on water quality which in turn affected the ecology of wastewater
stabilization ponds.
Key words: Stabilization pond, biofilm, baffle, algae, zooplankton
Introduction
Wastewater staELOL]DWLRQSRQGV:63¶VDUHZDVWHZDWHUWUHDWPHQWV\VWHPVWKDWXVHPXWXDO
relationship between bacteria and algae (Kayombo et al., 2002). These are the common
treatment technologies used in most developing countries. This is due to their simplicity,
low costs of construction, maintenance and operation (Veenstra and Alaerts, 1996).
However, they are not effective in nutrient removal especially nitrogen and this has been
attributed to low nitrifier biomass in the water column (McLean et al., 2000; Zimmo et al.,
2000). To improve nitrification in these ponds, attachment surfaces for the nitrifiers have
been used (Baskaran et al., 1992; Zhao and Wang, 1996; McLean et al., 2000; Schumacher
and Sekoulov, 2002).
This study incorporated baffles as attachment surface in pilot scale wastewater stabilization
ponds. The baffles were flat wooden plates coated with fibre glass material; they were
arranged in different configurations in the different ponds. Introduction of baffles affects
pond hydraulics and may change the pond ecology. :63¶VDUHFRPSOH[HFRV\VWHPVZKRVH
performance is related not only to hydraulic characteristics, pollution loading and climate
but also to biological communities like planktons and benthos. Physico-chemical variables
alone cannot explain pHUIRUPDQFH RI :63¶V $pproaches that take into account physicochemical and biological variables and their interactions should be adopted (Cauche et al.,
2000). Wastewater stabilization ponds mostly depend on oxygen production by algae
(Pearson, 2005) thus intensive algal grazing by zooplanktons can be detrimental to
treatment process (Lai and Lam, 1997; Pearson, 2005). For eutrophic lakes, zooplanktons
have been reported to release ammonia through excretion (Smith, 1978; Lehman, 1980;
Moss, 1988, Moegenburg and Vanni, 1991) though this is rapidly taken up by algae (Ganf
and Blazka, 1974; Moegenburg and Vanni, 1991). However, it is uncertain whether this has
an effect on wastewater treatment in wastewater stabilization ponds.
Development of algal-bacteria biofilms are influenced by many factors like the nature of
attachment surface, nutrients, light and other biological interactions. Different types of
algae prefer different growth conditions; their dominance and distribution on the biofilm
differs accordingly. For instance, Cyanobacteria and Euglena are tolerant to high organic
and nutrient loading (Wrigley and Torien, 1990; Sheheta and Badr, 1996). The motile
forms like Euglena and Chlamydomonas are known to migrate to areas with sufficient
illumination (Moss, 1988). The phytoplankton distribution and occurrence can also be
determined by grazing pressure of the herbivorous zooplanktons (Michael, 1987; Bakker
and Rijswijk, 1994; Lai and Lam, 1997).
Zooplanktons tend to occur in ponds with low BOD loading (Uhlman, 1980). There are
three major crustacean zooplanktons of ponds viz. copepods, cladocera and rotifers. These
have different sizes and different feeding habits although overlap in some instances. The
23
adult copepods are larger than cladocerans while the rotifers are the smallest in the group.
The copepods can be small-particle feeders (cyclopoid copepods) but generally feed on a
wider range of food particles (5-100μm). Copepods can prey on smaller zooplanktons,
larger colonies or masses of phytoplankton. They have eleven successive moults before
PDWXUDWLRQ LQWR DGXOW FRSHSRGV WKH ILUVW VL[ PRXOWV DUH MXYHQLOHV¶ UHIHUUHG WR DV QDXSOLL
(Moss, 1988).
The cladocera have a carapace which covers their bodies; they can be filter feeders or
raptorial i.e. actively grasp their prey e.g. Leptodora and Polyphemus. The herbivorous
genera include Daphnia and Bosmina. The filter feeders take up food particles in the range
of 1-50 μm. The particle feeders have thoracic limbs with hairs which convey food to their
mouth (Moss, 1988). The cladocera actively move through water using their large
branched second antennae giving them the common name of water fleas. Rotifers are
generally smaller in size and usually feed on small particles 1-20 μm in size. They are
suspension feeders; they have rhythmically beating cilia around the mouth which directs
water and suspended particles into their gut. Knowledge of zooplanktons of wastewater
stabilization ponds is limited as compared to information available on microalgae. The
zooplankton and insect larvae are known to play important role in the treatment process of
activated sludge and biological filters. Daphnia can polish algal rich effluents and could
have applications in aquaculture (Pearson, 2005).
The objective of this study was to investigate biofilm development on the baffles installed
in four pilot scale maturation ponds. Biofilm biomass, distribution and composition of
algae on the baffles were investigated. The diversity and biomass of zooplankton in the
pond water column of the pilot scale maturation ponds was further investigated.
Methodology
Description of pilot scale system
The pilot scale plant was set up at the Bugolobi Sewage Treatment Works (BSTW) in
Kampala- Uganda. The plant consisted of three systems: (i) an anaerobic tank (AT) of 10
m3 which fed the (ii) facultative pond (FP) (10m x 2.0m x 1.0m) at a flow rate of 2.1 m3 per
day and (iii) 4 fiber glass maturation ponds (MP) with different surface area for bacterial
attachment and different flow patterns. The total surface area for attachment in pond 1 (unbaffled) was 6.4 m2 while that of ponds 2, 3 and 4 was 23.2 m2 each. The surface area for
attachment in pond 1 was provided by the walls of the ponds while the extra surface area in
ponds 2, 3 and 4 was provided by installation of different configurations of 15 baffles of the
same area (0.56 m2 each).
Each maturation pond had dimensions of 4.0 m x 1.0 m x 1. 0 m (length, width and depth)
but wastewater was filled up to a depth of 0.8m. Pond 1 was a long open channel with
horizontal flow. Pond 2 had fifteen baffles placed vertically and parallel to the flow
direction and has horizontal flow pattern, similar to that of pond 1. Pond 3 also had fifteen
24
vertical baffles but placed such that the horizontal flow1 is directed around the baffles as
opposed to direct flow in pond 1 and 2, introducing plug flow characteristics. Pond 4 was
fitted with fifteen vertical baffles placed perpendicular2 to the influent flow direction. This
directed the flow in alternating upwards and downwards pattern. The layout of the pilot
scale ponds is shown in figure 2.
AT
1
E
f
f
l
u
e
n
t
2
FP
3
4
Figure 2: Top view of pilot scale wastewater stabilization ponds showing baffle arrangement and the flow
patterns
Operational conditions
Settled wastewater from the primary sedimentation tanks of BSTW was pumped daily into
the anaerobic tank. The mean residence time for the AT was 3 days. From the AT,
wastewater flowed continuously by gravity to the FP at flow rates of approximately 2.1 m 3
per day. The ponds were operated under two conditions referred to as period 1 and 2.
During period 1, the surface of the FP was left open to allow algae develop naturally. The
effluent of the FP was then fed into each maturation pond (Figure 2) at a flow rate of
0.5m3d-1. The influent of the maturation ponds during this period (Period 1) contained
average NH4-N and filtered COD of 34±7 and 94±34 mg l-1, respectively. In period 2, the
surface of the FP was covered using a plastic sheet to block light penetration. The major
aim of covering was to increase influent ammonia to the maturation ponds. The mean
influent NH4-N and filtered COD concentrations were 51±4 and 88±39 mg l-1; the flow
rates were similar to those of period 1. Covering the FP reduced the influent COD and the
1
Baffles placed 70% across the width of the pond but completely down the depth of the pond to induce
horizontal flow around the baffles
2
Baffles placed completely across the pond width with some baffles extending downwards up to70% of the
pond depth (from the surface) while others 70% from the bottom creating alternating upward and downward
flow pattern
25
reasons for this are unclear. The ammonia was higher during this time possibly due to
absence of ammonia oxidation, less uptake by algal biomass or less volatilization.
The theoretical retention time of the FP was 9.5 days while that of the MPs was 6.2 days.
Period 1 was 22 months and period 2 was 5 months. Grab samples were taken thrice a week
and analyzed for NH4-N, NO2-N, NO3-N, Kjeldahl±N, COD, BOD and TSS. All samples
were filtered except for TSS and Kjeldahl±N and were analyzed according to standard
methods (APHA, 1995). The pH, oxygen, temperature and light intensity were measured in
the field from 11.00 to 12.00 hours.
Growth of algal-bacterial biofilms
Dry weight
Biofilm wooden plates coated with fiber glass (same material as the baffles) of dimensions
3.0cm by 8.0 cm were used in this experiment. Six plates were vertically mounted on a
frame and suspended in each maturation pond at depth of 5cm, 45cm and 70 cm. During
period 1, the plates were left for six weeks to allow algal-bacterial biofilm to develop. The
biofilm material that developed consisted of algae, heterotrophic organisms, midge larvae
and detritus material. One plate was sampled each week; the whole biofilm was carefully
scraped off and dried at 105 o C to determine the dry weight as TSS (APHA, 1995). During
period 1, the dry weight was only determined for plates at 5cm depth. The procedures
above were repeated in period 2 but this time for five weeks with dry weight determined at
the three depths.
Wet weight
One biofilm plate from each depth of 5cm, 45cm and 70cm during each period was
FDUHIXOO\ VFUDSHG DQG WKH ELRILOP FROOHFWHG LQ PO YLDOV 7ZR WR WKUHH GURSV RI /XJRO¶V
Iodine (about 1% of the total volume) was immediately added to the samples. A known
volume of the sample was settled by sedimentation in a 2ml sedimentation chamber. The
minimum sedimentation time was 3 hours. After sedimentation at the chamber bottom, the
samples were analyzed using Utermohl inverted microscope technique. They were
observed under magnifications of 40, 100, 200, 400 and 1000 times where individuals of
different species/genera were identified and counted. Wet weight (μgl-1) estimation
involved calculating the specific biovolume (APHA, 1995) of individuals of each algal
species using its geometrical formula (Hillebrand et al., 1999) and multiplying by the
density of water (this method assumes the density of organism being equal to that of water).
The wet weights obtained in μgl-1 were multiplied by the total number of individuals of that
species to get the total weight per species. The sum of wet weights of all the species
expressed μgl-1 was obtained and wet weight recalculated for the original volume of sample
and divided by area of biofilm plate to get the biomass in gm-2. The wet weight biofilm
algal biomass obtained here were specific to only algae, they did not include other
organisms and detritus material as in the dry weight method.
Zooplankton
During period 2, zooplankton was sampled from the water column of the four maturation
ponds. Water samples in the top 5-20cm at the inlet, midpoint (2m) and outlet points of the
26
four ponds were sampled during day time using a one liter plastic container. Each sample
was filtered through a 53 μm sieve, stored in different sample bottles and preserved with
4% formaldehyde. The samples were transferred to the laboratory for analysis as described
by Ndawula et al., (2004). In the laboratory, the samples were washed over a 53 Pm sieve
to remove the fixative, re-diluted into sufficient volume to obtain workable counting
densities. Sub-samples of 2ml, 5mls and 10mls were taken from well agitated samples to
ensure homogeneous distribution of organisms. The sub-samples were introduced on a
counting chamber and examined under an inverted microscope at both X100 magnification
for taxonomic analysis and X40 magnification for counts to determine species composition
and abundance, respectively. Identifications were done to the lowest possible taxon using
published keys and figures (Sars, 1895; Rzoska, 1957; Brooks, 1957; Ruttner, 1974;
Pennak, 1978). Dry-biomass and densities were generated using the bio volume as
described in Ndawula (1998) and Manca and Comoli, (2000).
Results
The results for algal-bacterial biofilm dry weight at 5cm depth after 5 weeks are shown in
figure 2.1. The dry weight biomass at only 5cm depth can be compared between the two
periods (those of 45 and 70cm were not determined during period 1, see methodology). The
results for wet weight biofilm algal biomass are presented in table 2.1 and these can be used
for comparison at the three depths of the two periods.
140
Period 1
Period 2
100
TSS (mgl-1)
Dry weight (gm-2)
120
80
60
40
20
0
400
350
300
250
200
150
100
50
0
Pond 1 Pond 2 Pond 3 Pond 4
Figure 2.1: Algal-bacterial biofilm dry weight
at 5cm depth after 5 weeks incubation for
periods 1 and 2 in maturation ponds 1-4
Period 1
Period 2
Pond 1 Pond 2 Pond 3 Pond 4
Figure 2.2: TSS for periods 1 and 2 in
maturation ponds 1-4 at 5 cm depth (n = 61, 54)
Table 2.1 Wet weight biofilm algal biomass (gm-2) for the two periods estimated using bio volume of
cells
Period 2 (gm-2)
Pond
Period 1 (gm-2)
5cm
45cm
70cm
Total
5cm
45cm
70cm
Pond 1
1.03
0.65
0.49
0.73
1.06
0.16
2.2
Pond 2
5.74
1.66
0.93
7.13
10.4
0.51
8.3
P
Pondd 23
2.93
4.99
0.13
7.75
3.79
1.13
8.1
P
Pondd 43
0.16
0.06
1.22
8.57
9.99
6.06
1.4
27
algal
Total
2.0
18.0
12.7
24.6
TSS
The results for the effluent TSS of the maturation ponds are presented in figure 2.2. The
effluent TSS of ponds 1, 2, 3 and 4 during period 1 were 285± 89, 206 ± 90, 238 ± 83 and
160 ± 100 mg l-1 respectively. Kruskal-Wallis test showed a significant difference between
the effluent TSS of pond 1 and 2 (p<0.0001); but TSS of pond 3 did not significantly differ
from these two. The TSS of pond 4 was significantly lower than that of ponds 1 and 3
(p<0.0001). The effluent TSS of pond 1, 2, 3 and 4 during period 2 were 53±37, 46±30,
29±34 and 28±23 mg l-1 respectively. Non parametric test showed that the median TSS of
pond 1 and 2 were not significantly different, similar results were obtained for ponds 3 and
4. However, the former were significantly different from the latter. Comparisons between
the two periods showed that the TSS during period 1 was significantly higher than those of
period 2. The higher TSS during period 1 could have been partly due to entry of suspended
algae from the facultative pond (during period 1, the surface of the facultative pond was not
covered so the influent to the maturation pond included algae).
Dry Weight Biomass
During period 1, pond 3 had the highest algal-bacterial biofilm dry weight followed by
ponds 1, 2 and 4; the same order was observed during period 2 (Figure 2.1). Although the
biofilm biomass of pond 3 appeared to be higher than the other ponds in both periods, it
was difficult to make conclusions if it was significantly high due variations in data. In
terms of biofilm growth rates, pond 3 had the highest algal-bacterial biofilm growth rates in
both period 1 and 2 i.e. 3.1 and 3.6 gm-2 d-1 respectively. When each pond was compared
during the two periods, the algal-bacterial biofilm dry weights of period 1 were generally
higher than those in period 2 (Figure 2.1).
The results for algal-bacterial biofilm biomass (dry weight) development with time for
period 2 at three depths of 5cm, 45cm and 70 cm are shown by figures 2.3-2.6. It was also
seen that the biomass in pond 1, 2 and 3 were very dynamic; biofilm formation and
sloughing occurred simultaneously. Although the biofilm biomass at 5cm depth was most
of the time the highest, t-tests showed no significant difference between the biomass of this
depth and that of 45 and 70cm in all ponds except for pond 1 at 5 and 45cm depth. At 5cm
depth, pond 3 had the highest biomass of 76 g m-2 (at week 3), for the other ponds the
highest values are: 53 g m-2 (pond 1, week 5), 36 g m-2 (pond 2, week 3) and lastly 27g m-2
(pond 4, week 5). The results showed that the ponds behaved differently although they
received the same influent. Generally, the biomass in pond 4 at all depths was lower than
those of ponds 1, 2 and 3.
28
5cm
45cm
70cm
Dry weight (gm-2)
70.0
60.0
50.0
40.0
30.0
20.0
80.0
60.0
50.0
40.0
30.0
20.0
10.0
10.0
0.0
0.0
Week 1 Week 2 Week 3 Week 4 Week 5
Week 1Week 2Week 3Week 4Week 5
5cm
45cm
70cm
Figure 2.4: Algal-bacterial biofilm biomasses at
different depths of pond 2 during period 2
80.0
Dry weight (gm-2)
Dry weight (gm-2)
Figure 2.3: Algal-bacterial biofilm biomasses at
different depths of pond 1 during period 2
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
5cm
45cm
70cm
70.0
Dry weight (gm-2)
80.0
60.0
5cm
45cm
70cm
40.0
20.0
0.0
Week Week Week Week Week
1
2
3
4
5
Week 1 Week 2 Week 3 Week 4 Week 5
Figure 2.5: algal-bacterial biofilm biomasses at
different depths of pond 3 during period 2
Figure 2.6: algal-bacterial biofilm biomasses at
different depths of pond 4 during period 2
Wet weight biomass
The results for wet weight biofilm algae are shown in table 2.1; the full list of all the algal
species and their wet weight at different depths during period 1 and 2 are given in the
appendix 1. During period 1, the total algal wet weight of ponds 2 and 3 were equal but
higher than those of ponds 1 and 4. In period 2, pond 4 had the highest total algal wet
weight followed by pond 2, 3 and 1 respectively (Table 2.1). The results showed that the
total algal wet weight of ponds 2, 3, and 4 during period 1 were lower than those of period
2.
Oxygen profile
The oxygen concentrations in the water column are given in figure 2.7. The oxygen
concentrations in all ponds followed typical pond oxygen profiles with concentrations
decreasing with depth. At 5cm depth, the oxygen levels were above 15 mg l -1 and did not
29
significantly differ during the two periods. At 45 and 70 cm depth, the oxygen
concentrations during period 2 were significantly higher than those of period 1.
25.0
20.0
Oxygen (mgl-1)
5cm Period 1
5cm period 2
15.0
45cm Period 1
45cm Period 2
10.0
70cm Period 1
70cm Period 2
5.0
0.0
MP1
MP2
MP3
MP4
Figure 2.7: Oxygen concentrations at 5cm, 45cm and 70cm in the maturation
ponds during period 1 and 2.
Algal composition in biofilm
Figure 2.8 shows the baffles of pond 2 during period 1. The algal biofilms appeared to be
thick and green at the top of the plates. The common observation in all biofilms was
existence of the blood worm casings. The worms made their casings in the biofilm and
sometimes dislodged it (Figure 2.8). The algal composition of the biofilm of the maturation
ponds during period 1 and 2 at different depths are shown in table 2.2. The algal genera
were similar to the common ones found in the water column of maturation ponds (Mara and
Pearson, 1987; Pearson, 2005; Shanthala et al., 2009).
30
Figure 2.8: Baffles of pond 2 during period 1, note the dislodging of biofilm by Chironomidae larvae as
shown by the arrow
Table 2.2 Distribution of major algae groups on the biofilm plates at 5, 45 and 70cm depth of the maturation
ponds during period 1 and 2.
Depth
Algae group
Period 1
Period 2
1
2
3
4
1
2
3
4
++
+
++
++
+
5cm
Cyanobacteria
+
++
++
+
Cryptophytes
+
+
+
+
Diatoms
+
+
+
+
++
+
+
Euglenaphyte
++
+
+
+
+
+
++
Chlorophyta
+
+
+
45cm
Cyanobacteria
Cryptophytes
Diatoms
Euglenaphyte
Chlorophyta
Dinoflagellates
++
+
+
+
-
++
+
+
+
+
+
+
+
++
-
++
++
+
Cyanobacteria
+
Cryptophytes
+
+
+
Diatoms
++
Euglenaphyte
+
Chlorophyta
The symbol ++ shows the most dominant group based on wet biomass
70cm
+
+
++
+
+
-
+
+
+
+
++
-
+
+
++
+
-
+
+
++
+
-
++
+
+
+
-
++
+
+
+
+
+
+
++
+
+
+
++
+
++
+
+
+
++
+
+
+
Zooplankton composition
In the maturation ponds of this study, the three major groups of zooplankton found in the
water column at 5-20cm depth were; copepod, cladocera and rotifera (Table 2.3). Midge
larvae (blood worms) of Chironomidae were present and were seen attached on the
biofilms. However they were not enumerated; only micro invertebrates in the water column
were counted. From table 2.3, it was seen that copepods and cladocera were either low in
numbers or absent. The crustacean zooplanktons exhibit diurnal migrations moving to the
31
water surface at night and to the bottom during day. The copepods can detect and avoid
mechanical shock (Moss, 1988), it is possible that most crustacean zooplanktons detected
mechanical disturbance by the sampler and eluded being sampled.
Copepods were represented by only two species; Tropocyclops confinnis and Afrocyclops
sp., the young stage (Nauplius larvae) dominated all ponds (Table 2.3). Cladocera were
absent in ponds 2 and 3; cladocerans Ceriodaphnia cornuta was found in pond 1 while
Miona micrura was found in only pond 4. The rotifers were mainly common in ponds 1, 2
and 4 with Branchionus angularis being dominant in ponds 1 and 2 while Branchionus
plicatilis were dominant in pond 4. The total zooplankton biomass in the four maturation
ponds is shown in figure 2.9. Pond 4 had the highest biomass mainly contributed by the
Miona micrura cladocera. Although rotifers had high numerical abundances in pond 1
(Table 2.3), their contribution in terms of biomass was lower (Figure 2.9) compared to that
of Moina micrura (pond 4) mainly due to their small sizes. Miona micrura copepods feed
on a wide range of food sizes (Pagano, 2008) hence are effective grazers. The less grazing
pressure by zooplankton on algae in pond 1 compared to pond 4 could explain the
significantly higher (p<0.0001) median effluent TSS of pond 1 (49 mg l-1) than that of pond
4 (26 mg l-1) (Lampert et al., 1986).
Table 2.3 Distributions of zooplanktons (top 5-20cm depth) at inlet, midpoint and outlet of the
ponds
Zooplankton
Numbers per liter
Pond 1
Pond 2
Pond 3
inlet mid outlet inlet mid outlet inlet mid outlet inlet
Copepoda
Nauplius larvae
1
10
12
11
2
3
20
3
2
5
Tropocyclops confinnis
1
Cyclopoid copepodite
2
1
1
1
1
Afrocyclops sp
1
Cladocera
1
Ceriodaphnia cornuta
103
Miona micrura
Rotifera
1
B.calyciflora
1
1
Keratella tropica
1
1
1
1
Filinia opoliensis
4
4
Keratetla cochlearis
1
3
10
18
156
1482 564
B. angularis
1
0
Lecane bulla
1
1
1
1
Synchaeta sp
90
5
4
2
B.plicatilis
32
maturation
Pond 4
mid outlet
2
-
-
386
1
1
1
6
1
0
Zooplankton biomass (μg l-1)
1800
1600
1400
1200
1000
800
600
400
200
0
Pond 1
Pond 2
Pond 3
Pond 4
Figure 2.9: Total zooplankton biomass on basis of size of organism in the four maturation ponds during
period 2
Discussion
Dry weight biomass
The results for both period 1and 2 showed that the biofilm biomass dry weight in pond 3
was higher than in the rest of the ponds. However, it was difficult to get clear differences
between the dry weights of the four ponds. This is due to the variation of dry weights which
could have been caused by different components of the biofilm i.e. algae, heterotrophs,
midge larvae and detritus. The different components and their relative contribution to the
dry weight may have varied in the different ponds hence causing the differences. However,
comparison of dry weights between the two periods showed that algal-bacterial biofilm dry
weight of period 1 were higher than those of period 2 (Figure 2.1). Visual inspection of the
biofilm during algal counts and identification showed that the biofilm during period 1 (as
compared to that of period 2) was thicker in size and mainly consisted of detritus material.
This could be due to importation of algae from the facultative pond. During period 1, the
surface of the facultative pond was open hence more algae entered the maturation pond; it
is possible that more algae attached to the biofilm plates forming thick layers. When
biofilms develop and become thick, the inner layers become inactive and die due to light
and nutrient limitation (Lazarova and Manem, 1995). The inactive layers become detritus
material which supplies more organic matter to heterotrophs via biomass lyses (Wolf et al.,
2007). Barranguet et al., (2005) reported that when algal-bacterial biofilm develop under
low light, the proportion of heterotrophs to algae increases. The bulk water TSS of period 1
was significantly higher than period 2 (Figure 2.2) and this could have reduced light
penetration during period 1. Under these conditions, there is a possibility that the
heterotrophic biomass in period 1 was higher than that of period 2. The other explanation
for the differences in algal-bacterial biofilm dry weights during the two periods could be
due to the differences in influent BOD concentrations which resulted from change of
operational conditions. It was found that filtered influent BOD of the maturation ponds
during period 1 (72±45.1 mg l-1) was significantly higher (p<0.0001) than that of period 2
33
(29.4±9.2 mg l-1). The higher influent BOD during period 1 is thought to have favored
growth of more heterotrophic bacteria partly accounting for the higher dry weight during
this period.
The results for biofilm biomass of the maturation ponds during period 2 depicted
fluctuations in the biomass with time as can be seen in figures 2.3 ± 2.6. The reasons for
this are not clear; it is thought to be due to changing pond conditions or to the effect of
grazing by zooplankton. Midge larvae of Chironomidae made their cases in the algal
biofilm (Figure 2.8) probably dislodging and feeding on biofilm matter (Lamberti, 1996).
The results showed the dynamic nature of the algal-bacterial biofilms of WSP; probably
steady state biofilms in these systems are short lived or are not reached at all. This may be
of advantage to wastewater treatment i.e. in terms of renewal of biofilms or to the
disadvantage i.e. dislodging of the already established attached biomass. The effect of this
phenomenon requires further research.
Wet weight biomass
Two key observations were made from the results of the wet weight algal biofilm: First, all
the values of wet weight of the algal biofilm (Table 2.1) were lower than those of dry
weight algal-bacterial biofilm (Figure 2.1). Secondly, the algal wet weight biofilm during
period 2 were higher than in period 1 (Table 2.1); results which are opposite to those of dry
weight algal-bacterial biofilm. The wet weight algal biomass is specific to only algae while
the algal-bacterial biofilm dry weight consists of dry weight of algae, heterotrophic
organisms, and midge larvae and detritus material. This led to higher algal-bacterial dry
weight values. Since the wet weight of the algal biofilm represents the living algae in the
biofilm, the percentage of active algae at 5cm depth present in the dry weight can be
estimated. The percentage of living algae present in the dry weight during period 1 were
1%, 7%, 2% and 0.2% in ponds 1, 2, 3 and 4. Those of period 2 were 1.4%, 21%, 10% and
32% for ponds 1, 2, 3 and 4 respectively. The percentage weight of algal material would be
accurately estimated by chlorophyll a measurement; this is recommended for future studies.
The advantage of using the bio volume method for biomass estimation was that information
on both biomass and algal types were obtained concurrently.
The total wet weight of algal biofilm of pond 1 during period 1 and 2 did not differ while
those of ponds 2, 3 and 4 were higher during period 2 (Table 2.1). It was seen that the algal
biomass in the deeper parts of the biofilm substantially increased; especially in pond 4. This
may be attributed to the significantly lower bulk water TSS of period 2 (Figure 2.2) which
could have allowed more light penetration in the deeper parts of the ponds. Additionally,
period 2 had higher influent ammonia therefore more possibilities for algal growth and
higher biofilm biomass. However, increase in influent ammonia would equally affect both
biofilm and bulk water algae but the bulk water TSS of the maturation ponds in period 2
was lower than in period 1. The lower bulk water TSS during period 2 could have been
caused either by absence of algae in the influent or due to the grazing pressure of
zooplanktons on algae. The surface of the facultative pond was covered during period 2 and
this did not permit algae to develop. Zooplanktons are known to prevail under more aerobic
conditions (Pearson, 2005) which existed during period 2. Increase in oxygen suggests
34
more algae and under such conditions more zooplankton, which could have kept the
biomass under control (algae and heterotrophs), so it seems there was a self controlling
system.
Effect of algal biomass on oxygen production
The oxygen concentrations of the ponds during period 2 significantly improved especially
at 45cm and 70cm pond depths (Figure 2.7). This is consistent with the wet biofilm algal
biomass which was higher in the deeper parts of the ponds during period 2. The
improvement of oxygen conditions and lower BOD during period 2 is important for
nitrogen removal in WSP. These conditions could favor growth of nitrifiers and this may
have had a positive effect on ammonia oxidation. The oxygen concentration in ponds 1, 2
and 3 at 45cm depth during period 2 were above 5 mg l-1. These results are in agreement
Kayombo et al., (2002) who found daily average oxygen levels at 30cm depth in maturation
ponds to range from 3.4 to 11.3 mg l-1. The maturation ponds have been shown to be
aerobic during day time and anaerobic during night time (Kayombo et al., 2002). This
implies that ammonia oxidation occurs during the aerobic phase followed by denitrification
during the anaerobic phase. The depth at which aerobic conditions exist in ponds is
important with respect to nitrogen removal; this increases both the aerobic volume and area
(on biofilms) for nitrifiers. To understand nitrogen removal in these ponds, it is vital to
study nitrification rates at different oxygen conditions and this can be related to oxygen
conditions in ponds.
Algal composition in biofilms
The dominant algae groups (based on wet biomass) in the biofilm during period 1 in pond 1
at 5cm depth were Chlorophyta (Chlamydomonas sp), pond 2 and 3 Cyanobacteria
(Planktolyngbya sp) and pond 4 Euglenophyta (Euglena sp). Chlamydomonas sp and
Euglena sp are motile algae and are least expected to be found attached on biofilms.
Probably they attached to prevent from being washed out of the ponds. The motile forms
especially Chlamydomonas sp is known to dominate ponds with turbid water (Mara and
Pearson, 1987) and can tolerate high organic loading. Chlamydomonas sp exhibit high
tolerance to sulphide which cannot be said of Euglena sp (Pearson, 2005). The results from
this study showed that the bulk water TSS in pond 1 (which was significantly higher than in
the other ponds) caused turbidity which favored Chlamydomonas sp. The dominant groups
in ponds 1 and 2 at 45cm were Cyanobacteria (Planktolyngbya sp), pond 3 Chlorophyta
(Gongosira sp) and pond 4 Diatoms (Gomphocybella sp). At deeper depths of 70cm,
Euglenophyta was dominant in pond 1 while Cyanobacteria (Planktolyngbya sp) dominated
in ponds 2, 3 and 4. It is known that Cyanobacteria and Euglenophytes thrive under high
organic and nutrient rich water (Moss, 1988; Sheheta and Badr, 1996). In terms of algal
species found in the biofilms, the combined numbers of species (5cm, 45cm and 70cm)
were 11, 15, 8 and 21 algal species in ponds 1, 2, 3 and 4 respectively (Table A1,
Appendix). The diversity of algae is known to increase with low organic loading; pond 4
had the lowest effluent BOD.
The dominant algae on the biofilm at 5cm, 45cm and 70 cm depth in pond 1 during period
2 were Cyanobacteria (Planktolyngbya sp) and Chlorophyta (Protoderma sp), table 2.2.
35
Those of ponds 2 were Euglenophyta (Trachelomonas sp and Phacus longicuada), pond 3
Cyanobacteria (Planktolyngbya sp and Planktolyngbya limnetica) and Euglenophyta and
pond 4 Cyanobacteria (Planktolyngbya sp, Plankotothrix sp and Planktolyngbya limnetica).
It was found that Cyanobacteria were most common at 5cm depth of the ponds and this
could be due to selective grazing pressure by zooplanktons. The zooplanktons tend to feed
on the smaller algae and avoid the toxic Cyanobacteria (Gilbert, 1996). The total number of
algal species during period 2 in ponds 1, 2, 3 and 4 were 17, 21, 17 and 14 (Table A2,
appendix). The species diversity was higher during period 2 (except pond 4) implying that
the water quality had improved in terms of organic loading (Pearson, 2005). Generally,
there was a shift in the dominant group of algae from period 1 to period 2 indicating that
the pond behavior was changing.
Zooplankton composition
Small bodied cladocerans like the Ceriodaphnia and Miona are known to be insensitive to
Cyanobacteria toxins (Hanazato, 1991; Bouvy et al., 2001). It is possible that they were
able to withstand the Cyanobacteria that dominated pond 1 and 4 during period 2 (Table
2.2). Miona also thrives under favorable trophic conditions created by fragmentation of
Cyanobacterial filaments by copepods and rotifers (Bouvy et al., 2001). Other, large
cladoceran species like Daphnia and Diaphanosoma are more sensitive to toxic filaments
of Cyanobacteria (Kirk and Gilbert, 1992) and this explains their absence in the ponds. It
was seen that Cyanobacteria also dominated pond 3 but Ceriodaphnia and Miona were not
present. This can be attributed to predation pressure; it is known that the last stages of
copepods and adults cyclopoids are carnivorous (Brandl, 1998; Bouvy et al., 2001). The
higher numbers of Nauplius larvae in pond 2 and 3 may have suppressed the population of
Miona micrura in these ponds.
There are a number of reasons for existence of rotifer Branchionus angularis in only ponds
1 and 2. These include tolerance to pollution and toxins, food availability and competition,
predation and mechanical disturbance. For instance Branchionus angularis has been found
to be most tolerant to pollution (Sladecek, 1983) and toxins of Cyanobacteria (Fulton and
Paerl, 1988; Kirk and Gilbert, 1992). Pond 1 had the highest effluent ammonia of 27.8±4.4
mg l-1. Rotifers are small organisms which prefer to feed on smaller food particles like
bacteria, detritus and algae (Starkweather, 1980). Smaller and palatable algal forms like
Chlorophyta and Euglenophytes were among the dominant groups during period 2 in ponds
1 and 2 (Table 2.2) and these favored Branchionus rotifers in these ponds. It is also known
that copepods and rotifers compete for the palatable forms of algae hence presence of more
copepod nauplii in pond 3 disadvantaged the rotifers.
It is reported that copepods prey on rotifers and further still, the larger zooplanktons
mechanically damage the rotifers (Gilbert and Stemberger, 1985; Burns and Gilbert, 1986,
Gilbert, 1988). Possibly the lower number of nauplii in pond 4 favored the existence of
Branchionus plicatilis in this pond. B.plicatilis can tolerate low oxygen concentrations
(Barrabin, 2000); the oxygen concentrations of pond 4 at both 45 and 70cm were
significantly lower than the other ponds and this could have favored them. Most of the
zooplanktons recovered in the samples were grazers of algal cells especially Brachionus,
36
Moina and Cyclops. Studies by Michael (1987), Moegenburg and Vanni, (1991); Bakker
and Rijswiik, (1994) and Lai and Lam, (1997) show that zooplankton can exert pressure on
algal populations through grazing. Possibly the smaller algal forms were grazed upon
(Bouvy et al., 2001) and this is one of the reasons for Cyanobacteria being common in the
ponds.
The effect of zooplankton in wastewater treatment has been largely discounted (Mitchell
and Williams, 1982) yet they have been found to release nutrients back into water (Smith,
1978; Lehman, 1980; Lampert et al., 1986; Moss, 1988; Lai and Lam, 1997). For instance,
Smith, (1978) found ammonia released by fed copepods to be 0.303 μg N individual-1 day-1.
The smaller herbivores like rotifers and protozoan have high metabolic rates and can
release even more nitrogen; for instance they have been found to release up >35-60% of
their own phosphorous content (Lehman, 1980). To demonstrate the effect of zooplankton
on wastewater treatment, the average number of nauplius larvae per liter of wastewater for
the four ponds can be calculated from table 2.3. These can be used to calculate the total
number of individuals for the whole pond volume of 3200 liters and using the ammonia
release rate of Smith, (1978); the ammonia release per day by the zooplankton can be
calculated. For instance, 10560, 26560, 35200 and 4800 nauplii were calculated for ponds
1, 2, 3 and 4 respectively. These gave the amount of ammonia released as 3.2, 8.0, 10.1 and
1.4 mg-N d-1, which is substantially lower that the influent ammonia load of 26,520 mg N d1
. Probably if the ammonia released by all the other zooplankton is considered, the amount
of ammonia released may be important. The algal uptake of ammonia excreted by
zooplankton and subsequent feeding of algae by zooplankton shows internal recycling of
nitrogen. As long as the zooplanktons remain in WKH SRQGV WKH\ GRQ¶W FRQWULEXWH LQ WKH
nitrogen removal. Generally, the effect of zooplankton on nitrogen cycle in wastewater
stabilization ponds is not fully understood and further studies in this area is recommended.
Conclusions
Generally, the algal bacterial biofilm dry weight biomass during period 1 was higher than
those of period 2. The difference could have been caused by more algae entering from the
facultative pond and attaching on baffles to form thick layers. Death of the algae in the
deeper layers could have increased the proportion on detritus material during period 1. The
conditions during period 1 i.e. less light, more influent BOD and detritus material could
have favored more heterotrophs during this time. Also the oxygen levels during period 2
were higher and this could have favored the zooplankton which grazed on the biofilm.
During period 2, the biofilm biomass varied with time, a constant phase was not attained as
expected. The biomass rose and dropped, probably caused by changes in pond
environment, zooplankton grazing or disturbance by the blood worms. The studies showed
that biofilm development can be attained in three weeks and it is possible to use biofilms in
wastewater stabilization ponds.
The wet algal biomass during period 2 was higher than in period 1; this caused significantly
higher oxygen concentration in the deeper parts of the ponds during period 2. This could
have created favorable conditions for growth of nitrifiers since competition from
heterotrophs is minimized by low BOD loading during period 2.
37
The algal diversity during period 1 was lower than that of period 2; an indication that the
water quality of the ponds improved during this time. The covering of FP during period 2
led to low BOD loading which improved the water quality. Low BOD is also known to
favor growth of nitrifiers which lead to more ammonia removal.
The studies also showed that there was diversity of zooplankton in the ponds which may
have been as a result of algal distribution, physico-chemical or hydraulic characteristics of
the ponds. However, the role of zooplankton in the nitrogen cycle in wastewater
stabilization ponds is still unclear; further investigations are recommended.
Acknowledgements
We would wish to extend our sincere thanks to the Netherlands Government (through
NUFFIC) and EU- SWITCH project number 018530 for providing financial assistance. We
also thank Aguzu Alex and Kigundu Vincent from the National Fisheries Research Institute
(NaFFIRI), Jinja ± Uganda for the assistance in identifying phytoplankton and
zooplanktons. Their contributions to this work are invaluable.
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Appendix 1: List 1 Algal composition on biofilm during period 1
Algal species
Pond 1
5cm depth
Cyanobacteria
Merismopedia tenuissima
Planktolyngbya contarta
Euglenophytes
Euglena acus
Strombomonas acuminatus
Chlorophyta
Chlamydomonas sp
45cm depth
Cyanobacteria
Merismopedia tenuissima
Planktolyngbya sp
Diatoms
Gomphocybella sp
Nitzschia sp
Euglenophytes
Euglena sp
Chlorophyta
Protoderma sp
70cm depth
Cyanobacteria
Merismopedia tenuissima
Planktolyngbya sp
Diatoms
Nitzschia sp
Euglenophytes
Euglena sp
Trachelomonas sp
Pond 2
5cm depth
Cyanobacteria
Planktolyngbya sp
Planktothrix sp
Diatoms
Gomphocybella sp
Nitzschia acicularis
Euglenophytes
Euglena sp
Trachelomonas sp
Chlorophyta
Protoderma sp
Pseudodendoclonum sp
Tetraedron trigonium
Biomass (gm-2)
Biomass (gm-2)
Algal species
45cm depth
Cyanobacteria
Merismopedia tenuissima
Planktolyngbya sp
Cryptophytes
Cryptomonas sp
Diatoms
Diatoma sp
Fragilaria sp
Gomphocybella sp
Dianoflagelates
Glenodinium sp
Euglenophytes
Euglena sp
Trachelomonas sp
Chlorophyta
Scenedesmus sp
Tetraedron trigonium
3.1x10-5
1.1x10-4
4.6x10-2
1.8x10-2
9.6x10-1
7.0x10-4
4.9x10-1
3.6x10-2
9.3x10-4
7.6x10-4
70cm depth
Cyanobacteria
Planktolyngbya sp
Cryptophytes
Cryptomonas sp
Diatoms
Diatoma sp
Gomphocybella sp
Nitszchia sp
4.9x10-2
2.4x10-3
2.3x10-1
9.8x10-3
9.4x10-1
5.6x10-2
7.0x10-2
5.4x10-2
1.3x10-1
1.1x10-1
1.2x10-1
1.6x10-1
1.4x10-2
3.4x10-3
7.4x10-1
8.5x10-2
2.2x10-2
8.0x10-2
9.6x10-3
1.4x10-3
Pond 3
5cm depth
Cyanobacteria
Planktolyngbya
Planktothrix sp
Diatoms
Diatoma sp
Gomphocybella
Chlorophyta
Gongosira sp
Protoderma sp
45cm depth
Cyanobacteria
Planktolyngbya
Planktolyngbya
Diatoms
Diatoma sp
Gomphocybella
Chlorophyta
Gongosira sp
2.4x10-1
1.6x10-2
4.8
9.9x10-2
4.9x10-2
1.1x10-2
3.6x10-1
4.3x10-2
3.0x10-1
4.6x10-2
6.2x10-3
43
sp
sp
2.6
1.2x10-2
1.3x10-2
1.7x10-2
2.2x10-1
3.2x10-2
limnetica
sp
5.5x10-1
1.8x10-3
sp
5.5x10-2
1.8x10-1
3.6
Continuation list 1, algal composition on biofilm during period 1
Algal species
Biomass (gm-2)
Protoderma sp
5.7x10-1
70cm depth
Cyanobacteria
Planktolyngbya sp
Diatoms
Gomphocybella sp
Chlorophyta
Tetraedron trigonium
Pond 4
5cm depth
Cyanobacteria
Aphanocapsa sp
Planktolyngbya limnetica
Diatoms
Epitheimia turgida
Fragilari a sp
Nitzschia acicularis
Euglenophytes
Euglena sp
Phacus curvicuada
Chlorophyta
Chlamydomonas sp
Coelastrum sp
45cm depth
Cyanobacteria
Planktolyngbya sp
Cryptophytes
Cryptomonas sp
Diatoms
Diatoma sp
Gomphocybella sp
Navicula sp
Nitzschia sp
Euglenophytes
Trachelomonas sp
Chlorophyta
Protoderma sp
Scenedesmus sp
70cm depth
Cyanobacteria
Merismopedia tenuissima
Planktolyngbya sp
Algal species
Diatoms
Gomphocybella sp
Navicula gastrum
Nitzschia sp
Euglenophytes
Euglena sp
Trachelomonas sp
Chlorophyta
Coelastrum sp
Oocystis lacutris
1.1x10-1
1.3x10-2
8.2x10-4
8.5x10-5
8.7x10-4
9.1x10-4
1.9x10-4
2.2x10-5
1.4x10-1
1.2x10-3
1.3x10-3
1.8x10-2
8.3x10-3
2.4x10-3
1.5x10-2
2.4x10-2
2.0x10-3
3.7x10-3
9.7x10-4
4.5x10-3
4.9x10-3
2.0x10-3
5.4x10-1
44
Biomass (gm-2)
4.3x10-1
8.0x10-2
1.6x10-2
4.6x10-2
2.6x10-2
3.8x10-2
4.4x10-2
Table A1 combined algal species list for period 1
Pond 1
Pond 2
Cryptomonas sp
Chlamydomonas sp
Diatoma sp
Euglena acus
Euglena sp
Euglena sp
Fragilaria sp
Gomphocybella sp
Glenodinium sp
Merismopedia tenuissima
Gomphocybella sp
Nitzschia sp
Merismopedia tenuissima
Planktolyngbya contarta
Nitzschia acicularis
Planktolyngbya sp
Planktolyngbya sp
Protoderma sp
Strombomonas acuminatus Planktothrix sp
Protoderma sp
Trachelomonas sp
Pseudodendoclonum sp
Scenedesmus sp
Tetraedron trigonium
Trachelomonas sp
Total
11
15
Table A2 combined algal species list for period 2
Pond 1
Pond 2
Aphanocapsa sp
Aphanocapsa sp
Chroococcus dispersus
Cyclostephanodiscus
Cryptomonas sp
Diatoma sp
Cyclostephanodiscus
Euglena sp
Merismopedia tenuissima Diatoma sp
Euglena sp
Navicula sp
Fragilaria sp
Nitzschia acicularis
Merismopedia tenuissima
Nitzschia sp
Navicula sp
Phacus longicuada
Planktolyngbya limnetica Nitzschia acicularis
Nitzschia sp
Planktolyngbya sp
Oocystis solitaria
Protoderma sp
Phacus longicuada
Rhodomonas sp
Planktolyngbya limnetica
Romeria graclie
Planktothrix sp
Stuarastrum trigonum
Protoderma sp
Tetraedron trigonum
Rhodomonas sp
Trachelomonas sp
Rhopolodia sp
Scenedesmus sp
Tetraedron trigonum
Trachelomonas sp
Total:
17
Pond 3
Diatoma sp
Gomphocybella sp
Gongosira sp
Planktolyngbya limnetica
Planktolyngbya sp
Planktothrix sp
Protoderma sp
Tetraedron trigonium
21
45
8
Pond 3
Aphanocapsa sp
Cyclotella sp
Diatoma sp
Epitheimia sp
Fragilaria sp
Merismopedia tenuissima
Navicula gastrum
Navicula sp
Nitzschia acicularis
Nitzschia sp
Phacus longicuada
Planktolyngbya limnetica
Planktolyngbya sp
Planktothrix sp
Protoderma sp
Tetraedron trigonum
Trachelomonas sp
17
Pond 4
Aphanocapsa sp
Chlamydomonas sp
Coelastrum sp
Cryptomonas sp
Diatoma sp
Epitheimia turgida
Euglena sp
Fragilaria sp
Gomphocybella sp
Merismopedia tenuissima
Navicula gastrum
Navicula sp
Nitzschia acicularis
Nitzschia sp
Oocystis lacutris
Phacus curvicuada
Planktolyngbya limnetica
Planktolyngbya sp
Protoderma sp
Scenedesmus sp
Trachelomonas sp
21
Pond 4
Aphanocapsa sp
Chroococcus sp
Cyclostephanodiscus
Diatoma sp
Gomhocymbella sp
Merismopedia tenuissima
Navicula gastrum
Navicula sp
Nitzschia sp
Phacus longicuada
Planktolyngbya limnetica
Planktolyngbya sp
Planktothrix sp
Protoderma sp
14
List 2 Algal composition on biofilm during period 2
Algal species
Pond 1
5cm depth
Cyanobacteria
Aphanocapsa sp
Merismopedia tenuissima
Planktolyngbya sp
Romeria graclie
Cryptophytes
Rhodomonas sp
Euglenophytes
Euglena sp
Phacus longicuada
Trachelomonas sp
Chlorophyta
Protoderma sp
Stuarastrum trigonum
Tetraedron trigonum
Diatoms
Diatoma sp
Nitzschia sp
45cm depth
Cyanobacteria
Aphanocapsa sp
Planktolyngbya limnetica
Cryptophytes
Rhodomonas sp
Diatoms
Diatoma sp
Navicula sp
Nitzschia acicularis
Euglenophytes
Euglena sp
Trachelomonas sp
Chlorophyta
Protoderma sp
Tetraedron trigonum
70cm depth
Cyanobacteria
Aphanocapsa sp
Merismopedia tenuissima
Planktolyngbya limnetica
Planktolyngbya sp
Diatoms
Cyclostephanodiscus
Diatoma sp
Nitzschia acicularis
Biomass (gm-2)
Algal species
Euglenophytes
Trachelomonas sp
Chlorophyta
Protoderma sp
Pond 2
5cm depth
Cyanobacteria
Aphanocapsa sp
Chroococcus dispersus
Merismopedia tenuissima
Planktolyngbya limnetica
Diatoms
Navicula sp
Diatoma sp
Fragilaria sp
Nitzschia acicularis
Nitzschia sp
Euglenophytes
Euglena sp
Trachelomonas sp
Chlorophyta
Protoderma sp
Scenedesmus sp
45cm depth
Cyanobacteria
Aphanocapsa sp
Merismopedia tenuissima
Planktolyngbya limnetica
Diatoms
Navicula sp
Nitzschia sp
Cyclostephanodiscus
Diatoma sp
Rhopolodia sp
Euglenophytes
Euglena sp
Phacus longicuada
Trachelomonas sp
Chlorophyta
Oocystis solitaria
Protoderma sp
Tetraedron trigonum
70cm depth
Cyanobacteria
Aphanocapsa sp
Planktolyngbya limnetica
Planktothrix sp
Cryptophytes
Cryptomonas sp
Rhodomonas sp
7.7x10-3
1.8x10-4
9.5x10-2
4.0x10-4
7.4x10-4
1.9x10-1
3.5x10-1
6.4x10-3
6.2x10-2
7.1x10-3
1.6x10-3
6.5x10-3
2.0x10-2
1.0x10-2
2.1x10-3
2.7x10-3
7.7x10-2
1.5x10-2
1.2x10-3
8.0x10-3
2.6x10-2
9.2x10-1
8.4x10-4
-2
1.4x10
6.5x10-5
9.9x10-3
5.2x10-3
8.8x10-3
3.6x10-2
4.7x10-3
46
Biomass (gm-2)
9.0x10-3
7.3x10-2
1.5x10-2
1.0x10-3
4.4x10-4
3.3x10-2
2.1x10-1
9.6x10-2
1.2x10-2
3.2x10-2
8.5x10-2
4.5x10-1
5.8
3.7x10-1
2.2x10-2
2.1x10-2
3.0x10-2
1.7
2.0x10-2
1.1x10-1
7.7x10-2
6.7x10-2
2.9x10-2
5.3x10-1
7.3
8.8x10-1
3.0x10-2
1.6x10-1
6.3x10-3
3.0x10-2
1.1x10-1
2.6x10-2
6.4x10-3
3.6x10-3
Continuation list 2, algal composition on biofilm during period 2
Algal species
Diatoms
Cyclostephanodiscus
Diatoma sp
Nitzschia sp
Rhopolodia sp
Euglenophytes
Trachelomonas sp
Chlorophyta
Protoderma sp
Pond 3
5cm depth
Cyanobacteria
Aphanocapsa sp
Planktolyngbya sp
Diatoms
Nitzschia sp
Diatom
Fragilaria sp
Navicula gastrum
Euglenophytes
Trachelomonas sp
Chlorophyta
Protoderma sp
45cm depth
Cyanobacteria
Planktothrix sp
Aphanocapsa sp
Merismopedia tenuissima
Planktolyngbya limnetica
Planktothrix sp
Diatoms
Cyclotella sp
Diatoma sp
Epitheimia sp
Navicula gastrum
Nitzschia acicularis
Nitzschia sp
Euglenophytes
Phacus longicuada
Chlorophyta
Protoderma sp
Tetraedron trigonum
70cm depth
Cyanobacteria
Aphanocapsa sp
Merismopedia tenuissima
Biomass (gm-2)
Algal species
Planktolyngbya limnetica
Planktothrix sp
Diatoms
Diatoma sp
Navicula sp
Nitzschia sp
Euglenophytes
Trachelomonas sp
Chlorophyta
Protoderma sp
Tetraedron trigonum
2.2x10-2
3.7x10-2
5.1x10-2
5.7x10-3
1.8x10-1
4.3x10-2
3.0x10-2
5.2
Pond 4
5cm depth
Cyanobacteria
Aphanocapsa sp
Planktolyngbya limnetica
Planktolyngbya sp
Planktothrix sp
Diatoms
Navicula gastrum
Navicula sp
Nitzschia sp
Chlorophyta
Protoderma sp
2.4x10-1
1.1x10-1
2.6x10-2
4.9x10-2
1.1x10-3
2.1
45cm depth
Cyanobacteria
Planktothrix sp
Aphanocapsa sp
Merismopedia tenuissima
Planktolyngbya limnetica
Diatoms
Nitzschia sp
Diatoma sp
Navicula sp
Euglenophytes
Phacus longicuada
Chlorophyta
Protoderma sp
3.8x10-1
1.6x10-2
1.8x10-3
4.2x10-1
7.3x10-2
8.2x10-3
3.7x10-2
1.9x10-2
2.1x10-2
4.3x10-2
5.0x10-2
Biomass (gm-2)
7x10-1
4.9x10-2
3.7x10-2
5.4x10-2
4.1x10-2
1.7x10-1
7.6x10-2
6.7x10-3
1.81x10-1
8.1x10-2
4.3
6.8x10-1
8.4x10-3
6.3x10-3
1.2
2.2
4.0
6.6x10-2
1.5x10-2
1.8x10-1
6.8x10-1
4.4x10-1
4.5x10-2
3.1
1.4
2.6
70 cm depth
Cyanobacteria
Planktothrix sp
Chroococcus sp
Merismopedia tenuissima
Planktolyngbya limnetica
Planktolyngbya sp
1.9x10-1
9.4x10-3
2.9x10-2
7.1x10-4
47
7.2x10-1
1.2x10-2
1.2x10-4
1.4
2.0
Continuation list 2, algal composition on biofilm during period 2
Algal species
Diatoms
Cyclostephanodiscus
Diatom
Gomhocymbella sp
Navicula gastrum
Nitzschia sp
Euglenophytes
Phacus longicuada
Chlorophyta
Protoderma sp
Biomass (gm-2)
3.2x10-2
2.3x10-2
4.4x10-2
1.1x10-2
7.5x10-2
1.4
2.8x10-1
48
Chapter 3
Comparison of hydraulic flow patterns of four pilot scale baffled wastewater
stabilization ponds
49
Chapter 3
Comparison of hydraulic flow patterns of four pilot scale baffled waste stabilization
ponds
Abstract
Four pilot scale wastewater stabilization ponds (WSP) were set up in Kampala ± Uganda.
Each pond had a length of 4m, width 1m and depth of 1m. The wastewater was filled up to
a depth of 0.8m. Pond 1 was un-baffled and operated as control while ponds 2, 3 and 4
were fitted with fifteen baffles having the same surface area but different baffle
configurations to induce different flow patterns. Tracer tests using lithium were performed,
the aim of the test was to investigate the effect of baffles on the hydraulic characteristics of
the ponds. The test was performed twice for all 4 ponds; the first test was run for 19 days
while the second one lasted 30 days. The results for test 1 showed that the tracer
concentration was not zero at the end time of the sample period; therefore a second run was
performed. The tracer curves (lithium concentration-time curves) during the two runs
looked similar, demonstrating the reproducibility of the test. Because the results for test 2
were more complete and therefore more reliable to use in the calculations, they are
presented in the abstract. The tracer curves looked similar for ponds 1 and 2 implying that
installing baffles parallel to the flow (as in pond 2) did not affect the flow pattern. The peak
of the tracer curves of ponds 1 and 2 were reached first, followed by that of pond 3 and 4.
The following parameters were calculated: the theoretical and actual (measured) mean
hydraulic retention time; dead volume; short circuiting index, dispersion number and
reactors in series. The theoretical mean hydraulic retention time were 6.2 days while the
actual mean hydraulic retention times for ponds 1, 2, 3 and 4 were higher, 7.6, 7.5, 9.2 and
8.1 days. This can be explained by the pond design which resulted in the tracer diffusing in
dead zones and being released slowly. This resulted in curves with long tails which gave
higher actual mean hydraulic retention time than the theoretical mean hydraulic retention
time. This resulted in negative dead volumes i.e. -23%,-21%,-49% and -60% for ponds 1, 2,
3 and 4 respectively. These negative values support the argument that the tracer diffuses in
the dead volumes and is slowly released later. When comparing the ponds mutually, it was
seen that the actual mean hydraulic retention times for ponds 3 and 4 for both tests were
higher than those of ponds 1 and 2. This was believed to be due to the longer travel time
and larger dead zones created by the baffle arrangements in ponds 3 and 4. The short
circuiting index was 0.87 for ponds 1 and 2 and this decreases from pond 3 to 4. The baffle
arrangement in pond 4 was effective in reducing short circuiting by 60%. According to both
the mixers in series and dispersion model, pond 1 and 2 behaved like mixed reactors in
series while pond 3 and 4 were best described by plug flow with moderate dispersion.
Key words: Tracer, stabilization ponds, baffles, dead volume, mean retention time
50
Introduction
The performance of wastewater stabilization ponds (WSP) is dependent on many factors;
inter alia, organic loading regime, and geometry, climatic and environmental conditions.
The hydraulic behavior of the ponds is also of principle importance in determining the
overall treatment efficiency (Short et al; 2010). Understanding the hydraulics of WSP is
vital in improving treatment performance (Nameche and Vasel, 1998; Shilton et al.,
2000).The objective of this study was to investigate the effect baffles on hydraulic
characteristics of wastewater stabilization ponds under tropical conditions. Kilani and
Ogunrombi (1984) and Muttamara and Puetpaiboon, (1997) studied the effect of baffles on
nitrogen removal, but at laboratory scale. Knowledge on the hydraulics and use of baffles in
ponds at pilot scale under tropical conditions is scanty.
Introduction of baffles in this research was considered with a dual purpose: (a) as support
surface for biofilm development (Chapter 2), in particular for N-removal (Baskaran et al.,
1992; Craggs et al., 2000; Mclean et al., 2000) and (b) improvement of the hydraulic
behavior (as in this chapter). However, introduction of baffles in ponds may affect the
hydraulic characteristics of the ponds. For instance undesirable effects such as
channelization, creation of dead zones and organic overloading (in-let area) have been
reported (Shilton and Harrison, 2003). For ponds installed with horizontal baffles, 50% and
90% baffle-width relative to pond-width causes channeling and short-circuiting. It is
recommended that a baffle width of 70% relative to pond width reduces channeling
(Shilton and Harrison, 2003).
Generally, effects of baffles on pond hydraulics are not addressed by traditional pond
design methods. It is difficult to reliably predict how different modifications and
interventions affect pond performance (Wood et al., 1995). Demonstrating hydrodynamic
problems using Computational Fluid Dynamics (CFD) has been suggested. The major
limitation is that computer programs are expensive and require expertise in application, and
are focused on large scale systems. Levenspiel, (1972); Shilton et al., (2000); Van der
Steen, (2000); Zimmo, (2003) and many others applied tracers to study pond hydraulics and
have found it useful. This approach was used in this study.
Theoretical background
In tracer studies, a tracer is introduced in the pond influent and its concentration at the
effluent is determined in a series of grab samples collected at specific time intervals
(Metcalf and Eddy, 2003). Tracer input in ponds is usually by two methods i.e. the step and
pulse method. In the step method, the tracer is continuously added until the effluent
concentration equals to influent concentration. In the pulse method, the tracer is added for
a short time; usually the time of addition is shorter relative to the theoretical retention time.
There are a number of tracers that are used in scientific research but most common ones
include Congo red, Fluorosilicic acid (H2SiF6), Hexafluoride gas, Lithium chloride (LiCl),
Potassium permanganate, Rhodamine WT and Sodium chloride. Lithium chloride was used
in this study due to its common application in studying natural systems. Besides, it can be
easily analyzed using the atomic absorption spectrophotometer. Rhodamine WT is sensitive
51
to light and temperature and requires a fluorometer, which is expensive equipment. Sodium
chloride is the cheapest and easiest option but has a tendency of forming density currents
unless mixed well (Metcalf and Eddy, 2003).
Tracer studies are important in assessing hydraulic characteristics of ponds. Normally, the
time which water stays in the pond is calculated by dividing the pond volume and flow rate
(Short et al., 2010). This is referred to as theoretical mean retention time and it assumes
that the whole pond volume is active. The theoretical mean retention time usually differs
from the actual (measured) mean retention time. This is caused by non-ideal flow in ponds
which create pockets of stagnant water (Van der Steen, 2000). Levenspiel, (1972) and
Agunwamba et al., (1992) describe reactors with non-ideal flow using the dispersion and
mixers in series model. The dispersion model assumes plug flow which is superimposed on
top with some degree of mixing. Depending on the intensity of intermixing, the prediction
of this model ranges from plug flow at one extreme to mixed flow at the other end. Absence
of intermixing represents an ideal plug flow situation while high mixing results in a
completely mixed system (Metcalf and Eddy, 2003). The parameter used describing
dispersion model is the dispersion number (d). The mixers in series model consider the
system to be divided into a series of mixed reactor tanks. The parameter used to describe
this model is the number of reactors in series (N). Since the behavior of the ponds was not
known, both models were tested and the one which described the system approximately
well was used.
The parameters (d) and (N) for both dispersion and the mixers in series model can be
calculated from the tracer response curve using mean retention time and variance.
The mean retention time (tm) is approximated if the concentration versus time tracer
response curve (C) is defined by discrete time measurements as shown in equation 1
(Levenspiel, 1999).
‫ݐ‬௠ ൌ
σ ௧೔ ஼೔ ο௧೔
(1)
σ ஼೔ ο௧೔
Where ‫ݐ‬௠
= mean retention time based on discrete time step measurements (days)
‫ݐ‬௜
= time at ݅ ௧௛ measurement (days)
‫ܥ‬௜
= tracer concentration ݅ ௧௛ measurement (g m-3)
ο‫ݐ‬௜
= time increment (days)
Variance ો2can be used to define the spread of the distribution (Levenspiel, 1999) as;
’
ߪଶ ൌ
‫׬‬଴ ‫ݐ‬௜ଶ ‫ܥ‬௜ ሺ‫ݐ‬௜ ሻ݀‫ݐ‬௜
’
‫׬‬଴ ‫ܥ‬௜ ሺ‫ݐ‬௜ ሻ݀‫ݐ‬௜
ଶ
െ ‫ݐ‬௠
52
Or in discrete form as:
ߪଶ ൌ
σ ௧೔మ ஼೔ ο௧೔
σ ஼೔ ο௧೔
ଶ
െ ‫ݐ‬௠
(2)
ߪ ଶ = variance based on discrete time measurements
Where
Dispersion model
Dispersion number (d) is defined by Levenspiel (1972) as the coefficient of dispersion D
divided by product of fluid velocity (u) and reactor length (L)
஽
݀ ൌ ௨௅
(3)
Where d = dispersion number, (no units)
D = coefficient of dispersion (m2s-1)
u = fluid velocity (ms-1)
L = length (m)
There is a relationship between variance of normalized tracer response ો2 ș, variance ો2 (c)
derived from tracer response curve C, mean retention time tm and dispersion number d for a
pulse tracer input (Metcalf and Eddy, 2003) and this is given by:
ߪఏଶ ൌ
ߪ௖ଶ
‫ܦ‬
ൌʹ
ଶ
‫ܮݑ‬
‫ݐ‬௠
ఙమ
Hence ʹ݀ ൌ ቀ௧ మ೎ ቁ
(4)
೘
Where ߪఏଶ
= variance of normalized tracer response C curve
ߪ௖ଶ
= variance derived from curve C (see equation 2)
‫ݐ‬௠
= mean retention time
This implies that once the mean retention time and variance have been calculated from
tracer results, dispersion number d can be calculated. The dispersion values can be used to
assess the degree of axial dispersion in wastewater treatment. If d = 0, ideal plug flow; less
than 0.05 - low dispersion; 0.05 to 0.25 ± moderate dispersion; more than 0.25 high
dispersion and when d tends to infinity, then the system is considered completely mixed
(Metcalf and Eddy 2003).
Mixers in series model
The number of complete mixed reactors in series (N) can be calculated using the mean
retention time and variance derived from tracer response curve C.
53
ܰൌ
మ
௧೘
(5)
ఙ೎మ
Where N = number of mixers in series.
The link between dispersion number (d) and number of mixers in series (N) can be
established through the Peclet number (Pe). Peclet number is the inverse of dispersion
number (d) and number of mixers in series (N) is a half the Peclet number (Metcalf and
Eddy, 2003). For instance, 5, 3, 2.5, 2.0 and 1.7 mixed reactors in series will be required to
simulate plug flow reactor with dispersion for dispersion numbers 0.1, 0.15, 0.20, 0.25 and
0.30 respectively.
Other parameters
After determination of mean theoretical retention time (tHRT), the fraction of dead volume
can be calculated as (Levenspiel, 1972):
‫ ݁݉ݑ݈݋ݒ݀ܽ݁ܦ‬ൌ ቀͳ െ ௧
௧೘
ಹೃ೅
ቁ ൈ ͳͲͲΨ
(6)
Where tHRT = theoretical hydraulic retention time (volume/flow rate)
The index of short circuiting Įs) indicates how fast the influent reaches the effluent point.
It is normally expressed as values that range from 0 to 1. When Įs approaches 1, the extent
of short circuiting can be considered large.
ߙ௦ ൌ
௧೘ ି௧೛
(7)
௧೘
Where
ߙ௦
‫ݐ‬௣
= index of short circuiting
=time taken to reach the maximum tracer concentration
Methodology
Description of pilot scale system
The pilot scale wastewater stabilization ponds used in this study was as described in chapter
2 (Figure 2). The operational conditions were also as described in chapter 2. After studying
the effect of baffles on the biofilm formation and ecology of the wastewater stabilization
ponds (Chapter 2), there was need to study the effect of baffles on the hydraulic
characteristics of the ponds; this was addressed in this chapter.
Tracer experiment
The tracer experiment was performed twice. For the first test, lithium sulfate solution was
used as the tracer. A solution of 21 liters containing 500 mg l-1 of lithium was prepared
using tap water. This was fed into each maturation pond with a volume of 3200 liters; a
maximum concentration of 3.3 mg l-1 of lithium was expected in the pond effluent
(assuming it is a completely mixed system).
54
Four buckets each containing 21 liters of lithium solution were prepared as described
above. The buckets were placed at the influent point of the maturation ponds and left for 2
hours to acclimatize to the environmental conditions. During this time, influent flow rates
of the ponds were measured. The influent flow rates varied but a mean of 0.35 l min -1 was
obtained for all ponds. After the 2 hours had elapsed, the influents of the ponds were
closed, and the lithium from the buckets fed into the ponds at a flow rate of 0.35 l min-1.
The buckets were fitted with rubber stoppers connected to silicon tubes. At the end of each
tube was a clip which was used in regulating the flows. After addition of lithium, the clips
were closed and influent points of the ponds immediately opened and wastewater continued
to flow at a rate 0.35l min -1. Samples were taken immediately and for the next 19 days.
For the second test, LiCl.H2O was used. It appeared that the lithium sulfate which was
purchased at the local market and used for the first test was of insufficient quality.
Therefore Lithium chloride, analytical grade (ACROS Organics, New Jersey) was obtained
from UNESCO-IHE laboratories and used. Thirty liters of the effluent of FP was collected
in plastic buckets and a lithium solution of 350 mg l-1 prepared. The buckets were put close
to the maturation ponds and left for 2 hours to acclimatize to the environmental conditions.
The solution was stirred and 25 liters added to each maturation pond at a rate of 0.36 l min-1
as described above. The experiment was run for 30 days because the first test showed that
19 days was not long enough. Samples were picked 3 times a day.
The addition of lithium to the ponds during the two tests can be considered as pulse input.
This is because the addition took less than 1.5 hours as compared to the theoretical
retention time of 149 hours (Metcalf and Eddy 2003).
Results
The results for the tracer studies during the two tests are presented in tables 3 and 3.1.
Table 3 shows the amount of lithium added and the time it took for addition to each
maturation pond. Table 3.1 shows the various parameters that are used to represent the
hydraulic characteristics in ponds. The hydraulic characteristics were calculated based on
Levenspiel, (1972, 1999) and Metcalf and Eddy, (2003) and the concentration-time data
obtained as described above.
Table 3 Volumes and time which lithium was added to the maturation ponds
Test
Parameter
Volume added (l)
1
Pond 1
19
Pond 2
19
Pond 3
21
Pond 4
21
2
Pond 1
Pond 2
Pond 3
Pond 4
25
25
25
25
55
Duration (Hrs)
1.60
1.00
1.25
1.25
1.14
1.14
1.14
1.14
Table 3.1 Parameters used to describe hydraulic characteristics of the maturation ponds
Test
Parameter
Pond 1
Pond 2
Pond 3
1
Theoretical HRT (tHRT, days)
6.3
6.2
6.2
Actual HRT (tm, days)
6.6
6.3
8.2
Mixers in series (N)
1.8
1.8
2.7
6KRUWFLUFXLWLQJLQGH[Įs)
0.88
0.88
0.66
Dispersion (d)
0.28
0.28
0.19
Dead volume (%)
-5.6
-2.0
-33
Recovery (%)
139
134
209
2
Theoretical HRT (tHRT, days)
Actual HRT (tm, days)
Mixers in series (N)
6KRUWFLUFXLWLQJLQGH[Įs)
Dispersion (d)
Dead volume (%)
Recovery (%)
6.2
7.6
1.6
0.87
0.30
-23
78
6.2
7.5
1.7
0.87
0.30
-21
77
6.2
9.2
1.9
0.78
0.26
-49
101
Pond 4
6.4
8.1
4.2
0.29
0.12
-26
195
6.2
9.9
2.9
0.39
0.17
-60
94
Figure 3 and 3.1 show the normalized tracer concentration as function of normalized time.
The results for both tests were similar and showed the curves to be skewed with long tails.
1.6
Pond 1
Pond 2
Pond 3
Pond 4
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
0
0.17
0.30
0.32
0.34
0.48
0.64
0.79
0.94
1.11
1.27
1.43
1.58
1.75
1.92
2.08
2.40
2.77
3.12
Normalized tracer (C0/Ci)
1.4
Normalized time (ti/tHRT)
Figure 3: Normalized lithium concentration-time curves for the maturation ponds during test 1
56
0.7
Pond 1
Pond 2
Pond 3
Pond 4
Normalized tracer (Co/Ci)
0.6
0.5
0.4
0.3
0.2
0.1
-0.1
0
0.1
0.3
0.4
0.6
0.7
0.9
1.0
1.2
1.3
1.5
1.8
2.1
2.4
2.7
3.0
3.3
3.6
3.9
4.2
0
Normalized time (ti/tHRT)
Figure 3.1: Normalized lithium concentration-time curves for the maturation ponds during test 2
Discussion
From table 3, it was seen that the volume of lithium solution added in the maturation ponds
during test 2 was slightly higher than that of test 1. The time taken to add lithium to the
ponds during the two tests also did not differ. This because the flow rate during period 2
was slightly higher than that of period 1. During test 1, the percentage recovery of lithium
was greater than 100% (Table 3.1). The total concentrations of lithium measured at the end
of the experiment were greater than the total initial lithium input. During this time, the
lithium was bought on the local market in Uganda and was not of analytical grade. This
could have accounted for this anomaly. In test 2, analytical grade anhydrous lithium
chloride (ACROS Organics, New Jersey) was used. The percentage recovery during this
test improved and the recovery were 78, 77, 101 and 94% in ponds 1, 2, 3 and 4.
The percentage dead volumes in all the maturation ponds were found to be negative (Table
3.1). This was also observed by Kilani and Ogurumbi (1984); Thackson et al., (1987) and
Zimmo (2003), who explained this phenomenon as being due to diffusion of the tracer into
dead volumes and subsequent release at a later stage. This causes the tracer curves to be
skewed and have longer tails (Shilton et al., 2000; Torres et al., 2000; Short et al., 2010)
(Figure 3 and 3.1) resulting in larger areas under the curves. The larger areas results in
longer actual (measured) hydraulic retention times than the theoretic mean retention time.
From equation 6, it is seen that the dead volume is calculated as the difference between 1
and the ratio of actual mean hydraulic retention time to theoretical mean hydraulic retention
time. If the actual mean retention times are higher than theoretical mean retention times, the
ratio will be larger than one hence negative dead volume values are obtained. For this
particular case, it seemed that the method for calculating dead volumes developed by
57
Levenspiel (1972) and Metcalf and Eddy (2003) cannot describe these results. Therefore,
no meaningful conclusions can be made basing on dead volumes.
In terms of flow patterns, both figures 3 and 3.1 showed that the flow patterns of the
respective ponds during period 1 and 2 were similar indicating that the results were
reproducible. The figures showed that the flow patterns of pond 1 and 2 were similar in
shape implying that the baffle arrangement in pond 2 did not affect the flow pattern. The
flow patterns in pond 3 and 4 were different from each other and from those of ponds 1 and
2. The lithium peak was reached first in ponds 1 and 2 then followed by pond 3 and lastly
pond 4 (Figure 3 and 3.1). The time taken to reach the tracer peak (tp) is important in the
calculation of the index of short circuiting. Ponds 1 and 2 had the highest degree of short
FLUFXLWLQJĮs = 0.87, Table 3.1, test 2). The degree of short circuiting decreased from pond
1 to 4 and this showed the positive effect of baffle configuration in reducing short circuiting
in algal ponds. Similar effects were observed by Short et al., (2010). Vertical baffles placed
perpendicular to the flow direction seemed to perform best in reducing VKRUWFLUFXLWLQJĮs =
0.39). Short circuiting in ponds is usually caused by jet flow (Shilton and Harrison, 2003;
Bracho et al., 2006) and wind blowing in the direction of flow (Shilton and Sweeny, 2005).
Installation of stub baffles at the inlet point is recommended as an approach to reduce short
circuiting in ponds (Shilton and Sweeney, 2005).
The theoretical mean retention times during the two tests were from 6.2 to 6.4 days (Table
3.1). The actual mean retention times during test 2 were 7.6, 7.5, 9.2 and 9.9 days which
were higher than 6.6, 6.3, 8.2 and 8.1 days for ponds 1, 2, 3 and 4 during test 1 (Table 3.1).
The reason why the actual mean retention time was lower in test 1 could be attributed to the
duration of the tracer experiments. In test 1, the tracer experiment was run for 19 days and
not all the tracer was measured giving incomplete tails (Shilton and Sweeny, 2005) in the
tracer curves (Figure 3). Therefore, the area under the curves were smaller than in test 2
where the experiment was run for 30 days and all the tracer curves were complete (Figure
3.1). When the tracer concentrations were extrapolated (Bracho et al., 2009) for test 1 to
give complete curves, the actual mean retention time for ponds 1, 2, 3 and 4 were 8.1, 7.2,
10.6 and 8.6 days which were closer to those of test 2. According to the calculations based
on Levenspiel, (1972) and Metcalf and Eddy (2003), the actual mean retention times of
ponds 1 and 2 during both tests were less than those of ponds 3 and 4. This implied that the
baffle configuration of ponds 3 and 4 increased the mean retention time by approximately 2
days. The baffles increased the travel distance resulting in higher retention times (Shilton
and Harrison, 2003). It is also thought that the increment in retention times could be due to
the effect of the tracer diffusing in the dead zones and subsequently being released slowly
causing long tails in tracer curves hence longer retention times.
During test 1, the data processing showed that pond 1 and 2 can be described by 2 ideally
mixed reactors in series; for pond 3 it was possible to describe it by 3 mixed reactors in
series and 4 mixed reactors in series for pond 4. For test 2, the data processed showed that
pond 1 and 2 were described by 1.6 and 1.7 ideally mixed reactors in series. According to
Metcalf and Eddy, (2003), 1.7 reactors in series simulate plug flow with dispersion number
(d) of 0.30 (see under section Mixers in series model). For (d) > 0.25, the system is
58
considered completely mixed (see under section Dispersion model). Therefore, ponds 1 and
2 can be approximated to behave as ideal mixers. According to mixers in series model,
pond 3 can be described by 2 ideally mixed reactors in series (Table 3.1). Two reactors in
series corresponds to (d) of 0.25 which can be considered as plug flow with moderate
dispersion. Pond 4 is described by 3 mixed reactors in series (Table 3.1) which corresponds
to (d) of 0.15. The (d) value within 0.05 and 0.25 is also considered as plug flow with
moderate dispersion (Metcalf and Eddy, 2003). According to Persson, (2000); the higher
the number of stirred reactors in series (N), the more plug flow the system becomes. Table
3.1 shows N increasing from pond 1 to 4 probably suggesting more plug flow behavior.
Therefore, ponds 3 and 4 are best described by plug flow with moderate dispersion. When
the tracer results of test 1 were extrapolated, 1.4, 1.4, 1.9 and 3.2 mixed reactors in series
were obtained for ponds 1, 2, 3 and 4 respectively. These results are similar to those
obtained in test 2 where complete tracer curves were obtained indicating reproducibility of
results.
Conclusions
The results of this study showed that the actual mean retention times based on the tracer
study calculations for all the ponds during the two tests were higher than the theoretical
mean retention times. This is probably caused by the tracer diffusing into dead volumes and
slowly being released. It was also found that the un-baffled pond 1 and baffled pond 2 have
similar hydraulic characteristics implying that installing baffles parallel to flow did not
affect the pond hydraulics. However, the flow pattern of pond 3 and 4 were different (i.e.
tracer peaks where reached at different times) indicating that the different baffle
configurations were responsible for this observation. It can then be concluded that although
addition of baffles can increase surface area for nitrifier growth, they have an effect on
pond hydraulics depending on the type of configuration chosen. Additionally, baffle
configuration in pond 1, 2 and 3 did not reduce short circuiting yet the baffle configuration
of pond 4 was effective in reducing short circuiting. Ponds 1 and 2 behaved like mixed
reactors while pond 3 and 4 were best described by plug flow with moderate dispersion.
References
Agunwamba, J.C., Egbuniwe, N. and Ademiluyi, J.O. (1992). Prediction of dispersion
number in waste stabilization ponds. Wat. Res. 26 (1), 85-89
Baskaran, K., Scott, P.H. and Connor, M.A. (1992). Biofilms as an Aid to Nitrogen
Removal in Sewage Treatment Lagoons. Wat. Sci. Tech. 26(7-8), 1707-1716
Bracho, N., Brissaud, F. and Vasel, J.L. (2009). Hydrodynamics of ponds Part ii practice.
In: 8th IWA Specialist Group Conference on Waste Stabilization Ponds, 26 - 29 April
2009, Belo Horizonte/MG Brazil
Bracho, N., Lloyd, B. and Aldana, G. (2006). Optimization of hydraulic performance to
maximize faecal coliform removal in maturation ponds. Wat Res 40, 1677-1685
59
Craggs, L.J., Tanner, C.C., Sukias, J.P.S. and Davies, C.R.J. (2000). Nitrification potential
of attached biofilms in dairy wastewater stabilization ponds. Wat .Sci. Tech.42 (10-11) 195202
Kilani, J.S. and Ogunrombi, J.A. (1984). Effects of baffles of the performance of model
waste stabilization ponds. Wat. Res. 18, 941-944
Levenspiel, O., (1972). Chemical Reaction Engineering. Second edition, John Wiley and
sons, New York
Levenspiel, O., (1999). Chemical Reaction Engineering. John Wiley and sons, New York
McLean B.M., Baskran, K., and Connor, M.A. (2000). The use of algal-bacterial biofilms
to enhance nitrification rates in lagoons: Experience under laboratory and pilot scale
conditions. Wat .Sci. Tech. 42(10-11), 187-194
Metcalf and Eddy, (2003). Wastewater engineering. Treatment and Reuse. Tchobanoglous,
G., Burton, F.L., Stensel, H.D (Eds). 4th Ed. McGraw Hill, Inc., USA
Muttamara, S. and Puetpaiboon, U. (1997). Roles of Baffles in Waste Stabilization Ponds.
Wat. Sci. Tech. 35(8) 275-284
Nameche, T.H and Vasel, J.L. (1998). Hydrodynamic studies and modelization for aerated
lagoons and waste stabilization ponds. Wat. Res. 32 (10). 3039-3045
Perrson, J. (2000). The hydraulic performance of ponds of various layouts. Urban Water 2,
243-250.
Shilton, A. and Sweeney, D. (2005). Hydraulic design. In: Pond Treatment Technology
(Ed). Shilton, IWA publishing, 189-217
Shilton, A., and Harrison, J. (2003). Guidelines for the Hydraulic design of waste
stabilization ponds, Institute of technology and engineering, Massey University, New
Zealand
Shilton, A., Wilks, T., Smyth, J. and Bickers, P. (2000). Tracer studies on a New Zealand
waste stabilization pond and analysis of treatment efficiency. Wat. Sci. Tech. 42 (10-11),
343-348
Short, M.D., Cromar, N.J., and Fallowfield, H.J. (2010). Hydrodynamic performance of
pilot-scale duckweed, algal-based, rock filter and attached-growth media reactors used for
waste stabilization pond research. Ecol. Eng. 36, 1700-1708.
Thackston, E.L., Shields, F.D. and Schroeder, P.R. (1987). Residence time distributions of
shallow basins. J. Env. Engrg. Div. ASCE, 113: 1319-1332
60
Torres, J.J., Soler, A., Saez, J and Llorens, M. (2000). Hydraulic performance of a deep
stabilization pond fed at 3.5 m depth. Wat. Res. 34 (3) 1042-1049
Van der Steen, N.P. (2000). Fecal coliform removal from UASB effluent in integrated
systems of algae and duckweed. PhD Thesis, Ben-Gurion University of Negev, Israel
Wood, M.G., Greenfield, P.F., Howes, T., Johns, M.R., Keller, J. (1995). Computational
Fluid Dynamic Modeling of Wastewater Ponds to Improve Design. Wat. Sci. Tech. 31(12),
111-118
Zimmo, O.R. (2003). Nitrogen transformations and removal mechanisms in algal and
duckweed waste stabilization ponds. PhD Dissertation, UNESCO-IHE, Wageningen
University, Netherlands
61
Chapter 4
Nitrification in bulk water and biofilms of algae wastewater stabilization ponds
Published as: Nitrification in bulk water and biofilms of algae wastewater stabilization
ponds. M.A. Babu, M.M. Mushi, N.P van der Steen, C.M. Hooijmans and H.J. Gijzen. Wat.
Sci. Tech. 55 (11), 93-101, 2007.
62
Chapter 4
Nitrification in bulk water and biofilms of algae wastewater stabilization ponds
Abstract
Nitrogen removal in wastewater stabilization ponds is poorly understood and effluent
monitoring data show a wide range of differences in ammonium. For effluent discharge into
the environment, low levels of nitrogen are recommended. Nitrification is limiting in
facultative wastewater stabilization ponds. The reason why nitrification is considered to be
limiting is attributed to low growth rate and wash out of the nitrifiers. Therefore to maintain
a population, attached growth is required. The aim of this research was to study the relative
contribution of bulk water and biofilms with respect to nitrification. The hypothesis was
that nitrification can be enhanced in wastewater stabilization ponds by increasing the
surface area for nitrifier attachment. In order to achieve this, transparent pond reactors
representing water columns in algae WSP were used. To discriminate between bulk and
biofilm activity, 5 day-batch activity tests were carried out with bulk water and biofilm
sampled from the pond reactors. The observed value for Rbulk5day was 2.7 X 10-4 g-N l-1 d-1
and for Rbiofilm was 1.50 g-N m-2 d ±1. During the 5 days of experiment with the biofilm,
ammonia reduction was rapid on the first day. Therefore, a short-term biofilm activity test
was performed to confirm this rapid decrease. Results revealed a nitrification rate, Rbiofilm,
of 2.13-N m-2 d-1 for the first 5 hours of the test, which was significantly higher than the
1.50 g-N m-2 d-1, observed on the first day of the 7-day biofilm activity test. Results of this
study demonstrated that biofilm nitrification rates were significantly higher than the bulk
water nitrification rates although oxygen concentration in the latter was kept high at 8.8 mg
l-1. This implies that biofilms could play an important role in improving nitrification
process in wastewater stabilization ponds. The volatilization rates were low even for
experiments were air was bubbled to keep the oxygen concentration high.
Key Words: Nitrification, Wastewater Stabilization Ponds, Bulk water, Biofilm,
Introduction
Nitrogen pollution on the world water bodies is increasing and effects have become visible
VLQFH WKH ¶V ZKHQ LQFUHDVLQJO\ UHSRUWV DUH JLYHQ RI HXWURSKLFDWLRQ RI ZDWHU ERGLHV
(Gijzen and Mulder, 2001). In response, high environmental standards and stringent
regulations are being adopted by developed nations. Many developing countries have
followed suit and have set strict standards, which in practice do not function because of
prohibitive costs for treatment plants required to satisfy those standards (Veenstra and
Alaerts, 1996). Several advanced treatment technological innovations have come up but
developing nations cannot afford them, yet urbanization and population is on the rise
(Gijzen and Khonker, 1997; Yu et al., 1997; Gijzen et al., 2004). This is compounded
further by the millennium development goal seven which advocates for reduction of half
the proportion of people without access to safe drinking water by 2015 ( WSSCC,2004).
Increase in accessibility to safe drinking water and sanitation implies generation of more
wastewater. It is estimated that 80-90% of water consumed is converted to wastewater
(Mara et al., 1992).
63
In trying to address the issues of wastewater treatment, most developing countries have
opted for wastewater stabilization ponds (WSP) as the major treatment technology. This is
due to its cost-effectiveness in construction and maintenance. In fact Mara and Pearson,
(1998) recommend the use WSP in developing regions. They have found them to perform
similar to advanced systems especially in COD removal. However, their major
shortcomings includes narrow zone for nitrification since the aerobic zone is limited to the
upper 0.50 m (Baskran et al., 1992); long hydraulic retention time and low attached
bacterial biomass (McLean et al., 2000; Zimmo et al., 2000); short-circuiting (Shilton et
al., 2000; Shilton and Harrison, 2003); high concentration of total suspended solids (TSS)
in the effluent (Mara, 2004); and large area for construction (Pearson, 1996).
This study mainly focused on limitation of nitrification due to lack of attachment surface
for nitrifiers. In this study, nitrification rates in the bulk water and in the biofilm were
investigated through a series of batch activity tests.
Materials and methods
Experimental set-up
Four transparent pond reactors with a surface area of 0.043 m 2, an effective depth of 0.95
meter and a volume of 0.041 m3 were used. The pond reactors A1-A3 were placed in series,
while A0 was a single reactor. The reactors simulated the water column in algae wastewater
stabilization ponds (Figure 4.1). Synthetic wastewater (Table 4.1) of moderate strength
(Metcalf and Eddy, 2003) was continuously fed at a flow rate of 0.66 l d-1 into A0 and A1,
which translated into a theoretical mean retention time of 2.6 days for each pond reactor.
Nitrifiers and denitrifiers were introduced into the system at the start of the experiment by
seeding with 100 ml of aerobically and anoxically grown activated sludge. Later on, green
algae were also introduced in the reactors and allowed to colonize the system. The set-up
was exposed to 12-hour light and dark regimes by illumination with a light intensity of 125129 μEm-2s-1. This provided sufficient light and represented natural conditions. The lamps
also provided heat that resulted in a mean ambient temperature of 24oC. The average
influent NH4-N and COD concentration was 40 mg l-1 NH4-N and 96 mg l-1 COD
respectively.
Bulk water activity tests
One liter of bulk water was collected from A3 and placed into two 2-liter glass beakers
(horizontal surface area 0.0177 m2). Similarly, synthetic wastewater (Table 4.1) devoid of
microorganisms was prepared with an ammonia concentration adjusted to 20 mg l-1 - values
similar to that of the effluent bulk water of A3 and placed into two beakers to serve as
control. All beakers were continuously aerated and exposed to light for 7 hours. Water
samples were collected on an hourly basis and the ammonium concentration determined
according to standard methods (APHA, 1995). The experiment was continued for 7 days.
Ammonia, pH and oxygen levels were monitored on a daily basis (APHA, 1995). All the
experiments were run in duplicates.
64
Tank
Pump
Effluent
A0
A1
A2
A3
Figure 4.1: Experimental set up showing flow patterns of A0, A1, A2 & A3
Table 4.1 Composition of synthetic wastewater (modified after Moussa et al., 2003)
Macro nutrients
Concentration (mg/l)
Micro nutrients solution
CH3OONH4
93.75
EDTA
NH4Cl
87.70
FeCl3.6H2O
NaH2PO4.H2O
26.70
H3BO3
MgSO4.7H2O
90.00
CuSO4.2H2O
CaCl2.2H2O
4.72
KI
KCl
36.00
MnCl2.4H2O
Micronutrient solution
0.6 (ml/l)
Na2MoO4.2H2O
ZnSO4.7H2O
CoCl.6H2O
Concentration (g/l)
10
1.5
0.15
0.03
0.18
0.12
0.06
0.12
0.15
Biofilm activity test (7 days duration)
Algal biofilm was carefully collected from the walls of A3 at 0.05m depth. A small round
sampler of an area of 2.7 x 10-3 m2 was found to collect an average of 3.30g ± 0.47 wet
weight of biofilm. Biofilm of 11.28g (equivalent to biofilm area of 0.0092 m2) and 11.30g
(0.0098 m2) were sampled for duplicate studies. These were placed in beakers (area 0.0177
m2) containing 1 liter of synthetic wastewater (ammonium concentration of 20 mg l-1)
devoid of microorganisms. The biofilms were carefully handled to avoid disintegration.
Control experiments using the same synthetic wastewater devoid of microorganisms and
biofilm were run in parallel. All the beakers were continuously exposed to 12-hour
light/dark regimes. The experiment was left to run for 7 days without aeration. Ammonia,
pH and oxygen were monitored according to APHA (1995) on a daily basis.
Biofilm activity test (7 hours duration)
After the 7 days duration of the experiment above, the biofilms were reused to run a shortterm experiment. At this time, the biofilm structure was still intact. This experiment lasted
for a period of seven hours. The biofilms were weighed and the wet weight had increased
from 11.28g to 12g (0.00981 m2) and 11.30g to 13.73g (0.01123 m2) respectively. The
65
same experimental procedures as above were repeated. Water samples were collected
hourly and ammonia concentration, pH and oxygen determined (APHA, 1995).
Biofilm plate activity tests-(7 hours duration)
To investigate further nitrification rates of biofilm from A3, glass biofilm plates of 0.03m
by 0.08m were suspended in this pond reactor at 0.05m depth (Van der Steen, 2000). The
plates were retrieved from the pond reactor after two weeks and hung in glass beakers
containing 0.5 liters of synthetic wastewater containing 20 mg l-1 NH4-N. Ammonium
nitrogen, nitrates, pH, dissolved oxygen and temperature were monitored (APHA, 1995)
after every two hours.
Ammonia Volatilization
Ammonia volatilization was calculated using Equation (1) developed by Zimmo et al.,
(2004):
Ammonia volatilization rate (g-N m-2 d-1)
= 3.3[NH3-N] +4.90,
(1)
Where, [NH3-N] is calculated from Emerson et al., (1975) as:
ଵ଴଴
Ψܷ݊݅‫ܪܰ݀݁ݏ݅݊݋‬ଷ ൌ ଵାଵ଴ሺ೛಼ೌష೛ಹሻ ,
(2)
The temperature and pH measured during the activity tests were used to calculate
percentage unionized ammonia which was used in equation 1 to calculate ammonia
volatilization rates.
Statistical analysis
For statistical analysis, the ammonia concentration was plotted against time and the linear
parts of the slopes of different experiments compared. For experiments which were run for
days, the time was converted to hours before the graphs were plotted. Regression analysis
using the F-test (95% confidence interval, at 0.05 levels) was used to check if the slopes for
different treatments were statistically different.
Results
The results for bulk water experiments are shown in figures 4.2 and 4.3. The 7 hour bulk
water test did not show any decrease in ammonia (Figure 4.2). A mean ammonia
concentration of 21.1 ± 0.6 mg l-1 NH4-N and 19.6 ± 1.1 mg l-1 NH4-N was measured in the
duplicate experiment 1 and 2, respectively. The control experiment showed a similar trend
with a mean ammonia concentration of 20-21 mg l-1 NH4-N. However, when the
experimental time of the bulk water test was increased to 5 days, there was a slight drop of
ammonia in the first four days followed by a rapid drop there after (Figure 4.3). The
concentrations dropped from 20.1 to 17.4 mg l-1 NH4-N and from 19.6 to 16.8 mg l-1 NH4N in duplicate 1 and 2, respectively. After the fourth day, the ammonia concentration
rapidly dropped to 9.5 mg l-1 NH4-N giving an overall ammonia decrease of 10.6 mg l-1
NH4-N for the entire period of five days (Figure 4.3).
66
The results for biofilm experiments are shown in figures 4.4 and 4.5; the biofilm
experiment run for 7 days showed a sharp drop of ammonia during the first day and became
constant in the rest of the days. A short term experiment was repeated (Figure 4.5) and this
showed a sharp decrease of ammonia within the 5 hours.
25.0
20.0
20.0
NH4-N ( mg/-1)
NH4-N (mgl-1)
25.0
15.0
10.0
5.0
15.0
10.0
5.0
0.0
0.0
1
2
3
4
5
6
7
1
2
Figure 4.2: Ammonia concentrations in
the 7 hour bulk water experiment. Mean
data for duplicate 1 and 2 were used for
the plot
4
5
Figure 4.3: Ammonia concentrations in the
5 day bulk water experiment. Mean data
for duplicates 1 and 2 were used for the
plot
30.0
30
25.0
NH4-N (mgl-1)
25
20
mg-N l-1
3
Time (days)
Time (Hours)
Control NH4
15
Biofilm NH4
10
Biofilm N03
20.0
15.0
10.0
Biofilm
Control
5.0
5
0.0
0
1
2
3
4
5
6
0
7
1
2
3
4
5
6
7
Time (hours)
Time (days)
Figure 4.4: Ammonia and nitrate in
biofilm experiment after 7 days. Mean
data for duplicate 1 and 2 were used for
Figure 4.5: Ammonia concentrations in
the 7 hour biofilm experiment. Mean data
for duplicate 1 and 2 were used for the
67
Table 4.2 Nitrification and volatilization rates of bulk water, biofilm and glass plates of A3
Experiment
Nitrification rate
Volatilization rate
Volatilization rate
(g-N m-2 d-1)
Treatment (g-N m-2d-1)
Control (g-N m-2d-1)
*Rbulk5day (g-N l-1d-1)
2.7 x 10-4
R biofilm (7days)
1.50
2.13
R biofilm (7hrs)
1.65
R biofilm (glass plates)
* Note that the units for R bulk are expressed in g-N l -1 d -1
8.4 x 10-3
5.9 x 10-3
1.02 x 10-2
5.7 x 10-3
6.1 x 10-3
5.9 x 10-3
6.7 x 10-3
Table 4.2 summarizes nitrification rates in bulk water and in the biofilm as well as the
volatilization rates. As can be seen, the bulk water nitrification rates were less important
than the biofilm nitrification rates. The slope for the ammonium reduction with time for the
5 day bulk water experiment (-0.19) were significantly lower than that of the 7 day biofilm
experiment (-0.7) (df = 2, 5; F = 14.5). This implies that the biofilm nitrification rates were
significantly higher than bulk water nitrification rates. The biofilm nitrification rates were
also substantially higher than volatilization rates. The volatilization rates in both the
treatments and control were within the same order of magnitude showing consistence in
results.
Discussion
Bulk water, 7 hours experiment
The short-term investigation on the activity of microorganisms in the bulk water did not
show any change in the ammonia concentration for the entire 7-hour period of incubation
(Figure 4.2). This indicated that the activity in the bulk water is low despite the fact that
oxygen and pH were 8.8 mg l-1 and 8 respectively. Dissolved oxygen of less than 0.50 mg l1
is thought to inhibit nitrification; in this case, oxygen concentration was high thus not
limiting. The pH values measured are also within the optimum pH ranges of 7.2-9.0
required for suspended growth (Metcalf and Eddy, 1991).
Nitrifiers can only maintain themselves while attached to surfaces that prevent wash out.
They rarely live as free suspended bacteria (Hammer and Knight, 1994). This could be the
major reason for the absence of nitrogen removal in the bulk water in the short term
experiment. This is in agreement with Zimmo et al., (2000) who noted lack of attachment
surface for nitrifiers and denitrifiers as a major limitation of nitrogen removal in algae
ponds. Despite the fact that the control experiment was aerated by bubbling of air, ammonia
reduction was low indicating that ammonia volatilization did not play an important role. An
ammonia volatilization rate of 8.4 x 10-3 g-N m-2d-1 was obtained, which was negligible.
Because the 7-hour experiment did not show any change in the ammonia concentration, the
experiment was continued for 5 extra days. The results showed a slight drop of ammonia
concentration for the first four days. The reason for this could probably be attributed to
68
growth of nitrifiers. Nitrifiers are known to be slow growers thus would require more time
to build their population.
Like in the bulk water, there was rapid drop of the ammonia concentration in the control
after four days. The overall ammonia decrease from the control was 6.5 and 8.7 mg l-1 NH4N in control 1 and 2 respectively. These values are slightly lower than the values from the
bulk water. Ammonia loss in the control is exclusively due to volatilization while in the
bulk water, both ammonia oxidation and volatilization may have been important. Taking
values of 10.6 mg l-1 NH4-N and 8.7 mg l-1 NH4-N as decrease in bulk water and control, it
can be assumed that 1.9 mg l-1 NH4-N was lost due to nitrification. This gave a bulk water
nitrification rate (Rbulk5day) of 2.7x10-4 g-N l-1 d-1. The result indicated very low nitrification
rates in the bulk water. This is in agreement with McLean et al., (2000) who made a similar
observation for algae lagoons. They observed that lagoons with a high density of
suspended algae had higher nitrification rates. The algae provided attachment surface for
nitrifier growth since they are known to prefer attached growth. The decrease of ammonia
in the control showed that long term bubbling of air strips the water of ammonia.
Biofilm activity test, 7 days experiment
The ammonium concentration in the biofilm experiment rapidly dropped from 18.0 to 3.8
mg l-1 NH4-N within one day (Figure 4.4). The concentrations then gradually dropped and
after the fifth day, very low concentrations (< 1 mg l-1 NH4-N) were measured. While the
ammonia concentrations dropped, nitrate levels built up (Figure 4.4) and became constant
after 3 days. However, the amount of nitrate that built up was lower than the ammonia
reduced, showing that nitrates did not accumulate. It is believed that nitrification and
denitrification occurred simultaneously, with denitrification occurring in the deeper anoxic
micro environments of the biofilm (Kuenen and Robertson, 1994). This could have resulted
in the low nitrate concentration. Presence of nitrates; although at lower concentrations was
clear evidence that nitrification occurred in the beakers (Pearson, 2005).
Biofilm nitrification rate (Rbiofilm) of 1.50 g-N m-2 d-1 was obtained for this experiment
which was comparable to 1.65 g-N m-2 d-1 obtained for the glass biofilm plates (Table 4.2).
This showed that the biofilm glass plates could be used to determine nitrification rates of
the pond reactors. In comparison to the biofilm experiment, the ammonia reduction in the
control experiment was small i.e. from 26.5 to 22.6 mg l-1 NH4-N with an average ammonia
concentration of 23.0 ± 2.8 mg l-1 NH4-N obtained after seven days of incubation (Figure
4.4). This showed that ammonia volatilization was minimal. Ammonia volatilization rate of
5.9 x 10-3 g-N m-2 d-1 was obtained for the duplicate control experiments (Table 4.2). The
slope of the control experiment (-0.21) was significantly different from those of biofilm
experiment (-0.7) (df = 2, 3; F = 29.9) implying that the biofilm nitrification rates were
significantly higher than the volatilization rates. It can then be concluded that ammonia loss
69
in the biofilm experiment was mostly due to nitrification. The results of biofilm experiment
in figure 4.4 showed a rapid decrease of ammonia during the first day, a short term
experiment was conducted to investigate this further and results are discussed below.
Biofilm activity test, 7 hour- experiments
A short-term biofilm activity test was performed to confirm this rapid decrease. Both
duplicates 1 and 2 containing biofilm samples showed a gradual drop in ammonia
concentration with time (Figure 4.5). A regression line was fitted and ammonia reduction
rate of 0.85 and 0.83 mg l-1 h-1 (R2= 0.93 and 0.86) were obtained for duplicate 1 and 2
respectively. The average values of these reduction rates gave a biofilm nitrification rate,
(Rbiofilm) of 2.13 g-N m-2 d-1 for the first 5 hours of the test, which was significantly higher
(df = 2,7; F= 5.2) than the 1.50 g-N m-2 d-1, observed on the first day of the seven-day
biofilm activity test (Table 4.2). The results for the short term experiment are more reliable
since the experiment was closely monitored. Both the Rbiofilm values for short and long term
experiments are comparable to 0.72-2.64 g-N m-2 d-1 (Leu et al., 1998) but higher than
0.48-0.72 g-N m-2 d-1 (Craggs et al., 2000) and 0.72-0.96 g-N m-2 d-1 (McLean et al.,
2000). The control experiment showed a constant ammonia concentration during the first
five hours of the experimental period. This again confirmed that volatilization was minimal.
These results are in agreement to those obtained by Zimmo et al., (2004), which indicated
that ammonia volatilization was negligible in wastewater stabilization ponds under these
conditions.
Conclusions
This study demonstrated the importance of attached growth in the process of improving
nitrification in wastewater stabilization ponds. The results showed that the biofilm
nitrification rates were significantly higher than bulk water nitrification rates. The
volatilization rates were low and probably play a negligible role in wastewater stabilization
ponds. Nitrates were found to accumulate in biofilm experiments although the
accumulation did not equal the concentration of ammonia reduced. Presence of nitrates
indicated nitrification process and the lower concentrations relative to amount of ammonia
reduced could be an indication of denitrification.
Acknowledgements
We are grateful for the support from the Netherlands government through Netherlands
Fellowship Program and the EU-Switch project contract number 60030361 for financial
DVVLVWDQFH 7KH DXWKRUV¶ DUH DOVR WKDQNIXO WR (GZLQ +HV 6KL :HQ[LQ DQG 81(6&2-IHE
laboratory staff for their assistance and support in the laboratory work.
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Mara, D.D., and Pearson, H.W. (1998). Design manual for waste stabilization ponds in
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McLean, B.M., Baskran, K., and Connor, M.A. (2000). The use of algal-bacterial biofilms
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(2003). Improved method for determination of ammonia and nitrite oxidation activities in
mixed bacterial culture. Appl. Microbiol Biotechnol. 63: 217-221
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Technology (Ed). Shilton, IWA publishing
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stabilization ponds, Institute of technology and engineering, Massey University, New
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343-348
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Zimmo, O.R. (2003). Nitrogen transformations and removal mechanisms in Algal and
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73
Chapter 5
Nitrification rates of algal-bacterial biofilms in wastewater stabilization ponds under
light and dark conditions
Published as:
M.A. Babu., E.M.A. Hes., N.P. van der Steen., C.M. Hooijmans and H.J. Gijzen.
Nitrification rates of algal-bacterial biofilms in wastewater stabilization ponds under light
and dark conditions. Ecological Engineering 36 (2010) 1741±1746
74
Chapter 5
Nitrification rates of algal-bacterial biofilms in wastewater stabilization ponds under
light and dark conditions
Abstract
The objective of this study was to investigate nitrification rates in algal-bacterial biofilms
of wastewater stabilization ponds (WSP) under different conditions of light, oxygen and
pH. Biofilms were grown on wooden plates of 6.0 cm by 8.0 cm by 0.4cm in a PVC tray
continuously fed with synthetic wastewater with initial NH4-N and COD concentrations of
40 mg l-1 and 100 mg l-1 respectively, under light intensity of 85-95 μEm-2s-1. Batch activity
tests were carried out by exposure of the plates to light conditions as above (to simulate day
time), dim light of 1.8- 2.2 μEm-2s-1 (to simulate reduced light as in deeper locations in
WSP) and dark conditions (to simulate night time). Dissolved oxygen concentration and pH
were controlled. At some experiments both parameters were kept constant and at others left
to vary as in WSP. Results showed biofilm nitrification rates of 0.95-1.82 g-Nm-2d-1 and
0.16-1.62 g-Nm-2d-1 for light and dark experiments. When the minimum DO was 4.1 mg l-1,
the biofilm nitrification rates under light and dark conditions did not differ significantly at
95% confidence. When the minimum DO in the dim light experiment was 3.2 mg l -1, the
nitrification rates under light and dim light conditions were 0.95 g-Nm-2d-1 and 0.56 g-Nm2 -1
d and these significantly differed. Further decrease of DO to 1.1 mg l-1 under dark
conditions resulted in more decrease of the nitrification rates to 0.16 g-Nm-2d-1. It therefore
seems that under these experimental conditions, biofilm nitrification rates are significantly
reduced at certain point when bulk water DO is between 3.2 and 4.1 mg l-1. As long as bulk
water DO under dark is high, light was not important in influencing the process of
nitrification.
Key words: Biofilm, nitrification, light intensity, WSP, oxygen, pH
Introduction
Wastewater stabilization ponds are used worldwide as an effective and low cost technology
for wastewater treatment. The major disadvantage of ponds is the large land area required
for construction and their limited efficiency in removing nitrogen. They are usually
designed as a sequence of systems (anaerobic reactor or pond, facultative ponds and
maturation ponds). It is suggested that biofilms could be introduced into maturation ponds
to reduce the required land area (Johnson and Mara, 2005; Xia et al., 2008) and improving
nitrogen removal. Introduction of biofilms into WSP has been investigated by McLean et
al., (2000) and seems to be effective in increasing nitrification rates but the effect of typical
variations in the pond environment (pH, temperature, light intensity, oxygen concentration)
on algal-bacterial biofilm nitrification is largely unknown. Various experiments on
nitrification conducted by Zimmo et al., (2004) were limited to bulk water nitrification.
Information on nitrification rates of algal-bacterial biofilms of WSP under different
conditions is still insufficient. Studies by Wolf et al., (2007) and Roeselers et al., (2008)
investigated phototrophic biofilms and suggest that biofilm systems have a future prospect
in wastewater treatment. Through modeling, Wolf et al., (2007) have shown enhanced
75
oxygen production within the phototrophic biofilms under light conditions and this could
be used to the advantage of the nitrifiers. This study was conducted at laboratories of
UNESCO-IHE, Delft and is part of other studies on pilot scale wastewater stabilization
ponds carried out in Uganda. The focus of this study was to investigate biofilm nitrification
rates under light and dark conditions as well as under different pH and oxygen
concentrations.
Methodology
Growth of biofilm
A PVC tray (55cm x 37cm x 9.0 cm; length, width and depth) was divided into 3
compartments by two equally spaced transparent acrylic plates (Figure 5). The
compartments were connected by openings at each opposite end. Sixty wooden biofilm
plates of 6.0 by 8.0 cm were suspended vertically and parallel to the flow. Wood was
chosen because it provides a rough surface which is thought to improve biofilm attachment.
Pump
Influent
Effluent
Figure 5: Experimental set up for growth of biofilm
The system was continuously fed with synthetic wastewater of ammonia and COD
concentration of 40 mg N l -1 and 100 mg l -1, respectively (Babu et al., 2007). The influent
flow rate was 0.96 l hr -1 and the effluent was recycled at a rate of 2.5 l hr -1 just before the
final outlet to ensure a uniform distribution of ammonia over the reactor. Enriched activated
sludge (100 ml) from Hoek van Holland municipal wastewater treatment plant was used as
inoculum to establish nitrifier and denitrifier populations. Algae (100ml) from the column
experiments of Babu et al., (2007) were introduced into the system. The set-up was
exposed to a 12 hour light regime of 85-95 μEm -2 s -1. The plates were left to develop
biofilms for about 60 days and then transferred to batch reactors for determination of the
76
nitrification rates. The same synthetic wastewater was used for the continuous flow and
batch systems.
Batch reactors for nitrification activity
The purpose of these experimental runs was to identify the effect of light and oxygen on
biofilm nitrification rates. Batch experiments were conducted in duplicates under different
conditions of light and oxygen and nitrification rates were determined in the laboratory
using batch tests as described below:
1. Light and dark conditions; oxygen and pH left to vary
Biofilm plates from the tray were removed, gently rinsed with distilled water and hung
vertically in two-liter glass beakers containing 1.1 l of fresh synthetic wastewater (not
seeded with nitrifiers) of ammonia concentration of 20 mg l-1 and COD of 100 mg l-1 (Babu
et al., 2007). Each beaker had one biofilm plate which was exposed to a light intensity of
85-95 μEm-2s-1 for a period of 8 hrs; this was lower than 133-176 μEm-2s-1 measured in
Uganda on a sunny day. Oxygen and pH were left to vary as in WSP. The temperature was
not controlled but was almost constant (22oC). Samples were taken after every two hours,
filtered and ammonia measured. Other parameters monitored included nitrate, DO, pH and
temperature. All parameters were determined according to APHA, (1995). The procedures
above were repeated but in this case, the beakers with biofilm were exposed to dark
conditions. Similarly, oxygen and pH were left to vary as in WSP. A control with only
synthetic wastewater without biofilm plates was also set up and exposed to light as above.
This was to determine ammonia loss by volatilization.
2. Light and dark conditions; oxygen and pH kept constant
The experiments as in (1) above were repeated using 0.5 liters of synthetic wastewater with
oxygen and pH kept constant. The DO was kept between 9.5 and 6.7 mg l-1 by continuous
bubbling of air while the pH was kept at 7.7 by addition of 150 mg l-1 of sodium
bicarbonate to the synthetic wastewater. Control experiments with only synthetic
wastewater with pH and oxygen conditions similar to biofilm experiments were also run.
3. Light and dark conditions; pH kept constant but oxygen left to vary
In this experiment, the pH was kept constant as described above but the oxygen
concentrations were left to vary with time. Control experiments without biofilm plates were
also run under these conditions.
4. Light, dim light and dark conditions; pH kept constant, oxygen kept constant as per
condition
The experiments above were repeated but this time under the conditions of bright light, dim
light and darkness. Bright light represented the top part of the WSP at day light while dim
light simulated the deeper and shaded parts of WSP during day. Dark conditions were to
simulate night time. The oxygen level under bright light was kept between 8-9 mg l-1 by
continuous bubbling with air. The oxygen level under dim light was kept between 3-5 mg
l-1 by periodic bubbling with air, while that under dark conditions was kept between 2.3-1.1
mg l-1 by periodic bubbling with air and nitrogen gas. The pH was kept constant as
77
described above. This experiment was run for 6 hours and not 8 hours as the others. Also,
0.5 liters of synthetic wastewater was used.
Algal uptake
Since ammonia removal is not only due to nitrification but also by algal growth, the
nitrification rates under light conditions were corrected for the nitrogen uptake by algae.
The data for biofilm biomass (dry weight) taken from the pilot scale studies (Chapter 2)
were used for correction. It was assumed that the ammonia uptake rates by algae in the pilot
scale and laboratory and was same. From pilot scale studies, the highest algal-bacterial
biofilm growth rate after 3 weeks in one of the ponds at 5 cm depth was 3.63 gm-2 d-1. If
6% dry weight of biomass is nitrogen (Lai and Lam, 1997), then the algal uptake rate under
light conditions is 0.22 g-N m-2 d-1. Therefore, the maximum amount of ammonium taken
up by algae under light conditions is 0.48 mg l-1 and 0.63 mg l-1 for experiments run during
6 and 8 hours respectively. These values were subtracted from the overall amount of
ammonium removed during the entire experimental period. The remainder gives the
minimum amount reduced by nitrification.
Analysis of results
For the purpose of description in the following texts, all slopes of ammonia used in
calculations of the nitrification rates of all experiments will be referred to as initial slopes.
The 'initial slopes' is defined under the results section. Regression analysis using the F-test
(95% confidence interval, at 0.05 levels) was used to check if the initial slopes for different
treatments were statistically different.
Results
General trends
The ammonia concentration in light experiments 1 and 2 decreased with time and became
constant after six hours (Figure 5.1). The slopes until six hours were used to calculate the
nitrification rates (initial slopes). In the light experiments 3 and 4, the decrease was more
linear (Figure 5.1) so the slope for the whole experimental time was taken as the initial
slope and used to calculate the nitrification rates. In the dark experiments 1 and 3, there was
a general decrease of ammonia with time until the DO were 4.1 mg l-1 and 2.3 mg l-1
(Figure 5.2a and b). Hereafter, the ammonia concentration started to increase with time.
The reasons for this are not clear but might be due to ammonia release, which could have
already started at the beginning of the experiment and resulted in a less steep slope. The
nitrification rates for dark experiments 1 and 3 were calculated basing on the initial slopes
until when ammonia concentration started to increase (Figure 5.2a and b). Dark experiment
2 behaved like the light experiments 3 and 4 where the ammonia decrease with time was
linear. The DO in this experiment was kept high by bubbling with air possibly accounting
for the similarity. In this case, the whole slope was used to calculate the nitrification rates.
Table 5 shows the nitrification rates for the all runs; note that the nitrification rates under
light conditions were corrected for algal uptake. Table 5 also shows statistical comparisons
between nitrification rates under light, dim light and dark conditions. The ranges of DO in
the various experiments are also given. From table 5, it was seen that there was general
78
trend of decrease of nitrification rates from light experiments 1-4 although the oxygen
levels were high under light conditions. This can be explained by a change in biofilm over
time. The experiments took several weeks to be performed. At the beginning, the biofilms
looked green but with time, they became dark and slimy. The algal-bacterial biofilms are
dynamic in nature and long term stability may not occur. This may have an impact on the
nitrification rates hence further studies are recommended.
The results of this study showed that it is difficult to conclude on the effect of bulk water
pH on biofilm nitrification rates. For instance, the minimum bulk water pH of light
experiment 1 was 7 while those for light experiments 2, 3 and 4 were kept constant at 8.
The nitrification rate of light experiment 1 was significantly different from the rest of the
light experiments. The nitrification rate of experiment 2 was significantly different from
those of experiment 3 and 4 but the rates of latter did not differ. It would be expected that
the nitrification rates of experiments 2, 3 and 4 would not differ because the pH was same.
Since only the bulk water and not biofilm pH was measured; explaining the differences in
biofilm nitrification rates based on bulk water pH is difficult. Probably the pH in the
biofilm was different from what was observed in the bulk water possibly accounting for the
results.
Exp 1
Exp 4
25.0
Exp 2
Cont
Exp 3
23.0
NH4-N (mgl-1)
21.0
19.0
17.0
15.0
13.0
11.0
9.0
7.0
0
2
4
6
8
Time ( Hours)
Figure 5.1: Trends of ammonia with time in light experiments 1-4, note that the scale of y-axis on the graph
was adjusted to show the trends more clearly.
79
0
2
4
6
Time (Hrs)
NH4-N dark
22.0
O2 dark
21.5
Oxygen (mgl-1)
O2 dark
10
9
8
7
6
5
4
3
2
1
0
22.5
NH4-N (mgl-1)
10
9
8
7
6
5
4
3
2
1
0
NH4-N dark
Oxygen (mgl-1)
NH4-N (mgl-1)
21.5
21.0
20.5
20.0
19.5
19.0
18.5
18.0
17.5
17.0
21.0
20.5
20.0
19.5
19.0
18.5
8
0
2
4
6
Time (Hrs)
8
Figure 5.2: (a) and (b). Variations of ammonia and oxygen with time in dark experiments 1 and 3. Note that the
scale on the y-axis for ammonia was adjusted to show the trends more clearly.
N03-N ( mg/l)
Oxygen ( mg/l)
10.0
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
0
2
4
6
Time ( Hrs)
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
0
8
2
4
6
8
Time ( Hrs)
Figure 5.3: Oxygen variations in experiment
Ÿ Ŷ DQG Ƈ V\PEROV RQ WKH JUDSKV
represent Control, Dark and Light conditions
80
Figure 5.4: Nitrate accumulations in experiment
ŸŶDQGƇV\PEROVRQWKHJUDSKVUHSUHVHQW
Control, Dark and Light conditions
Table 5, Summary of Biofilm nitrification rates (g-Nm-2d-1) for all the 4 different experiments
Experiment
Nitrification rates (g-Nm-2d-1) based
on initial ammonia slopes
DO ranges corresponding to the
initial slope of the ammonia curve
(mgl-1)
Light
Dark
Dim light
Dark
Dim light
1
Light after
correction
1.82±0.29
1.62±0.31
-
8.9 - 6.4
9.0 - 5.1
n.a.
Significance
( 95%
confidence)
Light Vs
dark
No
2
1.47 ±0.04
1.12±0.07
-
9.5 - 6.7
8.2 -7.7
n.a.
No
3
1.16±0.03
1.34±0.25
-
9.4 - 6.3
9.4 - 4.1
n.a.
No
4
0.95±0.24
0.16±0.12
0.56±0.22
9.8 - 8.3
2.3 - 1.1
4.9 - 3.2
Yes between
light and dark
Yes between
light and dim
light
Yes between
dim light and
dark
1234-
Light and dark conditions with pH, temperature and oxygen left to vary
Light and dark conditions with pH, temperature and oxygen kept constant
Light and dark conditions with pH and temperature kept constant and oxygen left to vary
Light, dim light and dark conditions
Discussion
Effect of different variables on nitrification rates
1. Light and dark conditions, oxygen, pH and temperature left to vary
The ammonium concentration dropped from 21 mg l-1 to 17 mg l-1 for the biofilms exposed
to light while under dark conditions, it dropped from 21 to 19 mg l-1. The ammonia
concentration in the control experiment dropped slightly throughout the experimental
period showing that ammonia loss by volatilization was minimal. After correction for algal
uptake, the nitrification rate for light condition 1 was 1.82±0.29 g-Nm-2d-1 while that of
dark condition 1 was 1.62±0.31 g-Nm-2d-1 respectively. These results are within the range
of those obtained by Leu et al., (1998); Craggs et al., (2000); McLean et al., (2000) and
Lydmark et al., (2007). The nitrification rate under light conditions was slightly higher than
the lowest nitrification rate of 1.70 g-Nm-2d-1 obtained by Salvetti et al., (2006) for movingbed biofilm reactors operated with influent ammonia and DO of 12 mg l-1 and 7.9 mg l-1
respectively. Although Salvetti et al., (2006) also obtained very high nitrification rates of
10.40 g-Nm-2d-1; pure oxygen was bubbled in their system; for our studies, oxygen was
naturally provided by algae. For this experiment, statistical test shows no significant
difference in the nitrification rate under dark and light conditions (Table 5). This is in
disagreement with Verdegem et al., (2005) who found higher nitrification rates under light
than dark conditions. A direct comparison to their work was not possible since they did not
report the oxygen concentrations in their studies. Light is important for oxygen production
by algal photosynthesis but if the DO in the dark is high, the effect of light ceases to be
important. In this experiment, the bulk DO corresponding to the initial slopes of the
ammonia in both light and dark conditions decreased from 8.9 to 6.4 and 9.0 to 5.1 mgl -1
oxygen respectively (Table 5). The DO in the dark decreased but remained above 3 mg l-1
81
even after 8 hours (Figure 5.3). Probably, the decrease of DO in bulk water did not
significantly affect the availability of oxygen to the nitrifier population in the biofilm (only
bulk water and not biofilm DO was measured).
The effect of oxygen can be demonstrated from calculations based on stoichiometry of
nitrification where 4.57 g of oxygen is required to oxidize 1g-N. The theoretical oxygen
consumption was calculated for experiment 1 as follows: The volume of synthetic
wastewater used in this experiment was 1.1 l and ammonium reductions under light and
dark conditions based on the initial slopes were 4.4 mg NH4-N and 2.2 mg NH4-N
respectively. If we assume that all the ammonium consumed was due to the nitrification
process, 18.3 mg l-1 and 9.1 mg l-1 of oxygen was required for complete oxidation of
ammonium under light and dark conditions. In reality, the DO decreased with only 2.5 mg
l-1 (Table 5) under light conditions compared to the required 18.3 mg l-1 from the
calculation. It is likely that the extra oxygen for the ammonia oxidation was produced by
the photosynthesizing algal biofilm (Wolf et al., 2007). This is in agreement with our
working hypothesis which suggested significant oxygen production in the algal biofilm. For
the dark experiment, the decrease in DO was 3.9 mg l-1 during the initial experimental
period (Table 5); this is less than 9.1 mg l-1 estimated from the calculations. Calculation of
aeration by diffusion of oxygen from the atmosphere showed that it was insignificant hence
the extra oxygen could not have been provided by this mechanism. There is a possibility
that the extra oxygen was provided by DO already present in the biofilm before it was
exposed to dark conditions. Alternatively, may be not all the ammonia reduction under dark
experiment was due to nitrification. Probably some of the ammonia was taken up by
bacteria for biomass development or there was an error in oxygen measurement at the end
of the experiment. The results in the dark experiment showed that the initial oxygen
concentration was sufficient to support nitrification.
From the above explanations, one of the mechanisms suggested to cause decrease of
ammonia in both the light and dark experiments was ammonia oxidation. The other
mechanism suggested was algal uptake. Nitrates accumulated during the experimental
period showing that nitrification occurred (Figure 5.4). Although there was accumulation,
the nitrate concentrations never exceeded 1 mg l-l. Studies by Verdegem et al., (2005)
suggest that algae prefer ammonium to nitrates as N-source. It is only when ammonium
concentration is less than 0.03 mg l-1 Total Ammonia Nitrogen (TAN i.e. NH4 + NH3) that
nitrite and nitrate uptake becomes important. Therefore nitrate uptake by algae could not
explain the low nitrate concentrations observed during the experiment. The most probable
explanation is denitrification since it has been found to occur in the deeper anoxic layers of
the biofilms (Kuenen and Robertson, 1994; Revsbech et al., 2005). From the estimations of
algal uptake (see under Material and Methods), it was seen that only 0.48 mg l-1 of NH4-N
was taken up by algae during the initial 6 hours of experiment 1 under light conditions.
This was about 12% of the total NH4-N removed during the light experiment. It is then
suggested that algal uptake is the second principle removal mechanism after nitrification.
Volatilization appeared to be negligible since the ammonia concentration in the control
experiments was almost constant during the entire experimental period.
82
2. Light & dark conditions, oxygen and pH kept constant
In this experiment, the ammonia under light and dark conditions decreased from 19.2 to
10.9 mg l-1 and 12.4 mg l-1 respectively. The nitrification rates were 1.47±0.04 g-Nm-2d-1
and 1.12±0.07 g-Nm-2d-1 under light and dark conditions, respectively (Table 5). There was
no significant difference (at 95% confidence interval) in nitrification rates and this could be
explained by DO concentrations. The bulk water DO concentrations under light and dark
conditions corresponding to the initial ammonia slopes were from 9.5-6.7 and 8.2-7.7 mg l-1
of oxygen respectively. The oxygen concentrations were kept high by bubbling with air and
this seemed to favor nitrification, results which are similar to those of Goncalves and
Oliviera, (1996). It appeared that keeping DO high under dark conditions had the same
effect on nitrification as that of light and the associated oxygen production by
photosynthesis. As long as there was sufficient oxygen in the bulk water, light intensity (as
per this experiment) did not seem to affect the nitrification process.
3. Light & dark conditions, pH kept constant but oxygen left to vary
The nitrification rates for this experiment were 1.16±0.03 g-Nm-2d-1 and 1.34±0.25 g-Nm2 -1
d under light and dark conditions respectively. The difference was not statistically
significant (Table 5). The minimum DO concentrations under light and dark conditions
were 6.3 and 4.1 mg l-1, so it seemed that the oxygen concentration under dark was still
sufficient to support nitrification to a similar magnitude to that of light conditions. Even
when the minimum DO in dark experiment 3 (4.1 mg l-1) was lower than that of dark
experiment 1 (5.1 mg l-1, table 5), the nitrification rates were not significantly different.
This implied that the DO concentration at this moment was still not limiting nitrification.
These results are in agreement with Baskaran et al., (1992) who found minimal effect of
light on nitrification as long bulk DO was still high enough to support the process.
4. Light, dim light & dark conditions
The nitrification rates for experiment 4 were 0.95±0.24, 0.56±0.22 and 0.16±0.12 g-Nmd for light, dim light and dark conditions, respectively. All the nitrification rates were
significantly different from each other (Table 5). The mean biofilm biomass for the light,
2 -1
dim light and dark experiments were 18.7 ± 1.1 g VSSm-2 ,18.8± 5.8 g VSSm-2 and 15.0±
0.2 g VSSm-2 (n=2), respectively. The biomass of light and dim light conditions were
similar hence cannot explain the difference between the two nitrification rates. In
experiment 3, it was seen that under dark conditions at minimum DO of 4.1 mg l -1, no
significant difference was observed between the nitrification rate of light and dark; it was
assumed that light did not have a direct effect on nitrification. However if the DO decreases
to a minimum of 3.2 mg l-1 as in dim light experiment 4 (Table 5), a significant difference
appeared. It seemed that at a certain point between 3.2 and 4.1 mg l-1 of bulk water DO
under the given experimental conditions, nitrification became significantly reduced. In fact,
further decrease of DO from a minimum of 3.2 mg l-1 under dim light to a minimum of 1.1
mg l-1 as under dark condition 4 significantly reduced nitrification rate. The limits of
oxygen for effective biofilm nitrification under maximum COD 30-40 mg l-1 is proposed to
be 2.5 mg l-1 (Chen et al., 1989) and it was uncertain if effective nitrification still occurred
83
at COD above 30-40 mg l-1 (Baskaran et al., 1992). Results from this study disagreed with
those of Chen et al., (1989) because the DO limit is higher i.e. between 3.2 and 4.1 mg l-1.
The difference can be explained by a higher COD of 100 mg l-1 of this study which
increased competition for DO between heterotrophs and nitrifiers. The higher bulk water
DO requirement could also be due to rate limiting diffusion of DO across the boundary
layer of the biofilm. This study provides a new insight that for algae-bacterial biofilms,
effective nitrification can still occur at COD levels of 100 mg l-1.
Perspectives for design
Results from this study showed that high nitrification rates can be achieved in algalbacterial biofilms under illuminated conditions. In tropical regions where there is sufficient
sunlight during day time, designers should consider installing baffles in wastewater
stabilization ponds to improve the nitrification capacity. This could especially benefit ponds
that are highly loaded that the bulk DO even under sunlight is low (Kayombo et al., 2002).
Under those conditions, algal biofilms could improve nitrification. One of the design
decisions to make is how deep the baffles should extend into the pond. It is obvious that
extending the baffles into the anaerobic zone does not improve nitrification. This research
could be used to estimate to what pond depth the baffles are useful; a depth until below the
photic zone, until the point where the bulk water DO is between 3.2 and 4.1 mg l -1 . This
depth could be appropriate for nitrification, since deeper than that point the nitrification
rates will decrease.
Conclusions
x This study investigated indirect effect of light intensity, via the oxygen availability
for the nitrifiers in the biofilm. This effect is absent when there is sufficient oxygen
in the bulk liquid, but at bulk liquid DO values between 3.2 and 4.1 mg l-1, biofilm
nitrification rates are significantly reduced. The rates are even further reduced when
the bulk oxygen concentration decreases below 1.1 mg l-1.
x The results from this study demonstrated that the simple methodology used can be
applied to investigate the effects of DO on algal-bacterial biofilm nitrification rates.
Acknowledgements
We are grateful for the financial support from the Netherlands government through
Netherlands Fellowship Program. We also appreciate the financial assistance from the EUSwitch project contract 018530. The authors are also thankful to UNESCO-IHE laboratory
staff for their assistance and support in the laboratory work.
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Lydmark, P., Almstrand, R., Samuelsson, C., Mattson, A., Sorrensson, F., Lingren, P.E.,
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McLean, B.M., Baskaran, K., Connor, M.A. (2000). The use of algal-bacterial biofilms to
enhance nitrification rates in lagoons: Experience under laboratory and pilot scale
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Revsbech, N.R., Jacobsen, J.P., Nielsen, L.P. (2005). Nitrogen transformations in
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Roeselers, G., van Loosdrecht, M.C.M., Muyzer, G. (2008). Phototrophic biofilms and
their potential applications. J Appl Phycol 20, 227-235
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Salvetti, R., Azzelino, A., Cinziani, R., Bonomo, L. (2006). Effects of temperature on
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Wolf, G., Picioreanu, C., van Loosdrecht, M.C.M. (2007). Kinetic Modeling of
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Xia, S., Lin, J., Wang, R. (2008). Nitrogen removal performance and microbial community
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86
Chapter 6
Effect of operational conditions on the nitrogen removal in a pilot scale baffled
wastewater stabilization ponds under tropical conditions
87
Chapter 6
Effect of operational conditions on the nitrogen removal in a pilot scale baffled
wastewater stabilization ponds under tropical conditions
Abstract
Four pilot scale wastewater stabilization ponds (WSP) were set up in Kampala ± Uganda
and operated under low (period 1, 0.0057 g NH4-N l-1d-1) and high (period 2, 0.0084 g
NH4-N l-1d-1) ammonia loadings. Pond 1 was operated as control while ponds 2, 3 and 4
were fitted with baffles having the same surface area for biofilm attachment but different
configurations to induce different flow patterns. The major aim of this study was to
investigate the performance in nitrogen removal of these ponds under the different
operational conditions. The results of period 1 showed that the control pond performed
better in nitrogen removal than the baffled ponds. This was probably due to the effect of
TSS on light penetration. The TSS during period 1 was significantly higher than period 2. It
is likely that the algae growing in the upper most layers blocked light penetration hence
affecting the development of algal biomass and nitrifiers in the deeper parts of the baffles.
,QDGGLWLRQ766LQHIIOXHQWVRI:63¶VLVXVXDOO\DOJDOPDWHULDOVRWKHKLJKHU766LQSRQG
could mean more ammonia uptake by algae. In ponds 1 and 3, ammonia removal was
positively related to effluent pH and organic nitrogen; increase in these variables in WSPs
is linked to photosynthesis. So transformation of ammonia to organic nitrogen was
important in all ponds during period 1. In period 2, the baffled ponds performed better than
the control pond. Pond 3 performed best, followed by ponds 2, 4 and 1 which removed the
least nitrogen. It is believed that the extra surface area for attachment of nitrifiers provided
by baffles caused this observation. During this period, the effluent TSS was significantly
lower and this could have improved the conditions on the baffles for nitrogen removal.
Day-time oxygen concentrations in the water column during period 2 were higher which
could have positively influenced nitrification-denitrification processes. When the two
periods were compared, the nitrogen removal efficiencies during period 2 was higher than
in period 1 in ponds 2, 3 and 4. It is believed that the higher influent ammonia, lower BOD
and higher aerobic surface area of baffles during period 2 could have played a role;
regression models of both periods showed that increase in nitrogen removal was correlated
with increased influent ammonia. The nitrogen removal efficiency of pond 1 reduced
during period 2 possibly due to lack of extra attachment surface area for nitrifiers in the
ponds
Key words: Stabilization ponds; biofilm; ammonia removal; sewage; tropical conditions
88
Introduction
This study focused on the use of wastewater stabilization ponds in wastewater treatment.
Wastewater stabilization ponds are advantageous to developing countries due to their
simplicity, cost effectiveness, easy operation and use of solar energy (Veenstra and Alerts,
1996). Solar energy is abundant for most periods of the year in tropical countries (Mara,
1997; 2004); therefore this favors the use of wastewater stabilization ponds. However, their
performance in the removal of nutrients such as nitrogen and phosphorous is less clear
(Mergaert et al., 1992; Lettinga et al., 1993). Limitation of nitrogen removal in particular,
has been associated with a narrow aerobic zone for nitrification (Baskran et al., 1992) and
lack of attachment surface for nitrifiers (Craggs et al., 2000). Nitrifiers are also slow
growers as compared to heterotrophic bacteria. In instances of high organic loading, they
are more likely to be outcompeted by the heterotrophs. Increasing the aerobic zone,
providing attachment surface and reducing competition from heterotrophs are possible
approaches of favoring nitrifier growth.
In this study, the performance of wastewater stabilization ponds incorporated with baffles
as attachment surface for nitrifiers was evaluated. The ponds were operated under two
conditions and comparisons in ammonia removal between the two operational conditions
were made. This chapter describes the performance of the ponds in terms of nitrogen
removal efficiencies and statistical analysis; nitrogen mass balances were assessed in a
subsequent study (Chapter 7).
Methodology
Description and operation of the pilot scale system
The pilot scale wastewater stabilization ponds are shown and described in chapter 2 (Figure
2). The operational conditions were also described in chapter 2.All the physico-chemical
parameters were analyzed according to APHA, (1995).
Statistical analysis
Statistical t-tests and multiple regressions using SPSS® and Fowler and Cohen, (2003) were
used in data analysis. Normal distribution was tested and data that did not satisfy the
condition of normal distribution after log transformation were tested using non-parametric
tests. Multiple regressions with categorical predictors (Shield, 2005) were done to compare
the performance of ammonia removal between the ponds during the same period.
The goodness of the fits from multiple regressions was assessed using statistical diagnostics
such as checking for influential cases that may bias the model. Influential cases are those
that exert undue influence over the parameters in the model. Sometimes few influential
cases bias the regression model (Shield, 2005). SPSS calculates the outcome of the model
(dependent variable) with or without a particular case and compares the outcome. If the
outcome did not change, then that particular case did not have undue influence on the
model. There are several methods used to check for influential cases but the one used here
LVWKH&RRN¶VGLVWDQFH&RRN¶VGLVWDQFHFRQVLGHUVWKHHIIHFWRIDVLQJOHFDVHRQWKHZKROH
PRGHO ,I &RRN¶V YDOXHV RI JUHDWHU WKDQ DUe obtained, then influential cases may be of
concern.
89
Results
The results for period 1 and period 2 are presented and discussed separately and
comparisons between the two operational conditions are made. The results are presented as
means ± standard deviations. Therefore, the error bars on the graphs show standard
deviations. In some cases, results have been presented as median ± standard deviation.
Ammonia removal in this study is defined as the ammonia loss when influent and effluent
ammonia concentrations are compared. Ammonia loss can be due to transformation to
organic nitrogen by algae which settle or are washed out as TSS in the effluent. The
ammonia can also be lost from the ponds through volatilization or transformed to oxidized
forms by nitrification and permanently removed through denitrification. The nitrogen
mass balances are presented in chapter 7.
Period 1
During period 1, the influent and effluent ammonia of the facultative pond were 79±9.0 and
34.2±6.9 mg l-1 respectively, 57% of the ammonia was removed. Therefore, the maturation
ponds received an influent ammonia concentration of 34.2±6.9 mg l-1. The average
ammonia removals during period 1 for the maturation pond 1, 2, 3 and 4 were 21.2±4.4,
20.8±4.2, 20.7±5.2 and 9.6±5.1 mg l-1 respectively (Figure 6). Multiple regressions using
ammonia removal and pond type were performed to test for the effect of pond type on
ammonia removal. First, pond 1 was compared to ponds 2, 3 and 4 (Table 6). The resulting
model could significantly explain 49% (F= 48.5; at p<0.001) of the variance of ammonia
removal between the ponds. The results showed that the ammonia removal of pond 1
significantly differed from all the other ponds (Table 67KHQHJDWLYHȕ-values in table 6
during the first run showed that ammonia removal was significantly less in ponds 2, 3 and 4
in comparison to pond 1.
Table 6 Results for multiple regressions of ammonia removal and the pond type during period 1
Run
Variable
t-value
Sig (p) 6WDQGDUGL]HGFRHIILFLHQWȕ
1
Pond 1 versus pond 2
-3.2
<0.001
-0.231
Pond 1 versus pond 3
-3.3
0.020
-0.235
Pond 1 versus pond 4
-11.5
<0.001
-0.830
2
Pond 2 versus pond 3
Pond 2 versus pond 4
Pond 3 versus pond 4
0.05
-8.4
-8.3
If p<0.05, then significant difference
90
0.957
<0.001
<0.001
-0.004
-0.598
-0.594
Table 6.1Calculated and measured Kjeldahl nitrogen during period 1 and 2
Period
Pond
Measured
N-uptake by algae
Calculated
effluent NH4-N (as 6% effluent TSS)
Kjeldahl-N
-1
-1
(mg-N l )
(mg-N l )
(mg-N l-1)
1
1
9.5
17.1
26.6
2
13.4
12.3
25.7
3
13.5
14.3
27.8
4
24.5
9.6
34.1
2
1
28.7
3.2
31.9
2
15.2
2.8
18.0
3
12.6
1.7
14.3
4
19.1
1.7
20.8
Measured Kjeldahl-N
(mg-N l-1)
18.6
22.8
23.5
32.6
33.3
18.8
16.5
22.1
Table 6.2 Results for multiple regressions of ammonia removal and the pond type during period 2
Run
Variable
t-value
Sig (p) 6WDQGDUGL]HGFRHIILFLHQWȕ
1
Pond 1 versus pond 2
11.8
<0.001
0.703
Pond 1 versus pond 3
13.9
<0.001
0.832
Pond 1 versus pond 4
8.7
<0.001
0.521
2
Pond 2 versus pond 3
Pond 2 versus pond 4
Pond 3 versus pond 4
2.2
-3.1
-5.2
0.032
0.003
<0.001
0.129
-0.182
-0.311
If p<0.05, then significant difference
Further multiple regressions were done by comparing ammonia removal of pond 2 with
ponds 3 and 4. Pond 3 was also compared to pond 4 (Table 6). The results indicated that
ammonia removal between pond 2 and 3 did not differ significantly. Pond 4 significantly
GLIIHUHGIURPSRQGDQGDQGWKHQHJDWLYHȕ-values indicated that its ammonia removal is
less than that of pond 2 and 3. In summary, pond 1 removed more ammonia followed by
pond 2 and 3; pond 4 removed the least amount of ammonia. Therefore under these
conditions, the un-baffled pond performed better in ammonia removal than the baffled
ones. The total nitrogen removal efficiency of pond 1, 2, 3 and 4 were 53% 48%, 47% and
32% respectively.
Additional statistical analyses were performed to determine factors that could explain the
difference of ammonia removal in the ponds. Multiple regressions were conducted for each
pond using ammonia removal (dependent variable) and the following independent
variables: influent ammonia, influent Kjeldahl nitrogen, influent and effluent BOD, pH,
temperature and effluent organic nitrogen. Effluent concentrations were assumed to be
equal to average concentrations in the pond, assuming perfect mixing. Effluent organic
nitrogen was included as a variable because it is a measure for ammonia uptake by algae in
the pond. Data for twelve months (December 2007 to 2008) were used for analysis.
91
Table 6.3 Regression equations 1-4 for maturation ponds 1 - 4 in period 1 while 5-8 for maturation ponds 1 4 in period 2
Period 1
Pond 1
ܰ‫ܪ‬ସ ܰோ௘௠ ൌ ͳǤͷ ൅ ሺͲǤ͸Ͷ ‫ܪ݂ܰ݊ܫ כ‬ସ ܰሻ ൅ ሺെ͵Ǥ͸ ‫ܪ݌݂݊ܫ כ‬ሻ ൅ ሺͶǤͲ ‫ܪ݌݂݂ܧ כ‬ሻ ൅ ሺͲǤʹͺ ‫ܰ݃ݎܱ݂݂ܧ כ‬ሻሺͳሻ
Pond 2
ܰ‫ܪ‬ସ ܰோ௘௠ ൌ ͹ǤͶ ൅ ሺͲǤ͵ͻ ‫ܪ݂ܰ݊ܫ כ‬ସ ܰሻሺʹሻ
Pond 3
ܰ‫ܪ‬ସ ܰோ௘௠ ൌ ʹ͵Ǥͺ ൅ ሺͲǤͷͳ ‫ܪ݂ܰ݊ܫ כ‬ସ ܰሻ ൅ ሺͳǤͶ ‫݌݂݉݁ܶ݊ܫ כ‬ሻ ൅ ሺെͳǤͻ ‫݌݂݂݉݁ܶܧ כ‬ሻ ൅ ሺͲǤͶͷ ‫ܦܱܤ݂݂ܧ כ‬ሻ
൅ ሺͲǤ͵Ͳ ‫ܰ݃ݎܱ݂݂ܧ כ‬ሻሺ͵ሻ
Pond 4
ܰ‫ܪ‬ସ ܰோ௘௠ ൌ െ͹Ǥ͵ ൅ ሺͲǤͶͻ ‫ܪ݂ܰ݊ܫ כ‬ସ ܰሻሺͶሻ
Period 2
Pond 1
ܰ‫ܪ‬ସ ܰோ௘௠ ൌ െͷ͸Ǥ͹ ൅ ሺͲǤ͹Ͷ ‫ܪ݂ܰ݊ܫ כ‬ସ ܰሻ ൅ ሺͲǤͳͷ ‫ܦܱܤ݂݊ܫ כ‬ሻ ൅ ሺ͵Ǥͺ ‫ܪ݌݂݂ܧ כ‬ሻ ൅ ሺͳǤ͵ ‫ܱ݂݂ܰܧ כ‬ଷ ܰሻ
൅ ሺെͲǤͲʹ ‫݈݇ܣ݂݂ܧ כ‬ሻ ൅ ሺെͲǤͲʹ ‫݂݂ܵܵܶܧ כ‬ሻ ൅ ሺͲǤͶͻ ‫ܰ݃ݎܱ݂݂ܧ כ‬ሻ ൅ ሺͲǤͷͺ ‫݌݂݂݉݁ܶܧ כ‬ሻሺͷሻ
Pond 2
݈‫ܪܰ݃݋‬ସ ܰோ௘௠ ൌ െͳǤͳ ൅ ሺͳǤͶ ‫ܪ݂ܰ݊ܫ݃݋݈ כ‬ସ ܰሻ ൅ ሺͲǤͷͳ ‫ܪ݌݂݂ܧ݃݋݈ כ‬ሻ ൅ ሺെͲǤͲͶ ‫݂݂ܵܵܶܧ݃݋݈ כ‬ሻ
൅ ሺെͲǤͲͻ ‫ܦܱܤ݂݂ܧ݃݋݈ כ‬ሻ ൅ ሺͲǤͳ͹Ͳ ‫ݕݔܱ݂݂ܧ݃݋݈ כ‬ͷܿ݉ሻሺ͸ሻ
Pond 3
ܰ‫ܪ‬ସ ܰோ௘௠ ൌ െ͵ͻǤ͵ ൅ ሺͳǤʹͺ ‫ܪ݂ܰ݊ܫ כ‬ସ ܰሻ ൅ ሺെͳǤͶ͹ ‫ܪ݌݂݊ܫ כ‬ሻ ൅ ሺʹǤͷͻ ‫ܪ݌݂݂ܧ כ‬ሻ ൅ ሺെͳǤ͵͵ ‫ݕݔܱ כ‬͹Ͳܿ݉ሻ
൅ ሺͲǤͷͲ ‫ܰ݃ݎܱ݂݂ܧ כ‬ሻሺ͹ሻ
Pond 4
ܰ‫ܪ‬ସ ܰோ௘௠ ൌ െ͹͹Ǥͺ ൅ ሺͳǤͻ ‫ܪ݂ܰ݊ܫ כ‬ସ ܰሻ ൅ ሺെͲǤ͵ͻ ‫݆ܰܭ݂݊ܫ כ‬ሻ ൅ ሺ͸ǤͶ ‫ܪ݌݂݂ܧ כ‬ሻ ൅ ሺെͲǤͷͲ ‫ܦܱܤ݂݂ܧ כ‬ሻ
൅ ሺͲǤͺͲ ‫ܰ݃ݎܱ݂݂ܧ כ‬ሻ ൅ ሺͲǤ͵͸ ‫ݕݔܱ כ‬ͷܿ݉ሻሺͺሻ
For pond 1, influent ammonia, day time influent and effluent pH as well as effluent organic
nitrogen were significantly correlated to ammonia removal (Table 6.3). For ponds 2 and 4,
it was only influent ammonia which was significantly correlated to ammonia removal. The
results for pond 3 showed that ammonia removal was significantly correlated to influent
ammonia, influent temperature, effluent temperature, effluent organic nitrogen and effluent
BOD (Table 6.3).
Period 2
The average ammonia concentration entering and leaving the facultative pond was 75± 5
and 51.2±4 mg l-1 respectively i.e. only 32% of ammonia was removed. A two sample
independent t-test showed a significant difference between the means (t (98) =26.7,
p<0.05). The mean effluent ammonia of maturation pond 1, 2, 3 and 4 were 28.7±4, 15.2±
4, 12.6±3 and 19.1±8 mg l-1 respectively. Statistical t-tests showed significant differences
between the influent and effluent ammonia of all the maturation ponds. This implied that
significant amounts of ammonia were removed by the maturation ponds. Similarly, the
effluent ammonia concentrations in all ponds were significantly different from each other
implying that the ponds behaved differently in ammonia removal.
92
60
Period 1
Period 2
40
BOD5 (mgl-1)
NH4-N removal (influent -effluent)
mgl-1
50
30
20
10
0
FP
MP1
MP2
MP3
80
70
60
50
40
30
20
10
0
Period 1
Period 2
FP
MP4
Figure 6: ammonia removal in the ponds in period 1
and 2 (n =37, 48).
MP1
MP2
MP3
MP4
Figure 6.1: Effluent BOD of the FP and
MP during period 1 and 2 (n= 37, 48)
Figure 6 shows ammonia removal in the FP and the maturation ponds. It was noticed that
during period 2, the FP pond removed less ammonia therefore the influent ammonia to the
maturation ponds increased. This was caused by the covering of the FP; which prevented
algal growth resulting into no ammonia uptake by algae. Lack of photosynthesis could have
affected pH consequently reducing ammonia volatilization. The absence of oxygen
production by algae resulted into anaerobic conditions thus no ammonia oxidation
occurred. Results during this time showed no oxidized forms of nitrogen in the influent of
the MP. The ammonia removals of the maturation ponds during period 2 were 22.6±4.4,
36.1±3.9, 38.6±4.8 and 32.1±7.7 mg l-1 in ponds 1, 2, 3 and 4 respectively (Figure 6). In
terms of nitrogen removal efficiency, ponds 1, 2, 3 and 4 removed 40%, 63%, 69% and
59% of total nitrogen respectively.
Multiple regressions were run for ammonia removal and pond type; first, pond 1 was
compared to the rest of the ponds. Thereafter, pond 2 was compared to the other ponds and
finally, pond 3 was compared to pond 4 (Table 6.2).The results for the first regression
showed that the ammonia removal between pond 1 and the rest of the ponds were
VLJQLILFDQWO\GLIIHUHQW7KHSRVLWLYHȕYDOXHVZKen pond 1 was compared to the rest of the
ponds indicated that all the baffled ponds 2, 3 and 4 removed more ammonia than unbaffled pond 1. This is in agreement with the trends shown in figure 6. For the second run,
the removal of pond 2 significantly differed IURPSRQGVDQG7KHSRVLWLYHȕYDOXHIRU
the comparison between pond 2 and 3 indicated that pond 3 removes more ammonia than
SRQG7KHQHJDWLYHȕYDOXHZKHQSRQGDQGZHUHFRPSDUHGWRSRQGLQGLFDWHd that
the removal of pond 4 is lower than in these two ponds. It is clear that pond 3 performed
best in ammonia removal, followed by pond 2, 4 and 1 respectively.
Further multiple regressions were performed for each pond to find out which variables were
responsible for the ammonia removal. Unlike during period 1, more variables were
included this time because more data was complete for statistical analysis. The independent
variables used in the regression were: Influents ammonia, Kjeldahl nitrogen, BOD, TSS,
93
alkalinity and pH. Others included; effluents pH (day time), nitrate, alkalinity, TSS,
temperature, COD, BOD, organic nitrogen and day time oxygen at 5cm, 45cm and 70 cm.
The regression equations for the maturation ponds during period 1 and 2 are shown in table
6.3. The regression models for all the ponds during period 1 and 2 were checked for prerequisites of multiple regressions. All the VIF values were below 10 and all the tolerance
values were above 0.1. These indicated that the assumption of lack of multicollinearity is
tenable. Also the Durbin-Watson values were within the limits of 1 and 3 hence adjacent
residuals were not correlated. The values for cooks distance were less than 1. Plots of
ammonia removal by regression model versus ammonia removal measured for period 1
gave R2 values of 0.71, 0.36, 0.46 and 0.44 for ponds 1, 2, 3 and 4 respectively. For period
2, the plots showed R2 values of 0.45, 0.81, 0.92 and 0.85 for ponds 1, 2, 3 and 4
respectively.
Comparison between period 1 and 2
Generally, the ammonia concentration in the influent of the FP did not change in the two
operational periods since the conditions remained the same in the anaerobic tank. About 79
± 9.0 and 75±5.0 mg l-1 of ammonia entered the FP during period 1 and 2 respectively.
However, the influent ammonia concentration of the maturation ponds increased from
34.2±6.9 to 51.2±3.8 mg l-1 from period 1 to period 2 when the FP was covered. Ammonia
removal in the FP pond during period 1 was 44.9±11.3 mg l-1 while that under period 2 was
23.7±5.0 mg l-1 (Figure 6). This is approximately a 50% reduction in ammonia removal.
25.0
Period 1
Period 2
400
350
300
250
200
150
100
50
0
5cm Period 1
5cm period 2
20.0
Oxygen (mgl-1)
TSS (mgl-1)
Comparisons of ammonia removal between the same ponds for the two different periods
showed that the ammonia removal during period 2 were significantly higher in all ponds,
i.e. pond 1 (t (140) = 16.4, p<0.05); ponds 2 and 3 almost doubled (t (140) = 9.9 and 11.0
p<0.01) and pond 4 (t (139) = 16.4, p<0.01). Total nitrogen removal efficiencies however
showed that the removal efficiency for pond 1 reduced from 53% in period 1 to 40% in
period 2. Those of ponds 2, 3 and 4 increased from 48%, 47% and 32% in period 1 to 63%,
69% and 59% respectively in period 2.
45cm Period 1
15.0
45cm Period 2
10.0
70cm Period 1
70cm Period 2
5.0
0.0
Inf FP Inf MP MP1
MP2 MP3
MP1
MP4
MP2
MP3
MP4
Figure 6.3: Day time oxygen measurements
(between 11.00-12.00 hrs) at different depths of the
maturation ponds during period 1 and 2(n= 26, 44)
Figure 6.2: Influent TSS of FP and MP and effluent
TSS of the four maturation ponds (n = 61, 48).
94
Period 1
Period 2
Period 1
Period 2
10
8
6
pH
KJ-N (mgl-1)
45
40
35
30
25
20
15
10
5
0
4
2
0
MP1
MP2
MP3
MP1
MP4
Figure 6.4: Median effluent Kjeldahl
nitrogen of MP during periods 1 and 2
MP2
MP3
MP4
Figure 6.5: Effluent pH (measured at 11.00 hrs)
in the MP during periods 1 and 2 (n= 37, 48)
Discussion
Effects of influent composition
Covering the FP during period 2 changed the influent composition of the maturation ponds;
for instance influent ammonia increased (Figure 6) while BOD decreased (Figure 6.1). This
resulted in differences in ammonia removal between the two periods; with more ammonia
being removed during period 2 (Figure 6). Increase in influent ammonia favored more
ammonia removal of all ponds. The decrease of influent BOD (significant, t (67) = 6.14,
p<0.05) during period 2 probably favored the growth of nitrifiers especially in the baffled
ponds where more attachment was provided (McLean et al., 2000). This resulted into
improvement of ammonia removal (Muttamara and Puetpaiboon, 1997). Additionally, the
effluent TSS of the maturation ponds during period 2 was significantly lower than during
period 1 (Figure 6.2). This is most likely due to absence of algae entering the maturation
ponds from the FP or due to inhibition of algal growth by sulfides. Lower bulk water algal
TSS allowed more light penetration into the ponds hence more growth and oxygen
production by the attached algae (Chapter 2). The day time oxygen concentrations in the
deeper parts of the ponds during period 2 were significantly higher than in period 1 (Figure
6.3) and this might have favored ammonia oxidation. Although the oxygen levels during
period 1 were lower, they were above 0.5 mg l-1 in all ponds which was sufficient to
support bulk water nitrification (Metcalf and Eddy, 2003).
Importance of attachment
During period 1, the control pond was found to remove more ammonia than the baffled
ponds. Constable et al., (1989) and McLean et al., (2000) made similar observations when
at certain times of the year, the performance of ponds without attachment surfaces were
comparable or even better that those that had attachment surfaces. During this period, they
observed high suspended nitrifier growth and this was attributed to high algal
concentrations; they suggested that algae provided more surface area for attachment. Others
like Xia et al., (2009) have shown in their experiments that increase in amount of
95
suspended sediments (<0.002mm) increased nitrification as well. They concluded that
increased surface area provided by suspended sediments was responsible for this
observation. Since the TSS of pond 1 was higher than the rest in this study, it could be
possible that algal TSS provided more surface area for attachment of nitrifiers. A direct
linkage between algae and nitrifier attachment has not been established yet and need to be
studied further.
During period 2, ponds with baffles performed better than the control pond. These results
are in agreement with those obtained by Baskaran et al., (1992), McLean et al., (2000) and
Zanotelli et al, (2002). The reason for better performance of the baffled ponds was due to
more attachment surface for nitrifiers in these ponds. There was more light penetration in
the ponds leading to increased aerobic biofilm area (Chapter 2). This provided more
suitable conditions for growth of nitrifiers in the biofilms.
Different processes of ammonia removal
Lai and Lam (1997) found increase in organic nitrogen to coincide with higher algal
biomass, suggesting conversion of inorganic nitrogen to organic nitrogen in algal cells. In
this study, the amount of nitrogen taken up by algae (organic nitrogen) was calculated as
6% dry weight of effluent TSS (Lai and Lam, 1997). The amount of ammonia taken up by
algae during period 1 and 2 are shown in Table 6.1. The uptake during period 1 was
significantly higher than in period 2 implying that ammonia uptake was important during
this period. This is also reflected in higher Kjeldahl nitrogen during period 1 (Figure 6.4).
Ammonia uptake by algae is a temporary removal mechanism that stores nitrogen when the
dead algae accumulate in the sediments or when algae are washed out of the ponds. In case
of accumulation, the nitrogen can be released after decomposition of sediments and internal
recycling of nitrogen occurs (Reed et al., 1995; Camargo Valero et al., 2009). Therefore,
ammonia uptake by algae cannot fully explain the performance of the ponds during the two
periods. The other possibility is loss of nitrogen to the atmosphere through ammonia
volatilization; this was found to be negligible, results similar to those of Zimmo et al.,
(2003). Therefore, it is more likely that nitrification-denitrification accounted for nitrogen
removal. This is in agreement with Somiya and Fujii (1984), Gross et al., (1994); Lai and
Lam, (1997) and Zimmo et al., (2004) who found that nitrification-denitrification was a
major pathway of nitrogen removal in wastewater stabilization ponds provided the right
conditions exist. Total nitrogen mass balances in this study (presented in next chapter)
showed that nitrification-denitrification was the major nitrogen removal mechanism during
both periods.
Factors affecting ammonia removal
Multiple regressions (Table 6.3) showed that different factors affected ammonia removal in
the four ponds differently making them behave differently. Influent ammonia was the only
common factor positively related to ammonia removal in all ponds in both periods. Most of
the results showed that ammonia removal was positively correlated to day time effluent pH
and effluent organic nitrogen. Usually increase in the day time pH of wastewater
stabilization ponds is linked to utilization of carbon dioxide during photosynthesis. This
results in more ammonia uptake by algae hence more organic nitrogen in the effluent
96
(Wrigley and Toerien, 1990; Lai and Lam, 1997). Increase in pH values can also lead to
ammonia loss by volatilization. However, ammonia loss by volatilization was negligible,
results similar to those of Zimmo et al., (2003). The pH values obtained were usually less
than 8 (Figure 6.5) and this resulted in lower ammonia loss by volatilization. In some
ponds, ammonia removal increased with an increase in effluent nitrates and a decrease in
effluent alkalinity; an indication of nitrification. Results from regression also showed that
decrease in effluent BOD increased ammonia removal.
Conclusions
During period 1, under conditions of lower ammonia and higher BOD loading, the unbaffled pond performed better in nitrogen removal than the baffled ones. During period 2,
all the baffled ponds performed better in nitrogen removal than the un-baffled one. Effluent
TSS during period 2 was significantly lower than during period 1. This could have allowed
more light penetration hence the higher algal biomass and oxygen concentration at deeper
parts of the pond biofilm. This led to improved conditions for ammonia oxidation during
period 2. Additionally, the lower BOD concentration in the influent during period 2 could
have favored growth of nitrifiers hence improving the ammonia removal. Among the
baffled ponds during period 2, pond 3 performed best, followed by ponds 2, 4 and pond 1
removed the least nitrogen. This demonstrated the importance of baffle configuration on the
removal processes. It implied that the baffle configuration of pond 3 could be included in
wastewater treatment designs for improving nitrogen removal in wastewater stabilization
ponds.
Acknowledgements
We are grateful for the financial support provided by the Netherlands government through
Netherlands Fellowship Program. We also appreciate the financial assistance from the EUSwitch project contract 018530. We are also thankful to the management and laboratory
staff of Bugolobi Sewage Treatment Plant for their assistance and support in this research.
References
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Craggs, L.J., Tanner, C.C., Sukias, J.P.S. and Davies, C.R.J. (2000). Nitrification potential
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Lai, P.C.C and Lam, P.K.S. (1997). Major pathways for nitrogen removal in wastewater
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Mara, D.D. (1997).Design Manual Waste Stabilization Ponds in India, Lagoon Technology
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Mara, D.D. (2004). Domestic wastewater treatment in developing countries. Earth scan,
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Mara, D.D., and Pearson, H.W. (1998). Design manual for waste stabilization ponds in
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McLean B.M., Baskran, K., and Connor, M.A. (2000). The use of algal-bacterial biofilms
to enhance nitrification rates in lagoons: Experience under laboratory and pilot scale
conditions. Wat .Sci. Tech. 42(10-11), 187-194
Mergaert, K., Vanderhaegen, B., Verstraete, W., (1992). Application and trends of pretreatment of municipal wastewater. Wat. Res. 26(10-11), 1025 ±1033
Metcalf and Eddy (2003). Wastewater Engineering. Treatment and Reuse. Tchobanoglous,
G., Burton, F.L., Stensel, H.D (Eds). 4th Ed. McGraw Hill, Inc., USA
Muttamara, S. and Puetpaiboon, U. (1997). Roles of Baffles in Waste Stabilization Ponds.
Wat. Sci. Tech. 35(8) 275-284
Reed, S.C., Crites, R.W., Middlebrooks, E.J. (1995). Natural Systems for Wastewater
Management and Treatment (2nd Ed). McGraw ± Hill Inc
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Shield, A. (2005). Discovering Statistics Using SPSS. Second Edition, Sage publication
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Somiya, I. and Fujii, S. (1984). Material Balances of organics and nutrients in an oxidation
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99
Chapter 7
Nitrogen mass balances for pilot scale biofilm stabilization ponds under tropical
conditions
In Press as: M.A. Babu., N.P. van der Steen., C.M. Hooijmans., H.J. Gijzen. Nitrogen
mass balances for pilot-scale biofilm stabilization ponds under tropical conditions.
Bioresource Technology, doi:10.1016/j.biortech.2010.12.003
100
Chapter 7
Nitrogen mass balances for pilot-scale biofilm stabilization ponds under tropical
conditions
Abstract
The main objective of this study was to model the mechanisms of nitrogen removal in
biofilm wastewater stabilization pond based on simple nitrogen mass balance equations.
Pilot scale biofilm maturation ponds were constructed at Bugolobi sewage treatment plant;
Kampala ± Uganda. The dimensions of the ponds were 4m x 1m by 0.8m depth; pond 1
served as control (without extra biofilm attachment surface) while in ponds 2, 3 and 4,
fifteen baffles were installed in each. The control pond had a total surface area of 8 m 2
while the baffled ones had 23.2 m2 each, including the pond wall. The baffles were
arranged differently to induce different flow patterns. The ponds were operated under two
loading conditions, i.e. period 1, 0.0057 g NH4-N l-1 d-1 and period 2, 0.0084 g NH4-N l-1 d1
. Total nitrogen and TKN mass balances were made. Bulk water and biofilm nitrification
rates were determined and used in the TKN mass balance. Results for total nitrogen mass
balance showed that for both periods, denitrification was the major removal mechanism.
Nitrogen uptake by algae was more important during period 1 than in period 2. The TKN
mass balance predicted well effluent TKN for period 2 than period 1 when influent
TKN/BOD ratio increased from 0.5 to 0.67. This could be due to fluctuations in algae
density and ammonia uptake during period 1. No conclusions on reliability of mass balance
model in period 1 were made. Under conditions as in period 2, the TKN mass balance
model was a useful tool to predict performance of biofilm waste stabilization.
Key words: Biofilm, Bulk water, Stabilization ponds, Nitrification, Kjeldahl, Mass
balances
101
Introduction
Nitrogen is causing eutrophication of water bodies worldwide and domestic wastewater is
among the major sources of nitrogen disposal into the environment (de Godos et al., 2010).
The major effects of nitrogen pollution are manifested in eutrophication of surface waters
as well as pollution of ground water. Algal blooms and proliferation of non desired aquatic
weeds have been associated with nutrient pollution (Bolan et al., 2009). Indirect effects of
nitrogen pollution range from the blue baby syndrome to fish kills, from loss of aesthetic
values of water to increased costs of water treatment. The possibility of nitrous greenhouse
gas emissions from polluted water bodies has also been suggested (Gijzen and Mulder,
2001; Martinez et al., 2009).
Many nations have adopted stringent effluent nitrogen standards, which are associated with
increased treatment costs and demands upgrading of the already existing wastewater
treatment (WWT) systems or construction of new systems. Simple and cost effective
systems like wastewater stabilization ponds (WSP) in the standard configurations will in
most cases not meet discharge standards. WSP research has shown large variations in
performance for nitrogen removal, but it remains unclear which mechanisms are
responsible for nitrogen removal under varying environmental conditions.
The principle routes of nitrogen transformation in wastewater stabilization ponds include
nitrification, denitrification, sedimentation, volatilization, plant uptake and ammonification.
In some studies, denitrification and sedimentation have been found to be the major removal
mechanism (Zimmo et al., 2004; Bolan et al., 2009). Nitrification was previously thought
not to occur in wastewater stabilization ponds due to low nitrate concentrations observed in
the effluent (Pearson, 2005). However, McLean et al., (2000), Wilkie and Mulbry, (2002),
Zimmo et al., (2004), Gonzalez et al., (2008), de Godos et al., (2010) and MolinuevoSalces et al., (2010) have shown nitrification to be an important process of nitrogen
removal. Others (Aslan and Kapdan, 2006; Bolan et al., 2009) have found ammonia
volatilization to be a major nitrogen removal mechanism. Nitrogen uptake by algae has also
been reported to be an important removal mechanism (Gonzalez et al., 2008; Camargo
Valero et al., 2009; de Godos et al., 2010); but upon death, sedimentation and
mineralization of algae, internal cycling of ammonia occurs and this reduces the ammonia
removal efficiency. All these mechanisms are an integral part of nitrogen removal
mechanisms in wastewater stabilization ponds but which one prevails over the other
depends much on the environmental and operational conditions (Zimmo et al., 2004;
Camargo Valero et al., 2009; Gonzalez et al., 2010).
Nitrification in WSPs could possibly be enhanced by providing attachment surface for slow
growing nitrifiers (Zimmo et al., 2004; Pearson, 2005; Gonzalez et al., 2008) in biofilm
waste stabilization ponds (BWSP). Biofilms have been found to improve nitrogen removal
in wastewater stabilization ponds (McLean et al., 2000; Craggs et al., 2000 and Mara and
Johnson, 2007). However, use of biofilms under tropical conditions and under different
flow conditions has not been studied.
102
The aim of this study was to model the mechanisms of nitrogen removal in BWSP based on
Total Kjeldahl Nitrogen (TKN) and total nitrogen (TN) mass balance equations. The
nitrification rate in the TKN balance was based on measurement of nitrification rates in
both the bulk water and biofilm in batch activity tests.
Methodology
Description of pilot scale system
The pilot scale wastewater stabilization ponds are shown and described in chapter 2 (Figure
2). The operational conditions were also described in chapter 2.All the physico-chemical
parameters were analyzed according to APHA, (1995). This study was a continuation of the
studies in chapter 6.
Nitrification batch activity tests
Nitrification rates in biofilms (gm-2d-1) were derived from the decrease in ammonia
concentration with time in batch experiments. The decrease can be the result of
volatilization, nitrification and algal uptake. Ammonia taken up by algae was measured
(Babu et al., 2010) and volatilization was found to be negligible. Subsequently nitrification
rates were calculated by subtracting the uptake by algae from the rate of ammonia decrease.
Biofilms used in the tests were grown in the maturation ponds on biofilm plates of
dimensions 3.0 by 8.0 cm which were vertically mounted on a frame and suspended in each
maturation pond at 5 cm depth for more than 30 days. At the start of the batch experiments,
the biofilm plates were rinsed with distilled water and biofilm nitrification batch activity
tests performed as described by Babu et al., (2007, 2010).
Bulk water activity tests were performed using MP effluent, and the observed decrease in
ammonia concentration was used to calculate the bulk water nitrification rates (g l-1d-1).
Ammonia volatilization was found to be negligible and ammonia uptake by algae during
the batch test was assumed to be the same as the average algae uptake rate in the ponds.
Average algae uptake rate in the bulk water in the ponds was obtained by subtracting the
influent organic nitrogen load (g d-1) from that of the effluent and by dividing by the pond
volume. The increase in organic nitrogen represents nitrogen taken up by algae because
organic nitrogen is mainly algal material (Wrigley and Toerien, 1990; McLean et al., 2000;
Camargo Valero et al., 2009).
Kjeldahl mass balance
Assumptions and limitations
The TKN mass balance (Equation 1) is a simplified model to describe TKN removal
processes and could be used to estimate effluent TKN. The nitrification rate constants in
Equation 1 were calculated from the nitrification rates at different pond depths, which were
calculated based on maximum nitrification rates (obtained from the activity tests) and
oxygen concentrations at depths of 5, 45 and 70 cm measured at 12.00 hours. That DO
value was taken as the representative value though it is realized that in reality DO fluctuates
strongly. Sensitivity analyses were done to check for the effect of this assumption on
prediction of effluent TKN. The oxygen fluctuations take place over the duration of 24
hours, while the HRT is 6.4 days. Therefore it is expected that the effect of short term
103
fluctuations in oxygen on effluent nitrogen will be small. If this expectation is not true, one
would need to measure oxygen profiles for all the four ponds on an hourly basis.
Kjeldahl mass balance equation
Nitrification rates of biofilm (R biofilm) and bulk water (R nitrbulk) obtained in the batch
activity tests were used as parameters in the TKN mass balance (Equation 1) (adapted from
Zimmo et al., 2004) to predict the effluent TKN.
ܸ௥௘௔௖௧௢௥ ൈ
ௗሾ்௄ேሿ
ௗ௧
ൌ ܳ ൈ ሾܶ‫ܰܭ‬ሿ௜௡௙ െ ܳ ൈ ሾܶ‫ܰܭ‬ሿ௘௙௙ െ ሺܴை௩௘௥௔௟௟ ൈ ܸ௥௘௔௖௧௢௥ ሻ ൅ ܷ௩௢௟ ൅ ܵ௦௘ௗ
(1)
Where
= Influent Kjeldahl Nitrogen (gm-3)
= Effluent Kjeldahl Nitrogen (gm-3)
= Overall nitrification rate (gm-3day-1), ((Asurface ™ 5nitrbulk[ ¨[ R overall
:™5biofilm[¨[9reactor
x
= depth below pond surface (m)
= Nitrification rate in the bulk (gm-3 d-1)
R nitrbulk
= pond surface (m2)
Asurface
= Nitrification rate in the biofilm (gm-2d-1)
R biofilm
W
= Total width of the biofilm (2 times total baffle width + total width
of walls) (m)
Q
= Flow rate (m3d-1)
= Ammonia volatilization (gd-1)
U vol
= TKN removal by sedimentation (gd-1)
S sed
= Total volume of pond (m3)
V reactor
ሾܶ‫ܰܭ‬ሿ௜௡௙
ሾܶ‫ܰܭ‬ሿ௘௙௙
All the terms in Equation (1) are expressed in gd-1. The influent and effluent nitrogen flows
as well as sedimentation and volatilization were converted to this unit and referred to as
nitrogen fluxes (N-fluxes). Nitrification and denitrification rates were also changed to this
unit and referred to as nitrogen conversions (N-conversions). Ammonia volatilization was
calculated using Equation (2) developed by Zimmo et al., (2004):
Ammonia volatilization rate (g-N m-2 d-1)
= 3.3[NH3-N] +4.90
(2)
Where [NH3-N] is calculated from Emerson et al., (1975) as:
ଵ଴଴
Ψܷ݊݅‫ܪܰ݀݁ݏ݅݊݋‬ଷ ൌ ଵାଵ଴ሺ೛಼ೌష೛ಹሻ
(3)
Where pKa is the ammonia dissociation constant
104
Sediment samples were collected using sediment traps and dried at 70oC, weighed and
digested for ammonia determination as described in APHA (1995).
Effect of oxygen profile in pond water column on nitrification rates
The calculation of N-conversions for both the bulk water and biofilm were based on the
Monod model (Figure 7), with a half saturation constant for the bulk water of 0.4gm-3 of
oxygen (Henze et al., 2000). For algal-bacterial biofilms, the apparent half saturation
constant is not known, but likely to be higher than 0.4 due to limitation by diffusion of
oxygen into the biofilm. Zero and first order kinetics were used (Figure 7) in calculating Nconversions in the biofilm. This is because results from previous studies (Babu et al., 2010)
indicated that the biofilm nitrification rates were not significantly affected by bulk water
oxygen concentration above 3.2 gm-3, below this value, the nitrification rates significantly
reduced. Therefore, it was assumed that nitrification rates above depths at which oxygen
was above 3.2 gm-3 was constant and similar to that measured at 5cm depth (Rmax). More
details of the calculation of the N-conversions by bulk water and biofilm are discussed
separately in the following sections.
Figure 7: Nitrification rates versus oxygen concentration. The thick dotted curve represents bulk water
nitrification rates while the solid straight lines show biofilm nitrification rates.
Bulk water N-conversions
The bulk water N-conversions were determined using bulk water nitrification rates
(measured at 5cm depth; Rmax) and oxygen at 5, 45 and 70 cm depths. The oxygen
concentration reduced with pond depth due to light limitation. This pattern was observed
105
even in pond 4 which had the up-and-down flow pattern. The flow rate was too low to
cause any change in the oxygen profile of this pond. Therefore all the four ponds had
similar oxygen profiles. First, the mean oxygen concentration of 5, 45 and 70 cm depths
were plotted against their respective depths and fitted with exponential curves. All the
exponential curves gave good fits with R2 values of 0.99. The equations of best fit were
used to recalculate oxygen concentrations at 5 cm intervals along the pond depth. The
recalculated oxygen concentrations together with Rmax were fitted in equation 4 to calculate
nitrification rates at different depths.
ܴ௡௜௧௥௕௨௟௞ ܺ ൌ ௄
஼బమ
బమ ା஼೚మ
‫ܴ כ‬ெ௔௫ ǡ
(4)
Where,
RNitrbulk(x) = Nitrification rate at given oxygen concentration in bulk water (gm-3d-1)
CO2(x) = Oxygen concentration at a given depth x (gm-3)
KO2 = 0.4 gm-3
Rmax = Maximum nitrification rates measured at 5cm depth (gm-3d-1).
The calculated nitrification rates were plotted against their respective depths and curves
fitted. Equations of best fit obtained were integrated using equation 5.
଴Ǥ଼
Bulk water N-conversion (gd-1) = ‫ܣ‬௦௨௥௙௔௖௘ ‫׬‬଴ ܴே௜௧௥ ሺ‫ݔ‬ሻ݀‫ݔ‬ǡ
(5)
Where,
Asurface = Pond surface area (m2)
It was found that Rnitr(x) in equation 5 could be represented by an expression in the form of
Rnitr (x) = ax2 + bx + c. Integrating this gave:
଴Ǥ଼
Bulk water N-conversions (gd-1) = Asurface ‫׬‬଴ ܴ݊݅‫ݎݐ‬ሺ‫ݔ‬ሻ ൌ ƒȀ͵ሺšሻଷ ൅ „Ȁʹሺšሻଶ ൅ …ሺšሻǤ
(6)
The values of a, b and c were obtained from the best fit, while 0.80 is the pond depth (m).
This procedure was repeated for all the ponds during the two operational periods.
Biofilm N-conversions
The biofilm N-conversion through biofilm nitrification Rmax was assumed to be constant for
depths where oxygen was above 3.2 gm-3 (Figure 7):
Biofilm N-conversions (gd-1) at O2 above 3.2 gm-3 = Rmax * AT3.2,
(7)
Where AT3.2 is the sum of biofilm surface area of the walls and the baffles until the depth x
at which the oxygen concentration was above 3.2 gm-3. The biofilm N-conversions where
the oxygen concentrations were lower than 3.2gm-3 were calculated using equation 8:
106
଴Ǥ଼
(8)
Biofilm N-conversions (gd-1) =ܹ ‫׬ כ‬௫ଷǤଶ ܴே௜௧௥௕௜௢௙௜௟௠ ሺ‫ݔ‬ሻ݀‫ݔ‬,
Where,
RNitri biofilm = biofilm nitrification rates (gm-2d-1) at oxygen concentration below 3.2gm-3.
x3.2 = depth at which oxygen concentration was below 3.2gm-3.
In order to obtain an expression for RNitr biofilm(x), Rmax for biofilm was divided by 3.2 to
obtain slope "a" (Figure 7). The slope was multiplied with the oxygen concentrations at
different depths to obtain nitrification rates at these depths and fitted with an exponential
equation of the type y = c*exp (-dx). This equation was integrated (Equation 9) to give
biofilm N-conversions (for oxygen concentration less than 3.2 gm-3) as:
Biofilm N-conversions (gd-1) = W*c (-1/d) Exp (-d*x 3.2) - W*c (-1/d) Exp (-d*0.8)
(9)
Where,
c and d = constants obtained from exponential of fitted equations
The N-conversions calculated from equation 9 were multiplied by 2 and 15 to get the Nconverted by the two surfaces of all the 15 baffles. The total biofilm N-conversion was
obtained by summing the N-conversion of equation 7 and 9 and that converted by the pond
walls; these were subsequently used in the TKN mass balance (Equation 1).
Sensitivity analysis
Sensitivity analysis was performed for period 2 to investigate the effect of oxygen
variations on the TKN mass balance model, by reducing the oxygen concentration at 5, 45
and 70 cm depth by 10% and 50% of the measured values. Reduction of 10% gave an
average oxygen concentration (at 5cm) of 16 mg l-1 while 50% reduction gave an average
of 8.5 mg l-1. The nitrification rates in the biofilm and bulk water were calculated under
these hypothetical oxygen conditions and inserted in the model to calculate effluent TKN.
Total Nitrogen mass balance
The total nitrogen (TN) mass balance was based on influent nitrogen load being equal to
the sums of effluent nitrogen load, volatilization, sedimentation and denitrification as
shown in equation (10).
ܴௗ௘௡ ൌ ܳ ‫ܰ כ‬௜௡ Ȃ ሾܳ ‫ܰ כ‬௢௨௧ ൅ ܸ‫ ݈݋‬൅ ܵ݁݀ሿ,
(10)
Where,
Rden
Q
Nin
Nout
Vol
= Denitrification rate (mg d-1)
= Flow rate (l d-1)
=TKNin + NO2-Nin + NO3-Nin (mg l-1)
= TKNout + NO2-Nout+ NO3-Nout (mg l-1)
= Volatilization rate (mg d-1)
107
Sed
= Sedimentation rate (mg d-1)
Volatilization and sedimentation were determined as described in the section under
Kjeldahl mass balance equation above while denitrification was calculated from equation
(10).
Results
The results of this study are presented in figures 7.1 to 7.3 and table 7 (a) ± (c). Figure 7.1
(a) showed that ammonia uptake by algae was more important during period 1. In both
periods, denitrification was the major nitrogen removal mechanism. Biofilm and bulk water
nitrification rates are shown in table 7 (a). The nitrification rates were obtained after
correcting for nitrogen taken up by algae during the batch tests. Algae uptake in biofilms
was calculated based on biofilm growth rates in the ponds. The highest algal-bacterial
biofilm growth rates in both period 1 and 2 were found in pond 3 at 5 cm depth; these were
3.1 and 3.6 gm-2 d-1 respectively. If an average of 6% of dry weight of biofilm is taken as
nitrogen (Wilkie and Mulbry, 2002), the algal uptake rate for the above algal growth rates
are 0.184 and 0.218 g-N m-2 d-1, respectively. The area of the biofilm plates was 0.0042m2
and the volume of synthetic wastewater was 0.5 liters. Therefore the ammonium taken up
by algae in the biofilm batch experiments was 0.40 and 0.46 gm-3 for period 1 and 2. The
ammonium uptake rate by algae during the bulk water batch experiments in both period 1
and 2 were 0.53 and 0.93 gm-3 d-1 NH4-N. These values were used to correct for algal
uptake as described in the methodology.
35000
30000
Denitrification
mg-N d-1
25000
Sedimentation
Volatilization
20000
NO3-N
15000
NO2-N
NH4-N
10000
OrgN
5000
0
Infponds Pond 1 Pond 2 Pond 3 Pond 4
Figure 7.1a: Nitrogen mass balances based on total nitrogen fluxes for operational period 1
108
35000
30000
Denitrification
mg-N d-1
25000
Sedimentation
Volatilization
20000
NO3-N
15000
NO2-N
NH4-N
10000
Org-N
5000
0
Infponds Pond 1 Pond 2 Pond 3 Pond 4
Figure 7.1b: Nitrogen mass balances based on total nitrogen fluxes for operational period 2
Pred
Meas
Influent
70.0
60.0
TKN (gm-3)
50.0
40.0
30.0
20.0
10.0
0.0
Pond 1
Pond 2
Pond 3
Pond 4
Figure 7.2a: Influent, predicted and measured Kjeldahl nitrogen in maturation ponds 1-4 during operational
period 1
109
Pred
Meas
Influent
70.0
60.0
TKN (gm-3)
50.0
40.0
30.0
20.0
10.0
0.0
Pond 1
Pond 2
Pond 3
Pond 4
Figure 7.2b: Influent, predicted and measured Kjeldahl nitrogen in maturation ponds 1-4 during operational
period 2
Period 1
200.0
Period 2
BOD5 (mg l-1)
150.0
100.0
50.0
0.0
Inf FP
Inf MP
Eff MP1
Eff MP2
Eff MP3
Eff MP4
Figure 7.3a: Comparison of influent and effluent pond BOD during operational period 1 and 2
110
400
Period 1
Period 2
350
300
TSS (mgl-1)
250
200
150
100
50
0
Inf FP
Inf MP
Eff MP1
Eff MP2
Eff MP3
Eff MP4
Figure 7.3b: Comparison of influent and effluent pond TSS during operational period 1 and 2
111
Period 1(n=8)
0.71±0.25
0.60±0.26
0.78±0.33
0.50±0.29
Period 2(n=5)
0.88±0.11
0.69±0.44
0.96±0.32
0.79±0.36
Biofilm nitrification rates
(gm-2d-1)
Period 1(n=8)
1.30±1.5
0.79±0.8
1.28±0.5
0.80±0.8
Period 2(n=5)
1.18±0.3
1.32±0.3
1.24±0.4
1.78±0.4
Bulk water nitrification rates
(gm-3d-1)
1
2
3
4
Pond
0.527±0.03
0.536±0.03
0.531±0.03
0.533±0.02
(m3d-1)
Flow rate
47.8±11.4
47.8±11.4
47.8±11.4
47.8±11.4
Influent
TKN
(gm-3)
25.1±6.1
25.5±6.0
25.3±6.0
25.4±6.0
Influent
TKN
(gd-1)
3.1
9.6
12.5
7.9
Biofilm
nitrification
(gd-1)
112
3.5
2.0
3.3
2.5
Bulk water
nitrification
(gd-1)
0.048±0.02
0.032±0.01
0.032±0.02
0.024±0.003
Volatilization
rate
(gd-1)
0.81
1.11
2.27
2.4
Sedimentation
rate
( gd-1)
Effluent
TKN
measured
(gm-3)
18.6±6
22.8±7
23.5±8
32.6±7
Table 7b Different parameters used in the mass balance model equation; the calculated and measured effluent TKN
values are also given (Period 1).
Pond 1
Pond 2
Pond 3
Pond 4
Pond
Table 7a Biofilm and bulk water nitrification rates for the four maturation ponds during period 1 and 2 (nitrification
rates corrected for algal uptake)
1
2
3
4
Pond
0.526±0.01
0.525±0.01
0.519±0.01
0.523±0.01
(m3d-1)
Flow rate
59.1±5.5
59.1±5.5
59.1±5.5
59.1±5.5
Influent
TKN
(gm-3)
31.1±2.9
31.0±2.9
30.7±2.8
30.9±2.9
Influent
TKN
(gd-1)
5.3
15.3
19.3
12.3
(gd-1)
Biofilm Fluxes
113
3.5
3.9
3.5
6.0
Bulk water
fluxes
(gd-1)
0.040±0.03
0.037±0.02
0.033±0.02
0.026±0.01
Volatilization
rate
(gd-1)
1.06
1.22
1.21
1.18
Sedimentation
rate
( gd-1)
Effluent
TKN
measured
(gm-3)
33.3±4
18.8±4
16.5±3
22.1±8
Table 7c Different parameters used in the mass balance model equation; the calculated and measured effluent TKN
values are also given (Period 2).
Discussion
Nitrification rates
The biofilm nitrification rates obtained in the two periods (Table 7a) were within the range of
other studies e.g. 0.48-0.72 gm-2 d-1 (Craggs et al., 2000), 0.72-0.96 gm-2 d-1 (McLean et al.,
2000) , 1.5-2.1 gm-2 d-1 (Babu et al., 2007) and 0.8 gm-2 d-1 (Lydmark et al., 2007). The bulk
water nitrification rates in the two periods were in the range of 0.8 - 1.8 gm-3d-1, which were
higher than 2.7x10-4 gm-3d-1 obtained by laboratory studies using exclusively synthetic
wastewater (Babu et al., 2007). This could imply that field conditions using sewage favored
more nitrifier growth in the bulk water.
Total Kjeldahl Nitrogen (TKN) and Total Nitrogen (TN) mass balances
The TKN mass balance (Tables 7b and 7c) showed that biofilm nitrification was the largest
contributor to the mass balance, followed by bulk water nitrification, showing the potential of
using biofilms to improve nitrogen removal. Volatilization was less than 1 gd-1 and this was in
line with Zimmo et al., (2003) and Camargo Valero and Mara (2007) who found that this process
was negligible for ammonia nitrogen removal in wastewater stabilization ponds; as long as pH
remained below 8 (Zimmo et al., 2003). Sedimentation fluxes obtained were in the range of 0.8
to 2.4 g d-1 and these were within the values obtained by Zimmo et al., (2004). The sediments
appeared dark green and this indicated that they were made up of mostly decayed algal material.
Upon re-mineralization, the decayed algal matter releases ammonia in the water column. Very
small quantities of sediments were collected and this could be an indication that most of it
decomposed rather than accumulated. In terms of distribution, more sediment was collected at
the inlet points of the ponds as compared to the middle and outlet positions.
The total nitrogen (TN) mass balance for period 1(Figure 7.1a) showed that denitrification
accounted for 47%, 44%, 38% and 22% of the total influent nitrogen in ponds 1, 2, 3 and 4
respectively. Net algal uptake (effluent organic nitrogen) accounted for 18%, 19%, 20% and 17%
of the total influent nitrogen in ponds 1, 2, 3 and 4, respectively. Denitrification is therefore a
more important mechanism than algal uptake, except for pond 4. Note that denitrification was
calculated as the difference of all terms in the mass balance. Therefore it represents not only
denitrification per se but also any other term not included in the mass balance or errors. Caution
should be taken when interpreting this term; it is an estimate of denitrification rather than true
denitrification.
For period 2, the estimated denitrification in ponds 1, 2, 3 and 4 accounted for 36%, 59%, 65%
and 56% of the total influent nitrogen (Figure 7.1b). Algal uptake accounted for 8%, 6%, 7% and
5% of the total influent nitrogen in ponds 1, 2, 3 and 4, respectively. Here the contribution of the
estimated denitrification was also larger than algal uptake, and the difference is more pronounced
than in period 1. Nitrification-denitrification being the most important removal mechanism is in
agreement with results of Zimmo et al., (2004); Bolan et al., (2009) and Gonzalez et al., (2010).
Sedimentation accounted for about 3-9% during both periods. Volatilization accounted for less
than 1% of the total influent nitrogen hence can be considered to be negligible (Zimmo et al.,
2003; Camargo Valero and Mara, 2007). The effluent organic nitrogen during period 1 was
significantly higher than in period 2 i.e. during period 1, 9.1, 9.3, 10 and 8.1 mg-N l-1 while in
period 2 they were 5.4, 3.6, 3.9 and 3.0 mg-N l-1 in ponds 1, 2, 3 and 4, respectively. Since
effluent organic nitrogen in wastewater stabilization ponds is mostly contributed by algal
114
material (Wrigley and Toerien, 1990), these results confirmed that algal uptake was a more
important nitrogen removal mechanism during period 1. The amount of organic nitrogen that left
the ponds in algal biomass can also be calculated as 6% of effluent TSS (Wilkie and Mulbry,
2002). The values thus obtained for period 1 for ponds 1, 2, 3 and 4 were 17.1, 12.3, 14.3 and 9.6
mg-N l-1. Those for period 2 were 3.2, 2.8, 1.7 and 1.7 mg-N l-1. The values during period 1 were
significantly higher than period 2 which was consistent with the organic nitrogen results,
indicating that estimation of organic nitrogen concentrations by TSS is reliable.
Using the TKN mass balance and nitrification rates to predict effluent TKN
The measured average effluent TKN for period 1 differed substantially from the predicted
effluent TKN and had large standard deviations (Figure 7.2a). The model equations apparently
did not predict the effluent values well under these unstable conditions, and this could be due to
the large fluctuations in the algae density and ammonia uptake by algae. The mass balance
discussed in the previous section showed that ammonia uptake by algae is an important
mechanism during period 1. Another reason for the large variations in effluent TKN could
indirectly be due to large fluctuations in influent BOD and TSS. The results for period 2 showed
that the mass balance equation closely predicted the effluent TKN values (Figure 7.2b). The
facultative pond was covered during this time; hence the maturation ponds received less TSS
(due to limited algae growth in the FP). The BOD was also lower as compared to period 1. Both
the influent BOD and TSS showed less variations (Figure 7.3a and 7.3b) and this could have
been reflected in the effluent TKN; giving more accurate results. Under the experimental
conditions, the TKN mass balance predicted better when the influent TKN/BOD ratio was 0.67
(period 2) than 0.5 (period 1). The TKN/BOD ratios are important in influencing the nitrifier
population. For instance, the higher ratio during period 2 could have led to higher nitrification
rates since growth of heterotrophic bacteria was less, primarily due to a lower organic loading
(Downing and Nerenberg, 2008). Growth conditions for nitrifiers were therefore more favorable
during period 2. And since the model is based on nitrification rates based on measurements under
favorable TKN/BOD ratios, it is understandable that the model predicted better under the higher
TKN/BOD ratios of period 2.
Despite the fact that only oxygen was taken into account during the calculations of Nconversions for nitrification and the other factors like pH and temperature were not considered,
the model seemed to estimate the effluent TKN for period 2 relatively well. If the other factors
would be considered, the model may still improve. More research on bulk water and biofilm
nitrification rates at different depths, the effects of pH and temperature as well as an intensive
campaign of measurements of oxygen profiles and other parameters during the whole day is
recommended.
Sensitivity analyses
Since the model during period 2 could closely predict the measured effluent TKN, sensitivity
analysis was performed for only this period. The results showed for all ponds a negligible effect
of a 10% reduction in dissolved oxygen concentration, namely an increase of less than 1%
effluent TKN. When oxygen concentration was reduced to 50% of its original value, the increase
in effluent TKN was less than 1% for pond 1 and 2, while for ponds 3 and 4 it was 73 and 53 %,
respectively. The ponds 3 and 4 had lower concentration of oxygen at 45 and 75cm so reduction
of 50% of oxygen greatly affects effluent TKN. Sensitivity of the model to variation in oxygen
115
concentrations is therefore very limited for variations of 10% or less. The oxygen variations in
the ponds usually occur over a time span of hours (Kayombo et al., 2002), while the mean
retention time of the ponds was 6.4 days. Short term changes in oxygen concentration are
therefore less likely to affect the effluent TKN.
Conclusions
Results showed that biofilm nitrification (and subsequent denitrification) was the major pathway
for TKN removal in BWSP, and more important than bulk water nitrification. Preference of
nitrifiers for attached growth could explain these results.
TN mass balances showed that nitrification-denitrification, algal uptake and sedimentation were
the principle nitrogen removal mechanisms in BWSP. The importance of nitrificationdenitrification was more pronounced when the influent TKN/BOD ratio was increased.
The TKN mass balance model predicted effluent TKN better when influent TKN/BOD ratio
increased from 0.5 to 0.67. Under the latter conditions, the model was a useful tool to predict
performance of BWSP.
Acknowledgements
We are grateful for the financial support provided by the Netherlands Government through
NUFFIC. We also appreciate financial assistance from the EU FP6 -SWITCH project - contract
018530. The authors are also thankful to the management and laboratory staff of Bugolobi
Sewage Treatment Plant for their assistance and support in this research.
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4587-4594.
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Summary
119
Summary
Domestic wastewater is a source of nitrogen in environmental systems. Nitrogen is known to be
a major pollutant to the aquatic system. It causes eutrophication which leads to excessive algal
growth or growth of other undesired water weeds such as water hyacinth. This results in
disruption of the oxygen balance, release of toxins, loss of biodiversity and increased costs of
water treatment; if the water resource is used as a source for drinking water production.
Wastewater stabilization ponds are treatment technologies that have been adopted by many
developing countries. This is due to being cheap to construct, operate and maintain than activated
sludge systems. However, they suffer from high effluent total suspended solids concentration
(TSS), short-circuiting, long hydraulic retention time and ineffectiveness in removing nutrients
like nitrogen. The problem of nitrogen removal is attributed to low nitrifier biomass present in
the water column. Several studies have shown that the introduction of attachment surface for
nitrifiers in the ponds improves nitrogen removal. However, information on the introduction of
baffles as attachment surface for nitrifiers under tropical conditions is scarce.
This study focused on the effects of incorporation of baffles in pilot scale wastewater
stabilization ponds. The pilot scale ponds were constructed at Bugolobi Sewage Treatment
Works (BSTW) in Kampala, Uganda, and operated under tropical conditions. Settled wastewater
was pumped from the sedimentation tank of BSTW into a 10 m3 plastic anaerobic tank (AT)
having a retention time of 3 days. The wastewater was then fed continuously by gravity at a flow
rate of 2.1m3 per day into a facultative pond (FP). The effluent of the FP was fed into four pilot
scale maturation ponds (MP) of length, width and water depth of 4m by 1m by 0.8m at flow rates
of 0.5m3 per day. The details of the design and operation of the pilot scale are presented in figure
2 chapter 2 of this thesis. Pond 1 was operated as control while in ponds 2, 3 and 4, fifteen
baffles of the same surface area were installed. The baffles had different configurations (pond 2:
parallel to the flow, pond 3 and 4: perpendicular to the flow) inducing different horizontal and
vertical flow patterns. The ponds were operated for two periods i.e. under an influent BOD of
72±45 mgl-1 and ammonia of 34±7 mgl-1 (period 1) and an influent BOD of 29±9 and ammonia
of 51±4 mgl-1 (period 2). Introduction of baffles in wastewater stabilization ponds can affect their
ecology, hydraulic characteristics and performance. This was studied and presented in different
chapters. Laboratory studies on bulk water and biofilm nitrification rates were conducted, to
complement the pilot scale studies.
The results of this study showed that nitrogen removal from wastewater can be improved by
addition of extra attachment surface for nitrifiers. Experiments discriminating biofilm and bulk
nitrification rates showed that biofilm nitrification rates were more important than bulk water
nitrification rates (Chapter 4). Further laboratory experiments also showed that biofilm
nitrification rates are significantly reduced at bulk water oxygen concentration of less than 3.2
mg l-1 (Chapter 5). The results for the pilot scale wastewater stabilization ponds showed that
during period 1, the control pond performed better than the ones that had extra attachment
surface (Chapter 6). Under such conditions, it was found that the bulk water TSS was high and
this prevented light penetration into the deeper parts of the ponds resulting in reduction of
aerobic biofilm area that is required for nitrification (Chapter 2). The higher BOD during period
1 also favored the growth of heterotrophic bacteria compared to the nitrifiers. Nitrifiers are slow
growers and under high BOD loading, they are outcompeted by heterotrophic bacteria. When the
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light conditions and corresponding algal activity of the FP were changed to increase the
ammonia concentration in the influent to the maturation ponds (by covering it with a black
plastic sheet) in period 2, the influent BOD of the maturation ponds decreased. Under these
conditions, it was found that the baffled ponds 2, 3 and 4 showed better N-removal than the
control pond 1. The removal efficiency was attributed to more attachment surface for nitrifiers in
ponds 2, 3 and 4. The TSS of the maturation ponds also decreased, allowing more light
penetration hence more aerobic biofilm area became available to the nitrifiers. Additionally, the
lower influent BOD during period 2 favored the growth of nitrifiers in the ponds. Therefore
under these conditions, nitrogen removal in the baffled ponds became better than in the control
pond. Among the baffled ponds, pond 3 performed better than ponds 2 and 4. The mean HRT of
pond 2 was 7.5 days while that of pond 3 was 9.2 days (Chapter 3), this could explain the
differences in performance. However, the mean HRT for pond 4 was 9.9 days, higher than that of
ponds 2 and 3; the reason why its performance was lower is unclear. It may be a result of
differences in the hydraulic flow and oxygen balance. This study showed that introduction of
baffles in wastewater stabilization ponds affects both the ecology and the hydraulic
characteristics of the ponds. The algal and zooplankton distribution in the four ponds differed,
but how this related to the nitrogen removal is still unclear. The tracer study showed that the flow
patterns of ponds 1 and 2 were similar indicating that the baffle arrangement in pond 2 did not
affect the flow pattern. However, the tracer curves for ponds 3 and 4 were different and these
ponds had a higher mean hydraulic retention.
This study also showed that nitrogen uptake by algae is significant in wastewater stabilization
ponds. However, internal nitrogen cycling is known to occur when the dead algae in sediments
are decomposed resulting in a release of nitrogen. Some of the nitrogen incorporated in algal
biomass leaves the ponds through washout. The pH in the ponds was mostly below 8 hence
ammonia volatilization was probably negligible. Total nitrogen mass balances showed that
nitrification-denitrification was the major nitrogen removal mechanism (Chapter 7).
Outlook
Management of nitrogen in WSP effluents can be aimed to achieve two objectives i.e. protecting
water sources, or reuse of nitrogen in agriculture. For protection of water resources, the aim is to
maximize N-removal and while for N-reuse in irrigation; the effluent concentration need to be
tailored to the type of crop. In this study, the effluent concentration of total nitrogen of pond 1, 2,
3 and 4 during period 2 was 36, 22, 18 and 24 mg l-1. About 15-24% of this amount was organic
nitrogen, the rest was ammonium. The effluent of all the ponds did not meet the European
Standards for disposal of total nitrogen (< 15 mg l-1) to sensitive surface waters. The effluents
met the permitted standards of 25mg l-1 filtered BOD (CEC, 1991). In order to maximize reuse
of resources, the effluents from the ponds can be reused in irrigation. Municipal wastewater with
20-85 mg l-1 total nitrogen causes no soil acidification (as observed from synthetic fertilizers)
and can increase productivity. The algae washed out in the effluents can add organic matter and
nutrients to the soil (WHO, 2006). The limitation of the reuse of pond effluents in agriculture is
the relatively higher TSS of >20 mg l-1 which may cause problems for drip irrigation. The study
has shown that the ponds had a variety of algal forms; this could be a potential for animal feed
production (Hosetti and Frost, 1995). The ammonia concentration in the pond effluent was still
sufficient for land scape irrigation provided the microbial quality is satisfactory. In tropical
121
regions with high solar radiation, wastewater from oxidation ponds has become acceptable for
irrigation (Hosetti and Patil, 1988).
In this study, several zooplanktons were identified; some forms like the rotifers are suitable food
source for small planktonic stages of fish larvae. The rotifers have both high nutritional value
and high daily rates of production. They are important sources of food for freshwater fish larvae
Rotifers of Branchionus species are considered to be suitable as the first food for fish (Lubzens,
1987; Martinez and Dodson, 1992). Although it is doubtful whether the effluents of the ponds in
this study can be used for aquaculture, presence of high nutritional fish food sources and
diversity of algae give an insight into the potential exploitation of these ponds for aquaculture
(Roche, 1995).
The results of this study showed that baffles can be incorporated in maturation ponds and
improve nitrogen removal under conditions of high influent ammonia and low BOD loading. In
this study, this was achieved by covering the facultative pond; the other option can be through
construction of deeper anaerobic ponds. This is advantageous because it saves land and the
anaerobic pond can be utilized for biogas production. The major disadvantage is the increased
costs of excavating deeper ponds, the trade-off between cost of land and cost of construction
should be considered. Introduction of baffles in wastewater stabilization ponds increases the
construction costs but cheap materials like wooden plates could be effective. To avoid the
problem of dead volume, baffle configuration of pond 2 can be used since this did not affect
hydraulic conditions of the ponds (Chapter 3). For up-scaling purposes, light plastic material can
be used as baffles and these can be easily suspended by floaters. This decreases the cost involved
in baffle installation and gives the flexibility of moving baffles to desired spacing. The baffle
area required per pond volume based on the performance of pond 3 was calculated as 5m 2m-3
with the aerobic depth of pond 3 of 0.48m. This means for every 1m 3, 5 baffles of 1m by 0.5m
will be required. For a maturation pond of 50 m by 1m by 100 m, the total surface area required
will be 25,000m2. This implies that 250 baffles with baffle spacing of 0.2m will be installed
widthwise across the pond and 100 rows lengthwise. In order to cater for a spacing of 0.2m
between the rows, the pond length should be adjusted to 120 m. This study shows that this
addition of attachment surfaces in wastewater stabilization ponds can improve the process of
nitrogen removal. Wastewater stabilization pond designs incorporating baffles is therefore
recommended.
In terms of other parameters, introduction of deeper oxygenated zone and deeper sunlight
penetration may also help in pathogen and BOD removal. The flow through anoxic and aerobic
zones created by baffles may be useful for both BOD and N removal (and P). The ultimate aim is
to optimize conditions to reach overall treatment objectives for a range of treatment parameters.
This study was limited to nitrogen removal, further research on the effect of baffles on pathogen
and phosphorous removal is recommended.
References
Council of the European Communities (1991). Council Directive of 21 May 1991 concerning
urban wastewater treatment (91/271/EEC). Official Journal of the European Communities,
L135/40 (30 May)
122
EPA (2004). Guidelines for water reuse. EPA 645-R-04-108. U.S. Environmental Protection
Agency, Washington, D.C
Hosetti, B.B and Frost, H. (1995). A review of the sustainable value of effluents and sludges
from wastewater stabilization ponds. Ecol. Eng. 5, 421-431
Hosetti, B.B and Patil, H.S. (1988). Evaluation of catalase activity in relation to physicochemical parameters in a polluted river. In: V.P. Agrawal and L.D. Chaturvedi (Eds). Threatened
habitats, Society of Biosciences, 393-404.
Lubzens, E. (1987). Raising rotifers for use in aquaculture. Hydrobiol. 147, 245-255.
Martinez, R.R and Dodson,S.I. (1992). Culture of the rotifer Branchionus calyciflorus Pallas.
Aquaculture, 105, 191-199.
Roche, K.F. (1995). Growth of the rotifer Branchionus calyciflorus Pallas in diary waste
stabilization ponds. Wat. Res. 29 (10), 2255-2260.
WHO (2006). Guidelines for Safe Use of Wastewater, Excreta and Grey water. Volume II,
Wastewater use in Agriculture
123
Samenvatting
124
Samenvatting
Huishoudelijk afvalwater is een bron van stikstof in ecosystemen. Stikstof staat bekend als één
van de belangrijkste vervuilende stoffen in aquatische systemen, die eutrofiëring veroorzaakt,
wat weer leidt tot overmatige groei van algen of groei van andere aquatische onkruiden zoals
waterhyacint. Dit veroorzaakt een verstoring van het zuurstof evenwicht, het vrijkomen van
toxische stoffen en een verlies aan biodiversiteit en een toename in kosten voor drinkwater
bereiding als dat oppervlaktewater daarvoor wordt gebruikt.
Oxidatievijvers vormen een zuiveringstechnologie die in veel ontwikkelingslanden toegepast
wordt. Dit komt doordat ze goedkoper zijn om aan te leggen, te bedrijven en te onderhouden dan
een actief slib systeem. Echter, deze technologie kampt met een hoog gehalte aan zwevende stof
(TSS), kortsluitingstromen, lange hydraulische verblijftijden en een lage efficiëntie voor de
verwijdering van nutriënten zoals stikstof. Het probleem met betrekking tot stikstof verwijdering
wordt toegeschreven aan een lage concentratie aan nitrificerende biomassa in de waterkolom.
Verscheidene studies hebben laten zien dat het introduceren van aanhechtingsoppervlakt voor
nitrificerende bacteriën in de vijver een verbetering in de stikstof verwijdering teweeg brengt.
Echter, kennis over de introductie van schotten die als aanhechtingsoppervlak dienen onder
tropische condities is schaars.
Deze studie onderzoekt in het bijzonder de effecten van de introductie van schotten in oxidatie
vijvers op proefschaal. De proefvijvers zijn gemaakt op het terrein van de Bugolobi Sewage
Treatment Works (BSTW) in Kampala, Oeganda, en werden bedreven onder tropische condities.
Voorbezonken afvalwater werd vanuit de voorbezinker van BSTW in een plastic anaërobe tank
(AT) gepompt, die een verblijftijd van 3 dagen had. Het afvalwater stroomde vervolgens continu
met behulp van verval en met een debiet van 2.1 m3 per dag in de facultatieve vijver (FP). Het
effluent van de FP stroomde naar vier stabilisatievijvers (MP) met een lengte, breedte en diepte
van 4 bij 1 bij 0.8 meter, met een debiet van 0.5 m3 per dag. De details van het ontwerp en het
bedrijven van de proefvijvers staan beschreven in Hoofdstuk 2 van dit proefschrift. Vijver 1
werd bedreven als controle, terwijl 15 schotten in de vijvers 2, 3 en 4, elk met dezelfde
afmetingen, geplaatst werden op verschillende wijze (vijver 2: parallel aan de stromingsrichting,
vijver 3 en 4: loodrecht op de stromingsrichting) zodat verschillende horizontale en verticale
stromingspatronen veroorzaakt werden. De vijvers werden bedreven voor twee periodes,
namelijk met een influent BOD van 72±45 mg l-1 en een ammonia concentratie van 34±7 mg l-1
(periode 1) en een influent BOD van 29±9 en een ammonia concentratie van 51±4 mg l-1
(periode 2). De introductie van schotten in de stabilisatievijvers kan invloed hebben op hun
ecologie, hydraulische eigenschappen en prestaties. Dit is onderzocht en wordt gepresenteerd in
de verschillende hoofdstukken van dit proefschrift. Laboratoriumonderzoek naar
nitrificatiesnelheden in bulk water en biofilm zijn ook uitgevoerd, om de pilot-schaal studies aan
te vullen.
De resultaten van deze studie laten zien dat stikstofverwijdering uit afvalwater verbeterd kan
worden door het toevoegen van extra aanhechtingsoppervlakte voor nitificeerders. Experimenten
die onderscheid maakten tussen nitrificatie snelheden in de biofilm en in de waterkolom lieten
zien dat nitrificatie in de biofilm belangrijker was dan in de waterkolom (Hoofdstuk 4).
Aanvullende laboratoriumexperimenten lieten bovendien zien dat nitrificatiesnelheden
significant afnamen bij zuurstofconcentraties in de waterkolom lager dan 3.2 mg l-1 (Hoofdstuk
5). Resultaten voor de proefvijvers lieten zien dat tijdens periode 1 de controle vijver het beter
125
deed dan de vijvers met extra aanhechtingsoppervlakte (Hoofdstuk 6). Onder die condities bleek
de zwevende stof in de waterkolom hoger te zijn en ook bleek dat dit het doordringen van licht in
de diepere lagen van de vijver voorkwam. Dit resulteerde in een verkleining van het oppervlak
aan aërobe biofilm, welke een voorwaarde is voor nitrificatie (Hoofdstuk 2). De hogere BOD
tijdens periode 1 stimuleerde ook de groei van heterotrofe bacteriën in vergelijking met de groei
van nitrificeerders. Nitrificeerders groeien langzaam en bij een hoge BOD belasting worden ze
verdreven door de competitie met heterotrofe bacteriën. Toen de beschikbaarheid van licht en de
bijbehorende algen activiteit van de FP veranderd werden om de concentratie ammonia te laten
toenemen in het influent voor de stabilisatievijvers, door het overkappen van de vijver met zwart
plastic (periode 2), nam de concentratie BOD in het influent voor de stabilisatievijvers af. Onder
deze condities werd gevonden dat de vijvers met schotten (vijvers 2, 3 en 4) een betere Nverwijdering bereikten dan de controle vijver 1. De verwijderingsefficiëntie werd toegeschreven
aan een toename van aanhechtingsoppervlak voor nitrificeerders in de vijvers 2, 3 en 4. De
zwevende stof in de stabilisatievijvers nam ook af, wat ervoor zorgde dat licht dieper in de
waterkolom door kon dringen, met als gevolg dat meer aërobe biofilm beschikbaar kwam voor
de nitrificeerders. Daarnaast is de lagere BZV influent waarde tijdens fase 2 van voordeel op de
groei van nitrificeerders in de vijvers. Daarom is onder deze omstandigheden de
stikstofverwijdering in de vijvers met schotten beter dan in de controle vijver. Van de vijvers met
schotten presteerde vijver 3 beter dan de vijvers 2 en 4. De gemiddelde verblijftijd in vijver 2
was 7,5 dagen, terwijl dat in vijver 3 9,2 dagen was (Hoofdstuk 3), wat het verschil in prestatie
kan verklaren. Echter, de gemiddelde verblijftijd voor de vijver 4 was 9,9 dagen, hoger dan dat
van vijvers 2 en 3; de reden waarom de prestatie lager was, is onduidelijk. Het is wellicht een
gevolg van verschillen in de hydraulische stroming en zuurstofbalans. Deze studie laat zien dat
het introduceren van schotten in oxidatievijvers zowel de ecologie als het stromingspatroon van
de vijvers beïnvloedt. De soorten algen en zoöplankton verschilden per vijver, maar hoe dit
gerelateerd is aan de verwijdering van stikstof is nog onduidelijk. Een studie naar spreiding in
verblijftijden, m.b.v een tracer, liet zien dat het stromingspatroon van de vijvers 1 en 2 gelijk
waren, wat erop duidt dat de schotten in vijver 2 het stromingspatroon niet beïnvloedden.
Echter, het stromingspatroon in de vijvers 3 en 4 waren anders en deze vijvers hadden dan ook
een hoger gemiddelde hydraulische verblijftijd.
Deze studie heeft ook laten zien dat opname van stikstof door algen in oxidatie vijvers
aanzienlijk is. Echter, een interne stikstof cyclus vindt plaats waarbij afgestorven algen in het
sediment afgebroken worden, waarbij weer stikstof vrijkomt. Een deel van de stikstof die in
algen biomassa opgenomen was, verlaat de vijver door middel van uitspoeling. De pH van de
vijvers was meestal beneden een waarde van 8, en daarom was vervluchtiging van ammonia
waarschijnlijk verwaarloosbaar. De massabalans voor totaal stikstof liet zien dat nitrificatiedenitrificatie het belangrijkste mechanisme was voor stikstofverwijdering (Hoofdstuk 7).
Outlook
Het beheer van stikstof in het effluent van WSP is gericht op bereiken van twee doelstellingen,
namelijk de bescherming van waterbronnen en het hergebruik van stikstof in de landbouw. Voor
de bescherming van de watervoorraden ligt de nadruk op het maximaliseren van de
stikstofverwijdering, terwijl voor stikstofhergebruik de concentratie in het effluent zou moeten
worden afgestemd op het soort gewas in geval van irrigatie. In deze studie bedroeg de effluent
concentratie van totaal stikstof van de vijvers 1, 2, 3 en 4 gedurende de periode 2: 36, 22, 18 en
126
24 mg l-1. Ongeveer 15-24% hiervan was organische stikstof, de rest was ammonium. De
effluent concentratie van vijver 3 is 50% lager dan van vijver 1, de controle. Echter, het
afvalwater van alle vijvers voldoet niet aan de Europese normen voor de lozing van stikstof
totaal (<15 mg l-1) op gevoelig oppervlaktewater. Het afvalwater voldoet wel aan de toegestane
normen van 25 mg l-1 gefilterd BOD (CEC, 1991). Met het oog op het optimaliseren van
hergebruik kan het effluent van de vijvers worden gebruikt voor irrigatie; huishoudelijk
afvalwater met 20-85 mg l-1 totaal stikstof veroorzaakt geen bodemverzuring (zoals
waargenomen bij synthetische meststoffen) en kan de productiviteit verhogen. De algen die met
het effluent uitspoelen kunnen organisch materiaal en voedingsstoffen toe voegen aan de bodem
(WHO, 2006). De beperking voor hergebruik in de landbouw is dat de TSS van het effluent in
alle vijvers > 20 mg l-1 was. Dit kan problemen veroorzaken in geval van druppel irrigatie. De
studie heeft laten zien dat er verschillende soorten algen in de vijvers groeiden, die potentieel
voor dierenvoeder gebruikt zouden kunnen worden (Hosetti en Frost, 1995). De ammonia
concentratie in het effluent was voldoende voor irrigatie, op voorwaarde dat de microbiële
kwaliteit voldoende is. In tropische regio's met veel zonlicht, is effluent uit stabilisatievijvers
volledig geaccepteerd voor irrigatie (Hosetti en Patil, 1988).
In deze studie werden verschillende soorten zooplankton geïdentificeerd; sommige typen zoals
rotiferen zijn geschikt als voedsel voor de plankton fase van vissenlarven. De rotiferen hebben
zowel een hoge voedingswaarde als een hoge dagelijkse productie. Ze vormen een belangrijke
voedselbron voor larven van zoetwatervis. Rotiferen van het soort Branchionus worden
verondersteld geschikt te zijn als eerste voedsel voor vissen (Lubzens, 1987; Martinez en
Dodson, 1992). Hoewel het twijfelachtig is of het effluent uit de vijvers van deze studie gebruikt
kan worden voor aquacultuur, geeft de aanwezigheid van goede voedingsbronnen voor vis en de
diversiteit aan algen een inzicht in het mogelijke gebruik van deze vijvers voor aquacultuur
(Roche, 1995).
Uit de resultaten van deze studie is gebleken dat schotten kunnen worden opgenomen in de
vijvers om de stikstofverwijdering te verbeteren, onder de voorwaarde van een hoge influent
ammonia concentratie en een lage BOD belasting. In deze studie werd dit bereikt door het
bedekken van de facultatieve vijver, een andere optie is het bouwen van anaërobe, diepere
vijvers. Dit is voordelig omdat het land bespaart en omdat de anaërobe vijver gebruikt kan
worden voor de productie van biogas. Het grote nadeel is de toegenomen kosten van het graven
van een diepe vijver, waarbij de kosten van grond moeten worden vergeleken met de kosten van
de bouw. Invoering van schotten in de stabilisatievijvers verhoogt de bouwkosten, maar
goedkope materialen, zoals houten platen, kunnen effectief zijn. Om het probleem van
ongebruikt volume te voorkomen kunnen schotten worden gebruikt zoals in vijver 2 omdat dit de
hydraulische conditie in de vijver niet beïnvloed (Hoofdstuk 3). Voor opschalingdoeleinden kan
een lichte kunststof worden gebruikt als schotmateriaal dat gemakkelijk kan worden geplaatst
door middel van drijvers. Dit vermindert de kosten die nodig zijn voor de installatie en omdat de
schotten op ieder gewenste afstand kunnen worden geplaatst is het systeem flexibel. Het
oppervlak van de schotten dat nodig is per vijvervolume gebaseerd op de prestaties van de vijver
3 werd berekend op 5m2m-3 uitgaande van een aërobe diepte van vijver 3 van 0.48m. Dit
betekent dat voor elke 1m3, 5 schotten van 1m bij 0,5m nodig zullen zijn. Voor een vijver van
50m bij 1m bij 100m is de totale oppervlakte die nodig is 25.000 m2. Dit houdt in dat er 250
schotten met een afstand van 0,2 m ertussen zullen moeten worden geïnstalleerd in de breedte
127
van de vijver en 100 rijen in de lengte. Om tegemoet te komen aan een afstand van 0,2 m tussen
de rijen moet de vijverlengte worden vergroot tot 120 m. Deze studie toont aan dat deze
toevoeging van aanhechtingsoppervlakken in stabilisatievijvers stikstofverwijdering kunnen
verbeteren. Het is daarom aanbevolen om bij het ontwerp van een stabilisatievijver schotten op te
nemen.
De invoering van een diepere zuurstofrijke zone o.a. door middel van het laten doordringen van
zonlicht kan ook helpen bij pathogeen en BZV verwijdering. De stroming door anoxische en
aërobe zones gecreëerd door schotten kunnen nuttig zijn voor zowel BZV en N-verwijdering (en
P). Het uiteindelijke doel is om de condities te optimaliseren zodat voor verschillende parameters
de behandelingsdoelstellingen kunnen worden bereikt. Deze studie was beperkt tot de
stikstofverwijdering, verder onderzoek naar het effect van schotten op pathogenen- en fosfaatverwijdering wordt aanbevolen
Referenties
Council of the European Communities (1991). Council Directive of 21 May 1991 concerning
urban wastewater treatment (91/271/EEC). Official Journal of the European Communities,
L135/40 (30 May)
EPA (2004). Guidelines for water reuse. EPA 645-R-04-108. U.S. Environmental Protection
Agency, Washington, D.C
Hosetti, B.B and Frost, H. (1995). A review of the sustainable value of effluents and sludges
from wastewater stabilization ponds. Ecol. Eng. 5, 421-431
Hosetti, B.B and Patil, H.S. (1988). Evaluation of catalase activity in relation to physicochemical parameters in a polluted river. In: V.P. Agrawal and L.D. Chaturvedi (Eds). Threatened
habitats, Society of Biosciences, 393-404.
Lubzens, E. (1987). Raising rotifers for use in aquaculture. Hydrobiol. 147, 245-255.
Martinez, R.R and Dodson,S.I. (1992). Culture of the rotifer Branchionus calyciflorus Pallas.
Aquaculture, 105, 191-199.
Roche, K.F. (1995). Growth of the rotifer Branchionus calyciflorus Pallas in diary waste
stabilization ponds. Wat. Res. 29 (10), 2255-2260.
WHO (2006). Guidelines for Safe Use of Wastewater, Excreta and Grey water. Volume II,
Wastewater use in Agriculture
128
Curriculum vitae
Mohammed Babu was born on March 21st 1973 in Mbale, Uganda. He went to St. Peters College
Tororo for secondary education and later joined Islamic University in Uganda, Mbale. He
graduated with a degree in B.Sc Educ (Bot-Zoo/Chem) with honors in 1998. In 1999, he was
awarded a fellowship by the Netherlands Government to study MSc in Environmental Science
and Technology at UNESCO-IHE Institute for Water Education, Delft-The Netherlands. He
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Netherlands government to study a PhD at UNESCO-IHE Institute for Water Education under
sandwich construction program. His PhD research was financed by EU-SWITCH project. Since
graduation in 2001 to date, he has been lecturing in the department of environmental studies at
the Islamic University in Uganda, Mbale.
His address in Uganda is; Islamic University in Uganda, P. O. Box 2555 Mbale, Uganda.
Telefax: +256454433502
Email: [email protected]
129
Discharge of nutrient rich wastewater causes eutrophication of surface water;
therefore wastewater treatment before discharge is required. Wastewater stabilization
ponds are low cost technology used by developing countries but not effective in
nitrogen removal due to low nitrifier biomass in the water column. Introduction of
surface area for attachment of nitrifiers has therefore been proposed.
This thesis reports the performance of pilot scale wastewater stabilization ponds
fitted with baffles. The effect of baffles on nitrogen removal under tropical and two
operational conditions was investigated. Under TKN/BOD ratio of 0.67, the baffled
ponds performed better in nitrogen removal than the control pond. Total nitrogen
mass balances showed that nitrification-denitrification, algal uptake and sedimentation
were principle nitrogen removal mechanisms in biofilm waste stabilization ponds
This study shows the potential of biofilms in improving nitrogen removal in
wastewater stabilization ponds. The BOD and TSS concentrations were sufficiently
low to permit for reuse in irrigation. If the objective is reuse and optimization of
resources, the effluents from the ponds had sufficient nitrogen content for use in
agriculture.
This research was jointly funded by the Netherlands Fellowship Program and the
EU-SWITCH project. SWITCH is supported by the European Commission
(6th Framework Programme) and contributes to the thematic priority area of
“Global Change and Ecosystems”.
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