spatial-temporal analysis of oxygen related processes in...
TRANSCRIPT
Faculty of Bioscience Engineering
Academic year 2013 – 2014
Spatial-temporal analysis of oxygen related processes in
facultative ponds
Juan Esteban Espinoza Palacios
Promotor: Prof. dr. ir. Peter Goethals
Tutor: Tan Pham Duy
Master’s dissertation submitted in partial fulfillment of the requirements for
the degree of
Master of Science in Environmental Sanitation
········································································································································································ i Copyright
COPYRIGHT
The author, the promoter and the tutor give permission to use this thesis for consultation and to copy
parts of it for personal use. Any other use is subject to the Laws of Copyright. Permission to produce any
material contained in this work should be obtained from the author.
© Gent University, August 2014
The Promoter:
prof. dr. ir. Peter Goethals
The Tutor:
Tan Pham Duy
The Author:
Juan Esteban Espinoza Palacios
········································································································································································
ii Copyright
········································································································································································ iii
Acknowledgment
Acknowledgment
First I would like to thank God for his protection and blessing every day and especially during this stage
of my life full of new knowledge and experiences.
I wish to express my sincere thanks to the people who in one way or another have made it possible to
fulfill this new academic stage, especially my promoter prof. dr. ir. Peter Goethals who trusted me to
work as master’s student in one of the projects that he is leading.
I would like to thank my tutor Tan Pham Duy and my classmate Belén with whom we work together
during the sampling campaign and during the development of this study, to the staff of the Waste Water
Treatment Plant Ucubamba and of the Sanitation Laboratory from ETAPA-EP, especially Eng. Galo
Durazno and Eng. Yolanda Torres.
I would like to make a special mention to Ziv Shkedy and Leacky Muchenev from Hasselt University for
their help with the statistical model developing; also to Wout Van Echelpoel whose assistance and
comments guided me to the successful finalization of my thesis work.
Special thanks to my parents Jaime y Kuky, my siblings Pao, Jaime Andres and Adrian, to all my family
and friends for their continuous support from the distance. Also to all my friends here in Gent with
whom I have spent great moments getting to know them and creating strong friendships.
Verito, thank you very much for your patience, for being there supporting me every time, for the words
of encouragement when needed and for just being YOU.
To all professors and assistants that during this 2 years have taught me new things that will be useful for
my professional development, to Sylvie and Veerle for their support and advice during this 2 years.
Finally I would like to thanks SENESCYT for their support through a scholarship and to EPMAPS - Quito
········································································································································································
iv
Acknowledgment
········································································································································································ v
Abstract
Abstract
The discharge of wastewater into surface waters has aroused great interest on its treatment in order to avoid environmental contamination. Stabilization pond system is the simplest form of wastewater treatment and part of its design corresponds to facultative ponds which are operated with both aerobic and anaerobic zones. This study focuses on the oxygen related processes present in the facultative ponds of the waste water treatment plant Ucubamba situated in Cuenca – Ecuador, for which the variability of the dissolved oxygen was analyzed based on surface graphs and by applying a statistical linear mixed effect model at two different depths (close to the surface and to the sediment layer) comparing the variation of its behavior within one pond and between the two ponds present in the system working in parallel. A predictive model for each depth and pond were also obtained using the basic linear mixed effect
model and the variables that showed to influence the oxygen related processes in the facultative ponds
in the hypothesis testing. With this obtained models, predictions under the same conditions were
calculated and analyzed for both ponds.
Macroinvertebrates were moreover collected using the artificial substrates technique in order to
determine the effect of oxygen related processes on this community.
The principal outcomes of this study refer to a difference in dissolved oxygen and biochemical oxygen
demand between ponds, to the presence of diurnal dissolved oxygen cycle in one of the ponds which
was studied with data concerning morning and afternoon samplings. Also when comparing the dissolved
oxygen behavior between ponds, a variability of the dissolved oxygen is present in the layer close to the
surface contrary to what happens close to the bottom, where there is not a variability of dissolved
oxygen.
Keywords: statistics, water treatment, tropical, Ecuador
········································································································································································
vi
Abstract
········································································································································································ vii
Resumen
Resumen
La descarga de agua residual en los cuerpos receptores ha despertado un gran interés en su tratamiento
para de esta manera evitar la contaminación del medio ambiente. El sistema de lagunas de
estabilización es la forma más simple de tratar el agua residual, estando presente en su diseño las
lagunas facultativas, las mismas que funcionan con una capa aeróbica y una anaeróbica.
Este estudio se enfoca en los procesos relacionados con el oxígeno presente en las lagunas facultativas
en la Planta de Tratamiento de Aguas Residuales Ucubamba situada en la ciudad de Cuenca – Ecuador,
para lo cual la variación del oxígeno disuelto fue analizada mediante gráficas de superficie y aplicando el
modelo estadístico de efectos mixtos a dos diferentes alturas (cerca de la superficie y del fondo de la
laguna) comparando el comportamiento del oxígeno disuelto en una misma laguna y entre las dos
lagunas presentes en el sistema.
Un modelo predictivo para cada profundidad y laguna se obtuvo usando el modelo estadístico de
efectos mixtos con las variables que mostraron influenciar los procesos relacionados con el oxígeno en
las lagunas facultativas en la prueba de hipótesis. Con estos modelos, predicciones bajo las mismas
condiciones fueron calculadas y analizadas para las dos lagunas.
También se realizó una campaña de muestreo de macroinvertebrados mediante el uso de sustratos
artificiales para determinar su influencia en los procesos relacionados con el oxígeno debido a su
actividad respiratoria mediante la cual consumen oxígeno.
Los principales resultados de este estudio se refieren a que existe una diferencia en las concentraciones
del oxígeno disuelto y la demanda bioquímica de oxigeno entre las lagunas, también se observó la
presencia de ciclo diario del oxígeno disuelto en una de las dos lagunas, de las cuales se disponían de
datos en la mañana y en la tarde. Al comparar el comportamiento del oxígeno disuelto entre las lagunas,
se encuentra variabilidad en la capa cerca de la superficie de la laguna contrario al fondo de la misma en
donde no se encuentra variabilidad del oxígeno disuelto.
Palabras clave: estadistica, tratamiento de agua, tropical, Ecuador
········································································································································································
viii
Resumen
········································································································································································ ix
Table of contents
Table of contents
Copyright i Acknowledgment iii Abstract v Resumen vii Table of contents ix List of tables xiii List of figures xv List of abbreviations xvii Part I Introduction 1 Part II Literature review 3 2.1 Waste stabilization ponds 3 2.2 Facultative ponds 5 2.2.1 Physical, biochemical and hydraulic processes 6 2.2.2 Influence of environmental conditions 8 2.2.3 Presence of algae in facultative ponds and its diversity 8 2.2.4 Macroinvertebrates diversity 10 2.3 Biological assessment of the aquatic environment 10 2.3.1 Belgian biotic index 12 2.3.2 Multimetric Macroinvertebrate Index Flanders 12 2.3.3 Biological Monitoring Working Party Score System 13 2.4 Variability of the dissolved oxygen in the facultative ponds 13 2.4.1 Testing of research hypotheses 15 Part III Research objectives and goals 17 Part IV Materials and methods 19 4.1 Waste stabilization pond system 19 4.2 Facultative ponds 21 4.2.1 Sampling scheme 22 4.2.2 Physical – Chemical parameters 22 4.2.3 Variability of the dissolved oxygen in the facultative ponds 23 4.2.4 Macroinvertebates 25 4.2.4.1 Sampling 25 4.2.4.2 Assessment of macroinvertebrate communities 25 4.2.4.2.1 Belgian Biotic Index (BBI) 25 4.2.4.2.2 Multimetric Macro Invertebrate Index Flanders (MMIF) 26
········································································································································································
x
Table of contents
4.2.4.2.3 Biological Monitoring Working Party Score System (BMWP) 27 4.2.5 Meteorological Data 28 Part V Results 29 5.1 Dissolved oxygen, chlorophyll and biological oxygen demand distribution 29 5.1.1 Facultative pond 1 vs. facultative pond 2 distribution in the morning 30 5.1.1.1 Close to the surface, 30 cm under the water level 30 5.1.1.2 Close to the bottom of the pond, 15 cm over the sediment layer 33 5.1.2 Facultative pond 1 in the morning vs. facultative pond 1 in the afternoon 35 5.1.2.1 Close to the surface, 30 cm under the water level 35 5.1.2.2 Close to the bottom of the pond, 15 cm over the sediment layer 37 5.2 Variability of the dissolved oxygen in the facultative ponds 39 5.2.1 Variability within each facultative pond 39 5.2.2 Facultative pond 1 vs. Facultative pond 2 40 5.2.3 Influence of chlorophyll 41 5.2.4 Influence of climatic conditions 42 5.2.5 Influence of other chemical parameters 43 5.3 Predicted model for describing variability of dissolved oxygen in facultative
ponds 44
5.3.1 Predicted model for dissolved oxygen 44 5.3.2 Prediction of dissolved oxygen concentrations 45 5.4 Predicted model for describing variability of biochemical oxygen demand in
facultative ponds 47
5.4.1 Predicted model for biochemical oxygen demand 47 5.4.2 Prediction of biochemical oxygen demand concentrations 48 5.5 Presence of macroinvertebrates in the facultative ponds 50 Part VI Discussion 51 6.1 Dissolved oxygen, chlorophyll and biological oxygen demand distribution 51 6.1.1 Facultative pond 1 vs. facultative pond 2 distribution in the morning 51 6.1.2 Facultative pond 1 in the morning vs. facultative pond 1 in the afternoon 52 6.1.3 Facultative ponds vs. maturation ponds 53 6.2 Variability of the dissolved oxygen in the facultative ponds 56 6.2.1 Variability within each facultative pond 56 6.2.2 Facultative pond 1 vs. Facultative pond 2 56 6.2.3 Influence of chlorophyll 57 6.2.4 Influence of climatic conditions 57 6.2.5 Influence of other chemical parameters 57 6.3 Predicted model for describing variability of dissolved oxygen in facultative
ponds 58
6.3.1 Predicted model for dissolved oxygen 58 6.3.2 Prediction of dissolved oxygen concentrations 59 6.4 Predicted model for describing variability of biochemical oxygen demand in
facultative ponds 60
6.4.1 Predicted model for biochemical oxygen demand 60 6.4.2 Prediction of biochemical oxygen demand concentrations 61 6.5 Presence of macroinvertebrates in the facultative ponds 61
········································································································································································ xi
Table of contents
Part VII Conclusions and recommendations 63
Part VIII References 65 Part IX Appendix 69 Appendix 1 Taxa list of aquatic macroinvertebrates for calculating the BBI with their
respective tolerance scores 69
Appendix 2 Taxa taken into account for calculating the MMIF with their respective tolerance score ranging from 10 for very pollution sensitive to 1 for very pollution tolerant taxa
70
Appendix 3 System for the BMWP index determination adapted for Colombia 71 Appendix 4 Influence of BOD, COD, Kjeldahl-N, Phosphorus and Total Solids over DO
variability in the facultative ponds 72
Appendix 5 Predicted model for dissolved oxygen: Pearson correlation coefficients 74 Appendix 6 Predicted model for biochemical oxygen demand: Pearson correlation
coefficients 76
········································································································································································
xii
Table of contents
········································································································································································ xiii List of tables
List of tables
Table 2.1 Overview of the key environmental factors affecting WSP performance 4 Table 2.2 Advantages and disadvantages of facultative ponds 5 Table 2.3 Overview of key processes in WSP reflecting the importance in a facultative pond 7 Table 2.4 Key algal genera present in facultative ponds 8 Table 2.5 Biological approaches to water quality assessment: Ecological Methods; uses,
advantages and disadvantages 11
Table 2.6 Advantages and disadvantages of macroinvertebrates and algae as indicators of
water quality 11
Table 2.7 Identification levels of macroinvertebrate taxa for calculating the BBI and MMIF 12 Table 2.8 Main characteristics of diferent types of lakes in Flanders (Belgium), as defined for
application of the MMIF 13
Table 4.1 Sampling periods for physical – chemical parameters 22 Table 4.2 Calculation of BBI 26 Table 4.3 Water quality classes corresponding to the BBI values 26 Table 4.4 Preliminary WFD quality class intervals proposed for the MMIF interval range 27 Table 4.5 Quality class, values and characteristics for the BMWP index 27 Table 5.1 Fixed effects p values for the basic linear mixed model with average DO
concentrations for the FP1 and FP2 40
Table 5.2 Fixed effects p values for the basic linear mixed model with DO concentrations for
the FP1 and FP2 40
Table 5.3 Fixed effects p values for the basic linear mixed model with DO concentrations
for the combination of the FPs 41
Table 5.4 DO analysis per rows and columns at each depth between FP1 and FP2 41 Table 5.5 Influence of chlorophyll in the DO within ponds 42 Table 5.6 Influence of climatic conditions in the DO within ponds 42 Table 5.7 Influence of climatic conditions in the DO between ponds 43
········································································································································································
xiv
List of tables
Table 5.8 Influence of turbidity in the DO within FPs 43 Table 5.9 Parameter values obtained during model development describing the variation of
DO 45
Table 5.10 Parameter values obtained during model development describing the variation of
BOD 47
Table 5.11 Water quality indexes of the FPs per row and column 50 Table 6.1 Fixed effects p values for the basic linear mixed model with DO concentrations
for the combination of the FPs and MPs 55
Table 6.2 DO analysis per rows and column at each depth between FPs and MPs 55 Table 6.3 Limitations of the predictive models 59
········································································································································································ xv List of figures
List of figures
Figure 2.1 Flow sheet of a system of stabilization ponds followed by maturation ponds in series
3
Figure 2.2 Simplified working principle of a facultative pond 6 Figure 2.3 Algae, light energy and oxygen in a FP 9 Figure 2.4 Diurnal Variation of Dissolved Oxygen in a Facultative Pond 9 Figure 4.1 WSP – Ucubamba location 19 Figure 4.2 Flow Diagram of WSP – Ucubamba 20 Figure 4.3 WSP – Ucubamba, Lines in parallel 20 Figure 4.4 Sampling scheme 22 Figure 5.1 DO measured in the FP1 per location 29 Figure 5.2 DO measured in the FP2 per location 29 Figure 5.3 Average DO measured in the FP1 per location vs. measurements in the morning
and in the afternoon 30
Figure 5.4 DO spatial distribution measured between 9:00 and 12:30 at 30 cm under the
water level 31
Figure 5.5 BOD spatial distribution measured between 9:00 and 12:30 at 30 cm under the
water level 31
Figure 5.6 Chlorophyll spatial distribution measured between 9:00 and 12:30 at 30 cm under
the water level 32
Figure 5.7 DO spatial distribution measured between 9:00 and 12:30 at 15 cm over the
sediment 33
Figure 5.8 BOD spatial distribution measured between 9:00 and 12:30 at 15 cm over the
sediment 34
Figure 5.9 Chlorophyll spatial distribution measured between 9:00 and 12:30 at 15 cm over
the sediment 34
Figure 5.10 DO spatial distribution in the FP1 at 30 cm under the water level 36 Figure 5.11 BOD spatial distribution in the FP1 at 30 cm under the water level 36
········································································································································································
xvi
List of figures
Figure 5.12 Chlorophyll spatial distribution in the FP1 at 30 cm under the water level 37 Figure 5.13 DO spatial distribution in the FP1 at 15 cm over the sediment 38 Figure 5.14 BOD spatial distribution in the FP1 at 15 cm over the sediment 38 Figure 5.15 Chlorophyll spatial distribution in the FP1 at 15 cm over the sediment 39 Figure 5.16 DO predictions in function of chlorophyll and timing at different BOD
concentrations in FP1 45
Figure 5.17 DO predictions in function of chlorophyll and timing at different BOD
concentrations in FP2 46
Figure 5.18 BOD predictions in function of chlorophyll and timing at different DO
concentrations in FP1 48
Figure 5.19 BOD predictions in function of chlorophyll and timing at different DO
concentrations in FP2 49
Figure 6.1 Average of DO concentrations vs. concentrations in the third sampling in FP2 51 Figure 6.2 Average BOD spatial distribution measured in the facultative and maturation
ponds 30 cm under the water level 54
Figure 6.3 Average BOD spatial distribution measured in the facultative and maturation
ponds 15 cm over the sediment surface 44
Figure 6.4 Conceptual model of DO processes in secondary facultative WSP 58
········································································································································································ xvii List of abbreviations
List of abbreviations
AP Aerated Pond
AS Artificial Substrates
BBI Belgian Biotic Index
BMWP Biological Monitoring Working Party
BOD Biochemical Oxygen Demand
BOD5 Biochemical Oxygen Demand after 5 days
COD Chemical Oxygen Demand
DO Dissolve Oxygen
FP Facultative pond
LMEM Linear Mixed Effect Model
MMIF Multimetric Macroinvertebrate Index Flanders
MP Maturation pond
PFP Primary Facultative Pond
SFP Secondary Facultative Pond
TS Tolerance Scores
WSP Waste Stabilization Pond
WWTP Wastewater Treatment Plant
········································································································································································
xviii
List of abbreviations
········································································································································································ 1 Introduction
Part I Introduction
The generation of wastewater is inevitable and its discharge into surface waters leads to several problems. Following this, wastewater treatment has become an important area of interest (Sah et al., 2012). The stabilization pond systems constitute the simplest form of wastewater treatment (von Sperling, 2007). Except in anaerobic ponds and the bottom of facultative ponds, oxygen is needed for bacterial oxidation of waste organics. A theoretical oxygen demand can be estimated from the biological oxygen demand (BOD) to be removed and the daily production of volatile suspended solids in the pond (Wang et al., 2009). The natural processes of stabilizing organic waste by bacterial oxidation and that of producing oxygen by algae through photosynthesis are fundamental in the treatment of sewage by waste stabilization ponds (WSPs) giving the importance to understand the seasonal and diurnal changes of dissolved oxygen which gives insight into the process mechanism involved and helps devising short and long term operational strategies (Kayombo et al., 2000; Tadesse et al., 2004).
In facultative ponds (FPs), the mutualistic relationship between microalgae (including cyanobacteria) and heterotrophic bacteria plays an important role. In addition, FPs also exhibit a high complexity because of the simultaneous existence of aerobic, facultative and anaerobic zones. The biochemical processes and the microbial population in these three zones are diverse; hence developing an all-encompassing model is a challenge (Sah et al., 2011).
Even though the production and consumption of dissolved oxygen (DO) in a FP has been studied and
reported in literature and research, to the best of my knowledge, an analysis of oxygen related
processes in FPs regarding its variability in ponds located in the same area has not been reported.
Since the WSP is designed so that the two lines work in parallel treating the same type of water under
the same climatic conditions, the DO behaviour in the FPs should be similar. In order to determine the
overall efficiency, in this study an evaluation based on the physico – chemical and biological parameters
will be performed in the FPs situated in the waste water treatment plant ucubamba in Cuenca, Ecuador.
········································································································································································
2
Introduction
················································································································································································ 3 Literature Review
Part II Literature review
2.1 Waste stabilization ponds
Waste stabilization ponds (WSPs) are a suitable and widespread technology for wastewater treatment in developing countries, especially in tropical climates. WSP, commonly known as lagoons, can be a combination of three different pond types viz. anaerobic (AP), facultative (FP) and maturation (MP) ponds (Figure 2.1). There are also modified versions such as wastewater storage and treatment reservoirs, aerated facultative lagoons (AFLs) or high-rate algal ponds (HRAPs) (Sah et al., 2011; Sah et al., 2012). Wastewater treatment in WSP mainly results from settling and complex symbiosis of bacteria and algae where the oxidation of organic matter is accomplished by bacteria in the presence of dissolved oxygen supplied by algal photosynthesis and surface re aeration (Beran and Kargi, 2005). In the AP, BOD removal is achieved by the sedimentation of settleable solids and their subsequent anaerobic digestion in the resulting sludge layer with the release of biogas (around 70 per cent methane and 30 per cent carbon dioxide) (Mara, 2004).
Figure 2.1. Flow sheet of a system of stabilization ponds followed by maturation ponds in series
(von Sperling, 2007) The first stage is the removal of large floating objects and heavy mineral particles and it comprises screening and grit removal. The WW flow should always be measured for determining diurnal flow variations and detecting any abnormal flow rates (Mara, 2004). This preliminary treatment of the system is also shown in Figure 2.1. The three ponds (AP, FP and MP) particularly differ from each other in geometry, hydraulic flow, important biochemical processes and efficiency in carbon, nutrient and pathogen removal. Anaerobic ponds are primarily designed to enhance settling and the subsequent bulk removal of organic load via the anaerobic digestion of particulate organic solids. A facultative pond is the second stage of treatment in WSP systems. It mainly focuses on the removal of BOD and nutrients, but can also partially remove pathogens. A maturation pond is the third stage of treatment in a conventional WSP system. This is a shallow basin in which an aerobic condition is maintained over the entire depth of the pond. Pathogen removal is the key function of a MP, though further removal of organic matter and nutrients is also accomplished (Sah et al., 2012). Overall efficiency of WSPs is a function of many interacting processes. Connections and relationships
between mixing, stratification and planktonic kinetics have been investigated in lakes and oceans, and the
findings are related to processes in ponds with several differences (e.g. organic load), therefore separate
detailed studies in wastewater ponds are needed (Gu and Stefan, 1995).
The important physical and chemical environmental factors which dictate the performance of a WSP are
light intensity, pH, dissolved oxygen (DO), wind and temperature (Table 2.1). The pond’s physical and
chemical environment is not only dynamic but also difficult to characterize.
················································································································································································
4
Literature Review
Many processes play a role, for example the effect of wind on mixing, the effect of mixing on temperature
and DO concentrations, etc. (Sah et al., 2012).
Regarding the removal of pathogenic organisms, a series of ponds including maturation ponds is capable of reaching very high removal efficiencies (von Sperling, 2007).
Table 2.1. Overview of the key environmental factors affecting WSP performance (after Sah et al., 2012)
Factors Description Importance Light Dynamic factor, can be considered as varying function of time,
season, weather, time of the day and spatially (i.e. throughout the pond and over the depth).
Algal productivity influences DO and pH, pathogen removal.
Dissolved oxygen
Oxygen dynamics is driven predominantly by photosynthesis, which is a function of light, light attenuation and organic loading. Consequently, oxygen shows variation diurnally, spatially (along length, breadth and depth) and between ponds with different organic loading. Important sources of oxygen in ponds are re-aeration and photosynthesis by photosynthetic algae.
Odor prevention, disinfection, biochemical oxidation of organic matter.
Temperature Most of the processes are temperature dependent; also temperature affects the hydraulic properties of water by stratification and de-stratification under the influence of sun and wind. It also affects the solubility of different substances.
Controls the rate of different biochemical reactions and governs the hydraulic properties affecting mixing conditions in the pond.
pH Dynamic factor, the pH in the pond is controlled by bicarbonate buffering system. Since pH depends on photosynthesis and organic loading, it shows similar temporal and spatial variation like oxygen.
Pathogens and nutrient removal, odor control.
Wind Dynamic natural environmental factor. Driving force for re -aeration of ponds, speed and direction controls hydraulic behavior and, hence, performance of the ponds.
Algae play a key role in sewage treatment in waste stabilization ponds by acting as oxygen generators via the process of photosynthesis and without them ponds would turn anaerobic. The bacteria in the pond decompose the biodegradable organic matter and release carbon dioxide, ammonia and nitrates. These are utilized by algae, together with sunlight and photosynthetic process releases oxygen enabling the bacteria to break down waste and accomplish reduction in biological oxygen demand level (Pearson et al., 1987; Shanthala et al., 2009). According to Pearson et al. (1987), apart from the algae importance in supplying oxygen for bacterial
oxidation of organic matter it is becoming apparent that its presence affects treatment in other ways like
assimilating organic matter and influencing the conditions which affect the die-off of microbial pathogens.
Algae also influence the pH in the water like it is mentioned in Kayombo et al. (2002), the diurnal pH change
in the ponds is usually followed by net algal uptake of CO2 during the day via photosynthesis and the
increase of CO2 during the night due to total bacteria and algae respiration.
················································································································································································ 5 Literature Review
2.2 Facultative Ponds
According to Shilton, 2005, this is the most common type of pond in use throughout the world. The term
facultative refers to the fact that these ponds operate with both aerobic and anaerobic zones. Facultative
ponds (FP) are the simplest variant of the stabilization ponds systems. Basically, the process consists of the
retention of wastewater for a period long enough, so that the natural organic matter stabilization processes
take place. They can be broadly classified as primary and secondary, based on the characteristics of the
influent. If the FP receives influent without pre-treatment, it is named as primary facultative pond (PFP)
whereas if the FP receives pre-treated influent from anaerobic pond, septic tank, PFP or shallow sewerage
systems, it is called a secondary facultative pond (SFP) (von Sperling, 2007; Sah et al., 2011; Shilton, 2005).
FP’s are usually 1.2 to 2.4 m in depth and are not mechanically mixed or aerated. Table 2.2 shows a list of
advantages and disadvantages of this type of ponds (EPA, 2002).
Two different operational modifications to FPs can be used. A common operational modification is the
"controlled discharge" mode, where pond discharge is prohibited during the winter months in cold climates
and/or during peak algal growth periods in the summer. In this approach, each cell in the system is isolated
and then discharged sequentially. A similar modification, the “hydrograph controlled release” (HCR), retains
liquid in the pond until flow volume and conditions in the receiving stream are adequate for discharge.
Table 2.2. Advantages and disadvantages of facultative ponds (after EPA, 2002)
Advantages Disadvantages
Moderately effective in removing settleable solids, BOD, pathogens, fecal coliform, and ammonia.
Settled sludge and inert material require periodic removal.
Easy to operate. Difficult to control or predict ammonia levels in effluent.
Require little energy, with systems designed to operate with gravitational flow.
Sludge accumulation will be higher in cold climates due to reduced microbial activity.
The quantity of removed material will be relatively small compared to other secondary treatment processes.
Mosquitoes and similar insect vectors can be a problem if emergent vegetation is not controlled.
Requires relatively large areas of land.
Strong odors occur when the aerobic blanket disappears and during spring and fall pond turnovers.
Burrowing animals may be a problem.
*Some of these advantages and disadvantages may also be valid for the other type of ponds.
················································································································································································
6
Literature Review
2.2.1 Physical, biochemical and hydraulic processes
The stabilization of organic matter occurs by natural processes. A set of physical and biochemical processes takes place in WSPs, especially in FPs where the mutualistic relationship between microalgae (including cyanobacteria) and heterotrophic bacteria plays an important role. The symbiosis between photosynthetic algae/cyanobacteria and heterotrophic bacteria is the key feature of FPs. Here, the stabilization of waste is the result of the oxidation of organic matter by aerobic and facultative bacteria as well as anaerobic processes in the benthic layer. Oxygen for oxidation is mainly provided by algal photosynthesis which is an integral part of FPs, though algal biomass also adds to the chemical oxygen demand (COD) load in the system. In addition, FPs also exhibit a high complexity because of the simultaneous existence of aerobic, facultative and anaerobic zones. The biochemical processes and the microbial population in these three zones are diverse (Sah et al., 2011; Sah et al., 2012). Figure 2.2 shows in a simplified way the working principle of a facultative pond where the presence of algae generates O2 and consumes CO2 through photosynthesis while the respiration of the bacteria consumes O2 and generates CO2. According to Sah et al. (2012) there are various physical–chemical and biological processes that determine
the pond performance. The function and relative importance of these processes in different FPs is briefly
described in Table 2.3.
Figure 2.2. Simplified working principle of a facultative pond (von Sperling, 2007)
················································································································································································ 7 Literature Review
Table 2.3. Overview of key processes in WSP reflecting the importance in a facultative pond (after Sah et al., 2012)
Processes Effect Description Function
Advection/diffusion Primary
Mechanisms for transport of dissolved substances and heat are gravitational movement, advection, molecular diffusion and turbulent diffusion.
Facilitates mixing in the pond and is one of the main factors for pond performance.
Decay of algae and bacteria
Primary Natural process of death.
Contributes to sediments, a pathway for BOD and nutrient removal as part of non-biodegradable microbial biomass.
Mineralization of OM by aerobic bacteria
Primary
Aerobic bacteria in the presence of oxygen assimilate organic carbon and nutrients for growth. During this, nitrification occurs. Also facilitates hydrolysis
BOD and nutrients removal.
Mineralization of OM by anaerobic bacteria
Primary Active in bottom sludge layer, assimilate organic matter and nutrients.
Carbon and sulphate removal
Mineralization of OM by facultative bacteria
Primary During growth facultative bacteria assimilate organic carbon and nutrients. Under anoxic conditions, denitrification occurs.
BOD and nutrients removal.
Re-aeration Primary Physical process of air–water exchange of dissolved oxygen.
Secondary role in contributing to DO concentration in the pond.
Adsorption Secondary Chemical process which depends upon pH, redox conditions, salinity, DO and temperature.
Removal of inorganic phosphate and ammonium-nitrogen by adsorption to bottom sludge.
Growth of algae Secondary Algae use CO2 and nutrients to fix carbon for growth via photosynthesis and in turn provide oxygen for aerobic bacteria.
Main source of DO in the pond, involved directly or indirectly in nutrient removal and pathogen removal.
The hydraulic regime of a pond determines the time the effluent water resides in the pond, affecting directly
the overall performance of the WSP (Shilton et al., 2000). The analysis of the pond hydraulics is an essential
step in the understanding of the effectiveness of the WSP, and this insight is needed as the basis for the
improvement of the plant operation (Alvarado et al., 2011).
················································································································································································
8
Literature Review
2.2.2 Influence of environmental conditions
Solar radiation causes the upper layers of the WSP to warm up. As a consequence of the difference in
density of the warmer and lighter upper layers and the colder and denser deeper layers in the pond, thermal
stratification occurs, causing a heterogeneous vertical distribution of BOD, algae and oxygen, because
vertical mixing is compromised (Alvarado, 2013; Chu and Soong, 1997).
According to Werker et.al. (2002), the temperature and seasonal conditions affect an array of both physical
and biological activities within the system.
In the FP the turbidity is high, causing motile algae present in the pond to be located in a 10 to 15 cm layer
that moves up and down the system in response to light intensity. This dense layer hinders the penetration
of solar radiation to the deeper layer of the pond. Non-motile algae settle to the dark zone of the pond
where they cease to produce oxygen. DO is only measurable in the upper layers of the pond with the entire
water column turning anoxic during the night. Therefore, mixing in WSPs is important for the proper
operation of the system and is mainly influenced by the ambient temperature and wind speed (Alvarado,
2013; Shilton, 2005; Tadesse et al., 2004).
2.2.3 Presence of algae in facultative ponds and its diversity
Algae play a fundamental role in FPs. Their concentration is much higher than that of bacteria, resulting in a
greenish appearance of the liquid at the pond surface. In terms of dry suspended solids, their concentration
is usually lower than 200 mg.L-1, although in terms of numbers they can reach counts in the range of 104 to
106 organisms per mL (von Sperling, 2007). Due to the higher organic loading compared with MP, FP has
fewer algal genera than maturation ponds and flagellate genera tend to predominate. The typical algal
genera found in facultative ponds presented by Shilton (2005) are described in Table 2.4.
Table 2.4. Key algal genera present in facultative ponds (after Shilton, 2005)
Euglenophyta Euglena Phacus
Chlorophyta Chlamydomonas Chlorogonium Eudorina Pandorina Pyrobotrys Chlorella Carteria Volvox
Chrysophyta Navicula
Cyanobacteria Oscillatoria Arthrospira
Owing to the requirement of light energy, most of the algae are located close to the pond surface where there is a high oxygen production. When deepening down into the pond, the light energy decreases, reducing the algal concentration (Figure 2.3). One reason why in the FP the effluent take-off level should be located just below a depth of 50 cm from the pond surface since the dense algal band rarely reaches such a depth, due to the absence of light, and in this way the carry-over of algal solids in the effluent towards the first maturation can be minimized (von Sperling, 2007; Shilton, 2005).
················································································································································································ 9 Literature Review
Algae can present serious drawbacks in surface waters because they can reproduce rapidly when conditions are favorable. One of the solutions to control the explosive growth of algae is to reduce the amount of carbon, nitrogen, phosphorus and some elements like iron and cobalt present in water (Metcalf and Eddy, 1995; Rojas, 2000).
Figure 2.3. Algae, light energy and oxygen in a FP (von Sperling, 2007)
As a result of the photosynthetic activities of the pond algae, there is a diurnal variation in the concentration of dissolved oxygen. After sunrise, the DO level gradually rises to a maximum in the mid-afternoon, after which it falls to a minimum during the night when photosynthesis ceases and algal respiratory activity consumes oxygen (Mara, 2004). This variation is presented in Figure 2.4 where is also shown how the DO concentrations depends also in the depth of the pond, showing higher values in the surface layer.
Figure 2.4. Diurnal Variation of Dissolved Oxygen in a Facultative Pond: ○ top 200 mm of pond; ● 800 mm below surface (Mara, 2004)
················································································································································································
10
Literature Review
2.2.4 Macroinvertebrates diversity
Macroinvertebrates are not a systematic unit but a diverse assemblage of taxa, grouped together based on
taxonomic restrictions, size and habitat. Generally, macroinvertebrates are considered as those invertebrate
animals inhabiting the aquatic environment that are large enough to be caught with a net or retained on a
sieve with a mesh size of 250 to 1000 μm, and thus can be seen with the unaided eye. The majority of the
aquatic macroinvertebrates has a benthic life and inhabits the bottom substrates. Some representatives of
the macroinvertebrates are pelagic and freely swim in the water column, or pleustonic and associated with
the water surface (Gabriels, 2007).
Few studies have investigated the environmental variables structuring the aquatic communities of
permanent natural ponds and the available results are variable. Despite the ecological and conservation role
of ponds, there is a paucity of knowledge regarding how these and other environmental factors, such as size,
hydrology, type of vegetation and physicochemical characteristics may structure aquatic macroinvertebrate
diversity in constructed ponds. Little information is available on whether there are differences between
macroinvertebrate communities in constructed and natural ponds, and on the factors determining such
differences (Becerra et al., 2009).
Chironomids, (Diptera: Chironomidae) are one of the most important groups of insects in worldwide aquatic
ecosystems. Many chironomids are associated with freshwater, but some species can tolerate and develop
in polluted waters, such as WSPs, where they become a dominant macroinvertebrate. High organic pollution
levels may also provide the necessary conditions for Asellus sp. and Oligochaeta to thrive (Becerra et al.,
2009; Broza et al., 2000).
2.3 Biological assessment of the aquatic environment According to WHO (1996), biological methods can be useful for providing systematic information on water quality, managing fisheries resources, defining clean waters by means of biological standards or standardized methods, providing an early warning mechanism and assessing water quality with respect to ecological, economic and political implications. Within the principal biological approaches to water quality assessment, ecological methods use two main approaches: methods based on community structure and methods based on “indicator” organisms (WHO, 1996). Their uses, advantages and disadvantages are presented in Table 2.5. The use of algae and macroinvertebrates in the ecological methods implies some advantages and disadvantages which are presented in the Table 2.6. It is frequently argued that the indicator organisms incorporated into biotic indices should be distributed world-wide. However, few animal and plant species have true global distributions apart from ciliated protozoa which are difficult to collect, preserve and identify. Those species which do occur world-wide probably have broad ecological requirements and are, therefore, generally not suitable as indicators (WHO, 1996). For this reason different methods are used worldwide, e.g. in Flanders, Belgium the Belgian Biotic Index (BBI) and Multimetric Macroinvertebrate Index Flanders (MMIF) methods are applied while in Colombia and in Cuenca, Ecuador, an adaptation of the Biological Monitoring Working Party score system (BMWP) is applied (ETAPA, 2011; Gabriels, 2007; Zamora, 2007).
················································································································································································ 11 Literature Review
Table 2.5. Biological approaches to water quality assessment: Ecological Methods; uses, advantages and disadvantages (after WHO, 1996)
Indicator species* Community studies**
Principal organisms used
- Invertebrates. - Plants. - Algae.
- Invertebrates
Major assessment uses - Basic surveys. - Impact surveys. - Trend monitoring.
- Impact surveys - Trend monitoring
Appropriate pollution sources of effects
- Organic matter pollution. - Nutrient enrichment. - Acidification.
- Organic matter pollution - Toxic wastes - Nutrient enrichment
Advantages
- Simple to perform. - Relatively cheap. - No special equipment or facilities needed.
- Simple to perform - Relatively cheap - No special equipment or facilities needed - Minimal biological expertise required
Disadvantages
- Localized use. - Knowledge of taxonomy required. - Susceptible to natural changes in aquatic environment.
- Relevance of some methods to aquatic systems not always tested - Susceptible to natural changes in aquatic environment
* e.g. biotic indices; ** e.g. diversity or similarity indices
Table 2.6. Advantages and disadvantages of macroinvertebrates and algae as indicators of water quality
(after Gabriels, 2007 and WHO, 1996)
Advantages Disadvantages
Macroinvertebrates: Operational issues
- Visible to human eye. - Easy to collect. - Ubiquitous.
- Sometimes difficult to identify - Quantitative sampling is difficult - Substrate type important when sampling
Macroinvertebrates: Interpretational issues
- Ecologically relevant. - Good taxonomic keys. - Relatively long life cycles. - Taxonomically diverse, integrating a wide range of stressors.
- Sometimes difficult to identify. - Quantitative sampling is difficult.
Algae: Operational issues
- Useful indicators of eutrophication and increases in turbidity.
- Not very useful for severe organic or fecal pollution.
Algae: Interpretational issues
- Pollution tolerances well documented
- Taxonomic expertise required. - Some sampling and enumeration problems with certain groups.
················································································································································································
12
Literature Review
2.3.1 Belgian Biotic Index
The Belgian Biotic Index (BBI) is a standardized method to assess biological quality of watercourses based on
the macroinvertebrate community. The BBI combines characteristics of the indices proposed by Woodiwiss
in the UK (1964, Trend Biotic Index) and Tuffery and Verneaux in France (1968, Indice Biotique). When all
macroinvertebrates from a sample are identified, a list is made of all taxa of which at least two individuals
were encountered (Gabriels, 2007).
Table 2.7 shows the identification levels of macroinvertebrate taxa for calculating the BBI. The taxa list of
aquatic macroinvertebrates for calculating the BBI with their respective tolerance scores (TS) is presented in
Appendix 1. A more detailed explanation of the BBI calculation is presented in part IV of this thesis:
“Materials and methods”.
Table 2.7. Identification levels of macroinvertebrate taxa for calculating the BBI and MMIF (after Gabriels, 2007; Gabriels et al., 2010)
Taxonomic Group Determination level of systematic units
Coleoptera Family
Crustacea Family
Oligochaeta Family
Trichoptera Family
Diptera Family, excl. Chironomidae
Ephemeroptera Genus
Hemiptera Genus
Hirudinea Genus
Megaloptera Genus
Mollusca Genus
Odonata Genus
Plathelminthes Genus
Plecoptera Genus
Diptera, Chironomidae
Group (thummi-plumosus or thummi-plumosus)
Coleoptera Family
2.3.2 Multimetric Macroinvertebrate Index Flanders
According to Gabriels et al. (2010), the Multimetric Macroinvertebrate Index Flanders (MMIF) is a type-
specific index, which means that index calculations depend on the type of river or lake a sampling site
belongs to. It combines the robustness of the BBI with the versatility of multimetric indices, allowing for an
adaptation of scoring criteria for each river or lake type to reflect the relative distance to reference
conditions. The taxonomic identification level of macroinvertebrates is the same as BBI (Table 2.7). Taxa
taken into account for calculating the MMIF with their respective tolerance score (TS) are presented in
Appendix 2.
················································································································································································ 13 Literature Review
The category of lakes used within the MMIF includes all stagnant water bodies with a surface area larger
than 0.5 km2. An overview, including their abbreviations and determining properties are presented in Table
2.8.
Table 2.8. Main characteristics of different types of lakes in Flanders (Belgium), as defined for
application of the MMIF. (after Gabriels et al., 2010)
Lake types Abbreviation Properties
Alkaline A pH ≥ 7.5
Circumneutral C 7.5 ≥ pH ≥ 6.5; no clay
Acidic Z pH < 6.5; only sand/sandy loam/loam
Very slightly brackish Bzl Na > 250mg.L-1; no sand/sandy loam/loam
A more detailed explanation of the MMIF calculation is presented in part IV of this thesis: “Materials and
methods”.
2.3.3 Biological Monitoring Working Party Score System
In the Biological Monitoring Working Party Score System (BMWP), the macroinvertebrate from the
sample is identified at family level. Each family gets the corresponding value, being the index value the
sum of all the obtained values. The system for the BMWP index determination (adapted for Colombia) is
presented in Appendix 3 (Zamora, 2007).
A more detailed explanation of the MMIF calculation is presented in the chapter IV of this thesis:
“Materials and methods”.
2.4 Variability of the dissolved oxygen in the facultative ponds
In WSPs, oxygen tension is an operational parameter that shows a great deal of daily and hourly
variation. The rate of oxygen production is a function of the concentration of algae and other forcing
functions. The respiratory oxygen required by aerobic bacteria for assimilation of OM is met by algae
producing photosynthetic oxygen without the need for additional aeration (Kayombo et al., 2000). DO in
the pond gradually decreases with increasing depth and eventually reaches zero at a level, also called
oxypause, which changes according to the respiration and photosynthetic activity during the day (Beran
and Kargi, 2005).
The influences of all the individual parameters need to be considered for modeling the oxygen related
processes in the FP. Statistical models can be applied for the hypothesis and prediction of such
variations and relationships.
········································································································································································
14
Literature Review
In hypothesis testing, the model coincides with the theoretical or empirical distribution of the test
statistic that is used to test the null hypothesis; in prediction, one of a class of models is developed so
that it predicts in some optimal way the behavior of a dependent variable (Adèr et al., 2008).
Different statistical models and modeling techniques e.g., general linear model (GLM), structural
equation models (SEMs), growth models, mixed-effects models can be used depending on the nature of
the raw data.
General linear model (GLM)
They rest on the assumptions of normality, linearity and homoscedasticity. According to Adèr et al., (2008), GLM is a generalization of classical analysis of (co)variance in which the dependent variable is continuous and the predictors are partly discrete and partly continuous. A generalization of GLM called generalized linear model allows to model data using other distributions than the Normal (Olsson, 2002).
Structural equation models (SEMs)
Also called “simultaneous equation models”, are a type of multivariate regression models. The structural
equations are meant to represent causal relationships among the variables in the model (Fox, 2002).
Partial differential equation based models
They describe the growth behavior over time. One of the ways to analyze time series involved in growth
studies is by using the state-space model which provides an effective basis for practical time series
analysis in a wide range of fields. (Harvey et al., 2004).
Mixed-effects Models
In mixed-effects models at least one of the covariates is a categorical covariate representing
experimental or observational “units” in the data set. The important characteristic of a categorical
covariate is that, at each observed value of the response, the covariate takes on the value of one of a set
of distinct levels (Bates, 2010).
Parameters associated with the particular levels of a covariate are sometimes called the “effects” of the
levels. If the set of possible levels of the covariate is fixed and reproducible, we model the covariate
using fixed-effects parameters. If the levels that we observed represent a random sample from the set
of all possible levels we incorporate random effects in the model (Bates, 2010).
········································································································································································ 15 Literature Review
When the response variable is linearly related to a set of explanatory variables, the mixed model
becomes a linear mixed-effects model. For this, fixed effects, random effects, and trial-level noise
contribute linearly to the dependent variable, and random effects and trial-level error are both normally
distributed and independent for differing trials (Barr et al., 2013).
The type of data that can be analyzed using the linear mixed model include longitudinal data, repeated
measurements data, growth and dose-response curve data, clustered data, multivariate data and
correlated data.
Longitudinal data denotes that each subject has been measured repeatedly on the same outcome at
several points in time. Repeated measurements data refers to data on subjects measured repeatedly
either under different conditions, or at different times, or both. In growth and dose-response curve
data, the subjects are ordinarily measured time after time at a common set of ages or doses. Clustered
data arise in populations that have a natural hierarchical structure. Multivariate data is obtained when
the same subject is measured on more than one outcome variable (Cnaan et al., 1997).
2.4.1 Testing of research hypotheses The scientific problem is on the testing of a research hypothesis of the effect of one or more substantive variables on one or more outcome variables. The substantive variables are called independent variables and the outcome variables are called dependent variables. Usually, the research hypothesis states that a change in the independent variable causes a change in the dependent variable (Adèr et al., 2008). The hypotheses which state a relation between independent and dependent variables are known as null hypothesis. The rejection of this null hypothesis gives empirical support to the substantive research hypothesis, and the non-rejection of the null hypothesis does not support the research hypothesis (Adèr et al., 2008).
········································································································································································
16
Literature Review
········································································································································································ 17 Research objectives and goals
Part III Research objectives and goals
The aim of this study is to evaluate the dissolved oxygen variability in the facultative ponds in the waste
water treatment plant Ucubamba, situated in Cuenca – Ecuador, based on the oxygen related processes.
For this analysis a series of specific objectives were constructed:
1. Evaluate the DO, chlorophyll and BOD distribution in each FP at two different depths.
2. Evaluate the DO variability within one pond and when comparing the two FPs present in the plant.
3. Determine the macroinvertebrates’ influence over the oxygen presence in the FP due to their
respiration processes.
4. Develop a predictive model for the DO concentration in each FP.
5. Develop a predictive model for the BOD, using DO as a predictor among others.
········································································································································································
18 Research objectives and goals
········································································································································································ 19 Materials and methods
Part IV Materials and methods
4.1 Waste stabilization pond system
The WSP – Ucubamba is located at the northwest of Cuenca in a South – Andean region of Ecuador (see
Figure 4.1) it has been working normally since November 1999. It covers a total area of 45 ha, it treats
an average flow rate of 1.2 m3.s-1 which comes from the combined sewage system from the city.
The city of Cuenca is situated inside a large valley in the middle of the Andean column with a variable
temperature between 7 – 15 oC in winter and 12 – 25 oC in summer. Its central park is located at 2550
meters over the level of the sea (Espinoza and Rengel, 2009).
Figure 4.1. WSP – Ucubamba location (Espinoza and Rengel, 2009)
A description of the treatment process is shown in Figure 4.2. In order to avoid that overflows come into
the plant during raining seasons or during cleaning works, an Incoming flow structure (1) is placed at the
end of the main sewer treatment which works as pressure breaker. The opening gate (2) secures that no
larger flows than the maximum allowable flow comes into the plant, the excess flows are diverted
through a by-pass structure (9) to the effluent discharge point. Solids and floating particles with
diameter more than 20 mm are removed by the coarse bar screen (3) while the sand particles with
diameter greater than 0.2 mm are removed by the grit chamber (4) placed with flow baffle structures at
the chamber entrance.
········································································································································································
20 Materials and methods
Figure 4.2. Flow Diagram of WSP – Ucubamba (Alvarado, 2005) After preliminary treatment, wastewater is divided in two identical flow lines which consist of Aerated (5), Facultative (6) and Maturation Ponds (7) (Figure 4.3). Aerated ponds have a total area of 6 ha (3 ha each pond) where aeration is done by mechanical floating
aerators of inclined axis. In this stage there is a reduction of organic loading and the oxygen in the water
is kept at adequate levels for biomass assimilation by aerobic microorganisms. The hydraulic retention
time is relatively short. The digestion of organic solids produced in this stage takes place in the
facultative ponds together with the removal of bacteria and intestinal nematodes. The facultative ponds
have an area of 26 ha (13 ha each pond). Finally, the maturation ponds have a similar purpose than the
facultative ponds with the difference that in these ponds there is hardly any accumulation of solids and
the increase of the pH, due to the photosynthetic activity, results in an important bacterial mortality.
The maturation ponds have an area of 13 ha (7.4 ha maturation 1 and 5.6 ha maturation 2) (Alvarado,
2005).
Figure 4.3. WSP – Ucubamba, Lines in parallel
········································································································································································ 21 Materials and methods
4.2 Facultative ponds
The water treated in the aerated ponds is discharged into the facultative ponds which need to fulfill two
fundamental requirements of a FP: have an adequate organic load and an oxygen balance that keeps the
aerobic conditions over the anaerobic layer situated in the bottom of the pond.
The main purpose of these ponds is:
- Store and assimilate the biological solids produced in the aerated ponds.
- Provide appropriate organic loading conditions and oxygen balance to get an appropriate
biomass of unicellular algae in the top of the pond.
- Submit the proper conditions of bacterial mortality, which occurs when the algae population is
feed basically from the carbonated system. At higher sunshine hours or when the
photosynthetic activity is greater, algae consume bicarbonates and carbonates, producing a
marked increase in the pH, therefore a bacterial mortality.
- Ensure adequate removal of intestinal nematodes, so that the treatment is in accordance with
recent WHO guidelines.
The system has two facultative ponds working in parallel with the following characteristics for each
pond:
- Area = 13 ha; depth = 2 m; volume = 26,000 m3; slope inclination = 2:1.
- The slopes of the ponds are lined with concrete metal armor, with asphalt on the boards of the
flagstones to prevent possible infiltration and vegetation growth.
- The bottom sealing gaps is performed based on the compacted clay.
- The wastewater enters the pond through a submerged pipe with an internal diameter of 0.9 m.
In order to prevent erosion of the waterproofing layer a structure for energy dissipation (5.3 m x
5.3 m) is located at the bottom of the pond.
- The output structure of the wastewater is comprised of a rectangular weir 10 m long, provided
in addition to a rotary damper for varying levels and a collector gallery and a loading drawer for
discharge through pipe to the next processing unit.
········································································································································································
22 Materials and methods
4.2.1 Sampling scheme
In order to get a representative sample for the whole pond, each pond was divided in 6 parts
longitudinally and 4 parts transversally as it is shown in Figure 4.4. Samples were taken at the beginning
(1,2,3), middle (7,8,9) and end (13,14,15) of each pond.
Figure 4.4. Sampling scheme
Oxygen related processes in a FP deal with the presence/absence of algae, load of BOD, the time of the
day where these processes are taking place among others. Different parameters where measured during
the sampling to get a better overview of their influence in the processes mentioned.
4.2.2 Physical – Chemical parameters
Physical – chemical information was measured from the Facultative Pond 1 (FP1) and 2 (FP2) in each one
of the points mentioned before (1,2,3,7,8,9,13,14,15) at two different depths, 30 cm under the water
level and 15 cm over the bottom layer of the pond, using two multiprobes, YSI 6600V2 and YSI 6920V2,
in three different sampling periods which are presented in Table 4.1.
Table 4.1. Sampling periods for physical – chemical parameters
Facultative Pond 1 Facultative Pond 2
Sampling 1 July 25, 2013 (12:23 – 16:55) July 26, 2013 (09:30 – 12:52)
Sampling 2 August 14, 2013 (13:15 – 15:31) August 15, 2013 (09:14 – 11:37)
Sampling 3 August 26, 2013 (09:34 – 11:46) August 27, 2013 (09:18 – 11:01)
········································································································································································ 23 Materials and methods
The parameters obtained are: Temperature (oC), Specific Conductivity (mS.cm-1), Conductivity (mS.cm-1),
Total Dissolve Solids (g.L-1), Salinity (ppt), pH, Nitrates (mg.L-1), Chlorides (mg.L-1), Ammonium (mg.L-1),
Chlorophyll (µg.L-1), Dissolved Oxygen (%), Dissolved Oxygen (mg.L-1) and Turbidity (NTU).
While taking the measurements with the probes, for each pond, integrated samples were taken per
column (C1 = 1,2,3; C2 = 7,8,9; C3 = 13,14,15) using an automatic sampler, Teledyne ISCO 6712, and
analyzed in ETAPA’s laboratory which is situated in Ucubamba (the same place where the WSP is
situated) and the following parameters were obtained: Biological Oxygen Demand after 5 days (mg.L-1),
Chemical Oxygen Demand (mg.L-1), Total Phosphorus (mg.L-1), Total Kjeldahl Nitrogen (mg.L-1) and Total
Solids (mg.L-1).
Once all the data have been collected, plots showing the spatial distribution within each FP for DO,
chlorophyll and BOD are constructed to check if their behavior follows what is mentioned in the theory,
e.g. DO values should be higher in the afternoon than in the morning, and also to have an overview if
the behavior of FP1 is similar to FP2 as it was designed.
In the case of FP1, sampling 1 and 2 were performed between 12:30 and 17:00 while sampling 3 took
place between 9:00 and 12:30. This allows an analysis and comparison of the ponds between morning
and afternoon distribution of the parameters.
Since all the samplings for FP2 were performed between 9:00 and 12:30 this data allows us to compare
it with the data obtained from FP1 during the same day period.
4.2.3 Variability of the dissolved oxygen in the facultative ponds
Due to the correlation between the repeated DO measurements in the different locations and depths,
the statistical model chosen for testing the hypothesis of this study and to obtain a predictive model is
the basic linear mixed effect model.
The hypotheses for the DO variability within one pond and between ponds were based in:
- DO variability over time.
- DO variability between depths.
- DO variability between transversal locations (lines).
- DO variability between longitudinal locations (columns).
- DO variability of the different combinations: Time and depth, time and lines, time and columns,
depth and time. For this hypothesis, the combination also involves pond type when the
variability within ponds is being studied.
- Influence of timing.
- Influence of chlorophyll.
- Influence of climatic conditions.
- Influence of measured chemical conditions (Water temperature, pH, conductivity, salinity,
turbidity, nitrate, ammonium, BOD, COD, Kjeldahl nitrogen, phosphorus.
The basic linear mixed effect model consists of the dependency of DO in different parameters:
········································································································································································
24 Materials and methods
DO (mg.L-1) = f(location, depth, time, timing).
Where time refers to the day when the sampling was performed and timing to the hour of the day.
This dependency, in order to test the variability within each FP, is expressed as follows:
Where:
Yijk: Response variable. For this case dissolved oxygen concentration.
bi,j: Random effect that expresses the variability of the response in different locations and
depths.
ijk: Measurement error within an observation.
i: Subscript for location (1,2,3,7,8,9,13,14,15).
j: Subscript for depth (surface, bottom).
k: Subscript for sampling campaign (T1, T2, T3)
In order to test the influence of the variability between facultative ponds, the model presented before
has been modified by adding this new fixed effect:
DO (mg.L-1) = f(location, depth, time, timing) * pond,
This is reflected in the following equation:
Analogously, the influence of chlorophyll, climatic conditions and other chemical parameters is
determined: Water temperature, pH, conductivity, turbidity, salinity, nitrate and ammonium, BOD, COD,
Kjeldahl-N, phosphorus and total solids.
The predictive model can now be obtained using the basic linear mixed effect model and the variables
that showed to influence the oxygen related processes in the FP in the hypothesis testing.
The statistical tool used was the PROC MIXED from SAS 9.3.
Since a FP focuses on the removal of BOD, by using the basic linear mixed effect model a new predicted
model was developed with BOD as response variable and DO among the other selected variables for the
previous model as predictors.
········································································································································································ 25 Materials and methods
4.2.4 Macroinvertebrates
The presence of macroinvertebrates in the ponds has an influence in the oxygen related processes due
to the respiratory activity consuming oxygen.
Macroinvertebrates’ samples were taken from each pond, using the artificial substrate technique, in the
same locations as the physical – chemical parameters but for this only one sample period was
performed on august 26 and 27, 2013 at 1 depth, which was the bottom of the ponds.
4.2.4.1 Sampling
Four weeks before sampling day, one bag of artificial substrates (AS) was placed into each sampling
point at the bottom of the ponds supported by buoys. These AS consisted of polypropylene bags, with a
volume of 5 liters, filled with substrates from a channel that collects rain water in the plant installations.
During the sampling, the content of each bag was transferred into plastic buckets filled with water from
the FPs and ethanol to preserve the sample.
When the buckets were opened, the content was poured over a sieve and rinsed with pressurized water
where stones and large inert materials were removed. After washing, macroinvertebrates were
collected from the surface of the sieve and were transferred to plastic bottles containing ethanol.
4.2.4.2 Assessment of macroinvertebrate communities
After sampling biological indicators, macroinvertebrates were identified by using a stereoscope and guidelines books (Bouchard, 2004; de Pauw and van Damme, 1999). Obtained data was used to estimate the Belgian Biotic Index (BBI), Multimetric Macroinvertebrate Index Flanders (MMIF) and Biological Monitoring Working Party Score System (BMWP) adapted for Colombia.
4.2.4.2.1 Belgian Biotic Index (BBI)
When all macroinvertebrates from a sample were identified, a list was made of all taxa of which at least
two individuals were encountered with their tolerance which can be found in the left column of the
Table 4.2.
Based on the previous list the class frequency and the number of taxa were also determined being the
number of taxa within the lowest tolerance class and the number of taxa of which at least two
individuals were found in the sample respectively.
The BBI value was found in the cross-table (Table 4.2), in the row with the lowest tolerance class and its associated class frequency, and in the column with the correct taxa richness class. BBI values correspond to water quality classes with their associated formal valuation, which are presented in Table 4.3.
········································································································································································
26 Materials and methods
Table 4.2. Calculation of BBI. (after Gabriels, 2007)
Tolerance class
Indicator group Frequency Number of taxa
0-1 2-5 6-10 11-15 = >16
1 Plecoptera = >2 - 7 8 9 10
Heptageniidae 1 5 6 7 8 9
2 Trichoptera = >2 - 6 7 8 9
1 5 5 6 7 8
3
Ancylidae >2 - 5 6 7 8
Ephemeroptera 1-2 3 4 5 6 7
(excl. Heptageniidae)
4
Aphelocheirus
> = 1 3 4 5 6 7 Odonata
Gammaridae
Mollusca (excl. Sphaeriidae)
5
Asellidae
>= 1 2 3 4 5 -
Hirudinea
Sphaeriidae
Hemiptera (excl. Aphelocheirus)
6
Tubificidae
> =1 1 2 3 - - Chironomus thummi-pulmosus
7 Syrphidae – Eristalinea > =1 0 1 1 - -
Table 4.3. Water quality classes corresponding to the BBI values. (Gabriels, 2007)
········································································································································································ 27 Materials and methods
4.2.4.2.2 Multimetric Macroinvertebrate Index Flanders (MMIF)
Based on the references, a scoring system was developed for each metric consisting of threshold values needed for assigning a score ranging from zero to four (four being assigned to the metric values that were nearest to the reference value). These criteria were developed by equally dividing the interval between an expert-based target reference value and a value corresponding to a bad ecological quality into five smaller intervals. The resulting scoring criteria were taken from Gabriels (2007). These five metric scores are summed and subsequently divided by 20 to obtain the final index, ranging from zero for a very poor ecological quality to one for a high biological quality (Gabriels, 2007). All these calculations were performed in an excel sheet for Alkaline (A) and Circumneutral (C) types of water due that pH is always higher than 6.5. Quality class boundary values were constructed by equally dividing the total range of MMIF values into five classes (Table 4.4).
Table 4.4. Preliminary WFD quality class intervals proposed for the MMIF interval range. (Gabriels, 2007)
4.2.4.2.3 Biological Monitoring Working Party Score System (BMWP)
All the macroinvertebrates that are present in the sample were identified at family level and given a
value based on Table 2.11. The BMWP index was obtained by adding each one of previous values
obtained.
This BMWP index is situated within the ranges presented in the Table 4.5 determining the water quality.
Table 4.5. Quality class, values and characteristics for the BMWP index. (Zamora, 2007)
········································································································································································
28 Materials and methods
4.2.5 Meteorological Data
Average air temperature (oC), solar radiation (W.m-2), wind speed (m.s-1) and rain (mm) data was taken
from the CELEC Hidropaute Meteorological Station situated in the coordinates -2.859308; -78.933909,
600 m (approx.) away from the WSP Ucubamba.
········································································································································································ 29 Results
Part V Results
5.1 Dissolved oxygen, chlorophyll and biological oxygen demand distribution
Since the synergy of algae and bacteria plays a significant role in facultative ponds, the parameters
analyzed at first hand are DO, chlorophyll and BOD. The influence of these parameters as well as other
parameters measured during fieldwork will be analyzed statistically in the next part (see Section 5.2).
Variation of DO per location at each sampling moment is shown in Figure 5.1 for FP1 and Figure 5.2 for FP2. As mentioned in Table 4.1, the two first samplings in the FP1 were performed between 12:00 and 17:00 while its third sampling and all the samplings regarding the FP2 were performed between 9:00 and 13:00.
It is noticeable in Figure 5.1 and Figure 5.2 that the second sampling campaign of the water column 30 cm underneath the water surface shows the highest DO concentration for almost all sampling locations. This trend is not clearly present regarding the samples collected 15 cm above the sediment.
Higher DO values can be found towards the center of the ponds, locations 7, 8 and 9.
Figure 5.1. DO measured in FP1 per location. a) 30 cm under the water level, b) 15 cm over the sediment
Figure 5.2. DO measured in FP2 per location. a) 30 cm under the water level, b) 15 cm over the sediment
········································································································································································
30 Results
The lack of information in locations 1 and 2, 15cm over the sediment layer, is due to the presence of accumulated sludge that, in this area, was almost reaching the water level.
Due to the unequal amount of data collected in the morning and in the afternoon, see Table 4.1, it is very important to analyze the distribution of the different parameters in the morning and in the afternoon separately. If an average of all measurements is taken into consideration some important results may be ignored as it is presented in Figure 5.3 with the results of FP1 where it can be observed that the average values tend to go closer to the afternoon data.
Figure 5.3. Average DO measured in the FP1 per location vs. measurements in the morning and in the
afternoon. a) 30 cm under the water level, b) 15 cm over the sediment
With the data collected during morning sampling campaigns it is possible to compare between FP1 and FP2. When adding the data collected in the afternoon the comparison within FP1 can be performed.
5.1.1 Facultative pond 1 vs. facultative pond 2 distribution in the morning
In order to compare both ponds, the data used for the analysis belong to the third sampling campaign due to the fact that for the FP1 this is the only morning campaign.
5.1.1.1 Close to the surface, 30 cm under the water level
Concerning the layer close to the water level, it can be seen in Figure 5.4 that DO, in both ponds, shows an increase from the inlet until the middle of the pond and then a decrease towards the outlet. In the inlet zone of FP1 (which is situated in the bottom of Figure 5.4 in order to keep the same scheme of the wastewater treatment plant) there is a steep increase compared to the DO in FP2. Furthermore, DO values in FP1 are higher than the ones in FP2.
BOD behavior in both ponds is presented in Figure 5.5 where it can be seen that there is a high BOD concentration in FP1. BOD tends to increase towards the center of the pond and from there it tends to decrease towards the outlet of the pond. On the other hand, in FP2, BOD tends to decrease from the inlet towards the outlet of the pond. Furthermore, the highest BOD concentration observed in FP2 is situated near the inlet, while FP1 shows highest BOD concentration near the center of the pond.
········································································································································································ 31 Results
Figure 5.4. DO spatial distribution measured between 9:00 and 12:30 at 30 cm under the water level.
a) Facultative pond 2, b) Facultative pond 1
Figure 5.5. BOD spatial distribution measured between 9:00 and 12:30 at 30 cm under the water level. a) Facultative pond 2, b) Facultative pond 1
········································································································································································
32 Results
The spatial distribution of chlorophyll 30 cm under the water level presented in Figure 5.6 also shows more irregularly, an increase from the inlet zone to a certain point of the pond where it starts to decrease towards the pond’s outlet. It is noticeable that FP2 seems to have higher chlorophyll concentration than FP1.
The big difference of chlorophyll data obtained during the same sampling campaigns leads to step decreases when plotting the surface graphs. In Figure 5.6 these occurrences are represented with dark colors which are not shown on the scale.
Figure 5.6. Chlorophyll spatial distribution measured between 9:00 and 12:30 at 30 cm under the water level. a) Facultative pond 2, b) Facultative pond 1
The dark areas in the graph represent the steep decrease of chlorophyll concentration within the pond.
········································································································································································ 33 Results
5.1.1.2 Close to the bottom of the pond, 15 cm over the sediment layer
Close to the sediment surface there is a decrease in DO concentration in both ponds (see Figure 5.7) compared with the upper layer which was expected according to literature and also reflected when comparing with Figure 5.1 and Figure 5.2.
When comparing between ponds, FP1 shows higher DO values towards the left – center while FP2 has a very uniform distribution (see Figure 5.7), as it is also observed in Figure 5.2b, along the pond.
BOD concentration close to the sediment surface decreases as it moves from the inlet of both ponds toward the outlet with higher values presented in FP1 compared to FP2 as depicted in Figure 5.8.
Chlorophyll is almost absent close to the bottom (Figure 5.9), nevertheless it seems to be present in higher concentrations in FP1 which differs from the behavior of chlorophyll close to the surface observed in Figure 5.6 where it seems to be higher in FP2.
The dark areas in Figure 5.9 represent the steep decrease of chlorophyll concentration within the pond.
Figure 5.7. DO spatial distribution measured between 9:00 and 12:30 at 15 cm over the sediment. a) Facultative pond 2, b) Facultative pond 1
········································································································································································
34 Results
Figure 5.8. BOD spatial distribution measured between 9:00 and 12:30 at 15 cm over the sediment. a) Facultative pond 2, b) Facultative pond 1
Figure 5.9. Chlorophyll spatial distribution measured between 9:00 and 12:30 at 15 cm over the sediment. a) Facultative pond 2, b) Facultative pond 1
········································································································································································ 35 Results
5.1.2 Facultative pond 1 in the morning vs. facultative pond 1 in the afternoon
The data used for the analysis corresponds to the third sampling campaign for the morning since it is the only campaign that took place at this time of the day, while the second sampling campaign was considered for the afternoon data due that the conditions are more likely to approach the conditions of the third campaign and considering that when the average value is used, other parameters (e.g. Temperature, flow rate) will be also averaged only for one scenario.
5.1.2.1 Close to the surface, 30 cm under the water level
When comparing the same pond close to the water level, in this case FP1, the data collected in the morning vs. the data collected in the afternoon clearly shows that in the afternoon the DO concentrations are higher than in the morning (Figure 5.10) with a decreasing behavior towards the outlet of the pond. Figure 5.11 shows that BOD concentration is higher in the morning when DO concentration is lower. In the afternoon, however, a rather uniform BOD concentration is observed along the pond comparing with DO distribution, especially in the afternoon (Figure 5.10a) where a high increase of DO concentration is observed towards the center of the pond. Concerning chlorophyll, its distribution is more uniform in the afternoon when the concentrations are higher compared with the morning concentrations, see Figure 5.12. The dark areas in Figure 5.12 represent the steep decrease of chlorophyll concentration within the pond.
········································································································································································
36 Results
Figure 5.10. DO spatial distribution in the FP1 at 30 cm under the water level. a) Measured between
12:30 and 17:00, b) measured between 9:00 and 12:30
Figure 5.11. BOD spatial distribution in the FP1 at 30 cm under the water level. a) Measured between 12:30 and 17:00, b) measured between 9:00 and 12:30
········································································································································································ 37 Results
Figure 5.12. Chlorophyll spatial distribution in the FP1 at 30 cm under the water level. a) Measured
between 12:30 and 17:00, b) measured between 9:00 and 12:30
5.1.2.2 Close to the bottom of the pond, 15 cm over the sediment layer
Close to the sediment layer, see Figure 5.13, there is a decrease of the DO concentrations compared with the top layer presented in Figure 5.10. Higher DO concentrations are present in the afternoon, mainly in the center of the pond which is also reflected in Figure 5.1b when comparing all measurements performed in the pond. A similar pattern is observed for BOD concentration (see Figure 5.14) with higher BOD concentrations in the afternoon, reaching a maximum value close to the center of the pond. In the morning however, BOD concentrations are lower and decrease along the pond.
········································································································································································
38 Results
Figure 5.13. DO spatial distribution in the FP1 at 15 cm over the sediment. a) Measured between
12:30 and 17:00, b) measured between 9:00 and 12:30
Figure 5.14. BOD spatial distribution in the FP1 at 15 cm over the sediment. a) Measured between 12:30 and 17:00, b) measured between 9:00 and 12:30
········································································································································································ 39 Results
Chlorophyll concentration close to the bottom of the pond varies among the different locations (see Figure 5.15). As such, it is hard to state whether chlorophyll concentration is higher in the morning or in the afternoon. It can, however, be stated that the chlorophyll concentration near the bottom is, in general, lower than the layer close to the surface at any time of the day.
*The dark areas in the graph represent the steep decrease of chlorophyll concentration within the pond.
Figure 5.15. Chlorophyll spatial distribution in the FP1 at 15 cm over the sediment. a) Measured
between 12:30 and 17:00, b) measured between 9:00 and 12:30
5.2 Variability of the dissolved oxygen in the facultative ponds
5.2.1 Variability within each facultative pond
After running the model using the average concentrations of DO it can be concluded that there is an
effect of time (day of sampling) and depth in DO concentration as well as the interaction of depth and
location at 5 % level of significance in both facultative ponds. The p values obtained are presented in
Table 5.1.
When using not only the average DO concentrations but all the obtained data, the estimate for the
random effect parameter differs from 0 (474.4 for FP1 and 106.8 for FP2), meaning that a correlation
exists between the measurements recorded for the same location and depth. There is a big change in
the outcomes from Table 5.1 since Depth*Location and Time are not significant anymore and
Depth*Time becomes significant for FP1 and no effect becomes significant in FP2. See Table 5.2.
········································································································································································
40 Results
Table 5.1. Fixed effects p values for the basic linear mixed model with average DO
concentrations for FP1 and FP2
Effect Pr > F
FP1 FP2
Depth*location 0.0115 0.0441
Time <0.0001 0.0074
Depth <0.0001 <0.0001
Depth*Time 0.1003 0.3462
Table 5.2. Fixed effects p values for the basic linear mixed model with DO concentrations for FP1 and FP2
Effect Pr > F
FP1 FP2
Depth*location 1 1
Timing 0.3196 0.8906
Time 0.1814 0.4936
Depth 0.0491 0.5929
Depth*Time 0.0059 0.1445
Timing*Depth 0.0333 0.2191
Timing*Time 0.312 0.4458
5.2.2 Facultative pond 1 vs. Facultative pond 2
Table 5.3 shows that there is no variation depending on the ponds (p = 0.2114) but analyzing different
correlations, there is variability depending on Depth, Pond-Depth-Location, Pond-Depth, Pond-Depth-
Day, Pond-Depth-Timing. Based on these significances, Depth is an important factor resulting in
significant different DO levels.
In order to get more specific results an analysis per rows and columns at each depth was also
performed.
········································································································································································ 41 Results
Table 5.3. Fixed effects p values for the basic linear mixed model with DO concentrations for the combination of FPs
Pr > F
Pond*Depth*Location 0.0009
Pond 0.2114
Timing*Pond 0.4969
Depth 0.0506
Pond*Time 0.2225
Pond*Depth 0.01
Pond*Depth*Time 0.0018
Timing*Pond*Depth 0.0251
Timing*Pond*Time 0.3677
When doing an analysis per row and column between each pond, a variability is observed only in the
measurements close to the surface (30 cm under the surface of the pond) with a 5 % of confidence,
while 15 cm over to the bottom no such variability is observed (see Table 5.4).
Table 5.4. DO analysis per rows and columns at each depth between FP1 and FP2
Label Pr > |t|
30cm: col 1: pond1-pond2 0.0048
30cm: col 2: pond1-pond2 0.0109
30cm: col 3: pond1-pond2 0.0205
15cm: col 1: pond1-pond2 0.6974
15cm: col 2: pond1-pond2 0.9708
15cm: col 2: pond1-pond2 0.7589
30cm: row 1: pond1-pond2 0.008
30cm: row 2: pond1-pond2 0.017
30cm: row 3: pond1-pond2 0.0075
15cm: row 1: pond1-pond2 0.7625
15cm: row 2: pond1-pond2 0.9678
15cm: row 3: pond1-pond2 0.8202
5.2.3 Influence of chlorophyll In order to test the influence of the variability considering chlorophyll, the model presented before has
been modified by adding this new fixed effect:
DO (mg.L-1) = f(location, depth, time, timing, chlorophyll)
Table 5.5 shows that in the FP1 there is no influence of the chlorophyll in the variability of DO while in
the FP2 there is an influence of chlorophyll and a correlation with depth and timing.
········································································································································································
42 Results
Table 5.5. Influence of chlorophyll on DO within ponds
Effect Pr > F
FP1 FP2
Chlorophyll 0.5275 0.0301
Chlorophyll*Depth 0.1889 0.013
Timing* Chlorophyll 0.7365 0.0476
Chlorophyll*Time 0.5993 0.297
5.2.4 Influence of climatic conditions
When taking into consideration the measured climatic conditions in the DO distribution within ponds,
the results show that there is no significant importance of these parameters, see Table 5.6. In FP1
Timing*Depth may have an influence over DO since it approaches to the significance level while in FP2
there is an influence of Depth*Location and also the wind speed approaches the significance level.
When comparing the effect between FPs, see Table 5.7, Pond*Depth*Location and Timing*Pond*Depth have and influence over the variability of DO in FPs, this last combination does not reach the 5 % level of significance but it approaches this value. Suddenly, ‘Depth’ has become a less important factor regarding significance, as seen in Table 5.3.
Table 5.6. Influence of climatic conditions on DO within ponds
Effect Pr > F
FP1 FP2
Depth*location 0.1154 0.0245
Timing 0.7126 0.7257
Time 0.4242 0.4887
Depth 0.9880 0.6649
Depth*Time 0.9507 0.1546
Timing*Depth 0.0666 0.6200
Timing*Time 0.4681 0.3726
Air temperature 0.6664 0.6163
Solar radiation 0.4600 0.4110
Wind speed 0.4000 0.0731
Air temperature*Depth 0.2887 0.3100
Solar radiation*Depth 0.4606 0.3459
Wind speed*Depth 0.4079 0.9698
········································································································································································ 43 Results
Table 5.7. Influence of climatic conditions on DO between ponds Effect Pr > F
Pond*Depth*location 0.021
Pond 0.3672
Timing*Pond 0.8728
Depth 0.8976
Pond*Time 0.498
Pond*Depth 0.8718
Pond*Depth*Time 0.671
Timing*Pond*Depth 0.0702
Timing*Pond*Time 0.4976
Air_Temp*Pond 0.8113
Solar_radiation*Pond 0.5504
Wind_speed*Pond 0.2341
Air_Temp*Pond*Depth 0.3227
Solar_radiation*Pond*Depth 0.5195
Wind_speed*Pond*Depth 0.5951
5.2.5 Influence of other chemical parameters Different measured chemical parameters like water temperature, pH, conductivity, turbidity, salinity, nitrate and ammonium were also considered while analyzing the variability of the DO in the facultative ponds. The result is that only turbidity has an influence in the FP1 by itself and when it is considered together with chlorophyll as shown in Table 5.8.
Table 5.8. Influence of turbidity on DO within FPs
Effect Pr > F
FP1 FP2
Turbidity 0.0362 0.332
Turbidity *Depth 0.121 0.1913
Turbidity * Chlorophyll 0.0368 0.1893
Turbidity *Day 0.1033 0.8086
When taking into account the data obtained in the laboratory for BOD, COD, Kjeldahl-N, Phosphorus and Total Solids there is no influence in the variability from these measurements. These results are presented in Appendix 4.
········································································································································································
44 Results
5.3 Predicted model for describing variability of dissolved oxygen in facultative ponds
5.3.1 Predicted model for dissolved oxygen For the prediction model of DO in the facultative ponds using linear mixed effect model (LMEM), the predictors considered are chlorophyll, BOD and timing. Four different scenarios are taken into account: FP1 30 cm under the water level, FP1 15 cm over the sediment layer, FP2 30 cm under the water level and FP2 15 cm over the sediment layer, resulting in four different models in total. Since the BOD data correspond to integrated samples per column, chlorophyll, DO and timing were also considered per column, using the average value for the model development. When generating the model for each scenario, the software also gives the covariance parameter estimates UN(1,1) and Residual which represent the variance related to location and error respectively. The general equation obtained for the different scenarios is:
Where:
βi: Estimated values for the model.
bi: Random intercept term for each column. Calculated as the inverse of the normal distribution
with mean 0 and standard deviation (σ) equal to (UN(1,1))2 for a random probability.
i: Measurement error within an observation. Calculated as the inverse of the normal
distribution with mean 0 and standard deviation (σ) equal to (Residual)2 for a random
probability.
i: Subscript for location (1,2,3,7,8,9,13,14,15).
The obtained values for each scenario are presented in Table 5.9 where Pearson’s correlation coefficient (r) calculated for a specific case is also shown. The obtained graphs regarding Pearson’s correlation coefficient are presented in Appendix 5. The obtained values for Pearson’s coefficient show a high correlation for a specific case of each model;
however the small sample size results in a high variability in the correlation measurements. What this
means is that there is a lot of uncertainty in the prediction causing a fluctuation between correlations of
a certain model. The variability is from both the measurement error and the random intercept.
If the sample size was large, it could be expected that on average most of the random terms would
always be close to their mean (zero) hence this would give you relatively constant predicted correlation.
········································································································································································ 45 Results
Table 5.9. Parameter values obtained during model development describing the variation of DO Scenario β0 β1 β 2 β 3 UN (1,1) Residual r
1. FP1 30 cm under the water level
-15.8563 0.03114 0.6822 0.1346 0.1813 14.6169 0.795
2. FP1 15 cm over the sediment layer
-12.3988 -0.00468 0.9256 0.1925 17.1859 2.9748 0.870
3. FP1 30 cm under the water level
-47.3293 0.03634 2.7220 0.4092 11.3645 2.8200 0.829
4. FP2 15 cm over the sediment layer
3.8636 0.00276 -0.1206 -0.08278 0.2713 1.6298 0.592
5.3.2 Prediction of dissolved oxygen concentrations Based on the prediction models obtained for each scenario, some surface graphs were plotted so that
the mean DO behavior can be directly estimated in function of chlorophyll, timing and BOD. The
predictions deal with a location of which it is assumed that there is zero variability from the mean and
no measurement error. Chlorophyll and BOD concentrations as well as the timing for the plots were
based on the data obtained in the sampling campaigns.
Figure 5.16 shows that higher DO concentrations towards mid-afternoon are expected. Close to the
surface layer these concentrations are reached when the chlorophyll concentration is high, while close
to the bottom these concentrations are reached in the absence of chlorophyll. It is also noticed that as
BOD concentration increases, DO concentration increases as well.
Figure 5.16. DO predictions in function of chlorophyll and timing at different BOD concentrations in FP1. a) Scenario 1: 30 cm under the water level, b) Scenario 2: 15 cm over the sediment
········································································································································································
46 Results
Predictions for the third and fourth scenario (related to FP2) clearly show a decrease in DO
concentration from the surface to the bottom of the pond (see Figure 5.17). Furthermore, higher values
of DO concentration are presented in the mid-afternoon in the presence of high chlorophyll
concentrations.
As expected there is more DO variation regarding DO concentrations in the scenario 3 (Figure 5.17a),
when the analysis is done close to the surface layer comparing to the layer close to the bottom (Figure
5.17b).
Figure 5.17. DO predictions in function of chlorophyll and timing at different BOD concentrations in FP2. a) Scenario 3: 30 cm under the water level, b) Scenario 4: 15 cm over the sediment
········································································································································································ 47 Results
5.4 Predicted model for describing variability of biochemical oxygen demand in facultative ponds
5.4.1 Predicted model for biochemical oxygen demand During the development of the prediction models for BOD, the same scenarios and considerations for DO were taken into account. The general equation obtained for the different scenarios is:
Where:
βi: Estimated values for the model.
bi: Random intercept term for each column. Calculated as the inverse of the normal distribution
with mean 0 and standard deviation σ equal to (UN(1,1))2 for a random probability.
i: Measurement error within an observation. Calculated as the inverse of the normal
distribution with mean 0 and standard deviation σ equal to (Residual)2 for a random
probability.
i: Subscript for location (1,2,3,7,8,9,13,14,15).
The obtained values for each scenario are presented in Table 5.10 where Pearson’s correlation coefficient (r) calculated for a specific case is also shown. The obtained graphs regarding Pearson’s correlation coefficient are presented in Appendix 6. As it was the case of the predicted model for DO concentration in Section 5.3.1, the obtained values for
Pearson’s coefficient show a high correlation for a specific case of each model with a high variability in
the correlation measurements meaning that there is a lot of uncertainty in the prediction causing a
fluctuation between correlations of a certain model. The variability is from both the measurement error
and the random intercept.
Table 5.10. Parameter values obtained during model development describing the variation of BOD Scenario β0 β1 β 2 β 3 UN (1,1) Residual r
1. FP1 30 cm under the water level
61.9447 -0.04128 -0.6418 0.4774 1.4595 53.0949 0.686
2. FP1 15 cm over the sediment layer
12.6795 -0.00335 1.7194 0.5542 6.2422 35.8363 0.833
3. FP1 30 cm under the water level
-47.3293 0.03634 2.722 0.4092 42.1666 0.7762 0.962
4. FP2 15 cm over the sediment layer
11.3298 0.009105 1.2897 -0.447 1.5851 9.374 0.779
········································································································································································
48 Results
5.4.2 Prediction of biochemical oxygen demand concentrations As well as for DO surface plots, BOD has been represented in function of chlorophyll, timing and DO
considering that the predictions deal with a location of which it is assumed that it has zero variability
from the mean and no measurement error.
Chlorophyll and DO concentrations as well as the timing for the plots were based on the data obtained
in the sampling campaigns.
As it is shown in Figure 5.18a, for the first scenario (30 cm underneath water surface in FP1) it is
predicted to obtain higher concentrations in the morning when chlorophyll concentration is low. For the
layer close to the sediments in FP1 the predicted BOD concentrations tend to be high in the afternoon in
the absence of chlorophyll (see Figure 5.18b). The second scenario shows lower BOD predicted
concentrations than the first scenario.
Figure 5.18. BOD predictions in function of chlorophyll and timing at different DO concentrations in FP1. a) Scenario 1: 30 cm under the water level, b) Scenario 2: 15 cm over the sediment
········································································································································································ 49 Results
The predicted BOD values close to the surface in FP2 (Figure 5.19a) show a similar behavior as the one
for FP1 (Figure 5.18a) nevertheless the values obtained are lower for FP2. The fourth scenario, Figure
5.19b, shows that the predicted BOD concentrations tend to be high in the afternoon in the presence of
high chlorophyll concentrations.
When comparing the second and fourth scenario (Figure 5.18b and Figure 5.19b) the higher BOD
concentrations predicted correspond to the FP1.
Figure 5.19. BOD predictions in function of chlorophyll and timing at different DO concentrations in FP2. a) Scenario 3: 30 cm under the water level, b) Scenario 4: 15 cm over the sediment
········································································································································································
50 Results
5.5 Presence of macroinvertebrates in the facultative ponds
There is no high presence of macroinvertebrates in the FPs as it is shown in Table 5.11, where the water quality of the ponds is also presented.
Table 5.11. Water quality indexes of the FPs per row (R) and column (C)
Location FP BMWPcol BBI MMIF
Value Qualification* Value Qualification* Value Qualification*
C1 1 3 HP 1 VP 0 B
C2 1 2 HP 2 VP 0.05 B
C3 1 - NM - NM - NM
R1 1 3 HP 2 VP 0.05 B
R2 1 3 HP 1 VP 0 B
R3 1 1 HP 1 VP 0 B
C1 2 6 HP 3 B 0.1 B
C2 2 2 HP 1 VP 0.05 B
C3 2 2 HP 1 VP 0.05 B
R1 2 3 HP 2 VP 0.05 B
R2 2 - NM - NM - NM
R3 2 6 HP 3 B 0.1 B
*HP = Heavily Polluted, VP = Very Poor, B = Bad quality, NM = No Macroinvertebrates present
········································································································································································ 51 Discussion
Part VI Discussion
6.1 Dissolved oxygen, chlorophyll and biological oxygen demand distribution
From Figure 5.1 it can be noticed that there is a clear decrease in the DO concentrations in the third sampling period for FP1. When comparing the DO concentrations reached in the FP1 in the first and second sampling with the ones obtained in FP2 (Figure 5.2) the ones from FP1 present higher concentrations.
This variation in the DO concentrations can be explained by considering the time of the day when the sampling took place (Table 4.1), where the third sampling in FP1 and all the samplings in FP2 were performed in the morning, between 09:00 and 12:00.
Due to provision of sunlight, algae, which play a very important role in FPs by being the source of oxygen, are able to produce oxygen leading to higher concentrations in the afternoon and ultimately resulting in a diurnal pattern (Kayombo et al., 2002; Tadesse et al., 2004). The collected samples approach this pattern.
As it was mentioned in Section 5.1, it is very important to analyze the distribution of the different parameters in the morning and in the afternoon separately. Furthermore, in Section 5.2.2 it is established that the correlation Pond-Depth-Timing has an influence on the variability of DO. For this reason an analysis between ponds with the morning samples as well as within FP1 in the morning and afternoon separately was done at different depths.
6.1.1 Facultative pond 1 vs. facultative pond 2 distribution in the morning
Since the data available from morning sampling in the FP1 comes from the third sampling period and in order to avoid that for one pond the parameters are averaged and for the other not, the third sampling campaign was also chosen from FP2 even though these values do not differ much from the average as it is shown in Figure 6.1. The lack of information in locations 1 and 2 in Figure 6.1b is due to the presence of accumulated.
Figure 6.1. Average of DO concentrations vs. concentrations in the third sampling in FP2 . a) 30 cm under the water level, b) 15 cm over the sediment
········································································································································································
52 Discussion
If we take into consideration the relation of DO and BOD, it is expected that higher BOD values will lower the DO concentration since BOD in general leads to consumption of DO by bacteria in order to degrade organic matter at any time of the day. This trend is not observed when comparing FP1 vs. FP2 (Figure 5.4 and Figure 5.5) where it can be seen that FP1 has higher values of DO as well as BOD than FP2.
The higher concentrations of DO and BOD in FP1 compared to FP2 suggest some irregularity in the load distribution in the inlet of the pond treatment.
The high concentration of DO in the ponds may be explained due to the presence of the aerators in the previous ponds of the system which can also cause upwelling of sludge resulting in higher BOD concentrations being brought to the next pond. The algae influence, which consume DO at certain moments during the day when there is no sunlight for their photosynthetic activity is another factor to be considered.
Regarding the behavior of DO near the bottom, Figure 5.7 shows a decrease in its concentration compared to the upper layer. In this bottom area, the light will hardly be available so the present algae will also consume DO. This DO behavior may also be due to the liquid flow pattern, which was analyzed for FP1 in previous studies (Alvarado, 2013) where a short circuit and a strong circular pattern around the pond is suggested, which is typical for this type of hydraulic system without baffle structures.
The presence of settled sludge consisting of BOD will also have an influence on the BOD distribution in the bottom presented in the Figure 5.8, higher layers of settled sludge were found during the sampling period close to the inlet of the ponds where a high concentration of BOD is also observed in the results. It was expected that FP2 will have higher BOD concentrations due that it has lower DO concentrations (Figure 5.7) however this was not observed. On the other hand, BOD concentrations close to the bottom layer are lower compared with the layer close to the water level. The low chlorophyll concentrations in the bottom of both ponds presented in Figure 5.9 and compared to the chlorophyll concentrations in the upper layer corresponds to the absence of light in the bottom of the pond (see Figure 2.3).
6.1.2 Facultative pond 1 in the morning vs. facultative pond 1 in the afternoon
As mentioned in Section 5.1.2 the data used for the analysis corresponds to the third sampling campaign for the morning and the second sampling campaign was considered for the afternoon data due that the conditions are more likely the third campaign and considering that when the average value is used, other parameters (e.g. Temperature, flow rate) will be also averaged only for one scenario.
As mentioned before it is expected that higher BOD values will lower the DO concentration since BOD in general leads to consumption of DO by the bacteria for the degradation of the organic matter. This behavior is observed in Figure 5.10 and Figure 5.11 where there are higher concentrations of DO together with lower concentrations of BOD in the afternoon compared with the morning. However, for BOD the difference between morning and afternoon is less noticeable than for DO.
········································································································································································ 53 Discussion
This correlation between DO and BOD found when comparing the same pond in the morning vs. the afternoon was not found when comparing between ponds as presented in Section 5.1.2 and Section 6.1.2, this could be due that data used when comparing between ponds was taken from morning samplings when BOD can present higher concentrations due to up concentration during the night when bacteria can not start the OM degradation since the pond do not reach the minimal oxygen level required. Chlorophyll distribution in Figure 5.12 shows how important the effect of timing is when the presence and behavior of DO is analyzed between different FPs, as the algae activity is linked to the sunlight, in particular during the day, algae produce DO. This explains the higher DO concentrations (Figure 5.10) linked to higher chlorophyll concentration (Figure 5.12) in the afternoon. In the layer close to the sediment the trend of higher BOD concentration reflecting lower DO concentration is not observed. The fact that there is an increase of BOD from the morning to the afternoon (Figure 5.14) when a small increase of DO takes place as well (Figure 5.12), could be due to the presence of settled sludge consisting of BOD, higher load in the afternoon, or, that there is a lower bacterial activity for the degradation of organic matter in the afternoon. Also, when the sun shines (or in general during the day), temperature rises. When no mixing occurs, stratification in the water column takes place. On top, the warmer water layers (which also are less dense) are situated. Due to lower density of the top layers, the density of algae and other suspended solids can become too high to be suspended in the upper layers and therefore start to sink, thereby increasing the BOD concentration near the bottom.
6.1.3 Facultative ponds vs. maturation ponds
Since the effluent of the FPs goes into the MPs, a comparison between these two ponds is performed
based on the data collected in this study for FPs and the data collected by Arevalo (2014) for the MPs.
A FP mainly focuses on the removal of BOD and nutrients, but can also partially remove pathogens while
a MP, being the third stage of treatment in a conventional WSP system, has as a key function for
pathogen removal (Sah et al., 2012). For this reason it is expected to have a decrease in BOD
concentrations from the inlet towards the outlet in the FP followed by a low and constant BOD
concentration along the MP.
········································································································································································
54 Discussion
Figure 6.2. Average BOD spatial distribution measured in the facultative and maturation ponds 30 cm under the water level. a) Line 2, b) Line 1
Figure 6.3. Average BOD spatial distribution measured in the facultative and maturation ponds 15 cm over the sediment surface. a) Line 2, b) Line 1
Figure 6.2 and Figure 6.3 show that the expected BOD behavior on average occurs in the WWTP –
Ucubamba for the layer close to the surface and to the sediments, respectively. The slight increase of
BOD observed in the effluent of MP1, may be due to algal biomass present (Arevalo, 2014).
Regarding DO concentrations, a statistical model was also developed to test the variability between maturation and facultative ponds. The obtained results show an effect by pond type on dissolved oxygen concentrations only for line 1. This can be explained due that FP1 has a great sludge accumulation, which provides higher BOD concentrations having an influence on the DO present in the pond.
········································································································································································ 55 Discussion
The p values obtained for FP1 vs. MP1 show a 5 % of confidence that the effect of depth on dissolved oxygen depends on the type of pond, which was expected due to the difference in characteristic between facultative and maturation ponds. There is an effect of day and hour of the day on DO depending on depth and type of pond reflecting the diurnal photosynthesis variation (see table 6.1). These outcomes also show that ‘Depth’ seems to be the most important factor.
Table 6.1. Fixed effects p values for the basic linear mixed model with DO concentrations
for the combination of the FPs and MPs
Effect Pr > F
Depth*Location*Pond 0.0004
Pond 0.0212
Timing*Pond 0.339
Depth*Pond 0.0015
Time*Pond 0.1168
Depth*Time*Pond 0.0001
Timing*Depth*Pond 0.0072
Timing*Time*Pond 0.2302
When doing an analysis per row and column between each pond, see Table 6.2, a variability is observed
only in the measurements close to the surface (30 cm under the surface of the pond) for line 1 with a 5
% of confidence, while 15 cm over to the bottom no such variability is observed.
It was expected that in MPs the presence of oxygen will be uniform from the surface to the bottom due
to the fact that there is light penetration along the water column since this ponds are not as deep as FPs
(Arevalo, 2014), however outcomes show that near the bottom FPs and MPs have similar DO
concentrations, this could be due that the design of the MPs in the WWTP Ucubamba is 2 m high.
Table 6.2. DO analysis per rows and columns at each depth between FPs and MPs
Effect Pr > |t|
15cm: col 1: Facultative1-maturation1 0.7997
15cm: col 2: Facultative1-maturation1 0.8507
15cm: col 3: Facultative1-maturation1 0.8991
15cm: row 1: Facultative1-maturation1 0.9324
15cm: row 2: Facultative1-maturation1 0.8652
15cm: row 3: Facultative1-maturation1 0.9292
30 cm: col 1: Facultative1-maturation1 0.0059
30 cm: col 2: Facultative1-maturation1 0.0013
30 cm: col 3: Facultative1-maturation1 0.0022
30 cm: row 1: Facultative1-maturation1 0.0019
30 cm: row 2: Facultative1-maturation1 0.0041
30 cm: row 3: Facultative1-maturation1 0.0024
········································································································································································
56 Discussion
6.2 Variability of the dissolved oxygen in the facultative ponds
6.2.1 Variability within each facultative pond
When analyzing only the average of the data it is clearly remarked by the results presented in Table 5.1
that there is an effect of time, depth and location (related to the depth) which leads to the statistical
analysis considering all the data separately.
In Table 5.2 it can be seen that depth and its correlation with time and timing have an effect on DO
concentrations in the FP1 at 5 % level of significance. From this table it can also be mentioned that there
is no expected variation in the DO related to the location in the facultative ponds due that the obtained
p values are higher than 5 %.
This variation of DO in the FP1 related to Depth, Depth-Time and Timing-Depth also shows the
importance of analyzing the results separately for the morning and the afternoon for each depth as it is
presented in Section 5.1.2. In the FP2 this variation is not observed and since all the samplings were
performed in the morning time the significances mentioned in Table 5.1 were expected to be still
present, this sudden change may be due that the small amount of data used for the statistical model.
The fact that in both ponds there is no influence of the day and time of the day (time and timing) over
DO as well as the absence of influence of depth in FP2 may also be related to the scarcity of data for the
statistical model development. In theory (i.e. von Sperling, 2007; Mara, 2004) and in some other studies
(i.e. Beran and Kargi, 2005; Kayombo et al., 2002; Tadesse et al., 2004) it is mentioned that FPs show
diurnal variations in DO which also decreases gradually in the pond with increasing depth.
6.2.2 Facultative pond 1 vs. Facultative pond 2
When comparing between ponds the influence of depth, time and timing is more noticeable than when
comparing only within one pond. Contrary to what was expected, the outcomes show that there is no
pond type influence, however when this factor is related with other factors (e.g. Timing*Depth) an
influence in the variability of DO in the FPs is observed.
As it is shown in Figure 5.4 comparing between ponds at the same time of the day and Figure 5.10 for
FP1 along the day, it was also expected that the correlation between timing and type of pond will have
an influence in DO considering the presence/absence of light. This result was not obtained.
Nevertheless, when adding extra information (Timing*Pond*Depth), there is an influence over DO.
The absence of variability near the bottom, as mentioned before, is the overall result of the DO added to
this lower level due to diffusion and transport via water flow and its consumption by the few algae
present, the presence of settled sludge consisting of BOD and higher bacterial concentration. This leads
to a lower DO concentration and limited dynamics.
········································································································································································ 57 Discussion
6.2.3 Influence of chlorophyll
The outcomes from Table 5.5 show only influence of the chlorophyll over DO for the FP2 which was also
expected for FP1 due that the main source of DO in secondary facultative ponds is from the process of
photosynthesis and the previous pond in the system Kayombo et al., 2002) which is, in this case, an AP.
This result can be related to the small number of samples collected during the field work.
Chlorophyll being more constant can be due to limited reproduction rate or the fact that the water can become a little warmer during the day and, consequently, less oxygen can be dissolved (and may be released to the atmosphere).
It can also be possible that at high concentrations too much algae are present, intercepting the light of other algae, or with other words: oxygen production can be as high at lower chlorophyll concentrations (with more efficient use).
Oxygen production can also be limited by a limited amount of carbon dioxide being present in the water.
6.2.4 Influence of climatic conditions It was expected that wind speed and solar radiation will have influence over DO processes in the FPs for
the flow mixture and photosynthesis process (Alvarado, 2013; Shilton 2005). Since algae are
autotrophic, the presence/absence of solar radiation will determine their oxygen production while the
presence of wind can alter the flow in the pond creating a mixture within the pond.
The effect referred to the type of pond, its depth and location means that in order to get stronger
conclusions regarding the influence of climatic conditions, specific climatic measurements should be
taken in each location studied or with a station situated in the same location than the WWTP.
6.2.5 Influence of other chemical parameters
It is expected that higher BOD values will lower the DO concentration and vice versa, so even though
there is no influence of BOD on DO in the FPs it will be considered for the development of the prediction
model.
These outcomes may be due that the sampling for this measurement was performed by using integrated samples per column and not independent samples per location (see section 4.2.2).
········································································································································································
58 Discussion
6.3 Predictive model for describing variability of dissolved oxygen in facultative ponds
6.3.1 Predictive model for dissolved oxygen
In the analysis of variability of the dissolved oxygen in the FPs (section 5.2), it was established that there
is an influence of time, timing, depth and chlorophyll on the DO variability. These outcomes were
different for each pond for different reasons that should be considered in future research (e.g. different
mixing in the ponds, resulting in more uniform concentrations in certain ponds). However, all the
predictors were considered for FP1 and FP2 in separate models.
Besides the separate model for each pond, one model was also developed with the measurements 30
cm under the water level and 15 cm over the sediment layer separately, this way the influence of the
depth is taken into consideration.
Because of the small amount of data collected it is expected that a lot of uncertainty is present in the predictions. For this reason and in order to keep the model simple, the day of the sampling (time) will not be considered as a predictor but the hour of the day considering the diurnal variation of the DO. This previous considerations give us four different models (two for each pond at two different depths) with DO as a response variable and chlorophyll and time as predictors. Kayombo et al., (2000) present a conceptual model of DO processes in secondary facultative WSP which is presented in Figure 6.4 in a simplified form, where photosynthesis, respiration by algae biomass and oxidation of OM are the leading processes. Since photosynthesis and respiration by algae involve algae concentration in the pond, when considering chlorophyll concentration these leading processes are considered in the predictive model.
Figure 6.4. Conceptual model of DO processes in secondary facultative WSP (after Kayombo et al., 2000)
········································································································································································ 59 Discussion
Light intensity and temperature, which are forcing factors for the photosynthesis (Kayombo et al., 2000),
are not being considered as primary input variables in this study.
Oxidation of OM is the leading process still missing in the predictive model. For this reason BOD will be a
third predictor in the model, since by definition, BOD test measures the molecular oxygen utilized during
a specific incubation period for the biochemical degradation of organic material (APHA et al., 2012).
When analyzing the correlation between measured and predicted data of the model developed for each
one of the four scenarios, a high variability was found in the correlation measurements as a
consequence of the small sample size, meaning that there is a lot of uncertainty in the prediction, hence
the fluctuation in correlations.
Since the variability originates from the measurement error and random intercept, in order to get a
relatively constant predicted correlation, the sample size should be increased in future research. As such
it can be expected that on average most of the random terms would always be close to their mean.
When applying the obtained models, their limitations, based on the data considered for model
developing and presented in Table 6.3, should be taken into consideration.
Table 6.3. Limitations of the predictive models
Facultative Pond 1 Facultative Pond 2
DO Up to 20 mg.L-1 Surface: Up to 15 mg.L-1 Bottom: Up to 2 mg.L-1
BOD Between 25 and 55 mg.L-1 Between 25 and 40 mg.L-1
Chlorophyll Up to 500 µg.L-1 Up to 500 µg.L-1
Timing Between 09:00 and 17:00 Between 09:00 and 12:00
Validated NO NO *The limitations correspond to both depths considered in this study, unless mentioned differently.
6.3.2 Prediction of dissolved oxygen concentrations In order to predict DO values from each pond at different depth by the use of the predicted models
already obtained, ranges for BOD, chlorophyll and timing were determined based on the data obtained
in the sampling campaign.
In the development of the predictive models, an uncertainty in the prediction was found based on the
variability from the measurement error and the random intercept, for this reason both parameters were
ignored for the prediction of DO concentrations assuming that the model will predict a concentration for
a location which is assumed to have zero variability from the mean and no measurement error.
Even though the chlorophyll range should be mostly within higher values when studying the layer close
to the surface and lower values when close to the bottom, a similar range from 0 to 500 µg.L-1 was
considered since during the sampling campaign values within this range were found in both layers.
Nevertheless when analyzing the results, when it refers to the surface layer, high chlorophyll
concentrations are considered and low concentrations for the layer close to the sediment as well.
········································································································································································
60 Discussion
As it was expected, in FP1 (Figure 5.16) higher DO concentrations were predicted in the afternoon in
both layers as well as higher DO concentrations in the layer close to the surface compared with the layer
close to the sediments.
It was also expected to get lower DO concentration values as BOD concentration increases which is not
the case for these scenarios (Figure 5.16); this may be due that since most of the data in the FP1 (2 out
of 3 sampling periods) was taken during the afternoon, when DO values are higher, the mean tends to
these high values and, as it was mentioned before, the predictions concern to a location which is
assumed that it has zero variability from the mean.
For the third and fourth scenario, see Figure 5.17, the predicted results 15 cm over the sediment layer
match the measured data, showing very low DO concentrations at every location. Regarding the layer
close to the surface it can be noticed that the predictions show an increase in the DO concentrations
towards the afternoon, being the values before noon within the range of the measured data and the
predicted data for the afternoon completely out of this range; this is due to the timing limitation, that
the model has for the third and fourth scenario, which is that for FP2 the range of time of the model
goes from 09:00 until 12:00 (Table 6.3).
In the analysis of the DO distribution between ponds, performed with the data collected, that was
presented in Figure 5.4 and Figure 5.7, it was noticed that FP1 has higher DO concentration in both
layers, while in the prediction presented in Figure 5.16 and Figure 5.17, this was only confirmed for the
layer 15 cm over the sediment surface and for the layer close to the surface before noon, this is due to
the model limitations for the timing in FP2 whose range is between 09:00 and 12:00 (see Table 6.3).
6.4 Predictive model for describing variability of biochemical oxygen demand in facultative ponds
6.4.1 Predictive model for biochemical oxygen demand In order to develop a BOD predictive model, the same predictors as for DO were considered but in this
case the response variable is BOD and DO was determined as predictor.
The four obtained models correspond to each scenario specified for DO prediction models, considering
also the limitations presented in Table 6.3
For each scenario, a high variability was also found in the correlation measurements as a consequence
of the small sample size, meaning that there is a high uncertainty in the prediction, what leads to related
fluctuation in correlations.
········································································································································································ 61 Discussion
6.4.2 Prediction of biochemical oxygen demand concentrations
Based on the uncertainty found in the models developed for each scenario for the prediction of BOD
concentrations, the measurement error and the random intercept were ignored assuming that the
model will predict a concentration for a location which is assumed to have zero variability from the
mean and no measurement error.
It is expected that higher BOD values will lead to lower DO concentration, which is not shown in the
predicted values since when increasing DO in FP1, BOD concentrations also increase. Nevertheless,
when comparing the predicted BOD values in Figure 5.18 with the predicted DO values in Figure 5.16, it
can be noticed that the lower DO concentrations predicted are situated where the lower values for BOD
are predicted.
Due to the timing limitations of the model for the third and fourth scenario, from 09:00 to 12:00 as it is
detailed in Table 6.3, in the afternoon the predicted BOD values are extremely low and high for the third
and fourth scenario, respectively (Figure 5.19).
6.5 Presence of macroinvertebrates in the facultative ponds
Wang et al., (2009) mention different studies where different species, including benthic species, of
insects have been reported in WWTP in United States For this reason it was expected that there would
be an influence of macroinvertebrates over the oxygen process due to their oxygen consumption during
their respiration process.
The absence of macroinvertebrates in the WWTP Ucubamba, presented in the results section, means
that there is no high influence of macroinvertebrates in the oxygen processes in the FP.
It should be kept in mind that the substrates used for the macroinvertebrates’ sampling, even though
they were taken from water bodies present in the same area than the FPs, is not found in WSPs,
additionally, due to the substrates bag’s weight they were close to the bottom of the pond and in some
areas the substrates may have been buried in the sludge layer.
········································································································································································
62 Discussion
········································································································································································ 63 Conclusions and recommendations
Part VII Conclusions and recommendations
Even though both FPs were designed and built to work in parallel treating the same type of waste water
their performance differs the one from the other. DO and BOD concentrations are higher in the FP1
even for the predicted values under the same conditions.
When comparing the BOD behavior from the FPs inlet towards the MPs outlet, for both lines, a decrease
in its concentrations occurs along FPs followed by a low and constant BOD concentration along the MPs
with a slight increase of BOD observed in the effluent of MP1.
The diurnal DO cycle is clearly present in FP1 whose information is available from samples taken in the
morning and in the afternoon, also, when comparing within FPs, there is a decrease of DO as more BOD
is present, this relation is not present when comparing between FPs being the FP1 the one with higher
DO and BOD concentrations.
When comparing the DO behavior between ponds (FP1 vs. FP2 and FPs vs. MPs), a variability of the DO
is present in the layer close to the surface contrary to what happens close to the bottom where there is
an absence of DO’s variability.
The low water quality indexes found in the FPs reflect that there is no macroinvertebrates’ influence
over the oxygen processes.
Different predictive models for the DO and BOD concentration were developed considering two depths
per pond, one close to the sediments layer and one close to the water level, which are presented in
sections 5.3., 5.4 and discussed in sections 6.3, 6.4. Each of these models has certain boundary
conditions related to DO, BOD, chlorophyll and timing.
Recommendations
All the objectives stated for this study were fulfilled with the techniques applied, nevertheless since this
is the first time that this type of work has been performed in these ponds, the obtained models have
certain limitations so it is recommended to reinforce the initial information with extra sampling
considering 24 hours cycles and sampling at the same time in the different ponds.
For further research it is recommended to put some extra effort in the influence of chlorophyll over
oxygen processes in FPs since this study shows that there is such influence only for FP2. The
measurements of wind speed and solar radiation in the same location than the WWTP is also
recommended.
The outcomes from the models developed were according to the expected values within their
limitations ranges; however, in order to advance to further research it is important to validate the
obtained models.
········································································································································································
64 Conclusions and recommendations
········································································································································································ 65 References
Part VIII References
Adèr, H. J., Mellenbergh, G. J., Hand, D. J., 2008, Advising on research methods: a consultant´s
companion. Rosmalen, The Netherlands: Johannes van Kessel Publishing.
Alvarado, A., 2005, Drying of stabilization pond sludge and its quality for reuse in land application. Msc
thesis, UNESCO-IHE.
Alvarado, A., 2013, Advanced dynamic modeling of wastewater treatment ponds. PhD thesis, Ghent
University, Belgium.
Alvarado, A., Vendantam, S., Durazno, G., Nopens, I., 2011, Hydraulic assessment of waste stabilization
ponds: Comparison of computational fluid dynamics simulations against tracer data. Maskana –
Universidad de Cuenca 2, 81-89.
APHA, American Public Health Association, American Water Works Association, Water Environment
Federation, 2012, Standard methods for the examination of water and wastewater. 22nd Edition.
Arevalo, M.B., 2014, Spatial-temporal analysis of oxygen related processes in facultative ponds. Msc
thesis, Ghent University, Belgium.
Bates, D. M., 2010, Lme4: Mixed-effects modeling with R. Madison: Springer.
Becerra Jurado, B., Callanan, M., Gioria, M., Baars, J.R., Harrington, R., Kelly-Quinn, M., 2009,
Comparison of macroinvertebrate community structure and driving environmental factors in natural
and wastewater treatment ponds. Hydrobiologia 634, 153-165.
Beran, B., Kargi, F., 2005, A dynamic mathematical model for wastewater stabilization ponds. Ecological
Modelling 181, 39-57.
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J., 2013, Random effects structure for confirmatory
hypothesis testing: keep it maximal. Journal of memory and language, 68, 255-278.
Bouchard, R.W., Jr., 2004, Guide to aquatic macroinvertebrates of the Upper Midwest. Water Resources
Center, University of Minnesota, St. Paul, MN. 208pp.
Broza, M., Halpern, M., Inbar, M., 2000, Non-biting midges (Diptera; Chironomidae) in waste
stabilization ponds: an intensifying nuisance in Israel. Water Science and Technology Vol 42. 1-2pp,
71-74.
Chu, C.R., Soong, C.K., 1997, Numerical simulation of wind-induced entrainment in a stably stratified
water basin. Journal of Hydraulic Research 35, 21-41.
de Pauw, N., van Damme, D., 1999, Manual for macroinvertebrate identification. BISEL Project.
Cnaan, A., Laird, N. M., Slasor, P., 1997, Using the general linear mixed model to analyse unbalanced
repeated measures and longitudinal data. Statistics in medicine, 16, 2349-2380.
Espinoza, J.E., Rengel, P.A., 2009, Evaluación Hidráulica de la Planta de Tratamiento de Aguas Residuales
Ucubamba – Cuenca. Tesis Previa a la Obtención del título de Ingeniero Civil.
········································································································································································
66 References
EPA, United States Environmental Protection Agency, 2002, Wastewater technology fact sheet:
Facultative lagoons.
ETAPA, Empresa Pública de Alcantarillado, Alcantarillado y Agua Potable - Cuenca, 2011, Estudio de la
calidad de los rios Tomebamba, Yanuncay y Tarqui agias arriba de las captaciones de agua para la
ciudad de Cuenca.
Fox, J. (2002). Structural equation models. Retrieved August 13, 2014, from
http://cran.rproject.org/doc/contrib/Fox-Companion/appendix-sems.pdf
Gu, R., Stefan, H.G., 1995, Stratification dynamics in wastewater stabilization ponds. Wat. Res. Vol. 29. 8,
1909-1923.
Gabriels, W., 2007, Multimetric assessment of freshwater macroinvertebrate communities in Flanders,
Belgium. PhD thesis. Faculty of Bioscience
Gabriels, W., Lock, K., De Pauw, N., Goethals, P., 2010, Multimetric Macroinvertebrate Index Flanders
(MMIF) for biological assessment of rivers and lakes in Flanders (Belgium). Limnologica. 40, 199-207.
Harvey, A., Koopman, S. J., & Shephard, N. (Eds.). (2004). State space and unobserved component
models: theory and applications. Cambridge, UK: Cambridge University Press
Kayombo, S., Mbwette, T. S. A., Mayo, A. W., Katima, J. H. Y., Jorgensen, S. E., 2000, Modelling diurnal
variation of dissolved oxygen in waste stabilization ponds. Ecological Modelling 127, 21-31.
Kayombo, S., Mbwette, T. S. A., Mayo, A. W., Katima, J. H. Y., Jorgensen, S. E., 2002, Diurnal cycles of
variation of physical-chemical parameters in waste stabilization ponds. Ecological Engineering 18,
287-291.
Mara,D., 2004, Domestic wastewater treatment in developing countries, Earthscan, London.
Metcalf and Eddy Inc., 2003, Wastewater engineering: Treatment and reuse. 4th Edition: New York,
McGraw-Hill.
Olsson, U. (2002). Generalized linear models: an applied approach. Lund: Studentlitteratur
Pearson, H.W., Mara, D.D., Mills, S.W., Smallman, D.J., 1987, Factors determining algal populations in
waste stabilization ponds and the influence of algae on pond performance. Wat. Sci. Tech. Vol. 19.
12, 131-140.
Romero, J.A., 2000, Tratamiento de aguas residuals: Teoria y principios de diseño. 1ra Edicion: Bogota,
Editorial escuela colombiana de ingenieria.
Sah, L., Rousseau, D.P.L., Hooijmans, C.M., Lens, P.N.L., 2011, 3D model for a secondary facultative
pond. Ecological Modelling 222, 1592-1603.
Sah, L., Rousseau, D.P.L., Hooijmans, C.M., 2012, Numerical modelling of waste stabilization ponds:
Where do we stand?. Water Air Soil Pollut 223, 3155-3171.
Shanthalla, M., Shankar, P., Hosmani, P., 2009, Diversity of phytoplanktons in a waste stabilization pond
at Shimoga Town, Karnataka State, India. Environ Monit Assess 151, 437-443.
········································································································································································ 67 References
Shilton, A., 2005, Pond treatment technology: London, IWA Publishing.
Shilton, A., Wilks, T., Smyth, J., Bickers, P., 2000, Tracer studies on a New Zealand waste stabilization
pond and analysis of treatment efficiency. Water Science and Technology. 42, 343-348.
Tadesse, I., Green, F.B., Puhakka, J.A., 2004, Seassonal and diurnal variations of temperature, pH and
dissolved oxygen in advanced integrated wastewater pond system (R) treating tannery effluent.
Water Research 38, 645-654.
von Sperling, M., 2007, Waste stabilization ponds: London, IWA Publishing.
Wang, L.K., Pereira, N.C., Hung, Y.-T., Shammas, N.K., 2009, Biological treatment processes. Handbook of
Environmental Engineering, Vol. 8, Humana Press.
Werker, A.G., Dougherty, J.M., McHenry, J.L., Van Loon, W.A., 2002, Treatment variability for wetland
wastewater treatment design in cold climates. Ecological Engineering 19, 1-11.
WHO, World Health Organization, 1996, Water quality assessments – A guide to use of biota, sediments
and water in environmental monitoring. Second Edition.
Zamora González, H., 2007, El indice BMWP y la evaluación biológica de la calidad del agua en los
ecosistemas acuáticos epicontinentales naturales de Colombia. Universidad del Cauca, Popayan.
········································································································································································
68 References
········································································································································································ 69 Appendix
Part IX Appendix
Appendix 1. Taxa list of aquatic macroinvertebrates for calculating the BBI with their respective tolerance scores. (Gabriels, 2007)
Taxon TS Plathelminthes
Bdellocephala -
Crenobia -
Dendrocoelum -
Dugesia s.l. -
Phagocata -
Planaria -
Polycelis -
Polychaeta
Ampharetidae 7
Oligochaeta
Aelosomatidae -
Branchiobdellidae -
Enchytraeidae -
Haplotaxidae -
Lumbricidae -
Lumbriculidae -
Naididae s.s. -
Tubificidae 6
Hirudinea
Cystobranchus 5
Dina 5
Erpobdella 5
Glossiphonia 5
Haementeria 5
Haemopis 5
Helobdella 5
Hemiclepsis 5
Hirudo 5
Piscicola 5
Theromyzon 5
Trocheta 5
Mollusca
Acroloxus 3
Ancylus 3
Anisus 4
Anodonta 4
Aplexa 4
Armiger 4
Bathyomphalus 4
Bithynia 4
Bythinella 4
Corbicula 4
Dreissena 4
Ferrissia 3
Gyraulus 4
Hippeutis 4
Lithoglyphus 4
Lymnaea s.l. 4
Margaritifera 4
Marstoniopsis 4
Menetus 4
Myxas 4
Physa s.s. 4
Physella 4
Pisidium 5
Planorbarius 4
Planorbis 4
Potamopyrgus 4
Pseudamnicola s.l. 4
Pseudanodonta 4
Segmentina 4
Sphaerium 5
Theodoxus 4
Unio 4
Valvata 4
Viviparus 4
Acari
Hydracarina s.l. -
Crustacea
Argulidae -
Asellidae 5
Astacidae -
Atyidae -
Cambaridae -
Chirocephalidae -
Corophiidae -
Crangonyctidae -
Gammaridae 4
Janiridae -
Leptestheriidae -
Limnadiidae -
Mysidae -
Palaemonidae -
Panopeidae -
Sphaeromatidae -
Talitridae -
Triopsidae -
Varunidae -
Diptera
Athericidae -
Blephariceridae -
Ceratopogonidae -
Chaoboridae -
Chironomidae: -
-non thummi-plumosus 6
-thummi-plumosus -
Culicidae -
Cylindrotomidae -
Dixidae -
Dolichopodidae -
Empididae -
Ephydridae -
Limoniidae -
Muscidae -
Psychodidae -
Ptychopteridae -
Rhagionidae -
Scatophagidae -
Sciomyzidae -
Simuliidae -
Stratiomyidae -
Syrphidae 7
Tabanidae -
Thaumaleidae -
Tipulidae -
Megaloptera
Sialis -
Coleoptera
Dryopidae -
Dytiscidae -
Elminthidae -
Gyrinidae -
Haliplidae -
Hydraenidae -
Hydrophilidae -
Hygrobiidae -
Noteridae -
Psephenidae -
Scirtidae -
Hemiptera
Aphelocheirus 4
Arctocorisa 5
Callicorixa 5
Corixa 5
Cymatia 5
Gerris s.l. 5
Glaenocorisa 5
Hebrus 5
Hesperocorixa 5
Hydrometra 5
llyocoris 5
Mesovelia 5
Micronecta 5
Microvelia 5
Naucoris 5
Nepa 5
Notonecta 5
Paracorixa 5
Plea 5
Ranatra 5
Sigara 5
Velia 5
Odonata
Aeshna 4
Anax 4
Brachytron 4
Calopteryx 4
Cercion 4
Ceriagrion 4
Coenagrion 4
Cordulegaster 4
Cordulia 4
Crocothemis 4
Enallagma 4
Epitheca 4
Erythromma s.s. 4
Gomphus 4
Ischnura 4
Lestes 4
Leucorrhinia 4
Libellula 4
Nehalennia 4
Onychogomphus 4
Ophiogomphus 4
Orthetrum 4
Oxygastra 4
Platycnemis 4
Pyrrhosoma 4
Somatochlora 4
Sympecma 4
Sympetrum 4
Ephemeroptera
Baetis 3
Brachycercus 3
Caenis 3
Centroptilum 3
Cloeon 3
Ecdyonurus 1
Epeorus 1
Ephemera 3
Ephemerella s.l. 3
Ephoron 3
Habroleptoides 3
Habrophlebia 3
Heptagenia s.l. 1
Isonychia 3
Leptophlebia s.s. 3
Metreletus 3
Oligoneuriella 3
Paraleptophlebia 3
Potamanthus 3
Procloeon 3
Rhitrogena 1
Siphlonurus 3
Trichoptera
Beraeidae 2
Brachycentridae 2
Ecnomidae 2
Glossosomatidae 2
Goeridae 2
Hydropsychidae -
Hydroptilidae 2
Lepidostomatidae 2
Leptoceridae 2
Limnephilidae s.l. 2
Molannidae 2
Odontoceridae 2
Philopotamidae -
Phryganeidae 2
Polycentropodidae -
Psychomyiidae -
Rhyacophilidae -
Sericostomatidae 2
Plecoptera
Amphinemura 1
Brachyptera 1
Capnia s.l. 1
Chloroperla s.l. 1
Dinocras 1
Isogenus 1
Isoperla 1
Leuctra 1
Marthamea 1
Nemoura 1
Nemurella 1
Perla 1
Perlodes 1
Protonemura 1
Rhabdiopteryx 1
Taeniopteryx 1
········································································································································································
70 Appendix
Appendix 2. Taxa taken into account for calculating the MMIF with their respective tolerance score ranging from 10 for very pollution sensitive to 1 for very pollution tolerant taxa. (Gabriels et al., 2010)
Taxon TS Plathelminthes
Bdellocephala 5
Crenobia 7
Dendrocoelum 5
Dugesia s.l. 5
Phagocata 5
Planaria 6
Polycelis 6
Polychaeta
Ampharetidae 3
Oligochaeta
Aelosomatidae 2
Branchiobdellidae 2
Enchytraeidae 2
Haplotaxidae 4
Lumbricidae 2
Lumbriculidae 2
Naididae s.s. 5
Tubificidae 1
Hirudinea
Cystobranchus 4
Dina 4
Erpobdella 3
Glossiphonia 4
Haementeria 4
Haemopis 4
Helobdella 4
Hemiclepsis 4
Hirudo 4
Piscicola 5
Theromyzon 4
Trocheta 4
Mollusca
Acroloxus 6
Ancylus 7
Anisus 5
Anodonta 6
Aplexa 6
Armiger 6
Bathyomphalus 5
Bithynia 5
Bythinella 8
Corbicula 5
Dreissena 5
Ferrissia 7
Gyraulus 6
Hippeutis 6
Lithoglyphus 6
Lymnaea s.l. 5
Margaritifera 10
Marstoniopsis 5
Menetus 5
Myxas 7
Physa s.s. 5
Physella 3
Pisidium 4
Planorbarius 5
Planorbis 6
Potamopyrgus 6
Pseudamnicola s.l. 5
Pseudanodonta 6
Segmentina 6
Sphaerium 4
Theodoxus 7
Unio 6
Valvata 6
Viviparus 6
Acari
Hydracarina s.l. 5
Crustacea
Argulidae 5
Asellidae 4
Astacidae 8
Atyidae 7
Cambaridae 6
Chirocephalidae 6
Corophiidae 5
Crangonyctidae 4
Gammaridae 5
Janiridae 5
Leptestheriidae 6
Limnadiidae 6
Mysidae 5
Palaemonidae 5
Panopeidae 4
Sphaeromatidae 4
Talitridae 5
Triopsidae 6
Varunidae 4
Diptera
Athericidae 7
Blephariceridae 7
Ceratopogonidae 3
Chaoboridae 3
Chironomidae:
-non thummi-plumosus 3
-thummi-plumosus 2
Culicidae 3
Cylindrotomidae 3
Dixidae 6
Dolichopodidae 3
Empididae 3
Ephydridae 3
Limoniidae 4
Muscidae 3
Psychodidae 3
Ptychopteridae 3
Rhagionidae 3
Scatophagidae 3
Sciomyzidae 3
Simuliidae 5
Stratiomyidae 4
Syrphidae 1
Tabanidae 3
Thaumaleidae 3
Tipulidae 3
Megaloptera
Sialis 5
Coleoptera
Dryopidae 6
Dytiscidae 5
Elminthidae 7
Gyrinidae 7
Haliplidae 6
Hydraenidae 6
Hydrophilidae 5
Hygrobiidae 5
Noteridae 5
Psephenidae 6
Scirtidae 7
Hemiptera
Aphelocheirus 8
Arctocorisa 5
Callicorixa 5
Corixa 5
Cymatia 6
Gerris s.l. 6
Glaenocorisa 5
Hebrus 6
Hesperocorixa 5
Hydrometra 6
llyocoris 5
Mesovelia 6
Micronecta 6
Microvelia 7
Naucoris 6
Nepa 6
Notonecta 5
Paracorixa 5
Plea 6
Ranatra 6
Sigara 5
Velia 7
Odonata
Aeshna 6
Anax 6
Brachytron 7
Calopteryx 8
Cercion 7
Ceriagrion 7
Coenagrion 6
Cordulegaster 9
Cordulia 7
Crocothemis 7
Enallagma 7
Epitheca 7
Erythromma s.s. 7
Gomphus 7
Ischnura 6
Lestes 7
Leucorrhinia 7
Libellula 7
Nehalennia 7
Onychogomphus 7
Ophiogomphus 7
Orthetrum 7
Oxygastra 7
Platycnemis 7
Pyrrhosoma 7
Somatochlora 7
Sympecma 7
Sympetrum 7
Ephemeroptera
Baetis 6
Brachycercus 7
Caenis 6
Centroptilum 7
Cloeon 6
Ecdyonurus 9
Epeorus 10
Ephemera 8
Ephemerella s.l. 8
Ephoron 9
Habroleptoides 8
Habrophlebia 8
Heptagenia s.l. 10
Isonychia 7
Leptophlebia s.s. 8
Metreletus 7
Oligoneuriella 7
Paraleptophlebia 8
Potamanthus 8
Procloeon 7
Rhitrogena 10
Siphlonurus 7
Trichoptera
Beraeidae 9
Brachycentridae 9
Ecnomidae 6
Glossosomatidae 9
Goeridae 9
Hydropsychidae 6
Hydroptilidae 8
Lepidostomatidae 9
Leptoceridae 8
Limnephilidae s.l. 8
Molannidae 9
Odontoceridae 9
Philopotamidae 6
Phryganeidae 9
Polycentropodidae 6
Psychomyiidae 7
Rhyacophilidae 8
Sericostomatidae 8
Plecoptera
Amphinemura 9
Brachyptera 10
Capnia s.l. 10
Chloroperla s.l. 10
Dinocras 10
Isogenus 10
Isoperla 10
Leuctra 9
Marthamea 10
Nemoura 8
Nemurella 8
Perla 10
Perlodes 10
Protonemura 9
Rhabdiopteryx 10
Taeniopteryx 10
········································································································································································ 71 Appendix
Appendix 3. System for the BMWP index determination adapted for Colombia. (Zamora, 2007)
Order Families Score
Plecoptera Ephemeroptera Coleoptera Odonata Diptera Unionoida Acari Hidroida
Perlidae Oligoneuridae, Euthyplociidae, Polymtarcyidae, Odontoceridae, Glossosomatidae, Rhyacophilidae, Calamoceratidae, Hydroptilidae, Anomalopsychidae, Atriplectididae. Psephenidae, Ptilodactylidae, Lampyridae. Polythoridae. Simullidae. Unionidae. Lymnessiidae. Hidridae.
10
Ephemeroptera Tricoptera Coleoptera Odonata Diptera Gordioidae Lepidoptera Mesogastropoda Hirudiniformes
Leptophlebiidae, Efemeridae. Hydrobiosidae, Philopotamidae, Xiphocentronidae. Gyrinidae, Scirtidae. Gomphidae, Megapodagrionidae, Coenagrionidae. Simullidae. Gordiidae, Chordodedae. Pyralidae. Ampullariidae. Hirudinae.
9
Ephemeroptera Tricoptera Coleoptera Odonata Hemiptera Diptera Decapoda Basommatophora
Baetidae, Caenidae. Hidropsychidae, Leptoceridae, Helicopsychidae. Dytiscidae, Dryopidae. Lestidae, Calopterygidae. Pleidae, Saldidae, Guerridae, Veliidae, Hebridae. Dixidae. Palaemonidae, Pseudothelpusidae. Chilinnidae.
8
Ephemeroptera Tricoptera Coleoptera Odonata Hemiptera Diptera Basommatophora Mesogastropoda Archeogastrpoda
Tricorythidae, Leptohyphidae. Polycentropodidae. Elmidae, Staphylinidae. Aeshnidae. Naucoridae, Notonectidae, Mesolveiidae, Corixidae. Psychodidae. Ancylidae, Planorbidae. Melaniidae, Hydrobiidae. Neritidae.
7
Coleoptera Odonata Hemiptera Diptera Megaloptera Decapoda Anphipoda Tricladida
Limnichidae, Lutrochidae. Libellulidae. Belostomatidae, Hydrometridae, Gelastocoridae, Nepidae. Dolichopodidae. Corydalidae, Sialidae. Atyidae. Hyalellidae. Planariidae, Dugesiidae.
6
Coleoptera Diptera Basommatophora
Chrysomelidae, Haliplidae, Curculionidae. Tabanidae, Stratiomyidae, Empididae. Thiaridae.
5
Coleoptera Diptera Basommatophora
Hidrophilidae, Noteridae, Hydraenidae, Noteridae. Tipulidae, Ceratopogonidae. Limnaeidae, Sphaeridae.
4
Diptera Basommatophora Glossiphoniiformes
Culicidae, Muscidae, Sciomizidae. Physidae. Glossiphoniidae, Cyclobdellidae, Cylicobdellidae.
3
Diptera Heplotaxida
Chironomidae, Ephydridae, Syrphidae. All the families except Tubifex
2
Heplotaxida Tubificidae (Tubifex) 1
········································································································································································
72 Appendix
Appendix 4. Influence of BOD, COD, Kjeldahl-N, Phosphorus and Total Solids over DO variability in the facultative ponds
Biochemical Oxygen Demand
Effect Pr > F
FP1 FP2
Depth*Column_sample 0.2620 0.8543
Timing 0.6696 0.4804
Day 0.2559 0.6463
Depth 0.1150 0.3336
Depth*Day 0.1620 0.5161
Timing*Depth 0.0929 0.3582
Timing*Day 0.2655 0.6477
BOD 0.5251 0.4835
BOD*Depth 0.3535 0.6046
Timing*BOD 0.7103 0.3933
Chemical Oxygen Demand
Effect Pr > F
FP1 FP2
Depth*Column_sample 0.9988 1
Timing 0.7134 0.7050
Day 0.9185 0.7472
Depth 0.3909 0.6599
Depth*Day 0.4163 0.3641
Timing*Depth 0.2615 0.3381
Timing*Day 0.9976 0.6773
COD 0.9087 0.9347
COD*Depth 0.7707 0.3891
Timing*COD 0.8332 0.8591
Total Kjeldahl-N
Effect Pr > F
FP1 FP2
Depth*Column_sample 0.2145 1
Timing 0.2752 0.2180
Day 0.3276 0.3200
Depth 0.2682 0.5656
Depth*Day 0.4761 0.2797
Timing*Depth 0.1305 0.6352
Timing*Day 0.4146 0.3108
Kjeldahl-N 0.2596 0.2193
Kjeldahl-N *Depth 0.5626 0.2477
Timing* Kjeldahl-N 0.2406 0.2182
········································································································································································ 73 Appendix
Phosphorous
Effect Pr > F
FP1 FP2
Depth*Column_sample 0.9365 0.9129
Timing 0.5467 0.9940
Day 0.9992 0.8703
Depth 0.4948 0.9061
Depth*Day 0.6799 0.8944
Timing*Depth 0.6377 0.8966
Timing*Day 0.9990 0.8548
Phosphorous 0.6094 0.8732
Phosphorous*Depth 0.6672 0.9094
Timing* Phosphorous 0.5884 0.9983
Total solids
Effect Pr > F
FP1 FP2
Depth*Column_sample 0.7355 0.3701
Timing 0.0990 0.6300
Day 0.8447 0.8011
Depth 0.114 0.5065
Depth*Day 0.0890 0.4189
Timing*Depth 0.0897 0.5874
Timing*Day 0.7379 0.7628
Total solids 0.1467 0.5300
Total solids*Depth 0.0933 0.6371
Timing* Total solids 0.7355 0.3701
········································································································································································
74 Appendix
Appendix 5. Predicted model for dissolved oxygen: Pearson correlation coefficients First scenario, FP1 30 cm under the water level; UN(1,1) = 0.1813; Residual = 14.169
Second scenario, FP1 15 cm over the sediment layer; UN(1,1) = 17.1859; Residual = 2.9748
Third scenario, FP2 30 cm under the water level; UN(1,1) = 11.3645; Residual = 2.82
r = 0.795
r = 0.870
········································································································································································ 75 Appendix
Fourth scenario, FP2 15 cm over the sediment layer; UN(1,1) = 0.2713; Residual = 1.6298
r = 0.829
r = 0.592
········································································································································································
76 Appendix
Appendix 6. Predicted model for biochemical oxygen demand: Pearson correlation coefficients
First scenario, FP1 30 cm under the water level; UN(1,1) = 1.4595; Residual = 53.0949
Second scenario, FP1 15 cm over the sediment layer; UN(1,1) = 6.2422; Residual = 35.8363
Third scenario, FP2 30 cm under the water level; UN(1,1) = 42.1666; Residual = 0.7762
r = 0.686
r = 0.833
········································································································································································ 77 Appendix
Fourth scenario, FP2 15 cm over the sediment layer; UN(1,1) = 1.5851; Residual = 9.374
r = 0.962
r = 0.779