spatial-temporal analysis of oxygen related processes...

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

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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

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ii Copyright

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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

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iv

Acknowledgment

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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

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Abstract

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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

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Resumen

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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

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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

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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

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Table of contents

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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

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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

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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

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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


Figure 5.18 BOD predictions in function of chlorophyll and timing at different DO


Figure 5.19 BOD predictions in function of chlorophyll and timing at different DO


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

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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

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xviii

List of abbreviations

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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.

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2

Introduction

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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.

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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.


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.

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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)


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).

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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).


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)

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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).


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.

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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.


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.

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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).

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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).

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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.

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18 Research objectives and goals

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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.

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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


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.

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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)


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:

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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:

𝑌𝑖𝑗𝑘 = 𝛽𝑗𝑘𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑖 ∗ 𝑑𝑒𝑝𝑡ℎ𝑖 + 𝜃𝑗𝑑𝑒𝑝𝑡ℎ𝑖 + 𝜓𝑘𝑡𝑖𝑚𝑒𝑖 + Ω𝑡𝑖𝑚𝑖𝑛𝑔𝑖 + 𝛾𝑗𝑘𝑡𝑖𝑚𝑒 ∗ 𝑑𝑒𝑝𝑡ℎ𝑖 + 𝜙𝑗𝑡𝑖𝑚𝑖𝑛𝑔𝑖 ∗𝑑𝑒𝑝𝑡ℎ𝑖 +𝜔𝑘𝑡𝑖𝑚𝑖𝑛𝑔𝑖 ∗ 𝑡𝑖𝑚𝑒𝑖 + 𝑏𝑖𝑗 + 𝜀𝑖𝑗𝑘

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. εijk: 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:

𝑌𝑖𝑗𝑘𝑙 = 𝛽𝑗𝑘𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑖 ∗ 𝑑𝑒𝑝𝑡ℎ𝑖 ∗ 𝑝𝑜𝑛𝑑𝑖 + 𝜃𝑗𝑙𝑑𝑒𝑝𝑡ℎ𝑖 ∗ 𝑝𝑜𝑛𝑑𝑖 + 𝜓𝑘𝑙𝑡𝑖𝑚𝑒𝑖 + Ω𝑙𝑡𝑖𝑚𝑖𝑛𝑔𝑖 ∗ 𝑝𝑜𝑛𝑑𝑖+ 𝛾𝑗𝑘𝑙𝑡𝑖𝑚𝑒 ∗ 𝑑𝑒𝑝𝑡ℎ𝑖 ∗ 𝑝𝑜𝑛𝑑𝑖 + 𝜙𝑗𝑙𝑡𝑖𝑚𝑖𝑛𝑔𝑖 ∗ 𝑑𝑒𝑝𝑡ℎ𝑖 ∗ 𝑝𝑜𝑛𝑑𝑖 + 𝜔𝑘𝑙𝑡𝑖𝑚𝑖𝑛𝑔𝑖 ∗ 𝑡𝑖𝑚𝑒𝑖∗ 𝑝𝑜𝑛𝑑𝑖 + 𝑏𝑖𝑗𝑙 + 𝜀𝑖𝑗𝑘𝑙

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.


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.

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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)


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)

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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.

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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

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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.

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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.


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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.

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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.


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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

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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.

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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


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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.

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38 Results

Figure 5.13. DO spatial distribution in the FP1 at 15 cm over the sediment. a) Measured between


Figure 5.14. BOD spatial distribution in the FP1 at 15 cm over the sediment. a) Measured between


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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.

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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


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.

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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.

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42 Results

Table 5.5. Influence of chlorophyll on DO within ponds


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

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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.

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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:

𝐷𝑂𝑖 = 𝛽0 + 𝛽1 × 𝑐ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑙𝑖 + 𝛽2 × 𝑇𝑖𝑚𝑖𝑛𝑔𝑖 + 𝛽3 × 𝐵𝑂𝐷𝑖 + 𝑏𝑖 + 𝜀𝑖 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. εi: 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.

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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


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

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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


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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:

𝐵𝑂𝐷𝑖 = 𝛽0 + 𝛽1 × 𝑐ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑙𝑖 + 𝛽2 × 𝑇𝑖𝑚𝑖𝑛𝑔𝑖 + 𝛽3 × 𝐷𝑂𝑖 + 𝑏𝑖 + 𝜀𝑖 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. εi: 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


4. FP2 15 cm over the sediment layer 11.3298 0.009105 1.2897 -0.447 1.5851 9.374 0.779

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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


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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


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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

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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

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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.

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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.

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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.

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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

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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.

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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).

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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)

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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.

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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.

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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.

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62 Discussion

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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.

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64 Conclusions and recommendations

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Bouchard, R.W., Jr., 2004, Guide to aquatic macroinvertebrates of the Upper Midwest. Water Resources Center, University of Minnesota, St. Paul, MN. 208pp.

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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.

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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.

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68 References

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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

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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

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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

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72 Appendix

Appendix 4. Influence of BOD, COD, Kjeldahl-N, Phosphorus and Total Solids over DO variability in the facultative ponds

Biochemical Oxygen Demand


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


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

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Phosphorous


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


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

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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

𝐷𝑂𝑖 = −15.8563 + 0.03114 × 𝑐ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑙𝑖 + 0.6822 × 𝑇𝑖𝑚𝑖𝑛𝑔𝑖 + 0.1346 × 𝐵𝑂𝐷𝑖 + 𝑏𝑖 + 𝜀𝑖

Second scenario, FP1 15 cm over the sediment layer; UN(1,1) = 17.1859; Residual = 2.9748

𝐷𝑂𝑖 = −12.3988− 0.00468 × 𝑐ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑙𝑖 + 0.9256 × 𝑇𝑖𝑚𝑖𝑛𝑔𝑖 + 0.1925 × 𝐵𝑂𝐷𝑖 + 𝑏𝑖 + 𝜀𝑖

Third scenario, FP2 30 cm under the water level; UN(1,1) = 11.3645; Residual = 2.82

𝐷𝑂𝑖 = −47.3293 + 0.03634 × 𝑐ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑙𝑖 + 2.722 × 𝑇𝑖𝑚𝑖𝑛𝑔𝑖 + 0.4092 × 𝐵𝑂𝐷𝑖 + 𝑏𝑖 + 𝜀𝑖

r = 0.795

r = 0.870

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Fourth scenario, FP2 15 cm over the sediment layer; UN(1,1) = 0.2713; Residual = 1.6298

𝐷𝑂𝑖 = 3.8636 + 0.00276 × 𝑐ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑙𝑖 − 0.1206 × 𝑇𝑖𝑚𝑖𝑛𝑔𝑖 − 0.08278 × 𝐵𝑂𝐷𝑖 + 𝑏𝑖 + 𝜀𝑖

r = 0.829

r = 0.592

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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

𝐵𝑂𝐷𝑖 = 61.9447 − 0.04128 × 𝑐ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑙𝑖 − 0.6418 × 𝑇𝑖𝑚𝑖𝑛𝑔𝑖 + 0.4774 × 𝐷𝑂𝑖 + 𝑏𝑖 + 𝜀𝑖

Second scenario, FP1 15 cm over the sediment layer; UN(1,1) = 6.2422; Residual = 35.8363

𝐵𝑂𝐷𝑖 = 12.6795 − 0.00335 × 𝑐ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑙𝑖 + 1.7194 × 𝑇𝑖𝑚𝑖𝑛𝑔𝑖 + 0.5542 × 𝐷𝑂𝑖 + 𝑏𝑖 + 𝜀𝑖

Third scenario, FP2 30 cm under the water level; UN(1,1) = 42.1666; Residual = 0.7762

𝐵𝑂𝐷𝑖 = −47.3293 + 0.03634 × 𝑐ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑙𝑖 + 2.722 × 𝑇𝑖𝑚𝑖𝑛𝑔𝑖 + 0.4092 × 𝐷𝑂𝑖 + 𝑏𝑖 + 𝜀𝑖

r = 0.686

r = 0.833

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Fourth scenario, FP2 15 cm over the sediment layer; UN(1,1) = 1.5851; Residual = 9.374

𝐵𝑂𝐷𝑖 = 11.3298 + 0.009105 × 𝑐ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑙𝑖 + 1.2897 × 𝑇𝑖𝑚𝑖𝑛𝑔𝑖 − 0.447 × 𝐷𝑂𝑖 + 𝑏𝑖 + 𝜀𝑖

r = 0.962

r = 0.779

spatial-temporal analysis of oxygen related processes...

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