Virus inactivation utilising naturally occurring enzymes in wastewater
Amanda Rachel Inglis
School of Physical and Chemical Sciences
University of Canterbury
This dissertation is submitted for the degree of
Doctor of Philosophy
2018 For Mum and Dad ii
I would like to acknowledge my supervisors, Dr. Louise Weaver, Matthew Ashworth and Professor Emily Parker. Without all of your encouragement, input and advice I would not have made it to this point! I would also like to thank the whole groundwater team at ESR, especially Judith, Bronwyn, Phil, and Lee for their day to day support and distractions. Thanks also go to Dr. Sarah Masters in the Chemistry Department at UC for all of the sage advice and support she supplied me with.
This project would not have been possible if it was not for the funding supplied by ESR and Scholarships awarded by ESR, the Chemistry Department at UC and the Biomolecular Interaction Centre at UC. Abstract
Adequate sewage treatment is vital for maintaining New Zealand’s economy as well as offering economic and sustainable methods of treating wastewater globally. Effective treatment is required to protect both public health and the environment from anthropogenic activities. Pathogen removal mechanisms are largely unknown and are likely to be complex due to the diverse environment. The purpose of this study was the investigation of natural virus inactivation in waste stabilisation ponds. By investigating these complex mechanisms, particularly the enzymatic virus removal, we can develop models of pathogen removal and increase the knowledge of these processes to enable the efficient, low-cost, running of waste stabilisation ponds. A laboratory-scale waste stabilisation pond system was developed in order to investigate pathogen behaviour in the wastewater matrix. The laboratory-scale WSP system was combined with enzyme assays, viral enumeration, and genomic sequencing to investigate the bacterial communities present in wastewater and their potential production of enzymes involved in virus inactivation. It was determined that the bacterial communities in laboratory-scale and full-scale WSPs were compositionally similar, with a shared core microbiome. The bacterial abundance and functions associated with the bacteria varied in response to pond properties, while enzyme activity leads to reduced viral concentrations. Sequencing, physiochemical properties, and microbial pathogen reduction indicated a successful laboratory-scale WSP was developed. Human enteric virus concentration was reduced in the presence of extracellular enzymes present in wastewater, indicating a possible alternative for virus removal than expensive chlorination or UV. Contents
Contents iv
List of Figures xi
List of Tables xiv
1 Introduction 17
1.1 Research Gaps ...... 19
1.2 Research objectives ...... 20
2 Literature review 21
2.1 Wastewater ...... 22
2.2 Managing the spread of disease ...... 23
2.3 Waste stabilisation ponds ...... 25
2.3.1 Treatment aims ...... 26
2.3.1.1 Pre-treatment ...... 28
2.3.1.2 Primary treatment ...... 28
2.3.1.3 Secondary treatment ...... 31 Contents v
2.3.1.4 Tertiary treatment ...... 32
2.3.2 Disinfection potential ...... 34
2.3.3 Effluent fate ...... 37
2.3.4 WSP biological community ...... 39
2.3.4.1 Bacteria in WSPs ...... 39
2.3.4.2 Algae in WSPs ...... 43
2.3.4.3 Protozoa and Metazoa in WSPs ...... 44
2.3.5 Pathogenic community ...... 47
2.3.5.1 Protozoan pathogens in WSPs ...... 47
2.3.5.2 Microbial pathogens in WSPs ...... 49
2.3.5.3 Viruses in WSPs ...... 51
2.3.6 Pathogen detection ...... 55
2.3.6.1 Indicator organisms ...... 56
2.3.6.2 Use of MS2 for enteric virus monitoring ...... 59
2.4 Monitoring wastewater treatment ...... 61
2.4.1 Parameters for defining successful treatment ...... 66
2.4.2 Design manuals ...... 68
2.5 Model wastewater systems ...... 69
3 Methods 76
3.1 Full-scale sample site and sampling methods ...... 76
3.2 Physicochemical parameters ...... 78 Contents vi
3.3 Total solids ...... 78
3.4 Bacterial plating ...... 79
3.5 Bacteriophage plaque assay ...... 82
3.6 Enzyme assays ...... 83
3.6.1 Enzyme inhibition ...... 87
3.6.2 Heat inactivation ...... 88
3.7 Protein determination ...... 88
3.7.1 Lowry protein assay ...... 88
3.8 Enterovirus end-point titration assay ...... 89
3.8.1 Preparation of stock echovirus ...... 91
3.9 Viral spike ...... 91
3.10 Sample preparation ...... 91
3.10.1 Filtration ...... 92
3.10.2 Protein concentration ...... 93
3.10.3 Chloroform extraction ...... 93
3.10.4 Solid phase extraction ...... 93
3.11 Mass spectrometry ...... 94
3.12 Bacterial and enzyme culture ...... 95
3.13 Pond design ...... 97
3.14 Sequencing ...... 97
3.14.1 Data analysis and storage ...... 98
3.15 Statistics ...... 101 Contents vii
4 Laboratory-scale waste stabilisation pond development 102
4.1 Introduction ...... 102
4.2 Methods ...... 104
4.2.1 Pond construction ...... 104
4.2.1.1 Pond set-up ...... 106
4.2.2 Monitoring and sample collection ...... 107
4.2.3 Bacterial plating ...... 108
4.2.4 Viral spike and enumeration ...... 108
4.2.5 Statistical analysis ...... 108
4.3 Results and Discussion ...... 109
4.3.1 Physicochemical fluctuations ...... 109
4.3.2 Microbial behaviour and survival ...... 114
4.3.3 Virus behaviour and survival ...... 117
4.4 Summary ...... 120
5 Enzyme activity in waste stabilisation pond wastewater and virus survival 122
5.1 Introduction ...... 122
5.2 Methods ...... 123
5.2.1 Sample collection ...... 124
5.2.2 Enzyme assays ...... 124
5.2.3 Viral spike and detection ...... 124
5.2.4 Enzyme inhibition ...... 125 Contents viii
5.2.5 Filtration ...... 125
5.2.6 Statistics ...... 125
5.3 Results and Discussion ...... 126
5.3.1 Enzyme activity in laboratory-scale WSP ...... 126
5.3.2 Enzyme activity in full-scale WSP fractions ...... 127
5.3.3 Enzymatic inactivation of viruses ...... 133
5.3.4 Enzyme activity in presence of virus ...... 138
5.4 Summary ...... 140
6 Enzyme production capabilities of bacterial communities in WSPs 142
6.1 Introduction ...... 142
6.2 Methods ...... 145
6.2.1 Sample sites ...... 145
6.2.2 Sequencing ...... 146
6.2.3 Mass spectrometry ...... 147
6.2.4 Statistics ...... 147
6.3 Results and Discussion ...... 148
6.3.1 Targeted sequencing ...... 148
6.3.2 Shotgun sequencing ...... 153
6.3.3 Pathogenic bacteria in WSPs ...... 155
6.3.4 Metadata correlation ...... 157
6.3.5 Functional categorisation ...... 159 Contents ix
6.3.5.1 Targeted sequence analysis ...... 159
6.3.5.2 Shotgun sequence analysis ...... 161
6.3.6 Potential enzymes produced ...... 163
6.4 Summary ...... 167
7 Conclusions and future work considerations 168
7.1 Development of a laboratory-scale WSP system for the investigation of virus survival and inactivation ...... 168
7.2 Enzyme activity and virus survival ...... 169
7.3 Microbial diversity and enzyme production in WSPs ...... 169
7.4 Future work ...... 170
References 173
A Pond design equations 232
A.1 Laboratory-scale design ...... 232
A.2 Design process ...... 236
A.3 Design results ...... 238
A.4 Scaling equations ...... 242
A.5 Settling velocity ...... 246
A.6 BOD loading ...... 247
A.7 Retention time ...... 248 Contents x
B Viral structure information 249
B.1 Echoviruses ...... 249
B.2 MS2 ...... 250
C Metagenomic and Metaproteomic analysis 252
D Statistical Results 256 List of Figures
2.1 Interactions occurring in a waste stabilisation pond ...... 26
2.2 Wastewater treatment plant diagram ...... 27
3.1 Aerial view of WSP treatment plant ...... 77
4.1 Laboratory-scale waste stabilisation pond system diagrams of inlet and outlet ports and baffle configurations (a) and flow directions (b) ...... 105
4.2 Photos of the laboratory-scale pond set-up showing pond and pump layout (a) and the sampling and outlet ports (b) ...... 106
4.3 Daily variation of pH and DO in the three ponds of a laboratory-scale WSP system...... 110
4.4 Physicochemical monitoring in a laboratory-scale WSP system over time from set-up, showing pH, temperature and dissolved oxygen...... 112
4.5 Reduction of E. coli throughout the ponds of the laboratory-scale WSP system 115
4.6 Behaviour of E. coli and enterococci in a laboratory-scale WSP system over time...... 116
4.7 Behaviour of Echovirus 7 and MS2 throughout a laboratory-scale WSP at 3, 5 and 10 days post-viral spike of the influent ...... 118 List of Figures xii
5.1 Protease activity throughout a laboratory-scale WSP system (n = 9) . . . . . 127
5.2 Esterase activity in whole and extracellular WSP wastewater from a full-scale
treatment system (nWhole = 18; nExtracellular = 27) ...... 128
5.3 Protease activity in whole and extracellular WSP wastewater from a full-scale treatment system (n = 32) ...... 129
5.4 Protease activity present in a full-scale WSP consisting of three ponds in series
(nPrimary = 3; nSecondary and Tertiary =6) ...... 130
5.5 Esterase activity present in fractions of full-scale WSP wastewater over time
(n24 = 3; n0, 48=6) ...... 132
5.6 Echovirus 7 concentrations in wastewater after 48 hours of incubation at 20 °C 134
5.7 Survival of Echovirus 7 in full-scale WSP wastewater fractions over time compared to a heat-treated (inactive) control ...... 135
5.8 MS2 bacteriophage concentrations in fractions of full-scale WSP wastewater 136
5.9 The effect of virus on protease activity in full-scale WSP wastewater . . . . 139
5.10 Esterase activity in the presence of virus in full-scale WSP wastewater . . . . 139
6.1 Phyla detected in three full-scale and three laboratory-scale wastewater samples by targeted genomic sequencing ...... 149
6.2 Phyla counts, determined by targeted sequencing, in stages of wastewater treatment ...... 150
6.3 Cladogram of genera determined to be present in both full- and laboratory-scale wastewater, indicating a wastewater core microbiome . . . . 152
6.4 Relative abundance of bacteria and viruses in wastewater determined by whole genome shotgun sequencing ...... 154 List of Figures xiii
6.5 Genera known to contain pathogenic species as detected by targeted (16S) and de novo (whole genome shotgun) sequencing techniques ...... 156
6.6 Correlation of pH, temperature and concentration with phyla abundance . . . 157
6.7 Collated functional categorisation based on OTUs detected by targeted sequencing ...... 160
6.8 Functional annotation of whole genome shotgun sequences determined by MG-RAST ...... 162
A.1 Full-scale waste stabilisation pond system layout ...... 234
A.2 Design concept drawings ...... 237
A.3 Rhodamine tracer test ...... 242
B.1 Atomic structure of Echovirus 7 with an inset of the penton protein the virus particle is comprised of ...... 249
B.2 Atomic structure of Echovirus with bound DAF/CD55 protein which is also shown in the inset ...... 250
C.1 Genetic sequencing estimation of pathogenic reduction through ponds of a laboratory-scale WSP ...... 253
C.2 Protocol for SigmaProt Intact Protein Standard ...... 254
D.1 Multiple comparison analysis of one-way ANOVA for pH and DO variation . 256
D.2 Tukey’s values ...... 257 List of Tables
2.1 Common chemical and physical characteristics of wastewater influent . . . . 23
2.2 Average pathogen and indicator log removal values for WSPs and conventional WWTPs ...... 36
2.3 Pathogens in WSPs and their associated diseases ...... 48
2.4 Protozoan pathogen prevalence in wastewater ...... 49
2.5 Prevalence of bacterial pathogens throughout WWTPs ...... 50
2.6 Common human enteric viruses and their properties ...... 52
2.7 Prevalence of Viral Pathogens in Wastewater ...... 54
2.8 Pathogens and indicators currently used for monitoring ...... 58
2.9 An example of a discharge monitoring program ...... 64
2.10 Common waste stabilisation pond design values ...... 67
2.11 Existing WSP design manuals and their applications ...... 70
2.13 Wastewater treatment models ...... 73
3.1 Tryptic glucose media recipes for the enumeration of MS2 bacteriophage . . 83
3.2 Sample treatments and their expected outcomes ...... 92 List of Tables xv
3.3 Running gradient conditions for LC-MS ...... 95
3.4 Synthetic wastewater composition ...... 96
3.5 Sample QC for whole genome and shotgun sequencing samples ...... 99
4.1 Pearson correlation coefficients of the relationships between pH, dissolved oxygen (DO) and temperature, within and between the ponds of the laboratory-scale WSP system ...... 113
6.1 Detection of intact protein standards by tandem mass spectroscopy . . . . . 164
6.2 Protein identification including protein source, number of matches and protein ID as determined by Mascot ...... 165
A.1 Comparison of laboratory-scale pond names and depths to those of the full-scale system ...... 239
A.2 Laboratory-scale pond dimensions ...... 239
A.3 Scaling factors ...... 240
C.1 MG RAST analysis data ...... 253
C.2 Wastewater project data supplied to MG-RAST ...... 255
C.3 Mascot search parameters ...... 255 List of Tables xvi
Glossary
Enteric relating to the intestines Eutrophic rich in nutrients resulting in rapid aging Inorganic compounds not containing carbon or oxygen Morbidity being diseased Mortality being subjected to death Organic compounds containing oxygen and carbon
Abbreviations
BOD Biological Oxygen Demand CFU Colony Forming Units COD Chemical Oxygen Demand EPA Environmental Protection Agency FAO Food and Agriculture Organization of the United Nations HRT Hydraulic Retention Time IWA International Water Association PFU Plaque Forming Unit UN United Nations WHO World Health Organisation Chapter 1
Introduction
In developing countries, where wastewater contamination and associated gastrointestinal diseases result in 500,000 deaths each year [1], simple and sustainable wastewater treatments are required. Waste stabilisation ponds (WSPs) are appropriate for these contexts, however, they need to be investigated to improve the quality of the wastewater being discharged to the environment and water resources or used for irrigation.
Viral and bacterial pathogen presence and survival in wastewater can lead to the contamination of food and water. With an estimated 80% of the world’s wastewater being discharged into receiving environments (lakes, rivers, oceans or land) with inadequate or no treatment, the possibility of contamination with pathogens is increased [2]. The ingestion of contaminated food or water can result in gastroenteric illness and potentially death. When wastewater treatment does occur, viruses are generally not directly monitored, instead, bacterial indicators are used to determine successful treatment. The bacterial indicators are bacteria that have been determined to have similar survival rates to the viruses they are used to indicate. This detection method has been used for the past century, relying on the assumption of comparable survival. In recent years, researchers have begun to ascertain that 18 this assumption is inaccurate, with viruses surviving far longer, under the same conditions as the bacteria commonly used to monitor their survival [3–7].
The need for effective pathogen removal as part of wastewater treatment is paramount worldwide. The needs in developing countries differ greatly than their established counterparts. Simple, effective and sustainable treatment systems are required to be useful in areas such as these.
In developed countries or those with high socio-economic value, there is a range of wastewater treatment systems available. These systems include mechanical systems, often requiring significant energy input, high costs associated with operation and maintenance and workers with high skill levels. Additional treatment such as UV or chlorine disinfection can be used to ensure high levels of inactivation of bacterial and viral pathogens. Natural wastewater treatment systems or non-mechanised systems constructed of natural resources, present a potential solution for wastewater treatment worldwide, including in developing countries. These systems are run without a significant input of power or energy, utilising natural energy such as sunlight. The lack of organised wastewater collection and treatment in developing countries increases the risk of transmission of pathogens derived from human waste [4]. A significant proportion of deaths associated with gastroenteric illnesses are localised to these developing countries [7–10]. The populations in these regions are often immune-compromised due to lack of food, water and nutrients, making them more susceptible to gastroenteric illnesses than healthy individuals [9–12]. It is because of these health implications that an investigation into systems suitable to developing regions and rural areas is of vital importance.
The natural processes involve a range of bacteria and other organisms as well as the enzymes they produce. Enzymes are important in a range of industries and have been for many centuries. The production of cheese, bread, wine and beer were among the first uses of enzymes in industry [13]. Further industrial use includes the synthesis of penicillins, 1.1 Research Gaps 19 manufacturing of fuels, textiles and pulp and papers industries [13, 14]. Bioremediation is becoming a common use for both microbes and enzymes [15]. As toxins are increasingly discharged into the environment, microbes and enzymes are used to metabolise the pollutants into safer compounds that are more readily removed. Bioremediation relies on microbial growth, which can often be slow and limited by nutrient availability. However, with advancing scientific technologies, formulated enzymes can be produced, especially at greater volumes and activities than what could be produced by microbes in-situ. This enzyme remediation has now become another significant application of enzymes in addition to those described previously [15, 16].
This work focuses on WSPs as a natural treatment system, when successfully run can achieve pathogen removal, improve food and water safety and sanitation. WSPs are lined basins, built to allow for the stabilisation and treatment of wastewater utilising natural processes involving sunlight, algae and bacteria.
This thesis aims to investigate the mechanisms contributing to wastewater treatment within natural wastewater treatment systems such as WSPs. There is a particular focus on the inactivation of human enteric viruses using enzymes that are naturally occurring in the WSPs.
1.1 Research Gaps
There is little information in the literature regarding the mechanisms involved in wastewater treatment within WSPs [17–20]. Significant knowledge gaps pertaining to wastewater treatment include virus survival and enzyme activity in natural wastewater treatment systems such as WSPs. It is yet to be investigated how different enzyme activities can relate to specific treatment aims and the effect upon resulting virus survival [17, 18, 21–25].
While wastewater treatment has occurred worldwide for the past century, there are still substantial gains to be made by technological improvements. Some of the factors preventing 1.2 Research objectives 20 this increased understanding include the process of pathogenic inactivation, the effect of environmental conditions on treatment efficiency and the role of sewage composition onthe resulting effluent [17, 19, 26–28]. Many of these factors have been considered singularly, but it is the compounding effect of multiple factors including environment, sewage composition and treatment type leaving our understanding of wastewater treatment behaviour wanting.
1.2 Research objectives
This research aims to investigate the treatment mechanisms involved in biological wastewater treatment in WSPs and includes the underlying mechanisms involving enzyme activity and their ability to inactivate human enteric viruses. This research focuses on simple and sustainable treatment systems, applicable to developing countries and rural areas with the aim to reduce the risks associated with the spread of viral pathogens from wastewater. Developing and improving low-cost and sustainable wastewater treatment can lead to a reduction of death and illness related to the inadequate treatment of wastewater. This research aims to increase the knowledge of the biological interactions occurring in WSPs. The specific objectives of this research were to:
• Develop a laboratory-scale model system for the investigation of virus survival and inactivation in WSPs.
• Compare enzyme activity and virus survival in both a laboratory-scale WSP and a full-scale WSP system.
• Identify the microbial diversity in WSPs and associated enzyme production in addition to the potential for virucidal activity shown by these enzymes. Chapter 2
Literature review
In populations where their potable water sources are contaminated with wastewater, illness can be spread through ingestion of contaminated water or food. The impact of inadequate wastewater treatment and disposal results in over two million people dying indirectly from gastroenteric illness each year [29, 30]. The contaminants causing these gastrointestinal illnesses are known as pathogens. These pathogens can be bacterial, viral or protozoan in origin and enter the wastewater treatment systems as a component of the human and animal waste that constitutes wastewater [31–39]. Bacterial pathogens have been well studied and a great deal is known about how they respond to different types of wastewater treatment [33, 36]. Additionally, there are guidelines regarding the treatment of these bacterial pathogens worldwide and are commonly used in many countries as an indication of successful wastewater treatment [30, 40–47]. Despite what is known about the bacterial pathogens present in wastewater, very little is known or understood in regards to viral pathogens [48, 49]. This lack of knowledge has arisen owing to two chief reasons; the cost of analysis of viruses and the reliance on outdated assumptions [5, 34, 48, 50–52]. These assumptions rely on the belief that virus behaviour can be monitored by bacterial indicators due to having similar behaviour to human enteric viruses or increased resistance to treatment 2.1 Wastewater 22 then the viruses they indicate for [34, 37, 49, 53–55]. Additionally, these bacterial indicators are well studied and easily cultured meaning they are easier and cheaper to detect than the viruses themselves [51, 52].
A large proportion of the deaths associated with gastroenteric illness are situated in developing countries or areas with low socio-economic value. In these areas, there is a lack of wastewater treatment or disposal and the majority of water sources are contaminated with pathogens originating from wastewater. A lack of money, ability and skills required to operate and maintain a treatment plant all contribute to the lack of effective wastewater treatment in developing areas [56–58].
2.1 Wastewater
Wastewater is water which has been adversely affected by human activities and intervention, including liquid waste from domestic households, industrial liquids (for example; brewing, pulp and paper industries) or municipal liquid waste products [59]. Wastewater can be divided into four main categories; domestic, industrial, infiltration and stormwater wastewater. Domestic wastewater is one of the biggest contributors, including human and animal excrement as well as greywater derived from cooking, bathing and washing [60–62].
General wastewater components, other than water, are; organic matter, soluble and insoluble inorganic particles, microorganisms and pathogens, toxic compounds, xenobiotics, pharmaceuticals and large solids including dead animals, toys and sanitary items [61, 62]. The organic matter is comprised of proteins, carbohydrates, fats, oils, amino acids, fatty acids and esters which are mainly sourced from domestic or industrial waste [47, 56, 62–66]. The inorganic matter includes nitrogen from many sources (including ammonia and urea), phosphate, metals and sand [56, 65, 67, 68]. This inorganic matter can be toxic or carcinogenic to humans and the environment. Other toxins include phenols and oils from 2.2 Managing the spread of disease 23 industrial waste. Xenobiotics can be introduced from pharmaceutical industries or domestic waste, both in human excrement and grey water [67]. Wastewater composition varies in terms of the supplying population and the types of influent a treatment plant receives. Table 2.1 shows the main components of wastewater at varying concentrations [69–71].
High concentrations of organic matter and nutrients in wastewater can be toxic to many plants and organisms [72]. If not reduced, discharge of this heavily loaded wastewater can lead to algal blooms, creating a eutrophic environment where the alteration of the receiving environment can harm the natural microbial and aquaculture community.
Table 2.1: Common chemical and physical characteristics of wastewater influent
Constituents Concentration (mg/L)
Strong Medium Weak
Total Solids 1200 720 350
Total Dissolved Solids 850 500 250
Total Suspended Solids 400 260 110
BOD5 375 235 105
COD 870 515 230
TOC 270 170 75
Total Nitrogen 85 50 20
Total Phosphorus 19 12 5 Adapted from [69–71]
2.2 Managing the spread of disease
Wastewater contains potentially pathogenic organisms leading to disease in humans and animals. These organisms can enter water resources and spread waterborne disease in 2.2 Managing the spread of disease 24 humans. With the increased global population current treatment regulations pertaining to disposal and irrigation with human or animal wastewater is no longer as safe as it may once have been [35, 73–75]. Reduction of human pathogens, especially enteric viruses, in wastewater needs to be demonstrated to minimise the public health risk [55]. This need is even greater if the wastewater is destined to be used for irrigation or land application purposes due to increased risk of human contact [76–78].
Most countries do not directly detect virus levels in wastewater, as bacterial indicators are typically used for biological monitoring unless a pathogenic outbreak occurs [79]. A direct viral analysis is not a routine measurement as a lack of current methods prevents the culture of a range of viruses [34, 55, 74]. The expense associated with the detection for those viruses that can be cultured inhibits the ability to undertake this analysis regularly. It is because of the high costs and lack of methods that indicator bacteria such as E. coli or faecal coliforms are used as a proxy to monitor the removal of pathogens [34, 74]. The correlation of bacterial indicators to enteric viruses in wastewater is unreliable with research by Rose et al. (1996) showing coliforms did not effectively predict pathogen inactivation [80]. The same finding has been discussed by Davies-Colley, Donnison and Speed (2000) and Hewitt et al. (2013) [81, 82]. Viral detection techniques or improved indicator organisms are required to more effectively represent disinfection and prevent the spread of disease [5,6, 48, 50].
The optimisation of wastewater treatment resulting in increased viral inactivation, using sustainable and simple methods, will consequently lessen associated gastroenteric illnesses and deaths. This will have a significant economic benefit on society due to potentially lower infrastructure costs and health costs associated with diarrheal disease.
Wastewater treatment systems including membrane bioreactors, trickling filters and sludge digesters require large energy inputs as well as highly skilled workers for efficient operation. Developing countries do not have the money or expertise for the installation or maintenance of these technological wastewater treatment systems, or the resources to manage them 2.3 Waste stabilisation ponds 25 effectively [83]. Instead, a low-cost, sustainable treatment solution is needed to effectively reduce bacterial and viral pathogens.
2.3 Waste stabilisation ponds
Waste stabilisation ponds (WSPs) are a low-cost, simple and sustainable solution to wastewater treatment and have been in existence for over 100 years [56, 84, 85]. These systems include oxidation ponds, facultative ponds and maturation ponds [56, 57, 86, 87]. The largest cost associated with these treatment ponds is the initial purchase of land and earthworks, with low ongoing operational and maintenance costs [33, 88, 89]. These low operational costs, due to minimal energy input requirements, are part of the reason these systems are a viable wastewater treatment option in low income and rural regions [56, 57, 83, 86, 87]. The skill level required to operate and manage the pond systems is not particularly high when the systems are well managed a noticeable difference in the public health and the receiving environment can be evident [90].
WSPs are typically shallow ponds and include aerobic, facultative and anaerobic zones, allowing for the settling of suspended solids, degradation of organic matter and pathogen removal by natural processes [33, 56, 83, 84, 91, 92]. Some of these interactions can be seen in Figure 2.1. Due to the low energy input requirements and lack of mechanical processes, WSPs are often considered as “simple” wastewater technology [20, 58, 89, 91, 93–95]. However, the range of biological interactions occurring within each pond is likely to be hugely complex and far more expansive than is currently known (Figure 2.1).
WSPs can be used as a stand-alone treatment method, a complete treatment system or as a tertiary treatment, used to polish effluent after other treatment processes [56]. Where ponds are a complete treatment system, they incorporate all the following stages of treatment; Primary, Secondary and Tertiary. 2.3 Waste stabilisation ponds 26
Figure 2.1: Interactions occurring in a waste stabilisation pond Schematic adapted from the works of Walmsley and Shilton (2005); Ashworth and Skinner (2011) and Mara, Mills and Pearson (1992) [65, 83, 87]
2.3.1 Treatment aims
History, culture and religion have seen a large shift in the view of wastewater treatment [96]. Romans developed sophisticated sewer systems and cesspools for collection and disposal of wastewater, but these were lost with the collapse of the Roman Empire in the late 400’s. The 1800’s saw the introduction of sewage farms in Australia and the Bazalgette sewer system in London to transport the sewage away from cities and the people dwelling in them [87]. The only form of treatment occurring was in response to the smell of the waste [96].
The development of science in the 20th Century resulted in a shift towards the management of wastewater and pollution. The British Royal Commission on Sewage Disposal Final Report in 1915 was pivotal in the advance of wastewater treatment and management as the report introduced the use of standard tests [97]. As science developed, the realities surrounding environmental pollution were realised, leading to developments in regard to wastewater treatment [87]. 2.3 Waste stabilisation ponds 27
The next major step in changing the view of wastewater treatment came in 1972 when The United States passed the Clean Water Act, previously the Federal Pollution Control Act, mandating the use of microorganisms to purify wastewater as a stage of treatment treatment [98, 99].
Removal of organics, nutrients and pathogens are the primary aims in the treatment of wastewater before it is discharged to the environment [57]. Different treatments occur in phases of wastewater treatment systems. Organic matter and solids reduction generally occur initially, followed by nutrient degradation and disinfection of pathogens. Biological treatment processes, such as those occurring within WSPs to result in nutrient degradation and disinfection, are predominantly driven by sunlight [56, 68, 83, 86, 90, 92, 100]. An overview of wastewater treatment generally occurring in a WWTP is shown in Figure 2.2. The primary, secondary and tertiary treatment stages can utilise a range of techniques and systems.
Figure 2.2: Wastewater treatment plant diagram 2.3 Waste stabilisation ponds 28
2.3.1.1 Pre-treatment
Screening and grit removal are mechanical processes occurring before any treatment [84, 87, 101, 102]. Large solids, grit, sand and seeds can be introduced into the system by people flushing inappropriate objects, through the stormwater system, general bathing, washing of fruits and vegetables or laundry [71]. By intercepting the large solids a build up of grit is prevented in the next phase of treatment, which would require removal or cleaning and would increase the costs associated with wastewater treatment [70, 71, 102–104].
2.3.1.2 Primary treatment
The aim of primary treatment is the removal of both organic and inorganic solids to produce a clarified effluent [61]. Sedimentation is the physical process of separating the solid and liquid constituents of wastewater. Settling of suspended solids from wastewater is controlled by the ability of the colloidal1 substances to form flocs or aggregates and is influenced greatly by the gravitational2 and frictional3 forces within the environment [105–107]. Suspended solids include proteins, lipids, DNA, carbohydrates, and humic substances [105]. The formation of flocs depends on physical, chemical and biological factors including the interactions between these particles owing to surface properties, size, density, morphology, microbial composition and chemical constituents [105, 107].
Different types of sedimentation can occur due to the effects of the surrounding environment [102, 103, 108]. Discrete particle and flocculant settling are the main types of sedimentation likely to occur in primary treatment stages of wastewater treatment. Discrete particle settling is the individual settling of particles as there are no major interactions with other particles due to low concentrations of solids. Flocculant settling involves more interaction between these
1Colloidal - suspended particles 2Gravitational - the influence of an item towards the earth 3Frictional - resistance of two items when moving against one another 2.3 Waste stabilisation ponds 29 suspended particles than in discrete sedimentation. This leads to coalescing of particles, or the formulation of flocs to increase the rate of sedimentation as these flocs are heavier than individual particles and will settle faster [101].
The settling velocity can be determined by combining frictional drag force and gravitational force equations (Equation 2.1 and 2.2) along with standard assumptions excluding the interaction of other variables upon the settling rate, thereby equating gravitational force to frictional drag force in terms of sedimentation (Equation 2.3)[101, 103, 108].
Gravitational force (FG) can be shown as (adapted from [104])
FG = (ρp − ρ)gVp (2.1)
And drag force (FD) can be shown by (adapted from [104])
CDApρVs 2 FD = 2 (2.2)
And discrete particle settling (Vs) is shown as [104]
1 2 4g(ρρ −ρ)d V = (2.3) s 3CDρ
ρ = fluid density
ρp = particle density
Vp = particle volume
CD = drag coefficient
Ap = particle area g = gravitational constant µ = absolute fluid viscosity d = particle diameter 2.3 Waste stabilisation ponds 30
The momentum or turbulence of water can overcome the specific gravity of particles, preventing settling. This relationship can be determined by the Froude and Reynolds numbers (Section A.4). By keeping the water static when in a pond or clarifier, the movement is decreased, allowing particles to settle, leading to the formation of a sludge layer and a clarified liquid. When in a consecutive facultative pond system, the flow rate throughthe ponds needs to be slow enough to allow the gravitational forces of the particles to overcome the drag forces to allow settling. In full-scale systems, wind can play a significant role in the settling velocity. Wind is often regarded as a driving force of pond flow, and if the wind speed is high enough, the flow may be great enough to overcome gravitational forces thus preventing settling of particles [109].
Convective cooling can also affect the settling velocity. Where there is a rapid temperature drop, the upper layer cools before the lower layers can equalise, thermally. The warmer lower layer convects upwards, exchanging with the colder, upper layer. This can result in an instant settling of particles in the cold upper layer. Whilst this process is known, it is very hard to examine, let alone quantify, as it is an immediate equalisation response [109].
Equation 2.4 can be used to determine the rate of water clarification:
Q = A (2.4) Vs
Where: A = pond surface area
Vs = settling velocity Q = flow rate
When designing primary sedimentation ponds, the pond size required can be determined by rearranging Equation 2.4 to solve for A, so particles with a terminal velocity equal to or greater than Vs will be removed. 2.3 Waste stabilisation ponds 31
The conformation and abundance of various biopolymers can also play a role in flocculation, as well as the predominance of metal ions, metal complexes and other low molecular weight species. The presence of pollution in wastewater can also affect the flocculating ability due to the interference of normal attractive forces occurring between particles [110]. Sedimentation can be achieved by the use of primary clarifiers or flocculation-aided sedimentation [54].
Sedimentation also aids pathogen removal [111]. Interactions between pathogens and suspended solids can lead to pathogen removal from the liquid phase due to sedimentation in the sludge layer, reducing the pathogen concentration available for release upon wastewater discharge [91].
The successful removal of solids improves the efficiency of the secondary and tertiary treatment. The settling of solids improves the clarity of the primary effluent. The lower turbidity water enables improved light disinfection of pathogens in the secondary and tertiary stages of treatment, aiding overall wastewater treatment [89, 91, 112].
2.3.1.3 Secondary treatment
Secondary treatment in WSPs encompasses the degradation of nutrients and any remaining organic content [61]. This content includes soluble organic matter, insoluble nutrients, microbes or fine particles, including any non-settleable solids. Degradation in WSPs occurs biologically, involving waterborne microorganisms including algae [56, 83, 86, 92, 100]. Sunlight stimulates algal growth in WSPs, which in turn produces oxygen via photosynthesis [64, 87, 90]. This oxygen is then utilised by the WSP bacterial community oxidising and degrading the organic matter that wastewater is comprised of [113]. The bacteria are involved in nutrient cycles including nitrogen, carbon and sulphur cycles as well as a range of degradation mechanisms. A significant number of interactions are likely to contribute to secondary treatment within WSPs but few are well understood. 2.3 Waste stabilisation ponds 32
Secondary biological treatment can also be utilised by mechanical systems, including activated sludge reactors [71, 114, 115].
2.3.1.4 Tertiary treatment
Tertiary treatment involves the polishing of effluent or the removal of pathogens from wastewater [56, 116, 117]. Wastewater to be discharged into the aquatic environment may later be removed downstream of reintroduction, for drinking water. Additional endpoints of wastewater effluent include land application, irrigation and groundwater recharge, all withthe possibility of human contact further down the line [34, 35, 73, 118–120]. The potential reuse of wastewater drives the need for successful disinfection of the pathogens in wastewater.
Current research focused on methods for disinfection of effluent, by natural, mechanical or chemical processes [121, 122]. Disinfection of wastewater is used to inactivate pathogens in order to protect public health [123]. Unknowingly disinfection has occurred since the mid 1800’s, when chlorine products were used to deodourise the wastewater, as scientists then believed it was the odour that resulted in disease [96]. With the development of technical UV light systems to disinfect wastewater, the use of chlorine is only now diminishing as we begin to understand the negative side effects, for humans and the environment, and by-products of its use.
Natural and mechanical disinfection
UV disinfection is one of the most common methods of disinfection treatment in the developed world. UV treatment can be natural (sunlight) or mechanical with the incorporation of UV lamp systems [33, 73, 112, 124]. Natural UV treatment primarily occurs as a tertiary treatment in maturation or polishing ponds and other natural treatment systems. Mechanical UV is often used as a disinfection process when improved disinfection is required [124–128]. UV 2.3 Waste stabilisation ponds 33 disinfection requires high amounts of energy to run the UV systems needed to treat large volumes of effluent. UV treatment is mainly effective for high clarity effluents, relyingon sufficient pre-sedimentation practices in primary treatment [84, 112].
UV dosage required can be inferred by gathering data from flow-rates, contact time, the transmittance of light, the turbidity of effluent, lamp age and fouling as well as potential outages or downtime [84, 112]. A dose of 5.8 mJ/cm2 has been determined for 99% disinfection of the protozoan parasite Cryptosporidium under certain conditions while a dose of 100 mJ/cm2 is required for the same level of disinfection for enteric viruses [112]. Studies have shown larger particles in the effluent may be creating protection for the pathogenic microorganisms and particles from the UV rays [79, 100, 121, 125, 128]. This indicates the importance of effective primary treatment in the overall quality of the resulting effluent. Protection of pathogens would reduce the disinfection efficiency and minimise the level of treatment possible. Thus indicating the importance of effective primary and secondary treatment and the reduction of turbidity in the overall quality of the resulting effluent [84].
Alternatively, extracellular enzymes, such as protease and nuclease, could also cause natural virus inactivation in biological treatment systems [28, 111, 129]. Studies of viral activity in effluent saturated soil and in seawater indicated not all viruses have the same susceptibility to enzymatic activity. Coxsackievirus A9 and Hepatitis A were inactivated by Pseudomonas aeruginosa extracellular enzymes, while Poliovirus 1 and MS2 phage are not inactivated by this enzymatic activity [28]. This data is indicative of what happens in soil and seawater, but it is possible the composition of wastewater could result in similar outcomes.
Chemical disinfection
The addition of chemicals to wastewater in order to inactivate pathogens has occurred with many wastewater treatment systems [84, 124, 130]. Chlorine has been the traditional chemical used in the past [4, 124]. Chlorine treatment creates toxic by-products, known as 2.3 Waste stabilisation ponds 34 disinfection by-products (DBPs). Chlorination DBPs include Trihalomethane, Haloacetic acids, Haloamides, Haloacetonitriles, Nitrosamines, Dichloroacetic acids and Chloroform [131–134]. These DBPs can cause a range of anthropogenic and environmentally detrimental effects [72, 131].
By-products are produced by the reaction of disinfectants (such as Chlorine and Ozone) with humic acids, amino acids and organic matter [84, 131, 135]. The dissolved organic matter in wastewater act as the precursors to the formation of toxic by-products. Chlorination disinfection is becoming less common as more becomes understood about the toxic by-products formed [136]. In New Zealand, chlorination is rarely used because of the production of DBPs [91, 131]. Additionally, some authors, such as Ma et al. (2013) have suggested insufficient pathogen inactivation is associated with chlorination treatment [19].
Peracetic acid (PAA) has been investigated as a possible replacement of chlorine treatment [122, 125, 136–138]. PAA has a higher oxidation potential than chlorine and does not create the toxic by-products chlorine treatment does [122, 136, 139, 140]. Currently more expensive than Chlorine, PAA cost is likely to decrease as it becomes more widely used and production increases. Thus, PAA could be an encouraging alternative to Chlorine treatment for the disinfection of treated effluent.
2.3.2 Disinfection potential
Disinfection of pathogens includes the removal of the pathogenic organisms or the inhibition of their ability to infect their hosts [4, 47, 79, 91, 128, 141]. Disinfection by UV damages various portions of the pathogen organism or particle, preventing it from infecting its host [127]. What is increasingly obvious is that well-maintained WSPs without any additional chemical or mechanical disinfection can treat wastewater to a similar level as some high-cost, high-technology treatment plants [20, 33, 89, 93, 94, 142, 143]. 2.3 Waste stabilisation ponds 35
Most studies identify sunlight as the only or main natural disinfection cause in WSPs and other aquatic environments [129, 142, 144–146]. As WSPs are often 1.2 - 1.5 m deep while UV wavelengths can penetrate only the top 20 - 30 cms, resulting in a lower disinfection potential than expected [100]. Additionally, WSPs still function in periods of low light, such as winter, thus it is likely that other methods of inactivation are occurring [25, 79, 89, 100, 129, 144, 147–151].
The concentration of suspended solids present in WSPs can influence the level of UV inactivation occurring, as the solid particles shield organisms from the UV rays [84, 112]. The lower the suspended solid concentration is, the better the UV inactivation effect is possible, increasing the overall disinfection potential.
The persistence of virus particles in the environment is a considerable concern, especially compared to the presence and removal of bacterial pathogens [5, 147, 152]. Studies have shown that many enteric viruses have very low infectious doses, as well as having high survival rates in many conditions, resulting in more virulent pathogens [153–155]. Human enteric pathogens are introduced into wastewater through the faeces of individuals infected with a particular virus. It has been shown that viruses commonly associated with gastrointestinal illness, such as Norovirus and Rotavirus, are shed at levels of 1011 viral particles per gram of faeces [82, 147, 156]. These high levels of viruses in wastewater can present significant hurdles in the treatment and discharge process considering as few as 18 viral particles can be all that is necessary to infect an individual [156].
Viral persistence in wastewater was thought to be demonstrated by indicator organisms, based on assumptions made 40 years ago, that bacteria and viruses were removed at similar rates [3, 157]. An increasing lack of correlation between viruses and indicator organisms has been overlooked until now due to increased populations resulting in wastewater reuse and reclamation in order to fight water shortages [34, 37, 38]. Indicator monitoring is the highest level of pathogen monitoring in developing countries and yet it is these countries that are 2.3 Waste stabilisation ponds 36 most at risk from waterborne viruses as wastewater reuse and drinking water contamination are rife, resulting in high morbidity and mortality rates [38].
The majority of virus removal monitoring is presently done indirectly via the quantitation of microbial indicators, failing to effectively monitor or detect virus survival [19, 52, 80, 152, 158]. Previous studies have suggested the inefficiencies of this method [48, 76, 77]. The “one size fits all” approach typically used for viral indicators can be highly inaccurate andmore research is required to develop virus-specific treatment aims before improvements in this area can be made [5, 32, 33, 50, 80–82, 89, 128, 142, 143, 147, 156, 159–162].
Table 2.2: Average pathogen and indicator log removal values for WSPs and conventional WWTPs
Mean Log Removal Pathogen Error Reference WWTP (WSP) AdV 1.95 0.09 [49, 163] GI NoV 1.64 0.76 [6, 163, 164] GII NoV 2.00 0.69 [6, 163, 164] GIV NoV 1.40 0.67 [163] EV 1.53 (1.99) 1.12 [5, 155, 163, 165–169] Giardia 2.35 (2.45) 1.87 [5, 165, 168, 170] Helminths (2.49) 2.13 [39, 170] Cryptosporidium 1.09 (1.66) 0.84 [5, 168, 170] E. coli 3.14 (1.65) 1.82 [5, 49, 163, 165, 166, 170–172] Enterococci 3.21 1.56 [5, 49, 168] Faecal coliforms 3.65 (1.33) 3.36 [165–168, 173] Faecal enterococci 3.48 (1.69) [172] Faecal streptococci 0.34 (1.65) 0.21 [165, 167, 170, 171] Total coliforms 4.02 (1.49) 2.32 [163, 167, 168, 170, 171] Somatic coliphage 1.87 0.80 [5, 49, 155, 172] GI F-RNA phage 0.83 0.25 [163] GII F-RNA phage 2.50 0.35 [163] GIII F-RNA phage 2.86 0.25 [163] AdV = Adenovirus; NoV = Norovirus; EV = Enterovirus
The use of bacterial indicators as wastewater quality markers has continued in part due to the high costs and lack of reliable and informative methods associated with direct viral analysis 2.3 Waste stabilisation ponds 37
[34, 37]. Using polymerase chain reaction (PCR), viruses can be detected in wastewater, but this method does not provide information in regards to the viability of the viruses present [5]. Cell culture methods can be more informative in relation to infectivity, as infected cells are distinct from healthy cells. However, only a limited number of viruses can currently be cultured, as the correct conditions have not yet been discovered for the remaining viruses of interest [5]. Neither of these methods provides information on the mechanisms involved in their inactivation thus limiting the ability to expand our knowledge regarding mechanisms occurring within WSPs.
2.3.3 Effluent fate
In the developed world the majority of treated and non-treated effluent is discharged into waterways, such as the sea, lakes, rivers and streams [174, 175]. Toze (2006) discussed the potential harm associated with discharging WSP effluent into bodies of water and the environmental degradation of the receiving waterways [72]. Algal blooms and eutrophication are common in discharge waters, due to high concentrations of nutrients (nitrogen, phosphate, carbon dioxide) in the effluent [176]. In addition to environmental degradation, studies have indicated that many viral gastrointestinal public health outbreaks can be linked back to a virally contaminated water source, polluted by raw sewage or treated effluents [19, 50, 53, 59, 156, 174, 177]. These studies have led to the investigation of virus survival and the effect on effluent fate.
Water and wastewater reuse technology has developed steadily over the last several decades with research investigating additional endpoints of wastewater effluent including irrigation, land application and groundwater recharge [38, 72, 73, 119, 178–181].
Irrigation with wastewater and subsequent research surrounding it has arisen due to climate change; where the increasing global population and the associated need for increased food 2.3 Waste stabilisation ponds 38 production are teamed with the reduction of water availability globally [72, 76, 182, 183]. These developments have led to the increased use of treated wastewater for agricultural irrigation [77]. Despite this recent surge in research, it has been suggested that irrigation is a possible pathway for the spread of disease for many years [35, 179, 184]. Tierney, Sullivan and Larkin (1977) presented data that indicated Poliovirus 1 survived for up to 96 days in soil that had been irrigated with sludge, and 89 days when the soil had been flooded with effluent [184]. Depending on the crops grown, it is possible that some will be harvested within this time period, increasing the risk of human contact with viruses.
Land application can consist of applying wastewater effluent or sludge to land. This effluent fate has been occurring in some form since the 1800’s with the risks of viral contamination not becoming evident until the late 1900’s. Moore, Sagik and Sorber (1981) investigated the potential for pathogen transport from land irrigation through to groundwater. The recovery of enteric viruses from soil and water of experimental lysimeters indicated that the application of wastewater presents a potential public health risk [185]. Similarly, Hurst et al. (1980) detected enterovirus survival in soil at land application sites [120].
More recently, Sidhu and Toze (2009) investigated the literature associated with land application of sludge and the potential for contamination of surface waters, groundwater, soil and the food chain [74]. This review determined there is still a major risk for this contamination and an increased need for improved monitoring and methods in regards to pathogen detection [74].
A more recently developed form of wastewater reuse is that of aquifer recharge, which is in use throughout Australia, Europe, USA and the Middle East [186, 187]. The groundwater present in aquifers is removed for drinking water and irrigation around the world. Levels in many countries are now desperately low, so treated wastewater may be an option to replenish the aquifers [178, 186–188]. Further studies have examined the behaviour of waterborne pathogens in groundwater as a result of groundwater recharge using reclaimed water [119]. 2.3 Waste stabilisation ponds 39
By reusing effluents, natural waterways can be protected, the associated public health risks minimised and benefits of effluent utilised to aid agriculture [4, 72]. However, depending on the intended fate of the wastewater, the level of treatment will likely vary. Research into reuse and recycling of wastewater has expanded the knowledge of the processes occurring in wastewater treatment, but mainly in regards to the breaking down of organic products and use of complex mechanical treatment systems [72]. More information on the role of microorganisms in pathogen inactivation is needed to ensure the safety of the environment and the public.
2.3.4 WSP biological community
The biological processes involved in wastewater treatment, especially in a WSP, are vastly complex and involve interactions between a range of different trophic levels [189]. A significant proportion of the processes in WSPs are likely undertaken by the microorganisms present. Microorganisms enable life on earth by breaking down waste and providing nutrients [87]. There are two main populations of microorganisms associated with the wastewater within WSPs; the population entering the treatment plant associated with the faecal matter, and the population residing in the WSPs [190]. The WSP community includes bacteria, algae, enzymes, protozoa and invertebrates, all of which are likely involved in various treatment mechanisms [191–194].
2.3.4.1 Bacteria in WSPs
The importance of bacteria in the environment is often underestimated, however, specific functional groups perform critical biogeochemical transformations of the molecules entering their environment [195–197]. The presence of biogeochemical cycling of carbon, nitrogen and phosphate in WSPs can be indicated by common bacteria involved in these processes 2.3 Waste stabilisation ponds 40
[197]. Methylococcaceae are involved in carbon cycling, while Nitrospira are involved in nitrogen cycling [196–198]. The role of bacteria in the degradation of organic matter, such as organic carbon, is vital in keeping all life cycles moving forward. Without degradation, the build-up of toxic products would overwhelm the environment, preventing organisms from living or surviving, let alone thriving. This role is especially vital in the treatment of wastewater in WSPs.
One of the great unknowns of WSP treatment is the composition of the biological community enhancing overall pathogen removal, to prevent the spread of disease when wastewater effluent is discharged into the environment. Metagenomic sequencing of bacterial communities in a range of waste stabilisation ponds would potentially provide site-specific data adding to the current knowledge of WSPs and their functions [199–202].
Analysis of the bacterial communities involved in wastewater treatment has previously been limited to anaerobic systems, sludge or membrane-associated treatments [203, 204]. More work is now focusing on wastewater as the pressure on wastewater treatment plants (WWTPs) increases [202].
The bacteria within environmental systems can be classified by the functions they perform or the energy source they utilise [205]. Additionally, the ability of the bacteria to utilise free molecular oxygen can allow for further classification.
Since the 1970’s the bacterial community in anaerobic treatment environments has begun to be ascertained [22, 206–208].
Anaerobic bacteria are those that cannot use free molecular oxygen for the degradation of substrates. These bacteria are often present in the lower zones of the ponds and include hydrolytic, fermentative and methanogenic bacteria [205]. Anaerobic digestion includes the hydrolysis and solubilisation of proteins, fats and polysaccharides by a range of fermentative bacteria. Hydrolytic bacteria produce extracellular enzymes enabling the hydrolysis of the 2.3 Waste stabilisation ponds 41 organic matter, resulting in molecules able to be assimilated by the same bacteria [26, 64, 205, 209].
Aerobic bacteria commonly present in waste stabilisation ponds include Beggiatoa alba, Sphaerotilus natans, Achromobacter, Alcaligenes, Flavobacterium and Pseudomonas species [84]. These bacteria are typically found in the upper, oxygenic zones of the ponds where sunlight penetrates the water column stimulating the photosynthetic production of oxygen. Oxygen also enters the upper layer of the ponds by diffusion from the atmosphere. The role of oxygen in aerobic respiration is seen in Equation 2.5.
Aerobic bacterial respiration [84]
C2H12O6 + 6O2 + enzymes → 6CO2 + 6H2O + new cells (2.5)
Aerobic digestion occurs in the presence of heterotrophs, utilising carbon from organic compounds as their energy source [205]. In WSPs, the heterotrophs degrade carbonaceous BOD (cBOD), aiding the reduction of BOD in order to meet treatment requirements.
In terms of the biological community in wastewater, bacteria are often classified by their functions relative to wastewater treatment.
Acetogenic bacteria are fermentative bacteria that degrade cBOD to acetate, carbon dioxide and hydrogen [205]. These acetogenic bacteria include Acetobacter, Syntrobacter and Syntrophomonas genera. The acetate produced by acetogenic bacteria can then be used by methanogenic bacteria to produce methane gas [64, 205]. Methanogenesis occurs by Methanosarcina, Methanomonas, Methanococcus and Methanobacterium [64, 205].
The fermentative bacteria capable of hydrolysing the organic matter anaerobically include Pseudomonas, Flavobacterium, Alcaligenes, Bacteroides, Bifidobacteria, Escherichia and Clostridium [205, 210]. These bacteria form simple, soluble substrates available for use by methanogenic bacteria. 2.3 Waste stabilisation ponds 42
Denitrifying bacteria are facultative anaerobes and can, therefore, utilise free molecular
- oxygen or nitrate (NO3 )[205]. By degrading cBOD in wastewater, the denitrifiers can return nitrogen to the atmosphere as molecular Nitrogen (N2) or Nitrous Oxide (N2O) [205]. Alcaligenes, Bacillus and Pseudomonas genera are all known to contain species capable of this function. Conversely, nitrifying bacteria are strict aerobes, utilising free molecular oxygen to oxidise substrates [205]. Nitrobacter and Nitrospira oxidise ionised ammonia
+ - - - (NH4 ) to nitrite (NO2 ) and nitrite (NO2 ) to nitrate (NO3 ).
Poly-P bacteria are also known as phosphate accumulating organisms (PAOs). These bacteria, including Acinetobacter, Aerobacter, Klebsiella and Beggiatoa, remove orthophosphate from the wastewater environment [205].
Sulphur-reducing bacteria perform anaerobic respiration by oxidising molecular hydrogen or organic compounds and reduce sulphate to hydrogen sulphide through dissimilatory sulphate reduction [113, 205]. This reduction is carried out by Desulfovibrio and Desulfotomaculum bacteria. These bacteria are thought of as nuisance organisms as the products from these reactions can be toxic to WSP. The prevention of the sulphate-reducing community development is paramount in maintaining pond function, especially if the influent has high sulfate concentrations [84]. Sulphur-oxidising bacteria add oxygen to oxidise inorganic sulphur [205]. The bacteria carrying out this function, including Thiobacillus, Beggiatoa and Thiothrix, gain energy from this oxidation reaction [205].
A range of other bacteria is present in WSPs, including filamentous bacteria and floc-forming bacteria. Many species of bacteria present in these categories also have a range of other functions already mentioned. By looking at the species of bacteria present in waste stabilisation ponds, and the enzymes they produce, we can work towards a better understanding of the biological processes occurring in WSPs leading to treated wastewater [17, 23, 25, 26]. 2.3 Waste stabilisation ponds 43
2.3.4.2 Algae in WSPs
Algae comprise up to 90% of the organic matter in WSPs [211]. This proportion can vary in response to seasonal and diurnal effects due to sunlight and temperature affecting the production of new algal cells [56, 87, 95, 212, 213]. Algal growth in WSPs provides oxygen via photosynthesis to support the aerobic bacteria present in WSPs [62, 83, 87, 90, 92, 214]. These bacteria utilise this oxygen to catabolise the organic matter in wastewater and produce carbon dioxide as a result [57, 62, 95, 189, 191].
This symbiotic relationship between algae and bacteria continues as the algae use the carbon dioxide from the bacterial catabolism to undergo cell growth and photosynthesis, completing the photosynthetic cycle [56, 57, 100, 212]. At night, as solar irradiation is reduced, the photosynthetic cycle is halted [57, 87, 95]. The algae then begin to take up oxygen and via respiration give off carbon dioxide, stored in the water to provide nutrients for algal photosynthesis during the daytime [57, 87]. This storage of carbon dioxide lowers the pH of the WSP overnight, whilst with the use of carbon dioxide during the daytime, the pH of the WSP increases [87, 95]. The composition of the algal population changes depending on the loading conditions of the WSP [87]. A pond with a low load, meaning a low concentration of sewage and nutrients, will often see the abundance of algal species optimal for grazing of higher microorganisms, such as Chironomid midges [87, 215, 216]. This can create undesirable effects due to swarms of midges and can affect the local area [87, 215]. A substantially overloaded pond will often result in failure, and the death of all algal species due to uninhabitable conditions like high sulphate levels or acidic pH resulting in little to no biological treatment occurring [87, 217].
Algal photosynthesis is one of the main drivers of pathogen disinfection as the photosynthetic process results in high dissolved oxygen content and high pH values which are detrimental to pathogen survival in waste stabilisation ponds [56, 191, 217]. The reduction of algae by protozoan grazing diminishes high pH and dissolved oxygen produced by the photosynthetic 2.3 Waste stabilisation ponds 44 environment and therefore reduces the natural disinfection process in the WSPs [64, 213]. As algae make up such a large proportion of the ponds organic matter, and discharge is monitored in terms of BOD, the more algae present in the pond results in a higher BOD concentration in the effluent, potentially leading to discharge consent exceedance [56, 83, 189, 191, 211].
In addition to photosynthesis, algae also play a role in nutrient uptake of phosphorous and nitrogen [62, 84, 213]. This uptake is important as high levels of nitrogen and phosphorous in effluent can result in eutrophication of the receiving waters[218]. In addition to algae directly assimilating phosphorous to produce polyphosphate granules, phosphorous precipitates out to calcium phosphate [213]. This precipitation occurs when the pH increases, commonly associated with algal photosynthesis [47, 62, 213]. Nitrogen in the form of ammonia is found
+ in solution as ammonium ion (NH4 ) and ammonia (NH3)[218]. At a neutral pH, ammonia and ammonium ion are in equilibrium (Equation 2.6)[218].
+ − NH3 + H2O NH4 + OH (2.6)
As the pH increases, the equilibrium moves to produce more ammonia [218]. Thus, when the pH increases due to algal photosynthesis, ammonium ions are lost [47, 62, 213].
There are a number of algal species commonly found in WSPs and include Euglena, Phacus, Chlamydomonas, Ankistrodesmus, Chlorella, Microactinium, Scenedesmus, Selenastrum, Dictyosphaerium and Volvox [56, 87, 90]. Chlorella and Euglena are not algae, but are cyanobacteria [205]. They are being classified with the algae due to their photosynthetic behaviour.
2.3.4.3 Protozoa and Metazoa in WSPs
The larger microorganisms, such as protozoa and metazoa, as part of the microbial community can have a significant effect on the overall wastewater treatment [189, 219]. 2.3 Waste stabilisation ponds 45
Protozoa are unicellular organisms that are mostly aerobic however some have been found in anaerobic environments [213]. Paramecium, free-living ciliates and amoeba are all protozoa [213]. Metazoa are multicellular microscopic organisms including rotifers and the microscopic crustaceans, Daphnia [62, 84, 213].
Protozoan and metazoan organisms prey on algae and bacteria and are involved in the control of these populations [62]. The metazoan Daphnia has been associated with a reduction of Cryptosporidium oocyst and Giardia cyst numbers and viability in WSPs [62, 84, 220]. In addition to pathogenic protozoa, Daphnia also feed on single-celled algae and diatoms, bacteria and detritus in the water and from the sludge [189, 213]. Rotifers and tardigrades are also present in WSPs and partake in bacterial and algal grazing, maintaining the populations of each and encouraging floc formations and settling [47, 62, 65, 84, 213]. Tardigrades feed on bacteria, fungi and protozoa while Rotifers feed predominantly on bacteria [213]. Tardigrades and rotifers can be used to indicate the health of WSPs [213]. Rotifers have been associated with cleaner waters and well-functioning WSPs [84]. Tardigrades are strict aerobes and are sensitive to the organic loading, toxicity and dissolved oxygen in WSPs. These changes appear to affect Tardigrades before any changes are seen in the bacterial populations of the ponds, indicating that Tardigrade monitoring could be a successful parameters to determine WSP health [213].
Protozoan grazing in WSPs can be beneficial or detrimental, dependent upon the levels of protozoa present [74]. Protozoa in WSPs include amoebae, flagellates and ciliates [84, 213]. The amoebae, considered to be one of the lowest forms of protozoa, tolerate low dissolved oxygen levels and increased BOD levels, commonly associated with highly polluted waters [213]. Amoebae feed on dispersed bacteria that are prevalent in wastewater influent [213]. Amoeba can also be found in environments with high DO and low BOD, such as contaminated drinking water. This survival can be due to the development of cysts, enabling the amoeba to resist harsh environments. This enables their survival in unfavourable 2.3 Waste stabilisation ponds 46 conditions when sufficient levels of food (bacteria) are available [221]. Free-swimming ciliates are considered an intermediate life form. These protozoa prefer environments with higher dissolved oxygen and lower BOD concentrations [213]. Crawling and stalked ciliates, higher life forms, ingest dispersed bacteria and favour wastewater with high dissolved oxygen concentrations and increased hydraulic retention time [213]. These protozoa contribute to the “cropping” and “coating” of the wastewater [213]. The “cropping” consists of the predation and reduction of bacteria and algae whilst the promoted flocculation of suspended particles is the “coating” action. Both actions lead to water purification [213].
The identification of these different protozoan and metazoan organisms can indicate thestage of wastewater treatment or the level of wastewater pollution. Amoebae are often associated with poor wastewater treatment or highly polluted waters, due to their tolerance of low dissolved oxygen and high BOD loading [84, 213]. Free-swimming ciliates (eg Paramecium) dominance indicates a moderate quality wastewater [47, 213]. Crawling ciliates, stalked ciliates and rotifers are associated with well functioning wastewater treatment or low pollution [47, 84, 213].
Algal predation by protozoa and metazoa can reduce the BOD by 80% [64]. However, this reduction of algae is not always desirable due to the significant roles algae play in wastewater treatment. The extent to which these organisms prey on algae needs control to ensure total loss of algae does not occur [62, 189].
It is highly possible that as protozoan grazing on bacteria and algae exists, some protozoa may be able to ingest viral particles [219]. It is, however, unknown if the ingestion of virus particles will cause inactivation or if the virus particles are passed through the grazer with little effect, thus acting as a form of protection for the viruses [91, 171, 220, 222]. 2.3 Waste stabilisation ponds 47
2.3.5 Pathogenic community
The biological community in WSPs contribute a considerate amount of the treatment processes in natural wastewater treatment systems such as WSPs. Also present in wastewater are the organisms with deleterious effects against humans and animals, called pathogens. These organisms can be protozoan, microbial or viral and if not removed or inactivated in wastewater, can potentially lead to the spread of disease such as gastroenteritis, giardiasis and hepatitis [38, 40, 56, 223]. By looking at systems continuously achieving their discharge consent conditions and comparing these to systems that have failed consent or have high pathogen counts, it may be possible to determine the microbial community composition which is critical for pathogen reduction in wastewater. Table 2.3 presents some of the pathogens found in wastewater and the diseases and symptoms associated with them.
2.3.5.1 Protozoan pathogens in WSPs
Protozoan parasites such as Giardia and Cryptosporidium, are pathogens of concern in wastewater, especially where the intended use includes land application. These can come in the form of cysts and oocysts and can be very resistant. Their survival in wastewater and water environs can be prolonged, ranging from one year in wastewater to potentially longer in wastewater sludge or biosolids [74, 128]. These pathogenic protozoa can be used as treatment performance indicators, for example, the reduction of Giardia cysts can indicate reduction of other potential organisms that are not as robust [74].
Concentrations of Giardia cysts and Cryptosporidium oocysts vary in different wastewater influents. The World Health Organisation Guidelines for the Safe Use of Wastewater, Excreta and Greywater, 2006, present average concentrations of excreted organisms in wastewater [40]. The levels of Cryptosporidium and Giardia are 1 - 104oocysts/L and 102 - 105 cysts/L, respectively. The data presented in the 2006 WHO Guidelines is from papers published 2.3 Waste stabilisation ponds 48
Table 2.3: Pathogens in WSPs and their associated diseases
Pathogen Disease or Symptom Poliovirus poliomyelitis Coxsackievirus meningitis, pneumonia, hepatitis, fever Echovirus meningitis, paralysis, encephalitis, fever Hepatitis A infectious hepatitis Rotavirus acute gastroenteritis; severe diarrhoea Human Calicivirus epidemic gastroenteritis; severe diarrhoea Reovirus respiratory infections; gastroenteritis Hepatitis E hepatitis Astrovirus gastroenteritis Adenovirus respiratory tract infections, gastroenteritis Ascaris digestive disturbances Trichuris trichiura diarrhoea; anaemia Salmonella spp salmonellosis/food poisoning Shigella spp bacillary dysentery Yersinia spp acute gastroenteritis Vibrio cholerae cholera Campylobacter jejuni gastroenteritis Pathogenic E. coli gastroenteritis Cryptosporidium gastroenteritis; cryptosporidiosis Entamoeba histolytica acute enteritis Giardia giardiasis; diarrhoea Toxoplasma gondii toxoplasmosis Collated from Gerba and Smith (2005), Butler (2015) and the World Health Organisation Guidelines for Wastewater Reuse (2006) [38, 40, 56] between 1980’s and 2000’s. More recently, Reinoso and Bécares (2008) presented influent pathogen concentrations [170]. Cryptosporidium was detected at an average concentration of 45.7 oocysts/L (± 7.55) while Giardia was more prevalent at a concentration of 280.94 cysts/L (± 99.14). The concentrations determined by Reinoso and Bécares (2008) are different from those presented in the WHO Guidelines, this difference however could be due to the type of wastewater and the intake population [170].
Table 2.4 presents concentrations of Giardia, Cryptosporidium and Helminths in different fractions of wastewater. 2.3 Waste stabilisation ponds 49
Table 2.4: Protozoan pathogen prevalence in wastewater
Pathogen Influent Anaerobic Pond Facultative Pond Effluent 1165 284(4) [165] 67.1 8.1(5) 2.4(5) 0.7(5) [171] 280 1(5) [170] 4000 900(5) 100(5) [180] Giardia (cysts/L) 2000 850(5) 800(5) [180] 0.4(1) [5] 2(2) [5] 10(3) [5] 4.5 1.2(1) [168] 6 4(4) [165] 14.9 3.9(5) 1.2(5) 0.4(5) [171] 45.7 1(5) [170] 70 20(5) 9(5) [180] Cryptosporidium (oocysts/L) 50 20(5) 5(5) [180] 0.13(1) [5] 2(2) [5] 10(3) [5] 3.5 0.7(1) [168] 1.8 0.1(5) 0(5) 0(5) [171] 9.56 1(5) [171] Helminths (eggs/L) 992.6 54(5) 0.2(5) 0.1(5) [39] 700 500(5) 100(5) [180] 600 500(5) 200(5) [180] Italicised values presented as log transformed (oo)cysts/100L. Treatment types indicated by superscript numbers: 1Activated Sludge treatments; 2Membrane Bioreactor treatments; 3Anaerobic treatment systems; 4Physical-Chemical treatments; 5Waste Stabilisation Pond treatments.
2.3.5.2 Microbial pathogens in WSPs
A wide range of bacterial pathogens are often present in the wastewater of WSPs. Common pathogens of interest include Campylobacter jejuni, Vibrio cholerae, Salmonella spp. and pathogenic E. coli [37, 38, 56]. Concentrations of some of these pathogens in wastewater are presented in Table 2.5.
These bacterial pathogens predominantly cause gastrointestinal type illnesses with diarrhoea, vomiting, fever and abdominal pains among the symptoms [38, 40, 56, 218, 232]. 2.3 Waste stabilisation ponds 50
Table 2.5: Prevalence of bacterial pathogens throughout WWTPs
Pathogen Influent Primary treatment Effluent References Campylobacter 70 0.2(5) 0(5) [224] 5.61 4.92(1) 4.58(1) Mycobacterium [225, 226] 1.1 0.7(4) 0(4) 0.6 0.1(5) -1.5(2) 20 1.9(2) 0(5) Salmonella 7890 14(4) [166, 224, 227–229] 1530 143(1) 310(1) 86.2 0.6(2) 20.6(1) Shigella 1 -1.5(2) [166, 228, 229] 1000(1) 485 8(5) 0(5) Vibrio [230, 231] 15(1) Italicised values presented as log copies/mL. All other values presented as CFU/L. Treatment types indicated by superscript numbers: 1Activated Sludge treatments; 2Membrane Bioreactor treatments; 3Anaerobic treatment systems; 4Physical-Chemical treatments; 5Waste Stabilisation Pond treatments.
The Salmonella group are among the most important bacteria in regards to human public health [218]. These bacteria are found naturally in many environments and readily infect both humans and animals. Salmonella transmission is commonly foodborne, but waterborne transmission has been detected. Concentrations of Salmonella in the wastewater of developed countries are up to 107 lower than those detected in the wastewater of developing countries (up to 109 organisms/100 mL) [62]. It is thought that ingestion of a high number (105 - 107) of organisms is required for the development of infection [62, 218]. The Shigella bacteria are similar to Salmonella group but have lower environmental survival rates and less likely to infect animals than the Salmonella bacteria [218]. Individuals infected with Shigella excrete up to 109 organisms per gram faeces, while only 101 - 102 organisms are required for shigellosis to develop [62].
Another significant cause of bacterial gastroenteritis is the bacteria Campylobacter [38, 218]. These thermotolerant bacteria are abundantly found in human and animal faeces and can 2.3 Waste stabilisation ponds 51 contaminate water sources directly through sewage contamination or by the indirect contamination from animal faeces (such as bird droppings) [218]. Campylobacter has been detected in raw sewage at concentrations ranging from 104 to 106 organisms/L, with the ingestion of as few as 500 organisms required for gastroenteritis to develop [62]. Gastroenteritis caused by Campylobacter is thought to result in the development of the paralytic illness, Guillain-Barré syndrome, in some cases, increasing the morbidity and mortality associated with Campylobacter infections [62, 218].
Vibrio cholerae is a bacteria found in Asia and Europe but has not yet been detected in New Zealand, Australia or the Pacific Islands. It is endemic to areas with poor sanitation and continual contamination of water sources. V. cholerae causes Cholera, which has associated acute gastrointestinal symptoms, upon the ingestion of 106 - 107 organisms, with infected persons excreting approximately 1013 particles a day [38, 218].
Additionally there are a range of opportunistic bacteria in wastewater that cause no harm to the majority of individuals. However, these predominantly gram-negative bacteria can result in illnesses in immunocompromised individuals, such as the young, old or those with auto immune diseases [62].
2.3.5.3 Viruses in WSPs
To minimise the outbreaks of viral gastroenteric illness associated with wastewater contamination of drinking, recreation and irrigation waters, waterborne human pathogens are of interest. Waterborne gastroenteric viruses with water and faecal-oral transmission modes include Norovirus, Rotavirus, Enterovirus and Adenoviruses [38, 40, 62, 218, 232]. These viruses are also major contributors of diarrheal death and illness worldwide. The most common route of transmission for gastroenteric illness is the faecal-oral route, which is why wastewater contamination is a large factor in the spread of these diseases [62, 175, 218]. 2.3 Waste stabilisation ponds 52 Yes Yes Yes Yes Yes Yes Yes Yes Yes Faeces Presence in Unknown R BF Routes R; BF F-O; R F-O; R F-O; R F-O; R F-O; R R; ENT F-O; BD Transmission Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Human Infection dsDNA dsDNA dsDNA ssRNA (-ve) ssRNA (-ve) ssRNA (+ve) ssRNA (+ve) ssRNA (+ve) ssRNA (+ve) ssRNA (+ve) Genetic Information Size 27 nm 38 nm 75 nm 27 nm 32 nm 100 nm 900 nm 70-90 nm 75-160 nm 150-200 nm E E E Virion NE, IC NE, IC NE, IC (PT=3) (PT=3) NE, DC (PT=25) Properties (T=2,13) E, IC (T=16) NE, IC (T=3) NE, IC (T=1) Table 2.6: Common human enteric viruses and their properties Family Ortho- Reoviridae Filoviridae Data collated from [ 6 , 9 38 , 49 , 62 , 118 , 120 , 147 , 155 , 175 , 218 , 233 – 257 ] Hepeviridae myxoviridae Caliciviridae Adenoviridae Herpesviridae Picornaviridae Coronaviridae Picornaviridae A) Group (Hep E) Influenza Ebolavirus Where IC = Icosahedral; DC = Double Capsid; PT = Pseudo T; NE = Non-enveloped; E = Enveloped; F-O = Faecal-Oral Route; Rotaviruses Noroviruses Virus or Virus Enteroviruses Adenoviruses Coronavirinae Herpes viruses Orthohepevirus Hepatovirus (Hep R = Respiratory; BF = Body Fluids; BD = Blood; ENT = Enteric; ss = Single Stranded; ds = Double Stranded; +ve = Positive sense and -ve = Negative sense 2.3 Waste stabilisation ponds 53
Table 2.6 shows the virion properties and genetic information of some enteric viruses. The majority of the gastrointestinal viruses are non-enveloped, meaning the protein capsid is the outermost layer, while enveloped viruses have another layer surrounding the capsid, made up of phospholipids, glycoproteins and lipoproteins [258, 259]. Enveloped viruses tend to be more sensitive to the environment and treatments than their non-enveloped counterparts due to the lipid layer being susceptible to neutralisation in the presence of solvents and the effect of heat and dryness [251]. The neutralisation of the envelope hinders virus-host cell interactions and progeny release as the receptors for this are located on the envelope and are also neutralised.
The capsid of non-enveloped viruses have a very low lipid content, instead consisting of predominantly proteins [175, 260–262]. This composition makes the capsid much more resistant to the same physical and chemical agents that affect the lipids of the enveloped viruses, resulting in highly virulent viruses, with higher survival rates and often infectious at lower doses [47, 62, 175, 259, 263, 264].
The process of inactivation differs between the types of treatment and within each type of treatment where with modification of either the viral protein coat or the nucleic acids ofthe virus [265]. Physical factors include UV irradiation and temperature while chemical factors include heavy metals, organic chelators, chlorine disinfection or peracetic acid disinfection [122, 124, 125, 128, 136, 266–269]. Disruption of genome replication is commonly due to RNA damage where failure of genome injection can be due to host-binding proteins being interrupted [265].
Some human viruses are susceptible to enzymatic proteolysis [25, 111, 129, 270–272]. Something must be capable of proteolytic degradation of virus particles, otherwise they would cover all of earth’s surfaces [111]. Both Bacillus subtilis and Pseudomonas aeruginosa display antiviral activity, and some human viruses are susceptible to proteolysis [111]. 2.3 Waste stabilisation ponds 54
Table 2.7: Prevalence of Viral Pathogens in Wastewater
Virus Influent Effluent References 9 9 9 4.7 6(1) 7(2) 7(5) 4(1) 4.6 3.85 5.93 2.7 2.1(2) 1.5(3) 2.9(1) 3.6(1) [5, 82, 155, NoV 8 8 8 5.63 7(5) 3(1) 3.8 5.1 163, 166, 235, 5 4.8 5.2 5.08 1.3(2) 2.5(1) 3.5(2) 0(1) 273, 274] 2.48 6.2 5.46 4.66 3.1(1) 3.9(1) 3.6(1) 2.28 2.46 2.47 2.15 1.59 0.85 1.0(1) 0.7(1) 2.10 5.32 2.99 2.97 -0.9 -1.5 2.46 2.2 [155, 163, EV 3.95 3.19 6.2 2.24 -1.77 0(1) 1.75(1) 3.16(4) 165–168, 185, 4.0 2.92 1.7 2.27 2.48(3) 2.2(3) 2.32(2) 0.3 274, 275] 1.08(1) 1.1 4.0(2) 1.7 7.15 5.82 5.87 2.83 5.79(1) 5.87(1) 0(1) 0(1) [82, 163, 274, AdV 5.85 5.1 5.2 6.34 6.15(1) 5.1(1) 4.9(1) 4.46(1) 276, 277] 7.86 2.9 4.93 3.94(1) 3.57(1) 4.93(1) 2.59(1) 3.67 2.88 4.74 4.49 2.78(1) 2.96(1) 3.68(1) 4.46(1) 2.21 2.59 4.96 4.49 2.87(5) 2.83(5) 2.87(5) 2.72(5) [9, 155, 166, RV 2.19 0.99 3.73 2.65 3.23 2.88(1) 3.01(1) 3.9(2) 224] 6.5 2.53 0.3 2.34 4.38(1) 0.3(5) All values presented as Log transformed. Italicised values represent concentration determined by cell culture (PFU/L), all remaining values represent concentration determined by PCR (Genome copies/L). NoV = Norovirus (all genotypes); EV = Enterovirus; AdV = Human Adenovirus; RV = Rotavirus. Treatment types indicated by superscript numbers: 1Activated Sludge treatments; 2Membrane Bioreactor treatments; 3Anaerobic treatment systems; 4Physical-Chemical treatments; 5Waste Stabilisation Pond treatments.
Table 2.7 presents virus concentrations collated from the literature. The viruses included are those that are linked to public health outbreaks due to wastewater contamination of food and water sources [47, 62, 118, 258, 261, 262]. The data shows a wide range of viral concentrations can be found in all wastewater fractions, including effluent. Viruses have low infectious doses with as low as 10 particles required to result in illness [62, 175]. This concentration is exceeded in all wastewater fractions for all viruses analysed. 2.3 Waste stabilisation ponds 55
2.3.6 Pathogen detection
Pathogens are disease causing organisms that arise from multiple origins, such as viruses, protozoa or bacteria. There is increasing evidence that viral pathogens, in addition to well studied bacterial and protozoan pathogens, are linked to many gastroenteritis outbreaks through the contamination of food or water. Despite this evidence, these viruses are not required to be monitored throughout wastewater treatment [278].
Two methods of virus detection are available, molecular and cell culture. Molecular detection of viral particles has progressed significantly, however this technique still has disadvantages associated with it, including the limit of detection and lack of distinction between infectious and non-infectious particles [5, 82, 158, 279]. However, with sufficient method development and use of standards, molecular methods can quantify the concentration of viral genomes detected. Without the inclusion of standards, molecular detection is limited to presence/absence determination. Reliance on molecular methods can be misleading as the concentration presented is often taken as the concentration of infectious particles [119, 280]. Further studies have determined that viral genomes detected by molecular methods persist longer than infectious viral particles. Alternatively, cell culture methods allows for the detection of infectious viruses and determination of the concentration present in a sample. Viral detection by cell culture is limited due to a lack of available methods for a range of enteric viruses, such as Norovirus [34]. Additionally, the costs associated with viral cell culture are high, deterring the inclusion of viral detection in monitoring regimes where it is not a regulation. New molecular methods are currently being establish to improve infectious particle detection, but for now, where possible, cell culture is the best method available to determine the levels of infectious viruses present [281, 282].
A significant contributors to gastroenteritis worldwide is Norovirus [6, 158, 274]. Despite many years of research, breakthroughs into culturing this virus are only just being made. The possibility of a standard culture method for Norovirus is still however a long way off. The 2.3 Waste stabilisation ponds 56 culture of these viruses uses mammalian cell lines which themselves, and the reagents required are very costly. The time taken to culture the viruses also increases the costs associated with these methods. Levels of viruses are often too low to confidently detect and quantify in natural wastewater meaning concentration of large volumes of water is required. This concentration is time consuming and may introduce bias into the resulting detection of viruses. Despite these low levels, the potential for infection is still present, as viruses often have much lower infectious doses than their bacterial counterparts [74].
Bacterial pathogens, such as Mycobacteria, are also not directly monitored due to difficulties in detection methods. The ability to distinguish non-pathogenic bacterial species from the pathogenic ones is elusive at best, making robust detection of bacterial pathogens difficult [283, 284].
Due to these difficulties, indicator organisms are used instead. A range of indicators are monitored to determine the likely survival of both bacterial and viral pathogens.
2.3.6.1 Indicator organisms
The ideal indicator organism has the following attributes [47, 118]:
• ubiquitous presence when pathogens are present,
• similar survival characteristics to the pathogen of interest,
• unable to multiply in wastewater,
• non-pathogenic to humans,
• low detection limit,
• low cost and rapid detection. 2.3 Waste stabilisation ponds 57
It is unlikely there are organisms that fit all of the above criteria, however some organisms are likely to be more appropriate than others. The indicators for bacterial pathogens are generally undisputed for their suitability, but the same cannot be said for viral and protozoan pathogens [47]. Table 2.8 presents common indicator organisms and the pathogens they are used to monitor.
Total coliform bacteria are used to indicate the overall bacteriological quality of water, while faecal coliforms are used to specifically indicate for faecal pollution of water[47, 168, 232]. The total coliforms include Citrobacter, Enterobacter, Escherichia, Klebsiella, Serratia and Yersinia while Escherichia, specifically E. coli, is a faecal coliform indicator.
Intestinal enterococci or faecal enterococci are also faecal pollution indicators and include Streptococcus and Enterococcus. However, the enumeration and isolation methods for these indicators are less reliable than for faecal coliforms [47].
Another indicator for faecal pollution is Clostridium perfringens [47, 165, 168]. This bacteria indicates for past pollution or for pollution in the presence of toxins. C. perfringens is more resilient than other faecal coliforms, and will survive while other faecal indicators have been inactivated [47].
Bacteriophage, or viruses infecting bacteria, have also been suggested as additional indicator organisms. Two main types of bacteriophage are used as indicators, the somatic coliphage and f-specific phage. Somatic coliphage are viruses that infect coliform bacteria via the cellwall. The constant presence of somatic phage in sewage, faeces and wastewaters means they are suitable candidates for faecal pollution indication. Somatic coliphages belong to the groups I and II DNA viruses and include PRD1, φX174 and λ phage.
F-specific phage are viruses that infect the host bacteria via the F-pili on the hosts surface. Bacteria with the F-pili are distinguished as ’male’ bacteria, and F-specific phage can also be called male-specific phage. F-specific phage that have genetic information in the formofRNA have little relationship to human faecal pollution as they are not often found in human faeces. 2.3 Waste stabilisation ponds 58 Reference [ 39 , 47 , 62 ] [ 47 , 165 , 218 ] [ 34 , 47 , 62 , 118 , 218 , 223 ] [ 47 , 62 , 118 , 163 , 218 , 285 ] Indicators direct detection Clostridium perfringens Somatic phage or F-RNA phage ; faecal coliforms; total coliforms; intestinal enterococci E. coli V. cholerae ; Table 2.8: Pathogens and indicators currently used for monitoring Helminths Campylobacter Pathogens ; AdV = Adenovirus; RV = Rotavirus; EV = Enterovirus; Hep A = Hepatitis A; NLV = Norwalk-like Virus Cryptosporidium; Giardia E. coli AdV; RV; EV; Hep A; NLV ; Shigella 2.3 Waste stabilisation ponds 59
Therefore F-specific phage are not useful as faecal pollution indicators. However, theyare commonly found in sewage at high concentrations and are relatively resistant to chlorination, so may act as an indicator of treatment efficiency, to determine the success of wastewater treatment processes. F-specific phage are in Group IV of the RNA viruses and include MS2,
Qβ and GA as examples.
2.3.6.2 Use of MS2 for enteric virus monitoring
MS2 particles have many similar characteristics to those of enteric virus. These similarities include particle size and structure [286]. Both MS2 and enteroviruses are icosahedral shape, 20-25 nm in diameter and contain a single stranded RNA genome. MS2 (and other F-RNA phage) are excreted by humans consistently while enteric viruses are shed by humans during infection only [287, 288].
In wastewater treatment the removal of phage such as MS2 is more similar to that of infectious enteric viruses than what is seen with faecal indicator bacteria [288]. Historic studies found phage were to be more resistant to chlorination than Poliovirus, while a recent study found faecal indicator bacteria are more effectively removed by wastewater treatment that phage or enteric virus particles [5, 286, 289, 290].
The properties of MS2 presented here align with the attributes desired for suitable indicator organisms. In addition to these properties enumeration of MS2 is simple, low cost and rapid, further contributing to the evidence that MS2 (or F-RNA phage) could be a suitable indicator organism.
The ability of phage to multiply in the environment has been contested in the literature. Indicator organisms ideally should not replicate in the environment [52], but a recent review by Jofre et al. (2016) suggested if this replication was to occur it is likely the levels would not greatly contribute to the phage numbers contributed by faecal pollution [291]. The host 2.3 Waste stabilisation ponds 60 strains used for phage enumeration in standard methods are optimised to avoid detection of these naturally replicating phage [291].
There is, however, a lack of clear correlation between MS2 (or other F-RNA phage) and enteric virus survival [291]. Hatard et al. (2018) suggests this lack of consistent correlation may be due to the viruses selected for analysis [279]. Many studies include norovirus (NoV) when comparing survival to F-RNA phage. The detection of NoV is limited to viral genome detection using molecular methods because, as discussed previously, there is currently no methods available for the cultivation of NoV. Negative correlations between phage and viral genome detection of NoV is therefore unsurprising as viral pathogens are known to survive longer than infectious viral particles [279].
Further investigations are required to determine if this negative correlation can be dismissed as an artefact of the detection methods used. This investigation would be reliant upon cultivation and enumeration of infectious viral particles.
These studies suggest that the current indicators used to monitor virus removal in wastewater treatment systems are ineffective, as it is evident that some viral particles are still present in the treated wastewater. These viruses can be present in numbers high enough to cause viral illnesses, especially as viruses have much lower infectious doses than the indicator organisms used for monitoring [33, 82, 143]. The indicators in use may not inform on virus removal but if monitored throughout a WWTP or WSP then the indicators can at least provide information of successful treatment or microbial pathogen removal. Whilst the development of improved viral indicators and detection methods are required, the use of these current indicators should continue to be employed as a minimum requirement. The inclusion of MS2 as an additional indicator could prove to be beneficial in monitoring wastewater treatment and viral persistence despite the contrasting evidence in the literature. 2.4 Monitoring wastewater treatment 61
2.4 Monitoring wastewater treatment
Wastewater monitoring is used to help protect our environment and manage the water resources available to us [44]. Monitoring of wastewater discharge occurs as a requisite of discharge consent or statutory regulations of each individual country or region where the pond is located. The type and scale of monitoring will be reflected by the community served by the wastewater treatment plant, the composition of the wastewater, the eventual use or discharge of the effluent and the environment receiving the effluent. Commonly, the scaleof the monitoring program will be reflected by potential adverse effects to the environment and human health [44, 214].
There are four main types of monitoring; baseline, compliance, trend and investigative. Baseline monitoring occurs in the receiving environment prior to the construction of a wastewater treatment plant. This data is used to determine the suitability of the environment to receive wastewater effluent [44, 292].
Compliance monitoring is the monitoring of discharge prior to entering the receiving environment, defined by discharge consent [44]. Trend monitoring determines the behaviour of effluent or receiving environment over time, used to identify long term effects, diurnal variations and seasonal variations. Investigative monitoring generally occurs when discharge consent is exceeded or predetermined levels are exceeded. This is often used to inform the cause of why the consent was not reached [44].
Differences between these schemes and the analysis undertaken produce results which may not be suitable for comparisons. Further to this, within these schemes there are sampling differences such as if grab samples or composite samples are taken. When these and other variables are not consistent, comparisons of the results will include a high level of unacceptable uncertainty. 2.4 Monitoring wastewater treatment 62
The review of current monitoring guidelines will aid the identification of the variables commonly sampled differently and how the overall procedures differ. Building on these current monitoring scheme guidelines, steps can be taken towards the development of a New Zealand specific monitoring program aiding the success of WSPs throughout NZ. Thelackof consistent monitoring programs adds to the lack of consistent WSP design and therefore inconsistent wastewater treatment worldwide. By defining a suitable monitoring program, the comparison of results will be more accurate and relevant. The monitoring of a laboratory-scale WSP will aid the development of a suitable monitoring program for WSPs. Compliance monitoring is the most common type of monitoring program for the evaluation of wastewater treatment success. The New Zealand Oxidation Pond Monitoring Guidelines, developed in 2002, are based on a risk-centric approach [44]. These guidelines were recently updated, in 2017, however they refer to the monitoring program from the 2002 guidelines [43]. These guidelines require the characterisation of the wastewater components, the receiving environment under both normal and unusual conditions, as well as the potential effects upon humans and the environment [43, 44].
Wastewater treatment is typically monitored using testing determining the physical and chemical (physicochemical) properties of the wastewater on a day-to-day basis. Two of the analyses used to monitor treatment are biochemical oxygen demand (BOD) and chemical oxygen demand (COD) [57]. BOD is a measure of how much dissolved oxygen (DO) is used by microorganisms when oxidising organic matter while COD measures what is chemically oxidised as opposed to the biologically active microorganisms. COD is less specific than BOD as it does not take into account the bacterial mass. Both measurements, reported in mg/L or g/m3, give an indication of the organic pollution in the wastewater inferred by the amount of oxygen required to break down the organics. A low oxygen demand could indicate either there isn’t adequate biological activity in the wastewater to break down the organics, or 2.4 Monitoring wastewater treatment 63 there are sufficiently low levels of organic matter requiring reduced oxygen. Equation 2.7 describes organic and inorganic oxidation by mixed heterotrophic organisms:
−kt Lt = Loe (2.7)
Where Lt = BOD at time t; Lo = initial BOD at time 0; k = rate constant.
The BOD rate constant, k, varies from 0.15 to 0.30 in raw wastewater compared to 0.05 to 0.10 in surface water, indicating a much higher organic matter level in wastewater [62]. BOD is a principal test used to indicate the efficiency of wastewater treatment plants and the potential effect of the effluents on receiving waters. The standard test involves aliquots of wastewater are diluted with a buffer and saturated with oxygen [62], measuring the DO at the start of the five day incubation period, then again after five days in a dark room at 20 °C.These conditions prevent any photosynthetic production of oxygen, so the oxygen levels remaining after oxidation of organic matter are being measured.
Detection and quantitation of microbes can also be performed to investigate the reduction of harmful pathogens to safe levels before discharge of effluent. Common indicator bacteria tested include Escherichia coli, faecal coliforms and enterococci bacteria. Often, the majority of analyses are done prior to discharge to determine proper treatment of effluent, or the overall reduction in BOD and pathogens. However the levels of reduction throughout the treatment process are often not well understood. This lack of between-pond modelling prevents knowledge regarding specific processes and treatment stages being gained.
To determine an appropriate monitoring program, characterisation of the wastewater, the treatment plant and the receiving environment is required. Effective monitoring needs to take into account quality control, sample time, sample location and sample type (composite or grab samples). Further logistics to be considered are the time taken to transport samples to 2.4 Monitoring wastewater treatment 64 lab for analysis, time taken for analysis and how samples are stored. A microbiological and physicochemical monitoring scheme is shown in Table 2.9 with relative sampling occurrence.
Table 2.9: An example of a discharge monitoring program
Parameter Occurrence Parameter Occurrence Temperature Daily to Weekly Fats, oils and grease Monthly pH Daily to Weekly Total Nitrogen Weekly to Monthly TSS Fortnightly Ammonia Weekly to Monthly Turbidity Fortnightly Nitrate and Nitrite Quarterly Total and Reactive VSS Quarterly Monthly Phosphorous BOD Fortnightly Metals Quarterly to Annually COD Monthly Microbiological Daily to Weekly Foam and Daily Pathogens Monthly to Quarterly Scum Monitoring scheme of wastewater effluent prior to discharge. Occurrence is dependent on pre-determined risk for specific location and wastewater. Foam and scum monitoring occurs by visual checks. Fats, oils andgrease monitoring occurs if industry waste contributes to wastewater composition. Table adapted from the New Zealand Oxidation Pond Monitoring Guidelines [44, 87].
Pearson, Mara and Bartone (1987) developed guidelines for the minimum evaluation of WSPs and these guidelines are still relatively applicable today [90]. The authors suggested the first step in compiling data on a WSP system included the physical description ofthe WSP system and its location. This includes the geographical location and description of the site, the population the WSP serves and the climate data for the region. Only with this data will the physicochemical and microbiological monitoring be useful to determine WSP treatment efficacy and for application to other WSPs.
In terms of the WSP system, the pond arrangement and types should be recorded, as well as any pre- and post-treatment in use [43, 90]. Flow measurements through the WSP system should be taken, including in and out of the system, and identification of the destination of the effluent is important. For each pond in the system the dimensions and inlet/outlet configurations are to be recorded, as well as if ponds are lined and what with[43]. If baffles or any extra features are present in the pond these should be noted. The flows and loading the 2.4 Monitoring wastewater treatment 65 ponds are designed for should be recorded, as well as actual flows and loads that have been recorded and any other sampling information [43, 90]. To monitor the diurnal changes in the pond, in order to get a full range of pH and DO values, these should be measured at least once each season at 0800, 1200 and 1600 hours.
Samples can be collected as grab samples (one off samples) or composite samples collected over specified time periods. Sampling of the raw sewage, ponds or effluent will dependon the analyses to be done and the aim of analyses, determining treatment efficacy, pollution or pathogen reduction. Analysis of raw sewage is performed only if there appears to be any changes in pond function or to identify the level of treatment occurring in the pond system [43]. Where the final effluent is used for irrigation of crops it may be worthwhile toinclude further analyses such as electrical conductivity, sodium adsorption and nematode eggs. The results from analyses should represent mean values which can then be used to determine the theoretical retention time and removal of BOD, SS and FC in each pond and each series of ponds [90]. These analyses are not typically undertaken between ponds unless wastewater treatment processes appear to be ineffective, producing effluents with quality below discharge consent requirements. Sampling to determine WSP performance should occur weekly for five weeks each season to identify a full range of values dependent upon temperature and climate as well as influent properties [90]. This distribution of sampling would enable the incorporation of a wide range of environmental conditions, allowing for a more complete understanding of wastewater treatment to be developed. The sample size will need to be determined to result in the effect size desired as well as achieving the specified significance levelα ( ) and probability (p). This would result in statistically significant results, where the result seen is not likely to be due to chance.
Prior to sampling, limits need to be set to determine what qualifies as successful treatment. Most limits are based on the guidelines produced and updated by WHO, and it is up to the individual country or region to set their own limits from these. However all too often, the 2.4 Monitoring wastewater treatment 66
WHO guidelines are being used as distinct limit values in themselves, without reference to the immediate location, climate and level of treatment required of the WSP in question [293].
Standards NZ have standards in place for on-site domestic wastewater management (AS/NZS 1547:2012 and 1546:2008) and sampling programs (AS/NZS 5667:1998) [294–296]. The British Standards Institute have developed standards relating to Wastewater Treatment Plants (BS 12255) [297]. The ISO standard, ISO 16075:2015, covers the use of treated wastewater for irrigation [298]. None of these standards relate directly to WSPs specifically, so some inference is required to apply them to WSP systems.
None of these monitoring programs refer to viral analysis, with a reliance on physicochemical and microbiological tests. As irrigation with wastewater effluents, and increased human contact with water becomes more commonplace, viral monitoring guidelines need to be put in place.
2.4.1 Parameters for defining successful treatment
Success in terms of wastewater treatment is the reduction of influent pollutant levels, including organics and pathogens, to levels sufficient to meet resource consent requirements for effluent discharge and reintroduction to the environment. By ensuring the BOD, TSSand COD are at maintainable levels and the WSPs are not overloaded and the prevention of anoxic conditions (including high pH, reduction of biological activity), the stabilisation of wastewater can occur. WSPs run optimally with consistent influent concentrations, hence the prevention of overloading is foremost in regards to efficient function. Common values expected for influent are presented in Table 2.10. The wide range of retention time values reflects on the wide range of population sizes, influent load possible and climatic conditions. 2.4 Monitoring wastewater treatment 67
Table 2.10: Common waste stabilisation pond design values
Parameter Value Unit
BOD5 Loading 80 – 200 kg/ha/day Optimal pH 6.5 – 9
Optimal DO 2 – 8 g/m3
Depth 1.5 m
Hydraulic Retention Time (HRT) 8 – 30 days
Pond Size 2:1 - 3:1 L x W ratio Adapted from [44, 45, 84, 101]
Treatment efficiency for BOD is often defined as a limit of filtered effluent (25mg/L).To determine if effluent needs filtering or not in order to reach this desired level of treatment Mara (2005) designed Equation 2.8 in order to take into account the potential effect of filtration [211]. This equation is based on first order rate removal constant for BOD removal andthe proportion of algal matter making up the total BOD of the wastewater effluent.
Common first order rate removal of BOD(k1(20)) for a primary facultative pond is k1(20)= 0.3 d-1, with up to 90% of the BOD being algae. Filtered effluent BOD can be estimated using Equation 2.8:
Le( filtered) = 0.3Le(un filtered) (2.8)
Successful pathogen removal for a WSP system can vary in terms of the final use of the effluent. For wastewater reuse in irrigation, the effluent is recommended to have no morethan one human intestinal nematode egg per litre and no more than 105 faecal coliform bacteria per 100 mL [40, 46, 299]. 2.4 Monitoring wastewater treatment 68
Little information is available on the direct removal of viruses [155, 164, 274]. The data collected primarily looks at the levels of viruses (or phage) in the influent and effluent, not the individual stages of treatment. Simmons and Xagoraraki (2011) determined the removal of Human Adenovirus and Enterovirus in four wastewater treatment processes; MBR, Activated Sludge, Oxidation Ditch and Rotating Biological Contactors, with either UV or Chlorination disinfection [274]. Many researchers investigated viral concentration using qPCR and/or cell culture detection methods. The molecular method, qPCR, does not distinguish between infectious and non-infectious viruses. Therefore this method should not be relied on to determine the pathogenicity or viral load of wastewater. Compilation of this data is difficult as these studies investigated different viruses or phage, using different methods and reported in various ways. This makes it difficult to compare the values present in the literature.
A significant aim of wastewater treatment is the reduction of microbial values by aspecified number of logs. Utilising this log reduction as a success parameter for viral monitoring would not be sufficient for showing adequate viral reduction. The low infectious doses associated with viral infections means enough virus particles may remain in order to result in viral illnesses.
2.4.2 Design manuals
Design manuals can include model and design equations, monitoring schemes, operation and maintenance and construction processes as shown in Table 2.11. However, very few manuals incorporate multiple or all of these processes.
Manual users are customarily given a portion of the information and lack an understanding of the remaining factors. Without sufficient knowledge of the total processes occurring, management and maintenance of WSPs may suffer. Operators may modify the way in which 2.5 Model wastewater systems 69 situations are dealt if there was a greater understanding of all the processes involved and the effect of the modifications on the resulting effluent.
Many design manuals for the construction of municipal WSPs take into account the operational variables including the population to be serviced, loading rates and desired pathogen and organic removal. The civil engineering factors, including pond hydraulics, pond shape, inlet and outlet shape and baffle design, are often not included [44, 87]. These differences in the design manuals (and subsequent ponds) result in difficulty determining the effect of both the design and operational variables, and how they contribute towards overall wastewater treatment. Hydraulic modelling has shown design variables have the ability to influence the hydraulic properties of wastewater in a WSP system, including the lengthof time the water resides in the system. Both the pond hydraulics and retention time are important in treatment efficacy.
Many design models have been used previously to design WSPs or predict the hydraulic performance of a pond (Table 2.11). Some core equations historically utilised in pond design do not differ and are still commonly used today.
It is increasingly evident throughout the literature that these manuals focus significantly on the physicochemical and hydraulic values of WSP operation. By failing to consider pathogen removal mechanisms at the design stage, the resulting WSPs may not function optimally in terms of virus and bacterial removal.
2.5 Model wastewater systems
Model-scale studies use microcosms or computer modelling to assess the viability of new procedures or processes without committing large amounts of money, time and other resources to produce a full-scale system with potentially unknown success [300–302]. These studies 2.5 Model wastewater systems 70 (1998) Reference Ray (2002) Shilton and MfE (2005) Harrison (2003) Spellman (2014) Ashworth (2001) Mara and Pearson Used for Planners, Planners, Planners, Planners, Operators Operators Operators Operators Operators Designers Designers, Designers, monitoring and operation Type of Manual Design and models Regulations, design, hydraulic processes) Planning, design and (Hydraulic processes) Operation and models Regulations, design and Overview of WSP design operation and monitoring operation (Biological and Table 2.11: Existing WSP design manuals and their applications Manual Operation Design Manual Design of WSP’s stabilization ponds in Monitoring Guidelines Handbook of Water and Mediterranean countries Design manual for waste Waste Stabilisation Pond NZ Municipal Wastewater Oxidation Pond Guidelines Wastewater Treatment Plant Guidelines for the Hydraulic 2.5 Model wastewater systems 71 are commonly used to aid mathematical modelling, understanding chemical and hydrological processes and the testing of materials and methods [303].
Most studies of miniaturised wastewater treatment have been used to investigate the hydraulic properties and organic loading of systems, such as anaerobic reactors, as opposed to microbiological and biochemical processes involved in the treatment of wastewater [109, 304]. However, the lack of knowledge surrounding the microbial and biochemical aspects of WSPs ultimately hinders the management and maintenance of the overall treatment outcomes [94, 305, 306].
This often leads to the requirement of additional costly treatment stages such as UV and chemical disinfection [305]. Fritz, Middleton and Meredith (1979) suggested poor management and maintenance are mainly due to a lack of understanding in regard to pond design and biochemical processes contributing to wastewater treatment [56, 305, 306].
The successful application of modelling requires consistent use of equations, identification of parameter ranges and taking into account all possible aspects that may change the outcome of the modelling study. Dimensional analysis allows the inference of physical quantities, such as volume or geometry, and their relationship to the fundamental dimensions of what is being studied, in this case a pond. For dimensional analysis to be accurately surmised, the physical quantities being analysed must be expressed in terms of the same parameters, where the parameters include, but are not limited to: mass, length and time.
Of the existing modelling studies very few use the same parameters to monitor physical quantities and vice versa, few use the same physical quantities to accurately calculate the dimensional analysis. It is this lack of continuity of parameters and physical quantities limiting accurate models and design for wastewater treatment in both the industrial and academic realms.
Multiple studies have been undertaken to investigate design using model systems [20, 305, 307, 308] or full-scale systems [309]. Very few studies successfully incorporate both model 2.5 Model wastewater systems 72 and full-scale system design and the relationship between the two. The lack of dimensional analysis is especially evident in terms of determining how the model systems apply to real world applications and is one of the most common aspects missing in model design. Also missing is a lack of understanding of the complex processes occurring in the ponds [94, 310].
When using model systems to study full-scale ponds, some parameters do not respond linearly to scaling, reducing the accuracy of these models. However, these uncertainties may be managed and understood if the scaling of parameters is quantifiable. Linearity of a model can apply to either the variables or parameters, or both, and can allow for quantification of the parameters to be scaled. Properties affected during minimisation include chemical, physical and hydraulic properties. However, if well designed, the effect of scaling should be definable and thus able to be accounted for[109].
Some parameters are more significantly affected by the scaling process, these can therefore be considered as limiting parameters. Setting a minimum or maximum scaling value for the limiting parameter can act as a precedent for the scaling of the complete model. Of the limited model systems available, many lack basic pond design and geometric similarity to full-scale ponds, resulting in model systems producing results unlikely ever to occur in the real world and thus not a great help in extending our knowledge of wastewater treatment processes [310]. Without building new treatment systems of various sizes it is difficult to analyse the effect of volume and depth and the ratio between these parameters and others in conjunction with biological factors.
Models are based on empirical design equations or reactor theory equations, with various parameters taken into consideration [308, 309, 311]. Different factors affecting models include the flow theory (Non-Ideal vs. Completely Mixed), order of kinetics and the incorporation of equations to determine effect of temperature and location [109]. A small number of these equation based models can be seen in Table 2.13. 2.5 Model wastewater systems 73
Table 2.13: Wastewater treatment models
Author Type Model aim Factors Equation Theory Persson and Pollutant Residence Non-Ideal Empirical Wittgren (2003) Removal time Flow Geometry, viscosity, Fischer Empirical Pond Hydraulics Dispersion loading volume McGarry and Empirical BOD Removal Areal BOD Pescod Larsen Empirical BOD Removal MOT Mara Empirical Surface Loading Temperature Environmental Fritz et al. (1979) Mechanistic BOD Removal Mass Balance Steady-state Effects Nameche and Pond Peclet Empirical Pond Hydraulics Dispersion Vasel (1998) Geometry Number BOD, Thirumurthi Thirumurthi Rational BOD Removal Standard Dispersion (1974) (1969) Conditions Area, Depth Agunwamba Cost and Mathematical Plug Flow (1991) minimisation Treatment level Data compiled from [90, 123, 214, 305, 308, 309, 311–313]
Empirical equations are those based on observation and experience instead of theoretically derived relationships. Many authors have gathered data from the literature to aid in the development of empirical models applying real world results to design of new ponds [308, 309]. Rational design equations are based on empirical equations but refer to specific functionality predictions and incorporate a wider range of parameters.
Models based on reactor theory include the determination of transformations occurring in a specified system and how fast transformations will be. Reactor theory incorporates reaction rate and mass balance of the components of the system. Mechanistic models are based on the basic elements having direct correspondence to underlying mechanisms in the system being modelled. 2.5 Model wastewater systems 74
A study by Finney and Middlebrooks in 1980 tested the effectiveness of existing WSP design models [309]. The models were based on empirical equations and rational design equations. The empirical equations included those from Larsen [314], McGarry and Pescod [315] and Gloyna [316]. Rational design equations investigated included those from Marais and Thirumurthi [309, 313, 317]. Larsen’s 1974 empirical equation estimates pond surface area while McGarry and Pescod’s 1970 equation measures BOD removal in terms of BOD loading [309, 314, 315]. Gloyna’s 1976 empirical equation determines pond surface area incorporating flow rates, temperature, pond volume, algal toxicity, sulphide oxygen demand and influent BOD concentrations [309, 316].
Marais’ 1970 kinetic models included assumptions of hydraulic flow, and measures faecal coliform to determine pond effectivity [309, 317]. Two models assume instant mixing, no seepage and no settling. The first model suggests degradation is a first-order reaction, independent of temperature, while the second suggests it is dependent on temperature and utilises the Arrhenius equation to assess this. Thirumurthi (1974) rejects the theory that completely mixed flow models should be used for pond design as the ponds show non-ideal flow patterns. Thirumurthi produced a reactor theory equation accounting for mean retention time as well as influent and effluent BOD concentration, temperature and toxic chemicals [313]. Both empirical and kinetic models were used to determine the BOD loading and the associated pond surface area. Treatment success was monitored by suspended solids, faecal coliforms and BOD reduction. These models only consider physicochemical, hydraulic and microbial aspects of treatment. The use of indicators to determine viral removal indicates a significant portion of the equation is missing.
This thesis aims to use current knowledge of these model systems and full-scale WSPs to determine a potential model for hydraulic, microbial and pathogenic treatment of wastewater in a WSP. Using the models already discussed, steps can be taken to address their failings in order to construct a physical model taking into account a greater range of variables. The model 2.5 Model wastewater systems 75
WSP system will be used for the direct investigation of human enteric pathogens within the pond and their behaviour in response to a range of factors likely to be observed in a full-scale system under the effect of the environment. Chapter 3
Methods
These general methods were used for all experiments unless otherwise stated. Any modification to these general methods will be discussed in the appropriate chapter.
3.1 Full-scale sample site and sampling methods
Wastewater samples were collected from a wastewater treatment plant in the South Island of New Zealand. The plant serves a population of approximately 6,600 people, through a series of waste stabilisation ponds (Figure 3.1), before being pumped to a larger treatment plant where the effluent is treated with UV before being discharged into the ocean.
An average daily influent load of 5,6003 m /day is split between two primary facultative ponds after initial grit screening (Ponds 1A and 1B, Figure 3.1). The effluent from these primary ponds is then directed to the single secondary facultative pond. Neither the primary or secondary facultative ponds have baffles but occasionally have mechanical aerators in use. The tertiary maturation pond is configured of four cells separated by rock filters. 3.1 Full-scale sample site and sampling methods 77
Figure 3.1: Aerial view of WSP treatment plant 1 = Raw Sewage; 1A and 1B = Primary facultative ponds, influent split evenly between two ponds; 2 = Secondary facultative pond and 3 = Tertiary maturation pond. Arrows indicate intake and exit points of each pond while # indicates the location the samples were collected. Original image from GoogleMaps.
The pond design plans from 2002 show that Pond 1A has a depth of 1.3 m and an area of 54,000 m2 while Pond 1B has a depth of 1.5 m and an area of 39,200 m2. Pond 2 is 1.7 m deep and 20,000 m2 in area and Pond 3 is 0.8 m deep, with an area of 11,000 m2. The pond depths can change depending on the volume of sludge present at a point of time. Pond 1B had recently been desludge at the time of sampling (within 1 year previously) while Pond 1A was overdue for desludging. Due to this, the volume of wastewater in Pond 1A was likely lower than in Pond 1B. Additionally, observation indicated that Pond 1A was not as healthy as Pond 1B, as a greater level of scum, a cloudier wastewater and odour were present. Due to this, Pond 1B was used for sampling for this work.
Samples were collected from 30 cm below the pond surface by peristaltic pump or grab samples using an extendable pole. The wastewater was collected in sterile containers and immediately sealed and labelled with date, time and location of sample. All samples were kept on ice and shielded from sunlight once collected. They were transported and processed 3.2 Physicochemical parameters 78 in the lab within 1-2 hours of sampling in accordance with the APHA Standard Methods for the Examination of Water and Wastewater [318].
3.2 Physicochemical parameters
Physicochemical parameters were monitored at the time of sampling and additional treatment plant monitoring data were kindly supplied by the regional council involved. For time scale studies in both full-scale and laboratory-scale systems, continuous monitoring was used, where dissolved oxygen (DO), pH and temperature were measured every 30 minutes using a Hach HQD portable meter with attached Hach PH301 and LDO101 HQD Intelli series probes for pH and DO respectively [319].
Probes and meter were purchased from Thermo Fisher Scientific, NZ. The DO probe was calibrated with saturated oxygen, while the pH probe was calibrated using standard pH Buffers (pH of 4, 7 and 10), purchased from Thermo Fisher Scientific. Both probes were calibrated at the start of each experiment, or when the probe alerted to calibration being required, as per Hach instructions.
3.3 Total solids
Total solids were measured using a method adapted from the APHA method 2540B [318] where 100 mL sample was used instead of the suggested 500 - 1000 mL. The beakers were pre-dried, weighed with anti-bumping granules and correctly labelled. Each sample was analysed in duplicate.
100 mL of the WSP water was measured into the labelled beaker and placed in a drying oven (103 °C - 105 °C). Once the samples had completely evaporated the beakers were placed in a desiccator to cool at room temperature. The dry weight was then recorded and the desiccation 3.4 Bacterial plating 79 step repeated until three consistent weights were obtained, The total solids were calculated using Equation 3.1.
(A − B) × 1000 = mgtotal solids/L (3.1) SampleVolume, mL
3.4 Bacterial plating
Various bacterial plating techniques are used to enumerate and identify specific bacterial species present in wastewater. Sterile controls were used to ensure the bacterial growth seen was due to presence in wastewater and not from external contamination from during the sample preparation steps. Unless otherwise stated the plating method used was pour plating as per the APHA Standard Methods with 1 mL of sample dilutions [318]. All bacteriological enumeration methods were used in Chapters 4 and 5.
Heterotrophic plate counts (HPC)
BD Difco™ R2A (218263, Fort Richard Laboratories) agar is a low nutrient medium for the enumeration of slow growing bacteria commonly present in potable water. This media enables the growth of these bacteria without being overgrown by faster growing species that would normally grow on a broader culture medium.
Dehydrated R2A media was made up to 100% (18.2 g/L) with distilled water and steralised before use by autoclaving at 121 ºC for 15 minutes. This could be used after steralisation, or refrigerated for up to 4 weeks. If using pre-made agar, sterile medium was microwaved until molten, and cooled to 48ºC in a water bath before using.
Serial dilutions were made in peptone diluents and 1 mL of each dilution was plated in a sterile, vented petri dish (LBS60001X, LabServ®, Thermo Fisher Scientific™ NZ). Approximately 15 mL of warm molten agar was poured on top of sample dilution in the appropriate petri dish. The sample and agar were mixed by rotating the plate three times in one direction, then three 3.4 Bacterial plating 80 times in the opposite direction, followed by moving up and down and side to side three times each, as per APHA Standard Method 9215 B [318].
Once the agar had set, the plates were inverted and incubated at 22 ± 2 ºC for 4 to 7 days. After incubation, colonies were promptly counted and results were recorded including sterility controls.
Bacterial colony forming units (CFU) per mL were calculated using the following equation:
Colonies counted Volume o f sample in dish, mL = CFU/mL (3.2)
Triplicates of each dilution were plated, with three dilutions generally chosen for analysis. Additional controls included a diluent control to confirm the sterility of the peptone usedto dilute samples and a sterility control to confirm the sterility of the R2A agar. These controls minimise the chance that bacterial growth seen is from a contamination source.
Escherichia coli detection
Oxoid Brilliance™ E. coli/Coliform Selective Agar (PO5176A, Thermo Fisher Scientific™ NZ) was used for the detection and enumeration of Escherichia coli and other coliforms, differentiated by their colony colour when grown due to the cleavage of chromogenic agents in the media, Rose-Gal and X-Glu. Rose-Gal detects β-galactosidase activity, while β-glucuronidase activity was detected by X-Glu. Brilliance™ agar was made up as per the manufacturer’s specifications and was used immediately after making.
E. coli colonies are purple while other coliform colonies appear pink and other organisms are blue or are inhibited. Agar was made up according to the manufacturers guidelines, where the dehydrated agar powder was made up with a specific volume of deionised, sterile water. The agar was microwaved in short bursts at 50% power until the agar was fully dissolved and cooled to 48 ºC in a water bath before use. 3.4 Bacterial plating 81
Sample dilutions were prepared in peptone diluents and 1 mL of dilutions were dispensed into petri dishes, in triplicate. Aseptically, 5 - 10 mL of agar was poured into the petri dishes and mixed with the sample by swirling. Agar was set on a flat surface [318]. Plates were inverted and incubated for 24 hours at 37± 2 ºC. Colony forming units per mL were calculated using Equation 3.2.
E. coli (purple) was used as a positive control, while Klebsiella pneumoniae (pink) was used as a negative control. Dilution and sterility controls were also included as described previously. Samples were plated in triplicate, with three dilutions of each sample.
Enterococci detection
Merck Chromocultr Enterococci Agar (100950, Merck NZ) was used for the detection and enumeration of Enterococci bacteria including Enterococcus faecalis and E. faecium. The chromogenic agents in this media were cleaved by enterococci, producing red colonies, where the presence of Aerococcus species were shown by blue/violet colonies and Streptococcus species were identified by turquoise colonies. The agar was made up following the directions as per the manufacturer’s specifications, with 33 g in 1 L of warm, sterile water. The agar was fully dissolved by microwaving at 50% power for short bursts, followed by cooling in a water bath to 48 ºC before use.
Sample dilutions (1 mL) were dispensed into petri dishes, in triplicate. Five to 10 mL of agar was aseptically poured on top of sample dilutions, plates were mixed as per APHA standard methods [318]. Once plates were set they were inverted and incubated at 35 ± 2 ºC for 24 hours. Colony forming units per mL were calculated as above using Equation 3.2.
E. faecalis was plated as a positive control, while Streptococcus pneumoniae was used to represent the negative control. Dilution and sterility controls were also included as described previously. Samples were plated in triplicate with three dilutions of each sample. 3.5 Bacteriophage plaque assay 82
3.5 Bacteriophage plaque assay
MS2 phage plaque enumeration methods were used in Chapters 4 and 5 to determine the survival of MS2 phage throughout wastewater treatment.
MS2 is a male-specific bacterial virus that infects E. coli with f-pili and can be found in the environment as well as being used in laboratory-systems. Detection of this phage uses a double layer agar technique with the addition of E. coli F-amp as the host [318]. As MS2 is light sensitive it should always be kept wrapped in foil and when the plates are set put them directly into a dark incubator.
TGA agar for the base and overlay were made up as directed in Table 3.1. When required, the TGA base agar was melted in the microwave and placed in a water bath (48 ˚C) to cool.
Prior to pouring the base plates, a 1.5 mg/mL antibiotic solution was added to the agar (at 10 mL per L of agar). Agar was then poured into petri dishes to just cover the dish base (~12 mL). Plates were dried for at least 1 hour prior to storage or use.
The overlay (O/L) agar was melted in the microwave at the time of the assay. Once molten, the agar was cooled in water bath to 48 ˚C and a 1.5 mg/mL antibiotic solution was added (at 1 mL per 100 mL agar). Before dispensing the O/L, warm sterile water was added to the O/L at 50 mL per 100 mL of agar followed by gentle swirled to mix. Three mL of diluted O/L was added to each warm tube. E. coli F-amp (200 µL) was added to tubes in small batches so as to pour immediately after adding sample. Sample dilutions (1 mL) were added to tubes and gently vortexed to mix. Agar containing sample and host was poured onto base agar, swirling the plate while pouring to ensure an even spread.
Appropriate controls were prepared including a Drop test (pour O/L with F-amp, set and add drop of MS2), Sterility Control (nothing added, just agar base), O/L Control (Pour on some
O/L, no added phage or F-amp), H2O Control (Add 1 mL H2O to O/L and pour on) and F-amp Beginning and End Controls. F-amp Beginning and End Controls were used to check that the 3.6 Enzyme assays 83
Table 3.1: Tryptic glucose media recipes for the enumeration of MS2 bacteriophage
Base Agar Overlay (O/L) Agar Tryptone 10.0 g 20.0 g Dextrose 1.0 g 2.0 g NaCl 5.0 g 10.0 g Agar 15.0 g 15.0 g Yeast Extract 10.0 g CaCl2.2H2O 0.15 g (10 mL) Deionised H2O 1000 mL 1000 mL All components were added to deionised water and microwaved to dissolve agar. Media was autoclaved at 121 ˚C for 15 minutes. At this stage, media could be kept at 4 ˚C for up to 3 months. Media was made up according to the standard APHA method [318].
F-amp host was still alive and in same growth phase as the rest of the experiment. Additionally these controls indicated if any contamination had occurred during the experiment. All samples were plated in triplicate, with three dilutions of each sample.
Plates were incubated at 36.5 ± 2 ºC for 18 to 24 hours, all with overlay agar facing down, except the Drop Control. The clearings or plaques were counted and recorded, along with controls and used to calculate plaque forming units (PFU) per mL, calculated using Eq. 3.2.
3.6 Enzyme assays
Enzyme assays were used to determine the presence and activity of selected enzymes in wastewater samples. These methods were used in Chapters 4 and 5.
Fluorescent enzyme assays were set up using Nunc™ Microwell™ 96-Well black polystyrene plates with Non-Treated Surface, Radiation Steralised and have a working volume 50-250 μL/well (NUN237107, Thermo Fisher Scientific™ NZ). Plates are sealed with Axygenr PCR-SP AxySealr Clear Polyester Films (Global Science, VWR) suitable for enzyme assay application. All enzyme assays were run on the BMG LABTECH
CLARIOstarr. 3.6 Enzyme assays 84
For both Esterase and Protease assays, samples were plated in replicates. The number of replicates depended on the assay and number of total samples but generally fell between five and eight replicates.
Fluorescein diacetate
Fluorescein diacetate (FDA) cleavage is used as a measure for total microbial esterase activity in wastewater at 485-15 nm (excitation) and 528-20 nm (emission) on the BMG LABTECH
CLARIOstarr microplate reader. A standard curve using fluorescein sodium salt was used to determine a specific range of fluorescence likely to be seen in wastewater samples, witha range of dilutions to give an even spread of concentrations. FDA was cleaved by enzymes with esterase ability to produce a fluorescent signal (Fluorescein). This signal was used to infer the amount of enzyme activity present in each well by using the fluorescein standard curve.
Controls were required for both the standard and sample plates to account for any background fluorescence present in the wastewater samples of buffers as well as the baseline measurement for the plates themselves. Controls were included in all assays. These controls include a positive control, comprising of the fluorescein sodium salt for the standard curve, and fluorescein diacetate and sample for the FDA assay. The negative controls included the phosphate buffer only for both the standard curve and assay. Background control and sample control determine the background fluorescence associated with the dye and samples respectively and are made up with the dye or sample and buffer.
Using the BMG CLARIOstarr data analysis software MARS, the relative fluorescence was used to extract information regarding the presence of enzyme activity in sample. Standard curves were only used if R value was above 0.98, if the value was lower than this, the standard curve and samples were rerun to get a more accurate result.
Fluorescein standard curve 3.6 Enzyme assays 85
In micro tubes, standard dilutions of Fluorescein sodium salt were made up with phosphate buffer. Standard dilutions were added to rows of a 96 well plate, at 250 μL/well of a black 96 well plate. Once all dilutions were plated a sealing film was applied and the whole plate was vortexed for 30 sec at 400 rpm. Plates were incubated at 28 ˚C for 4 hours, wrapped in tinfoil to exclude light. After incubation, the plates were vortexed for 30 sec at 400 rpm. The sealing film and lid were removed and the fluorescence of the plates were read usingtheFDA protocol (485-15/ 528-20 nm).
FDA assay
Samples were plated as replicates at a volume of 235 μL/well. Fluorescein diacetate Dye was added to the sample wells to give a final well volume of 250 μL/well. Included were sample controls (250 μL per sample, n > 3), dye control (235 μL phosphate buffer and 15 μL of FDA Dye, n > 6) and blank controls (250 μL phosphate buffer, n > 6). Plates were sealed with sealing film and vortexed for 30 sec at 400 rpm. After vortexing the plates werethen incubated at 28 ˚C in the dark for 4 hours. Following incubation, plates were again vortexed for 30 sec at 400 rpm. The sealing film was removed and plates were read using the FDA protocol (485-20/ 528-20 nm).
Esterase activity calculations
Standard curve determination:
To determine the net fluorescence, the average fluorescence of the negative control was subtracted from the fluorescence of all standard wells. The replicates for each standard concentration were averaged. The net fluorescence was plotted against the Fluorescein concentrations, followed by trendline and slope equation determination.
Assay calculation:
The average fluorescence of the negative control was subtracted from the total fluorescence of all samples to give the net fluorescence. The equation determined by the standard curve 3.6 Enzyme assays 86 was rearranged to solve for X (the sample concentration) where Y was the net fluorescence of the sample. The resulting concentration was divided by the incubation time to get concentration per unit time. Previous dilutions and incubation time were accounted for the result in concentration per unit time per volume (commonly mL) of activity.
Protease
General protease activity was monitored with the Thermo Scientific™ Pierce™ Fluorescent Protease Assay Kit (23266, Thermo Fisher Scientific™ NZ), with FITC-Casein as the fluorescent dye, following the microplate assay protocol provided. FITC-Casein isa fluorescently labelled Casein derivative that is cleaved by trypsin or protease enzymes. Controls included a positive control, comprising of the FTC-casein dye and Trypsin for the standard curve, and FTC-casein dye and sample for the assay. The negative controls included the phosphate buffer and dye. Background control and sample control determine the background fluorescence associated with the dye and samples respectively and are madeup with the dye or sample and buffer.
Protease standard curve
Trypsin standard dilutions or buffer were added to each well of a 96 well plate. To the same plate, FTC-casein working dye was added to appropriate wells (i.e. not to wells used for buffer and sample controls). Plate controls included the blank (buffer only) and negative control (FTC-casein + Buffer). The plate was sealed and vortexed for 30 sec at 400 rpm. After vortexing, the plate was covered with tin foil and incubated at 28 ˚C for 1 hour followed by another vortex for 30 sec at 400 rpm. The fluorescence was detected using the protease protocol (485-20/ 538-20 nm).
Protease sample assay
The assay was completed as above, replacing the trypsin standard with unknown samples. 3.6 Enzyme assays 87
Protease activity calculation
Standard curve determination:
The average fluorescence of the blank or control wells was subtracted from the standard wells to give the net fluorescence. Net fluorescence was averaged for each standard concentration. A plot of the average net fluorescence against trypsin concentration was used to determine the slope equation for the standard curve.
Assay calculation:
The average blank fluorescence from all sample wells was removed to give thenet fluorescence. The equation determined using the standard curve was rearranged tosolvefor X (protease concentration) where Y was the net fluorescence of the samples. The protease concentration was divided by the incubation time to determine the concentration per unit time. Dilutions and incubation time were accounted for to result in the concentration per unit time per volume (commonly mL) of activity.
3.6.1 Enzyme inhibition
A broad spectrum protease inhibitor cocktail was purchased from Thermo Fisher Scientific™(PIE78429). The Halt™ Protease Inhibitor Cocktail contained AEBSF, Aprotinin, Bestatin, E64, Leupeptin and Pepstatin A to give a 100X inhibition concentration. The cocktail, used at a 1X concentration, caused reversible and irreversible inhibition of serine, cysteine, amine and aspartic acid proteases. To a 100 mL sample of wastewater, 1 mL of Protease Inhibitor Cocktail was added and mixed thoroughly. The sample was stored at room temperature and excluded from light for 1 hour prior to use in an experiment. 3.7 Protein determination 88
3.6.2 Heat inactivation
To produce a sample where bacteria and a wider range of enzymes were inactivated, wastewater was treated with heat. Wastewater was autoclaved in glass schott bottles at 121 °C for 1.5 hours. Samples were cooled and used in further analysis.
3.7 Protein determination
Protein concentration was determined using the Pierce™ Modified Lowry Protein Absorbance Assay (PIE23240, Thermo Fisher Scientific™ NZ) adapted for a 96-well microplate assay.
3.7.1 Lowry protein assay
Preparation of standards and Folin-Ciocalteu reagent:
The albumin standard (BSA) was used to create a range of standard dilution concentrations. Additionally, 1X Folin-Ciocalteu reagent was prepared by diluting the supplied 2X reagent 1:1 with ultra-pure water. Because the diluted reagent was unstable, 1X Folin-Ciocalteu reagent was prepared on the same day of use.
Microplate procedure:
Each standard and unknown sample replicate were pipetted into a well of a 96-well plate (NUN167008, Thermo Fisher Scientific™ NZ). Modified Lowry Reagent was added toeach well at nearly the same moment using a multichannel pipette, followed by immediate mixing on plate vortex for 30 seconds. The plates were covered and incubated at room temperature (RT) for exactly 10 minutes.
After incubation, prepared 1X Folin-Ciocalteu Reagent was added to each well using a multi-channel pipettor, followed by immediate mixing on plate vortex for 30 seconds. The 3.8 Enterovirus end-point titration assay 89 plates were again covered and incubated at RT for 30 minutes. The absorbance was measured at 750 nm on a plate reader.
The average absorbance value of the blanks was subtracted from the absorbance value of standard dilutions to give the net absorbance. A standard curve was produced by plotting the average Net Absorbance values for each BSA standard vs. its concentration in μg/mL. A trendline was added to the standard curve plot and the slope equation was determined. The blank absorbance values were subtracted from the sample absorbances. The sample concentrations were calculated by rearranging the standard curve equation to solve for X using the net absorbance as Y.
Sample dilutions and incubation times were accounted for to determine the protein concentration per volume per unit time.
3.8 Enterovirus end-point titration assay
Buffalo Green Monkey (BGM) kidney cells were cultured and used to assay and prepare stocks of the enterovirus, Echovirus 7.
Cell culture:
Reconstitution of BGM cells was done firstly, removing a cell vial from liquid nitrogen and thawing. Once cells were defrosted, warm M199 growth medium (12561056, Life Technologies™, Thermo Fisher Scientific™ NZ) was added to2 a75cm tissue culture flask (NUN156472, Thermo Fisher Scientific™ NZ) and cells were added dropwise. The tissue flask was rocked to mix and incubated for 24 hrs at 37 °C,5%CO2. After initial incubation, the old medium was aspirated off and fresh growth medium was added. The flask was then incubated until cells reach confluence (commonly 4-5 days). 3.8 Enterovirus end-point titration assay 90
Once the cells were confluent, the cell cultures were split to propagate more cells. Theold medium was poured from tissue flask and was PBS was added to wash the cells. Oncethe cells were washed, the PBS was removed and Trypsin-EDTA (73924, Gibco®) was added to separate the cells. Incubation with Trypsin-EDTA took between 3 - 5 minutes under the same conditions as previously stated, until the cells started lifting from the flask. Fresh growth medium was added and the cells were pipetted to give a single cell suspension. From this suspension, the cells were split at a 1:8 ratio, with some suspension used to further passage the cells if needed. A volume of the cell suspension was used to conduct the enterovirus end point assay.
The cells were transferred into a 50 mL centrifuge tube and spun at 400 x G for 5 minutes. Once cells were centrifuged, the supernatant was removed and the cells were resuspended in 4% FBS M199 or to give 105 cells/mL (FBS 10091148, Life Technologies™, Thermo Fisher Scientific™ NZ). A range of dilutions of environmental sample were created by transferring 0.1 mL of sample into 0.9 mL of fresh M199 medium, including a negative control (M199 only).
A multi-dispenser was used to transfer 0.1 mL of the sample dilutions to the appropriate wells of a 96 well microplate. The resuspended cells were dispensed into each well using a multichannel pipette and the 96-well plate was sealed and incubated for up to 7 days in a
CO2 incubator at 37 °C and 5% CO2. The positive and negative wells were recorded at 4 days until 7 days. Positive wells were identified by cell cytopathy, or lysed cells dueto infection with virus. The tissue culture infectious dose 50 (TCID50) was calculated according to the method of Reed and Muench [320]. Briefly, the logarithm of the dilution showing mortality above 50% is taken away from the sum of the difference of the logarithms multiplied by the logarithm of the dilution factor. This results in the logarithm of 50% endpoint dilution, or TCID50. 3.9 Viral spike 91
3.8.1 Preparation of stock echovirus
Using confluent cells from initial passage steps, the old medium was discarded and cellswere washed with PBS. Echovirus 7 stock was added to fresh M199 medium and was subsequently added to the flask of washed cells. The flask was rocked gently to mix and was incubated for an hour, rocking every 15 mins. After incubation, M199 with 2% Fetal Bovine Serum was added and further incubation occurred until 80% of the cells were cytopathic (3-5 days).
At this stage, the flask of cells was put through a freeze thaw cycle where the flask waskeptat -80°C until frozen, followed by incubation until thawed. This cycle was repeated three times. The contents of the flask were transferred to a centrifuge tube and was centrifuged at2,000 rpm for 10 minutes. The supernatant was aliquoted into 1 mL vials and stored at -80 °C for future use. The final cell count was determined using a hemocytometer.
3.9 Viral spike
Echovirus 7 (EV7) and MS2 were obtained from storage at -80 °C. Stocks were thawed and sonicated in a water bath sonicator. After sonication, EV7 and MS2 stocks were vortexed to sufficiently re-suspend all viral particles. EV7 and MS2 were spiked into known volumes of wastewater to result in a final concentration of5 10 cells/mL. This method was utilised in Chapters 4 and 5.
3.10 Sample preparation
Throughout this project, two main sample treatment types were studied, whole and extracellular wastewater or bacterial culture.
1. The whole wastewater is used as a control to identify how all the organisms present in the system affect one another. This includes bacteria, chemicals and nutrients. 3.10 Sample preparation 92
Table 3.2: Sample treatments and their expected outcomes
Expected Treatment Components Outcomes Protozoa, algae, Highest virus Whole Sample bacteria and inactivation enzymes Extracellular Extracellular Enzymes enzymatic virus inactivation
2. Extracellular samples are run through a Millipore™ 0.22 μm filter, these filters are used to remove bacteria and larger microorganisms [321].
Table 3.2 shows the purpose of each treatment, including filtration to remove the bacteria and higher organisms from the solution, keeping the chemical makeup or the solution as intact as possible, as well as any extracellular activity. Cell-associated enzymes were likely filtered out with the bacteria they were attached to, so any enzyme activity seen in the filtered samples was attributed to extracellular enzymes.
All work for the Chloroform extraction and protein digestion were carried out in a fume hood.
3.10.1 Filtration
Fractions of wastewater were produced by filtering a known volume of wastewater through sterile 8 μm, 0.8 μm and 0.22 μm filter membranes. All filters were Mixed Cellulose Ester membranes, with a diameter of 47 mm. The membranes were used with sterile vacuum filtration units, singularly or with manifolds. All filters and manifolds were steralised with ethanol prior to use. This method was used for Chapters 4 and 5. 3.10 Sample preparation 93
3.10.2 Protein concentration
Concentration using a 10,000 Da MWCO1 centrifugal concentrator. Wastewater was applied to the upper chamber of the centrifugal concentrator, followed by centrifugation at 4,000 x G for 20 minutes, or until the volume of sample had travelled through the membrane. The eluent was transferred back into the upper chamber and spun again. This step was repeated 5 times in order to minimise the salt content of the protein concentrate. This concentration method was used for Chapter 6.
3.10.3 Chloroform extraction
Bacterial residue was removed by Chloroform extraction. Initially, the protein concentrate was sonicated for 2 min in a water bath sonicator. Chloroform was then added to the sample at a 1:1 ratio followed by vortexing until samples looked milky (> 2 min). Alternatively, when a large number of samples were being extracted, a shaking platform was used at 200 rpm for 20 minutes. The mixed samples were centrifuged at 10,000 x G for 20 minutes and the upper aqueous layer containing the extracted sample was isolated and used for further analysis. This methods was used for sample preparation for viral analysis (Chapters 4 and 5) and for sample clean up (Chapter 6).
3.10.4 Solid phase extraction
HyperSep™ C18 solid phase extraction (SPE) cartridges were used for protein extraction (60108-305, Thermo Fisher Scientific™ NZ). Before use, cartridges were conditioned with
50% Acetic Acid and ddH2O. Up to 4 mL of sample was loaded onto conditioned cartridges followed by a wash step with 10% AcOH, 10% MeOH and ddH2O. The sample was eluted in
1Molecular Weight Cut-Off 3.11 Mass spectrometry 94
CHCl3, MeOH or Acetone. Following elution, the solvent was evaporated with nitrogen and reconstituted in 500 μL ddH2O for further analysis by chromatography or mass spectrometry. This sample preparation method was utilised in Chapter 6.
3.11 Mass spectrometry
Mass spectrometry is becoming a vital tool in the realm of meta-omics and identification of unknown proteins. Liquid-Chromatography Mass Spectrometry (LC-MS) was used to identify peptides present in the extracellular wastewater matrix, as shown in Chapter 6. Extracellular wastewater samples were concentrated using 10 kDa and 3 kDa molecular weight cut-off (MWCO) centrifugal devices. The concentrated protein was then run through a solid phase extraction (SPE) cartridge and peptides were eluted with Acetonitrile. Solvent was evaporated under nitrogen and samples were reconstituted with water.
Samples were initially separated on Thermo Fisher HPLC (Dionex) with subsequent mass spectrometry detection on a Bruker MaXis 3G Ultra High Resolution Quadrupole Time of Flight (UHR-QToF) machine with electrospray ionisation (ESI). The column used for both HPLC and mass spectrometry was the Agilent Zorbax 300 SB-C18 column.
Standards
In order to determine the correct running conditions for the detection of intact proteins, the SigmaProt Intact Protein LC-MS Standard (MSRT2, Sigma-Aldrich® NZ) was used. The standard contained 9 common proteins; Ribonuclease, Insulin, Lysozyme, Transferrin, BSA,
Trypsin Inhibitor, β-Lactoglobulin A, Carbonic Anhydrase and Lactate Dehydrogenase. To run the sample the running conditions, shown in Table 3.3, as suggested in the associated 3.12 Bacterial and enzyme culture 95
Table 3.3: Running gradient conditions for LC-MS
Time (Minutes) %B 0.0 20 20.0 60 20.5 80 22.5 80 23.0 20 30 20 Where Solvent A = Water (with 0.1% TFA) and Solvent B = ACN (with 0.1% TFA) protocol from Sigma-Aldrich were used (Appendix C.2). After dissolving the standard in
0.1% TFA/Water, 5 μL was added to a 500 μL wastewater or water sample for LC-MS/MS.
Running Conditions
The gradient ran over 30 minutes, with up to 80% of Solvent B. Solvent A contained water (with 0.1% TFA), while Solvent B contained ACN (with 0.1% TFA) 3.3. A total volume of
10 μL was injected for each sample with a flow rate of 0.2 mL/minute.
The ionisation source used was Electrospray ionisation (ESI) with positive ion polarity. The capillary was set at 4000 V and the collision cell radio frequency (RF) was set at 1200.0 Vpp. Scan range from 100 m/z to 3000 m/z.
Deconvoluted spectra and resulting mass peaks were submitted to the online Mascot server (Matrix Science, www.matrixscience.com) and ProteoSAFe (University of California, San Diego; www.proteomics.ucsd.edu/ProteoSAFe/index.jsp). Search parameters for these can be found in Appendix C.3.
3.12 Bacterial and enzyme culture
Nutrient broth and modified nutrient broth were used to culture bacteria from wastewater. 3.12 Bacterial and enzyme culture 96
Table 3.4: Synthetic wastewater composition
Name Formula 10x Concentration Mass Peptone 1600 mg/L 1.6 g Urea CH4N2O 300 mg/L 0.3 g Monopotassium KH PO 300 mg/L 0.3 g phosphate 2 4 Magnesium Sulphate MgSO4.7H2O 300 mg/L 0.3 g Heptahydrate Calcium Chloride CaCl2.2H2O 40 mg/L 0.04 g Dihydrate Glucose C6H12O6 1500 mg/L 1.5 g Sodium Acetate C2H3NaO2 3000 mg/L 3 g Ammonium NH Cl 1400 mg/L 1.4 g Chloride 4 Ferrous Sulfate FeSO4 50 mg/L 0.05 g Beef Extract 1000 mg/L 1 g The above components were dissolved in 1 L of water, followed by filter steralisation, ready for use (adapted from [322–325]).
The modified nutrient broth was made up as a concentrated nutrient broth andafter steralisation, sterile synthetic wastewater was added to dilute the broth to the concentration of standard nutrient broth. The synthetic wastewater used was based on recipe from Zhang et al. (2015) and OECD: Test 303 [322, 323] and can be seen in Table 3.4.
Warmed broth was inoculated 1:10 with wastewater and incubated overnight at 35 °C with shaking (90 rpm). The optical density (OD) was checked after 18-20 hours of growth at 600 nm wavelength to identify log phase growth. Log phase growth was detected when the OD600 absorbance values were between 0.5 and 1. When the correct OD was obtained, the culture was decanted into centrifuge tubes and bacteria pelleted at 12,000 x G. The supernatant was aspirated, taking care not to dislodge the pellet which was then resuspended in 1 mL Milli-Q Water and transferred to 2 mL microtubes. The bacteria was repelleted in the 3.13 Pond design 97 micro-centrifuge at 10,000 x G for 20 minutes. The supernatant was aspirated off, taking care not to disturb the bacterial cell pellet.
At this stage bacteria was grown on R2A agar for use in further cultures. A sterile loop was used to streak the bacterial inoculant onto agar plates, followed by incubation at 22 f°ƒC for 4-7 days. The remaining pellet were be frozen at -80 °C for storage until further analysis was completed.
3.13 Pond design
Pond design equations were adapted from Shilton [109, 310, 326, 327] for use in the development of a laboratory-scale WSP system. The ponds were constructed out of plastic containers, valves and teflon tubing with influent and effluent containers to allowfora semi-continuous flow. Further design and details are shown in Chapter4.
3.14 Sequencing
Wastewater samples were sent to the NGS sequencing unit, Macrogen Inc in Seoul, Korea for 16S metagenome and whole genome shotgun sequencing. The results for these methods are presented in Chapter 6.
Genetic information was extracted by Macrogen Inc. using PowerSoil extraction kits. Following extraction, 16S rRNA targeted sequencing was run on the Illumina MiSeq 300bp paired-end system, targeting the V3-V4 region, using the 16S Metagenome Protocol. The resulting sequences had the adapters trimmed by Macrogen. Whole genome shotgun sequencing was run on the Illumina HiSeq 2500 100 bp paired-end system with a TruSeq Nano DNA library with a 350 bp insert. The resulting sequences were trimmed by Macrogen.
Ten samples were submitted for both targeted and shotgun sequencing, all ten samples passed quality control for targeted sequencing while only three samples passed the quality control 3.14 Sequencing 98 for shotgun sequencing. This disparity is due to the methods involved in each of the sequencing techniques and the concentrations of genetic information required by each. The 16S sequencing utilises PCR to amplify specific gene sequences, depending on the primer sequences chosen. The PCR amplification used in 16S sequencing allows for detection of low concentration genetic information.
Whole genome shotgun sequencing does not include the PCR amplification used in 16S sequencing. Without amplification, the method relies on concentrations of genomic information high enough to allow for extraction, digestion and reconstruction of gene sequences. Table 3.5 presents the concentrations of each sample and the result of quality control (QC) for each sequencing technique.
3.14.1 Data analysis and storage
Raw reads from Macrogen were checked for quality and pre-processed before analysis. Targeted sequencing:
Processing of the sequences prior to analysis included joining the paired-ends followed by filtering the resulting reads with a quality score of 20 (Q20). The joined and filtered reads were then merged into a single file for chimera removal (Q20). The resulting targeted sequences were analysed using the Quantitative Insights Into Microbial Ecology (QIIME) pipeline and Greengenes database, run in a Jupyter Notebook environment [116, 328]. The QIIME output included Operational Taxonomic Unit (OTU) classification of the targeted sequences identified.
Operational Taxonomic Units (OTUs) were picked with a quality score of 20, using the open_reference protocol, picking OTUs against the Greengenes database using the uclust algorithm. The core diversity was determined by random subsampling to a depth of 50 subsamples to result in a rarified value that all higher-level samples will be subsampled down to. This was based on the counts per sample determined by the biom table output. QIIME 3.14 Sequencing 99 (nM) 393.77 316.03 265.87 Concentration 1 1 1 1 1 2 2 Reason DNA degraded DNA degraded Concentration low Concentration low Concentration low Concentration low Concentration low Fail Fail Fail Fail Fail Fail Fail Pass Pass Pass Shotgun Pass/Fail Smeared band on 1% agarose gel. 2 g) μ 0.29 3.80 0.22 0.25 0.25 0.21 1.77 0.37 0.23 0.39 ( Concentration Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Targeted Pass/Fail L) No band detected on 1% agarose gel; μ 1 Table 3.5: Sample QC for whole genome and shotgun sequencing samples 1.9 1.49 1.63 1.64 1.38 2.45 1.54 2.57 25.32 11.83 (ng/ Concentration Sample ID 06102015L 10062015R 30062015R 11122015L1 11122015L2 16022016L1 04022016HS 16022016L91 16022016L101 16022016L111 3.14 Sequencing 100 analysis was followed by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt; V1.0.0; Huttenhower Lab) to infer the functional categorisation of the samples [329]. The same OTUs were utilised to determine the core microbiome, computed and plotted using MEGAN Community Edition (Version 6.12.3, [330])
QIIME and PICRUSt results were analysed in R and plotted with ggplot [331].
Shotgun sequencing:
The whole genome shotgun sequencing was analysed by MG RAST, Kaiju and Kraken [332– 334]. The three programs have been developed specifically for the analysis of metagenomic samples. Kaiju (v1.4.4) works by directly aligning sequences to curated protein databases. Sequences were analysed against four databases, the complete reference genomes from NCBI RefSeq, representative genomes from proGenomes and the non-redundant protein database with and without eukaryotic sequences.
At the time of analysis the NCBI RefSeq database contained 23 million protein sequences, proGenomes contained 19 million protein sequences and the non-redundant database contained 96 million protein sequences, 9 million of which were eukaryotic. This study presents results attained using the proGenomes database. Kraken (V1.0) matched k-mers against a database using the lowest common ancestor (LCA) for identification [333]. K-mers are the subsequences possible within a specified length (k). Both Kaiju and Kraken were utilised and the results were compared to investigate if differences in sequence analysis methods was evident.
MG RAST (V4.0.3) used k-mers for functional annotation instead of OTUs as used in PICRUSt analysis. The functional categorisation determined a range of subsystem pathways were represented in the wastewater samples [334]. 3.15 Statistics 101
Results were analysed in R and plotted with ggplot and phyloseq.
3.15 Statistics
SigmaPlot 12.5 was used for plotting graphs. AcaStat was used for statistical analyses (Version 2200.2.2, © 2018 AcaStat Software). Data was stored in Excel (Microsoft, 2013) and SigmaPlot 12.5.
All data presented is calculated as arithmetic mean values of all samples unless otherwise indicated. The error is determined as the standard error of the mean (SEM) of the log-transformed values where applicable.
The level of Type I error (α) for all statistical analyses was set to 0.05, or a 5% chance of detecting a false positive. To be statistically significant, the achieved p value will be below that of α = 0.05, where two-tailed p statistics are used. Chapter 4
Laboratory-scale waste stabilisation pond development
4.1 Introduction
There are many parameters suggested in the literature to define a well-functioning WSP [101]. Some of these parameters are dissolved oxygen, pH and the reduction of biological organic demand [44, 45]. Using a model system, these parameters can be characterised and therefore can be used for the investigation of bacterial species present in successful and poor-functioning systems to determine a core microbiome indicating pond behaviour. The development of a model-scale system for biochemical behaviour of wastewater is an unchartered territory. This thesis will describe the investigation of a model system to build upon the knowledge of biochemical behaviour in wastewater. Model systems aim to study flow rates, pond sizes, addition of baffles and the effect of influent concentration. Laboratory or bench-scale models are used in a wide range of research fields such as engineering, chemistry and biology, in order to determine the operating parameters of a new method or design without committing large amounts of time and money, thus reducing associated financial risks [300–302]. 4.1 Introduction 103
This study is among the first to develop a functioning WSP system at the microcosm scale incorporating hydraulic, physicochemical and microbial parameters [20, 304, 335]. Larger pilot scale studies (50-100 m3) are more commonly used for these ’proof of concept’ studies [93, 109]. Previous research into the improvement of WSPs focused on the hydraulic characteristics and its effect on overall treatment efficacy [20, 109, 336]. The smaller bench-scale systems have not been thought to display the same hydraulic characteristics and have as such been discounted from wastewater research [310]. Additional studies have looked at pilot-scale systems or bench-scale bioreactors for the treatment of alternative wastes [301, 302].
The system presented here provides a realistic and relative representation of both the hydraulic function and chemical and microbial processes, thus enabling monitoring and determination of microbiological activity in response to hydraulic and physicochemical parameters. Whilst the hydraulic behaviour of these bench-scale systems is significant, the lack of incorporation of hydraulics with microbiological and physicochemical parameters in previous models has resulted in poorly run WSPs [308, 311]. This has increased the likelihood of contamination of the receiving environment and potential for public health incidents [310].
The aim of this study was to develop a laboratory-scale waste stabilisation pond that accurately represented the hydraulic, microbiological and physicochemical behaviour of full-scale systems. Upon successful development, this system was used to investigate variations in a range of common design parameters. The WSP could then be used to investigate the effects of wastewater treatment when physicochemical parameters were varied, within ranges seen in the typical operational environment.
The objectives required to achieve this aim were to:
• Design and engineer a laboratory-scale model WSP based on a functioning full-scale system. 4.2 Methods 104
• Develop parameters for the monitoring and detection of viral and microbial pathogens to determine applicability of current monitoring procedures.
• Determine bacterial populations which enable nutrient cycling, pathogen inactivation and organic degradation, indicating successful wastewater treatment.
4.2 Methods
The experimental methods used in sample collection, treatment and analysis are presented in Chapter3.
4.2.1 Pond construction
The final design consisted of three ’ponds’ in series with final working volumes of24.6L, 11.1 L and 8.2 L, as presented in Appendix A.2. The initial pond had the largest volume (24.6 L) as shown in Figures 4.1a and 4.1b. Each pond was painted black on the outside, up to the water level, to prevent light affecting the lower pond depths; plastic above the water level was left clear to allow sunlight to penetrate, thus reducing the effect of shading.
The three ponds all had galvanised steel baffles which were joined to the containers by rivets and sealed with a silicone sealant. The baffles were made to be the full depth of each specific pond; fitted from the bottom to the water surface, as well as two2 thirds( /3) the width of the pond as per Olukanni and Ducoste (2011) [20]. Figure 4.1b shows the effect of the baffles on the flow of wastewater through the pond system. Additional ports were in place forsampling locations; this ensured sampling occurred at the same place each time, minimising some of the error involved in the sampling procedure (Figure 4.2b). 4.2 Methods 105
(a) Dimensions for laboratory-scale waste stabilisation ponds.
(b) Flow direction in laboratory-scale WSP system
Figure 4.1: Laboratory-scale waste stabilisation pond system diagrams of inlet and outlet ports and baffle configurations (a) and flow directions (b)
Pond inlets were positioned at approximately 2/3 the depth of the pond to prevent the influent flowing across the pond surface directly to the outlet pipe. The outlet was positioned belowthe water surface in the upper UV zone where the most disinfection occurred, but below the algal layer to ensure the algal community was not drawn out with the effluent so the community remained in the pond. Both inlet and outlets were positioned on the sides of the pond where the baffles joined the pond walls as shown in Figures 4.2a and 4.1b.
The light source consisted of two lamp tubes in a Fluoro 4’ light; a Sunlight Bulb emitting wavelengths of 400 nm - 700 nm and a Tropical Bulb emitting wavelengths of 380 nm - 720 nm. These bulbs were chosen to represent the natural sunlight that drives pond treatment. Both bulbs were rated at 40 Watts. Direct pathogen inactivation, or disinfection, is known to 4.2 Methods 106
(a) Pond layout development (b) Pond outlet and sampling ports
Figure 4.2: Photos of the laboratory-scale pond set-up showing pond and pump layout (a) and the sampling and outlet ports (b) occur at the 254 nm wavelength [112, 121, 159, 174, 337–340]. The aim of this system is to mimic what is occurring in the environment therefore sunlight wavelengths are desired, including UVB (280-320 nm), UVA (320-400 nm) and visible (400-700 nm) [337–340]. The disinfection wavelength (254 nm) does not occur naturally and is not included in the lamp to prevent direct viral disinfection affecting the overall viral reduction. The light set up was positioned 800 mm above the base of all the ponds, directly overhead to minimise any shadowing. The lights were set on a timer to match daylight hours, in order to maintain similar exposure as to what was occurring in the full scale system due to seasonal variation in daylight hours.
4.2.1.1 Pond set-up
Samples from the full-scale system were used to develop the laboratory-scale WSP system. Samples were collected as described in Section 3.1. Twenty-five to 30 L of wastewater from Pond 1B; 15 L of Pond 2 and 10 L of Pond 3 was collected by peristaltic pump from the full-scale WSP system. Additionally, one litre of sludge was collected from the same ponds. 4.2 Methods 107
The sludge and wastewater was added into the corresponding laboratory-scale ponds and left to settle for 24 hours.
Raw sewage was also collected from the full-scale system, in addition to further volumes of Pond 1B. The raw sewage was filtered to remove large solids and was diluted with primary wastewater from Pond 1B to create an influent reservoir. The raw sewage was diluted to reduce the organic loading to prevent system overloading. Once the sludge had settled, running of the system was begun at a flow rate of 1.38 L/s. Further information regarding laboratory-scale system set up can be found in AppendixA.
Fresh sewage and primary wastewater was collected each week. The sewage was filtered upon arrival in the laboratory and was kept in the refrigerator until use. When the influent was in use, 20 L was kept on a shaker, packed with ice and covered with insulated foam to prevent sunlight and keep the temperature low. The influent was monitored to ensure the pH and dissolved oxygen (DO) were within the expected parameters. Any deviation of pH and DO indicated deterioration of influent and if this occurred, the influent was replaced with fresh influent.
4.2.2 Monitoring and sample collection
Dissolved oxygen (DO) and pH were measured every 30 minutes using specific pH and DO probes connected to a data-logger (Section 3.2). Temperature and conductivity were recorded from the same probes at the same time intervals.
Samples were taken at set time points during the experiments using a sterile syringe. Samples were taken from the sample ports at the outlet end of each pond. At the same time samples of influent and effluent were also collected. Samples were held in 35 mL containers andstored below 10°C until analysis. All samples were analysed within 2 hours of sample collection. 4.2 Methods 108
4.2.3 Bacterial plating
Heterotrophic plate counts (HPC) were used to enumerate the heterotrophic bacteria present in both the full-scale WSP wastewater used for developing the laboratory-scale system, and to monitor treatment throughout the laboratory-scale WSP itself.
E. coli and enterococci analysis was also utilised in the analysis of the full-scale WSP wastewater used to create the influent reservoir. The survival of E. coli and enterococci throughout the laboratory-scale WSP system was monitored to determine the success of the laboratory-scale WSP development.
Further details of these bacterial plating methods can be found in Section 3.4.
4.2.4 Viral spike and enumeration
Known concentrations of MS2 phage and Echovirus 7 (EV7) were inoculated into the laboratory-scale WSP system as presented in Section 3.9. The behaviour of MS2 and EV7 throughout the WSP system was used to determine the success of the development of the laboratory-scale system. The detection of MS2 and EV7 was as described in Sections 3.5 and
3.8. The TCID50 values determined by the Reed and Muench method were converted to
PFU/mL in order to compare with the MS2 analysis results. Briefly, the 50% titre 50(TCID ) was divided by the volume of viral inoculum plated (i.e. 0.1 mL) to result in TCID50/mL. To convert to PFU/mL, the TCID50/mL is multiplied by a constant, 0.7.
4.2.5 Statistical analysis
Box and Whisker plots show the median value and 99th percentiles. 4.3 Results and Discussion 109
One-way ANOVA (F) analysis was used to analyse the differences of pH and DO concentrations between pond types. Additionally, Pearson correlation (r) was calculated for each pond to assess the relationship of pH and DO.
One-way ANOVA was used to investigate the variation of E. coli concentrations throughout the laboratory-scale WSP system.
Pearson correlation was used to investigate the relationship of E. coli and enterococci concentrations by time, while Kruskal-Wallis ANOVA (X2) was used to analyse the variation of means of E. coli and enterococci in each pond and over time (days).
Kruskal-Wallis ANOVA analysis was used to analyse the variations of mean concentrations of EV7 and MS2 between groups.
4.3 Results and Discussion
4.3.1 Physicochemical fluctuations
The daily behaviour of an environmental system is influenced by temperature and daylight hours and the effect of these upon pH and DO. The average temperature of the laboratory-scale WSP system over the period of time when data was collected was 19°C. The fluctuations of both the pH and DO are shown in Figure 4.3 for each of the three ponds in the laboratory-scale system. The greatest fluctuations were seen in Pond A where the pH ranged between 7.5 and 8.2 and the DO ranged between 7.9 mg/L and 9.2 mg/L. These fluctuations were less pronounced in Pond B and Pond C with pH ranging from 8.3 - 8.4 and 8.4 - 8.5 respectively. The DO ranged between 8.9 mg/L and 9.5 mg/L for Pond B and 8.8 mg/L and 9.1 mg/L for Pond C. These ranges are well within the optimal pH and DO ranges expected for waste stabilisation ponds [63, 94]. 4.3 Results and Discussion 110
A difference between pH and DO concentrations in the pond types over a 24 hour period were found (F = 207.4, p = 0.0001, df = 5, ω2= 0.8776). Tukey’s test was used to further explore the ANOVA results. The mean pH of Ponds B and C were not found to be statistically different with a mean difference of -0.1058 (q = 3.702, p = 0.0998, df = 138, 95% CI = -0.2227 to 0.01101). Additionally, the mean difference (-0.01425) of DO concentrations between Pond A and Pond C was not significant (q = 0.4985, p = 0.9998, df = 138, 95% CI = -0.1311 to 0.1026). The differences in pH and DO concentrations for the remaining comparisons were found to be statistically significant, with all Tukey’s values (q) being greater than the critical q value (2.77) and p < 0.0001. All comparison analysis results can be found in AppendixD in Figures D.1 and D.2.
Figure 4.3: Daily variation of pH and DO in the three ponds of a laboratory-scale WSP system.
Values are averages with error bars calculated as SEM values, n = 24.
In Pond A, no statistical correlation was determined between pH and DO (r = 0.3768, p = 0.0695, n = 24). A negative correlation was determined between pH and DO in Pond B with r = -0.6272 (p = 0.001, n = 24, Cohen’s d = 0.74). An r2 value of 0.3934 indicates that 39.3% of the variance is shared with the other variable. A positive correlation between pH and DO was found in Pond C (r = 0.442, p = 0.0306, n = 24, d = 0.45). An r2 of 0.1954 4.3 Results and Discussion 111 indicates 19.54% of the variation of one variable is shared with the other. The causes for the greater fluctuations seen in Pond A in Figure 4.3 could include the greater depth compared to Ponds B and C; and the higher loading of the influent entering the pond. The greater depth may have resulted in less sunlight reaching the lower depths of the pond [150, 341], potentially limiting the algal growth and bacterial activity compared to Ponds B and C; however this difference in the model system is only a fraction of what would be seen in a real world situation.
The fluctuations during the daylight hours were expected as a response to increased photosynthetic activity of algae and stimulation of the production of oxygen and usage of carbon dioxide by bacteria. This increase in activity resulted in increased levels of DO and acidity of the environment, as supported by Fritz, Middleton and Meredith (1979) [305]. Daily monitoring of natural pond systems shows diurnal trends of pH and DO values. A diurnal trend is the presence of two peaks of DO and pH in response to increased photosynthetic behaviour occurring during the daylight hours. In this laboratory-scale system, the DO increases at 7.00 a.m. and 11.00 p.m. to form two peaks, but no significant pH diurnal trends are seen. There is often a delayed effect of the algal activity in ponds, resulting in a lag of the peaks, explaining the increased DO seen in Figure 4.3 prior and post daylight hours [86]. The diurnal trend in the laboratory-scale WSP was not a defined as has been seen in full-scale WSPs [95, 100, 212]. This is likely due to the holistic effect of temperature and sunlight occurring in the real world, making a more significant change in the pond temperature and activity than was seen in the laboratory-scale system. Wallace, Champagne and Hall (2016) presented the average DO over a two week period for primary, secondary and tertiary ponds was 13 ± 3.2 mg/L, 5.4 ± 2.8 mg/L and 6.3 ± 2.4 mg/L respectively. Similarly, the average pH for each pond was 9.2 ± 0.2, 8.1 ± 0.4 and 8.3 ± 0.2 for the primary, secondary and tertiary ponds [212]. Further, Craggs et al. (2004) presented the results of two experiments showing the effect of a lower algal concentration (measured as 4.3 Results and Discussion 112
Chlorophyll a) on the concentration and behaviour of pH and DO [100]. The second experiment had a greatly reduced algal population while the temperature and insolation remained the same. This algal level resulted in lower DO concentrations, albeit still exhibiting diurnal behaviour, while the pH of the system was greatly reduced and very little diurnal behaviour was seen [100].
The laboratory-scale system was run over 120 days. Figure 4.4 shows measurements of pH, DO and temperature recordings that were taken daily during this time.
Figure 4.4: Physicochemical monitoring in a laboratory-scale WSP system over time from set-up, showing pH, temperature and dissolved oxygen.
Fluctuations in pH, DO and temperature were greatest in the first 20 to 30 days of the experiment, but reduced after this time period. The calculated retention time for this system (using flow rate and volume) indicated a theoretical retention time of 14.2 days.This retention time was considered to be a full treatment cycle, with all wastewater being treated by the time it exited the pond system. With an experimental run of 120 days, more than 8 full treatment cycles were thought to be achieved.
The fluctuations initially seen in this system occurred over the first and second treatment cycles, with the greatest drop in both pH and DO occurring in the first 10 to 15 days. Further fluctuations were detected over the next 10 to 15 days but to a lesser extent. The stabilisation 4.3 Results and Discussion 113 that occurred over the first 30 days potentially correlates to two full treatment cycles having occurred, with the development of a stable environment after this time. When setting up a new full scale system, with a total retention time of 30 to 40 days, and sewage is being added into water filled ponds, much like the set up of this system, 7 to 14 days is commonly the time required for an algal and microbiological community to develop [70]. Alternatively, for systems being started from scratch and filled to working volume with fresh sewage, these are commonly left to settle and stabilise for 1 to 2 months, equating to approximately two retention time cycles.
Table 4.1: Pearson correlation coefficients of the relationships between pH, dissolved oxygen (DO) and temperature, within and between the ponds of the laboratory-scale WSP system
r A r B r C A pH A DO B pH B DO C pH C DO p Temp p Temp p Temp 0.8095 -0.775 0.9106 -0.789 0.8985 -0.75 pH pH pH 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.8095 -0.881 0.9106 -0.8081 0.8985 -0.613 DO DO DO 0.0001 0.0001 0.0001 0.0001 0.0001 0.0007 -0.775 -0.881 -0.789 -0.808 -0.75 -0.613 Temp Temp Temp 0.0001 0.0001 0.0001 0.0001 0.0001 0.0007 Pearson Correlation, n = 27.
Correlation coefficients (r) are presented in Table 4.1. A negative correlation between temperature and pH in Pond A was found (r = -0.7745, p = 0.0001, n = 27, r2 = 0.5998, d = 0.82). Additionally, a negative correlation was also determined between temperature and DO concentration in Pond A (r = -0.8812, p = 0.0001, n = 27, r2 = 0.774, d = 0.97). Conversely, a positive correlation was found for pH and DO concentrations in Pond A (r = 0.8095, p = 0.0001, n = 27, r2 = 0.656, d = 0.97) where 65.6% of variation was shared. The same relationships were found in Ponds B and C, with negative correlations between pH and temperature, where Pond B had a correlation coefficient of r = -0.789 (p = 0.0001, n = 27, r2 = 0.6226) and Pond C had a correlation coefficient of r = -0.749 (p = 0.0001, n = 27, r2 = 0.5621). The relationship between temperature and DO concentrations were also found 4.3 Results and Discussion 114 to be negative, with correlation coefficients of r = -0.8081 (p = 0.0001, n=27,r2 = 0.653) and r = -0.6128 (p = 0.0007, n = 27, r2 = 0.375) in Ponds B and C respectively. Positive correlations were determined between pH and DO concentration in Pond B (r = 0.9106, p = 0.0001, n = 27, r2 = 0.829) and Pond C (r = 0.899, p = 0.0001, n = 27, r2 = 0.807).
As the daily temperature changed, the pH and DO changed in response, with a reduction in frequency seen over time. Upon stabilisation, the pH remained at 7.6 in all three ponds, whilst the DO increased from an average of 3.8 mg/L in Pond A to 5.3 mg/L and 6.1 mg/L in Ponds B and C, respectively. The temperature in Ponds A and B remained at an average of 20°C, but dropped to 19.5°C in Pond C. This drop in temperature, while only minimal, could be attributed to the smaller volume of Pond C and being furthest away from the large volume of Pond A which has increased ability to maintain temperature.
Overall, the resulting physicochemical monitoring of a laboratory-scale system indicated a successful waste stabilisation pond development, supported by microbial analyses showing biological treatment of wastewater treatment had occurred. The pH and DO are the common day to day parameters used to observe WSP function. These parameters were chosen, and will remain central to wastewater treatment in time, as they are cost effective and easy to perform, with sufficient knowledge about how the two respond to the environment. Large variations in pH and DO over short time frames indicate that undesirable conditions may be developing in the pond.
4.3.2 Microbial behaviour and survival
The laboratory-scale WSP resulted in a reduction of E. coli throughout the consecutive ponds, as presented in Figure 4.5. A one-way ANOVA revealed that the mean E. coli concentrations between the ponds were different (F = 145.4, p = 0.0001, df = 3, 20, r2 = 0.956). Multiple comparison analysis (Tukey’s test) was used to assess the relationship between the mean E. 4.3 Results and Discussion 115
coli concentrations (log10 CFU/mL) for the Influent (4.728), Pond A (2.83), Pond B (0.333) and Pond C (0.00). A reduction of 4.78 log10CFU/mL occurred from the influent through to Pond C (q = 25.58, p = 0.0001, df = 20). The difference in means between Ponds B and C were not found to be statistical (q = 1.803, p = 0.5884, df = 20) with a mean difference of
0.333 log10CFU/mL.
The total reduction could be attributed to the natural treatment occurring within the wastewater, as a heterotrophic bacterial community was maintained in the laboratory-scale system thus indicating the reduction in indicator bacteria was unlikely to be due to a toxic environment. Heterotrophic bacteria can be thought of as an indication of the biological activity occurring within a waste stabilisation pond, and the maintenance of heterotrophic bacteria is a good indication that pond environment is able to host biological life [20, 342]. The presented data is not a time resolved study throughout the system, it is an average of all samples taken over time and would incorporate any differences seen due to the influent entering the pond at any one time.
Figure 4.5: Reduction of E. coli throughout the ponds of the laboratory-scale WSP system
E. coli reduction in reference to a sustained heterotrophic bacterial community (dashed line) in all ponds. Linear regression analysis and 95% confidence, n = 6.
Figure 4.6 compares the survival of E. coli in the ponds to that of enterococci, over one week. Concentrations of E. coli and enterococci entering the system at this time are different, with 4.3 Results and Discussion 116 enterococci present at concentrations 1 log lower than E. coli, but the same downward trend is seen over time in Pond A. The lower enterococci concentration is likely due to lower excretion in faeces in humans, with up to 3 log lower concentration (in CFU/mL) than E. coli per gram of human faeces [343–345].
A negative correlation was determined for both E. coli (r = -0.83, p = 0.0001, n = 16) and enterococci (r = -0.733, p = 0.002, n =15) with time. A positive correlation was found between E. coli and enterococci (r = 0.67, p = 0.009, n = 14).
Figure 4.6: Behaviour of E. coli and enterococci in a laboratory-scale WSP system over time.
Error bars are calculated as SEM values (n = 3)
Values in the literature indicate this difference is also seen upon the molecular detection of both E. coli and enterococci, with detection of up to 7 genome copy (gc) log10/100 mL for
E. coli and 4 gc log10/100 mL for enterococci [346]. It is also thought that enterococci is more resistant to die-off or inactivation than E. coli particles. Ponds B and C also show a downward trend for E. coli concentration, but the trend for enterococci differs as it increased after 7 days in both Ponds B and C.
The mean concentrations of E. coli were found to be different over time with mean log10 concentrations of 26.46 CFU/mL, 28 CFU/mL, 20.28 CFU/mL and 12.58 CFU/mL at days 0, 3, 7 and 10, respectively (X2= 10.93, p = 0.0121, df = 3) . These results support those 4.3 Results and Discussion 117 seen for enterococci over the same time period, indicating that the lower influent concentration did not have an effect on the overall reduction. The means of enterococci at day 0 (30.38 log10CFU/mL), day 3 (21.33 log10CFU/mL), day 7 (17 log10CFU/mL) and day 2 10 (16.13 log10 CFU/mL) were found to be different (X = 10.1086, p = 0.0177, df = 3, d = 0.7).
Similar differences between the means of E. coli and enterococci were found between ponds
A, B and C. The mean concentrations of enterococci in Pond A (29.61 log10CFU/mL), Pond
B (17.92 log10CFU/mL) and Pond C (12.92 log10CFU/mL) were statistically different (X2 = 15.237, p = 0.0005, df = 2). The mean concentrations of E. coli in Ponds A (29.14 log 10 CFU/mL), B (20.08 log10CFU/mL) and C (11.46 log10CFU/mL) were also found to be different (X2 = 15.201, p = 0.0005, df = 2, d = 0.70).
As these analyses were not time resolved, the concentrations in Ponds B and C would not necessarily be relative to what is seen in Pond A at this time. The retention time was calculated as 14.2 days for a particle to move through the entire volume of the system, thus analysis of bacterial indicators would need to occur in 14.2 day increments to monitor the reduction through the system in a time-resolved manner.
4.3.3 Virus behaviour and survival
Known concentrations of a human enteric virus Echovirus 7 (EV7), and a bacteriophage, MS2 were added to the influent of the laboratory-scale system. The resulting concentrations were monitored throughout the system over time to determine their survival in a functioning waste stabilisation pond, seen in Figure 4.7. Increasing concentrations of EV7 and MS2 occurred 3 days after the initial spike event, as the influent, containing the virus, began to flow through to Ponds A and B. A Kruskal-Wallis test showed there was not astatistical difference in MS2 concentration between the ponds of the laboratory-scale WSP (X2 = 4.3 Results and Discussion 118
3.7274, p = 0.1551, df = 2) or between the times analysed (X2 = 2.5129, p = 0.2847, df = 2). The mean ranks of MS2 concentrations by pond type were 27.93, 26.39 and 18.8 log10PFU/mL for Ponds A, B and C, respectively. Further, the mean ranks of MS2 concentrations over time were 23.87 log10PFU/mL at day 3, 28.28 log10PFU/mL at day 5 and
20.6 log10PFU/mL at day 10.
Figure 4.7: Behaviour of Echovirus 7 and MS2 throughout a laboratory-scale WSP at 3, 5 and 10 days post-viral spike of the influent
Survival of EV7 and MS2 in the wastewater of a laboratory-scale WSP, after a known concentration (105) of
each virus was spiked in. (nMS2 = 3, nEV7 = 4).
EV7 concentrations were different between pond types, with mean ranks of 8, 4.5 and 2.5
2 log10 PFU/mL in Ponds A, B and C respectively (X = 6.3051, p = 0.0427, df = 2). The rank means of EV7 concentration over time were not found to be statistically different (X2 =
0.8362, p = 0.6583, df = 2) with mean concentrations of 3.83 log10 PFU/mL at day 3, 5.5 log10
PFU/mL at day 5 and 5.66 log10 PFU/mL at day 10.
MS2 bacteriophage is known to be present naturally in both the wastewater and recreational water environments [288, 347]. The host bacteria for MS2 can be introduced by both human and animal faeces as well as soil and can result in a range of abundance levels. The presence of MS2 in the environment, along with similar diffusion routes and morphologies as enteric viruses has lead to its use as a faecal contamination indicator organism [288, 347, 348]. Prior analysis of the wastewater before addition of MS2 indicated a concentration of 105 had been 4.3 Results and Discussion 119 present in the influent up to 10 days prior to the concentration spike, which may be whysome MS2 is seen in Ponds B and C within three days of spike addition. However, as this has not been tested for, the presence of MS2 in Pond C by day 5 could also be due to short circuiting. Using the calculated theoretical retention time, viral breakthrough should not occur in Pond B until 10 days after the initial spike. The theoretical retention time was determined as 14.2 days from the influent to the effluent of Pond C. The movement of MS2 through the laboratory-scale system, seen in Figure 4.7 indicates the possibility of a shorter actual retention time.
Commonly, the current belief is that microbial indicators successfully indicate viral removal in wastewater. This experiment shows this may not occur in all situations, and may be specific to individual viral types under specific environmental conditions. By monitoring only the bacterial indicators, there is little knowledge that can be inferred in regard to virus survival, as shown by the differential behaviour of both the human enteric virus, Echovirus 7, and the bacteriophage, MS2.
During the operation of the model system, several complicating issues were encountered, including:
Influent reservoir ran dry:
During the running of the laboratory-scale system, the influent level dropped below that of the intake tube, resulting in no influent being introduced into the system but wastewater effluent still exiting the system; resulting in reduced volumes in the ponds. Upon this occurrence, the ponds were topped up with treated wastewater, diluted 1:2 with MilliQ-H2O and the influent reservoir was replaced. Any analysis using samples within one week post-event were not included as results may be affected by diluted wastewater introduced directly into the ponds.
Daphnia over-population:
During initial experimental runs of the laboratory-scale system, a visible loss of algae occurred in the secondary and tertiary ponds. Over two days, as the water became clearer, 4.4 Summary 120 small invertebrates could be seen on the surface of the ponds. Microscopic identification showed an abundance of Daphnia magna and Daphnia pulex, microscopic crustaceans. The growing population of Daphnia resulted in organic matter being removed at an increased rate, potentially masking the effect of microbial wastewater treatment. When this event was identified, the ponds were stopped and drained. This behaviour has previously been referred to in the literature, where analysis of a range of BOD methods lead to the filtration of protozoa from a sewage seed to prevent bacterial predation [349]. Therefore, subsequent experiments used wastewater and sewage passed through a 63 μm sieve to remove larger invertebrates and protozoa. The Daphnia over-population led to a protocol revision, and along with the reduced pond levels, resulted in a longer time for the project to get up and running.
4.4 Summary
The construction of a laboratory-scale WSP system for the treatment of wastewater was successful. By taking into account hydraulic, physicochemical and microbial parameters, the monitoring of the WSP system provided information regarding the treatment efficacy and enabled changes to be made to the loading and operation before the environment became toxic.
The variations in both DO and pH are greatest when other parameters in the pond are fluctuating, including temperature and organic matter concentration. As these parameters begin to level out over time as the ponds begin to stabilise, so to do the pH and DO values. This stabilisation indicates the successful development of the microbial community within the laboratory-scale WSP, enabling treatment of the organic matter to occur.
These results were supported by the maintenance of a steady concentration of heterotrophic bacteria in conjunction with the decreasing E. coli and enterococci concentrations throughout 4.4 Summary 121 the pond system, over time. The viral analyses were not found to be as straightforward as with the microbial analyses. However, a reduction in EV7 over time and throughout the pond stages was determined. MS2 behaviour was more variable and further studies would be required to determine the reasons behind this. The variation in behaviour between EV7 and MS2 indicate that the use of MS2 as an indicator for human enteric viruses may not be suitable, however, further studies incorporating a larger range of human viruses would be required.
Theoretical retention time calculated, and rhodamine dye analysis (Appendix A.3) of flow indicated acceptable hydraulic behaviour. However, both the microbial and viral analyses appeared to indicate the decreased retention time in the system, even when design and calculation had determined an acceptable retention time. This difference between the theorised and observed retention time could result from short circuiting and the role of dead zones within the pond, despite the presence of baffles. Dead zones, or areas where wastewater flow doesn’t reach, would reduce the effective pond volume and therefore decrease the overall retention time. A shorter retention time than that of the design can lead to overloading of the WSP system, resulting in discharge of wastewater that may not have been as effectively treated as intended. The longer the length of time the wastewater resides in the pond system, the more likely treatment, in terms of organic degradation and pathogen inactivation, will occur. This behaviour would need to be investigated further to determine the cause in order to account for any reduction in retention time.
This chapter ultimately presents the development of a laboratory-scale WSP, allowing for the use of the system in subsequent chapters. Chapter 5
Enzyme activity in waste stabilisation pond wastewater and virus survival
5.1 Introduction
This chapter considers the investigation of enzyme presence and activity in the wastewater of laboratory and full-scale waste stabilisation ponds (WSPs). In addition, the effect of specific enzymes on the survival of human pathogens and faecal indicators were explored. The functions of enzymes are important in the biological treatment of wastewater [26, 57, 350]. Extracellular enzymes have been the subject of many environmental studies in the past, due to ease of analysis (no cell disruption required) and likely increased dispersion in a large environment, such as wastewater [350]. Enzyme activity can be affected not only by the substrates present in the wastewater, but also the stage of wastewater treatment in which they occur [23, 26, 57, 350]. Waste stabilisation pond treatment incorporates various processes at each stage, therefore it is probable that both the enzyme families present, as well as the activity of enzymes, in the pond system will adapt in response to the treatment occurring [26, 57, 350]. 5.2 Methods 123
The laboratory-scale WSP system outlined in Chapter 3 was utilised to investigate the survival of human enteric viruses in a WSP and the effect of enzyme activity on virus survival. The laboratory-scale system was beneficial because known concentrations of viruses could be monitored throughout the system. Low and fluctuating concentrations of viruses in the environment make accurate survival studies difficult. Previous virus survival studies have determined survival or transport in synthetic wastewater medium, closed wastewater systems and distilled water. Fractions of wastewater were obtained by filtration based on particle size to isolate separate fractions of wastewater in order to investigate virus-inactivating properties.
A laboratory-scale WSP system will be used to investigate the physical, chemical and biological components and their effects on overall wastewater quality. Enzymes present in the extracellular fraction of wastewater will have viral inactivation activity under certain conditions. To test this hypothesis the following aims were undertaken:
• Determine the presence and activity of enzymes in wastewater, comparing extracellular enzyme activity to the enzyme activity in whole WSP wastewater.
• Compare the survival of the human enteric virus, Echovirus 7, and the bacteriophage, MS2, in a laboratory-scale WSP system. Further investigate the behaviour of EV7 and MS2 in the wastewater of a full-scale WSP system.
• Investigate whether viral presence has an effect on enzyme abundance and activity.
5.2 Methods
Unless otherwise stated, all sample collection, treatment and analysis occurred as outlined in Chapter3. 5.2 Methods 124
5.2.1 Sample collection
Full-scale wastewater was collected from the waste stabilisation pond treatment plant in the South Island of New Zealand, as detailed in Section 3.1. Figure 3.1 identifies the location of sampling for each pond at this site. Samples were returned to the laboratory and prepared for analysis within 2 hours of collection.
Time-series and viral analyses in full-scale wastewater were undertaken in the laboratory. Samples were collected from the full-scale WSP system in sterile containers and were treated upon arrival at the laboratory. The time-series studies were run in a controlled environment, where temperature was set at specified levels, and samples were rotated to keep in suspension. An untreated wastewater sample was alway included in the study as a control.
Wastewater samples were collected from the laboratory-scale WSP system using the in-built sampling ports as shown in Figures 4.1b and 4.2b.
5.2.2 Enzyme assays
Esterase activity was assessed in both laboratory-scale and full-scale wastewater using a Fluorescein Diacetate cleavage fluorescence assay. Protease activity was also determined in laboratory-scale and full-scale wastewater using a fluorescent assay detecting cleavage of casein. Full methods are in Section 3.6.
5.2.3 Viral spike and detection
Echovirus 7 (EV7) and MS2 were used to investigate the survival of viruses in laboratory-scale and full-scale wastewaters. For the laboratory-scale investigations, the WSP system influent was spiked with EV7 and MS2 to a final concentration of 105 cells/mL for each virus. The influent was drawn through the pond system at 1.38 L/s, with samples being taken fromeach 5.2 Methods 125 pond over time. The full-scale wastewater was collected and treated, followed by addition of EV7 and MS2 to a final concentration of5 10 cells/mL for each sample containing virus. An enterovirus end-point titration assay was used to detect EV7 survival, using a BGM cell line, while a two-layer tryptic glucose agar was used to enumerate MS2. Further details on these methods are in Sections 3.5 and 3.8.
5.2.4 Enzyme inhibition
Protease inhibition occurred in the presence of a broad spectrum protease inhibitor cocktail, Thermo Fisher™ Halt™ Protease Inhibitor Cocktail (100X), added to a final concentration of 1X (Section 3.6.1). Heat treatment was used as an alternative inhibition method (Section 3.6.2) to produce a wider range of inactivation.
5.2.5 Filtration
Filtration studies were used to determine the contribution of wastewater communities to virus inactivation. Three membrane sizes were used to fraction the wastewater, 8 μm, 0.8 μm and 0.22 μm, resulting in three fraction ranges; 8 - 0.9 μm, 0.8 - 0.23 μm and ≥ 0.22 μm. Whole wastewater was analysed for bacterial growth, enzyme activity and virus survival, alongside the three fractions using the methods previously described in Chapter 3.10.1.
To determine which organisms contributed to the esterase activity, WSP wastewater was separated by filtration to create fractions containing organisms based onsize
5.2.6 Statistics
One-way ANOVA was used to investigate the variance of enzyme activity in the influent, effluent and ponds of the laboratory-scale WSP system. Additionally, one-way ANOVA 5.3 Results and Discussion 126 analysis of esterase and protease activity variation between whole and extracellular wastewater was performed. Pearson correlation was used to investigate the relationship between esterase activity, time and filtration.
Two-way ANOVA analysis was used to investigate esterase activity over time and the variations in EV7 concentrations in relation to filtration and enzyme inhibition. Three-Way ANOVA was used to investigate the protease activity differences between and within a range of treatments.
Two-way repeated measures ANOVA was used to investigate the effect of time and treatment on EV7 concentration, while Kruskal-Wallis one-way ANOVA analysis determined the variation of MS2 concentration between treatment
5.3 Results and Discussion
Enzyme activity was detected in whole and extracellular primary wastewater of a full-scale WSP treatment system. The enzymes assayed were protease and esterase enzymes.
5.3.1 Enzyme activity in laboratory-scale WSP
Esterase activity was greater than that of protease activity in the laboratory-scale WSP wastewater. A significant reduction of esterase activity was observed upon filtration of wastewater (Tukey’s, q = 5.058, p < 0.05). Activity was reduced from 97 pmol/mL/hr to 20 pmol/mL/hr.
These esterase values are similar to those determined by Burns and Dick (2005), where lake eutrophication gave esterase activity from 50 to 250 pmol/mL/hr as eutrophication increased [351]. This correlation indicates the conditions within the laboratory-scale WSP are similar enough to other real world environments to elicit similar enzyme activities. 5.3 Results and Discussion 127
Figure 5.1: Protease activity throughout a laboratory-scale WSP system (n = 9)
Protease activity, determined by a fluorescent signal released upon cleavage of casein substrate, was greatest in the incoming influent of the laboratory-scale waste stabilisation pond. This influent was comprised of primary wastewater and raw sewage from a full-scale WSP. The mean concentrations; 47.91, 10.19, 8.87, 11.79 and 8.13 pmol/mL/hr in the influent, ponds A, B, C and effluent respectively, were different (F =1937.39, p = 0.0001, df = 4, ω2 = 0.9942). Values in the literature often consider protease activity in terms of moles of tyrosine released from the proteolytic degradation of casein therefore direct comparison is difficult between these results and literature values.
5.3.2 Enzyme activity in full-scale WSP fractions
Figure 5.2 shows the activity of esterase enzymes in whole and extracellular wastewater from a full-scale WSP system. This system was used for the design of the laboratory-scale WSP and formed the influent and wastewater for the laboratory-scale WSP (Section 3.1). 5.3 Results and Discussion 128
Figure 5.2: Esterase activity in whole and extracellular WSP wastewater from a full-scale treatment system (nWhole = 18; nExtracellular = 27)
The mean esterase activity in whole wastewater was reduced by 2927.17 pmol/mL/hr upon filtration with a 0.22 μm mixed cellulose ester membrane (Figure 5.2). This filtration removed bacteria and bacterial-associated enzymes, creating an extracellular fraction of WSP wastewater. The esterase activity in whole wastewater (6433 pmol/mL/hr) and extracellular wastewater (3505.83 pmol/mL/hr ) were found to be different (F = 22.19, p = 0.0001, df = 1,
43, ω2= 0.3201).
Figure 5.3 presents the activity of protease enzymes determined in whole and extracellular wastewater. The reduction of activity from whole to extracellular wastewater of a full scale treatment system was significant with a difference of 58.38 pmol/mL/hr between thetwo treatments (One-way ANOVA; F = 33.07, p = 0.0001, df = 1, 30, ω2= 0.5006).
The results shown in Figures 5.2 and 5.3 indicate that a proportion of the enzymes present in wastewater were intracellular or bound to organisms or particles larger than 0.22 μm in size. Extracellular or unbound esterase enzymes appeared to contribute 35% of the total esterase activity detected. Similarly, extracellular protease enzymes contributed 44% of total protease 5.3 Results and Discussion 129
Figure 5.3: Protease activity in whole and extracellular WSP wastewater from a full-scale treatment system (n = 32) activity detected. These results indicate that > 50% of the enzyme activity was cell-associated. This could include membrane-bound enzymes, exoenzymes or enzymes associated with larger organisms.
Ertugrul, Donmez and Tacak (2007) found differences between intracellular and extracellular lipase activity dependent upon the growth medium used [352]. In most mediums tested, intracellular lipase had greater activity than that of the extracellular lipase, indicating a greater proportion of lipase enzymes in this specific environment being cell-associated. Yan, Zheng, Du and Li (2014) also found that lipase were present in both extracellular and intracellular fractions [353]. Mukherji, Patil and Prabhune (2015) stated that protease enzymes can be found both extracellularly and intracellularly, and that the extracellular proteases tend to be present as inactive precursors, or zymogens, that need cleavage before they are active on a wide range of substrates in the extracellular matrix [354].
Despite the loss of activity, this fractionation study further confirmed enzyme presence in the extracellular portion of wastewater. In addition to esterase activity in wastewater fractions, 5.3 Results and Discussion 130 protease activity was examined throughout a WSP wastewater treatment plant. Three ponds in series incorporated the three stages of wastewater treatment; primary, secondary and tertiary treatment.
Figure 5.4: Protease activity present in a full-scale WSP consisting of three ponds in series (nPrimary = 3; nSecondary and Tertiary = 6)
Figure 5.4 shows increased protease activity occurred in the secondary WSP, with the lowest protease activity in the primary WSP. One-way ANOVA analysis found the mean protease activities were different between the primary, secondary and tertiary WSPs (F = 37.79, p = 0.0001, df = 2, 12). The mean protease activity was lowest in the primary WSP (34.36 pmol/mL/hr) with the highest activity in the secondary WSP (97.502 pmol/mL/hr). Bonferroni post-hoc comparison of means found the protease activity to be different between the primary and secondary ponds, with a mean difference of 63.14 pmol/mL/hr (p = 0.0001) and between the secondary and tertiary ponds with a mean difference of 45.52 pmol/mL/hr (p = 0.0003). However, the difference in mean protease activity between the primary and tertiary ponds was not found to be significant (Mean difference = 17.62 pmol/mL/hr, p = 0.2591). 5.3 Results and Discussion 131
The primary pond received raw sewage, with grit screening as the only prior treatment. The secondary pond received wastewater from the primary pond after an average of 15 days retention, once sedimentation had removed the majority of solids. The tertiary pond received the wastewater from the secondary pond after 8 days retention time. The wastewater stayed in the tertiary pond for approximately three days retention time before it was discharged. The main treatment in the primary pond was the sedimentation of the solid matter, while the secondary pond was dominated by biological degradation of organic matter. Minimal sedimentation occured in the laboratory-scale system, with the majority of the sediment collecting nearer the inlet. Natural disinfection processes occurred in the tertiary pond.
The levels of protease activity, as seen in Figure 5.4, were consistent with the biological treatments occurring in each pond. As the majority of biological treatment tends to occur in the secondary pond, it is likely that enzymes will be more active in that environment. This supports the increased enzyme activity detected in the secondary pond of the full-scale WSP wastewater treatment system. Fischer, Wolff and Emmerling (2013) found that enzymes, including α and β Glucosidases, have greater activities in the biological treatment stages than in the influent and effluents [17]. The findings by Fischer et al. (2013) support the results presented here [17].
The behaviour of esterase varied over time (Figure 5.5). A positive Pearson correlation was found between esterase activity and time (r = 0.928, p = 0.0001, n = 45) where 86.5% of the variance is shared between the two variables. A positive correlation was also determined between esterase activity and filter size, however this correlation was not found tobe significant (r = 0.148, p = 0.331, n = 45).
The 8 μm and 0.8 μm samples showed increases in esterase activity of 49,283 pmol/mL/hr and 44,700 pmol/mL/hr respectively from 0 to 48 hours. These differences in activity were both found to be significant, t = 35.67 (Paired t-test, p = 0.0001, df = 10) and t = 58.67 (p = 0.0001, 5.3 Results and Discussion 132
Figure 5.5: Esterase activity present in fractions of full-scale WSP wastewater over time (n24 = 3; n0, 48= 6)
DF = 10) respectively. The extracellular fraction, 0.22 μm, also increased from 0 to 48 hours by 35,528 pmol/mL/hr (Paired t-test, t = 77.27, p = 0.0001 and df = 10).
The esterase activity means in the three fraction ranges, 8 μm and above, 0.8 - 0.23 μm and < 0.22 μm, were different at 0, 24 and 48 hours, with the differences between the fractions increasing with time. At 0 hours, esterase activity in the extracellular wastewater fraction (0.22
μm) was different to that of the 0.8 μm fraction (t = 43.10, p = 0.0001, df = 10, ω2 = 0.9936) and the 8 μm fraction (t = 39.046, p = 0.0001, df = 10, ω2 = 0.9922). The esterase activity in the 0.8 μm and 8 μm fractions were also different (t = 11.56, p = 0.0001, df = 10, ω2 = 0.917).
After 24 hours of incubation a difference in esterase activity was identified between both
8 μm and 0.8 μm and the 0.22 μm fraction. The relationship between the 8 μm and 0.22 μm fractions at 24 hours was significant (t = 129.72, p = 0.0001, df = 4, ω2 = 0.9993) as was the relationship between the 0.8 μm and 0.22 μm wastewater fractions (t = 90.47, p = 0.0001, df = 4, ω2= 0.9993). The esterase activity between the 0.8 μm and 8 μm fractions was found to be different (t = 33.899, p = 0.0001, df = 4, ω2= 0.9948).
At 48 hours, the differences in esterase activity between the fractions were less pronounced with the difference in esterase activity between the 0.22 μm and 0.8 μm fractions at t = 10.99, 5.3 Results and Discussion 133 p = 0.0001, df = 10, ω2= 0.909. Differences were also found between 0.22 μm and 8 μm (t = 9.79, p = 0.0001, df = 10, ω2= 0.887) and 8 μm and 0.8 μm (t = 2.84, p = 0.0176, df = 10, ω2= 0.37).
The 8 μm membrane removed larger organisms and protozoa as these organisms are often 8 - 11 μm in size. The 0.8 μm membrane removed most algal cells and the 0.22 μm membrane removed all culturable bacteria let through the previous filters [321].
Lenhard (1967) introduces some of these differences in enzyme activity based on wastewater treatment and components. The protease activity determined in whole wastewater amounts to
8.26 μM/mL/min, with wastewater effluent having an activity of 0.272 μM/mL/min, calculated by absorbance at 280 nm [355]. The large difference between these values indicates a significant reduction in enzyme activity throughout the treatment process. Minimal work has been done regarding enzyme activities in the wastewater of waste stabilisation ponds, but several authors have presented data on enzymes in other wastewater sources, such as stirred tank reactors, olive mill wastes and wastewater sludges [352, 356, 357].
5.3.3 Enzymatic inactivation of viruses
MS2 and Echovirus 7 (EV7) survivals were reduced in the presence of active enzymes in wastewater. The survival of the viruses, EV7 and MS2, were investigated in natural WSP wastewater. The wastewater was collected from the full-scale WSP system in New Zealand and was treated and studied in closed aliquots in the laboratory in order to monitor the survival of viruses over time. The samples were treated prior to viral inoculation, with time series analysis occurring from the time of inoculation (0 Hours).
Figure 5.6 shows the variations of EV7 concentration in whole and extracellular wastewater comparing the effect of an enzyme inhibitor creating in an inactivated sample. Two-Way 5.3 Results and Discussion 134
Figure 5.6: Echovirus 7 concentrations in wastewater after 48 hours of incubation at 20 °C
ANOVA analysis of the variation of EV7 found that all interactions were significantly different (F = 3740, p = 0.0001, df = 1, 34). Post-hoc Sidak multiple comparisons found the
EV7 concentration in whole wastewater was reduced by 0.6472 log10PFU/mL (t = 42.21, p = 0.0001, df = 34) after 48 hours of incubation at 20 °C upon inhibition. Conversely, the EV7 concentration in the extracellular fraction of wastewater was increased in the inactivated sample by 1.342 log10 PFU/mL (t = 46.78, p = 0.0001, df = 34).
The removal of suspended solids and other organisms lead to decreased EV7 concentration in the extracellular sample of wastewater. By removing interfering substances, the virus particles present in the extracellular wastewater are more susceptible to attack by the free-living enzymes. Inhibition of the extracellular sample of wastewater by addition of a protease inhibitor saw higher EV7 concentration, indicating some enzyme activity had been diminished by the inhibition treatment. The majority of EV7 die-off in whole and extracellular wastewater occurred within the first 24 hours, with a plateau seen between 24 and 52 hours.
Figure 5.7 presents the effect of filtration on virus survival of EV7. Whole WSP wastewater was separated to create fractions as detailed previously (Method 3.10.1) with the addition on 5.3 Results and Discussion 135
Figure 5.7: Survival of Echovirus 7 in full-scale WSP wastewater fractions over time compared to a heat-treated (inactive) control an inhibited wastewater sample (Heat-treatment inactivation) and virus survival over time was quantified.
All interactions (Time and Treatments) were different (F = 32.48, p = 0.0001, df = 18, 48). Additionally, the variation within timepoints and treatments were found to have significant variations of EV7 concentration (FTime = 110, p = 0.0001, df = 6, 48; FTreatments = 7.845, p = 0.0019, df = 3, 8). Time contributed the greatest influence on EV7 concentration, with 39.04% of the variation attributed to time.
Over time, the relationships between the treatments varied, as determined by Tukey’s multiple comparison tests. At 0 hours, the EV7 concentrations between the heat-treated and
0.22 μm, 8 and 0.8 μm, 8 and 0.22 μm and 0.8 and 0.22 μm samples were to vary with mean differences of -1.625 (q = 15.92, p = 0.0001, df = 56), -0.75 (q = 7.348, p = 0.0001, df = 56), -2 (q = 19.6, p = 0.0001, df = 56) and -1.25 (q = 12.25, p = 0.0001, df = 56) respectively. At 2 hours post-inoculation, EV7 concentrations in the heat-treated and 0.22 μm and 8 and 0.22 μm samples were different. The heat-treated and 0.22 μm samples had a mean difference of -0.625 (q = 6.124, p = 0.0004, df = 56) as did the 8 and 0.22 μm samples (Mean diff. = -0.625, q = 6.124, p = 0.0004, df = 56). 5.3 Results and Discussion 136
There were no significant differences in EV7 concentration between any treatments at4and 24 hours post-inoculation. At 28 hours, differences in EV7 concentration were found between 8 μm and 0.8 and 0.22 μm samples with mean differences of -0.625 (q = 6.124, p = 0.0004, df = 56) and -0.5 (q = 4.899, p = 0.0055, df = 56) respectively. At 48 hours, both the heat-treated and 8 μm samples had different EV7 concentrations than the 0.8 μm sample, with mean differences of -0.625 (q = 6.124, p = 0.0004, df = 56) and -0.75 (q = 7.348, p = 0.0001, df = 56) respectively. Additionally, the EV7 concentration in the 0.8 μm sample was significantly different to that of the 0.22 μm sample (Mean difference = 0.75, q = 7.348, p = 0.0001, df = 56).
At 52 hours post-inoculation, the EV7 concentration in the heat-treated sample was significantly different to those in the8 μm and 0.8 μm samples, both with mean differences of 0.5 (q = 4.899, p = 0.0055, df = 56).
Over the total 52 hours, the sample treatments with different EV7 concentrations were 8 μm and 0.22 μm, with a mean difference of -0.5536 (q = 6.589, p = 0.0071, df = 8).
Figure 5.8: MS2 bacteriophage concentrations in fractions of full-scale WSP wastewater
Figure 5.8 shows a reduction of MS2 survival as the fraction size decreased to 0.8 μm, with a slight increase in survival observed in the 0.22 μm sample. Kruskal-Wallis one-way ANOVA found the differences in MS2 concentration between treatment (X2 = 24.08, p = 0.0001, 5.3 Results and Discussion 137
nGroup = 4). Multiple comparisons found the difference between the whole sample and the 0.8 μm and 0.22 μm samples were significant (Dunn’s; q = 3.232, p = 0.0072 and q = 2.663, p = 0.0468 respectively) with MS2 mean ranks reduced by 27.3 and 22.3 respectively. The
MS2 survival in the 8 μm sample was also found to be higher than that in the 0.8 μm and 0.22 μm samples (Dunn’s, q = 4.062, p = 0.0003 and q = 3.227, p = 0.0076 respectively). No significant difference was found between the whole and8 μm samples (Dunn’s, q = 0.491 and p = 0.999) nor between the 0.22 μm and 0.8 μm samples (Dunn’s, q = 0.753 and p = 0.999).
MS2 survival was reduced in extracellular wastewater compared to whole wastewater. EV7 showed similar behaviour in one experiment, but showed increased survival in extracellular wastewater in another experiment. This variation in EV7 survival could be a result of the specific organisms or enzymes present in the wastewater at the time of the experiment. The reduction of pathogen survival in extracellular wastewater may be due to the reduction of colloidal matter or solids, removed by the filtration. In the absence of these colloidal solids, viruses may be more susceptible to enzymatic inactivation than if colloidal solids were present as they may act to shield the viruses from various inactivation mechanisms.
The effect of filtration on the survival of EV7 and MS2 was further investigated with theuse of the 8 μm and 0.8 μm fractions. MS2 showed a decrease in survival from whole wastewater to the 8 μm and 0.8 μm fractions. Despite this, a slight increase occurred from the 0.8 μm to the 0.22 μm or extracellular wastewater fraction. Unlike MS2, EV7 showed an increase in survival as the wastewater fractions decreased in size, with the greatest survival evident in the extracellular sample.
These results indicate a difference in inactivation mechanisms and therefore survival of MS2 and EV7. As the two pathogens are known to infect different cells, with MS2 infecting E. coli cells and EV7 infecting human cells, a difference in behaviour is not surprising. Previous work by researchers has indicated that enzymes have the ability to reduce viral infectivity [28, 111, 129, 272]. Zajac and Crowell (1965) determined selective inhibition of 5.3 Results and Discussion 138 particular virus particles by a range of enzymes. This 1965 study showed that elastase, when present with viable cells, prevented binding of Coxsackievirus B3 (CVB3) and Poliovirus 1 (PV1). Alternatively, the presence of pancreatin inhibited attachment of CVB3 and PV1 to the viable cells. Zajac and Crowell (1965) also determined that the enzymes inactivated the viral receptors on the surface of living cells, as pre-treatment resulted in reduced binding of CVB3 and PV1 [272]. A study at a similar time by Cliver and Herrmann (1972) indicated that Coxsackievirus A9 was more sensitive to protease inactivation than PV1 [111]. Additionally, Cliver and Herrmann (1972) also suggested that low molecular weight substances are likely involved in this inactivation.
Many years later Nasser, Glozman and Nitzan (2002) expanded on the work by Cliver and Herrmann (1972), supporting the differential inactivation of CA9 and PV1 in the presence of protease [28]. Nasser et al. (2002) found that proteinase-pronase and elastase activities selectively reduced CA9 survival, while little effect was apparent for Hepatitis A (HAV), PV1 or MS2. These enzymatic viral reduction experiments have been studied mainly in marine waters and soil [22, 28, 129, 144].
5.3.4 Enzyme activity in presence of virus
Whilst determining the effect of enzyme activity on virus survival, varied responses of protease and esterase activities were detected upon the addition of MS2 and EV7 at known virus concentrations. 5.3 Results and Discussion 139
Figure 5.9: The effect of virus on protease activity in full-scale WSP wastewater
Box and Whisker Plot showing median and 99% confidence intervals (n = 45).
The variance seen between samples with added virus (EV7 and MS2) and those without were different (F = 9.308, p = 0.004, df = 1). Figure 5.9 shows increased protease activity upon the addition of virus particles at both 0 and 48 hours.
Figure 5.10: Esterase activity in the presence of virus in full-scale WSP wastewater Box and Whisker plot showing median and 99% confidence intervals (n = 72)
Figure 5.10 shows an increase in esterase activity in the presence of virus over time (F = 40.19, p = 0.001, df = 2). The increased protease and esterase activity in full-scale wastewater indicates a potential viral-infection response of the organisms present in the 5.4 Summary 140 wastewater, or perhaps the viruses utilise enzymes as part of their infection or replication mechanisms.
The potential stimulation of protease and esterase activities in the presence of EV7 and MS2 could lead to enzyme activity being used as an indication of increased viral load entering the waste stabilisation pond. This inflammatory response has been studied in terms of enzymes in the human body. A 1952 paper by Pardee and Kunkee presented initial work on the effect of bacteriophage infection on enzyme activity of the infected host cell [358]. This early study indicated a potential increase in activity seen for one specific enzyme in the study, but a more in-depth study was required to further this work. If viruses cause an inflammatory response in proteases, and potentially a range of enzymes, this increased enzyme activity could indicate that a longer retention time, or extended UV exposure is required to inactivate the larger proportion of viruses in the system [350]. The use of enzyme detection as an indicator could result in faster response to changes in wastewater contamination and would be much more cost effective than direct virus analysis as it currently stands.
5.4 Summary
Enzyme activity was detected in both whole and extracellular wastewater. Both esterase and protease enzyme activities were reduced upon filtration to create an extracellular sample, however enzyme activity was still observed in the extracellular fraction of wastewater. Both EV7 and MS2 concentrations were reduced in extracellular samples, however one EV7 study found an increased EV7 concentration in the extracellular fraction, indicating that varying conditions may impact virus survival.
This chapter indicates protease and esterase enzymes may have potential inactivation behaviours towards EV7 and MS2 viruses under certain conditions. Further work is required to quantify these findings and to analyse a wider range of enzymes and viruses of interest. 5.4 Summary 141
To aid the determination of specific enzymes present in wastewater that may also have viral inactivation abilities, targeted and shotgun sequencing were done in conjunction with whole protein mass spectrometry to help identify specific enzymes in wastewater. This work is presented in the following chapter. Chapter 6
Enzyme production capabilities of bacterial communities in WSPs
6.1 Introduction
The reduction of virus survival in the presence of extracellular enzymes of wastewater was presented in the previous chapter. Building on this finding, the potential enzymes capable of virus reduction are investigated utilising metagenomic sequencing of bacterial producers and mass spectrometric analysis of proteins and enzymes in wastewater. Due to the treatment occurring in each stage of treatment, the enzyme activity in wastewater would vary as a result of the treatment occurring. This change in activity would also be seen as a shift in biological communities at the phylum level throughout wastewater treatment, identified through genomic sequencing.
Current knowledge regarding microbial functions in WSPs generally pertains to specific roles. The presence and survival of pathogenic organisms have been extensively investigated [41, 89, 100, 359] as have those involved in nutrient metabolism [72]. Investigations into the microbial roles in phosphate and nitrogen metabolism, among others, has resulted in the 6.1 Introduction 143 detection of microbes required for specific nutrient metabolism pathways enabling effective wastewater treatment [198, 322, 360–364]. The detection of bacteria involved in the degradation of micro-pollutants in wastewater is becoming a significant area of research, with significant steps being made in the identification of the microbes and degradation pathways involved [365–368].
Unlike the specific studies previously mentioned, this work focuses on the general roles and activities occurring within WSPs leading to wastewater treatment. This more “holistic” approach will provide a more comprehensive understanding of the mechanisms and interactions contributing to biological wastewater treatment occurring within WSPs. Two sequencing approaches, targeted and shotgun sequencing, were used to develop a broad overview of the community within WSPs and their functions.
The well-established method of targeted sequencing, based on an amplification method where a specific piece of genetic material (DNA or RNA) is selected as the amplicon, was adopted [369–372]. The chosen amplicon is amplified by polymerase chain reaction (PCR) to produce multiple copies of the target sequence, followed by sequencing of the resulting copies. The 16S ribosomal RNA (rRNA) gene, is a commonly used amplicon for bacterial sequencing [370, 373, 374]. Despite being a well-established method, there are limitations associated with this technique. Whilst the 16S gene is a phylogenetically and taxonomically informative marker, the presence of the PCR amplification step prior to sequencing introduces a significant bias in the resulting sequences [375]. Sequencing and assembly errors introduce artificial sequences or chimaeras (incorrectly assembled amplicons) reducing the sensitivity and making the identification of bacteria difficult. Targeted sequencing allows for the determination of the taxonomic composition of the community but is unable to determine the functional ability associated with the detected taxa, limiting the practicability of the sequencing data. In addition to these significant limitations, the targeted sequencing 6.1 Introduction 144 technique relies on previously identified, taxonomically informative genetic markers, making the detection of novel microbes difficult [370].
As sequencing technologies have developed, a new sequencing method has enabled researchers to determine both the presence and function of microorganisms in complex samples [372, 375–377]. Shotgun sequencing is the direct sequencing of environmental DNA, without the amplification step involved in targeted sequencing, reducing the biases relating to this process [334, 370, 375]. Once the genetic information is extracted, the DNA is fragmented, with all fragments being sequenced to produce a range of reads. These reads can be aligned to genetic locations of all genomes present in the sample, not restricted to bacterial organisms. Shotgun sequencing allows for the determination of the taxonomic composition of the sample as in targeted sequencing but also allows for biological functions to be determined from the coding sequences of the genome [370]. Analysis of the resulting sequences from both targeted and shotgun sequencing requires the employment of separate computational analysis programs due to the different sequencing technology and types of sequences produced [116, 328, 330, 332–334, 376].
The two sequencing techniques result in different views of the same microbial communities [369]. In addition to these metagenomic sequencing techniques, high-resolution mass spectrometry using electrospray ionisation enables the detection of peptides and intact proteins from complex sample mixtures. Genomic rearrangements, such as horizontal gene transfer, can be used by microbes to respond to environmental changes. These adaptations can be missed by metagenomic sequencing, therefore mass spectrometry is used for a proteomic investigation [378, 379]. The utilisation of mass spectrometry allows for the determination of the proteins being expressed in the wastewater sample at a specific moment in time, under specific conditions in order to determine gene and cellular functionality [378]. As mass spectrometry incorporates protein fractionation to allow for improved protein detection, the collection of fractions containing proteins of interest is achievable. This 6.2 Methods 145 fractionation can occur online or prior to mass spectrometry and many mass spectrometric systems now allow for automatic fraction collection. Protein expression can be cross-referenced to the functional roles identified by shotgun sequencing in order to identify the enzymes that play an active role in the treatment of wastewater. The isolated fractions containing proteins of interest can further be used in the development of virus inactivation assays.
This systems-level approach, using multiple analytical techniques, will allow for greater knowledge to be developed in regards to genetic composition and protein expression of WSPs as a biological wastewater treatment system. This includes the potential for enzymatic virus inactivation.
The specific objectives used to test this hypothesis were:
• Investigation of bacterial abundance in laboratory-scale and full-scale WSPs.
• Determination of the functional ability of these bacterial communities.
• Identification of enzymes produced by these bacterial communities.
6.2 Methods
Unless otherwise stated, all sample collection, treatment and analysis occurred as directed in Chapter3.
6.2.1 Sample sites
Ten samples were submitted for sequence analysis, consisting of a range of WSP types and locations. The laboratory-scale wastewater samples include individual pond samples (Primary, secondary and tertiary) as well as combined wastewater samples including raw 6.2 Methods 146 sewage and wastewater. One laboratory-scale sample included bacterial extraction from bacterial growth cultures. This sample would be expected to have differences to the direct wastewater extractions, as the culture method would likely bias the growth of some species.
Two locations were used for the full-scale wastewater samples in order to investigate the effect of geographical location and influent composition on the bacterial community in wastewater. Site 2 was the same site used for the construction of the laboratory-scale WSP system and other analyses presented in this work (3.1). Two samples (a and b), taken from two sampling points within the same calendar month, were included in the sequencing.
Site 1 was another full-scale WSP system that serviced a smaller, tourist location, with fluctuating influent loads, located approximately 100 km from the primary full-scale WSP system site.
The collection and treatment of all samples (excluding the bacterial extraction sample) were the same, with extraction performed directly on the wastewater as is.
6.2.2 Sequencing
Targeted sequencing of the V3-V4 region of the 16S rRNA gene resulted in 23,501,816 total raw reads, with an average of 2,350,182 reads per sample prior to any quality parameters being applied. The forward and reverse targeted sequences were joined and filtered to result in average join lengths of 444 base pairs (bps) and an average of 650,838 read counts. Chimaera analysis was done on joined and filtered sequences with a quality threshold ofQ20. This analysis identified 6.68% of the sequences were chimeric or incorrectly assembled. These sequences were removed and the remaining 5,833,729 non-chimeric sequences were used for subsequent analysis. Within the QIIME pipeline, operational taxonomic units (OTUs) were selected, resulting in an average count of 579,402 OTUs per sample, ranging from 358,504 OTUs to 788,678 OTUs per sample. For functional annotation of the targeted 6.2 Methods 147 sequences, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used [329]. PICRUSt annotation uses the OTUs identified by QIIME analysis to assign functions. This is based on pre-calculated gene copies per organism.
Additionally, shotgun sequencing resulted in 59,623,138 total raw reads with an average of 19,874,379 reads per sample. This is in excess of 8 times more reads per sample than the targeted sequencing. The forward and reverse shotgun sequences were joined and filtered, resulting in an average joined length of 168 bps and an average of 94,747 read counts. As the QIIME pipeline searches for OTUs, it was not applicable to the analysis of the shotgun sequences. Two programs developed specifically for the analysis of metagenomic samples, Kaiju (Version 1.4.4) and Kraken (Version 1.0) were used [332, 333]. Functional categorisation by MG RAST resulted in a range of subsystem pathways, represented in the wastewater samples [334]. MG RAST uses k-mers for the functional annotation instead of OTUs as used in PICRUSt analysis.
6.2.3 Mass spectrometry
Analysis of the deconvoluted spectra resulting from tandem mass spectrometry used the Mascot server [380, 381]. Mascot was run using the MS/MS Ion Search with all parameters used detailed in Appendix C.3. The top scoring peptides resulting from Mascot were used to determine the biological processes and molecular functionality they possessed. Function annotation and inference used KEGG, InterPro and UniProt databases [382–386].
6.2.4 Statistics
Two-way ANOVA was used to investigate the variation between the wastewater types in the laboratory-scale system and the core microbiome genera in full-scale and laboratory-scale wastewater, as detected by targeted sequencing. 6.3 Results and Discussion 148
6.3 Results and Discussion
Sequencing resulted in an indication of the core microbiome present for biological wastewater treatment in WSPs. The detection of these core bacteria in the laboratory-scale WSP further supports the development of a successful laboratory-scale WSP, as described in Chapter4, that can be used to investigate behaviour in terms of virus survival and enzymes. Sequencing also identified enzymes present in wastewater based on functional categorisation fromthe genomic sequencing of wastewater samples. Additionally, mass spectrometry was utilised to identify enzyme proteins extracted from wastewater samples. The enzymes determined by both methods were compared to determine any correlations in detection.
6.3.1 Targeted sequencing
Targeted sequencing identified phyla present in samples of wastewater from a range ofWSP systems. Figure 6.1 presents the abundance of phyla detected in six samples, three full-scale and three laboratory-scale WSP samples. These results illustrate the similarities between all samples, supporting the hypothesis that the successful development of a laboratory-scale WSP system will likely result in the development of a similar bacterial community as the full-scale WSP. The phyla detected in the laboratory-scale WSP is representative of the native flora of the full-scale WSP, as the wastewater used to develop the laboratory-scale was collected from this full-scale WSP system.
At the species level, five species were identified in all samples sequenced. These species were Lactobacillus johnsonii, Flavobacterium succinicans, Pseudoxanthomonas mexicana, Sphingobium xenophagum and Propionibacterium acnes. Lactobacillus johnsonii is an intestinal bacteria involved in the fermentation of sugars [387]. Pseudoxanthomonas mexicana has been isolated from human urine and Propionibacterium acnes, a common causative agent for human acne, has also been found in the gastrointestinal tract of humans 6.3 Results and Discussion 149
Figure 6.1: Phyla detected in three full-scale and three laboratory-scale wastewater samples by targeted genomic sequencing Bubble size determined by sequence read counts on a log scale of assigned reads (5,726,314 reads).
[388, 389]. Knowing the role of these bacteria and their normal habitat, the presence of these species in wastewater is understandable as it is likely they enter the WSP with the incoming waste community. Sphingobium xenophagum has previously been isolated from biological wastewater treatment facilities [390]. This organism is known to degrade alkylphenols, such as nonylphenol isomers utilising the enzyme flavin monooxygenase [390–392]. Alkylphenols are commonly used surfactants (detergents or emulsifiers) that are transformed during biological wastewater treatment [390, 392]. The metabolites produced by this transformation, alkylphenol polyethoxylates, have been shown to have endocrine disruption abilities and are found in wastewater effluent and the lakes and surface waters where wastewater discharge occurs [367, 391–393]. The presence of Sphingobium xenophagum in these wastewater 6.3 Results and Discussion 150 samples indicates a presence of surfactants and the need for degradation of these environmental pollutants.
Figure 6.2: Phyla counts, determined by targeted sequencing, in stages of wastewater treatment Primary = Initial facultative pond; Secondary = Maturation pond; Tertiary = Maturation pond; Combined = Composite sample of raw sewage and primary, secondary and tertiary samples.
Figure 6.2 shows a phylogenetic shift throughout wastewater treatment stages in a laboratory-scale biological WSP system, as determined by a difference in OTU reads. The samples included a combined wastewater sample consisting of raw sewage, primary, secondary and tertiary wastewaters. Raw sewage was not analysed individually due to a limitation of the number of samples sent for sequencing. Two-way ANOVA found the phyla varied by 95.03% (F = 57.37, p = 0.0001, df = 23, 69) while no variation was found between the pond types overall (F = 0.00, p = 0.999, df = 3, 69). The phylum Actinobacteria was 6.3 Results and Discussion 151 reduced by 12.54% from the combined sample to the secondary wastewater sample (q = 3.774, p = 0.0457, df = 69), as was Proteobacteria with a reduction of 18.93% from the combined sample to the primary wastewater sample (q = 5.697, p = 0.0008, df = 69). Bacteroidetes were increased from the combined sample to the primary wastewater sample (Mean diff. = 15.94, q = 4.796, p = 0.0062, df = 69) and to the secondary wastewater sample (Mean diff. = 14.84, q = 4.467, p = 0.0123, df = 69).
Proteobacteria increased by 23.42 and 22.87% from the primary wastewater sample to the secondary (q = 7.049, p = 0.0001, df = 69) and tertiary (q = 6.882, p = 0.0001, df = 69) wastewater samples, respectively.
The tertiary sample appears to have the greatest difference in community composition compared to all other samples, with the loss of Firmicutes and reduction of Bacteroidetes apparent. The Firmicutes are predominantly gastrointestinal bacteria which would likely be introduced into the WSP with the incoming influent [394, 395]. The reduction of Firmicutes in the tertiary stage of treatment indicates the WSP community begins to dominate the wastewater as opposed to the influent community. As Firmicutes prefer intestinal environments, their reduced survival outside the intestine was be expected.
The Bacteroidetes are found in soil, water and intestinal environments [395–398]. As they are reduced in the tertiary stage of treatment, the majority of the bacteroidetes present in the initial stages are likely to be intestinal bacteria, associated with the sewage entering the WSP system.
These findings are in agreements with those previously described by McLellan et al. (2010), Ye and Zhang (2013) and Kaevska, Videnska and Vasickova (2016) [225, 399, 400]. McLellan (2010) and Ye (2013) both found Proteobacteria, Firmicutes, Bacteroidetes and Actinobacteria dominating the influent or sewage samples [399, 400]. This is comparable with the findings of the primary wastewater presented in this study (Figure 6.2). The findings recently presented by Kaevska et al. (2016) are similar to the results presented here, with the notable absence of 6.3 Results and Discussion 152
Actinobacteria in the influent seen in the study by Kaevska. The differences in the dominant phyla in each of these studies may be attributed to the geographical location of the study, the population size serviced by the WWTP and the type of wastewater the WWTP receives.
Figure 6.3: Cladogram of genera determined to be present in both full- and laboratory-scale wastewater, indicating a wastewater core microbiome Node size determined by log scale of reads assigned (3,446,491 reads)
Analysis of the targeted sequences resulted in the determination of the core microbiome associated with wastewater [330, 401]. This was calculated by identifying all taxa present in 50% of the sample OTU reads. Figure 6.3 shows the core taxa present in wastewater of both the laboratory-scale and full-scale WSPs analysed. A major group present was the Terrabacteria group, containing Actinobacteria, Cyanobacteria and Firmicutes. The unranked clade of terrabacteria groups prokaryotes that appear to have evolved due to environmental pressures [402]. This group contains approximately two-thirds of all prokaryotes including 6.3 Results and Discussion 153
Deinococcus-Thermus and Chloroflexi in addition to the phyla detected in the wastewater samples. Statistical analysis was undertaken to determine if the bacterial communities in the full-scale and laboratory-scale WSPs were similar. Variation was found between the genera detected within both the full-scale and laboratory-scale wastewater samples (F = 10.06, p = 0.0001, df = 273, 548). No statistical variation in genera was found between the full-scale and laboratory-scale wastewater samples (F = 1.7 x10-12, p = 0.999, df = 1, 548). These results provide further evidence of the successful development of a laboratory-scale WSP. The core microbiome only presents the bacteria commonly present in the WSPs tested. Further experiments investigating the omission of these core bacteria would be needed to provide data to support this finding, however, this was outside the scope of this work.
6.3.2 Shotgun sequencing
The main contributors to the bacterial community determined from shotgun whole genome sequencing (WGS) were in agreement with those identified by targeted sequencing at phylum level (Figures 6.2 and 6.4). 6.3 Results and Discussion 154
Figure 6.4: Relative abundance of bacteria and viruses in wastewater determined by whole genome shotgun sequencing Two geographically separate WSPs, Site 1 and 2. Two samples taken from Site 2 within the same calendar month indicates some community variation. Viruses mislabelled as a phyla due to computational errors.
The full-scale wastewater samples presented in Figure 6.4 show a variation in bacteria and viruses between the two sample locations in Site 2 compared to the those in Site 1. Kaiju analysis of the shotgun sequencing data showed proteobacteria was the most abundant phyla in the two sample sites with varying abundances for the remaining phyla (Figure 6.4). A greater abundance of Firmicutes was present in Site 1 full-scale WSP compared to the Site 2 full-scale WSP samples 6.4. The lower population served at Site 1 and the large variation in influent volume could contribute to the variation in phyla. The sequence analysis performed using Kaiju was in agreement with the Kraken in regards to the four most dominant phyla present in each sample. 6.3 Results and Discussion 155
A large proportion of the reads in each sample were unclassified for both Kaiju and Kraken analyses. The diversity of the organisms present in natural environments, such as WSPs, can lead to many organisms not being classified. Additionally, the bacterial taxa may have been assigned to clades, but not to any specific genus, resulting in unclassified sequences. The classification of sequences can be highly reliant on the databases that were chosenfor identification. With newer techniques, such as whole genome shotgun sequencing, the databases and analytical techniques may not be as advanced compared to techniques that have been in use for much longer.
6.3.3 Pathogenic bacteria in WSPs
There are 30 genera commonly identified as containing pathogenic species known to infect humans however not all species within these genera are pathogenic [395, 403]. Of these 30 genera, 19 were detected by targeted sequencing of 10 wastewater samples (Figure 6.5). Comparatively, shotgun sequencing of 3 wastewater samples resulted in the detection of all 30 potentially pathogenic genera (Figure 6.5).
The shotgun sequencing was performed on three of the same samples analysed by targeted sequencing. The targeted sequencing resulted in a maximum of 13 pathogenic genera being detected in a single sample, while shotgun sequencing determined the presence of all pathogenic genera in a single sample. These results highlight the effect of sample preparation and sequencing analysis between the two techniques. Additional to the pathogenic genera detected shotgun sequencing allowed for the detection of pathogens at a species level.
Using the targeted sequencing results, the reduction of the genera, known to contain pathogenic species, throughout the WSPs was determined. A total of one log reduction of sequence counts was observed throughout the laboratory-scale WSP (Figure C.1). While not all the species in these genera will be pathogenic, it provides an initial overview of the genera 6.3 Results and Discussion 156 (whole genome shotgun) de novo Figure 6.5: Generasequencing techniques known to contain pathogenic species as detected by targeted (16S) and 6.3 Results and Discussion 157 behaviour. Further investigations and greater in-depth sequencing are needed to determine the abundance of pathogenic species.
6.3.4 Metadata correlation
Figure 6.6 presents the correlations between temperature, pH, DNA concentration and the phyla detected in all samples as a result of targeted sequencing. The correlation shows Fusobacteria and Firmicutes were positively correlated to DNA concentration, temperature and pH. When each or all of the properties increased, the abundance of these phyla increased also.
Figure 6.6: Correlation of pH, temperature and concentration with phyla abundance Correlation plot with red spheres indicating positive correlations and blue spheres indicating negative correlations. Intensity of colour and angle of sphere indicates the strength of the correlation. Darker reds and blues are more strongly correlated than the fainter colours. Concentration refers to the DNA concentration.
Acidobacteria abundance was negatively correlated to pH, where Acidobacteria become more abundant as the environment became more acidic. Acidobacteria are known acidophiles 6.3 Results and Discussion 158 and thrive in acidic conditions where the pH is below 2, supporting the correlation presented here [404]. Actinobacteria and Tenericutes were negatively correlated with all three properties. Actinobacteria are mesophilic bacteria commonly found in aquatic and terrestrial environments [405]. As pH increased, the abundance of Actinobacteria decreased. Optimal growth of Actinobacteria occurs between pH 6 and 9, with some Streptomyces strains capable of growing at acidic pH (~3.5) [405]. The negative correlation of Actinobacteria with both pH and temperature support this data. Tenericutes may have increased sensitivity to environmental factors due to the lack of cell wall [395]. Thus any changes in pH and temperature would likely result in a decreased abundance of Tenericutes.
The Firmicutes and Caldiserica were strongly positively correlated to pH indicating these phyla prefer an environment with a higher pH. Caldiserica are anaerobic chemoheterotrophs, using sulphuric compounds as electron donors for respiration [406]. Aerobic environments and thus higher levels of dissolved oxygen are linked to lower, or acidic pH. That Caldiserica increases with pH increases support a prevalence for an anaerobic environment and lower dissolved oxygen, supporting the correlation present in this study.
Nitrospira were those most positively correlated with temperature, as temperature increases so too does the abundance of Nitrospira. This phylum has been detected in a range of environs, including wastewater sludge and hot springs [198, 407]. The ability of the Nitrospira to survive in conditions such as hot springs indicate the positive correlation with temperature could be expected. Additionally, Nitrospira is known to be involved in the nitrogen cycle, therefore its presence suggests a potential function in the nitrification process in wastewater treatment [198]. 6.3 Results and Discussion 159
6.3.5 Functional categorisation
The functions of the identified bacteria were predicted by using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) from the 16S rRNA sequences identified, and by MG RAST for shotgun sequences [329, 334].
6.3.5.1 Targeted sequence analysis
There are three main steps in PICRUSt analysis, including copy number prediction, number normalisation and the metagenome prediction. Number prediction and number normalisation are needed in order for the OTU counts to be comparable to the relative abundance of the organisms instead of the abundance of 16S rRNA. This normalisation is based on pre-calculated gene copies per organism [329]. The functional pathways identified by PICRUSt analysis were then collated into seven core pathways, as seen in Figure 6.7. These pathways, determined using the Kyoto Encyclopedia of Genes and Genomes (KEGG) include cellular processes, environmental information processes, genetic information processes, human disease, metabolism and organismal systems [382? , 383].
PICRUSt analyses of the targeted sequences show a large variation in functionality between the samples. The secondary pond appears to have the greatest abundance of unclassified functions (Figure 6.7). The biological activity occurring within the secondary pond is not well understood and this is represented by this large abundance of unknown functions.
Genetic information processing functions were well represented, including DNA repair and recombination proteins and DNA replication proteins. Environmental information processing functions included transporters and secretion systems. Transporters, such as the ABC transporters, are important in cell viability, virulence and pathogenicity [383, 408]. The processes with a large number of reads associated include those involved in xenobiotic degradation, nitrogen and methane metabolism, amino acid metabolism, photosynthesis and 6.3 Results and Discussion 160
Figure 6.7: Collated functional categorisation based on OTUs detected by targeted sequencing
LS = laboratory-scale WSP; FS = Full-scale WSP; 1,2,3,4 = sample number (composite samples of 1°, 2° and 3°); 1° = Primary pond; 2° = Secondary pond; 3° = Tertiary pond signal transduction. The functions presented here would all contribute to the biological treatment of wastewater. Xenobiotic degradation would be utilised in the degradation of pharmaceuticals and drugs entering the WSP with the influent community. Photosynthesis drives the aerobic respiration process, providing oxygen for the bacteria to utilise, allowing for the degradation of organic matter, thus contributing to the main aim of wastewater treatment.
The results in this section indicate that the functionality of the bacterial community in wastewater varies in regard to treatment stages of WSPs. This is supported by the correlations identified in the previous section where the bacterial abundance of some phyla varied relating to temperature, pH and dissolved oxygen. 6.3 Results and Discussion 161
6.3.5.2 Shotgun sequence analysis
The functional categorisation of shotgun sequences determined a range of subsystem pathways were represented in the wastewater samples [334]. Some of the functions identified included nitrogen, sulfur and phosphorous metabolism, RNA and DNA metabolism, virulence, disease and defence (Figure 6.8).
The functions of the greatest abundance detected relate to carbohydrates and are often involved in organic matter degradation [409]. Enzymes involved in carbohydrate pathways include L-lactate permease, ABC transporters and ribulose bisphosphate carboxylases. The ABC transporters were also identified by the PICRUSt analysis of the targeted sequencing. However, the functional annotation by PICRUSt categorised the ABC transporters in the environmental information processing group as opposed to carbohydrate related processes (Figure 6.7). Transport is a function that is required in the majority of functional categories thus it is not surprising that the ABC transporters appear in more than one categorisation [408]. Clustering-based systems were another functional category detected by MG RAST. The clustering systems have been implicated in metabolic pathways, microbial metabolism in diverse environments, the biosynthesis of secondary metabolites and xenobiotic degradation. This group included a range of oxidoreductase, transferase and isomerase enzymes. Oxidoreductase enzymes catalyse the oxidation of a substrate, which acts as a hydrogen donor [47]. This oxidation is essentially the degradation of organic matter, hence WSPs can be referred to as Oxidation Ponds [62].
The functional categorisation of genomic sequencing has identified enzymes and proteins encoded for by bacteria in WSPs. The presence of genes indicates potential functions but does not determine if the enzymes and proteins are being expressed at the time of sampling. To determine the proteins and enzymes active in wastewater at a given point in time, analysis of the protein expression was undertaken using mass spectrometry. 6.3 Results and Discussion 162 Figure 6.8: Functional annotation of whole genome shotgun sequences determined by MG-RAST 6.3 Results and Discussion 163
6.3.6 Potential enzymes produced
Tandem mass spectrometry was used to detect enzymes and proteins in WSP wastewater using intact protein detection, or top-down proteomics. The enzymes detected as a result of mass spectrometry were used to indicate functional processes occurring in the wastewater, in conjunction with the functional categorisation from the metagenomic sequencing.
As this is still a developing area of research, a significant amount of time was spent on the modification of existing methods, including running conditions, spectra interpretation and identification of proteins. To aid this development, an intact protein LC-MS standardwas used (SigmaProt MSRT2, Sigma-Aldrich). The standard was run and the initial spectral analysis by Bruker Compass software showed peaks corresponding to the masses of the proteins in the LC-MS standard. Additionally, the peaks were eluted from the column within the elution time range, specified for each standard protein, as indicated in the protocol in Appendix C.2. The deconvoluted spectra were exported as generic mascot files to enable further analysis using Mascot (Matrix Science).
The spectra from the protein standard were uploaded to the Mascot platform and analysed using settings as shown in Appendix C.3. Despite manually identifying the peaks of the standard proteins, Mascot failed to match any peaks. This indicates an issue in regard to how Mascot interprets the spectra. To investigate this, another platform was used in order to compare results. The standard protein masses detected and the relating peptide sequences are shown in Table 6.1. Further analysis of these peptides using ScanProSite (https://prosite.expasy.org/scanprosite/) resulted in a range of enzymes and proteins containing these motifs, yet none directly matched the protein standards.
Nevertheless, a range of wastewater samples were analysed by tandem mass spectrometry, using the same running conditions as for the standard. Mascot analysis of the spectra from the 6.3 Results and Discussion 164
Table 6.1: Detection of intact protein standards by tandem mass spectroscopy
Protein Calculated Mass Elution time Peptide Sequence Peak Mass 15,062 4.6 GAGVVLT 15,063.008 Ribonuclease 15,224 5.0 VAANGDS 15,220.996 15,386 5.6 GGSADAP 15,387.121 CLGGGGS 14,304.459 Lysozyme 14,305 9.0 EDSTFLM 14,301.474 14.3 GAPEEVL 18,361.676 18,363 14.5 DPLPSVL 18,366.002 β-Lactoglobulin A 14.8 GLTETPY 18,364.818 14.4 LPYTETG 18,687.338 18,688 14.7 LGEEYAD 18,687.287 Peptide motifs detected by Mascot analysis of a protein standard, SigmaProt MSRT2. wastewater samples identified potential proteins present in the wastewater. The top scoring proteins are seen in Table 6.2.
The proteins identified presented a range of molecular functions and biological processes.
The protein AAA40409.1, the T cell receptor β-chain V2 precursor, is a component of the T cell receptor molecule. This molecule recognises antigen fragments bound to histocompatible molecules, displaying antigen binding and immunoglobulin receptor binding functionality. The biological processes involving these proteins include T cell receptor pathway signalling, defence and immune responses and complement activation. It is, however, unlikely there was an abundance of mammalian cells present in the wastewater, on the surface of which these T-cell receptors would be located. BLAST analysis of the motif assigned to protein AAA40409.1 against metagenomes resulted in the identification of asparagine synthetase as a potential protein match. The analysis determined there was an 83% sequence and identity similarity. The ligase enzyme, asparagine synthetase (EC 6.3.5.4), forms carbon-nitrogen bonds and is present in the metabolism of secondary metabolites in addition to the metabolism of alanine, aspartate and glutamate. The detection of this enzyme in the wastewater sample is supported by its presence in the groundwater metagenome. 6.3 Results and Discussion 165 4 4 1 2 1 1 2 2 1 2 2 1 2 2 2 2 2 2 10 Matches 28 24 28 15 15 12 13 15 15 12 21 25 12 11 20 25 25 25 25 Score ET1 Xac29-1 sp. MG 06-1683 08DC60 group 96.0932 subsp. bulgaricus Source Acidilobus Homo sapiens Mus musculus Escherichia coli Trypanosoma cruzi Bacillus cereus Anolis alvarezdeltoroi Hippophae rhamnoides Caenorhabditis brenneri Escherichia coli Amblyomma americanum Influenza A virus (H5N8) Fusarium proliferatum Chlamydia psittaci Chlamydia psittaci Natronorubrum texcoconense uncultured Human immunodeficiency virus 1 Xanthomonas axonopodis Lactobacillus delbrueckii -chain V2 precursor β uORF Protein Matrix protein 2 Chalcone synthase Putative lipoprotein DNA gyrase, B subunit Unnamed protein product GapA binding peptide SR1P Protein of unknown function Cytochrome C oxidase subunit Truncated envelope glycoprotein Interstitial retinol binding protein 3 T cell receptor Hypothetical protein XAC29_22299 Hypothetical protein MGAcid_10980 Hypothetical protein EC960932_1300 Uncharacterised protein FPRO_13309 Hypothetical protein CAEBREN_11216 Hypothetical protein SAMN04515672_1385 Table 6.2: Protein identification including protein source, number of matches and protein ID as by Mascot determined AII77576.1 GenPept ID EPJ24219.1 EPJ32986.1 SDJ66571.1 ESQ21157.1 EGT54154.1 CZR47642.1 AKT08850.1 ALN12105.1 CDR75928.1 ABA38785.1 CAA25215.1 ABD90548.1 AGH79814.1 AAA40409.1 EKW67672.1 AMN92734.1 AKN108861.1 WP_088113684.1 6.3 Results and Discussion 166
Chalcone synthase (ABA38785.1) of the acyltransferase enzyme class (EC 2.3.1.74) are involved in the biosynthesis of secondary metabolites, specifically flavonoid. The acyltransferase enzymes were also detected by metagenomic sequencing, classified in the clustering-based systems, associated with fatty acid elongation and degradation in addition to secondary metabolite biosynthesis.
The DNA Gyrase B subunit, belonging to the DNA topoisomerases (EC 5.99.1.3), is involved in DNA replication and DNA repair and recombination processes. As WSPs are a biological environment with cellular growth and death continuously occurring, DNA replication, repair and recombination processes would be vital to WSP function.
The hypothetical protein XAC29_22299 has 100% identity to a DUF3018 Domain-containing protein (WP_003491150.1). The DUF3018 domain, a Domain of Unknown Function, is conserved in proteobacteria which were dominant in the genomic sequence analysis of these samples. A PFam search of the DUF3018 domain showed that two proteins, Antitoxin MazE and Homoserine O-succinyltransferase, both contain the DUF3018 domain architecture, indicating possible functionality associated with the domain.
The cytochrome C oxidase transmembrane protein complex consists of the cytochrome C oxidase subunit 1 protein (AKT08850.1). The oxidoreductase, cytochrome C oxidase, containing copper, utilises oxygen as the electron acceptor. The biological processes that cytochrome C oxidase enzymes likely play a role in include aerobic respiration, oxidative phosphorylation and electron transport. These functions are all required for organic matter degradation as discussed in Section 6.3.5.
Other proteins detected of interest were the unnamed protein product of E. coli (CAA25215.1). BLAST analysis of this peptide presented with a conserved domain, Lysozyme_like Superfamily. Additionally, 100% similarity to a transglycosylase enzyme was observed. The transglycosylase enzyme (P46022) has roles in cell wall organisation and peptidoglycan biosynthesis as well as having transferase activity. Both transglycosylase and 6.4 Summary 167 peptidoglycan biosynthesis were identified as functions associated with targeted sequencing, determined by PICRUSt, indicating the detection of this enzyme in the wastewater supports these findings. The Lactobacillus sp. protein (CDR75928.1) showed 100% similarity to Peptidase C14, where both proteins contained the WD40 conserved domain. Peptidase C14 has endopeptidase activity and is likely involved in peptidoglycan biosynthesis and degradation. Peptidoglycans and glycoproteins are present in viral capsids. Enzymes involved in peptidoglycan pathways could potentially be involved in viral inactivation by capsid cleavage, however, more work is required to investigate this.
The interpretation of the spectra and resulting proteins requires further development in order to improve the detection of enzymes.
6.4 Summary
The targeted and shotgun sequencing provided further evidence supporting the development of a laboratory-scale WSP system. The adoption of the shotgun whole genome sequencing can aid the researcher to determine if the potential biases introduced through sample preparation and PCR may have effects on the detection of bacterial genera in a sample, as has previously been found with targeted sequencing.
Functional analysis of both targeted and shotgun sequences identified the bacterial community played roles in cellular, environmental and genetic processes, metabolism and human disease. These processes support the biological activity thought to occur in order to treat wastewater in WSPs.
Oxidoreductase, transferase and isomerase enzymes were detected by tandem mass spectrometric analysis of wastewater. Additionally, these enzymes were associated with the functional categories identified by the genomic sequence analyses, indicating the valueof mass spectrometric detection in addition to genome sequencing. Chapter 7
Conclusions and future work considerations
This work contributes to improving the understanding of waste stabilisation ponds and the processes occurring within them. Additionally, the intact protein mass spectrometry analysis provides new avenues of enzyme and protein investigation in a range of scientific fields. The achievement of the aims, as outlined in Section 1.2, are detailed below.
7.1 Development of a laboratory-scale WSP system for the
investigation of virus survival and inactivation
The successful development of the laboratory-scale WSP system was substantiated by the maintenance of an active biological community and aerobic state of the wastewater, as shown by pH, dissolved oxygen and microbial monitoring. Supporting the physicochemical and microbial data, genomic analyses of wastewater indicated no significant differences were observed between the bacterial communities of the laboratory-scale and full-scale WSPs, 7.2 Enzyme activity and virus survival 169 indicating the presence of a stable microbial community. The laboratory-scale system was designed to incorporate realistic pond geometry, previously often overlooked in model studies [305]. The effect of scaling was elucidated, enabling the potential application of the design to be scaled up in future. The laboratory-scale WSP system was utilised to safely and accurately monitor viral and bacterial pathogen survival throughout wastewater treatment. The data presented here indicate the development of this system will be a vital tool in the development of virus detection methods in wastewater in addition to thr further investigation of the mechanisms occurring in real-world wastewater treatment systems.
7.2 Enzyme activity and virus survival
Extracellular enzymes were shown to be capable of viral inactivation in wastewater as seen by a reduction of the bacteriophage MS2 and the enterovirus EV7. This inactivation of viruses was analysed in the laboratory-scale WSP and the wastewater of a full-scale WSPs. A further reduction of enzyme presence, therefore activity, in wastewater resulted in a reduction of virus inactivation. However, some virus inactivation did occur when extracellular enzymes only were present in the wastewater. This result supports the hypothesis that extracellular enzymes may play a role in virus inactivation in WSPs. The bacteriophage MS2 behaved slightly differently to that of the enteric virus, EV7, in samples where enzyme activity had been inhibited. Ultimately, a greater loss of infective EV7 was seen in the presence of extracellular enzymes compared to the reduction of MS2 in the same wastewater samples.
7.3 Microbial diversity and enzyme production in WSPs
The enzymes involved in this inactivation activity are thought to be produced by the bacterial community present in WSPs. Genomic sequencing was used to identify the dominant bacteria 7.4 Future work 170 in both the laboratory-scale and full-scale WSPs. The phyla detected in highest abundance in all samples were Proteobacteria, Actinobacteria, Bacteroidetes and Firmicutes. Further analysis of the genomic sequences identified the bacterial community shift throughout the wastewater treatment process occurring in WSPs.
Functional analysis of the bacterial community in WSPs identified processes potentially occurring in wastewater treatment and the enzymes associated with these processes. These enzymes included oxidoreductases, isomerases, transferases and transporters. These enzymes are commonly associated with a range of functional pathways including environmental monitoring, xenobiotic degradation, nitrogen metabolism, virulence and genetic information processing. The presence of these pathways supports the mechanisms thought to occur in the biological treatment of wastewater.
Further to the genomic identification of enzymes in wastewater, tandem mass spectrometry was utilised to detect proteins and enzymes present in wastewater at the time of sampling. Where the genomic analysis detected genes capable of enzyme production, the mass spectrometry detects the expressed proteins. The proteins identified by intact protein mass spectrometry included oxidoreductases, isomerases and transferase enzymes, in correlation with those predicted by the genomic sequencing.
The identification of proteins by tandem mass spectrometry was limited by thelackof available appropriate databases for matching intact protein spectra. The mass spectrometry results presented in this study are preliminary and further work on this technique is required.
7.4 Future work
Whilst this work has endeavoured to solve many of the issue outlined in the introduction, there is still a significant amount of work to be done in regards to improving wastewater treatment worldwide. These difficulties include a lack of understanding surrounding biological 7.4 Future work 171 wastewater treatment and the associated virus removal. This work has presented evidence that virus survival in WSPs may differ to that of the bacterial indicators currently utilised for pathogenic monitoring. It is becoming increasingly more evident that bacterial indicators are not sufficient to determine pathogen reduction throughout wastewater treatment. This hasbeen highlighted throughout recent history, but with direct virus analysis costs too high, there was not thought to be a suitable solution [48].
This work has attempted to improve what is known in terms of the survival of two viral organisms, commonly used for pathogen studies and an indication of virus removal in wastewater worldwide. This natural pathogen inactivation could contribute to wastewater treatment, minimising the need for additional disinfection systems. The inactivation of viruses requires a great deal of further research, as this will likely determine the applicability of successful enzymatic inactivation. The lack of information regarding many viruses, including EV7, highlights the knowledge gaps surrounding human enteric viruses and their inactivation mechanisms. It is currently unknown if this inhibition would be reversible or if recovery of infection could occur if the activity of the enzymes is reduced. This inactivation can occur via damage to the viruses themselves, their receptors or by preventing binding to host cells, thus requiring greater investigation [129, 272, 410].
To accurately identify viral inactivation there is an increasing need for improved viral detection (directly or indirectly). By investigating inactivation mechanisms, enzymes involved and physicochemical characteristics, a more robust indirect viral detection method may be developed.
Further to this, the development of an intact cell culture enzymatic viral inactivation assay will enable researchers to distinguish specific enzymes capable of inactivating viruses and the mechanisms by which this may occur. Preliminary work (by the author, not published here) has involved the development of an in vivo viral culture assay to determine the effect of protease enzymes on virus inactivation. The potential enzymatic inactivation of pathogens 7.4 Future work 172 could occur via yet unknown processes and to different extents. These processes may not be unearthed until a significant development in scientific techniques has occurred. A significant limitation to the investigation of virus survival is the lack of culturable virus assays, such as an assay for Norovirus. Work focussing on viral enumeration will be vital for furthering this area of research.
Another significant issue identified in this study was the lack of consistent standards in regards to wastewater treatment, treatment design and monitoring regimes. Development of robust design, operation and maintenance guidelines including the effects of variation in the system and how to deal with uncertainties can only occur when the regulation and governance changes. For this to happen there needs to be a recognition that there are problems with current systems. Improving the monitoring standards may lead to the development of affordable virus detection to allow for direct monitoring in wastewater, which has not yet occurred due to lack of regulations driving the need. With increasing reuse of wastewater due to population growth and water scarcity, stricter regulations regarding wastewater treatment will be required. Regulations should take into account all variables likely to affect the resulting effluent, including location, population, treatment type and destination of waste. The irrigation of raw crops would require stricter regulations and lower microbial and viral values to minimise the risks associated with public health outbreaks. References
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[415] NIWA. Cliflo - national climate database. URL http://cliflo.niwa.co.nz/.
[416] www.metservice.com. MetService - Meteorological Service of New Zealand. URL www.metservice.com.
[417] Hladilek, M. D. et al. Microbial community structure of a freshwater system receiving wastewater effluent. Environmental Monitoring and Assessment 188, 1–10 (2016).
[418] Belila, A., Snoussi, M. & Hassan, A. Rapid qualitative characterization of bacterial community in eutrophicated wastewater stabilization plant by T-RFLP method based on 16S rRNA genes 28, 135–143 (2012). References 226
[419] Kaleli, H. A. & Islam, M. R. Effect of temperature on the growth of wastewater bacteria. Toxicological and Environmental Chemistry 59, 111–123 (1997).
[420] Watters, G. Z. The hydraulics of waste stabilization ponds. Report Paper 18, Utah Water Research Laboratory, Utah State University (1973).
[421] Coggins, L. X., Sounness, J., Zheng, L., Ghisalberti, M. & Ghadouani, A. Impact of hydrodynamic reconfiguration with baffles on treatment performance inwaste stabilisation ponds: A full-scale experiment. Water 10, 109 (2018).
[422] Li, M., Zhang, H., Lemckert, C., Roiko, A. & Stratton, H. On the hydrodynamics and treatment efficiency of waste stabilisation ponds: From a literature review to a strategic evaluation framework. Journal of Cleaner Production 183, 495–514 (2018).
[423] Johansson, H. E., Liljas, L. & Uhlenbeck, O. C. RNA recognition by the MS2 phage coat protein. Seminars in Virology 8, 176–185 (1997).
[424] Jain, R. & Srivastava, R. Metabolic investigation of host/pathogen interaction using MS2-infected Escherichia coli. BMC Systems Biology 3 (2009).
[425] Haramoto, E., Fujino, S. & Otagiri, M. Distinct behaviors of infectious F-specific RNA coliphage genogroups at a wastewater treatment plant. Science of the Total Environment 520, 32–38 (2015).
[426] Ogorzaly, L., Tissier, A., Bertrand, I., Maul, A. & Gantzer, C. Relationship between F-specific RNA phage genogroups, faecal pollution indicators and human adenoviruses in river water. Water Research 43, 1257–1264 (2009).
[427] Valegard, K., Liljas, L., Fridborg, K. & Unge, T. The three-dimensional structure of the bacterial virus MS2. Nature 345, 36–41 (1990). References 227
[428] Kovacs, E. W. et al. Dual-surface-modified bacteriophage MS2 as an ideal scaffold for a viral capsid-based drug delivery system. Bioconjugate Chemistry 18, 1140–1147 (2007).
[429] Withey, S., Cartmell, E., Avery, L. M. & Stephenson, T. Bacteriophages-potential for application in wastewater treatment processes. Science of the Total Environment 339, 1–18 (2005). Publication strategy
The following is the intended publication strategy for the work included in this thesis. The pieces of work within each chapter essentially form the basis of each paper.
Chapter 4
This succinct piece of work presenting the development of a hydraulically, biologically and biochemically robust laboratory-scale WSP.
• Submission to Journal of Applied Microbiology or Environmental Science and Technology
Chapter 5
More work on virus survival studies, including the development of an in vivo culture assay monitoring the effect of enzymes on virus survival.
• Submission to Viral Pollution of The Environment, Journal of Water and Health, ISME Journal References 229
Chapter 6
Further sequencing studies, including longitudinal and latitudinal survey of microbial communities in WSPs.
• Submission to PloS One, Environmental Microbiology, Science of the Total Environment
Mass spectrometric detection of intact proteins from environmental samples. This could be a technical note or method development paper with a small amount of work.
• Submission to Nature Communications; Microbial Informatics and Experimentation Achievements
Throughout the course of this PhD I have been awarded the following:
2017 KiwiNet Emerging Innovators Award
2014-2017 ESR Ph. D Vision Scholarship
2016 Canterbury Branch Federation of Graduate Women Travel Scholarship for International Conference attendance
2014 New Zealand Microbiological Society Grant-in-Aid for Conference Attendance
I have presented this work at the following conferences and scientific meetings:
2016 Joint New Zealand Microbiological Society and New Zealand Biochemistry and Molecular Biology Conference; Christchurch, New Zealand
2016 11th IWA Specialist Group Conference on Wastewater Pond Technologies; Leeds, United Kingdom.
2016 ‘Omics Symposium; Christchurch, New Zealand
2015 New Zealand Microbiological Society Conference; Rotorua, New Zealand
2014 Joint New Zealand Microbiological Society and New Zealand Biochemistry and Molecular Biology Conference; Wellington, New Zealand References 231
Additional to my PhD work I have also taken part in a range of other activities in order to develop my own and other students skills:
• Demonstrating Chemistry 1st and 2nd Year Laboratories
• Tutorial Help for First and Second Year students
• Science Outreach Programmes with primary school students
• Supervision of intern students
All of these achievements have aided my professional development and the ability to discuss my research with a wide range of audiences. My demonstrating work has hopefully encouraged younger students to consider biochemistry as a viable future. Appendix A
Pond design equations
A.1 Laboratory-scale design
Design equations are used in both industry and research to predict the behaviour of ponds within given parameters. Many of these equations have been developed to aid WSP construction and operation. Subsequent studies of existing ponds and models show that many stabilisation ponds have been found to perform poorly upon the variation of parameters. This inability to incorporate parameter variation makes them ineffectual in the real world as conditions will always be variable.
Various parameters are used to assess a ponds performance, with different ranges depending on pond type, load type, location and eventual destination of effluent. The most commonly used parameters when designing any waste stabilisation pond include determining the settling velocity, the maximum BOD loading and the ideal retention time ensuring sufficient time for treatment to occur.
Other parameters commonly factored into pond design include dispersion, the addition of baffles and the positioning of inlet and outlet pipes[311, 326, 327, 411–413]. Various studies have investigate the optimal arrangements for each of these. [109, 303, 310, 311, 327, 414]. A.1 Laboratory-scale design 233
Pond design guidelines and research have produced a range of common pond design values. Table 2.10 shows a compilation of these pond design values suggested for facultative waste stabilisation ponds. Facultative ponds are commonly designed to deal with BOD loading rates of 80-200 kg/ha/day (in ponds with a depth of 1.5 m) as the initial organic removal step, over an 8 - 30 day retention time.
Laboratory-scale design factors
The design factors to be considered in the development of this laboratory-scale WSP system originate from the full-scale WSP system that is being modelled as the basis for this laboratory-scale system design.
The wastewater treatment plant being modelled is located in the South Island, New Zealand and has an oceanic climate, with a mean daily atmospheric temperature of 11.5°C (5.9°C - 17°C).[415, 416] There are over 6,600 connections carrying sewage to the plant, with an average daily influent load of 5,6003 m /day. The flow rate changes throughout the dayas the treatment plant is gravity fed, meaning the flow of waste is unregulated and enters the plant dependent upon usage of the system and demand. The WWTP consists of four waste stabilisation ponds as seen in Figure A.1. Two of the four ponds (1A and 1B) run in parallel, followed by two ponds in series (2 and 3). The facultative ponds (1A and 1B) have a total combined volume of 128,800 m3. The flow of wastewater entering the pond system is equally split, after the grit screen, by a splitting chamber, to continuously load Ponds 1A and 1B with the same volume and flow of wastewater.
The theoretical hydraulic retention time (HRT) for a facultative pond (θ,(A.2)) is calculated with the pond area, pond depth, influent flow and effluent flow
θ = AD[1/2 (Qi + Qe)] (A.1) A.1 Laboratory-scale design 234
Figure A.1: Full-scale waste stabilisation pond system layout Schematic view of the layout of the full scale waste stabilisation pond treatment plant used in the design of the laboratory-scale system.
Or when seepage or evaporation is negligible [327]:
V θ = (A.2) Q
Where: A = pond area (m2); D = pond depth (m); Q = flow 3(m /day); V = pond volume (m3)
Using equation A.2 the HRT for the full scale plant used in this study can be calculated from the pond values, first by determining the pond volumes using the data from the 2002 design plans:
Pond 1A = 50,000 m2 x 1.3 m Pond 1B = 40,000 m2 x 1.5 m Pond 2 = 20,000 m2 x 1.7 m Pond 3 = 11,100 m2 x 0.8 m V = 170,100 m3 total volume Q = 5600 m3/day
3 = 170,100m θ 5600m3/day
θ = 30 days A.1 Laboratory-scale design 235
The total theoretical retention time for the whole system is 30 days, but practically this is dependent on hydraulic flow and the occurrence of short-circuiting. Ponds 1A and 1Bhavea theoretical retention time of 15-16 days each, Pond 2 has a retention time of >8 days before moving through to Pond 3. The theoretical retention time in Pond 3 is 3 days before the effluent is pumped to another treatment system for discharge 1. Using this equation and retention time, the pond volumes and retention time for the laboratory-scale system can be determined.
There is little data available concerning the microbiological species and behaviour in WSPs, and what is available has been collected for research purposes [417–419]. The information gained in these studies utilise the advances in science and sequencing capabilities to garner a glimpse of the microbial communities in a range of wastewater types [417, 418]. A large limitation to improving our understanding of WSPs is the lack of microbiological analysis, not only in the whole system but also at each pond treatment stage. The development of a laboratory-scale WSP system can be used to gain more information about the treatment occurring at each stage of the pond system, in order to improve understanding of overall wastewater treatment.
Operational considerations
The physical development of the laboratory WSP system is the initial limitation of the design and construction of an effective treatment system. The materials used need to be obtainable and cost-effective and construction needs to be reproducible.
Design Plan 1 included the use of cylindrical mesocosms used for previous experiments (Figure A.2a). The mesocosms already had sampling ports built in, as well as ports for pH and DO probes. However, this design was not thought to be suitable for effective representation of WSP performance, primarily due to the cylindrical design which may
1The effluent is added to the flow for a larger municipal system including a UV disinfection systemandan ocean outfall pipe. A.2 Design process 236 increase the possibility of short-circuiting. All existing mesocosm vessels were the same dimensions, thus the level of water in each was adjusted to achieve the 1/3 reduction in pond size from primary and secondary. Compounding this, the construction material of the mesocosms were a thick plastic, blocking out sunlight. The pond level in the second and third mesocosms would be low, and the high, solid walls of these ponds may provide shading from light, which wouldn’t occur in a natural system. This difference would introduce unnecessary uncertainty that could not be easily accounted for when applying the design to a real-world situation.
The second design, Figure A.2b, was planned with rectangular pond shapes with a very little overhang of pond walls to reduce shading as much as possible, especially when the pond size from primary to secondary is reduced by one third (1/3). This design was based on commercially available plastic containers.
A.2 Design process
The Stokes Law equation (2.3) was outlined in the Introduction. Settling of two-thirds of the total organic matter is the aim for the initial pond in the treatment process. Where the settling velocity determines the reduction of suspended solids, the BOD loading can be used to determine pond size for a specific load or the maximum BOD load that can be received by an existing pond. This measurement is in terms of the biological matter to be oxidised within the pond by the bacterial community.
The mean residence time for the wastewater in the pond system to achieve a certain level of treatment needs to be determined. If the flow rate through the pond system is too fast, the wastewater will not have enough time in the pond system for the necessary treatment to occur. However, if the flow rate is too low, too much sedimentation will occur, leading to increased sludge build up and costly desludging will likely be required more often. Hence, A.2 Design process 237 (c) Design Plan 3 (b) Design Plan 2 Figure A.2: Design concept drawings (a) Design Plan 1 A.3 Design results 238 the retention time needed for sufficient treatment of wastewater should be determined inthe planning stages, so the correct pond size can be selected for the ideal flow rate.
The literature dictates that WSPs receive influent at mid-depth or below, where a pond depth is 1.5 m, the influent pipe enters at 1 m below the pond surface [87, 93, 327]. By releasing the influent at mid-depth, the likelihood of the waste moving across the surface of thepond to the outlet is decreased, reducing short-circuiting within the pond. If the inlet is too low in the pond, it can cause a build up of sludge and localised organic matter overload [93, 420]. Inlet jetting can have a major driving force within the pond, where, in the absence of wind, the inlet drives the pond hydraulics [109]. Other aspects considered in pond design have been discussed in the literature [20, 87, 211, 308].
The large size of the ponds and keeping the influent velocity low can help to prevent short-circuiting. Currently, the average and maximum inlet velocities at this plant are 40 L/second and 180 L/second respectively. Some short-circuiting is still likely to occur in the full-scale system as Ponds 1A, 1B and 2 have no baffles present to direct the wastewater flow.
Resource consents commonly only require effluent monitoring before the effluent is discharged from the WWTP. When a WSP system includes a series of ponds (Facultative, Maturation and Polishing Ponds) there is no regulatory requirement to monitor the microbiological aspects at each stage of the system.
A.3 Design results
Using the full-scale system described previously, a laboratory-scale system was designed and the results are shown here. Laboratory-scale pond names and types are shown in Table A.1. The laboratory-scale system included a combined initial pond (A), equivalent to both Ponds 1A and 1B in the full-scale WWTP. The secondary pond of the full-scale system is deeper than the primary and tertiary ponds, however, this is not reflected in the laboratory-scale design. A.3 Design results 239
Table A.1: Comparison of laboratory-scale pond names and depths to those of the full-scale system
Model Scale Full Scale Pond Depth Pond Depth Pond Type Pond M Pond F Ponds 1A Anaerobic Pond A 200 mm 1.4 m and 1B Facultative Facultative Pond B 100 mm Pond 2 1.7 m Maturation Maturation Pond C 100 mm Pond 3 0.8 m Polishing
Model or laboratory-scale pond depth (Pond DepthM) compared to the full scale pond depth (Pond DepthF) and their relative pond types. Note the pond depth for ponds 1A and 1B are an average of the two individual depths.
Table A.2: Laboratory-scale pond dimensions
Pond A Pond B Pond C Length 410 mm 420 mm 350 mm Width 300 mm 265 mm 235 mm Depth 200 mm 100 mm 100 mm Total volume 30 L 19 L 9 L Working volume 24.6 L 11.13 L 8.2 L Baffle length 200 mm 177 mm 147 mm Baffle height 200 mm 100 mm 100 mm Freeboard 50 mm 75 mm 10 mm Working measurements for final pond and baffle dimensions for the three ponds of the laboratory-scale WSP system.
Common pond design indicates the first pond should be deeper than the following ponds, but depending on the individual case this may change.
The dimension of the containers or “ponds” are shown in Table A.2. The scaling factors can be determined using the depth of a laboratory-scale pond, combined with the full scale system pond depth and areas. Pond A is designed to have a depth of 200 mm, while Ponds B and C have depths of 100 mm to aid the 2/3 reduction in pond size without changing the pond surface area greatly.
Using equation A.5, the fluid depth scaling factor (SL) can be determined using the pond depths of the full scale system and planned laboratory-scale pond depths. A.3 Design results 240
Table A.3: Scaling factors
Pond A Pond B Pond C Full Scale retention time 30 days 8 days 3 days Scaling factor 2.65 4.12 2.8 Resulting reduced retention time 11.3 days 1.9 days 1 days Full Scale flow rate 180 L/s 180 L/s 180 L/s Scaling factor 129.6 1191.6 181 Resulting flow rate 1.38 L/s 0.15 L/s 0.99 L/s Calculated scaling factors for each pond of the laboratory-scale WSP system.
1.4m For Pond A, SL = 0.2m resulting in a scaling factor of SL = 7 1.7m For Pond B, SL = 0.1m resulting in a scaling factor of SL = 17 0.8m For Pond C, SL = 0.1m resulting in a scaling factor of SL = 8
The resulting scaling factors can be used to determine the scaling factors for volume, area, flow rate and retention time as shown in Table A.3[307, 310, 336]. The resulting flow rate for the laboratory-scale ponds differ from one another as they use the scaling factors that result from the pond depth analysis. The flow rate for Pond A, 1.38 L/s, was used as the baseflow rate for all three laboratory-scale ponds, as the flow was controlled by a single motor with multiple tube channels, allowing pumping of wastewater through all ponds to be at the same rate.
Using the equation for the flow rate scaling factor, equation A.9, input of the relevant depth scaling factor can result in a scaling factor for determining the ideal flow rate for each pond in
2.5 the system. Where SQ = SL then SQPondA= 129.6; SQ PondB= 1191.6 and SQ PondC= 181.
Similarly, the equation for retention time scaling, equation A.10, is used with the input of relevant depth scaling factors.
0.5 Where ST = SL then ST PondA= 2.65; ST PondB= 4.12 and ST PondC= 2.8.
With the physical parameters determined for each pond, the baffle configurations can be investigated, as well as the positioning of inlet and outlet pipes. A.3 Design results 241
Olukanni and Ducoste (2011) preformed CFD modelling studies to investigate the optimal baffle, inlet and outlet pipe configurations [20]. The ideal baffle configuration suggested isa two baffle system, 2/3 of the pond width, evenly spaced along the length of the pond [20].
It is known that wind may provide enough shear force on the surface of the pond to influence the hydraulics of the pond system, but this has not been taken into account for this laboratory-scale study, as it has been conducted inside with minimal wind. Additionally, when baffles are present, the effect of wind driving is minimal compared to theeffecton hydraulic flow of the baffle driving force.
Once constructed, all ponds were filled with wastewater from the corresponding ponds from the full scale WSP system. Influent was then introduced by pump inflow. Using the monitoring guidelines discussed previously, the performance of the laboratory-scale WSP system was determined for each experimental run. Runs were developments of methods, building on each previous run to determine optimal running conditions for the study.
Set up
An initial test of the laboratory-scale waste stabilisation pond system was run to identify any hydrological flow issues or identify if any leaks were present in the ponds. All pondswere filled to working volumes with water and as no leaks were identified, a stock solutionof Rhodamine dye was introduced to the system to visualise the flow through the pond system. The rhodamine was pumped into the pond using the same pump configuration as what is used in all further experiments, to help identify the effect of inlet jetting. As seen in Figure A.3, the baffles encouraged flow through the system without short circuiting or dead zones highlighted by the presence of the pink rhodamine. The rhodamine leaked under the first baffle identifying the need for it to be resealed to prevent wastewater from moving under the baffles and reducing the retention time in the system. A.4 Scaling equations 242
(a) (b) (c)
Figure A.3: Rhodamine tracer test Rhodamine dye flow through laboratory-scale pond system to test hydraulic properties ofpond.
A.4 Scaling equations
As identified in Section 3.1, a large range of pond design equations exist. These can beapplied in the design of a miniaturised pond system, or for a full scale pond system. Where full scale pond system is the basis of design, model scale systems can be determined by using scaling equations such as the Froude Number and the Reynolds Number.
The Froude Number
Many mathematical equations have been used in the empirical modelling of pond design and function. One equation commonly used, when analysing model pond designs for scaling to or from full scale ponds, is the Froude equation. The Froude number (Fr) is a relationship of the inertial forces and gravitational forces of a fluid and is important in fluid flows where free surface area is present (not enclosed flow) [104, 109]. The Froude number can be determined as follows:
υ Fr = √ (A.3) gy
Where: A.4 Scaling equations 243
υ = the velocity (m/s); g = gravity (m/s2); y= depth of fluid (m)
When this equation is used for a scaling relationship Frm (Model Froude) is equal to Fr f (Full scale Froude), which can be shown as:
υm υ f √ = √ (A.4) ym y f
ym We know that y= Depth, so is the scaling factor for “Depth” or length, (SL) where: y f
y f SL = (A.5) ym and the scaling factor for velocity (SV ) is
υ f SV = (A.6) υm then
2 SL = SV (A.7)
The pond “length” or depth is generally standard around 1.5 m, so this could be used as a starting point to input the equations.
To calculate the scaling factors for pond flowrate, the scaling factor needs to be replaced with the equation for flowrate (A.8):
Q = Aυ (A.8) A.4 Scaling equations 244 where;
2.5 SQ = SL (A.9)
Time (T) is also scaled and can be calculated using velocity as its units are Length and Time;
SL SV = ST rearranging to solve for time;
0.5 ST = SL (A.10)
A larger Froude number indicates a more significant role of inertia compared to that of gravitational force. A lower Froude number suggests gravitational force outperforms inertia, where the critical balance of gravitational and inertial forces is a Froude value of 1. When Froude values exceed 1, the fluid has a high velocity flow and is called supercritical flow while below 1 the fluid has subcritical flow, characterised by slow moving fluid. The balance of gravitational and interial forces is the critical flow desired for WSPs, as this will result ina flow rate that maintains particle sedimentation and allows ideal retention time tobemet.
The Reynolds Number
Where the Froude number indicates the relationship between gravitational and inertial forces, the Reynolds number is a measure of inertial forces compared to viscous forces.
Reynolds Number (Re) can be shown as:
Vy Re = ν (A.11) A.4 Scaling equations 245 where:
V = velocity (m/s); y = characteristic length (m);
ν = kinematic viscosity (m2/d)
A Reynolds number > 2000 is indicative of laminar flow, while an Reynolds number < 2000 suggests Turbulent flow of the fluid. Laminar flow behaviour is dominated by viscous forces, while inertial forces take charge in Turbulent flow systems. A balance of viscous and laminar flows is ideal for WSPs. Too much turbulence causes resuspension of sediment, whilefully laminar flow conditions will result in short-circuiting and subsequent reduction in treatment.
If using the Reynolds number to assess a design, it can be used in two ways, for the investigation of inlet pipes or of the whole pond. When determining the Reynolds number for inlet pipes, the characteristic length (y) used is the diameter of the inlet pipe. However, for pond investigation, the characteristic length used to determine the Reynolds number is instead the pond depth, or the hydraulic mean depth which also takes in to account the actual pond depth.
Use of scaling equations
When using these equations (Fr and Re) to account for the suitability of model designs, both numbers cannot be used in the scaling of one model, unless the kinematic velocity is changed which is unlikely to occur in the real world. It is, therefore, up to the designer to decide if the Froude number or the Reynolds number is more applicable to a certain setting with some studies investigating the effect these calculations have on overall success of pond modelling systems [109]. Many authors have suggested that for WSP design specifically, the Froude number is more applicable, however Reynolds number always needs to be kept in mind to ensure the flow in both model and full scale systems is turbulent and to define if inletdriving A.5 Settling velocity 246 force is laminar or turbulent, changing how the pond flow occurs [109]. If the Reynolds number is sufficiently small in laboratory-scale systems, laminar flow can dominate, resulting in short-circuiting of the pond system. The pathogens present in the wastewater are transported through the pond before sufficient inactivation can occur.
A.5 Settling velocity
The Stokes Law equation (2.3) was outlined in the Introduction. The settling velocity as discussed, is used to identify the flow rate range needed to achieve optimal particle settling. The optimal amount of settling can be determined by the organic loading expected, using the BOD equation (A.12) or by the pond size available. Settling of two thirds of the total organic matter is the aim for the primary pond in the treatment process. Settling velocity can also be affected by wind force and convective cooling [109]. While wind is a significant factor in full-scale systems, only one early study was found to have specifically investigated the role of wind on pond hydraulics [420]. A recent study by Coggins et al. (2018) used modelling to determine that wind will likely have a significant effect on pond hydrodynamics when the pond is un-baffled. For baffled ponds, the modelling suggested the wind had little effect on the hydrodynamics [421]. The evidence in the literature regarding the effect of wind is contradictory. Some researchers suggest the effect of wind to be a positive force, by increasing pond aeration and mixing. Alternatively, some researchers concluded that the effect of wind was a negative force by affecting the pond hydrodynamics and leading to short-circuiting [422]. Shilton (2001) found that flow patterns were ultimately dependent upon which forceis stronger, wind or inlet momentum [109, 422]. For the laboratory-scale system wind was not included as a variable as the development of a wind source and quantitation of the wind speed was outside the scope of this work. Additionally, as the evidence suggests, the role of wind is very specific to each system and could be negated by a stronger inlet momentum. A.6 BOD loading 247
A.6 BOD loading
Where the settling velocity determines the reduction of suspended solids, the BOD loading can be used to determine pond size for a specific load or the maximum BOD load that can be received by an existing pond. This measurement is in terms of the biological matter to be oxidised within the pond by the bacterial community.
BOD(mg/L)×In f luent Flow BOD5 Loading = Pond Area (A.12)
Anaerobic ponds are designed based on volumetric BOD loading, while facultative ponds are designed based on surface BOD loading as seen in Equation A.13[211].
T−25 λs = 350(1.107 − 0.002T) (A.13)
Where T = mean air temperature of the coldest month in °C.
Common design values of 84 kg/ha/day for facultative ponds have been suggested by many authors [211]. This is especially important in New Zealand compared to sub-tropical and tropical climates as the colder mean air temperature (T ≤ 8°C) has an associated minimum design value of 80 kg/ha/day from which the suggested pond area can be calculated using Equation A.14[211].
A f = 10LiQ/λs (A.14)
Where Li is the influent BOD in mg/L [211]. A.7 Retention time 248
A.7 Retention time
Theoretical retention time, flow rate and pond size
PondVolume HDT (θ) = In f luent Flow
Facultative pond retention time can be calculated using Equation A.15 where the standard pond depth is 1.5 m and seepage is negligible [211].
θ f = A f D f / 2Qi − 0.001eA f (A.15)
Where D f = facultative pond depth (m);
3 Qi = Influent flow (m /day); e = net evaporation (mm/day).
The mean residence time for the wastewater in the pond system to achieve a certain level of treatment needs to be determined. If the flow rate through the pond system is too fast, the wastewater will not have enough time in the pond system for the necessary treatment to occur. However if the flow rate is too low, too much sedimentation will occur, leading to increased sludge build up and costly desludging will likely be required more often. Hence, the retention time needed for sufficient treatment of wastewater should be determined in the planning stages, so the correct pond size can be selected for the ideal flow rate. Appendix B
Viral structure information
B.1 Echoviruses
Echoviruses are members of the Enterovirus B genus. Echo stands for Enteric Cytopathic Human Orphan. Structure is shown in Figure B.1.
Figure B.1: Atomic structure of Echovirus 7 with an inset of the penton protein the virus particle is comprised of Echovirus 7 capsid with a zoomed view of a singular penton protein with ’Puff’ and ’Knob’ regions highlighted. Figure adapted from Plevka et al. 2010 [249].
Echovirus receptors and binding sites
Binding to CD55/DAF is also seen for Echovirus 7. Binding to these receptors has been linked to common motif regions in the VP2 and VP3 proteins in contact with DAF. The “Puff” region B.2 MS2 250 on VP2 is 51 residues long (residues 129-180), while the “Knob” region is located on VP3 at residues 58 to 69 (11 residues in length) [249]. The structure shown in Figure B.2 visualises the binding of CD55/DAF to the Echovirus capsid.
Figure B.2: Atomic structure of Echovirus with bound DAF/CD55 protein which is also shown in the inset The icosahedral structure of Echovirus as seen in Figure B.1 but with additions on the surface of the viral capsid. These proteins are the DAF/CD55 receptors to highlight how binding to cell surface receptors would occur. Inset of the DAF/CD55 protein unit with binding sites for EV7 highlighted in pink. Figure adapted from Plevka et al. 2010 [249].
B.2 MS2
MS2 is a lytic RNA bacteriophage or a virus that infects bacteria. MS2 specifically infects male Escherichia coli [287, 423, 424]. MS2 is a member of Group I of four genogroups of bacteriophages (GI, GII, GIII, GIV) and is from the family Leviviridae [425]. Leviviridae is split into two genera, Levivirus and Allolevivirus.
Levivirus contains genogroups I and II, where Allolevivirus contains genogroups III and IV. The F-RNA phages of genogroups II and III are commonly found in human sewage, while F-RNA phage in genogroups I and IV are found in animal wastes [426]. GI F-RNA phage can survive for long periods in river water, seawater and treated sewage compareed to the other genogroups. The GIII F-RNA are inactivated more quickly by heat-treatment than GI F-RNA phage, indicating potential that GI is more environmentally persistent than other genogroups [425]. B.2 MS2 251
MS2 contains positive sense, single-strand of RNA (3569 nts long) in a T=3 icosahedral capsid, with 27 nm capsid made up of 180 copies of a 13.7 kDa subunit. The virion also contains one copy of the A-protein, a maturation protein at 44 kDa [427]. The capsid of MS2 also contains 32 pores at 1.8 nm in diameter. MS2 is harmless to humans but has many features corresponding to eukaryotic-infecting viruses. Upon infection of E. coli by MS2, an up-regulation of amino acid biosynthesis in the host is seen, resulting in viral protein production [424].
The capsid of MS2 also has reactive amines [427, 428]. T=3 structures have two dimer arrangements, C/C dimers and A/B dimers. C/C dimers are located at the two-fold axis of symmetry and the A/B dimers are at a quasi-two-fold axis and are superimposed on the C/C dimers. The coat protein (the 13.7 kDa subunit) protects the viral RNA and also acts as a translational repressor of the phage-encoded replicase gene. Phage adsorption and entry are mediated by specific receptors, being carbohydrates, proteins and lipopolysaccharides onthe host cells surface [429].
Infection of male E. coli occurs by injection of RNA and A-protein. The cell surface host site for MS2 is the sex pilus (or fertility (F) fimbriae) of E. coli bacteria, coded by the F-plasmid of E. coli (e.g. E. coli K-12) [52].
MS2 Phage coat protein binds to the RNA hairpin at a unique site on the phage genome. Binding results in the repression of translation of the phage-encoded subunit of replicase. This competitive inhibitor blocks ribosomal entry. This RNA-Protein interaction also identifies phage RNA for assembly into virus particles once the phage coat protein increases in concentration to sufficient levels [423].
Jain and Srivastava (2009) separate the infection process into three stages. The early transient period is the initiation of infection. The middle steady state period occurs when viral protein synthesis has displaced the host protein synthesis. The final period, the late transient stage is when all biosynthesis has been impaired and lysis is approaching [424]. Appendix C
Metagenomic and Metaproteomic analysis 253
Figure C.1: Genetic sequencing estimation of pathogenic reduction through ponds of a laboratory-scale WSP
Table C.1: MG RAST analysis data Process Measurement Value bp count 2,039,846,041.00 Sequence count 20,160,754.00 Upload Mean sequence length 101.00 ± 4 Mean GC percent 54 ± 13% Artificial Duplicate Reads Sequence count 1,227,157.00 bp count 1,860,825,445.00 Sequence count 18,532,691.00 Post QC Mean sequence length 100.00 ± 5 Mean GC percent 54 ± 13% Predicted protein features 10,434,445.00 Processed Predicted rRNA features 26,875.00 Identified protein features 4,269,566.00 Alignment Identified rRNA features 13,363.00 Annotation Identified functional categories 3,192,166.00 254 Figure C.2: Protocol for SigmaProt Intact Protein Standard 255
Table C.2: Wastewater project data supplied to MG-RAST Parameter Investigation Type WGS Study Name wastewater_enzymes Latitude and Longitude -43 173 Country/Location New Zealand Collection Date 04-02-2016 Environment (Biome) aquatic environment Environment (Feature) microbial feature Environment (Material) wastewater Environmental Package wastewater|sludge Sequencing Method illumina
Table C.3: Mascot search parameters
Search parameters Type of search MS/MS Ion Search Error tolerant All significant protein hits Enzyme NoCleave Mass values Average Protein Mass Unrestricted Peptide Mass tolerance ± 5 Da Fragment Mass tolerance ± 5 Da Max missed cleavages 1 Instrument type ESI-QUAD-TOF Number of queries 301 Database 1 NCBIprot 20171024 Database 2 SwissProt 2017_11 Appendix D
Statistical Results
Figure D.1: Multiple comparison analysis of one-way ANOVA for pH and DO variation 257 Figure D.2: Tukey’s values