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PATHOGEN REMOVAL THROUGH BIOLOGICAL FILTRATION
AND
QUANTITATIVE MICROBIAL RISK ASSESSMENTS
FOR DRINKING WATER PURIFICATION
BY
JOSHUA GORDON ELLIOTT
A THESIS SUBMITTED IN CONFORMITY WITH THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE
DEPARTMENT OF CIVIL ENGINEERING UNIVERSITY OF TORONTO
© COPYRIGHT BY JOSHUA GORDON ELLIOTT 2015
ii
PATHOGEN REMOVAL THROUGH BIOLOGICAL FILTRATION AND QUANTITATIVE MICROBIAL RISK ASSESSMENTS FOR DRINKING WATER PURIFICATION
Joshua Elliott Master of Applied Science, 2015 Graduate Department of Civil Engineering University of Toronto
ABSTRACTBiological filtration is a novel concept for drinking water purification that allows for the
colonization of rapid granular filters with native bacterial organisms in order to reduce organic
compounds in the final treated effluent. There is little published material on the efficacy of these
filters for the removal of pathogens, specifically protozoa such as cryptosporidium and giardia
which are difficult to inactivate using chlorine disinfection. This study utilizes aerobic
endospores as a surrogate for cryptosporidium to characterize the removal performance of
biologically active filters. Biological filtration was shown to achieve < 1-log10 removal of
aerobic spores while conventional filters achieved > 3-log10 removal. In addition, Quantitative
Microbial Risk Assessments were conducted for 10 Canadian drinking water utilities. Several of
these risk assessments were based upon filter performance estimates derived from aerobic spore
removal results. Most utilities are below the 10-6 DALY pp/yr risk threshold established by the
World Health Organization.
iii
ACKNOWLEDGEMENTSThis research was supported by the Natural Sciences and Engineering Research Council of
Canada (NSERC) Chair in Drinking Water Research. Exceptional gratitude is extended to my
academic and research supervisor, Dr Robert Andrews, who has provided ample opportunity to
explore and excel in the field of drinking water treatment.
Many thanks to the City of Ottawa's Drinking Water Services staff for patience and support
as I completed this academic journey. Special thanks to Ian Douglas, Stephanie MacFayden and
Teresa Brooks for continued discussions and development regarding the Health Canada QMRA
model. I would like to also express sincere appreciation to David Scott (City of Toronto), and
John Armour (Peterborough Utilities) for allowing access to their excellent pilot and research
facilities. Additional thanks for the collaborators on the WRF Tailored Collaborative Project
despite adjustments in the project scope and schedule. This includes the participating utilities
(Waterloo, Vancouver, Toronto, Ottawa).
I also appreciate the participation and contributions of all of the NSERC chair utility partners
involved in the QMRA project including the City of Toronto, Elgin Area and Huron Water
Systems, City of Barrie, City of Peterborough, Region of York, Region of Durham.
Special thanks to all of my supportive family, friends and colleagues, both in Ottawa and
Toronto, who have endured my extended academic pursuits.
iv
TABLEOFCONTENTSAbstract ........................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................ vii
Table of Figures ............................................................................................................................. ix
Nomenclature ................................................................................................................................. xi
1. Introduction ............................................................................................................................. 1
1.1. Research Objectives ......................................................................................................... 1
1.2. Summary of Chapters ....................................................................................................... 1
2. Literature Review.................................................................................................................... 3
2.1. Introduction ...................................................................................................................... 3
2.2. Surrogates for Cryptosporidium ....................................................................................... 5
2.3. Conventional Filtration for Particle Removal .................................................................. 8
2.4. Factors Affecting Particle Removal in Conventional Filtration ....................................... 9
2.5. Biological Filtration for Removal of Dissolved Organics ............................................. 10
2.6. Factors affecting DOC removal in biological filters ...................................................... 12
2.7. Biological filtration for pathogen removal ..................................................................... 13
2.8. Enhanced Biological Filtration ....................................................................................... 16
2.9. Spore Enumeration Methods .......................................................................................... 18
2.10. Research Gaps ............................................................................................................ 19
3. Methods and Materials .......................................................................................................... 21
3.1. Measured Variables ........................................................................................................ 21
3.2. Biological indicators ...................................................................................................... 22
3.3. Methods .......................................................................................................................... 24
3.3.1. Bacillus Atrophaeus Growth and Culture Methods ................................................ 24
3.3.2. Bacillus Atrophaeus Growth Procedure .................................................................. 25
3.3.3. Bacillus Atrophaeus Enumeration Procedure ......................................................... 28
4. Aerobic Spore Removal Through Granular Filters and Applications to Quantitative Microbial Risk Assessments ......................................................................................................... 31
4.1. Abstract .......................................................................................................................... 31
4.2. Keywords ....................................................................................................................... 31
4.3. Introduction .................................................................................................................... 31
v
4.4. Materials and Methods ................................................................................................... 34
4.4.1. Pilot Plant Characteristics ....................................................................................... 34
4.4.2. Spore Preparation .................................................................................................... 35
4.4.3. Sampling Protocol ................................................................................................... 35
4.4.4. Water Quality Parameters ....................................................................................... 36
4.4.5. Risk Analysis & Statistical Methodology ............................................................... 37
4.5. Results ............................................................................................................................ 38
4.5.1. Aerobic Spore Removal .......................................................................................... 38
4.5.2. QMRA Results ........................................................................................................ 46
4.6. Conclusions .................................................................................................................... 49
5. Quantitative Microbial Risk Assessments for 10 Canadian Water Utilities ......................... 51
5.1. Abstract .......................................................................................................................... 51
5.2. Keywords ....................................................................................................................... 51
5.3. Introduction .................................................................................................................... 51
5.4. Methods .......................................................................................................................... 53
5.4.1. Raw Water Pathogens ............................................................................................. 54
5.4.2. Process Assessments ............................................................................................... 58
5.5. QMRA Analysis ............................................................................................................. 61
5.6. Considering Non-Detects ............................................................................................... 71
5.7. Conclusions .................................................................................................................... 74
6. Overall Conclusions .............................................................................................................. 75
7. References ............................................................................................................................. 76
8. Appendix ............................................................................................................................... 86
8.1. Pilot Plant Configurations for Aerobic Spore Trials ...................................................... 86
8.2. Plant A Spore Challenge Studies ................................................................................... 89
8.3. Plant B Spore Challenge Studies .................................................................................... 92
8.4. Plant C Spore Challenge Studies .................................................................................... 95
8.5. QMRA Data ................................................................................................................... 97
8.6. Plant A ............................................................................................................................ 97
8.7. Plant B .......................................................................................................................... 102
8.8. Plant C .......................................................................................................................... 106
8.9. Plant D .......................................................................................................................... 110
vi
8.10. Plant E....................................................................................................................... 112
8.11. Plant F ....................................................................................................................... 117
8.12. Plant G ...................................................................................................................... 122
8.13. Plant H ...................................................................................................................... 126
8.14. Plant I ........................................................................................................................ 131
8.15. Plant J ....................................................................................................................... 133
8.16. Matrix Recovery Results .......................................................................................... 135
vii
LISTOFTABLES
Table 4-1: Source water characteristics for the three pilot plants. ................................................ 34
Table 4-2: Analysis of Variance (ANOVA) results for pilot scale biofilters at Plant B and C. ... 45
Table 4-3: Summary of observed aerobic spore removals and the related literature values for other pathogens included in the Health Canada risk model. ......................................................... 48
Table 4-4: Annual average disinfection conditions for the 3 plants investigated. ........................ 49
Table 5-1: Dose response models and parameters for five pathogens in the Health Canada model........................................................................................................................................................ 53
Table 5-2: Illness risks given infection for the five reference pathogens. .................................... 54
Table 5-3: Summary of DALY risk values for the 5 pathogens in the Health Canada model. ... 54
Table 5-4: Summary of raw water pathogen results for the 5 pathogens of interest .................... 55
Table 5-5: Pathogen monitoring data for all plants, MDL's are corrected for recovery. .............. 57
Table 5-6: Summary of treatment processes implemented by the various utilities. ..................... 59
Table 5-7: Log removal credits granted by the Health Canada QMRA model based on the data collected by KWR. ........................................................................................................................ 60
Table 5-8: Comparing alternative methods for calculating the mean cryptosporidium concentration at all 10 plants. ....................................................................................................... 73
Table 5-9: Comparing alternate methods for calculating virus concentrations. ........................... 73
Table 8-1: A comparison between raw and settled water aerobic spore counts during spiking. .. 89
Table 8-2: A comparison between settled water aerobic spore counts and filter effluent counts for the alum treatment train. ............................................................................................................... 90
Table 8-3: A comparison between settled water aerobic spore counts and filter effluent counts for the ferric treatment train. ............................................................................................................... 91
Table 8-4: Spore counts at the Plant B pilot. ................................................................................ 92
Table 8-5: Spore counts for both conventional filters at Plant B. ................................................. 93
Table 8-6: Spore counts for biological filters at Plant B. ............................................................. 93
Table 8-7: Spore counts for biological filters at Plant B. ............................................................. 94
Table 8-8: Spore counts for biological filters at Plant B. ............................................................. 94
Table 8-9: Spore counts at the Plant C pilot location. .................................................................. 95
Table 8-10: Spore counts at the Plant C pilot location. ................................................................ 95
Table 8-11: Spore counts at the Plant C pilot location. ................................................................ 96
Table 8-12: Results of pathogen monitoring for the 12 month sampling period at Plant A. ........ 97
Table 8-13: Monthly risk results at Plant A based on pathogen monitoring data and monthly averages for process effectiveness. ............................................................................................... 98
viii
Table 8-14: Pathogen Log-Inactivation by Chlorine Disinfection at the Plant A. ....................... 99
Table 8-15: Results of pathogen monitoring for Plant B for the first 12 month sampling period...................................................................................................................................................... 102
Table 8-16: Monthly risk results for Plant B based on pathogen monitoring data and monthly averages for process effectiveness. ............................................................................................. 103
Table 8-17: Pathogen Log-Inactivation for Plant B by Chlorine Disinfection .......................... 103
Table 8-18: Results of pathogen monitoring at Plant C. Values are reported as #/100 L. ......... 106
Table 8-19: Monthly risk results for Plant C based on pathogen monitoring data and monthly averages for process effectiveness. ............................................................................................. 107
Table 8-20: Pathogen Log-Inactivation by Chlorine Disinfection at Plant C. ........................... 107
Table 8-21: Results of pathogen monitoring for Plant E. ........................................................... 112
Table 8-22: Monthly risk results at Plant E based on pathogen monitoring data and monthly averages for process effectiveness. ............................................................................................. 113
Table 8-23: Pathogen Log-Inactivation by Chlorine Disinfection at Plant E. ........................... 114
Table 8-24: Results of pathogen monitoring for Plant F. ........................................................... 117
Table 8-25: Monthly risk results at Plant F based on pathogen monitoring data and monthly averages for process effectiveness. ............................................................................................. 118
Table 8-26: Pathogen Log-Inactivation by Chlorine Disinfection at Plant F. ............................ 119
Table 8-27: Results of pathogen monitoring at Plant G. ............................................................ 122
Table 8-28: Monthly risk results at Plant G based on pathogen monitoring data and monthly averages for process effectiveness. ............................................................................................. 123
Table 8-29: Pathogen Log-Inactivation by Chlorine Disinfection at Plant G. .......................... 123
Table 8-30: Results of pathogen monitoring at Plant H. ............................................................ 126
Table 8-31: Monthly risk results for Plant H based on pathogen monitoring data and monthly averages for process effectiveness. ............................................................................................. 127
Table 8-32: Pathogen Log-Inactivation by Chlorine Disinfection at Plant H. .......................... 128
Table 8-33: Results of pathogen monitoring for Plant I. ............................................................ 131
Table 8-34: Monthly risk results for Plant I based on pathogen monitoring data and monthly averages for process effectiveness. ............................................................................................. 131
Table 8-35: Pathogen Log-Inactivation by Chlorine Disinfection for Plant I. .......................... 132
Table 8-36: Protozoa recovery results for all 10 plants. ............................................................. 135
Table 8-37: Virus recovery results for all 10 plants. .................................................................. 136
Table 8-38: Bacteria recovery results for all 10 plants. .............................................................. 137
ix
TABLEOFFIGURESFigure 2-1: A sample of published studies comparing the removal of aerobic spores, microspheres and cryptosporidium oocysts in biologically active granular media filters. ........... 15
Figure 4-1: Turbidity profile during typical filter run for Plant A showing stable filter operation following ripening spike. .............................................................................................................. 36
Figure 4-2: Aerobic spore removal for a range of coagulation and filtration conditions. ............ 39
Figure 4-3: Aerobic spore removal vs filter effluent turbidity for all filter trials. ........................ 40
Figure 4-4: ATP concentrations for Plant B. ................................................................................ 42
Figure 4-5: ATP concentrations for Plant C. ................................................................................ 42
Figure 4-6: Relationship between aerobic spore removal and biofilm proteins for plants B and C........................................................................................................................................................ 45
Figure 4-7: Comparison of QMRA results for all 3 plants assuming average experimental physical removal and annual averages for disinfection conditions. ............................................. 49
Figure 5-1: QMRA risk estimates for (a) cryptosporidium, (b) giardia, (c) enteric viruses. ........ 62
Figure 5-2: QMRA risk outputs for (a) Plant A, (b) Plant C, (c) Plant G..................................... 63
Figure 5-3: Normalized probability distribution for the annual DALY risk at Plant C. ............... 64
Figure 5-4: Surface plots of microbial risk for Plant D as a function of increasing pathogen concentrations and decreasing chlorine residual for primary disinfection. .................................. 66
Figure 5-5: Surface plots of microbial risk for Plant D as a function of increasing pathogen concentrations and decreasing chlorine residual for primary disinfection. .................................. 67
Figure 5-6: Surface plots of microbial risk for Plant B as a function of varying chlorine disinfection and UV fluence configurations. ................................................................................ 69
Figure 5-7: Surface plots of microbial risk for Plant D as a function of varying chlorine disinfection and filter effluent turbidity. ....................................................................................... 70
Figure 8-1: Process flow diagram for Plant A showing the sampling locations for aerobic spores........................................................................................................................................................ 86
Figure 8-2: Process flow diagram for Plant B showing the sampling locations for aerobic spores........................................................................................................................................................ 87
Figure 8-3: Process flow diagram for Plant C showing the sampling locations for aerobic spores........................................................................................................................................................ 88
Figure 8-4: Monthly chlorine residual values for the clearwell at Plant A. ................................ 100
Figure 8-5: Monthly pH values for disinfection calculations at Plant A. ................................... 100
Figure 8-6: Monthly temperature values based on raw water measurements at Plant A. ........... 101
Figure 8-7: Monthly flowrate for the clearwell at Plant A. ........................................................ 101
Figure 8-8: Monthly chlorine residual values for the clearwell at Plant B. ................................ 104
Figure 8-9: Monthly pH values for disinfection calculations at Plant B. ................................... 104
x
Figure 8-10: Monthly temperature values based on raw water measurements at Plant B. ......... 105
Figure 8-11: Monthly flowrate through the clearwell at Plant B. ............................................... 105
Figure 8-12: Monthly chlorine residual values measured at Plant C. ......................................... 108
Figure 8-13: Monthly pH values for disinfection calculations at Plant C. ................................. 108
Figure 8-14: Monthly temperature values based on raw water measurements at Plant C. ......... 109
Figure 8-15: Monthly flowrate data for the Plant C. .................................................................. 109
Figure 8-16: Monthly chlorine residual values for chlorine disinfection for Plant D. ............... 110
Figure 8-17: Monthly pH values for chlorine disinfection at Plant D. ....................................... 110
Figure 8-18: Monthly temperature values for chlorine disinfection at Plant D. ......................... 111
Figure 8-19: Monthly flowrate for chlorine disinfection for Plant D. ........................................ 111
Figure 8-20: Monthly chlorine residual values for the chlorine contact tank at Plant E. ........... 115
Figure 8-21: Monthly pH values for disinfection calculations at Plant E. .................................. 115
Figure 8-22: Monthly temperature values based on raw water measurements at Plant E. ......... 116
Figure 8-23: Monthly flowrate data for Plant E. ......................................................................... 116
Figure 8-24: Monthly chlorine residual values for Chlorine Contact Tank #1 at Plant F. ......... 120
Figure 8-25: Monthly pH values for Chlorine Contact Tank #1 at Plant F. ............................... 120
Figure 8-26: Monthly temperature values for Chlorine Contact Tank #1 at Plant F. ................. 121
Figure 8-27: Monthly flowrate for Chlorine Contact Tank #1 at Plant F. .................................. 121
Figure 8-28: Monthly chlorine residual values for chlorine disinfection at Plant G. ................. 124
Figure 8-29: Monthly pH values for chlorine disinfection at Plant G. ....................................... 124
Figure 8-30: Monthly temperature values for chlorine disinfection at Plant G. ......................... 125
Figure 8-31: Monthly flowrate for chlorine disinfection at Plant G. .......................................... 125
Figure 8-32: Monthly chlorine residual values for the north clearwell at Plant H. .................... 129
Figure 8-33: Monthly pH values for disinfection calculations at Plant H. ................................. 129
Figure 8-34: Monthly temperature values based on raw water measurements at Plant H. ......... 130
Figure 8-35: Monthly overall flowrates for Plant H. .................................................................. 130
Figure 8-36: Monthly chlorine residual values for the clearwell at Plant J. ............................... 133
Figure 8-37: Monthly pH values for disinfection calculations at Plant J. .................................. 133
Figure 8-38: Monthly temperature values based on raw water measurements at Plant J. .......... 134
Figure 8-39: Monthly overall flowrates for Plant J. ................................................................... 134
xi
NOMENCLATURE% Percent
Alum Aluminum sulphate
ANOVA Analysis of variance
Anth Anthracite
ATP Adenosine triphosphate
BSA Bovine serum albumin
C Carbon
CFU Colony forming unit
cm centimeter
CT Concentration * Time
DALY Disability adjusted life year
DBP Disinfection byproduct
ddH2O Distilled water
°C Degrees Celsius
DEUF Dead end ultrafiltration
DI Deionized
DNA Deoxyribonucleic acid
DOC Dissolved organic carbon
EBCT Empty bed contact time
EDTA Ethylenediaminetetraacetic acid
ENT Enteric
EPS Extracellular polymeric substances
ETSW Extended terminal subfluidization wash
g grams
GAC Granular activated carbon
hr Hours
H2O2 Hydrogen peroxide
HAA Haloacetic acid
HSD Honest significant difference
IMS immunomagnetic separation
KWR Water Cycle Research Institute
L Liter
LB Lysogeny broth
xii
log10 Base 10 logarithm
LT2 Long term treatment rule 2
mg milligram
MIB 2-Methylisoborneol
min Minute
MPN Most Probable Number
N Nitrogen or Number
NCSTR n - continuous stirred tank reactor
ND Non-detect
ng nanogram
NOM Natural organic matter
NSERC Natural Sciences and Engineering Research Council of Canada
NTU Nephelometric Turbidity Units
P Phosphorus
PACl Polyaluminum chloride
PDF Probability distribution function or portable document format
pH power of Hydrogen
PO4-P Phosphorus
pp Per person
Pr Proteins
PS Polysaccharides
QMRA Quantitative microbial risk assessment
qPCR Quantitative polymerase chain reaction
RPM Revolutions per minute
THM Trihalomethanes
UF Ultrafiltration
μg microgram
μM micrometer
USEPA United States Environmental Protection Agency
UV Ultraviolet
UV254 Ultraviolet Absorbance at 254 nm
yr Year
1
1. INTRODUCTION
1.1. RESEARCHOBJECTIVES
1) To utilize aerobic endospores as surrogates for giardia cysts and cryptosporidium
oocysts to estimate the log10 removal of protozoa through biologically active granular
media filters in Ottawa, Peterborough and Toronto.
2) To determine the effects of nutrient supplementation and biofilm growth on the
removal of aerobic endospores through biologically active granular media filters.
3) To provide Quantitative Microbial Risk Assessments for 10 Canadian utilities based
on raw water pathogen sampling, engineering assessments, and experimental
estimates of physical removal performance where available.
1.2. SUMMARYOFCHAPTERS
Chapter 2 provides an overview of biological filtration as it pertains to the removal of
pathogens and dissolved organic matter. A literature review discusses surrogates for
pathogens in drinking water treatment as well as past experimental results that serve as
reference points for estimating biological filtration performance.
Chapter 3 reveals the experimental research methods employed in this thesis, including
microbiological techniques for handling aerobic spores, as well as some discussion
concerning statistical approaches utilized in the evaluation of filtration performance.
Chapter 4 presents a stand-alone research paper discussing experimental results
pertaining to aerobic spore removal through biological filtration at 3 pilot drinking water
research facilities. The results of this experimental work was also utilized in Quantitative
Microbial Risk Assessments for these 3 utilities based on aerobic spores as a surrogate
for pathogen removal performance.
2
Chapter 5 presents a stand-alone paper addressing the Quantitative Microbial Risk
Assessments for ten (10) Canadian drinking water utilities. Four (4) of the utilities were
participants in a tailored collaborative research projected conducted by the Water
Research Foundation. The remaining utilities were NSERC chair partners.
3
2. LITERATUREREVIEW
2.1. INTRODUCTION
Drinking water treatment focuses primarily on the reduction of risk that results from the
presence of harmful biological and chemical contaminants. Microbiological contaminants can
include bacteria, viruses as well as protozoan species. These pathogens are commonly found in
natural watersheds as a result of fecal contamination from both animal and human activities in
and around the watershed. Bacteria and viruses can be effectively treated primarily with
disinfection processes (chemical, ultraviolet treatment) while protozoan contaminants such as
cryptosporidium and giardia are more resistant to treatment via disinfection. These pathogens
are difficult to inactivate using traditional chemical disinfection strategies so many treatment
plants rely on physical removal of these protozoa through rapid granular filtration.
The design and operation of conventional drinking water plants is based upon assumed
performance credits for each process unit. For example, in Ontario, treatment plants are required
to achieve an overall 2-log10 reduction in cryptosporidium as well as a 3-log10 reduction in
giardia and 4-log10 reduction of viruses. The reduction is assumed to be achieved through a
combination of physical removal (sedimentation and filtration) as well as disinfection processes
(chemical, ultraviolet treatment). In most cases the disinfection stage has been well
characterized with published data that shows the relationship between the process conditions
(temperature, pH, chemical dose, contact time) and the inactivation for each pathogen. In effect,
if there is a shift in the operational conditions in the plant there would be a subsequent change in
the disinfection credit for that particular process.
Physical treatment stages are given far less regulatory consideration and are assigned
fixed performance credits regardless of the process conditions at the plant. While filter
4
performance is commonly reported in terms of effluent turbidity, there is no consistent,
quantifiable relationship between filter turbidity and pathogen removal performance.
Furthermore, there are several possible design variations for granular media filters. The filter
bed may consist of a single media type (mono-media) or the plant may feature dual- or tri-media
filters depending on the design specification when it was built. In practice there is also a wide
range of media characteristics (effective size and uniformity coefficients) that can affect a filter's
ability to remove turbidity and pathogens. As a result, very few treatment facility are able to
ascertain what level of physical removal they are achieving through their process apart from
relying on studies conducted at similar plants that may not be indicative of the local plant
performance.
While drinking water filtration has traditionally been preceded by a chemical disinfection
step, there has been a recent shift in the industry towards delaying chemical disinfection until
after the filtration stage. By eliminating or minimizing upstream disinfection processes, native
bacteria are able to pass through the treatment processes that lie upstream of the filter process
and actively colonize in the bed of the filter. These biological organisms are capable of
absorbing and consuming chemical compounds derived from natural and anthropogenic sources.
Natural Organic Matter (NOM) in drinking water can react with chlorine during the disinfection
stage and produce compounds known as disinfection by-products (DBP's) that can contribute to
taste and odour complaints and may also increase the carcinogenic potential of the water.
The bacteria also tend to alter the surface characteristics of the granular media by coating
the surface with EPS (extracellular polymeric substances). This EPS slime coat is produced
when the organisms are stressed either by environmental factors or a lack of nutrients in the
water. While EPS is generally benign and doesn't typically affect the chemical stability of the
5
filtered water, its effects on particle removal are less certain. EPS has also been attributed to
increased headloss development within filter beds leading to much shorter run times.
Conventional (non-biologically active) filters have been the subject of numerous
performance studies, however, biologically active filters have only recently been examined with
regard to particle and pathogen removal. Removal performance in biological filter has been
shown to be highly variable, not unlike conventional (non-biologically active) filters.
Furthermore, the active supplementation of biofilters with essential nutrients has been an even
more remote field of study but will be a necessary realm of investigation as more plants opt to
operate their filters in an enhanced biological mode.
For the purposes of Quantitative Microbial Risk Analysis (QMRA) it is important to
accurately represent the overall removal efficiency of a treatment process. Until now, the Health
Canada model has approximated the removal credit attributed to the filters by taking a mean
value derived from a comprehensive review of recent research activities. While this is an
excellent approximation when computing the risk over a group of treatment facilities, it may fail
to represent the intricacies of one particular treatment plants capabilities. Additionally,
biofiltration (intentional or coincidental) is not directly addressed in the Health Canada model
and most certainly will contribute to the variability in treatment plant performance and
subsequent overall risk to the drinking water consumers.
2.2. SURROGATESFORCRYPTOSPORIDIUM
In order to meet acceptable risk targets in Canada, a treatment plant must achieve a final
cryptosporidium count of 1.3 x 10-4 oocysts per liter (Health Canada, 2013). This low target
concentration is difficult to quantify using typical analytical methods, given that a detection limit
of 1 per 100 L is still at least an order of magnitude too high. The Health Canada target can also
6
be expressed as 1 oocyst in 7692 L which is beyond the lower limit of quantification for existing
sampling and analytical methods (typically as low as 1 oocyst per 100 L according to USEPA
Method 1623).
Due to the limitations of existing analytical methods and the inherent variability in raw
water protozoa counts (Weiss et al., 2005), many groups have opted to estimate the effluent
concentrations of oocysts in the final effluent from a treatment plant (Health Canada 2013). This
strategy requires knowledge of both the influent concentration of oocysts entering the plant as
well as a reasonable understanding of the typical physical removal performance of the filtration
units within the plants. Filters are typically evaluated according to a logarithmic scale in which
3-log10 equates to 99.9% removal of the target contaminant. It is increasingly important for plant
operators to have an understanding of their specific plant’s ability to remove these target
pathogens, while at the same time it can be time consuming and cost prohibitive to conduct
direct cryptosporidium removal studies within the plants. Pilot studies and surrogate studies are
therefore the best opportunity for most practitioners to characterize their plant’s performance in
terms of particle and pathogen removal (Hijnen et al., 2000; Hsu and Yeh, 2003; Huertas et al.,
2003; Galofre et al., 2004).
Bacterial spores and microspheres are the most common surrogates used to determine
removal through filtration units. Both spores and microspheres have similar size (< 5μm) and
shape characteristics as native cryptosporidium oocysts (Emelko 2003; Hsu and Huang, 2002)
and have been shown to be a conservative estimate for the removal of oocysts in most treatment
plants (Brown and Cornwell, 2007; Galofre et al., 2004). Muhammad et al. (2008) determined
that aerobic spores, while conservative, were more representative of cryptosporidium oocysts in
a study conducted on point-of-use filtration systems. There have been reported cases of aerobic
7
spores re-growing or sloughing off during the course of an extended filter run (Mazoua and
Chauveheid, 2005; Heller et al., 2007). In light of this, several articles suggest using non-
biological agents such as polystyrene microspheres as an additional surrogate to consider in
pathogen transport studies (Passmore et al., 2010; Amburgey et al., 2005). Unfortunately the
surface characteristics of microspheres are not identical to cryptosporidium oocysts and can
exhibit significant surface charges (Hsu and Huang, 2002). These charges may limit the
attachment of microspheres to the filter media grains. There is ongoing development in this area
by Pang et al. (2012) who are trying to modify the surface functional groups on polystyrene
microspheres to better mimic the surface charges on native cryptosporidium oocysts.
Several studies have also looked at riverbank filtration as an example of a natural
biological filtration system. These studies often use naturally occurring bacterial spores and
protozoan oocysts to document the overall removal rates as the water traverses through the
saturated granular media riverbanks (Weiss et al., 2005; Gollnitz et al., 2005). These studies are
useful in the comparison between the removal of cryptosporidium and bacterial spores, however
the results cannot be directly compared to engineered rapid granular filtration systems (Weiss et
al., 2005). In general, the authors concluded that aerobic spores should be considered a
conservative indicator organism and a useful surrogate for cryptosporidium oocysts because the
detection limits for aerobic spores are slow low (usually 1 per sample) when compared to the
typical analysis for cryptosporidium.
The USEPA LT2 guidance toolbox provides accommodation for the use of aerobic spores
as a validation tool for the removal of cryptosporidium. Plants achieving greater than 0.5-log10
removal of aerobic spores through sedimentation are granted an additional 0.5-log10 credit for the
8
removal of cryptosporidium. Additional credits are also available for demonstrated removal
through granular riverbank filtration systems.
Recently, a QMRA exercise was performed by Jaidi et al. (2009) which considered the
relationship between aerobic spore removal and cryptosporidium oocyst removal demonstrated
in literature. This research determined that there was a linear correlation between aerobic spores
and cryptosporidium removal based on 15 peer reviewed papers. This information was then
utilized in a QMRA model to predict overall risk of illness. Notably, this paper compared several
methods of estimating pathogen removal through a treatment plant including turbidity and
particle counts and aerobic spore surrogate trials. Aerobic spore removal corresponded best with
regional epidemiological data from the region of interest. Other recent QMRA work has
considered turbidity to be an indicator of plant performance, Bastos et al. (2013) concluded that
it is necessary to consider the variability in both turbidity and aerobic spore removal when
evaluating risk through QMRA processes.
2.3. CONVENTIONALFILTRATIONFORPARTICLEREMOVAL
Rapid granular filtration has traditionally been used as a means of particle removal in
drinking water treatment trains. Generally preceded by a chemical conditioning step
(coagulation), conventional filtration is effective for the removal of particles that contribute to
turbidity as well as pathogenic particles (Hu 2002). The effectiveness of conventional filtration
was the subject of much research in the early 2000’s after the serious cryptosporidium outbreak
in Milwaukee. Research focused on quantifying the removal of protozoan oocysts through
granular media filtration because chemical disinfection has been shown to be largely ineffective
for cryptosporidium and giardia (Edzwald et al., 2000).
9
Conventional treatment plants consisting of coagulation, sedimentation, and rapid
granular filtration are assigned a 2-log10 credit for cryptosporidium removal according to the US
EPA (1989). Many researchers have demonstrated much higher removals including a study
conducted by Emelko et al. (2001) that achieved between 4.5 and 5-log10 removal of
cryptosporidium in dual media sand and anthracite filters. Several other researchers have shown
more modest removal values that are commonly between 2 and 3-log10 removal (Edzwald et al.,
2001; States et al., 2002; Gitis 2008; Nieminski et al., 2000). The variability in reported log
removal performance is thought to be related to differences in media characteristics or
operational practices.
2.4. FACTORSAFFECTINGPARTICLEREMOVALINCONVENTIONALFILTRATION
Emelko et al. (2003) evaluated the effect of media type by including trials with trimedia
configurations (sand/anthracite/garnet) and found that the trimedia filter performed only slightly
better than dual media configurations (4.9-log10 versus 4.6-log10). Other studies have shown
much lower cryptosporidium removal of 2-log10 but also demonstrated that removal was not
dependent on media configuration (mono-, dual-, tri-media) (Harrington et al., 2003). Filter
depth can have an impact on the overall removal performance of a filter. Gitis (2008) found that
80cm mono-media sand filters achieved 4-log10 removal of cryptosporidium while 40cm sand
filters could only demonstrate 3-log10 removal.
Effective coagulation has been shown to have a large impact on the overall performance
of rapid granular filtration units. In one study, rapid sand filters with no coagulant achieved 50%
removal of cryptosporidium oocysts while filters that used either 10 or 20 mg/L of alum
achieved up to 95% removal of the dosed oocysts (Gitis, 2008). Coagulation is thought to
neutralize the surface charges on oocysts, allowing them to attach more readily to the media
10
grains. It is important to note that the optimum coagulation conditions (pH and dose) for
turbidity removal may not be the optimum for cryptosporidium removal (States et al., 2002).
Alternatively, Shaw et al. (2000) proposed modifying the surface properties of the granular
media by affixing an iron-aluminum oxide coating, which in their study resulted in drastic
increase in the filter attachment coefficient by up to 2.9 times.
Operational practices such as optimizing backwash intensity and implementing a ripening
stage in the backwash sequence have been shown to improve pathogen removal and retention in
the filter bed (Amburgey et al., 2003; Glasgow and Wheatley, 2001). In both of these studies,
researchers showed that the ripening stage of a filter run often resulted in much lower particle
removal rates and that this water should either be wasted or returned to the plant influent.
Amburgey et al. (2003) also proposed an extended terminal sub-fluidization wash (ETSW) that
was shown to stabilize the pathogens and other small particles in the filter bed and thereby
reducing the number of particles that breakthrough the filter upon start-up. Variable flow
conditions within the filter (rapidly fluctuating flow rates) have been shown to result in lower
removal values than stable flow operations (Lipp and Baldauf, 2000; Glasgow and Wheatley,
2001).
2.5. BIOLOGICALFILTRATIONFORREMOVALOFDISSOLVEDORGANICS
A more recent advancement in the field of drinking water treatment is the operation of
biologically active rapid granular filtration units. These units are similar to conventional
filtration but can be distinguished from conventional treatment by the absence of any disinfectant
residual in the filter influent. These filters have been shown to reduce natural organic matter
(NOM) through the biologically active organisms that colonize on the surface of the media
11
grains. Biologically active filters may be used solely for the reduction of NOM or they may also
be employed for simultaneous particle removal (Persson et al., 2005).
The practice of biofiltration involves the colonization of granular media filters with
bacterial cells. These bacteria are generally native species from the raw water source that have
managed to get through the upstream treatment processes (namely coagulation). As they are
native organisms from the watershed, it is expected that they would readily degrade naturally
occurring dissolved organic material that is found in the influent water. Bacteria have been
shown to form biofilms on the surface of granular media that consists of a mix of live and dead
bacterial cells held together in a matrix of extracellular polymeric substances (EPS) (Searcy et
al., 2006).
DOC reductions have been demonstrated in passive biological filtration units to be
approximately 80-90%. When looking specifically at known taste and odour compounds, 2-
Methylisoborneol (MIB) and geosmin, typical reductions can be up to 97% in both biological
GAC and expanded clay media (Persson et al., 2007). Similar research has shown removal of
these compounds to be 89% and 51% respectively (Elhadhi et al., 2004). These results are a
promising indication that biofiltration can be used to mitigate taste and odour episodes and may
lessen the replacement frequency for GAC to control taste and odour issues. The ability for
biological filters to absorb MIB and geosmin was best during sustained periods of elevated
contaminant concentrations and was shown to drop if the filters were not exposed to a
continuous concentration of the contaminants. This supports the notion that the reduction in
MIB and geosmin was due primarily to the presence of biological activity rather than the
absorptive capacity of the granular media (Zhu et al., 2010).
12
Additionally, by reducing the DOC levels in the filter effluent there is also a reduction in
DBP formation potential. Typically characterized using trihalomethanes (THM) and haloacetic
acid (HAA) concentrations, DBPs are thought to contribute to taste and odour issues and may
also have significant health effects. Biofiltration using GAC has been shown to achieve 30%
reductions in THM’s while anthracite biofilters achieved closer to 12% reductions in THM’s.
HAA’s generally follow a similar trend with 36% reductions using GAC and 20% using
anthracite filters (Mirzaei Barzi, 2008).
2.6. FACTORSAFFECTINGDOCREMOVALINBIOLOGICALFILTERS
The media characteristics can greatly affect the level of biofilm growth and activity
within a filter. It has been demonstrated that granular activated carbon (GAC) can support far
more biological activity than anthracite based filters as measured by both EPS and ATP
(Papineau et al., 2012). A study by Mohanram et al. (2010) also concluded that different media
materials would achieve varying levels of microsphere or pathogen removal. Media age was
also shown to play a role in the removal performance of slow sand filtration units. Other groups
observed increased removal from 2.6-log10 to 3.9-log10 for cryptosporidium oocysts after the
filters had been acclimatized over a 1 year period (Deloyde, 2007).
Unfortunately, biological activity within filters has been shown to be sensitive to
environmental factors such as temperature and pH. Halle et al. (2009) found significant
differences between filters operated at 1ºC and greater than 10ºC, with warmer temperatures
achieving 15% reduction in DOC and virtually no reduction during the cold winter conditions.
Wide shifts in water chemistry can disrupt the healthy cells within the biofilm and cause
shedding leading to increased biological matter in the filter effluent. Biofilm structural changes
were noted after 7-days of phosphorus limited conditions in a study conducted by Liu et al.
13
(2006). Additionally, it may require a significant amount of time to acclimatize the bacterial
populations (ranging from 1 month up to 18 weeks) (Liu 2001; Papineau et al., 2012). During
this acclimatization period, biological filters achieve sub-optimal removal of dissolved organic
material.
Empty Bed Contact Time (EBCT) has also been proposed as an important operational
parameter for biofiltration units however there are conflicting reports as to its effect on the
removal of dissolved organics and pathogen particles. EBCT is defined as the bed depth divided
by the superficial (approach) velocity of the water stream (Huck 1999). This measure can be
used to compare various filter conditions when looking at biofiltration performance. In general,
a higher EBCT will lead to increased contact time between the water and the biofilm layer. This
should result in higher removals of dissolved organics, but this is not always the case. Halle et
al. (2009) showed that a 3-fold increase in EBCT from 5 to 14 minutes did not result in
reportable increase in dissolved organic reductions.
2.7. BIOLOGICALFILTRATIONFORPATHOGENREMOVAL
Efforts have been made to identify instances in the literature where researchers have
investigated the effectiveness of biological filtration for the purpose of pathogenic particle
removal. Several of the papers written in the European context stipulate that the filters are being
operated in a biological mode (Hijnen et al., 2010; Heller et al., 2007) or are at least downstream
of a dedicated oxidant quenching system (Mazoua and Chauveheid, 2005). Several studies have
investigated the direct impact of biofilm growth in rapid filtration units, but many of these
studies have been conducted using glass beads or other artificial surfaces (Dai and Hozalski,
2002; Helmi et al., 2008; Li et al., 2006) rather than conventional sand and anthracite or GAC
filter media (Amburgey et al., 2005; Papineau et al., 2012). There is limited research into the
14
pathogen removal effectiveness of biologically enhanced (supplemented) filters at this time.
While several papers have supplemented biofilm growth with small organic compounds, as well
as nitrogen and phosphorus sources (Papineau et al., 2012; Dai and Hozalski, 2002), there is no
little evidence of any research into the effectiveness of using nutrient supplementation as an
operational parameter to optimize pathogen removal.
There are conflicting results regarding the ability of biofilters to remove pathogens. In
general, most recent research shows an increase in pathogen removal when the filter media have
an active biofilm layer (Papineau et al., 2012; Hijnen et al., 2010; Mazoua and Chauveheid,
2005; Amburgey et al., 2005). These papers employed either fresh or aged granular anthracite or
granular activated carbon as the primary filtration media. Studies that used glass filter beads to
study the direct effects of biofilm rather than the combined effects of filtration media and biofilm
often demonstrated lower pathogen removals (Dai and Hozalski, 2002) but this may not reflect
the intricacies and removal performance of typical drinking water filtration units. Interestingly,
there are two common methods of reporting removal rates in biological filters. When removal
rates are shown to be less than 1-log10, authors generally report in terms of percentage removal
(Dai and Hozalski, 2002; Papineau et al., 2012) whereas studies than demonstrate greater than 1-
log10 usually opt to report removal performance in terms of logarithmic units (Hijnen et al.,
2010; Mazoua and Chauveheid, 2005).
15
Figure 2-1: A sample of published studies comparing the removal of aerobic spores, microspheres and cryptosporidium oocysts in biologically active granular media filters.
Many drinking water plants, if they are using granular media filtration as their primary
physical pathogen barrier, will use a chemical coagulant upstream of the filters to change the
surface properties of the pathogenic particles in the water. Studies that have included coagulant
as part of their experimental design often find statistically significant differences when
comparing biological and non-biological filtration units using coagulated influent sources (Dai
and Hozalski, 2002). Dai found 50% cryptosporidium removal in non-biological filters
coagulated with 0.01 M Ca2+ ions while the biologically active filters using the same coagulant
dose only showed a 15% reduction in cryptosporidium. The removal was not necessarily due to
the coagulant’s effect on NOM because the researchers had several filters that were artificially
spiked with NOM during the trials. All of the biological filters functioned in a similar manner
regardless of the NOM dosage. The specific coagulant being used could also be responsible for
removal performance as the Dai paper also noted that using Aluminum based coagulants resulted
in far greater cryptosporidium removal of approximately 70%. Conflicting results were found by
Abudalo et al. (2010) where differing levels of DOC were injected into the influent of several
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Log Rem
oval
Cryptosporidium Microspheres Spores
16
filters. They found that natural organic material plays an important role in determining the
attachment efficiency of pathogen particles in biological filtration units. Cryptosporidium
removal varied from 30-60% based on changes in dissolved organic material (Abudalo et al.,
2010). Additionally, several studies have documented the effects of various water chemistry
parameters on the surface charges of pathogenic particles (Hsu and Yeh, 2003; Hsu and Chuang,
2002). Similar to the findings in conventional filtration studies, it has been shown that incorrect
coagulant doses can cause increased pathogen breakthrough in biological filters and this is due in
part to the inadequate neutralization of surface charges (Emelko, 2003; Amburgey et al., 2005).
EPS can play a role in the physical removal of pathogenic particles by occupying up to
10% of the available void space between filter particles (Mauclaire, 2004). Primarily comprised
of polysaccharides and proteins, EPS is usually produced in greater quantities when the bacteria
are considered to be under stressed conditions (Fang et al., 2009). These polymeric films lead to
rapid increases in filter headloss and can significantly shorten runtimes. There are few very
studies that directly relate the effects of EPS on pathogen removal.
2.8. ENHANCEDBIOLOGICALFILTRATION
The most proactive demonstration of biofilter supplementation was conducted by
Lauderdale et al. (2012) and they concluded that phosphorus was the limiting nutrient for the
watershed that they were studying. Upon supplementation with a phosphorus dose of 0.02 mg/L
as PO4-P their filters began to achieve a 75% greater reduction of DOC than the control filter.
Filter headloss was reduced by 15% as well resulting in much longer run times at the Arlington,
TX pilot facility. While Lauderdale’s work was conducted on an oligotrophic water source,
phosphorus was also shown to be a limiting nutrient in northern boreal watersheds as well. The
work of Lehtola et al. (2002) also concluded that phosphorus supplementation at levels between
17
1-5 µg/L proved to increase microbial activity within the biofilm. Most researchers agree that
the optimum ratio of nutrients for microbial growth and biostability is 100:10:1 molar
equivalents of elemental carbon, nitrogen and phosphorus. In many cases it is suggested to
supply a slight excess of phosphorus to ensure that it is no longer the limiting nutrient for biofilm
growth. Fang et al. (2009) utilized a dose ranging from 30 to 300 µg/L to achieve EPS
reductions between 77-81%. One additional study supporting the notion of phosphorus
supplementation was conducted by Sang (2003). They showed the addition of 25µg/L of PO4-P
achieved a 15% reduction in BDOC (biodegradable organic carbon). Sang (2003) also noted that
the oxygen demand in the supplemented filters increased by 13-22% leading to the conclusion
that supplementary oxygen sources may also be beneficial for biofilter operation.
Ozone was a primary candidate for increasing the available oxidative capacity of the
influent water. Vokes (2007) used a combination of ozone and biofiltration to achieve
biostability for the effluent which had earlier been identified as having very high chloramine
demand. The use of ozone and biofiltration led to the reduction of taste and odour episodes and
also achieved great reductions in THM and HAA levels (>80% reduction and >66% reduction,
respectively). Lauderdale et al. (2012) opted to apply hydrogen peroxide as an upstream oxidant.
This additional oxidant provided a supplementary source of dissolved oxygen through the
activation of the peroxidase enzymes in many of the bacterial colonies. The result of this
hydrogen peroxide addition was a further reduction of headloss by up to 60% when compared to
passive biological filters and a very sharp decrease in the amount of EPS material contained in
the filter units. In terms of particle removal, Wu et al. (2008) found that ozone does not affect
the overall particle count in the effluent stream with both their ozonated and control filters
allowing roughly 50 particles/mL through the filter. Urfer et al. (2000) had experimented earlier
18
with periodic ozone and hydrogen peroxide dosing schemes and found that occasional
application of oxidants did not result in any significant shift in the filter’s ability to remove
dissolved organic contaminants.
2.9. SPOREENUMERATIONMETHODS
Aerobic spores are isolated from environmental samples by using Standard Method 9218 B.
This involves heat treating the sample at 75°C for 15 minutes to eliminate any vegetative cells
(Huertas et al., 2003) and then concentrating through vacuum filtration onto 0.45µm membrane
(Brown and Cornwell, 2007; Hijnen et al., 2000; Heller et al., 2007). The filter material varies
between researchers, with some using polycarbonate (Amburgey et al., 2005; Emelko 2003) and
others using nitrocellulose (Heurtas et al., 2003; Chae et al., 2008) In general, 1 to 5 liters of
sample are required to accumulate a sufficient number of colonies on the surface of the
membrane (Brown and Cornwell, 2007) with a target of less than 100 colonies per plate (Huck et
al., 2002). The membrane filters are then incubated on a nutrient supplemented agar plate. Some
researchers used trypan-blue to allow for easier identification of colonies. Incubation
temperatures ranged from 30°C to 37°C while incubation times ranged from 22 hours) to a more
typical 24 hours. Visual plate counts may be conducted without the aid of a microscope
(Mazoua and Chauveheid, 2005; Galofre et al., 2004; Huck et al., 2002).
Another potential method for isolating the particles of interest is the use of an
ultrafiltration membrane cartridge. Hill et al. (2007) have achieved 91% recovery rates of
cryptosporidium oocysts when filtering up to 100 liters of tap water. This could allow for time-
weighted sampling on biofilter effluents. One concern with this method is the possibility of
biopolymers fouling the membranes prior to the completion of the 100L filtration exercise.
19
Nonetheless, ultrafiltration can be considered a viable option for recovering aerobic spores and
cryptosporidium oocysts from large water samples during spiking activities at pilot scale.
2.10. RESEARCHGAPS
To date, there is very limited information in the literature regarding pathogen and particle
removal through enhanced or engineered biological filtration units. While research continues for
biologically active filters (consider Hijnen et al., 2010), there is a lack of research that considers
filtration units that utilize nutrient supplementation for either improved DOC reductions or
reduced headloss development. The closest available study was conducted by Papineau et al.
(2012) who looked at the effects of media aging on biofilm development as well as
cryptosporidium capture and retention. They used a constant dose of nutrient supplementation
(C, N and P) to quickly achieve a biofilm prior to their 18 week study. The nutrient
supplementation was set a 100:10:1 ratio as described in the literature with a base dose of 1mg/L
as C, 100µg/L as N, and 10µg/L as P. This established dosage was not altered at any time during
the study so it is not considered to be an engineered biofiltration study. Furthermore, Papineau et
al. (2012) conducted their work on bench scale filters and it is unknown at this time whether pilot
and full scale filters will produce comparable removal performance. Time series data that
explorers the changes in filter performance throughout the duration of a filter run is also an area
that could further the knowledge in this field. It is generally understood that biological activity in
the filter will change over time; it is not known whether this will have a significant effect on the
filter’s capacity to remove microorganisms.
The natural conclusion is that there is a significant body of work that needs to investigate
the effect of enhanced biological filtration on pathogenic particle removal. Aerobic spores have
been shown to be a conservative surrogate for cryptosporidium oocysts and will be used to
20
demonstrate the capacity of biological filtration for the removal of potentially harmful
pathogens. Several related operating parameters will be investigated including water
temperature, filter flow rate (and related EBCT), TOC/DOC removal and turbidity removal.
The final niche for this project involves Quantitative Microbial Risk Assessments
(QMRA). At the present time there has been minimal research into the effect of enhanced
biological filtration on the overall risk resulting from the consumption of drinking water treated
in this manner. While it is understood that aerobic spores are not a perfect surrogate for
cryptosporidium oocysts, it is believed that they will at the very least provide some insight into
the relative risk that may result from operating in an enhanced biological mode.
21
3. METHODSANDMATERIALS
3.1. MEASUREDVARIABLES
Spore concentrations have been determined in both the influent and effluent streams for
each filter in order to determine the overall removal rate across each process unit. In order to
completely characterize the filter performance at all points during a filter run, samples were also
taken following the initial ripening spike. This time series data highlights the relationship
between runtime, headloss development, EPS quantities and pathogen removal capacities as the
filter matures.
It was initially hypothesized that cold water filters would exhibit higher quantities of EPS
resulting in lower removals of aerobic spores. This is consistent with the results demonstrated by
Papineau et al. (2012) who saw significant reductions in cryptosporidium removal between
summer and winter. Their study looked at three different filter media designs (sand, anthracite
and GAC) and all exhibited the large reductions in removal capacity during the winter months.
In this particular study, warm water conditions resulted in 50, 65 and 90% removals respectively
for sand, anthracite and GAC. The same filters achieved approximately 10, 30, and 40%
removals during cold water conditions. This temperature-effect hypothesis was tested by
comparing results from summer and winter conditions during periods of similar water quality
and supplementation regimes. While it may not be possible to achieve identical conditions,
ANOVA statistical analysis can be used to test for the main effect of temperature on the removal
of aerobic spores. For the purposes of this study, cold water is considered to be less than 10
degrees Celsius which is typically observed for approximately 6 months of the year in Ontario
waters from November to April.
22
Turbidity was evaluated at the same time that the aerobic spores samples were collected
either through online turbidimeters or bench top measurements. Turbidity and spore removal at
the three pilot facilities were expected to be loosely correlated which is similar to the patterns
demonstrated by several instances in the literature (Harrington et al., 2003).
Empty Bed Contact Time (EBCT) is known to play a role in the removal of DOC through
biologically active filters. Longer EBCT’s at low flow rates were expected to produce greater
DOC removal but were not anticipated to increase removal of aerobic spores. In the case of high
flow rates, the filters were expected to have lower DOC removal performance and depending on
the hydraulic conditions within the filter bed they may experience shedding or breakthrough of
the aerobic spore particles. As noted by Mazoua and Chauveheid (2005), aerobic spores may be
present in higher concentrations than the influent if the filter flow rate is changed and particles
that were initially trapped within the filter bed are sheared away from being attached to the
media grains. A gradual ramping of the flowrate was necessary to prevent this shearing of the
aerobic spores from the media. An additional operating condition of interest may be the
intentional shearing of these particles to simulate poor operational practices with sudden flow
rate changes.
3.2. BIOLOGICALINDICATORS
The biological activity within the filter bed was monitored using Adenosine Triphosphate
(ATP) levels garnered from media samples within the bed. While the ATP measurements are
semi-quantitative, they useful for comparing relative levels of biological activity within different
filters. ATP indicates that the cells are metabolically active, but does not indicate whether they
are in a growth or maintenance phase and certainly does not indicate whether the bacterial
colonies are under environmental stress from either water quality parameters or nutritional
23
depravation. The most suitable kit for evaluating ATP is the Luminultra kit. Every ATP sample
requires that the filter be drained and a small sample of media (1 teaspoon) must be removed
from the filter. This sampling procedure may be disruptive to the biofilm so the sampling is
conducted on an as required basis, generally following an aerobic spore removal study so as to
minimize the disturbance of the filter media prior to conducting the removal study.
In order to monitor the levels of EPS associated with the filter’s biological colonies, the
media must be removed from the filter and treated with a physical/chemical process to isolate the
EPS substances. Various physical methods for EPS extraction have been proposed including
centrifugation or sonification. In most cases the EPS molecules are extracted into either a Milli-
Q water phase or ethanol solvent phase from which the EPS is then precipitated out of solution.
Additionally, a cation exchange resin (CER) may also be used to isolate the polymeric molecules
from the cellular matrix (Mclellan, 2013). Following this isolation step the EPS is processed
according to the Lowry method (1951) to quantify the levels of proteins and within the sample.
Polysaccharides within the EPS are measured using the Dubois method (1956). This method
yields a yellow-orange colour when sugars are exposed to phenol and concentrated sulphuric
acid. The colorimetric response can then be used to determine the levels of carbohydrates within
the sample.
High levels of EPS are indicative of poor nutrient availability or environmental
conditions that are not conducive to biological activity (cold water, non-ideal water chemistry or
pH conditions). Using the methods outlined above, EPS can be further subdivided into
polysaccharides as well as protein components. This information may be used to characterize
the surface characteristics and therefore the impact that EPS has on the removal of pathogenic
particles.
24
3.3. METHODS
3.3.1. BACILLUSATROPHAEUSGROWTHANDCULTUREMETHODS
Bacterial endospores are present in most natural watersheds, in varying concentrations.
Depending on environmental conditions, bacteria may be in their normal vegetative state,
actively consuming nutrients and following a normal reproductive cycle, or they may have
formed endospores as a result of an environmental shock factor. Shock factors include high
temperatures, low nutrient availability, or poor water chemistry characteristics. Upon exposure
to unfavourable conditions, some aerobic bacteria will form endospores which are a very robust
encapsulation of the key cellular components such as DNA and a few basic cell organelles.
These endospores are capable of enduring long periods of very harsh conditions and are then
able to be reconstituted into vegetative cells when conditions are more favourable.
For the purposes of this research, both the vegetative stage and the endospore stage must be
considered. The vegetative state prove to be important during spiking activities where artificially
high concentrations of Bacillus atrophaeus are spiked into the influent stream of the filter. In
order to reach these artificially high concentrations, Bacillus atrophaeus are grown up in the lab
to concentrations reaching 108 CFU mL-1. These high concentrations require a special nutrient
supplemented culture as well as close monitoring to ensure that the spores continue to grow to
the desired concentration. The concentration can be monitored using optical density
measurements of the growth medium. Due to the obligate aerobic nature of Bacillus atrophaeus,
adequate oxygen must be provided at all times to ensure the continued growth of the culture
solution.
While Bacillus atrophaeus is likely to grow on any nutrient rich media, Harwood et al.
(1990) have proposed Spizizen's minimal medium for a more selective growth of Bacillus
25
atrophaeus. This medium can also be supplemented with glucose to prevent autolyzation of the
cells prior to utilization in filter challenge studies. Following the exponential growth phase, as
determined by optical density (OD) measurements, the culture can be sporulated using either
simple exhaustion of the nutrients in the culture media, or through a deliberate step-down
method in which the nutrient concentration is reduced over a period of days. It is recommended
that the solution be brought to at least 70°C for 15 minutes to eliminate any remaining vegetative
cells prior to final processing and purification of the endospores. Following sporulation, the
spores can be washed with Milli-Q water and centrifuged to remove any detritus or cellular
waste products. After a series of sequential wash steps the final centrifuged pellet can be stored
at -20°C or resuspended into Milli-Q water with phosphate buffer prior to spiking the spores in a
filter challenge study.
3.3.2. BACILLUSATROPHAEUSGROWTHPROCEDURE
This procedure is appropriate for the growth and preservation of both native aerobic spores
and pure culture strains of bacillus atrophaeus. The objective of this procedure is to
significantly increase the concentration of the bacillus vegetative cells according to typical
exponential bacterial propagation patterns until a point where they can be sporulated and then
stored for future studies.
26
Materials
Equipment Reagents
250 mL Erlenmeyer flasks
Propane Burner
Lighter
Nutrient broth appropriate to organism (LB Broth or other suitable medium)
Incubator at 37˚C with orbital shaker Difco Sporulation Medium
Pipette
Sterile Pipette Tips
Autoclave bag
Autoclave (standard cycle of 121°C for 20min)
35 ml conical tubes for centrifugation
Broth Preparation
1. Rehydrate LB powder in distilled water according to manufacturer’s instructions on the bottle. Final prepared volume should be 100 mL. Autoclave solution (121°C for 20 min) prior to inoculation. Bacterial Growth phase
2. Add an isolated colony from a spread plate culture or a preserved sample to the LB broth flask.
Incubate flask for 22-24 hours at 37ºC while agitating with orbital shaker set to 160 RPM. Approximate counts of bacilli and growth phase can be determined using Optical Density measurements (λ=600nm) at periodic intervals.
Bacterial sporulation
3. Bacterial sporulation will occur when growth is limited by nutrients or oxygen or may be fostered by transferring into a flask containing Difco Sporulation Medium (DSM). Prepare 100 mL of DSM according to following proportions. Autoclave the prepared solution prior to inoculation.
27
Difco Sporulation Medium (per liter) 8 g of Nutrient broth powder 10 ml of 10% (w/v) KCl 10 ml of 1.2% (w/v) MgSO4•7H2O ~1.5 ml of 1 M NaOH (pH to 7.6)
4. Inoculate the DSM solution with 100µL of the broth prepared in steps 2-3.
5. Incubate for 72 hours at 37ºC while agitating with orbital shaker set to 160 RPM. Validation of sporulation can be achieved by heat fixing a sample to a microscope slide and then staining with malachite green. Spores are green, vegetative cells are red.
Washing bacterial spores
6. Spores are isolated from broth and extracellular material by a 3 stage washing and pelletizing process (10min, 10000 x g). Acceptable washing solutions include ddH2O, Ringer’s solution, or 10mM EDTA.
7. Aseptically transfer the cooled culture to sterile 35 ml conical tubes for centrifugation. Balance the conical tubes prior to centrifugation to achieve proper weight distribution for centrifugation using sterile ddH2O. Centrifuge for 10 min at 10000 x g.
8. After centrifugation, remove the supernatant by pipetting. Do not disturb the pellet. Approximately 5 ml sterile ddH2O can be added to each tube to suspend the pellet. Vortex each pellet to suspend the spores and transfer the suspension to one conical tube while keeping the suspension sterile. Centrifuge for 10 min at 10000 x g. A volume of sterile ddH2O is added to the tube containing the pellet of spores to reach a total volume of 30 ml. Vortex the tube well to suspend the spores (washing).
9. Centrifuge the suspension again as described above. Remove the supernatant without disturbing the pellet of spores. Add 14 ml of ddH2O to the tube.
Storage of final bacterial spore solution
The concentrated spore solution can be stored at 4°C for up to 30 days in labelled sealed vials. Longer storage may be possible but viability of the spores would require validation prior to use in other experiments.
Waste Disposal and Clean-up
Pipette tips, and leftover sample should be autoclaved prior to disposal. Disposal will be in unmarked (non-biohazard) autoclave bags placed in regular waste bins. Large liquid volumes of sample (0.5 to 1 liter) will be autoclaved and disposed of down the sanitary drain. Cleanup and disinfection of BSC and benchtop surfaces will use Virox Rescue Sporicide with a contact time of 10 minutes followed by activation of the UV lamp in the cabinet for 10 minutes. It is necessary to vacate the room while the UV lamp is on, and warn others not to enter until it is shut off.
28
3.3.3. BACILLUSATROPHAEUSENUMERATIONPROCEDURE
Bacillus atrophaeus spores can be isolated from most background bacteria in the sample by
heating to a temperature of 70ºC for 20 minutes. This effectively inactivates all vegetative cells
and leave only the aerobic endospores of interest. Following the heat treatment the samples are
enumerated using the membrane filtration method. The membrane filtration method involves
filtering a large volume of sample through a 0.45 µm filter which results in the bacterial spores
being trapped on the surface of the membrane. Depending on the suspended solids or the
turbidity of the sample, volumes between 0.5 L and 5 liters can be filtered through the membrane
unit. An appropriately sized membrane unit must be selected depending on the volume being
filtered. A 5-10 L sample may require large disc filtration units. Following filtration, the
membranes are placed on nutrient agar and allowed to incubate for 22-24 hours at 35ºC After a
the desired incubation period has elapsed the bacterial colonies were counted and by multiplied
by the dilution factor, the amount of bacteria present in the sample was determined.
Materials
Equipment Reagents
propane burner appropriate Microbiological media
Lighter 70% ethanol
sterile Petri dishes
Erlenmeyer Flasks
Hot Water Bath @ 70ºC
Incubator at 37 ˚C
Vacuum Manifold
0.45 um membrane filter
Pipette
Sterile Pipette Tips
Sterile Forceps
29
Method Outline
Sampling and Storage
1. If working with water, collect samples in sterile polypropylene or glass bottles, leaving enough air space to allow thorough mixing before enumeration, and store in the dark at 4°C. If water is chlorinated, add sterile sodium thiosulfate to bottles before sampling to quench residual chlorine.
2. If collecting samples from a port exposed to the air, spray it with 70% ethanol and wipe clean before taking the sample.
3. Analyse samples within 24 hours.
Media Preparation
1. Rehydrate agar in distilled water according to manufacturer’s instructions on the bottle.
2. If indicated on bottle, heat to boiling to dissolve agar. Remove from heat and autoclave for 20 minutes.
3. When the agar has cooled enough to comfortably hold the bottle, dispense into sterile Petri dishes. When solidified, store plates inverted at 4-5°C. Discard unused agar plates after 4 weeks or sooner if they begin to dry out.
Bacterial Enumeration
1. Roughly measure desired volume plus some excess into Erlenmeyer flask in circulating hot water bath set to 70ºC. Ensure that there is a similar volume of water also allocated for monitoring the temperature of the samples. After the samples have reached 70ºC, keep the samples partially submerged in the water bath for 20 minutes to ensure complete inactivation of vegetative cells.
2. Using sterile forceps, place the membrane element on the vacuum manifold. Secure the sample cup on top of the membrane and flush the membrane surface with 50 mL of sterile DI water.
3. Measure the desired sample volume using either an appropriate volumetric flask or graduated cylinder. Appropriate volumes are expected to be between 100 mL and 1 L. Dilutions of 1 or 10 mL in 100 mL may also be necessary if spore counts are found to exceed 100 per membrane.
4. Start vacuum pump and wet the membrane with DI water. Pour sample into sample cup and allow sample to completely pass through the filter element.
5. Rinse sample cup by flushing with 100 mL of sterile DI water. Shut off vacuum pump.
30
6. Using sterile forceps remove the membrane from the vacuum manifold and place on the nutrient agar plate.
7. Incubate plates for 22-24 hours at 37ºC. Counts of Bacillus atrophaeus colonies can be performed using visual plate counts following a grid pattern.
8. Compute the bacterial endospore concentration in CFU/100mL using the following:
bacterial endospore CFU/100 mL=colonies counted × dilution factor
mL of sample plates
Waste Disposal and Clean-up
Incubated plates, pipette tips and leftover sample should be autoclaved prior to disposal. Disposal will be in unmarked (non-biohazard) autoclave bags placed in regular waste bins. Cleanup and disinfection of BSC will use Virox Rescue Sporicide with a contact time of 10 minutes followed by activation of the UV lamp in the cabinet for 10 minutes. It is necessary to vacate the room while the UV lamp is on, and warn others not to enter until it is shut off.
31
4. AEROBICSPOREREMOVALTHROUGHGRANULARFILTERSANDAPPLICATIONSTOQUANTITATIVEMICROBIALRISKASSESSMENTS
4.1. ABSTRACT
Biological filtration has recently received renewed interest for its applicability as a
pretreatment to achieve removal of dissolved organic carbon including disinfection by-product
precursors, however there has been limited studies regarding its efficacy for physical removal of
pathogenic particles. This study experimentally determined log-removal values for aerobic
spores at three drinking water treatment facilities through a variety of filter configurations
including anthracite, GAC, as well as the addition of nutrient supplementation with nitrogen and
phosphorus. Biological filters were shown to achieve < 1-log10 reduction of aerobic spores,
while conventional filtration achieved > 3-log10 removal. Aerobic spore removal was found to
be correlated with biofilm protein and polysaccharide levels when considering biological filters.
Quantitative Microbial Risk Assessments were also conducted based on the demonstrated spore
removal at each drinking water facility.
4.2. KEYWORDS
Biological Filtration, Aerobic Spores, Quantitative Microbial Risk Assessments
4.3. INTRODUCTION
Conventional rapid granular filtration is a common water treatment process for the
removal of pathogens and serves as a primary barrier against microbial risk for many drinking
water plants. Quantitative Microbial Risk Analysis (QMRA) provides a framework for
calculating the risk of illness and overall health burden associated with pathogens in drinking
water. Several common QMRA methods utilize source water pathogen concentrations and
estimates of treatment plant performance as input data. Due to the inherent variability in raw
water pathogen counts (Weiss et al., 2005), and highly variable plant performance (KWR 2010),
32
many studies have opted to estimate the concentration of pathogens in the final treated water.
(Health Canada 2013). This strategy requires knowledge of both the influent concentration as
well as a reasonable understanding of the typical filtration removal performance. Pilot studies
incorporating surrogate organisms have been used to characterize operational performance in
terms of particle and pathogen removal (Hijnen et al., 2000; Hsu and Yeh, 2003; Huertas et al.,
2003; Galofre et al., 2004).
Disability adjusted life years (DALY’s) are a common metric for evaluating health risks
associated with drinking water. Health Canada has adopted the World Health Organization
(WHO) standard of 10-6 DALY’s per person, per year as an acceptable risk threshold. In order to
maintain risk levels below this threshold, a treatment plant must achieve a final cryptosporidium
count of less than 1.3 x 10-4 oocysts per liter (Health Canada, 2013). This low target
concentration is difficult to quantify using typical analytical methods. Bacterial spores and
microspheres have been used as surrogates to determine removal through filtration units as both
have similar size and shape characteristics as native cryptosporidium oocysts (Emelko 2003; Hsu
and Huang, 2002) and have been shown to provide conservative estimates for the removal of
oocysts in most treatment facilities (Brown and Cornwell, 2007; Galofre et al., 2004).
Muhammad et al. (2008) observed that aerobic spores, while conservative, were more
representative of cryptosporidium oocysts than microspheres in a study conducted using point-
of-use filtration systems. Jaidi et al. (2009) reported a linear correlation between aerobic spores
and cryptosporidium removal based on a review of 15 other studies; this information was then
utilized in a QMRA model to predict overall risk of illness. Bastos et al. (2013) concluded that it
is necessary to consider variability in both turbidity and aerobic spore removal when evaluating
risk through QMRA processes. Tfaily et al. (2015) have also reiterated the importance of
33
accurate process assessments including carefully characterizing the performance of disinfection
stages and the usefulness of spores as potential surrogate for pathogen transport.
Granular media filters may be colonized by bacteria and biofilm when they are not
preceded by a chemical disinfectant. Biologically active filters serve to reduce natural organic
matter as well as provide pathogen removal (Persson et al., 2005). Limited pathogen removal
(<1.0 log10) has been demonstrated through biologically active granular filters (Hijnen et al.,
2010; Heller et al., 2007; Mazoua and Chauveheid, 2005). Several researchers have investigated
the impact of biofilm growth in rapid filtration units, however many have been conducted using
glass beads or using other artificial surfaces (Dai and Hozalski, 2002; Helmi et al., 2008; Li et
al., 2006) rather than anthracite or GAC filter media. Papineau et al. (2012) found that biofilm
improved filter performance with respect to pathogen removal. This was attributed to an increase
in polysaccharide accumulation on the media surface. Limited research exists with respect to
pathogen removal effectiveness of biological granular media filters; especially those which may
receive nutrients to enhance biological performance. While several studies have supplemented
biofilm growth with carbon, nitrogen and phosphorus, (Papineau et al., 2012; Dai and Hozalski,
2002), there is very limited evidence regarding the effectiveness of using nutrient
supplementation as an operational parameter to optimize pathogen removal.
The objective of this pilot-scale study was to compare the effectiveness of biological and
conventional filters with respect to aerobic spore removal as it relates to biological activity and
operational performance. This data was then employed as a surrogate for cryptosporidium to
determine the risk of illness and health burden at three Ontario drinking water plants using
QMRA. The facilities were located on varying surface water sources and allowed for
comparison of 19 discreet pilot-scale filtration configurations (including both conventional and
34
biological), using a range of chemical coagulants, filtration media types, and operational
parameters.
4.4. MATERIALSANDMETHODS
4.4.1. PILOTPLANTCHARACTERISTICS
Typical source water characteristics for the three pilot plants are shown in Table 4-1. Two of
the plants (A & B) are located on river sources with relatively high DOC and low turbidity while
plant C is located on a lake with low DOC and turbidity.
Table 4-1: Source water characteristics for the three pilot plants.
Plant A Plant B Plant C
Temperature (°C) 17-21 18-23 9
Alkalinity
(mg/L CaCO3) 25-40 70-80 80-90
pH 7.3-7.5 7.7-7.9 7.6-7.9
DOC (mg/L) 4-7 3-5 2-3
Turbidity (NTU) 3-5 1-4 0.4-1
Plants A and B both utilize similar pilot facilities with two parallel treatment trains
equipped with tapered flocculation and laminar plate settling thereby allowing for side-by-side
comparison of operating conditions. Plant A was equipped with a total of four 15.2 cm (6”)
glass filter columns fed from the settled water tanks. Plant B had two 15.2 cm (6”) glass filter
columns fed by settled water as well as a further two glass filter columns and four smaller
7.62cm (3”) acrylic filter columns that were fed directly from the river source water. Plant C was
operated as a direct filtration pilot facility with tapered flocculation preceding three 15.2 cm (6”)
glass filter columns. An additional four 7.62cm (3”) acrylic filter columns were fed directly
from the Lake Ontario source water. Figures depicting the layout of all three pilot plants are
available in the Appendix. Seasonal chlorination of the raw water (above 12°C) is practiced at
35
Plants B and C for zebra mussel control however any remaining residual was quenched using
sodium thiosulphate or sodium bisulphite prior to biological filtration.
4.4.2. SPOREPREPARATION
Bacterial endospores were prepared from glycerol based stocks of ATCC 9372 stored at -
80°C and reanimated through an overnight culture in nutrient broth (Difco) incubated at 37°C
and 120 RPM. A 1mL aliquot of the overnight culture was then transferred to 400 mL
Erlenmeyer flasks of liquid sporulation media consisting of nutrient broth supplemented with
additional mineral salts (MnSO4, CaCl2, MgSO4) and incubated for up to 7 days in an orbital
incubator at 30°C and 120 RPM. Spores were purified by serial centrifugation at 9000 RPM for
10 mins. Supernatant was drawn off and spores were re-suspended in sterile DI water. This
method has been adapted from Harwood and Cutting (1990). Storage was at 4°C for up to 2
weeks, at which point the pellet was re-suspended in filter effluent prior to injection into pilot
plant. Sporulation and heat resistance were verified prior to inoculation of pilot plant systems.
Inoculation or spiking was conducted over a 24 hour period, allowing all treatment processes to
adjust or acclimatize to the presence of aerobic spores.
4.4.3. SAMPLINGPROTOCOL
Sampling was conducted approximately 24 hours following a filter backwash to avoid
ripening periods (Figure 4-1). Aerobic spore enumeration was performed by collecting 1 liter
samples from locations upstream and downstream of selected treatment process units. Sequential
samples were collected in sterile, triple rinsed, glass bottles. Samples were filtered using sterile
0.45μm nitrocellulose membranes (Millipore HAWG047) and placed in an oven at 80°C for 20
minutes to inactivate all vegetative bacteria, leaving only the heat resistant spores as viable for
potential growth on the membrane surface as per methods described by Hill et al. (2012). Each
36
membrane was then placed on nutrient agar and incubated for 24 hours at 37°C. Counts of 20-
200 colonies forming units (CFU) per membrane were achieved by further dilution with
phosphate buffered saline (PBS) as needed.
Figure 4-1: Turbidity profile during typical filter run for Plant A showing stable filter operation following ripening spike.
4.4.4. WATERQUALITYPARAMETERS
Turbidity measurements were conducted using a benchtop Hach turbidimeter for small (3”)
filters and online flowing samples through a Hach 1720E for the large diameter (6”) filters. DOC
was measured using a wet oxidation method as described in Standard Method 5310 D (APHA,
2012) with an O-I Corporation Model 1010 TOC Analyzer (College Station, Texas, USA). LC-
OCD analyses were conducted at the University of Waterloo (Waterloo, ON) according to a
method described by Huber et al. (2011).
ATP concentrations were assayed using a LuminUltra Deposit Surface Analysis kit (DSA-
100C, Fredericton, NB) following the manufacturer’s instructions. Extracellular polymeric
substance (EPS) was extracted in Tris-EDTA buffer (10mM Tris, 10mM EDTA), where 10mL
was added to 2g of media, shaken at 300 rpm for 4h at 4°C and centrifuged (Liu and Fang,
37
2002). The supernatant was passed through a 0.45μM filter and stored at -20°C. Protein and
polysaccharide components of EPS were quantified according to Pierce™ BCA (Thermo Scientific)
and DuBois et al. (1956) methods, respectively. For protein quantification, a CE 3055 Single Beam
Cecil UV/Visible Spectrophotometer (Cambridge, England) was used; a Hach Odyssey DR/2500
Scanning Spectrophotometer (Mississauga, ON) was employed for polysaccharide measurements.
4.4.5. RISKANALYSIS&STATISTICALMETHODOLOGY
Quantitative Microbial Risk Assessments were conducted using a probabilistic risk model
developed by Health Canada and prepared in Microsoft Excel (version 13_07). Full-scale
process data for the three plants was incorporated into monthly risk assessment calculations.
Raw water pathogen concentrations were determined using a dead-end ultrafilter cartridge and
enumerated according to methods established and executed by Tetratech Environmental
(Burlington, VT). Cryptosporidium oocysts and giardia cysts were enumerated by
immunomagnetic separation (IMS) methods similar to USEPA method 1623 (employing antigen
coated magnetic microbeads, specific to cryptosporidium or giardia). Enteric viruses were
enumerated by cell culture on three independent cell-lines. Campylobacter and e. coli O157
were also enumerated by culture methods using organism-specific growth media. Recovery
varied for each site, and a pooled total of 10 sites was used to provide a representative dataset.
Statistical analysis for correlation were performed using ANOVA; treatment comparisons were
evaluated with Tukey’s Honest Significant Difference (HSD) test. All statistical calculations
were performed in R (version i386 3.0.2).
38
4.5. RESULTS
4.5.1. AEROBICSPOREREMOVAL
Aerobic spore removal was quantified by measuring both influent and effluent concentrations
for the various process trains. For all trials, the average influent concentrations were
approximately 103 CFU/mL while effluent concentrations were < 1 CFU/mL for conventional
filters and approximately 102 CFU/mL for most biological filters. Log removal values were
based on a minimum of 4 time-series samples collected at 15 minutes intervals from both
influent and effluent sampling locations. Biofilters fed directly with raw water performed
poorly in terms spore removal, with most achieving less than 1.0-log10 removal of aerobic spores
(Figure 4-2). Results published by Persson et al. (2005) reported 60-90% (0.4 to 1.0 log10)
removal of particles through biofiltration,; Papineau et al. (2012) observed cryptosporidium
removal of up to 71% (0.54-log10) through spent GAC filters. Experimental results from this
research suggest that coagulation and subsequent rapid granular filtration perform better than
biological granular filtration alone. When evaluated at α=0.05, all biological filters at Plant B
achieved comparable spore removal. For plant C, most biofilters achieved 0.11 to 0.15-log10
whereas the application of in-line low-dose coagulant addition (0.2 mg/L PACl) resulted in
significantly (p<0.05) higher aerobic spore removal (0.52-log10).
Conventional filtration at Plant A allowed for comparison of coagulant chemicals as well as
media type, with anthracite based filters achieving slightly lower (median removal of 2.95-log10
and 3.25-log10, for alum and ferric sulphate, respectively). GAC-based filters achieved median
removal of 3.29-log10 and 3.44-log10, for alum and ferric sulphate respectively. Ferric sulphate
coagulation and GAC filters both contributed to an increase in aerobic spore removal. Plant C
demonstrated similar trends with a PACl coagulated GAC filter achieving 3.56-log10 compared
to its anthracite based counterpart which achieved only 3.27-log10. Plant B demonstrated the
39
highest median removals for both the alum and PACl coagulated anthracite-based filters (median
removals of 3.60-log10 and 3.85-log10, respectively).
Figure 4-2: Aerobic spore removal for a range of coagulation and filtration conditions.
The relationship between turbidity and pathogen removal is specific to source water
characteristics. Emelko et al. (2006) reported that filter turbidity values of less than 0.10 NTU
were indicative of consistent pathogen removal performance greater than 3.0-log10. Mazoua and
Chauveheid (2005) found that turbidity values between 0.07 and 0.12 NTU were equally likely
to be associated with low pathogen counts in filtered water. Figure 4-3, which compares the
results of all three pilot plants, shows filters with turbidity values > 0.15 NTU to have
significantly higher breakthrough of aerobic spores. Maintaining effluent turbidity values of <
40
0.15 NTU could be considered as a potential regulatory limit to indicate that filters are achieving
effective removal of protozoan pathogens.
Conventional filtration, preceded by flocculation and sedimentation resulted in aerobic
spore removal exceeding 2.5-log10 (median results ranging from 2.9-log10 to 3.9-log10).
Sedimentation prior to conventional filtration also achieved greater than 1.0-log10 removal
(typically 1.2 to 1.6-log10, data not shown). Limited observations of direct filtration at Plant C
showed median removal results ranging from 3.3 to 3.6-log10, similar to past results (Emelko
(2003); Galofré et al. (2013); Brown and Cornwell (2007)). Regulatory credits for these
processes are typically 2.0-log10 removal of cryptosporidium which is significantly lower than
the experimental results observed in this study and reported in literature.
Figure 4-3: Aerobic spore removal vs filter effluent turbidity for all filter trials.
Vertical bars represent ±1 standard deviation.
Biological activity on filtration media was monitored on a monthly basis and was quantified by
measuring ATP and EPS (including proteins and polysaccharides). ATP values exceeding 100
ng/g of media were considered to be indicative of a biologically active filter and this is
0
1
2
3
4
5
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
Aer
obic
Spo
re R
emov
al (
Log 1
0)
Effluent Turbidity (NTU)
Plant A Plant B Plant C
41
comparable to the studies by Pharand et al. (2014) who observed typical ATP levels in biological
filters of 102-103 ng/cm3. As such, all filters that were not preceded by coagulation were deemed
to be biologically active according to this metric. Additionally, the anthracite filter at Plant A
that was preceded by alum coagulation and sedimentation was the only biologically active
“conventional filter” with an average ATP value of 105 ng/g. The other filters at Plant A,
including both GAC filters, were below the 100 ng/g threshold with values ranging from 32 ng/g
to 91 ng/g. Conventional filters at Plant B and direct filters at Plant C also had average ATP
values < 100 ng/g, with averages of 57 ng/g and 65 ng/g respectively. Figure 4-4 and 4-5 show
average ATP concentrations for Plant B and C and highlight the significant difference (p<0.05)
between ATP levels in conventional filters vs biological filters directly receiving raw water.
When compared at a significance level of α=0.05, all biological filters exhibited similar ATP
levels. The only notable exception was the nutrient enhanced filter (supplemented with 0.5 mg/L
of both nitrogen and phosphorus) which had slightly elevated average ATP levels (2374 ng/g)
when compared to a control filter (1748 ng/g) (p=0.16). No significant correlation with ATP
activity and water temperature was observed. In addition, ATP was not statistically related to
aerobic spore removal (p>0.05).
42
Figure 4-4: ATP concentrations for Plant B.
Vertical bars represent ±1 standard deviation.
Figure 4-5: ATP concentrations for Plant C.
Vertical bars represent ±1 standard deviation.
Biofilm was also quantified using measures of proteins and polysaccharides; both
discrete fractions of the extracellular polymeric substances (EPS) excreted by bacteria on the
surface of filter media. Proteins and polysaccharides have previously been shown to have strong
1000
10000
100000
1000000
10000000
Anth40 mg/L
Alum
Anth Anth0.5 mg/L
N & P
Anth Anth0.2 mg/L
H2O2
Anth 0.2 mg/L
Alum
GAC
AT
P (
pg/g
)
1000
10000
100000
1000000
GAC0.8 mg/L
PACl
Anth0.8 mg/L
PACl
GAC0.8 mg/L
PACl
GAC GAC0.5 mg/L
N & P
GAC0.2 mg/L
PACl
GAC0.2 mg/L
H2O2
AT
P (
pg/g
)
43
correlations with each other; Papineau et al. (2012) reported r = 0.96 between these parameters in
bench scale GAC and anthracite filters. For Plants B and C, linear relationships were observed
with of r = 0.70 and r = 0.72 respectively. There is conflicting evidence in the literature
regarding the impact of biofilms on pathogen removal. Dai and Hozalski (2002) examined
cryptosporidium removal through filters filled with glass beads and measured clean-bed
cryptosporidium removal of 53%. Comparatively, biofilm coated filter beads removed only 21%
of cryptosporidium oocysts. Hijnen et al. (2010) reported lower removals with increased biofilm
development (2.7-log10 in fresh GAC filters, versus 1.3-log10 observed in GAC filters that had
been actively filtering for 2 years). Papineau et al. (2012), however, observed an increase in
cryptosporidium removal with elevated biofilm levels (40% removal in virgin GAC beds versus
71% in GAC that had significant accumulation of biofilm EPS). Figure 4-6 shows that proteins
are a strong predictor of spore removal performance (p<0.01). Proteins also correlated will with
ATP levels (p<0.01) and DOC removal (p=0.02) as shown in Table 4-2. Protein concentrations
at Plant B ranged from 382 μg/g to 941 μg/g, with the lowest levels observed for the H2O2 pre-
filter addition and highest values associated with nitrogen and phosphorus. Azzeh et al. (2014)
observed a 48% reduction in headloss in a biological filter by the addition of H2O2 as a result of
reducing the accumulation of EPS compounds. The addition of 0.5 mg/L of nitrogen (NH4) and
phosphorus (PO4) resulted in decreased spore removal performance for Plant B (median removal
of 0.66-log10) when compared to anthracite-based control filters (median removals ranging from
0.82 to 1.00-log10). H2O2 and nutrient addition both resulted in negligible impact to aerobic
spore removal at Plant C. Increased polysaccharide levels were typically indicative of greater
spore removal performance (p<0.01), but were not found to be significant predictors of ATP
concentrations or DOC reductions for any of the biofilters.
44
Plant C, which draws water from Lake Ontario had significantly lower protein levels (40-
109 μg/g). Polysaccharide concentrations for all filters were observed to be between 7 and 27
μg/g. This may be due in part to the lower concentration of dissolved organic carbon (~2.5 mg/L)
in the source water of plant C when compared to >6 mg/L for Plant B. The filter supplemented
with 0.2 mg/L of H2O2 was found to have lower levels of both proteins and polysaccharides
when compared to all other filters including the control, inline coagulant, and nutrient
supplemented filters.
Inline addition of coagulants immediately upstream of filters was also investigated as a
potential pre-treatment for biological filtration. Alum and PACl were both found to decrease
proteins and polysaccharides, but were subsequently observed to increase pathogen capture
within the filter as summarized in Table 4-4. This suggests that the mechanism of surface charge
adjustment accomplished through coagulation had a greater impact on pathogen removal than the
development of EPS for both Plants B and C.
GAC media had significantly higher (p<0.01) mean protein concentrations when
compared to anthracite based filters, but was not shown to have elevated ATP levels. This was
especially prominent at Plant C where there was lower levels of influent DOC (~2.5 mg/L).
When evaluating both conventional and direct filtration, the results indicate that GAC is superior
to anthracite for pathogen removal which may potentially be attributed to the increased surface
area and pore volume (Emelko 2006). For all conventional filters, the ATP and EPS (protein and
polysaccharide) levels were significantly lower (p<0.05) than their biological counterparts which
indicates that the increased pathogen removal performance was not associated with accumulated
EPS material on the media particles but rather the effectiveness of coagulation.
45
Biological filtration is often monitored with respect to the removal of organics using either
direct DOC analysis or UV254 measurements. Aerobic spore removal was not found to be a
function of DOC or UV254 reductions for these filters (p=0.16, 0.29; respectively). EBCT was
also not shown to be a predictor of biofiltration performance in terms of aerobic spore removal.
Table 4-2: Analysis of Variance (ANOVA) results for pilot scale biofilters at Plant B and C.
Figure 4-6: Relationship between aerobic spore removal and biofilm proteins for plants B and C.
y = 0.0009x + 0.2387R = 0.76
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 200 400 600 800 1000
Aer
obic
Spo
re R
emov
al (
Log 1
0)
Biofilm Protein Concentration (μg/g)
Plant B Plant C
Turbidity
(NTU) ΔUV
(cm-1) ΔDOC (mg/L)
Spore Removal
(log10) ATP
(ng/g) Polysaccharides
(ug/g) Proteins
(ug/g)
Turbidity (NTU) - 0.50 0.04 <0.01 0.59 0.52 0.69
ΔUV (cm-1) 0.50 - 0.16 0.29 0.08 0.46 0.07
ΔDOC (mg/L) 0.04 0.16 - 0.16 0.92 0.21 0.02
Spore Removal(log10) <0.01 0.29 0.16 - 0.32 0.01 <0.01
ATP (ng/g) 0.59 0.08 0.92 0.32 - 0.63 <0.01
Polysaccharides (μg/g) 0.52 0.46 0.21 <0.01 0.63 - <0.01
Proteins (ug/g) 0.69 0.07 0.02 <0.01 <0.01 <0.01 - p-values < 0.05 are shown in bold to highlight significant relationships.
46
4.5.2. QMRARESULTS
By considering aerobic spore removal to be a conservative indication of cryptosporidium
reduction through granular filtration it is possible to calculate the overall health burden for the
three water treatment facilities in this study. Removal performance for the additional 4 pathogens
(giardia, rotavirus, E. coli O157 and campylobacter) included in the Health Canada model was
assumed to follow observations reported in the “Elimination of Micro-organisms by Water
Treatment Process” (KWR 2010). Each pathogen was granted a percentage of the removal credit
associated with cryptosporidium. As such, all pathogen removals have been related to the
aerobic spore removal demonstrated at each facility. Table 4-2 shows the average aerobic spore
removal performance observed at these facilities including upstream coagulation, flocculation
and sedimentation where appropriate. In general, slightly higher removals were observed in this
study when compared to the average literature performance reported for conventional and direct
filtration. Aerobic spore removal through non-coagulated biological filters were slightly lower
than those presented in literature, but this may be due to the effect of source water conditions
observed at Plants B and C. Disinfection was characterized according to the results presented in
Table 4-4 which summarizes annual average physical-chemical conditions for disinfection at the
3 plants.
A risk analysis plot was produced based upon the experimental removals observed at
these facilities for both conventional and direct filtration, as well as biological filtration (Figure
4-7). While physical removal has been experimentally validated through this work, there was
no effort to quantify the effectiveness of disinfection following either conventional or biological
filtration. All disinfection credits were based upon the Health Canada QMRA model predictions.
Due to the extended contact times available at these facilities, and the fact that many are
47
targeting >2-log10 inactivation of giardia, the inactivation of bacteria and viruses is often greater
than the 8-log10 maximum limit in the Health Canada model. Biological filtration (without
adequate coagulation upstream) was shown to be an insufficient barrier against microbial illness,
particularly for the protozoan pathogens (cryptosporidium & giardia) which consistently exceed
the WHO’s target of 10-6 DALY/pp/yr.
48
Table 4-3: Summary of observed aerobic spore removals and the related literature values for other pathogens included in the Health Canada risk model.
Experimental
Results Estimated Removal Based on Literature
Plant Chemical Additions
Process Description
Media Type
Aerobic Spore Removal (log10)
Giardia (log10)
Viruses(log10)
Bacteria (log10)
mean±sd mean mean mean
A
30 mg/L Alum Coagulation, Flocculation,
Sedimentation 1.27±0.15 1.10 1.20 1.06
40 mg/L Ferric Coagulation, Flocculation,
Sedimentation 1.09±0.11 0.94 1.03 0.91
30 mg/L Alum Conventional
Filtration Anthracite 2.96±0.15 2.35 1.36 1.06
30 mg/L Alum Conventional
Filtration GAC 3.29±0.17 2.61 1.51 1.18
40 mg/L Ferric Conventional
Filtration Anthracite 3.21±0.14 2.55 1.47 1.15
40 mg/L Ferric Conventional
Filtration GAC 3.47±0.14 2.76 1.59 1.25
B
60 mg/L Alum Coagulation, Flocculation,
Sedimentation 1.43±0.30 1.24 1.35 1.19
8 mg/L PACl Coagulation, Flocculation,
Sedimentation 1.31±0.28 1.13 1.24 1.09
60 mg/L Alum Conventional
Filtration Anthracite 3.62±0.80 2.88 1.66 1.30
8 mg/L PACl Conventional
Filtration Anthracite 3.79±0.57 3.01 1.74 1.36
Biological Filtration
Anthracite 0.93±0.22 1.03 0.64 0.46
0.5 mg/L N&P Biological Filtration
Anthracite 0.83±0.32 0.92 0.57 0.41
0.5 mg/L H2O2 Biological Filtration
Anthracite 0.73±0.23 0.81 0.51 0.36
0.2 mg/L Alum Biological Filtration
Anthracite 0.78±0.23 0.86 0.54 0.38
Biological Filtration
GAC 0.83±0.13 0.92 0.57 0.41
C
0.8 mg/L PACl Direct
Filtration GAC 3.26±0.02 3.14 0.64 1.49
0.8 mg/L PACl Direct
Filtration Anthracite 3.29±0.07 3.17 0.65 1.50
0.8 mg/L PACl Conventional
Filtration GAC 3.56±0.07 3.43 0.70 1.63
Biological Filtration
GAC 0.14±0.06 0.15 0.10 0.07
0.5 mg/L N&P Biological Filtration
GAC 0.14±0.02 0.15 0.10 0.07
0.2 mg/L PACl Biological Filtration
GAC 0.53±0.02 0.59 0.37 0.26
0.5 mg/L H2O2 Biological Filtration
GAC 0.11±0.01 0.12 0.08 0.05
49
Table 4-4: Annual average disinfection conditions for the 3 plants investigated.
Plant ID
Contact Time (min)
Chlorine Residual (mg/L)
pH Temperature
(°C) Baffle Factor
A 60.4 1.20 5.84 9.8 0.67
B 176.0 1.61 6.96 10.3 0.8
C 116.6 1.69 7.67 6.3 0.6
Figure 4-7: Comparison of QMRA results for all 3 plants assuming average experimental physical removal and annual averages for disinfection conditions.
4.6. CONCLUSIONS
Biological filtration in the absence of coagulation has been shown achieve significant
reductions in microbial risk by demonstrating up to 1-log10 reduction in aerobic spores (a
surrogate for cryptosporidium). Filtration performance was dependent on treatment
configuration, with nutrient supplemented GAC filters showing higher pathogen removal
A1 A2 A3 A4 B1 B2 B3 B4 B5 B6 B7 C1 C2 C3 C4 C5 C6 C7
DA
LY p
p/yr
Crypto Giardia Virus Campy Ecoli
WHO Target (10-6 DALY pp/yr) 10-6
10-9
10-12
10-15
10-3
100
50
capabilities when compared to those that were supplemented with hydrogen peroxide to reduce
headloss. Furthermore, aerobic spore removal performance was shown to be related to the level
of EPS on the filter media, as the concentration of proteins in the biofilm were found to have a
significant impact on aerobic spore removal performance. Conventional filtration (filters assisted
with coagulation), consistently achieved greater than 3-log10 for all three facilities regardless of
media configuration and coagulant applied.
51
5. QUANTITATIVEMICROBIALRISKASSESSMENTSFOR10CANADIANWATER
UTILITIES
5.1. ABSTRACT
Quantitative Microbial Risk Assessments (QMRA) were conducted for 10 drinking water
facilities in Canada. Monthly risk estimates were based upon raw water samples for a suite of
reference pathogens as well as detailed processes assessments that considered the variability of
disinfection processes at the individual utilities. The majority of the utilities (9 out of 10)
maintained risk levels significantly below the World Health Organization’s target of 10-6 DALY
pp/yr. Cryptosporidium was shown to be the most significant contributor for microbial risk at all
10 utilities. Plant specific treatment failure scenarios were also evaluated; most treatment
facilities were capable of withstanding partial filtration and disinfection failures as a result of
following the multi-barrier approach.
5.2. KEYWORDS
QMRA, Microbial Risk, Drinking Water, Canada
5.3. INTRODUCTION
Quantitative Microbial Risk Analysis is a means by which water utilities and regulatory
bodies can estimate the potential health burden for drinking water consumers (WHO 2011). The
results of a Quantitative Microbial Risk Assessment may be used to infer the current level of risk
or may be used to evaluate future microbial risk levels under varying raw water conditions and
plant performance scenarios (Jaidi et al., 2009). The most widely accepted QMRA model in the
Canadian context was produced by Health Canada and has undergone several revisions and
updates since its initial release in 2008 (Health Canada 2013). Recent applications of the Health
Canada model include Tfaily et al. (2015) who considered the implications of disinfection at 17
52
Canadian drinking water treatment facilities based on raw water surveys for a limited suite of
pathogens including cryptosporidium, giardia and indirect estimates for E. coli levels.
The current Health Canada model (version 13_04) considers the risk resulting from
potential exposure to five model organisms; namely cryptosporidium, giardia, rotavirus (a
model enteric virus strain), campylobacter, and enterohemorhagic e.coli O157. Many of these
organisms have been implicated in well-known waterborne illness outbreaks including
Milwaukee (cryptosporidium in 1993), Sydney (giardia in 1998), and Walkerton (E. coli in
2000). Furthermore, the treatment of these organisms requires a multi-barrier approach to
effectively reduce risk for drinking water consumers. USEPA risk assessments in 1991
determined that the acceptable level of risk related to drinking water was 1 illness for every 10
000 persons. According to Lechevallier and Hubel (2004), to produce an acceptable level of risk
(10-4 risk of infection) associated with cryptosporidium oocysts, the final treated water
concentration should be below 1 oocyst per 290,000 L. The World Health Organization
proposed the Disability Adjusted Life Year (DALY) as a more generalized metric of health
burden and established a drinking water threshold at 10-6 DALY/pp/year. This represents a lower
guideline for protozoan pathogens, however it is quite stringent for pathogens with more severe
symptomatic expressions such as e. coli and rotavirus. Both thresholds should be considered
when estimating the risk associated with drinking water consumption.
Application of QMRA is quite common in the food industry and is gaining recognition in
drinking water treatment. QMRA provides pertinent information to utility operators and
regulators regarding the effectiveness of their treatment processes and an understanding of the
importance of maintaining source water quality. Most models focus on annualized risk, rather
than incorporating short term episodic events in their calculations.
53
5.4. METHODS
The Health Canada QMRA model estimate the physical removal performance based on a
literature survey conducted by KWR to through various drinking water processes. The survey
included both full scale and pilot scale treatment investigations and ranked studies based on the
quality of work performed. These weighted mean values are one of the primary inputs to the
Health Canada model, although it also provides for the use of upper or lower removal estimates
based on 1 standard deviation from the weighted means to provide a broader estimate of the
variability in removal performance. Primary disinfection in the Health Canada model is
estimated through an NCSTR calculation module which incorporates parameters such as
disinfectant residual decay, hydraulic baffling factor, mean hydraulic residence time, temperature
and pH. Ultraviolet disinfection credits are granted according to regulatory inactivation
relationships (USEPA, 1991). All dose response curves are specific to each organism and have
been based in literature studies (either epidemiologically-based or human feeding trials) as
shown in Table 5-1.
Table 5-1: Dose response models and parameters for five pathogens in the Health Canada model.
Pathogen r α β Equation Model Source
Crypto 0.018 - - Pinfection = 1 – e-µVr Poisson Messner et al., 2001
Giardia 0.01982 - - Pinfection = 1 – e-µVr Poisson Rose & Gerba, 1991
Rota - 0.265 0.4415 Pinfection = 1 – (1+d/β)-α Beta-poisson Haas, 1999
Campy - 0.024 0.011 Pinfection = 1 – (1+d/β)-α Beta-poisson Teunis et al., 2005
E.coli O157 - 0.0571 2.2183 Pinfection = 1 – (1+d/β)-α Beta-poisson Stachan, 2005
where: d = dose (#pathogens ingested) =µV µ= mean pathogen concentration (# per liter) V = volume of water consumed (liters) r, α, β = coefficients
Infection and illness relationships were based on literature studies with most pathogens
having infection rates between 0.7 and 1. These factors are presented in Table 5-2. In the event
54
that illness information was not available (particularly for e.coli and campylobacter), the risk of
illness given a positive infection was deemed to be 1.0. Both the infection and illness
correlations were based on the assumption that all pathogens are infectious.
Table 5-2: Illness risks given infection for the five reference pathogens.
Pathogen r Reference
Crypto 0.7 Casman et al, 2000
Giardia 0.4 Nash et al. 1987
Rota 0.88 Havelaar & Melse, 2003
Campy 1.0 Illness used directly in dose response model
E.coli O157 1.0 Illness used directly in dose response model
Disability adjusted life-years (DALY’s) were calculated for every illness predicted by the
model. Individual illnesses had a subset of symptoms, each of which was assigned a probability
and severity of the outcome. The age distribution in the Canadian population was also
considered in the determination of the relative impact of symptom duration on the overall DALY
risk factor. Table 5-3 provides a summary of DALY risk values.
Table 5-3: Summary of DALY risk values for the 5 pathogens in the Health Canada model.
Crypto Giardia Rotavirus Campylobacter Ecoli O157
DALY’s due to morbidity (YLD)
1.29E-03 1.29E-03 4.31E-03 3.19E-03 1.42E-02
DALY’s due to mortality (LYL)
4.15E-04 4.15E-04 4.15E-03 1.41E-03 1.04E-02
TOTAL DALY’s 1.70E-03 1.70E-03 8.46E-03 4.60E-03 2.45E-02
5.4.1. RAWWATERPATHOGENS
The pathogen survey conducted in this study utilized a dead end ultrafiltration (DEUF)
cartridge to filter raw source water. The cartridges were then shipped by courier to TetraTech
Environmental (Burlington, VT) for processing and enumeration of the pathogenic particles
55
captured on the membrane. The cartridges were backwashed with a solution containing 0.5%
(v/v) Tween 80, 0.01% w/v sodium hexametaphosphate and 10 μL Antifoam Y-30 emulsion in 1
L of deionized water. The total backwash volume was then subdivided into aliquots for the
various pathogen enumeration procedures. Cryptosporidium and giardia were enumerated using
an immunomagnetic separation technique adapted from USEPA Method 1623 while total enteric
viruses, as well as campylobacter and E. coli O157 were enumerated via pathogen specific
culture methods. Matrix spike recoveries conducted at each site indicated that the cartridge and
subsequent processing efforts had overall recoveries rates of approximately 35-45% for
cryptosporidium and giardia, 69% for campylobacter and for E. coli O157 and 2% for enteric
viruses. Source water concentrations reported in Table 5-4 are corrected for recovery.
Table 5-4: Summary of raw water pathogen results for the 5 pathogens of interest
Raw Water Concentration (min-max, average) [# / 100L)
Crypto Giardia Virus Campy E.coli
A 4.9-26.6,
8.1 4.9-26.6,
8.1 108.3-2729.3,
542.9 18.1-44.4,
23.6 18.1-44.4,
23.6
B 4.9-67,
16.1 4.9-67,
16.1 110.3-6004.4,
1151.3 18-717.2,
177.1 18-36.8,
23.1
C 1.1-7,
5.1 1.1-7,
5.1 123.4-3920.2,
1042.9 10.6-45.7,
20.3 10.6-22.3,
19.1
D 1.4-6.6,
5.1 1.4-33.3,
11.2 158-15383.2,
3866.4 10.4-1226.8,
227.8 9.9-24.3,
19.1
E 1.4-6.2,
4.5 1.4-10.1,
4.8 87.4-8436,
3028.6 8.5-8165.1,
547.7 8.5-23.1,
18.3
F 1.2-7.1,
5.4 1.2-7.1,
5.4 134.1-7642,
777.9 10.7-23.1,
18.4 10.7-23.1,
18.8
G 1.2-10.5,
5.3 1.2-7,
5.1 108.3-3424,
548.9 10.4-23.9,
19 10.4-23.9,
19
H 2.8-11.1,
5.5 2.8-141,
17.1 154.1-58555,
16071.1 10.6-3198.5,
471.5 10.6-21.7,
19.3
I 0.4-10.1,
4 0.4-10.1,
4.1 61-2381.9,
446.5 3.9-81.7,
22.6 3.9-25.2,
14.2
J 1.1-2.9,
1.8 1.1-5.9,
1.9 47.7-794,
268.3 4.4-10.8,
6.5 4.4-10.8,
6.5
56
Petterson et al. (2007) emphasized the importance of considering matrix recovery effects
in QMRA calculations. Recovery results for the DEUF cartridges varied widely between source
water matrices. Average recovery for cryptosporidium and giardia via the adapted version of
Method 1623 averaged 36% and 45% for cryptosporidium and giardia respectively. While these
results are typical for a subjective multi-step quantitative method, there were some results on the
low end that are concerning for risk analysis, namely the results less than 20% for giardia at
Plant A, B, F, and G. A recovery of less than 20% effectively multiplies the risk by 5 times if
recovery is taken into account in microbial risk calculations. For many of these plants with low
recovery results there have been no positive results, however the recovery adjusted risk
calculations suggest a potentially elevated level of risk due to the low recovery levels for the
protozoa sampling methods.
In many cases, it was deemed more appropriate to use average recovery values across all
sampling sites rather than using the site specific recovery data. This was particularly evident for
virus recovery results where many of the sites demonstrated recoveries of less than 1%. Across
16 independent trials, the average recovery rate for adenovirus was 1.88%; most sites
encountered near 0% recovery for adenoviruses during this study. The only significant recovery
of adenovirus was recorded for the Plant E which achieved 8-9% across 3 independent trials, all
other sites observed less than 1%. Average recovery for E.coli O157 and campylobacter by
culture was approximately 69% after removing several results that had unreasonable recovery
values (700%-6582%).
Following 18 months of sampling, data for the 10 plants in the study revealed very few
positive detections for protozoan, bacterial and viral pathogens. Protozoan counts were
performed by immunomagnetic separation (IMS) while bacteria and viruses were counted with
57
both direct culture and quantitative polymerase chain reaction (qPCR) methods. Out of a total of
1290 counts performed, only 185 resulted in positive detections. This equates to a detection rate
of 14% for all samples. Cryptosporidium was detected in only 2 samples, while giardia was
found in 11. Viruses were detected in approximately half of the samples, while campylobacter
was observed in 17% of samples. E. coli O157 was never detected via culture methods, but
results show 52 positive detections via qPCR. Campylobacter on the other hand was detected 28
times by culture but resulted in zero detections by qPCR. A summary of the detection results is
found in Table 5-5.
Table 5-5: Pathogen monitoring data for all plants, MDL's are corrected for recovery.
In all cases, utility partners were requested to collect 100 L using a dead-end
ultrafiltration (DEUF) cartridge. While this was typically adhered to, there were several
deviations from this practice with some sites filtering as little as 45 L or as much as 341 L
depending on the throughput of the cartridge. Lower sample volumes did not appear to be
correlated with high influent turbidity during the sampling period. Ongerth and Frhat (2013)
have previously reported that the frequency of detection is highly dependent on sample volume
Crypto oocysts
Giardia cysts
Enteric Viruses Culture
CampyCulture
E. coli O157:H7 Culture
E. coli O157:H7
qPCR
Campy qPCR
AdenovirusqPCR
# detect 2 11 88 28 0 52 0 4
# of samples
163 163 153 163 161 163 162 162
Average MDL
(/100L) 12.4 12.4 309.8 36.0 35.9 1587.8 1708.1 1688.9
max MDL
(/100L) 134.1 134.1 936.3 88.8 88.8 6289.3 12578.6 12578.6
58
analyzed. Regulatory monitoring of 10 L samples required by the USEPA has resulted in 90%
non-detect values with over 60% of plants not observing any positive cryptosporidium results.
Quantitative PCR (qPCR) methods were not adopted for risk assessment purposes in this
study. It was unclear how genome copies related to infective units for both bacterial and viral
pathogens. In most cases where there was a non-detect, the qPCR data would produce even
higher risk values than the culture methods reported above. This was due to the very small
sample volumes that are processed with this method. Often sample volumes were less than 100
mL, resulting in MDL’s that were greater than 1000 units per 100 L.
5.4.2. PROCESSASSESSMENTS
Process assessments provided an overall evaluation of a treatment plant’s ability to
adequately treat pathogens that may be found in source water. A complete assessment considers
the process flow diagram for a treatment plant as well as specific parameters related to
disinfection which are sensitive to changes in operational practices and strategies. Care was
taken to document disinfection basin volumes, flowrates, disinfectant residual concentrations, as
well as water quality parameters such as temperature and pH. Filter turbidity values were
considered as indications of adequate filtration performance but were not utilized as inputs for
the Health Canada model. Table 5-6 contains a summary of the treatment technologies
employed at each of the treatment facilities in this study.
59
Table 5-6: Summary of treatment processes implemented by the various utilities.
Conventional treatment (coagulation, flocculation, sedimentation, followed by rapid
granular filtration) is a common method of water purification in North America. Six of the
plants studied had conventional treatment followed by varying methods of disinfection. Less
common treatment options included two plants that were equipped with ultrafiltration
membranes as well as one plant that focused on direct filtration for pathogen removal. In
addition, there was one plant that relied on a well-managed watershed protection plan and had no
physical treatment barriers for the removal of pathogens. UV disinfection was practiced at four
of the plants as a barrier against cryptosporidium and to mitigate risk in the event of
compromised effectiveness of upstream treatment processes.
Disinfection methods varied across the 10 plants, however, almost all plants utilized free
chlorine for primary disinfection. Notable exceptions include Plant I and J; Plant I had a large
scale ozone contactor for primary disinfection, mainly to meet regulatory giardia inactivation
requirements while Plant J implemented a ozonation unit upstream of the filters which provides
Plant Location
Physical Removal Inactivation
Coag/Floc/Sed Rapid
FiltrationDirect
Filtration Ultrafiltration Cl2 Ozone UV
A Y Y Y
B Y Y Y Y
C Y Y
D Y Y Y
E Y Y Y
F Y Y
G Y Y Y
H Y Y Y Y Y
I Y Y Y*
J Y Y Y *UV disinfection was commissioned during this study
60
both primary disinfection and taste and odour control. The remainder of the plants used large
contact basins to achieve appropriate CT (concentration*time) values to meet their regulatory
mandates for the inactivation of giardia. In most cases disinfection exceeded the maximum
allowable log-reduction limits in the Health Canada model. The Health Canada model only
allows for disinfection credits equivalent to twice the observed inactivation based on literature
studies. Sensitivity analyses (discussed below) highlights the operational conditions when the
plants may achieve less than the maximum log-inactivation credits.
According to the log removal credits predicted by the Health Canada model, which are
based primarily upon the KWR Elimination of Waterborne Pathogens report as shown in Table
5-7, all plants in this study exceeded the MOE’s required credit values of 2-log10 crypto, 3-log10
giardia, 4-log10 virus with the exception of plant I, prior to the commissioning of UV
disinfection upgrades.
Table 5-7: Log removal credits granted by the Health Canada QMRA model based on the data collected by KWR.
Process Category Crypto Giardia Virus Campy E.coli
Coagulation/Flocculation/Sedimentation 1.86 1.61 1.76 1.55 1.55
Rapid Granular Filtration 2.41 1.92 1.11 0.87 0.87
Ultrafiltration Membrane 6.41 6.18 4.12 8 8
Direct Filtration 2.97 2.86 0.59 1.36 1.36
Free Chlorine Disinfection Variable: depends on operating conditions
Ozone Disinfection Variable: depends on operating conditions
Ultraviolet Disinfection Variable: depends on operating conditions
61
5.5. QMRAANALYSIS
Baseline risk assessments were conducted for monthly intervals corresponding to raw
water sampling conducted at each utility. Physical removal estimates were determined based on
the treatment plant categorization as per the Health Canada model. Inactivation performance for
each month was estimated using an N-CSTR module included in the Health Canada model with
monthly average disinfection parameters (pH, temperature, residual disinfectant, contact time).
Figure 5-1 summarizes results and demonstrates that all plants were able to meet the treatment
target of 10-6 DALY/pp/yr for giardia and viruses, however, there it is also possible that Plant I
may be above the WHO target for cryptosporidium due to the lack of physical filtration barriers
at this facility.
Overall risk values may also be shown on a monthly basis (Figure 5-2). Conventional
filtration was able to satisfy the WHO threshold of 10-6 DALY/pp/yr as shown in Figure 5-2 (a).
Direct filtration plants are more susceptible to risks resulting from protozoan pathogens such as
giardia and cryptosporidium due to the reduced pathogen removal through coagulation and rapid
granular media filtration as shown in Figure 5-2 (b). Free chlorine, ozone and ultraviolet
disinfection are all effective processes for the reduction of bacterial and viral risk, however,
ultraviolet was shown to be much more effective for the reduction of protozoan risk factors
(Figure 5-2 (c)).
62
Figure 5-1: QMRA risk estimates for (a) cryptosporidium, (b) giardia, (c) enteric viruses.
Error bars represent minimum and maximum values, while inner boxes show 25, 50, and 75th percentiles. Dashed lines represent WHO target value of 10-6 DALY/pp/yr.
1.E-15
1.E-12
1.E-09
1.E-06
1.E-03
A B C D E F G H I J
DA
LY p
er p
erso
n, p
er y
ear
10-3
10-6
10-9
10-12
10-15
1E-21
1E-18
1E-15
1E-12
1E-09
1E-06
0.001
A B C D E F G H I J
DA
LY p
er p
erso
n, p
er y
ear
10-3
10-6
10-9
10-12
10-15
10-18
10-21
1E-21
1E-18
1E-15
1E-12
1E-09
1E-06
0.001
A B C D E F G H I J
DA
LY p
er p
erso
n, p
er y
ear
10-3
10-6
10-9
10-12
10-15
10-18
10-21
(a)
(b)
(c)
63
Figure 5-2: QMRA risk outputs for (a) Plant A, (b) Plant C, (c) Plant G.
(a)
(b)
(c)
64
All plants were also subjected to a baseline risk analysis for the sampling occasion that
demonstrated the maximum risk. Normalized probability distributions are an alternative method
for displaying maximum risk scenarios (Figure 5-3). These plots highlight the range of risk
values as well as the relative risk ranking among all 5 reference pathogens. Typical conventional
treatment facilities show all five reference pathogens levels to be well below the 10-6
DALY/pp/yr threshold. More robust treatment plants (membranes coupled with UV) have
negligible risk as most pathogens result in risk below the 10-15 DALY/pp/yr minimum risk cut-
off in the Health Canada model.
Figure 5-3: Normalized probability distribution for the annual DALY risk at Plant C.
The WHO recommends an annual risk threshold of 10-6 DALY/pp/yr however a wide
range of plant performance may existdepending on disinfection conditions or raw water quality.
A sensitivity analysis allowed for the comparison of various process operational parameters to
determine their effect on overall risk values. Each plot allowed for the consideration of 2
independent parameters (x,y pairs) over 20 intervals for a total of 400 data points (z axis, risk
values) per plot. Pathogen concentrations were assumed to be the recovery corrected maximum
observed unless otherwise stated. Disinfection conditions (pH and temperature) were held
65
constant at values representing typical winter treatment scenarios as this was the most vulnerable
time for most chemical disinfection processes. Five scenarios were evaluated at each plant.
Chlorine decay was considered negligible for this analysis.
Figure 5-4 shows the impact of increased pathogen concentrations as a result of potential source
water contamination events (sewage overflows, agricultural run-off, etc) coincident with
treatment failures resulting in compromised coagulation/filtration. Cryptosporidium and giardia
are shown to be particularly sensitive to the effectiveness of physical removal and require nearly
100% effectiveness of the physical barriers to avoid breaching the 10-6 DALY pp/yr threshold. A
linear relationship was observed between overall microbial risk and pathogen concentration as
well as physical removal effectiveness over the range of values evaluated in this study. Figure 5-
5 shows the impact of disinfectant concentration and contact time on overall risk and
demonstrates that a decrease in chlorine residual had little impact on risk related to
cryptosporidium with typical reductions from chlorine of approximately 0-0.05 log10. Giardia,
even in cold water conditions, had a much greater response to varying chlorine residuals, often
resulting in 1-2 log10 reduction. E. coli O157, campylobacter and enteric viruses had a distinct
response to low levels of chlorination and typically achieve maximum disinfection credits at low
concentration*time values (<10 mg*min/L) suggesting that most plants would be able to tolerate
potential low chlorine residual plant upset events.
66
A B
C D
E
Figure 5-4: Surface plots of microbial risk for Plant D as a function of increasing pathogen concentrations and decreasing chlorine residual for primary disinfection.
(A) cryptosporidium, (B) giardia, (C) rotavirus, (D) campylobacter and (E) E. coli O157.
67
Figure 5-5: Surface plots of microbial risk for Plant D as a function of increasing pathogen concentrations and decreasing chlorine residual for primary disinfection.
(A) cryptosporidium, (B) giardia, (C) rotavirus, (D) campylobacter and (E) E. coli O157.
A B
C D
E
68
The addition of UV at Plants B and G resulted in a dramatic downward shift for all five pathogen
risk plots. In most cases these plants can withstand a complete chlorination failure without
exceeding the WHO target of 10-6 DALY/pp/year if the UV system is maintained at typical
fluence levels as shown for Plant B in Figure 5-6. The overall risk outcomes for cryptosporidium
and giardia have non-linear profiles with respect to UV fluence suggesting a tailing effect as
reflected by the relevant inactivation studies.
Using an estimate of filter performance based on turbidity measurements it was possible to
generate the plot shown in Figure 5-7. Data was based on several pilot plant filter challenge
studies which correlated the impact of high turbidity events with the removal of cryptosporidium
through granular media filtration (Barbeau and Douglas, unpublished). A notable non-linear
deterioration in overall microbial risk can be observed as filter turbidity values exceed 0.2 NTU
suggesting that plants should strive to maintain filter turbidity values less than 0.2 NTU.
69
A B
C D
E
Figure 5-6: Surface plots of microbial risk for Plant B as a function of varying chlorine disinfection and UV fluence configurations.
(A) cryptosporidium, (B) giardia, (C) rotavirus, (D) campylobacter and (E) E. coli O157.
70
Figure 5-7: Surface plots of microbial risk for Plant D as a function of varying chlorine disinfection and filter effluent turbidity.
(A) cryptosporidium, (B) giardia, (C) rotavirus, (D) campylobacter and (E) E. coli O157. Filter turbidity can be approximately related to log removal performance as documented by Douglas & Barbeau (unpublished work).
A B
C D
E
71
5.6. CONSIDERINGNON‐DETECTS
Evaluating the impact of non-detect measurements on the overall risk for each of the
utilities proved to have a moderate impact on reported risk values. Considering that there were
only 2 positive results for cryptosporidium (single oocyst observations at both Plant D and G),
the significance of how non-detects are handled can have a significant impact in the microbial
risk results. Three methods for handling non-detects were evaluated, including replacing the
non-detect values with half the detection limit, the full value of the detection limit and another
proposed method which sums all sample volumes from non-detect samples and generates a very
low detection limit based on the possibility that only 1 organism was detected in the total
summated volume. This alternative method was based on the tandem application of both the
Poisson and binomial distributions. The Poisson distribution was used to evaluate the chance of
getting a discrete value of x in a sample, given the concentration (u) in the source water and the
effective sample volume (V) according to Equation 1.
[Eq 1]
Where: x = number of ingested pathogens u = concentration of pathogen in the sample (#/L) V = volume of sample consumed (L)
72
The probability of getting the same result (0 in a sample) in a series of subsequent
sampling events was given by the compliment of the binomial distribution. The binomial
distribution was given by:
[Eq 2]
Where: x = number of positive sampling events n = total number of grab samples evaluated
The compliment was equal to unity minus the binomial probability distribution function
(PDF), where the binomial PDF was based on the total number of trials (n), the number of
successes (x) and the probability of each successful event. By setting x = 0, the probability (P) of
failing to detect a pathogen in n sequential samples can be calculated. This strategy was best
applied to sequential samples with equal volumes, but could also be expanded to variable sample
volumes as well by evaluating the product of all sequential probabilities of detection (see Poisson
distribution above) and then taking the compliment to give the probability of repeatedly getting
non-detects given a concentration in the source water and a series of sample volumes. See Table
5-8 for comparisons between traditional methods (1/2 detection limit) and the proposed method
of calculating the upper 95th confidence limit. The third method implemented the ProUCL tool
provided by the USEPA for calculating upper 95th confidence limits based on datasets that
contain non-detect values and the results are shown in Table 5-9. In general, the method selected
for representing non-detects has a limited impact on the final risk calculations with most
pathogens displaying less than 1-log difference between the maximum and minimum estimates.
73
Table 5-8: Comparing alternative methods for calculating the mean cryptosporidium concentration at all 10 plants.
Table 5-9: Comparing alternate methods for calculating virus concentrations.
Plant Location
Cryptosporidium Concentration Estimates (#/100L) Mean
Full Detection Limit
for ND values
Mean ½ Detection Limit
for ND values
UCL based on Poisson & Beta
distributions
A 16.2 8.1 3.2
B 32.3 16.1 3.7
C 10.1 5.1 1.0
D 10.2 5.1 1.4
E 9.2 4.5 1.2
F 10.8 5.4 1.1
G 10.2 5.3 1.1
H 11.1 5.5 1.8
I 3.5 1.8 0.6
J 7.4 4.0 0.7
Plant Location
Virus Concentration Estimates (#/100L) Mean
½ Detection Limit for ND values
ProUCL Mean
ProUCL 95th UCL
A 542.9 578.6 1069
B 1151 1180 2360
C 1042 1081 1527
D 3866 3888 5476
E 3029 3037 4157
F 778 816.6 1469
G 549 581.9 872
H 16071 16087 38263*
I 268.3 285 390.1
J 446.5 452.3 683.6**
Most 95th UCL calculated with Kaplan-Meier (KM) Statistics using Normal Critical Values *95% KM Chebyshev UCL
**95% KM (Percentile Bootstrap) UCL
74
5.7. CONCLUSIONS
Risk assessments conducted with the Health Canada QMRA model have shown that all
participating water treatment plants are capable of meeting the WHO’s target of 10-6 DALY per
person per year when all microbial barriers are in place. Risk associated with cryptosporidium
was best controlled by physical removal (filtration) and ultraviolet disinfection. Sensitivity
analysis for these plants demonstrated the importance of effective primary disinfection for the
control of bacterial and viral pathogens, however, in most cases disinfection practices far
exceeded the requirements to meet risk targets. Alternative methods for calculating mean values
for datasets with high frequencies of non-detects were investigated and could be attributed to a
shift in risk values by up to 1-log10 if proposed methods are implemented for multiple sequential
non-detects.
75
6. OVERALLCONCLUSIONSAerobic spore removal studies have been proven to be a valuable tool for assessing the
removal performance for both direct biological filtration as well as conventional filtration.
Biological filtration typically achieved less than 1-log10 removal while conventional filters
achieved greater than 3-log10 removal. Pathogen removal was found to be related to EPS
(extrapolymeric substances) attached to the filter media, with higher protein concentrations
leading to increased pathogen removal. QMRA calculations for both biological and conventional
filtration highlighted the importance of both physical and chemical barriers in order to reduce the
risk of illness related to drinking water. Direct biological filtration provided insignificant
physical removal of pathogens and would require an additional barrier if implemented at full
scale facilities.
The completion of Quantitative Microbial Risk Assessments (QMRA) for 10 water utilities
across Canada has demonstrated that most utilities are providing adequate barriers for the
reduction of drinking water illnesses. 9 out of 10 plants were found to be consistently below the
WHO’s threshold of 10-6 DALY/pp/yr. Most conventional filtration plants are also well prepared
for potential increases in pathogen loading rates resulting from potential disturbances in the
source water. Additional barriers such as ozone and UV have been shown to provide further
microbial protection in cases where pathogens are highly prevalent in the watershed.
76
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86
8. APPENDIX
8.1. PILOTPLANTCONFIGURATIONSFORAEROBICSPORETRIALS
Figure 8-1: Process flow diagram for Plant A showing the sampling locations for aerobic spores.
87
3-speed tapered flocculationPlate Assisted Sedimentation
Raw Water Tank
3-speed tapered flocculationPlate Assisted Sedimentation
Alum Addition
PACl Addition
Ant
hrac
ite/S
and
(Con
trol
)
Ant
hrac
ite/S
and
(Per
oxid
e)
Ant
hrac
ite/S
and
(Inl
ine
Alu
m)
GA
C/S
and
(No
trea
tmen
t)
Ant
hrac
ite/S
and
(Con
trol
)
Ant
hrac
ite/S
and
(NH
3 &
PO
4) Ant
hrac
ite/S
and
(PA
Cl)
Ant
hrac
ite/S
and
(Alu
m)
SS S S S S
S S
Sand
Anthracite
GAC
S
S
S
S Sample Location
Figure 8-2: Process flow diagram for Plant B showing the sampling locations for aerobic spores.
88
Figure 8-3: Process flow diagram for Plant C showing the sampling locations for aerobic spores.
89
8.2. PLANTASPORECHALLENGESTUDIES
Table 8-1: A comparison between raw and settled water aerobic spore counts during spiking.
Sample Location
Raw
Water Alum SW Ferric SW
CFU/mL CFU/mL Log
Reduction CFU/mL
Log Reduction
6750 460 1.1 514 1.1
6450 510 1.1 588 1.0
6400 453 1.2 516 1.1
6550 489 1.1 586 1.0
6000 569 1.1 608 1.0
6533
N= 23 10 10
Min 5000 410 1.0 380 0.9
Max 8000 620 1.2 880 1.2
Avg 6672 496 1.1 573 1.0
Std Dev 1180 68.7 0.06 162 0.14
90
Table 8-2: A comparison between settled water aerobic spore counts and filter effluent counts for the alum treatment train.
Sample Location
Alum SW CF1 (Sand/Anthracite) CF2 (Sand/GAC)
CFU/mL CFU/mL Log
Reduction CFU/mL
Log Reduction
460 0.59 2.9 0.23 3.3
510 0.48 3.0 0.25 3.3
453 0.39 3.1 0.31 3.2
489 0.43 3.1 0.27 3.3
569
N= 10 8 8
Min 410 0.24 2.9 0.20 3.2
Max 620 0.64 3.3 0.32 3.4
Avg 496 0.47 3.0 0.26 3.3
Std Dev 68.7 0.1 0.14 0.04 0.06
91
Table 8-3: A comparison between settled water aerobic spore counts and filter effluent counts for the ferric treatment train.
Sample Location
Ferric SW CF3 (Sand/Anthracite) CF4 (Sand/GAC)
CFU/mL CFU/mL Log
Reduction CFU/mL
Log Reduction
514 0.31 3.3 0.26 3.3
588 0.29 3.3 0.26 3.4
516 0.31 3.3 0.24 3.4
586 0.23 3.4 0.21 3.4
608
N = 10 8 8
Min 380 0.22 3.2 0.19 3.3
Max 880 0.37 3.4 0.32 3.5
Avg 573 0.29 3.3 0.25 3.4
Std Dev 162 0.06 0.1 0.05 0.1
92
8.3. PLANTBSPORECHALLENGESTUDIES
Table 8-4: Spore counts at the Plant B pilot.
Sample Location
Raw
Water Alum SW PACl SW
CFU/mL CFU/mL Log
Reduction CFU/mL
Log Reduction
17500 240 1.8 340 1.6
14000 260 1.8 400 1.6
13000 300 1.7 480 1.5
10500 260 1.8 340 1.6
14000 460 1.5 560 1.4
16500 300 1.7 700 1.3
17500 370 1.6 380 1.6
17000 400 1.6 420 1.6
18000 270 1.7 360 1.6
13500 460 1.5 510 1.5
N= 10 10 10
Min 10500 240 1.5 340 1.3
Max 18000 460 1.8 700 1.6
Avg 15150 332 1.67 449 1.5
Std Dev 2495 84 0.11 115 0.10
93
Table 8-5: Spore counts for both conventional filters at Plant B.
CF1
(Alum, Sand/Anthracite)
CF2 (PACl, Sand/Anthracite)
CFU/mL Log
Reduction CFU/mL
Log Reduction
0.017 4.4 0.012 4.4
0.012 4.6 0.008 4.6
0.028 4.2 0.006 4.7
0.018 4.4 0.012 4.4
N= 8 8
Min 0.012 4.2 0.006 4.4
Max 0.028 4.6 0.012 4.7
Avg 0.018 4.4 0.0095 4.6
Std Dev 0.006 0.15 0.003 0.15
Table 8-6: Spore counts for biological filters at Plant B.
BF1 BF2
CFU/mL Log
Reduction CFU/mL
Log Reduction
1110 1.1 1210 1.1
1150 1.1 2700 0.7
1150 1.1 1350 1.1
900 1.2 1000 1.2
N= 8 8
Min 900 1.1 1000 0.7
Max 1150 1.2 2700 1.2
Avg 1078 1.15 1565 1.0
Std Dev 120 0.05 770 0.2
94
Table 8-7: Spore counts for biological filters at Plant B.
BF3 BF4
CFU/mL Log
Reduction CFU/mL
Log Reduction
2800 0.7 1850 0.9
1080 1.1 1450 1.0
870 1.2 1370 1.0
1200 1.1 1600 1.0
N= 8 8
Min 870 0.7 1370 0.9
Max 2800 1.2 1850 1.0
Avg 1488 1.05 1568 0.99
Std Dev 885 0.22 211 0.06
Table 8-8: Spore counts for biological filters at Plant B.
Time BF5 BF6
min CFU/mL Log
Reduction CFU/mL
Log Reduction
2900 0.7 800 1.3
2600 0.8 1000 1.2
3300 0.7 600 1.4
3300 0.7 1100 1.1
N= 8 - 8 -
Min 2600 0.7 600 1.1
Max 3300 0.8 1100 1.4
Avg 3025 0.70 875 1.24
Std Dev 340 0.05 221 0.12
95
8.4. PLANTCSPORECHALLENGESTUDIES
Table 8-9: Spore counts at the Plant C pilot location.
Sample Location
Raw
Water DF1 DF2 DF3
CFU/mL CFU/mL Log
Reduction CFU/mL
Log Reduction
CFU/mL Log
Reduction 3900 2.42 3.28 2.3 3.3 1.5 3.5
4800 2.56 3.25 1.9 3.4 1.34 3.5
5400 2.52 3.26 2.6 3.2 1.16 3.6
4200 2.64 3.24 2.64 3.2 1.04 3.6
N= 8 8 8 8
Min 3900 2.42 3.24 1.9 3.2 1.04 3.5
Max 5400 2.64 3.28 2.64 3.4 1.5 3.6
Avg 4575 2.54 3.26 2.36 3.29 1.26 3.6
Std Dev 665 0.09 0.02 0.34 0.07 0.2 0.07
Table 8-10: Spore counts at the Plant C pilot location.
Sample Location
Raw
Water BF7 BF8
CFU/mL CFU/mLLog
Reduction CFU/mL
Log Reduction
1970 1480 0.1 1450 0.1
1980 1600 0.1 1410 0.1
1830 1200 0.2 1500 0.1
1870 1270 0.2 1520 0.1
1790
N= 8 8 8
Min 1790 1200 0.1 1410 0.1
Max 1870 1600 0.2 1520 0.1
Avg 1888 1388 0.14 1470 0.11
Std Dev 84 185 0.06 50 0.01
96
Table 8-11: Spore counts at the Plant C pilot location.
Sample Location
Raw
Water BF9 BF10
CFU/mL CFU/mLLog
Reduction CFU/mL
Log Reduction
1970 570 0.5 1310 0.2
1980 570 0.5 1450 0.1
1830 580 0.5 1330 0.2
1870 520 0.6 1350 0.1
1790
N= 8 8 8
Min 1790 520 0.5 1310 0.1
Max 1870 580 0.6 1450 0.2
Avg 1888 560 0.53 1360 0.14
Std Dev 84 27 0.02 62 0.02
97
8.5. QMRADATA
8.6. PLANTA
Table 8-12: Results of pathogen monitoring for the 12 month sampling period at Plant A.
Date Volume Sampled
Crypto Giardia Virus
16-Oct-12 102.2 <5.0 <5.0 <5.8
13-Nov-12 98.4 <18.9 <18.9 <11.5
4-Dec-12 98.4 <4.4 <4.4 <4.4
15-Jan-13 98.4 <4.9 <4.9 6.0
5-Feb-13 98.4 <3.9 <3.9 <6.4
14-Mar-13 45.4 <8.5 <8.5 15.6
2-Apr-13 98.4 <3.9 <3.9 <6.7
15-May-13 98.4 <4.0 <4.0 <8.1
18-Jun-13 98.4 <3.5 <3.5 <5.1
8-Jul-13 101.3 <3.7 <3.7 <5.7
12-Aug-13 98.4 <4.2 <4.2 10.0
10-Sep-13 98.4 <4.0 <4.0 <9.0 Bold figures denote positive microbiological results. Values are reported as #/100 L.
98
Table 8-13: Monthly risk results at Plant A based on pathogen monitoring data and monthly averages for process effectiveness.
DALY per person per year
Crypto Giardia Rotavirus
Oct 2012 5.4E-08 4.5E-10 2.8E-11
Nov 2012 2.0E-07 8.3E-09 5.5E-11
Dec 2012 4.7E-08 4.1E-09 2.1E-11
Jan 2013 5.2E-08 9.1E-09 2.9E-11
Feb 2013 4.2E-08 8.7E-09 3.0E-11
Mar 2013 9.1E-08 1.9E-08 7.5E-11
Apr 2013 4.1E-08 5.0E-09 3.2E-11
May 2013 4.2E-08 1.9E-09 3.9E-11
Jun 2013 3.8E-08 7.0E-10 2.4E-11
Jul 2013 4.0E-08 1.4E-10 2.7E-11
Aug 2013 4.5E-08 2.8E-11 4.8E-11
Sept 2013 4.3E-08 2.2E-11 4.3E-11
99
Table 8-14: Pathogen Log-Inactivation by Chlorine Disinfection at the Plant A.
Log Inactivation
Crypto Giardia Rotavirus
Oct 2012 0.02 2.65 8
Nov 2012 0.03 1.96 8
Dec 2012 0.03 1.63 8
Jan 2013 0.03 1.33 8
Feb 2013 0.03 1.26 8
Mar 2013 0.03 1.25 8
Apr 2013 0.03 1.49 8
May 2013 0.02 1.92 8
Jun 2013 0.02 2.30 8
Jul 2013 0.02 3.03 8
Aug 2013 0.02 3.78 8
Sept 2013 0.02 3.86 8
100
Figure 8-4: Monthly chlorine residual values for the clearwell at Plant A.
Figure 8-5: Monthly pH values for disinfection calculations at Plant A.
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
Clearwell Chlorine Residual (mg/L)
6.60
6.80
7.00
7.20
7.40
Settled W
ater pH
101
Figure 8-6: Monthly temperature values based on raw water measurements at Plant A.
Figure 8-7: Monthly flowrate for the clearwell at Plant A.
0
5
10
15
20
25
30
Temperature (°C)
0
20
40
60
80
100
120
Daily Plant Flow (ML/d)
102
8.7. PLANTB
Table 8-15: Results of pathogen monitoring for Plant B for the first 12 month sampling period.
Date Volume Sampled
Crypto Giardia Virus
16-Oct-12 98.4 <30.1 <30.1 <8
13-Nov-12 98.4 <47.6 <47.6 <15
4-Dec-12 100.3 <5.0 <5.0 <7
15-Jan-13 98.4 <5.0 <5.0 103
5-Feb-13 98.4 <3.9 <3.9 14
14-Mar-13 53 <21.1 <21.1 121
2-Apr-13 109.8 <3.5 <3.5 <4
15-May-13 98.4 <4.1 <4.1 <7
18-Jun-13 98.4 <3.5 <3.5 <5
8-Jul-13 101.2 <3.8 <3.8 <5
12-Aug-13 98.4 <4.2 <4.2 <10
10-Sep-13 98.4 <5.9 <5.9 8
Bold figures denote positive microbiological results. Values are reported as #/100 L.
103
Table 8-16: Monthly risk results for Plant B based on pathogen monitoring data and monthly averages for process effectiveness.
DALY per person per year
Crypto Giardia Rotavirus
Oct 2012 3.2E-12 2.5E-13 3.9E-16
Nov 2012 6.5E-12 1.5E-12 7.3E-16
Dec 2012 5.2E-13 3.4E-13 3.3E-16
Jan 2013 5.2E-13 7.6E-13 4.9E-15
Feb 2013 4.4E-13 6.7E-13 6.7E-16
Mar 2013 2.2E-12 2.6E-12 5.8E-15
Apr 2013 3.7E-13 3.7E-13 2.1E-16
May 2013 4.33E-13 1.5E-13 3.4E-16
Jun 2013 3.7E-13 4.7E-14 2.6E-16
Jul 2013 4.0E-13 2.9E-14 2.6E-16
Aug 2013 4.5E-13 2.3E-15 4.8E-16
Sep 2013 6.3E-13 2.0E-14 4.0E-16
Table 8-17: Pathogen Log-Inactivation for Plant B by Chlorine Disinfection
Log Inactivation
Crypto Giardia Rotavirus
Oct 2012 0.03 2.67 8
Nov 2012 0.03 2.09 8
Dec 2012 0.03 1.76 8
Jan 2013 0.03 1.42 8
Feb 2013 0.04 1.36 8
Mar 2013 0.04 1.51 8
Apr 2013 0.03 1.57 8
May 2013 0.03 2.02 8
Jun 2013 0.03 2.47 8
Jul 2013 0.03 2.72 8
Aug 2013 0.03 3.86 8
Sept 2013 0.03 3.07 8
104
Figure 8-8: Monthly chlorine residual values for the clearwell at Plant B.
Figure 8-9: Monthly pH values for disinfection calculations at Plant B.
0.0
0.5
1.0
1.5
2.0
Chlorine Residual (mg/L)
6.2
6.4
6.6
6.8
7.0
7.2
7.4
Clearwell pH
105
Figure 8-10: Monthly temperature values based on raw water measurements at Plant B.
Figure 8-11: Monthly flowrate through the clearwell at Plant B.
0
5
10
15
20
25
Temperature (°C)
0
10
20
30
40
50
60
70
80
Daily Plant Flow (ML/d)
106
8.8. PLANTC
Table 8-18: Results of pathogen monitoring at Plant C. Values are reported as #/100 L.
Date Volume Sampled
(L)
Crypto Giardia Virus
12-Sep-12 100 <5.0 <5.0 <8.9
2-Oct-12 100 <5.0 <5.0 <6.4
14-Nov-12 100 <3.7 <3.7 <7.8
11-Dec-12 100 <4.3 <4.3 <8.1
15-Jan-13 100 <4.2 <4.2 15.0
10-Feb-13 100 <4.0 <4.0 <6.4
5-Mar-13 100 <3.3 <3.3 <5.3
2-Apr-13 100 <3.6 <3.6 <7.0
14-May-13 100 <4.0 <4.0 <6.3
4-Jun-13 100 <3.7 <3.7 <6.5
9-Jul-13 100 <4.1 <4.1 <5.0
13-Aug-13 100 <4.0 <4.0 18.0
Bold figures denote positive microbiological results.
107
Table 8-19: Monthly risk results for Plant C based on pathogen monitoring data and monthly averages for process effectiveness.
DALY per person per year
Crypto Giardia Rotavirus
Sep 2012 8.6E-07 1.5E-14 8.2E-09
Oct 2012 8.5E-07 9.3E-15 5.9E-09
Nov 2012 6.3E-07 5.1E-13 7.2E-09
Dec 2012 7.7E-07 1.6E-11 7.5E-09
Jan 2013 7.4E-07 1.2E-10 1.4E-08
Feb 2013 7.1E-07 2.3E-10 5.9E-09
Mar 2013 5.9E-07 1.0E-10 4.9E-09
Apr 2013 6.3E-07 8.7E-11 6.4E-09
May 2013 7.2E-07 7.6E-11 5.8E-09
Jun 2013 6.6E-07 8.6E-12 6.0E-09
Jul 2013 7.5E-07 7.5E-12 4.6E-09
Aug 2013 7.4E-07 1.2E-12 1.7E-08
Table 8-20: Pathogen Log-Inactivation by Chlorine Disinfection at Plant C.
Log Inactivation
Crypto Giardia Rotavirus
Sep 2012 0.1 7.8 8.0
Oct 2012 0.1 8.0 8.0
Nov 2012 0.1 6.1 8.0
Dec 2012 0.1 4.7 8.0
Jan 2013 0.1 3.8 8.0
Feb 2013 0.1 3.5 8.0
Mar 2013 0.1 3.8 8.0
Apr 2013 0.1 3.9 8.0
May 2013 0.1 4.0 8.0
Jun 2013 0.1 4.9 8.0
Jul 2013 0.1 5.0 8.0
Aug 2013 0.1 5.8 8.0
108
Figure 8-12: Monthly chlorine residual values measured at Plant C.
Figure 8-13: Monthly pH values for disinfection calculations at Plant C.
0.00
0.50
1.00
1.50
2.00
2.50
3.00
Chlorine Residual (mg/L)
6.00
6.50
7.00
7.50
8.00
8.50
9.00
pH
109
Figure 8-14: Monthly temperature values based on raw water measurements at Plant C.
Figure 8-15: Monthly flowrate data for the Plant C.
0
5
10
15
20
25
30
Temperature (°C)
0
20
40
60
80
100
Daily Flowrate (ML/d)
110
8.9. PLANTD
Figure 8-16: Monthly chlorine residual values for chlorine disinfection for Plant D.
Figure 8-17: Monthly pH values for chlorine disinfection at Plant D.
0.0
0.5
1.0
1.5
2.0
Cl2 Residual (mg/L)
5.0
5.2
5.4
5.6
5.8
6.0
6.2
pH
111
Figure 8-18: Monthly temperature values for chlorine disinfection at Plant D.
Figure 8-19: Monthly flowrate for chlorine disinfection for Plant D.
0
5
10
15
20
25
30
Temperature (°C)
0
50
100
150
200
250
300
Flow (ML/d)
112
8.10. PLANTE
Table 8-21: Results of pathogen monitoring for Plant E.
Date
Volume Sampled
(L)
Crypto Giardia Virus
16-Jan-13 113.6 <3.6 3.6 21
4-Feb-13 234.7 <1.6 <1.6 16
5-Mar-13 124.9 <3.1 <3.1 69
1-Apr-13 94.6 <4.1 <4.1 97
1-May-13 102.2 <3.5 <3.5 7
5-Jun-13 94.6 <4.0 <4.0 14
10-Jul-13 106 <3.5 <3.5 <3.5
13-Aug-13 98.4 <3.9 <3.9 59
16-Sep-13 109.8 <3.5 <3.5 45
9-Oct-13 98.4 <4.0 <4.0 <7.9
6-Nov-13 98.4 <3.9 <3.9 1320
3-Dec-13 94.6 <4.1 <4.1 1520
Bold values denote positive observations. Values are reported as #/100L.
113
Table 8-22: Monthly risk results at Plant E based on pathogen monitoring data and monthly averages for process effectiveness.
DALY per person per year
Crypto Giardia Rotavirus
Jan 2013 3.4E-08 6.9E-11 1.0E-10
Feb 2013 1.5E-08 1.8E-11 7.7E-11
Mar 2013 2.9E-08 3.4E-11 3.3E-10
Apr 2013 3.9E-08 7.9E-12 4.6E-10
May 2013 3.4E-08 6.0E-15 3.4E-11
Jun 2013 3.9E-08 1.6E-15 6.7E-11
Jul 2013 3.4E-08 1.4E-15 1.7E-11
Aug 2013 3.8E-08 1.6E-15 2.8E-10
Sep 2013 3.3E-08 1.4E-15 2.2E-10
Oct 2013 3.8E-08 1.6E-15 3.8E-11
Nov 2013 3.6E-08 3.4E-12 6.3E-09
Dec 2013 3.9E-08 4.0E-11 7.3E-09
114
Table 8-23: Pathogen Log-Inactivation by Chlorine Disinfection at Plant E.
Log Inactivation
Crypto Giardia Rotavirus
Jan 2013 0.08 3.31 8.00
Feb 2013 0.09 3.56 8.00
Mar 2013 0.08 3.56 8.00
Apr 2013 0.08 4.31 8.00
May 2013 0.07 7.36 8.00
Jun 2013 0.08 8.00 8.00
Jul 2013 0.07 8.00 8.00
Aug 2013 0.07 8.00 8.00
Sep 2013 0.08 8.00 8.00
Oct 2013 0.08 8.00 8.00
Nov 2013 0.08 4.66 8.00
Dec 2013 0.08 3.61 8.00
115
Figure 8-20: Monthly chlorine residual values for the chlorine contact tank at Plant E.
Figure 8-21: Monthly pH values for disinfection calculations at Plant E.
0.00
0.50
1.00
1.50
2.00
2.50
Chlorine Residual (mg/L)
6.40
6.50
6.60
6.70
6.80
6.90
7.00
7.10
7.20
pH
116
Figure 8-22: Monthly temperature values based on raw water measurements at Plant E.
Figure 8-23: Monthly flowrate data for Plant E.
0
5
10
15
20
25
30
Temperature (°C)
0
10
20
30
40
50
60
Daily Plant Flow (ML/d)
117
8.11. PLANTF
Table 8-24: Results of pathogen monitoring for Plant F.
Date Volume Sampled
Crypto Giardia Virus
13-Jun-12 100 <5 <5 <41
4-Jul-12 100 <5 <5 <31
13-Aug-12 100 <5 <5 <17
27-Aug-12 100 <5 <5 <24
1-Oct-12 100 <5 <5 <7
5-Nov-12 100 <5 <5 <8
3-Dec-12 100 <4.4 <4.4 17
21-Jan-13 94.6 <4.4 <4.4 <8
4-Feb-13 100 <4.2 <4.2 24
5-Mar-13 100 <3.4 <3.4 <6
2-Apr-13 100 <3.8 <3.8 6
29-Apr-13 100 <3.8 <3.8 <7
118
Table 8-25: Monthly risk results at Plant F based on pathogen monitoring data and monthly averages for process effectiveness.
DALY per person per year
Crypto Giardia Rotavirus
June 2012 1.34E-10 5.45E-13 2.25E-13
July 2012 1.34E-10 2.54E-13 1.7E-13
Aug 2012 1.33E-10 1.82E-13 9.32E-14
Sept 2012 1.31E-10 5.96E-14 1.32E-13
Oct 2012 1.3E-10 4.62E-14 3.84E-14
Nov 2012 1.31E-10 1.45E-13 4.39E-14
Dec 2012 1.13E-10 3.91E-13 9.32E-14
Jan 2013 1.17E-10 1.28E-12 4.39E-14
Feb 2013 1.07E-10 7.24E-13 1.32E-13
Mar 2013 8.7E-11 5.21E-13 3.29E-14
Apr 2013 1E-10 1.06E-12 3.29E-14
May 2013 1.02E-10 7.04E-13 3.84E-14
119
Table 8-26: Pathogen Log-Inactivation by Chlorine Disinfection at Plant F.
Log Inactivation
Crypto Giardia Rotavirus
June 2012 0.055 3.54 8
July 2012 0.056 3.04 8
Aug 2012 0.056 2.55 8
Sept 2012 0.051 2.05 8
Oct 2012 0.059 2.26 8
Nov 2012 0.058 2.32 8
Dec 2012 0.048 2.06 8
Jan 2013 0.044 2.24 8
Feb 2013 0.047 2.80 8
Mar 2013 0.042 2.71 8
Apr 2013 0.037 2.58 8
May 2013 0.039 2.81 8
120
Figure 8-24: Monthly chlorine residual values for Chlorine Contact Tank #1 at Plant F.
Figure 8-25: Monthly pH values for Chlorine Contact Tank #1 at Plant F.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Chlorine Residual (mg/L)
7
7.2
7.4
7.6
7.8
8
8.2
pH
121
Figure 8-26: Monthly temperature values for Chlorine Contact Tank #1 at Plant F.
Figure 8-27: Monthly flowrate for Chlorine Contact Tank #1 at Plant F.
0
2
4
6
8
10
12
14
16
18
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
Temperature (C )
0
50
100
150
200
250
300
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
CT (m
g/L*min)
122
8.12. PLANTG
Table 8-27: Results of pathogen monitoring at Plant G.
Date Volume Sampled
(L)
Crypto Giardia Virus
13-Sep-12 100 5.0 5.0 15.4
2-Oct-12 100.2 5.0 5.0 7.9
12-Nov-12 100.2 4.6 4.6 8.3
6-Dec-12 100.8 4.6 4.6 9.2
16-Jan-13 100.4 3.7 3.7 4.4
5-Feb-13 100.2 3.8 3.8 5.0
5-Mar-13 100.2 3.7 3.7 14.0
3-Apr-13 100.2 3.7 3.7 5.9
14-May-13 100.3 3.8 3.8 8.0
3-Jun-13 103.4 3.6 3.6 5.6
10-Jul-13 100.6 3.4 3.4 4.9
13-Aug-13 100.3 3.8 3.8 8.0
Bold figures denote positive microbiological results. Values are reported as #/100 L.
123
Table 8-28: Monthly risk results at Plant G based on pathogen monitoring data and monthly averages for process effectiveness.
DALY per person per year
Crypto Giardia Rotavirus
Sept 2012 3.2E-14 3.5E-17 4.6E-15
Oct 2012 3.4E-14 2.3E-16 2.4E-15
Nov 2012 3.1E-14 3.5E-16 2.5E-15
Dec 2012 3.1E-14 7.4E-16 2.7E-15
Jan 2013 2.4E-14 6.1E-16 1.3E-15
Feb 2013 2.4E-14 3.4E-16 1.5E-15
Mar 2013 2.5E-14 9.5E-16 4.2E-15
Apr 2013 2.5E-14 7.7E-16 1.8E-15
May 2013 2.6E-14 5.6E-16 2.4E-15
Jun 2013 2.4E-14 2.0E-16 1.7E-15
Jul 2013 2.3E-14 2.2E-16 1.5E-15
Aug 2013 2.6E-14 1.1E-16 2.4E-15
Table 8-29: Pathogen Log-Inactivation by Chlorine Disinfection at Plant G.
Log Inactivation
Crypto Giardia Rotavirus
Sept 2012 0.08 3.12 8.00
Oct 2012 0.06 2.29 8.00
Nov 2012 0.07 2.07 8.00
Dec 2012 0.07 1.75 8.00
Jan 2013 0.08 1.74 8.00
Feb 2013 0.09 2.01 8.00
Mar 2013 0.06 1.55 8.00
Apr 2013 0.07 1.64 8.00
May 2013 0.06 1.79 8.00
Jun 2013 0.07 2.21 8.00
Jul 2013 0.05 2.14 8.00
Aug 2013 0.06 2.49 8.00
124
Figure 8-28: Monthly chlorine residual values for chlorine disinfection at Plant G.
Figure 8-29: Monthly pH values for chlorine disinfection at Plant G.
0.00
0.50
1.00
1.50
2.00
2.50
3.00
Cl R
esidual (mg/L)
6.00
6.50
7.00
7.50
8.00
8.50
9.00
Disinfection pH
125
Figure 8-30: Monthly temperature values for chlorine disinfection at Plant G.
Figure 8-31: Monthly flowrate for chlorine disinfection at Plant G.
0
5
10
15
20
25
Temperature (°C)
0
5
10
15
20
25
30
35
Daily Plant Flow (ML/d)
126
8.13. PLANTH
Table 8-30: Results of pathogen monitoring at Plant H.
Date Volume Sampled
Crypto Giardia Virus
2-Feb-13 100 <4 4 116
4-Mar-13 100 <4 <4 804
2-Apr-13 100 <4 <4 103
1-May-13 100 <4 <4 58
3-Jun-13 100 <4 <4 <6
9-Jul-13 100 <4 <4 12
2-Aug-13 100 <4 <4 <9
11-Sep-13 100 <4 4 28
7-Oct-13 100 <4 8 1060
5-Nov-13 100 <4 4 12
2-Dec-13 100 <4 50 N/A
14-Jan-14 100 <4 4 N/A Bold figures denote positive microbiological results. Values are reported as #/100 L.
127
Table 8-31: Monthly risk results for Plant H based on pathogen monitoring data and monthly averages for process effectiveness.
DALY per person per year
Crypto Giardia Rotavirus
Feb 2013 2.87E-13 2E-17 5.55E-19
Mar 2013 1.26E-13 8.18E-19 3.85E-18
Apr 203 5.28E-14 3.99E-20 4.93E-19
May 2013 1.68E-14 7.79E-21 2.78E-19
Jun 2013 2.28E-14 4E-21 2.97E-20
Jul 2013 8.14E-15 1.25E-20 5.75E-20
Aug 2013 8.01E-14 5.95E-20 4.09E-20
Sep 2013 1.11E-13 8.83E-19 1.34E-19
Oct 2013 3.56E-13 5.9E-17 5.08E-18
Nov 2013 2.95E-13 8.22E-18 5.75E-20
Dec 2013 3.12E-13 2.44E-16 9.65E-29
Jan 2014 3.09E-13 1.76E-17 9.65E-29
128
Table 8-32: Pathogen Log-Inactivation by Chlorine Disinfection at Plant H.
Log Inactivation
Crypto Giardia Rotavirus
Feb 2013 0.04 1.92 8.00
Mar 2013 0.07 3.27 8.00
Apr 203 0.07 4.59 8.00
May 2013 0.06 5.28 8.00
Jun 2013 0.05 5.59 8.00
Jul 2013 0.05 5.11 8.00
Aug 2013 0.05 4.43 8.00
Sep 2013 0.05 3.24 8.00
Oct 2013 0.06 2.47 8.00
Nov 2013 0.07 2.28 8.00
Dec 2013 0.06 1.91 8.00
Jan 2014 0.07 1.96 8.00
129
Figure 8-32: Monthly chlorine residual values for the north clearwell at Plant H.
Figure 8-33: Monthly pH values for disinfection calculations at Plant H.
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
Chlorine Residual (mg/L)
6.60
6.80
7.00
7.20
7.40
7.60
7.80
pH
130
Figure 8-34: Monthly temperature values based on raw water measurements at Plant H.
Figure 8-35: Monthly overall flowrates for Plant H.
0
5
10
15
20
25
30
Temperature (°C)
0
100
200
300
400
500
600
700
800
900
Flow Rate (L/sec))
131
8.14. PLANTI
Table 8-33: Results of pathogen monitoring for Plant I.
Date Volume Sampled
Crypto Giardia Virus
5-Mar-13 167.7 2.9 5.9 198.5
3-Apr-13 230.5 2.3 2.3 347.4
29-Apr-13 226.7 2.4 2.4 73.0
4-Jun-13 289.4 1.9 1.9 54.8
27-Jun-13 340 1.6 1.6 53.1
8-Aug-13 341.4 1.6 1.6 47.7
10-Sep-13 420 2.5 2.5 47.7
8-Oct-13 373.3 1.5 1.5 794.0
5-Nov-13 312.8 1.7 1.7 99.2
3-Dec-13 341.4 1.6 1.6 51.5
7-Jan-14 369.1 1.5 1.5 446.6
5-Feb-14 372.7 1.5 1.5 595.5 Bold figures denote positive microbiological results. Values are reported as #/100 L.
Table 8-34: Monthly risk results for Plant I based on pathogen monitoring data and monthly averages for process effectiveness.
DALY per person per year
Crypto Giardia Rotavirus
Mar 2013 0.00019 4.12E-06 1.42E-08
Apr 203 0.000156 3.2E-06 2.48E-08
May 2013 7.02E-05 1.55E-10 5.22E-13
Jun 2013 4.7E-05 6.12E-11 3.92E-13
Jul 2013 4.59E-05 7.12E-10 3.8E-13
Aug 2013 1.83E-05 1.77E-10 3.42E-13
Sep 2013 5.44E-05 3.01E-10 3.42E-13
Oct 2013 2.68E-05 6.13E-11 5.68E-12
Nov 2013 3.9E-05 5.53E-10 7.1E-13
Dec 2013 3.26E-05 5.53E-10 3.69E-13
Jan 2014 4.31E-05 5.91E-10 3.2E-12
Feb 2014 4.29E-05 5.92E-10 4.26E-12
132
Table 8-35: Pathogen Log-Inactivation by Chlorine Disinfection for Plant I.
Log Inactivation
Crypto Giardia Rotavirus
Feb 2013 0.031992 1.831286 8
Mar 2013 0.025227 1.543228 8
Apr 2013 0.028553 1.866771 8
May 2013 0.029247 2.178016 8
Jun 2013 0.011606 1.025455 8
Jul 2013 0.015891 1.634528 8
Aug 2013 0.014804 1.59822 8
Sep 2013 0.024509 2.065895 8
Oct 2013 0.016121 1.162368 8
Nov 2013 0.020042 1.14024 8
Dec 2013 0.020243 1.080966 8
Jan 2014 0.020305 1.073879 8
133
8.15. PLANTJ
Figure 8-36: Monthly chlorine residual values for the clearwell at Plant J.
Figure 8-37: Monthly pH values for disinfection calculations at Plant J.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Chlorine Residual (mg/L)
6.5
7.0
7.5
8.0
8.5
Disinfection pH
134
Figure 8-38: Monthly temperature values based on raw water measurements at Plant J.
Figure 8-39: Monthly overall flowrates for Plant J.
0
5
10
15
20
25
Temperature (°C)
0
100
200
300
400
500
600
Total Plant Flow (ML/d)
135
8.16. MATRIXRECOVERYRESULTS
Table 8-36: Protozoa recovery results for all 10 plants.
Location
Giardia %
Recovery
Crypto %
RecoveryF 9.2 9.2
F 35.2 62.7
C 75 72.6
B 13.4 30.6
A 18.5 25.9
D 69.4 35.4
D 74.7 66.3
E 10.9 32.8
J 7.5 24.3
I 0 0
I 52.2 72.4
I 59.4 36.8
H 82.5 9.1
H 94.9 20.1
H 96.1 50.8
G 24.2 73.6
Average % 45.2 38.9
Std Dev. % 33.9 24.6
136
Table 8-37: Virus recovery results for all 10 plants.
Location
Virus %
Recovery
F 0
F 188.2*
C 9
C 9.4
C 9
D 0
D 0
E 0
J 0.1
J 0.2
I 0
I 0
H 0
H 0.1
G 0.8
G 0.7
G 0.7
Average 1.9
Std Dev. 3.6
*Data excluded from summary statistics.
137
Table 8-38: Bacteria recovery results for all 10 plants.
Location Ecoli/Campy% Recovery
C 0.1
C 0.1
G 0.1
G 0.1
D 722.4*
D 3170.6*
D 6582.1*
H 77.8
H 34.8
H 48.5
I 57.4
I 48.2
I 55.8
B 99.4
B 101.4
E 102.4
E 92.5
E 106.9
E 34.2
J 90.9
J 80.9
F 86.6
F 104.5
D 91.2
D 113.2
H 102.8
H 100.8
H 121.2
I 55.7
I 59.2
I 59.7
I 84.9
Average 69.4
Std Dev. 36.8
*Data excluded from summary statistics.
