APPLICATIONS OF PRINCIPAL COMPONENT ANALYSIS OF ... · EXCITATION-EMISSION MATRICES FOR CHARACTERIZATION OF NATURAL ORGANIC MATTER IN WATER TREATMENT Nicolás Miguel Peleato Master

APPLICATIONS OF PRINCIPAL COMPONENT ANALYSIS OF

FLUORESCENCE EXCITATION-EMISSION MATRICES FOR

CHARACTERIZATION OF NATURAL ORGANIC MATTER

IN WATER TREATMENT

by

Nicolás Miguel Peleato

A thesis submitted in conformity with the requirements

for the degree of Master of Applied Science

Graduate Department of Civil Engineering

University of Toronto

© Copyright by Nicolás Miguel Peleato 2013

ii

APPLICATIONS OF PRINCIPAL COMPONENT ANALYSIS OF FLUORESCENCE

EXCITATION-EMISSION MATRICES FOR CHARACTERIZATION OF NATURAL

ORGANIC MATTER IN WATER TREATMENT

Nicolás Miguel Peleato

Master of Applied Science, 2013

Graduate Department of Civil Engineering

University of Toronto

ABSTRACT

Quantification of natural organic matter (NOM) in water is limited by the complex and

varied nature of compounds found in natural waters. Current characterization techniques, which

identify and quantify fractions of NOM, are often expensive and time consuming suggesting the

need for rapid and accurate characterization methods. In this work, principal component analysis

of fluorescence excitation-emission matrices (FEEM-PCA) was investigated as a NOM

characterization technique. Through the use of jar tests and disinfection by-product formation

tests, FEEM-PCA was shown to be a good surrogate for disinfection by-product precursors.

FEEM-PCA was also applied in order to characterize differences in humic-like, protein-like, and

Rayleigh scattering between multiple source waters and due to differing treatment processes. A

decrease in Rayleigh scattering influence was observed for a deep lake intake, and multiple

processes were found to significantly affect humic-like substances, protein-like, and Rayleigh

scattering fractions.

iii

AKNOWLEDGEMENTS

This work was funded in part by the Natural Sciences and Engineering Research Council

of Canada (NSERC) Char in Drinking Water Research, as well as through an Ontario Graduate

Scholarship.

I would like to thank my supervisor, Professor Robert C. Andrews, for his guidance,

expertise, and support of my research. My thanks also go out to Professor Susan Andrews for

her assistance with analytical chemistry and troubleshooting chromatography issues. Assistance

from Jennifer Lee, Jim Wang, John Armour, Sabrina Diemert, Dana Zheng, Liz Taylor-

Edmonds, and Alex Balmus was also greatly appreciated.

This work would not have been possible without invaluable support from the

Peterborough Utilities Commission (PUC) and Toronto Water with special thanks to Kevan

Light, René Gagnon, and David Scott.

Finally, my appreciation goes out to my parents, family, friends, and Aidan for their love

and continual support.

iv

TABLE OF CONTENTS

ABSTRACT ............................................................................................................................... ii

AKNOWLEDGEMENTS ......................................................................................................... iii

TABLE OF CONTENTS .......................................................................................................... iv

LIST OF TABLES .................................................................................................................. viii

LIST OF FIGURES .................................................................................................................... x

NOMENCLATURE ................................................................................................................. xii

1 Introduction ........................................................................................................................... 1

1.1 Background .................................................................................................................. 1

1.2 Objectives ..................................................................................................................... 2

2 Literature Review .................................................................................................................. 3

2.1 Disinfection by-products .............................................................................................. 3

2.1.1 DBP formation theory ........................................................................................... 3

2.1.2 Modelling DBP formation .................................................................................... 4

2.2 Measurement techniques for natural organic matter .................................................... 7

2.2.1 Liquid chromatography – organic carbon detection ............................................. 8

2.2.2 Fluorescence excitation-emission matrices ........................................................... 8

2.3 Multivariate analysis of fluorescence spectra ............................................................ 11

2.4 Principal component analysis ..................................................................................... 12

2.4.1 Mathematical definition and derivation .............................................................. 12

2.5 Application to natural waters ..................................................................................... 14

3 Materials and methods ......................................................................................................... 15

3.1 Experimental protocols .............................................................................................. 15

3.1.1 Jar tests and DBP formation ............................................................................... 15

3.1.2 Peterborough pilot plant ...................................................................................... 17

3.2 Analytical methods .................................................................................................... 19

v

3.2.1 Quality control .................................................................................................... 19

3.2.2 Total and dissolved organic carbon .................................................................... 19

3.2.3 Ultraviolet absorbance ........................................................................................ 20

3.2.4 Liquid chromatography – organic carbon detection ........................................... 22

3.2.5 Chlorine residuals ............................................................................................... 23

3.2.6 pH measurements ................................................................................................ 24

3.2.7 Fluorescence excitation-emission matrices ......................................................... 24

3.2.8 Principal component analysis ............................................................................. 24

3.2.9 Trihalomethanes .................................................................................................. 25

3.2.10 Haloacetic acids .................................................................................................. 29

4 Disinfection By-Product Modelling using Principal Component Analysis of Fluorescence

Excitation-Emission Matrices ............................................................................................. 36

4.1 Introduction ................................................................................................................ 36

4.2 Method overview ....................................................................................................... 38

4.2.1 Source waters ...................................................................................................... 38

4.2.2 Jar tests ................................................................................................................ 38

4.2.3 Disinfection by-product formation and analysis ................................................. 39

4.2.4 DOC, TOC, and UVA measurements ................................................................. 39

4.2.5 Fluorescence spectra collection .......................................................................... 40

4.2.6 Fluorescence data analysis .................................................................................. 40

4.3 Results and discussion ............................................................................................... 41

4.3.1 Conventional parameter results from jar tests .................................................... 41

4.3.2 Fluorescence results from jar tests ...................................................................... 43

4.3.3 Relationships between NOM indicators ............................................................. 47

4.3.4 Disinfection by-product formation prediction .................................................... 48

4.4 Conclusions ................................................................................................................ 54

5 Contributions of spatial, temporal, and treatment impacts on natural organic matter

character using principal component analysis of fluorescence spectra ............................... 55

5.1 Introduction ................................................................................................................ 55

5.2 Method overview ....................................................................................................... 56

vi

5.2.1 Water treatment plant characteristics .................................................................. 56

5.2.2 Total organic carbon ........................................................................................... 57

5.2.3 Liquid chromatography – organic carbon detection ........................................... 59

5.2.4 Fluorescence spectra collection .......................................................................... 59

5.2.5 Statistical analysis ............................................................................................... 61


5.3.1 Analysis of raw water ......................................................................................... 61

5.3.2 Analysis of treatment efficiency ......................................................................... 66

5.4 Conclusions ................................................................................................................ 70

6 Pilot-scale coagulation optimization including NOM characterization with principal

component analysis of fluorescence spectra ........................................................................ 71

6.1 Introduction ................................................................................................................ 71

6.2 Methods ...................................................................................................................... 72

6.2.1 Pilot plant ............................................................................................................ 72

6.2.2 Fluorescence excitation-emission matrices ......................................................... 73

6.2.3 Principal component analysis ............................................................................. 74

6.2.4 Statistical assessment .......................................................................................... 74

6.2.5 Tukey’s method .................................................................................................. 75


6.4 Performance evaluation using fluorescence spectra .................................................. 78

6.4.1 Spectral features of Peterborough water ............................................................. 78

6.4.2 Results of principal component analysis and model selection ............................ 80

6.4.3 Performance of coagulation on NOM fraction removal ..................................... 81

6.4.4 Performance of filtration on NOM fraction removal .......................................... 83

6.4.5 Performance of FEEM-PCA scores related to common NOM indicators .......... 84

6.5 Conclusions ................................................................................................................ 86

7 Summary, Conclusions and Recommendations .................................................................. 87

7.1 Summary .................................................................................................................... 87

7.2 Conclusions ................................................................................................................ 87

vii

7.3 Recommendations ...................................................................................................... 88

REFERENCES ......................................................................................................................... 89

8 Appendices .......................................................................................................................... 98

8.1 Raw Data .................................................................................................................... 98

8.1.1 Disinfection by-product modelling (Section 4) .................................................. 98

8.1.2 Toronto NOM project data tables ..................................................................... 108

8.1.3 Peterborough pilot coagulant optimization data tables ..................................... 115

8.2 Calculations .............................................................................................................. 118

8.2.1 Analysis of variance .......................................................................................... 118

8.2.2 Tukey’s method ................................................................................................ 118

8.2.3 Statistics on water quality differences at Peterborough Pilot ........................... 119

8.3 Process flow diagrams of City of Toronto water treatment plants .......................... 121

8.4 Program code ........................................................................................................... 126

8.4.1 Real RLS files (Python 2.6) .............................................................................. 126

8.4.2 Manipulate for PCA input (Python 2.6) ............................................................ 128

8.4.3 Deconvolute loadings (Python 2.6) ................................................................... 130

8.5 Increased THM formation in PACl treated water .................................................... 131

8.5.1 Background ....................................................................................................... 131

8.5.2 Method: pH adjusted DBP Formation Tests ..................................................... 131

8.5.3 Results and Discussion ..................................................................................... 134

8.5.4 Controls and Blanks .......................................................................................... 136

8.5.5 Water quality ..................................................................................................... 137

8.5.6 Comparison of pilot and jar test results ............................................................ 138

8.5.7 DBP Formation Results .................................................................................... 139

8.5.8 Conclusion ........................................................................................................ 144

viii

LIST OF TABLES

Table 2.1: Selected existing DBP models ...................................................................................... 6

Table 2.2: Commonly reported excitation/emission location of NOM fluorophores .................... 9

Table 3.1: Jar test reagents ........................................................................................................... 15

Table 3.2: Jar test method outline ................................................................................................ 16

Table 3.3: Total and dissolved organic carbon instrument conditions ........................................ 20

Table 3.4: Accuracy and method detection limit of TOC analysis .............................................. 20

Table 3.5: Description of LC-OCD fractions (Huber et al., 2011) .............................................. 23

Table 3.6: THM analysis instrument conditions .......................................................................... 25

Table 3.7: Accuracy and method detection limit of THM analysis ............................................. 26

Table 3.8: Trihalomethane analysis method outline .................................................................... 28

Table 3.9: Stock HAA specie concentrations .............................................................................. 30

Table 3.10: HAA analysis instrument conditions ........................................................................ 30

Table 3.11: Calibration range and check standard target concentrations for HAAs ................... 31

Table 3.12: Quality control standard deviation and HAA method detection limits .................... 32

Table 3.13: HAA analysis method outline ................................................................................... 33

Table 4.1: Source water characteristicsa ...................................................................................... 38

Table 4.2: Excitation/emission peak location for each water source ........................................... 43

Table 4.3: Variance explained by each PC for each sample set .................................................. 45

Table 4.4: Linear relationship strength (R2) between FEEM-PCA and traditional NOM

indicators .................................................................................................................... 48

Table 4.5: Results of non-linear regression for various NOM indicators and total THM

concentrations ............................................................................................................ 53

Table 4.6: Results of non-linear regression for various NOM indicators and total HAA

concentrations ............................................................................................................ 53

Table 5.1: Intake descriptions (CTC Source Protection Committee, 2012) ................................ 57

Table 5.2: Summary of treatment processes ................................................................................ 57

Table 5.3: Excitation/emission location of peaks for each water source ..................................... 64

Table 5.4: Variance explained by PCA model ............................................................................. 64

ix

Table 5.5: Significant changes in LC-OCD fractions between raw and treated water ................ 68

Table 6.1: Peterborough raw water quality yearly ranges ........................................................... 72

Table 6.2: Variance (%) explained by PCs for Models 1 and 2 .................................................. 80

Table 6.3: Strength of linear correlation between FEEM-PCA and common NOM indicators .. 85

Table 8.1: PC 1 Scores ................................................................................................................. 99

Table 8.2: PC 2 Scores ................................................................................................................. 99

Table 8.3: TOC .......................................................................................................................... 100

Table 8.4: UVA .......................................................................................................................... 100

Table 8.5: SUVA ....................................................................................................................... 101

Table 8.6: THMs ........................................................................................................................ 102

Table 8.7: HAAs ........................................................................................................................ 103

Table 8.8: Raw water LC-OCD results ...................................................................................... 108

Table 8.9: Treated water LC-OCD results ................................................................................. 109

Table 8.10: Treated water PC scores ......................................................................................... 110

Table 8.11: Raw water PC scores .............................................................................................. 111

Table 8.12: Raw water TOC ...................................................................................................... 113

Table 8.13: Treated water TOC ................................................................................................. 114

Table 8.14: FEEM-PCA scores .................................................................................................. 115

Table 8.15: Statistical comparison of pilot and full-scale treatment trains: water quality

parameters ................................................................................................................ 116

Table 8.16: Statistical comparison of pilot and full-scale treatment trains: chlorine residual and

disinfection by-products ........................................................................................... 117

Table 8.17: Jar Test Water Quality T-Test Results .................................................................... 135

Table 8.18: Pilot Plant Water Quality T-Test Results ............................................................... 137

Table 8.19: T-Tests for Normalized TTHM Jar Test Results .................................................... 141

Table 8.20: T-Tests for Normalized TTHM Pilot Plant Results ................................................ 142

Table 8.21: T-Tests for Normalized THAA Jar Test Results .................................................... 144

Table 8.22: T-Tests for Normalized THAA Pilot Plant Results ................................................ 144

x

LIST OF FIGURES

Figure 2.1: Example freshwater FEEM with humic acid (α), fulvic acid (β), and protein-like (δ)

peaks. (a) top-down view, (b) 3D view. (Peiris et al., 2010). ................................... 10

Figure 3.1: Process flow diagram of Peterborough water treatment plant .................................. 18

Figure 3.2: Example TOC calibration .......................................................................................... 21

Figure 3.3: TOC quality control chart (July to Sep., 2012) ......................................................... 21

Figure 3.4: LC-OCD chromatogram of surface water (Huber et al., 2011). ............................... 22

Figure 3.5: Example calibration chart for THM species (July 2012) .......................................... 26

Figure 3.6: Trichloromethane (TCM) quality control - low concentration target: 10 ug/L ......... 27

Figure 3.7: Trichloromethane (TCM) quality control - high concentration target: 60 ug/L ........ 27

Figure 3.8: Example HAA calibration curves (July 2012) .......................................................... 31

Figure 3.9: Example HAA quality control chart for DCAA ........................................................ 32

Figure 4.1: DOC, UVA, and SUVA results for all waters over the range of coagulant doses .... 42

Figure 4.2: Example fluorescence spectra (Otonabee River raw water)...................................... 43

Figure 4.3: Fluorescence spectra of Ottawa River raw water ...................................................... 44

Figure 4.4: Fluorescence spectra of Lake Simcoe raw water ...................................................... 44

Figure 4.5: Flourescence specctra of Lake Ontario raw water .................................................... 44

Figure 4.6: Loading plots for three principal components of the ‘all samples’ model from sample

set XA. ........................................................................................................................ 46

Figure 4.7: Scores from all sample PCA model (XA) .................................................................. 47

Figure 4.8: Total THMs concentration vs. alum dose. ................................................................ 50

Figure 4.9: Total HAAs concentration vs. alum dose ................................................................... 50

Figure 4.10: Measured values vs. predicted values of THMs from non-linear regressions of

source-specific models ............................................................................................... 52

Figure 5.1: Toronto WTP intake locations. ................................................................................. 58

Figure 5.2: Raw water LC-OCD results ...................................................................................... 62

Figure 5.3: Example raw water spectra from all four water treatment plants .............................. 63

Figure 5.4: Loading plots for first 3 principal component ........................................................... 65

Figure 5.5: Averages and MSD for raw water FEEM-PCA scores ............................................. 66

xi

Figure 5.6: Comparison of treatment performance for all plants: TOC ...................................... 67

Figure 5.7: Comparison of treatment performance for all plants: LC-OCD ................................ 68

Figure 5.8: Average difference in PC score due to treatment ...................................................... 69

Figure 6.1: Simplified process flow diagram of pilot and full-scale treatment train ................... 72

Figure 6.2: Example FEEM illustrating scattering regions (FORS and SORS) .......................... 74

Figure 6.3: TOC and UVA of pilot plant settled water ................................................................ 77

Figure 6.4 Average monthly total loss of head for pilot plant filter ............................................ 78

Figure 6.5: Raw water FEEM – 2D view .................................................................................... 79

Figure 6.6: Raw water FEEM - 3D view ..................................................................................... 79

Figure 6.7: Raw and treated water samples (Model 1), PC 1 loading plot ................................. 82

Figure 6.8: Raw and treated water samples (Model 1), PC 2 loading plot .................................. 82

Figure 6.9: Treated water samples (Model 2), PC 1 loading plot ................................................ 82

Figure 6.10: Treated water samples (Model 2), PC 2 loading plot .............................................. 83

Figure 6.11: Impact of coagulation on PC scores. Averages and MDL (bars) for each treatment

train (n = 9 – 10). ....................................................................................................... 83

Figure 6.12: Impact of filtration on PC scores. Averages and MDL (bars) for each treatment train

(n = 9 – 10). ................................................................................................................ 84

Figure 8.1: THM speciation graphs, chlorine = 2.5 mg/L ......................................................... 104

Figure 8.2: THM speciation graphs, chlorine = 3.5 mg/L ......................................................... 105

Figure 8.3: HAA speciation graphs, chlorine = 2.5 mg/L ......................................................... 106

Figure 8.4: HAA speciation graphs, chlorine = 3.5 mg/L ......................................................... 107

Figure 8.5: F.J Horgan process flow diagram ............................................................................ 122

Figure 8.6: Island process flow diagram .................................................................................... 123

Figure 8.7: R.C. Harris process flow diagram ........................................................................... 124

Figure 8.8: R.L. Clark process flow diagram ............................................................................. 125

Figure 8.9: UVA vs. TOC for Jar Test Results: Full Range ...................................................... 135

Figure 8.10: UVA vs. TOC for jar test results: filtered water samples ..................................... 136

Figure 8.11: pH vs. Total THM Concentration for Jar Test Samples ........................................ 140

Figure 8.12: pH vs Total THM Concentration for Pilot Plant Samples ..................................... 141

Figure 8.13: pH vs. total HAA concentration jar tests ............................................................... 142

Figure 8.14: pH vs. total HAA concentration pilot samples ...................................................... 143

xii

NOMENCLATURE

% Percent

oC Degrees Celsius

γ Fraction of integral fluorescence emission

ε Molar absorptivity

λ Wavelength (nm)

μ Average

φ Quantum yield

General form of principal component

A Absorbance

a.u. Arbitrary units

Al2(SO4)3 Aluminum sulphate

Alum Aluminum sulphate

ANN Artificial neural networks

ANOVA Analysis of variance

b Path length

BCAA Bromochloroacetic acid

BDCAA Bromodichloroacetic acid

BDCM Bromodichloromethane

Br- Bromide

c Concentration

C Covariance matrix

CDBM Chlorodibromomethane

DBAA Dibromochloroacetic acid

DBPs Disinfection by-products

DCAA Dichloroacetic acid

DOC Dissolved organic carbon (mg/L)

DPD N,N-diethyl-p-phenylenediamine

F Fluorescence intensity

FEEM Fluorescence excitation-emission matrix

xiii

FEEM-PCA Principal component analysis of fluorescence excitation-emission matrices

FORS First order Rayleigh scattering

GAC Granular activated carbon

GC-ECD Gas chromatography with electron capture detection

H2SO4 Sulfuric acid

HAA5 Sum of five haloacetic acids: Monochloroacetic acid (MCAA),

Dichloroacetic acid (DCAA), Trichloroacetic acid (TCAA),

Monobromoacetic acid (MBAA) and Dibromoacetic acid (DBAA)

HAA9 Sum of nine haloacetic acids: Monochloroacetic acid (MCAA),

Dichloroacetic acid (DCAA), Trichloroacetic acid (TCAA),

Monobromoacetic acid (MBAA), Dibromoacetic acid (DBAA),

Bromochloroacetic acid (BCAA), Bromodichloroacetic acid (BDCAA),

ChlorodibromoaceticaAcid (CDBAA), and Tribromoacetic acid (TBAA)

HAAs Haloacetic acids

HI 705 Hyper+Ion 705 PACl

I Intensity

KHP Potassium hydrogen phthalate

L Litre(s)

LC-OCD Liquid chromatography with organic carbon detection

LMW Low molecular weight

MAE Mean absolute error

MBAA Monobromoacetic acid

MCAA Monochloroacetic acid

MDL Method detection limit

mg/L Milligrams per litre

Milli-Q® Purified water from Millipore Corporation equipment

mL Millilitre(s)

MSD Minimum significance difference

MSE Mean square error

MTBE Methyl-tert-butyl ether

MX Mutagen-x

μL Microlitre(s)

xiv

µg/L Microgram(s) per litre

n Number of samples

NaOCl Sodium hypochlorite

nm Nanometer

NOM Natural organic matter

OCD Organic carbon detection

OND Organic nitrogen detection

p Loading matrix

PACl Polyaluminum chloride

PARAFAC Parallel factor analysis

PC Principal component

PCA Principal component analysis

PFD Process flow diagram

pH Hydrogen ion concentration

PLS Partial least squares

R2 Pearson’s correlation coefficient

SCADA Supervisory control and data acquisition

SD Standard deviation

SORS Second order Rayleigh scattering

SUVA Specific ultraviolet absorbance

T Temperature

t Time

t Scores matrix

TBM Tribromomethane

TBAA Tribromoacetic acid

TCAA Trichloroacetic acid

TCM Trichloromethane

THMs Trihalomethanes

THM4 Sum of four trihalomethanes: Tribromomethan (TBM), Trichloromethane

(TCM), Chlorodibromomethane (CDBM), and Bromodichloromethane

(BDCM)

TOC Total organic carbon, mg/L

xv

TOX Total organic halides

USEPA United States Environmental Protection Agency

UV Ultraviolet

UVA Ultraviolet absorbance

V Volume

WTP Water treatment plant

1

1 Introduction

1.1 Background

Organic matter in naturally occurring waters is comprised of a complex heterogeneous

mixture of humic acid, fulvic acid, proteins, carbohydrates, and other organic compound classes

(Her et al., 2003). The monitoring of natural organic matter (NOM) has proven to be of great

importance in the water treatment field. NOM has been shown to be related to disinfection by-

product formation, negatively affects water treatment processes such as membrane filtration

(Peiris et al., 2009), and promotes biological growth in distribution systems (Baghoth et al.,

2011). The diversity of compounds within the definition of NOM is problematic when

considering removal techniques (Pifer and Fairey, 2012). There is evidence that both functional

groups and molecular weight distributions of compounds considered to be components of NOM

are integral to determining treatability (Matilainen et al., 2011).

Quantification of NOM has traditionally been through gross measurement parameters

such as total and dissolved organic carbon (TOC/DOC). These parameters represent all

oxidizable organic content in the water and do not provide information on specific compound

classes within the definition of NOM. Given the evidence that size, functionality, and structure

are important to treatability and reactivity, NOM quantification techniques with the ability to

identify specific compound classes are of interest for a variety of applications in the water

treatment field (Barrett et al., 2000).

NOM characterization using fluorescence spectra has been gaining traction as a

promising method (Zepp et al., 2004; Bieroza et al., 2011). This type of analysis is completed

through the collection of three dimensional fluorescence excitation-emission matrices (FEEM),

which represent fluorescence intensity at various excitation-emission pairs. In comparison to

other NOM characterization techniques, including liquid chromatography - organic carbon

detection (LC-OCD), FEEM analysis is fast and provides consistent results with high sensitivity

(Peiris et al., 2008). Traditional FEEM analysis involves identifying the location and magnitude

of peaks of fluorescence intensity. The position (excitation-emission region) of the peak can be

correlated with a specific compound and the maximum intensity of the peak with concentration

(Bieroza et al., 2011). This method, referred to as ‘peak picking,’ is limited in that it neglects to

capture the heterogeneity of NOM fractions (Peiris et al., 2010). The fluorescence intensity at

specific excitation/emission pairs is influenced by several variables including concentration,

quantum yield, and absorptivity. Molecular structure has a noted effect on some of these factors

2

which can be common among different molecules. By ‘peak picking’ the nature of overlapping

structural properties between distinct compounds and many of the potentially relevant

chromophores are neglected (Persson and Wedborg, 2001).

To further extract information and, in particular, changes in NOM composition between

several samples, multivariate analysis techniques can be employed. Common advanced analysis

techniques involve using two-way principal component analysis (PCA) and multi-way parallel

factor (PARAFAC) analysis (Peiris et al., 2010). These multivariate techniques reduce the

dimensionality of the data set (Persson and Wedborg, 2001), simplifying analysis, while

incorporating the entire fluorescence spectrum (Bieroza et al., 2011).

1.2 Objectives

Specific research objectives for this study can be summarized as follows:

1. Investigate the applicability of principal component analysis of fluorescence

excitation-emission matrices (FEEM-PCA) for characterization of NOM.

2. Compare FEEM-PCA as an indicator of disinfection by-product (DBP) precursor

material to traditional NOM measures.

3. Investigate spatial and temporal changes in NOM characteristics between raw waters

for treatment facilities in a given watershed using FEEM-PCA.

4. Determine changes to NOM fractions due to various treatment processes with FEEM-

PCA and other characterization techniques.

3

2 Literature Review

2.1 Disinfection by-products

Major DBPs classes produced from chlorination or natural waters include

trihalomethanes (THMs), haloacetic acids (HAAs), haloacetonitriles, halopicrin, cyanogen

chloride and bromide, chloral hydrate, and mutagen-x (MX) (Oxenford, 1997). Despite the

identification of hundreds of distinct DBP types, 30 - 60% of the total organic halides (TOX)

formed have been accounted for (Singer, 1994; Krasner et al., 2006). Control and regulations for

water utilities using chlorine typically revolve around concentrations of trihalomethanes (THMs)

and a subset of haloacetic acids (HAAs) (Beggs et al., 2009), which occur at the highest

concentrations (Hua and Reckhow, 2008). The regulation of only certain classes addresses the

impracticality of measuring all DBPs and provides a surrogate for other un-identified compounds

(Hrudey, 2009).

Coinciding with the discovery of THMs in drinking water in the 1970s, the USEPA

released findings of an association between consumption of chlorinated water and increased

cancer risk. This spurred the classification of chloroform as a carcinogen and ultimately the

regulation of THMs in drinking water by the USEPA (Singer, 1994). Furthermore, it opened up

a field of necessary research into the potential health effects of chlorination DBPs on humans.

There is some indication of potential carcinogenicity and adverse health effects from chlorinated

DBP exposure (McBean et al., 2008; USEPA, 1999), although current evidence has not linked

realistic human consumption levels of regulated DBPs in drinking water, THMs and HAA5, and

elevated human health risk (Hrudey, 2009; Wang et al., 2007).

2.1.1 DBP formation theory

Disinfection by-products are formed through the oxidation of NOM present in natural

source waters (Oxenford, 1997; Sadiq and Rodriguez, 2004). NOM is comprised of a complex

heterogeneous mixture of humic acid, fulvic acid, proteins, carbohydrates, and other organic

compound classes (Her et al., 2003). Each of these classes has unique reactivity with

disinfectants to form DBPs (Barrett et al., 2000), although multiple studies have identified

humic-like substances to be the largest contributor to DBP formation (Childress et al., 1999;

Liang and Singer, 2003).

4

In addition to precursor concentrations and reactivity, several other factors affect the rate

and total DBP formation including pH, temperature, reaction time, chlorine dose, and bromide

ion concentration (Chen and Westerhoff, 2010). Increased reaction times generally lead to

greater DBP formation and consumption of additional chlorine residual (Rodriguez and Sérodes,

1999). The effect of pH is specific to DPB specie; THM formation potential increases with

increasing pH, while formation potential for HAAs and most other DBP classes are reduced with

increasing pH (Sadiq and Rodriguez, 2004). Increasing chlorine dose as well as water

temperature are also associated with increasing DBP formation potential on the basis of reagent

availability and reaction kinetics (Gallard and U. von Gunten, 2002).

2.1.2 Modelling DBP formation

Control of DBP formation is typically achieved through the reduction of precursor

material (Singer, 1994; Oxenford, 1996). While it is possible to remove DBPs once they have

formed, it is more cost efficient to limit the production through NOM reduction (Sadiq and

Rodriguez, 2004). Predictive DBP models have been successfully linked to NOM estimation

parameters which estimate the amount of DBP precursor material, including total organic carbon

(TOC), dissolved organic carbon (DOC), UV-absorbance (UVA) at 254 nm, and specific UV-

absorbance (SUVA) (Chen and Westerhoff, 2010; Chowdhury et al., 2009). Results from DBP

modelling studies show conflicting results regarding which NOM surrogate is most appropriate

for DBP formation prediction. Some studies indicate that SUVA (DOC divided by UVA at 254

nm) has been shown to correlate best with DBP formation (Kitis et al., 2001; Barrett et al.,

2000). In contrast, Uyak and Toroz (2007) found UVA more closely correlated with DBP

formation potential than DOC or SUVA while other authors have reported greater prediction

strength using DOC (Sohn et al., 2004). Based on the EPA database of reported DBP

concentrations, Sohn et al. (2004) presented DOC based models had a significantly greater

predictability when compared to using UVA.

Chowdhury et al. (2009) provided a comprehensive list of reported DBP models and their

strength (as R2). This list has been adapted and is presented as Table 2.1 to illustrate historically

successful model types and parameters used for DBP modelling. Of particular note is the lack of

NOM fraction information used in previous models. Although linear regression techniques have

been applied, it has been shown that empirical power based models best describe DBP formation

(Amy et al., 1987; Sohn et al., 2004). A generalized form of a power function for THM

prediction is shown as equation 2.1.

5

where, NOM is the concentration of NOM, Br- is concentration of bromide, T is

temperature, Cl2 is chlorine dose, and t is time. k, a, b, c, d, e, and f are constants

determined through regression. Coefficients can be found through several methods,

although forward stepwise regression has typically been used (Amy et al., 1998).

Due to the complexity of compound types within NOM and their unique reactivity in

DBP formation (Barrett et al., 2000; Chen et al., 2008) it is postulated that methods which can

characterize NOM fractions will improve the accuracy of DBP formation models. Although

significant work has been done on relating specific constituents of NOM with DBP formation

potential (Minear and Amy, 1996; Hua and Reckhow, 2007), little work has focused on including

fractionation information into predictive DBP models (Chowdhury et al., 2009). Furthermore,

NOM characterisation has commonly relied on chemical fractionation using adsorption resins

(Matilainen et al., 2011) or physical fractionation using size exclusion columns (Huber et al.,

2011; Pifer and Fairey, 2012). These NOM fractionation techniques are limited for widespread

application due to high instrument and sample costs, long analysis time, and required personnel

expertise.

Hua and Reckhow (2007) performed a comprehensive study on DBP formation potential

from NOM fractions obtained using XAD resins. Through total organic halides (TOX)

measurements, the authors found that hydrophobic, high molecular weight compounds produced

a greater amount of unidentifiable organohalides. Furthermore, they concluded that THM

precursors were generally less hydrophobic than those for HAAs. Yang et al. (2008) used

regional integration of fluorescence excitation-emission matrices (FEEMs) for determining DBP

formation of NOM fraction extracts. The results from regional integration performed worse for

prediction of chloroform and nitrile formation in comparison to SUVA. Pifer and Fairey (2012)

presented increased correlation of chloroform formation with maximum intensities from parallel

factor (PARAFAC) analysis of FEEMs coupled with SUVA in comparison to SUVA by itself.

(2.1)

6

Table 2.1: Selected existing DBP models

Source Model R2

Rodriguez et al. (2000) a) THMs = 0.044(DOC)1.030

(t)0.262

(pH)1.149

(D)0.277

(T)0.968

b) THMs = 1.392(DOC)1.092

(pH)0.531

(T)0.255

a) 0.90

b) 0.34

Serodes et al. (2003) a) log(HAAs) = 2.72+0.653(TOC)+0.458(D)+0.295(t)

b) log(HAAs) = 1.33+2.612(TOC)+0.102(D)0.255(T)+0.102(t)

c) HAAs = -8.202+4.869(TOC)+1.053(D)+0.364(t)

d) THMs = 16.9+16.0(TOC)+3.319(D)-1.135(T)+1.139(t)

e) log(THMs) = -0.101+0.335THMo+3.914(TOC)+0.117(t)

f) THMs – 21.2+2.447(D)+0.499(t)

a) 0.89

b) 0.80

c) 0.92

d) 0.78

e) 0.89

f) 0.56

Sohn et al. (2004) a) THMs = 10-1.385

(DOC)1.098

(D)0.152

(Br)0.068

(T)0.609

(pH)1.601

(t)0.263

b) THMs = 0.42(UVA)0.482

(D)0.339

(Br-)0.023

(T)0.617

(pH)1.601

(t)0.261

c) THMs = 0.283(DOC*UVA)0.421

(D)0.145

(Br-)0.041

(T)0.614

(pH)1.606

(t)0.261

d) THMs = 3.296(DOC)0.801

(D)0.261

(Br)0.223

(t)0.264

e) THMs = 75.7(UVA)0.593

(D)0.332

(Br)0.0603

(t)0.264

f) THMs = 23.9(DOC*UVA)0.403

(D)0.225

(Br-)0.414

(t)0.264

g) THMs = (THM @ pH 7.5, T = 20oC)*1.156

(pH-7.5)1.0263

(T-20)

h) HAA6 = 9.98(DOC)0.935

(D)0.443

(Br)-0.031

(T)0.387

(pH)-0.655

(t)0.178

i) HAA6 = 171.4(UVA)0.584

(D)0.398

(Br)-0.091

(T)0.396

(pH)-0.645

(t)0.178

j) HAA6 = 101.2(DOC*UVA)0.452

(D)0.194

(Br)-0.0698

(T)0.346

(pH)-0.6235

(t)0.18

k) HAA6 = 5.228(DOC)0.585

(D)0.565

(Br)-0.031

(t)0.153

l) HAA6 = 63.7(UVA)0.419

(D)0.640

(Br)-0.066

(t)0.161

m) HAA6 = 30.7(DOC*UVA)0.302

(D)0.541

(Br)-0.012

(t)0.161

n) HAA6 = (HAA6 @ pH 7.5, T = 20oC)*0.932

(pH-7.5)1.02

(T-20)

a) 0.90

b) 0.70

c) 0.81

d) 0.87

e) 0.90

f) 0.92

g) 0.92

h) 0.87

i) 0.80

j) 0.85

k) 0.92

l) 0.92

m) 0.94

n) 0.85

Uyak et al. (2005) THMs = 0.0707(TOC+3.2)1.314

(pH-4.0)1.496

(D-2.5)-0.197

(T+10)0.724

0.98

Hong et al. (2007) THMs = 10-1.375

t0.258

(D/DOC)0.194

pH1.695

T0.507

(Br-)0.218

BDCM = 10-3.201

t0.297

pH1.695

T0.507

(Br-)0.218

TCM = 10-0.748

t0.210

(D/DOC)0.221

pH1.374

T0.532

(Br-)-0.184

0.87

0.87

0.86

Note: DBP concentrations from all models reported in µg/L. THM: trihalomethanes, HAA: haloacetic acid, D: chlorine dose

(mg/L), DOC: dissolved organic carbon, TOC: total organic carbon, UVA: ultraviolet absorbance, Br-: bromide concentration

(mg/L), pH: hydrogen ion concentration, t: time, T: temperature (oC)

7

2.2 Measurement techniques for natural organic matter

In both surface and ground waters, NOM consists of a wide array of compounds

including humic substances, carbohydrates, carboxylic acids, phenols, amino acids, and proteins

(Thurman, 1985; Her et al., 2003). Due to the complex variety of natural organic matter,

quantification is accomplished through the use of surrogates. Typical surrogate quantification

measures of NOM, such as TOC/DOC, only provide gross measurements (Gone et al., 2009).

Both TOC and DOC are measures of total mass of organic carbon, typically obtained through

complete oxidation (Matilainen et al., 2011). This means that TOC/DOC, while providing

information on the total concentration of organic material offers no information on NOM

character and reactivity.

Through a variety of methods it has been established that humic substances make up the

majority of organic carbon (40-60% of the dissolved fraction) in natural waters (Sohn et al.,

2007). Humic substances are a broad group which represent a heterogeneous mixture of high

molecular weight aromatic and aliphatic organic compounds characterized by functional groups

such as carboxyl, phenolic and/or enolic OH, alcoholic OH, and quinolic CO (Mobed et al.,

1996). In most natural freshwater sources, the second most abundant organic carbon fraction is

bio-polymers, which have molecular weights of 10 kDA or greater. This fraction tends not to

have a response to UVA indicating the lack of unsaturated structures however, does contain

organic nitrogen indicating the presence of polysaccharides, proteins, and amino acids (Huber et

al., 2011).

Another commonly used NOM surrogate is UVA, which has been shown to account for

specific structures and certain functional groups (Sadiq and Rodriguez, 2004). NOM has been

found to absorb light over a wide range of wavelengths, while inorganic constituents of natural

waters do not absorb above wavelengths of approximately 230 nm. The majority of NOM

chromophores contain aromatic functional groups such as phenols and various aromatic acids,

which absorb light in the UV region (< 400 nm), and are principally associated with humic

substances (Korshin et al., 1996). For the majority of applications in water treatment, UVA is

measured at a wavelength of 254 nm, which is thought to be most selective to aromatic

chromophores. Studies have shown that the aromaticity of DOC strongly defines its reactivity

with oxidants, such as chlorine, making UVA a theoretically ideal DBP precursor surrogate

(Weishaar et al., 2003).

8

In an effort to address the non-selectiveness of TOC/DOC, a measure of SUVA was

developed which incorporates a measure of aromaticity using UVA. SUVA is defined as the

ratio of UVA at 254 nm to DOC (Edzwald and Tobiason, 1999). As with UVA, it has been

thought that SUVA would perform well as a DBP precursor surrogate since it incorporates a

measure of aromaticity. However, Weishaar et al. (2003) showed that water samples with

similar SUVA values displayed a wide range of THM formation potentials. They concluded that

while SUVA was a good indicator of the DOC character, however did not provide enough

source-specific reactivity information for DBP formation prediction.

Two analysis techniques which have been shown to provide information on multiple

NOM fractions include liquid chromatography-organic carbon detection (LC-OCD) (Huber et

al., 2011) and multivariate analysis of fluorescence excitation-emission matrices (FEEM) (Zepp

et al., 2004; Bieroza et al, 2011).

2.2.1 Liquid chromatography – organic carbon detection

LC-OCD allows for simultaneous quantification of several organic matter fractions. It

consists of a DOC analyzer preceded by chromatographic separation on the basis of molecule

size and into hydrophilic and hydrophobic fractions (Huber and Frimmel, 1992). Hydrophilic

NOM is then divided into five subcategories: biopolymers, humic substances, building blocks of

humic substances, low molecular weight (LMW) acids and LMW neutrals. A UV detector

provides information regarding the specific UV absorbance (SUVA) and qualitative levels of

inorganic colloids for each previously identified category. Finally, an organic nitrogen detector is

used to provide additional information regarding the nature of specific fractions, such as the

fraction of bio-polymers considered to be proteins (Huber et al, 2011).

2.2.2 Fluorescence excitation-emission matrices

Analysis of fluorescence spectra is completed through collection of fluorescence

excitation-emission matrices (FEEM). These matrices represent fluorescence intensity at various

excitation-emission pairs. When compared to other NOM characterization techniques, including

liquid chromatograph organic carbon detection (LC-OCD), FEEM analysis is fast and provides

consistent results with high sensitivity (Peiris et al., 2008). Furthermore, fluorescence measures

are more selective and less susceptible to interferences when compared to NOM characterization

using UVA (Persson and Wedborg, 2001).

9

FEEM analysis is based on the realization that several components of NOM fractions

exhibit unique fluorescence signatures that allow them to be distinguished from each other in a

fluorescence spectrum (Coble et al., 1990; Zepp et al., 2004). Through FEEMs, typically humic

acid, fulvic acid, and aromatic proteins (tryptophan or tyrosine-like) compounds are identifiable

(Bieroza et al., 2011). There is evidence that humic substances originating from either terrestrial

or anthropogenic sources may be differentiated through FEEMs (McKnight et al., 2001). NOM

compounds typically identifiable through fluorescence and their reported unique excitation-

emission signatures are presented in Table 2.2. An example FEEM with humic acid fulvic acid,

protein-like, as well as first and second order Rayleigh scattering (FORS/SORS) peaks labelled,

is shown as Figure 2.1 (Peiris et al., 2010).

Table 2.2: Commonly reported excitation/emission location of NOM fluorophores

Compound group Excitation/Emission

wavelength (nm)

Description and source Author

Protein-like 275/<300 Amino acids, free or

bound in proteins

Hudson et al., 2007

275/310 Tyrosine-like Coble et al., 1996

275/340 Tryptophan-like Coble et al., 1996

Humic-like 260/380-460

Terrestial humic-like.

Peak A: humic acid

Coble et al., 1996

350/420-480 Terrestial humic-like. Peak

C: fulvic acid

Coble et al., 1996

300-370/400-500 Humic-like Murphy et al., 2008

2.2.2.1 Theory

Fluorescence occurs when chromophores are excited and then relax to a ground state

through the radiation of photons. Due to non-radiative decay, and other processes some of the

energy adsorbed is lost resulting in the emitted photon to not equal the excitation energy (Stokes’

shift) (Hudson et al., 2007). Fluorescence intensity (photons emitted) is dependent on several

factors including absorptivity, quantum yield, and concentration of the chromophore, as well as

cell path length (Roch, 1997; Persson and Wedborg, 2001).

10

Figure 2.1: Example freshwater FEEM with humic acid (α), fulvic acid (β), and protein-like (δ)

peaks. (a) top-down view, (b) 3D view. (Peiris et al., 2010).

The energy absorbed by a chromophore subjected to excitation photons can be described

by Beer’s law:

where, A is the absorbance, ε is the molar absorptivity of a specific chromophore, b is the

path length, and c is the concentration of chromophores.

The absorbance (A) is what contributes to the excitation of the chromophore. In complex

mixtures, such as NOM in water, multiple chromophores exist with distinct properties expressed

through the molar absorptivity (ε). Therefore, total absorbance of the sample is the sum of the

molar absorptivity and concentration of each chromophore multiplied by the path length (Persson

and Wedborg, 2001). After excitation at a specific wavelength, λi, the intensity of emission

through relaxation to the ground state is described by the quantum yield (φ) and the fraction of

the integral fluorescence emission and a specific wavelength (γ(λi)) (Roch, 1997). Quantum

yield is defined as the number of quanta emitted divided by the total quanta absorbed (Persson

and Wedborg, 2001), in effect the efficiency of fluorescence for a specific compound.

Therefore, for a given intensity of excitation light, Io(λi), the fluorescence of a chromophore, F,

can be expressed as follows (Roch, 1997):

(2.2)

(2.3)

11

For multiple chromophores in solution, as with Beer’s law, the total fluorescence is

additive (Persson and Wedborg, 2001). Factors such as absorptivity and quantum yield are

highly dependent on the structure, weight, and functional groups associated with a compound

(Baghoth et al., 2011). Therefore, the broad peaks observed in FEEMs of natural waters are

explained by the overlapping structural properties of NOM chromophores (Persson and

Wedborg, 2001).

2.3 Multivariate analysis of fluorescence spectra

Historically analysis of FEEMs has been completed through identifying location and

maximum intensity of fluorescence peaks. The position (excitation-emission region) of the

peaks are correlated with specific compound groups such as humic and fulvic acids, while the

maximum intensity is correlated with concentration (Her et al., 2003). As discussed in section

2.2.2.1, the fluorescence intensity is highly dependent on the molecular structure of the

chromophores, which can be similar between several distinct molecules. The method of tracking

concentration through the maximum intensity of fluorescence peaks, referred to as ‘peak

picking,’ does not capture the nature of overlapping structural properties between diverse

compounds and many of the potentially relevant chromophores are neglected (Persson and

Wedborg, 2001; Peiris et al., 2010).


several samples, multivariate analysis techniques can be employed. Multivariate analysis

methods such as PCA are able to reduce dimensionality of data sets by modelling variance

between samples. This allows for the incorporation of entire fluorescence spectrum in analysis,

creating a more complete representation of the heterogeneity of NOM (Bieroza et al., 2011).

Furthermore, information related to the fundamental properties of PCA can garner information

about the sample set being analyzed. PCs represent maximum variance in the sample set and,

therefore, describe the spectral areas which most significantly change between samples.

Several multivariate analysis techniques have been successfully applied to FEEM

analysis including PCA, partial least squares (PLS), parallel factor analysis (PARAFAC),

artificial neural networks (ANNs) and others (Persson and Wedborg, 2001; Stedmon et al.,

2003). Recent publications related to freshwaters have generally employed either PCA or

PARAFAC (Her et al., 2003; Peiris et al. 2010). Each of these analysis techniques vary in their

assumptions and algorithms for data decomposition. For example, PCA requires each PC to be

12

mutually independent with maximized variance representation (Joliffe, 1986). While there is

some evidence that PARAFAC produces variables more representative of individual NOM

compounds, it is not capable of analyzing negative values (Bieroza et al., 2011) and cannot

incorporate light scattering regions due to their non-linear response (Bahram et al., 2006).

Three types of scattering have been identified in typical FEEMs including Rayleigh,

Tyndall and Raman scattering, with Rayleigh scattering typically being the most pronounced

(Zepp et al. 2004). Although it eliminates the possibility of analyzing changes to scattering,

which have been associated with particulates (Peiris et al., 2010), modelling efficiency has been

shown to improve for PCA through the subtraction of scattering regions (Bahram et al., 2006).

2.4 Principal component analysis

PCA is a multivariate data analysis technique that is used to approximate a large data

matrix through observed patterns (Wold et al., 1987). Approximation of the data patterns is

achieved by obtaining new, mutually independent, variables that are mathematically represented

by linear combinations of the original variables. In generalized terms, equation 2.4, the original

data matrix X is decomposed into the product of two small matrices, ti and pi which are referred

to as the scores matrix and the loading matrix, respectively. The residuals generated from the

approximation are represented in another matrix E. For multiple samples, q, in the matrix X, the

product and residuals are summed (Peiris et al., 2010).

∑

2.4.1 Mathematical definition and derivation

Given a matrix of n random variables, X, one could describe patterns of the n variances

through all of the possible ⁄ covariances. For a small n, this would be realistically

feasible, however in applications where n is very large this method of describing variance

between random variables would be cumbersome. In essence, principal component analysis

attempts to describe these variances through a reduced set of variables, called principal

components (Joliffe, 1986).

Principal components are linear functions of the original set of variables (Wold et al.,

1987), X, therefore generally can be defined as , where θT is a vector of n constants,

(2.4)

13

transposed. The first PC ( ) describes the most variance in X. As part of PCA, individual PCs

are defined as mutually independent. Therefore, the second PC ( ) must also describe a

maximum amount of variance within X, while being uncorrelated with (Joliffe, 1986).

The PCs are calculated through finding the eigenvectors of the covariance matrix of X,

referred to as C. For any arbitrary i’th PC,

∑

Where, θi is the i`th eigenvector of C with associated eigenvalue, λi. A constraint is typically

added where θTθ = 1 so that by definition the variance of αi is equal to the i`th eigenvalue, λi.

As discussed, each subsequent PC maximizes the amount of variance represented. The

variance represented by a PC can be defined as follows with respect to the covariance matrix:

A common approach to finding the PCs is to use Lagrange multipliers (Joliffe, 1986).

For a Langrage multiplier, λ, equation 2.7 is maximized through differentiation (equation 2.8)

with respect to θ1 in order to find the first principal component:

[

]

Therefore, λ is the eigenvalue of C associated with the eigenvector, θ1. This leads to the

conclusion that in order to maximize variance represented by the PC, the eigenvalue has to be

maximized. In general, the i’th largest eigenvalue of the covariance matrix is associated with the

i’th PC. For PCs greater than 1, mutual independence must also be considered. Joliffe shows a

proof on how correlation between eigenvectors can be accounted for using the approach

discussed above (Joliffe, 1987).

(2.5)

(2.6)

(2.7)

(2.8)

14

2.5 Application to natural waters

Significant work with FEEM analysis for NOM characterization in marine and

freshwaters has been completed over the last couple of decades. Coble et al., 1990 analyzed

dissolved organic matter (DOM) in sea waters using FEEMs. They were successful in

identifying three distinct fluorophores and tracking changes in concentration with depth based on

maximum peak intensities. To-date, at least four NOM groups related to distinct FEEM peaks

have been identified to represent protein and humic substances (Zepp et al., 2004). Baker et al.,

(2002) used fluorescence spectrophotometry to identify the presence of pollution in catchments.

They reported that protein-like fluorescence correlated well with wastewater influence and were

able to estimate the impact on natural waters using this technique. Hudson et al. (2007) also

illustrated the use of fluorescence to detect anthropogenic sources of pollution in natural waters.

Peiris et al. (2011) has shown the application of FEEMs for prediction of ultrafiltration

membrane fouling events. Furthermore, membrane fouling for both ultrafiltration and

nanofiltration has been linked predominantly to the protein-like fraction (Peiris et al., 2010).

In light of these varied applications of FEEM analysis for NOM characterization, it is of

interest to further explore and refine the use of fluorescence spectra in the drinking water field.

The incorporation of multivariate analysis techniques for handling high-dimensionality of data

has significant promise for increasing analysis sensitivity and selectivity.

15

3 Materials and methods

3.1 Experimental protocols

3.1.1 Jar tests and DBP formation

Jar tests were conducted to mimic conventional water treatment processes including

coagulation, flocculation, sedimentation, and filtration. Two coagulant types were used in this

study: alum (General Chemical, Parsipanny, NJ), and Hyper+Ion 705 (HI705) polyaluminum

chloride (PACl) (General Chemical, Parsipanny, NJ). The jar tests were conducted using a PB-

700 Standard Jar Tester paddle stirrer with six square, acrylic 2-L jars (Phipps & Bird,

Richmond, VA). The protocol followed was adapted from the USEPA’s Enhanced Coagulation

Guidance Manual (USEPA, 1999). Following addition of the coagulant into each jar,

coagulation was simulated through rapid mix (100 RPM) for 1.5 minutes and flocculation by

reducing the mixing speed to 30 RPM for 15 minutes. The water was then allowed to stand (no

mixing) for 30 minutes to simulate sedimentation. Vacuum filtration was performed on the

settled water using 1.2 micron glass microfibre filters (Whatman 934-AH, Florham Park, NJ) to

simulate media filtration (Wassink, 2011). For specific analyses including DOC and LC-OCD

the water was filtered through 0.45 micron membrane filters (Pall Corporation Supor®-450, Port

Washington, NY) to ensure that particulates (> 0.45µm in size) had been removed. Following

filtration, chlorination was simulated by dosing sodium hypochlorite (12% Cl2, BioShop Canada,

Inc., Burlington, ON) and allowing a 24 hour reaction time in a 250 mL sealed bottle, without

headspace.

Reagents used in the jar tests are summarized in Table 3.1 and Table 3.2 provides a

method outline for the jar tests.

Table 3.1: Jar test reagents

Reagent Source

Aluminum sulphate (Al3(SO4)318H2O General Chemical (Parsipanny, NJ), 48.5%

Polyaluminum chloride (Hyper+Ion 705 PACl) General Chemical (Parsipanny, NJ), 100%

Sodium hypochlorite (NaOCl) BioShop Canada Inc. (Burlington, ON), 12%

16

Table 3.2: Jar test method outline

Raw water stored in refrigerator (4oC)

Allow for raw water to reach room temperature before testing (23±2oC)

Chlorine Solution Preparation

1. Fill a 100 mL volumetric flask with Milli-Q water and 1000 µL of 12% NaOCl stock

solution. Invert flask 5 times to ensure mixing.

2. Transfer to 125 mL amber bottle, cap with Teflon lined cap and store at 4oC Yields a 600

mg/L Chlorine Solution

3. Concentration of free chlorine in solution is checked using the DPD method

Jar Test

1. Take a 100 mL sample of raw water and measure pH. From this take duplicate 40 mL

sample for UV254 and TOC analysis

2. Fill six jars with 2 L of raw water using a 1 L graduated cylinder.

3. Add required volume of coagulant (and acid determined from previous step). Add acid

first and mix well prior to adding coagulant.

4. Using the jar testing apparatus, stir at 100 rpm for 90 seconds (rapid mix)

5. Reduce speed to 30 rpm for 15 minutes (flocculation)

6. Turn of stirrer, raise the paddles out of the sample and allow 30 minutes for settling.

7. Collect a 500 mL sample of the supernatant from each jar, filter using a 1.5 µm glass

fibre filter and measure pH.

8. Take duplicate 40 mL sample from the 500 mL for UV254 and TOC analysis

DBP Formation Test:

1. From the filtered supernatant, transfer just under 250 mL into a 250 mL bottle.

2. After adding chlorine, fill up the remainder of the headspace with Milli-Q water. Cap

with Teflon-lined screw caps and set out at room temperature (23 ± 2oC)

3. After 24 hours, measure free and total chlorine fractions using DPD method.

4. Quench chlorine with 0.050 g of ascorbic acid

5. Collect two 25 mL samples for DBP analysis in sealed vials with no headspace

17

3.1.2 Peterborough pilot plant

The Peterborough pilot plant was designed and operated to mimic the operation of the

full-scale water treatment plant located on the Otonabee River in the City of Peterborough,

Ontario. The full-scale plant was built in 1922 with expansions in 1952, 1965, and 1995. The

pilot plant, which is comprised of two identical treatment trains, was put online in 2011 to collect

data for process optimization work. To date, the pilot plant has been used principally to compare

the effects on water quality from the use of polyaluminum chloride (PACl) as a coagulant

compared to the currently used aluminum sulphate (alum).

A detailed process flow diagram is presented as Figure 3.1. Both the full-scale and pilot-

scale trains draw water from the same intake in the Otonabee River. When the temperature of

the raw water falls below 12oC, a free residual of approximately 0.05 mg/L chlorine is applied at

the intake for zebra mussel control. Following intake, the coagulant is added during a flash mix.

Flocculation is allowed for in multiple large tanks followed by sedimentation using parallel plate

settlers. Settled water then flows through granular dual media filters. The filter effluent is

combined prior to entering a chlorine contact tank for primary disinfection. After the initial dose

of chlorine and a hydraulic retention to allow for sufficient contact time, additional chlorine is

added for secondary disinfection. Hydrofluosilic acid and sodium silicate are also added at this

point for fluorination and corrosion control, respectively. Sodium silicate addition is intended to

elevate the pH, which is typically depressed through alum addition, for corrosion prevention

prior to entering the distribution system.

3.1.2.1 Operational scheme

The pilot plant was being used to observe the possible cost savings and process benefits

of using Hyper+Ion 705 (HI705) polyaluminum chloride (PACl) as a coagulant as opposed to

alum. For comparison of these two coagulants, the operation scheme was to match settled water

TOC for both pilot treatment trains as well as the full-scale plant. The full-scale plant coagulant

dose was selected by the WTP staff based on raw water quality. Pilot-scale doses of both alum

and PACl are selected based on monthly jar tests to match full-scale settled water TOC.

TOC measurements were collected every two minutes by one online analyzer (General

Electric Sievers 900 On-Line), which alternated between four sampling points: two pilot plant

trains, full-scale, and raw water. The analyzer cycled between the sampling points

approximately every 2.5 hours. The manufacturer reported accuracy of this machine was ±2% of

the TOC value (General Electric, 2005).

18

Figure 3.1: Process flow diagram of Peterborough water treatment plant

19

3.2 Analytical methods

3.2.1 Quality control

Quality control measures were used in TOC/DOC, UVA, THM, and HAA analyses.

These included calibration, setting quality control limits, and periodic quality checks. Details

pertaining to each analysis method are presented in their specific method descriptions. Quality

control limits were set based on Standard Method 1020 (APHA, 2005). Limits were set based on

the standard deviation of 8 quality control standards as follows:

Control limit was set to µ ± 3 SD

Warning limit was set to µ ± 2 SD

where, µ was the average response or concentration from 8 quality control standards, and SD

was the standard deviation.

If any of the following conditions were satisfied, the instrument would be recalibrated:

2 consecutive measurements outside the control limit

3 of 4 consecutive measurements outside the warning limit

Method detection limits were also calculated for each analysis method. The MDL was

determined through multiplication of the standard deviation from 8 quality control standards and

the critical value of the Student’s t distribution at a 99% confidence level.

3.2.2 Total and dissolved organic carbon

For jar tests, both total and dissolved organic carbon was analyzed using an O-I

Corporation model 1030 TOC Analyzer (College Station, Texas). Pilot plant TOC methods are

described in section 3.1.2. Organic carbon measurements were based on the wet oxidation

method as described in Standard Method 5310 D (APHA, 2005). For analysis of the dissolved

fraction, samples were first filtered using a 0.45 µm filter (Pall Corporation Supor®-450, Port

Washington, NY) (Edzwald and Tobiason, 1999). Equipment conditions are listed in Table 3.3.

Samples were collected in 40 mL amber glass vials with Teflon® lined screw caps (VWR

International, Mississauga, ON). For preservation they were acidified to a pH < 2 using 3 drops

of concentrated sulphuric acid. If storage was required, samples were refrigerated at 4oC until

analysis, which was typically performed within 1 – 3 days.

20

Table 3.3: Total and dissolved organic carbon instrument conditions

Parameter Condition

Sample volume 15 mL

Oxidant volume and type 1000 µL of 100 g/L sodium persulphate

Acid volume and type 200 µL of 5% phosphoric acid

Reaction time 150 seconds

Detection time 120 seconds

Purge gas Nitrogen

Calibration and quality control of organic carbon measurements was completed using

standards prepared from dry potassium hydrogen phthalate (KHP) (Sigma-Aldrich Corp.,

Oakville, ON) in Milli-Q® water. A six-point calibration (0, 0.625, 1.25, 2.5, 5, and 10 mg/L)

was completed approximately once per month or as dictated by quality control data. Every 10

samples run, one blank (Milli-Q® water) and one check standard prepared to 3.0 mg/L TOC was

run. Figure 3.2 shows an example TOC calibration and Figure 3.3 shows quality control

standards run for DBP modelling work presented in section 4 of this document. The limits in the

quality control chart was prepared based on 8 quality control standards (3.0 mg/L TOC) as

discussed in section 3.2.1. Table 3.4 reports the standard deviation and method detection limit

as found from the 8 quality control standards.

Table 3.4: Accuracy and method detection limit of TOC analysis

Parameter Value

Manufacturer reported accuracy ± 5 % of the measured value

Quality control standard deviation 0.10 mg/L

MDL (mg/L) 0.35 mg/L

3.2.3 Ultraviolet absorbance

Ultraviolet absorbance (UVA) was measured at 254 nm using a CE 3055

spectrophotometer (Cecil Instruments, Cambridge, England). The method followed is described

in Standard Method 5910 B (APHA, 2005). A quartz cuvette with 1 cm path length was used.

Prior to measurements, the spectrophotometer was zeroed using Milli-Q® water.

21

Figure 3.2: Example TOC calibration

Figure 3.3: TOC quality control chart (July to Sep., 2012)

Area count = 6217.8(TOC) + 2499 R² = 0.9989

0

10000

20000

30000

40000

50000

60000

70000

0 2 4 6 8 10 12

Are

a C

ou

nt

TOC (mg/L)

2.9

3

3.1

3.2

3.3

3.4

3.5

3.6

Tota

l org

anic

car

bo

n (

mg/

L)

Limit (3SD) Warning (2SD) Average

Otonabee River

Lake Ontario

Lake Simcoe

Ottawa River

22


Liquid chromatography – organic carbon detection (LC-OCD) analysis was conducted

using the method described by Huber et al. (2010). Samples were passed through a 0.45 µm

filter to remove any particulates prior to analyses. A weak cation exchange column (250 mm x

20mm, Toso, Japan) provided chromatographic separation of the NOM fractions. The mobile

phase used was a phosphate buffer exposed to UV at a flow rate of 1.1 mL/min (MLE, Dresden,

Germany). The samples first passed through the UVA detector (254 nm) and then through the

organic carbon detector (OCD). Prior to entering the OCD, the solution was acidified to convert

organic carbon to carbonic acid. During the acidification process, bound nitrogen (organic and

inorganic) was converted to nitrate which absorbs light in the UV region (Huber et al., 2011).

This allows for the simultaneous quantification of bound nitrogen in each separated fraction

using the UVA detector. Calibration was performed by the on-site technician using potassium

hydrogen phthalate. Data acquisition and processing was carried out using a customized

software program (ChromCALC, DOC-LABOR, Karlsruhe, Germany). All data from the OCD,

organic nitrogen detector (OND), and UVD were collected, however only OCD data was used in

the analysis. An example LC-OCD chromatogram is shown as Figure 3.4.

Figure 3.4: LC-OCD chromatogram of surface water (Huber et al., 2011).

Responses shown for organic carbon detection (OCD), UVA detection (UVD),

and organic nitrogen detection (OND). Peak A: biopolymers, Peak B: humic

substances, Peak C: building blocks, Peak D: low molecular weight acids, Peak

E: low molecular weight neutrals, Peak F: nitrate, Peak G: ammonium

23

Table 3.5: Description of LC-OCD fractions (Huber et al., 2011)

Fraction Description

Biopolymers High molecular weight based on elution time

Saturated compounds based on lack of UV absorbance

Non-ionic based on not being retained by cation and anion exchange resins

Some compounds contain nitrogen indicating presence of polysaccharides

and proteins

Humic

substances

Humic acids elute prior to fulvic acids

Contains carboxylic groups

One study placed 28% of organic carbon in Fulvic acids to be related to

aromatic structures, 42% for humic acids.

Building

blocks

Shoulder to humic substance peak indicating similar properties to humic

substances but with lower molecular weight

Breakdown products from humic substances

LMW acids Elute separately due to an ion chromatographic effect

Anions at neutral pH

LMW neutrals Low molecular weight and low ion density

Hydrophilic to amphiphilic as it elutes close to the permeation volume of the

column

Hydrophobic

organic carbon

Material which dos not elute in a standard run time due to strong

hydrophobicity

3.2.5 Chlorine residuals

Free chlorine residuals were measured with a HACH Odyssey D5/250 spectrophotometer

(HACH, Loveland, CO) following the DPD colourimetric method described in Standard Method

4500-Cl G (APHA, 2005). Prior to sample measurement a Milli-Q® blank was used to zero the

instrument. For sample measurement, one DPD pillow was added to 25 mL of sample in a clear

cuvette, capped, and shaken to rapidly mix. Absorbance was measured and free chlorine residual

was determined through the instrument’s internal calibration.

24

3.2.6 pH measurements

Sample pH was measured using a Model 8015 pH meter (VWR Scientific Inc.,

Mississauga, ON). A three point calibration (pH: 4, 7, and 10) was completed prior to measuring

a set of samples. During measurement samples were stirred using a magnetic stirrer and bar.


The FEEMs were collected using a Perkin Elmer Luminescence Spectrometer LS50B

with FLWinLab Version 3.0 (Waltham, Massachusetts, USA). The collection protocol stipulated

collection of intensity values at 10 nm increments within excitation-emission ranges of 250-380

nm and 300-600 nm, respectively. Scan rate was set to 600 nm/min, slit width was set to 10 nm,

and photomultiplier tube voltage was set to 800 V. UV-grade polymethylmetacrylate cuvettes

with four optical windows were used. To account for background noise and scattering

interference, spectra for Milli-Q© water was obtained using the same instrument settings.

Intensity values from the Milli-Q© samples were subtracted from intensity values of sample

spectra to reduce noise effects.

3.2.8 Principal component analysis

Each fluorescence sample produced a total of 4214 intensity values at unique excitation-

emission wavelength pairs. Prior to PCA, intensity data of each sample had to be arranged by

row. All data manipulation and organization of the spectrum was carried out using in-house data

manipulation scripts written in Python 2.6 (Python Software Foundation), which have been

included as section 8.4. Using the PLS Toolbox (Eigenvector Research Inc., Manson, WA) for

MATLAB 7.12.0 (MathWorks, Natick, MA), principal component analysis (PCA) was applied

to the fluorescent excitation-emission matrices.

The model input data was pre-processed using the PLS toolbox. Autoscaling was applied

to the intensity values to reduce bias. Autoscaling centers the data on the mean and scales each

variable to a unit standard deviation. Without autoscaling, the PCA technique would favour

higher intensity values and skew the components found to those with higher fluorescence

intensities, neglecting smaller peaks. For model validation, a cross-validation method of random

subsets (subset size of approximately 5-10% of the sample size) was selected. From the PCA

model, loading values and score values were collected for analysis.

25

3.2.9 Trihalomethanes

Trihalomethane (THM) analysis was conducted using the EPA Method 551.1. All four

THM species were analyzed (trichloromethane/chloroform, bromodichloromethane,

dibromochloromethan, and tribromomethane/bromoform). The liquid-liquid extraction method

outlined by the USEPA (USEPA, 1990) was carried out using MTBE (VWR International,

Mississauga, ON, ACS grade > 99.8%). Stock standard solutions (2000 µg/L of each specie)

from Supelco Inc. (Bellefonte, Pennsylvania, USA), were used for calibration and quality

control. All standards were kept in sealed glass vials at -4oC. MTBE extracts were analyzed with

a Hewlett Packard 5890 Series II Plus gas chromatograph (Mississauga, Ontario, CA) equipped

with an electron capture detector. A J&W Science DB-5.625 durabond column (length: 30m,

inner diameter: 0.25 mm, film: 0.25 µm) (Agilent Technologies Canada Inc., Mississauga,

Ontario, CA) was used. Injections were run in splitless mode, with helium as carrier gas and an

argon/methane (95%/5%) mix as makeup gas. Table 3.6 reports the instrument conditions and

temperature program used.

Table 3.6: THM analysis instrument conditions

Parameter Value

Injector temperature 200oC

Detector temperature 300oC

Temperature program

0 – 10 min: 35oC

10 – 16.25 min: 35oC – 60

oC

17.25 – 19.75 min: 60oC – 110

oC

21.25 – 25.58 min: 110oC – 240

oC

Carrier gas Helium

Makeup gas 95% argon, 5% methane

Flow rate 1.2 mL/min at 35oC

An eight-point calibration was completed at concentrations of 2, 4, 10, 20, 30, 40, 60, and

80 µg/L. A series of sixteen quality control standards were run to determine method detection

limits. Eight of the quality control standards were prepared to a concentration of 10 µg/L of each

specie with the remaining eight prepared to a target concentration of 60 µg/L. With every 10

samples analyzed, two check standards were run (10 and 60 µg/L) along with a blank (no

26

addition of stock to Milli-Q®). All blank standards analyzed as part of this work did not display

background interference. An example calibration chart of each THM specie is shown as Figure

3.5. Method detection limits and standard deviations (SD) for the THM analysis is reported in

Table 3.7. Example quality control charts (Figure 3.6 and Figure 3.7) for trichloromethane at

both low and high concentrations. An overview of the method is presented as Table 3.8.

Table 3.7: Accuracy and method detection limit of THM analysis

Parameter TCM BDCM DBCM TBM

low high low high low high low high

Quality control standard

deviation (µg/L) 0.7 3.9 0.9 2.4 0.9 1.8 0.6 2.2

MDL (µg/L) 2.5 NAa

3.0 NAa 3.0 NA 2.1 NA

a

Low: 10 µg/L target, high: 60 µg/L target.

NAa Not Applicable: high quality standards not used to determine MDL

Figure 3.5: Typical calibration chart for THM species (July 2012)

y = 0.8919x + 0.3251 R² = 0.9920

y = 4.4197x - 2.1403 R² = 0.9864

y = 3.8725x + 2.9656 R² = 0.9764

y = 1.8124x + 0.7383 R² = 0.9905

0

50

100

150

200

250

300

350

400

0 10 20 30 40 50 60 70 80 90

Are

a C

ou

nt

Concentration (µg/L)

trichloromethane bromodichloromethane dibromochloromethane tribromomethane

27

Figure 3.6: Trichloromethane (TCM) quality control - low concentration target: 10 ug/L

Figure 3.7: Trichloromethane (TCM) quality control - high concentration target: 60 ug/L

6

7

8

9

10

11

12

13

Tric

hlo

rom

eth

ane

Co

nce

ntr

atio

n (

ug/

L)

Otonabee Simcoe Ontario Ottawa

3 SD Limit 2 SD Warning Average

45

49

53

57

61

65

69

73

Tric

hlo

rom

eth

ane

Co

nce

ntr

atio

n (

ug/

L)



28

Table 3.8: Trihalomethane analysis method outline

Collect quenched samples in 25 mL amber vials

Store samples in the dark at 4oC for up to 14 days

Bring samples to room temperature, prior to analysis

All samples and standards are prepared in 40 mL amber glass vials

Blanks: Add 25 mL of Milli-Q water into 40 mL amber vials to be prepared alongside samples

Standard Working Solution: (10 µg/mL)

1. Fill a 5 mL volumetric flask partially with methanol

2. Using a 50 µL syringe, add 25 µL of THM stock (2000 µg/mL – Supelco 48140-U) to the

volumetric flask below the surface of the methanol.

3. Add methanol to the volumetric flask to the 5 mL mark.

4. Cap with a glass stopper and invert 5 times to ensure mixing

Calibration: (2 µg/L - 80 µg/L)

1. Add appropriate amounts of the standard working solution to 25 mL of Milli-Q to

achieve an 8 point calibration between 2 and 80 µg/L.

Quality Standards: (10 µg/L and 60 µg/L)

1. Quality standards of 10 µg/L and 60 µg/L are made by respectively adding 25 µL and

150 µL of the standard working solution into 25 mL Milli-Q samples.

2. 1 quality standard of each concentration is prepared for every 10 samples run

3. A set of 8 quality standards at each concentration is prepared for every calibration

prepared

Extraction

1. To the 40 mL amber glass vial:

2. Add 1 teaspoon (5 mL) of sodium sulphate using a scoop

3. Add 4 mL of MTBE extraction solvent

4. Cap with Teflon-lined silicon septa and screw cap

29

Table 3.8: Trihalomethane analysis method outline (continued)

5. Shake vial vigorously for approx. 10 seconds

6. Repeat for all samples and standards

7. Place all vials upright in the rack and shake for 3 minutes

8. Let the sample stand for 10 minutes prior to phase separation

9. Using a glass Pasteur pipette, extract the top organic layer (MTBE) from the vial and

transfer to a clean 1.8 mL GC vial. Allow for no headspace in the vial. Use a clean

pipette for each sample. Cap immediately

10. Place GC vials in freezer overnight to freeze out any remaining water

11. Once frozen, extract the top 1.5 mL from the GC vial and transfer to a new, clean 1.8 mL

GC vial

12. Analyze using GC ECD.

3.2.10 Haloacetic acids

Haloacetic acid analyses were performed using a liquid-liquid extraction method (EPA

Method 552.3) using MTBE (VWR International, Mississauga, ON, ACS grade > 99.8%). Nine

haloacetic acid species were included in the analysis: monochloroacetic acid (MCAA),

monobromoacetic acid (MBAA), dichloroacetic acid (DCAA), trichloroacetic acid (TCAA),

bromochloroacetic acid (BCAA), dibromoacetic acid (DBAA), bromodichloroacetic acid

(BDCAA), dibromochloroacetic acid (DBCAA), and tribromoacetic acid (TBAA). Standard

solutions from Supelco Inc. (Bellefonte, Pennsylvania, USA), were used for calibration and

quality control. Stock solution had variable concentration of each component (Table 3.9). All

standards were kept in sealed glass vials at -4oC. Samples were analyzed with a Hewlett Packard

5890 Series II Plus gas chromatograph (Mississauga, Ontario, CA) equipped with an electron

capture detector. A J&W Science DB-5.625 Durabond column (length: 30m, inner diameter:

0.25 mm, film: 0.25 µm) (Agilent Technologies Canada Inc., Mississauga, Ontario, CA) was

used. Injections were run in splitless mode, with helium as carrier gas and an argon/methane

(95%/5%) mix as makeup gas. Instrument conditions are summarized in Table 3.10.

A six-point calibration was completed at concentrations at varying concentration ranges,

dependent on the concentration of the specie within the stock solution. A series of eight quality

control standards prepared at a low dose were run to determine method detection limits. With

every 10 samples analyzed, a check standard was run along with a blank (no addition of stock to

30

Milli-Q®) (Table 3.11). All blank standards analyzed as part of this work did not display

background interference. An example calibration chart is shown as Figure 3.8 for all HAA

species. Table 3.12 reports the method detection limits and standard deviations for the THM

analysis. An example quality control chart for dichloroacetic acid (DCAA) at a target

concentration of 5.6 µg/L is shown as Figure 3.9. Check standards for Lake Ontario water

exceeded the quality limits (maximum of 3.1 µg/L away from average). An overview of the

HAA analysis method is presented as Table 3.13.

Table 3.9: Stock HAA specie concentrations

HAA specie Stock concentration (µg/L)

Bromoacetic Acid 387.9

Bromochloroacetic Acid 406.5

Bromodichloroacetic Acid 435.8

Chloroacetic Acid 573.1

Chlorodibromoacetic Acid 1003

Dibromoacetic Acid 212.3

Dichloroacetic Acid 590.4

Tribromoacetic Acid 2200

Trichloroacetic Acid 204.0

Table 3.10: HAA analysis instrument conditions

Parameter Value

Injector temperature 200oC

Detector temperature 300oC

Temperature program

0 – 9.5 min: 50oC

9.2 - 22 min: 50oC – 65

oC

22 - 24 min: 65oC – 85

oC

24 - 30 min: 85oC – 205

oC

Carrier gas Helium

Makeup gas 95% argon, 5% methane

Flow rate 1.2 mL/min at 35oC

31

Table 3.11: Calibration range and check standard target concentrations for HAAs

HAA Specie Calibration Range

(6 point calibration)

(µg/L)

Check Standard Target


Bromoacetic Acid 1-31 7.8

Bromochloroacetic Acid 1-33 8.1

Bromodichloroacetic Acid 1-35 8.7

Chloroacetic Acid 1-46 5.6

Chlorodibromoacetic Acid 2-80 20.1

Dibromoacetic Acid 0.5-17 4.3

Dichloroacetic Acid 1-47 5.6

Tribromoacetic Acid 4-176 44

Trichloroacetic Acid 0.5-16 4

Figure 3.8: Typical HAA calibration curves (July 2012)

MCAA: y = 0.0286x - 0.0996, R2 = 0.9828 MBAA: y = 0.2961x - 0.4946, R2 = 0.9973

DCAA: y = 0.6234x + 0.1464, R² = 0.983

TCAA: y = 0.4563x - 0.6356, R2 = 0.9953

BCAA: y = 0.8294x - 0.9536, R2 = 0.9934 DBCAA: y = 0.5731x + 1.0879, R² = 0.9913

DBAA: y = 0.5383x + 0.0883, R² = 0.9951

CDBAA: y = 0.0917x - 0.042, R2 = 0.9926 TBAA: y = 0.7677x - 4.2011, R2 = 0.9948

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80 90

Are

a C

ou

nt


MCAA MBAA DCAA TCAA BCAA DBAA DBCAA CDBAA TBAA

32

Figure 3.9: Example HAA quality control chart for DCAA

Table 3.12: Quality control standard deviation and HAA method detection limits

HAA Specie Quality Control Standard

Deviation (µg/L)

Method Detection Limit

(µg/L)

Bromoacetic Acid 1.5 5.2

Bromochloroacetic Acid 0.3 1.1

Bromodichloroacetic Acid 0.4 1.3

Chloroacetic Acid 0.2 0.7

Chlorodibromoacetic Acid 0.3 1.1

Dibromoacetic Acid 0.2 0.7

Dichloroacetic Acid 0.7 2.5

Tribromoacetic Acid 2.7 9.6

Trichloroacetic Acid 0.5 1.8

4

5

6

7

8

9

10

Dic

hlo

roac

eti

c ac

id C

on

cen

trat

ion

(u

g/L)

Otonabee Ontario Ottawa


33

Table 3.13: HAA analysis method outline

Collect quenched samples in 25 mL amber vials

Store samples in the dark at 4oC for up to 14 days

Bring samples to room temperature, prior to analysis

All samples and standards are prepared in 40 mL amber glass vials

Sulfonamide Solution:

1. Add 15 mL of dietylene glycol, 15 mL of ether and 3 g of N-methyl-N-nitroso-p-toluene

sulphonamide (Diazald) to a 40 mL amber vial

2. Shake until completely dissolved. This solution can be used to make diazomethane and

can be stored at 4oC for up to 30 days

Diazomethane Generation:

1. Prior to extraction, diazomethane must be produced

2. Set up generation apparatus as shown in Figure 6521:3 in Standard Methods (APHA,

2005).

3. Fill the first tube with ether to a depth of 3 cm

4. Add potassium hydroxide solution (370 g/L KOsolution in Milli-Q water) to the second

tube so that it is just touching the base of the impinge.

5. Add sulphonamide solution above the KOusing a long Pasteur pipette; ensure no mixing

occurs

6. Add 4 mL of MTBE to the last tube and put it in a beaker of ice such that the impinge is

submerged in the MTBE.

7. Connect the nitrogen gas feed to the in port of the apparatus

8. Slowly turn on the gas flow and bubble the nitrogen gas through the apparatus slowly

until the MTBE solution becomes yellow

9. This solution may be stored at 4oC in a 25 mL amber vial for up to 24 hours

10. Allow for the diazomethane to warm up to room temperature prior to use in extraction.

Blanks:

1. Add 25 mL of Milli-Q water into 40 mL amber vials to be prepared alongside samples

34

Table 3.13: HAA analysis method outline (continued)

Standard Working Solution: (10 µg/mL)

1. Fill a 5 mL volumetric flask partially with methanol

2. Using a 50 µL syringe, add 25 µL of HAA9 stock (2000 µg/mL Supelco-47787) to the

volumetric flask below the surface of the methanol.

3. Add methanol to the volumetric flask to the 5 mL mark.

4. Cap with a glass stopper and invert 5 times to ensure mixing

Calibration: (2 µg/L - 80 µg/L)

1. Add appropriate amounts of the standard working solution to 25 mL of Milli-Q to

achieve an 8 point calibration between 2 and 80 µg/L.

Quality Standards: (10 µg/L and 60 µg/L)

2. Quality standards of 10 µg/L and 60 µg/L are made by respectively adding 25 µL and

150 µL of the standard working solution into 25 mL Milli-Q samples.

3. 1 quality standard of each concentration is prepared for every 10 samples run

4. A set of 8 quality standards at each concentration is prepared for every calibration

prepared

Extraction

1. To the 40 mL amber glass vial:

2. Add 1 teaspoon (5 mL) of sodium sulphate using a scoop

3. Add 1 mL of sulphuric acid

4. Add 4 mL of MTBE extraction solvent

5. Cap with Teflon-lined silicon septa and screw cap

6. Shake vial vigorously for approx. 10 seconds

7. Repeat for all samples and standards

8. Place all vials upright in the rack and shake for 3 minutes

9. Let the sample stand for 10 minutes prior to phase separation

10. Using a glass Pasteur pipette, extract the top organic layer (MTBE) from the vial and

transfer to a clean 1.8 mL GC vial. Allow for 100 µL of headspace.

35

Table 3.13: HAA analysis method outline (continued)

11. Using an auto pipette, transfer 100 µL of diazomethane to the top of the vial and cap

12. Place GC vials in freezer overnight to freeze out any remaining water

13. Once frozen, extract the top 1.5 mL from the GC vial and transfer to a new, clean 1.8 mL

GC vial

14. Analyze using GC ECD.

36

4 Disinfection By-Product Modelling using Principal Component Analysis of

Fluorescence Excitation-Emission Matrices

4.1 Introduction

For decades the drinking water industry had understood the importance of monitoring and

studying the formation of by-products associated with disinfection processes. In North America

chlorine remains the most common disinfectant used by water utilities (AWWA, 2007) which,

when added to natural waters, forms organic halides through reactions with natural organic

material (NOM) (Richardson and Postigo, 2011). Control and regulations for utilities using

chlorine typically revolve around concentrations of two organic halide groups, trihalomethanes

(THMs) and haloacetic acids (HAAs) (Beggs et al., 2009). Since DBPs are principally formed

through the reaction of a disinfectant with NOM, any predictive model must incorporate NOM

concentration. Difficulties arise since NOM is comprised of a complex heterogeneous mixture

of humic acid, fulvic acid, proteins, carbohydrates, and other organic compound classes (Her et

al., 2003). Each of these classes has unique reactivity with disinfectants to form DBPs, which

implicates the importance of understanding the character, or concentrations of specific

compound classes, considered to be within the definition of NOM (Barrett et al., 2000).

Historically, predictive DBP models have been successfully linked to NOM estimation

parameters including total organic carbon (TOC), dissolved organic carbon (DOC), UV-

absorbance (UVA) at 254 nm, and specific UV-absorbance (SUVA) (Sadiq & Rodriguez, 2004).

In particular, SUVA (DOC divided by UVA at 254 nm), has been shown to correlate well with

DBP formation (Kitis et al., 2001; Barrett et al., 2000). SUVA may be used to provide an

estimate of humic content, which has been generally recognized as the NOM fraction most

reactive to form DBPs (Barrett et al., 2000). In contrast, both TOC and DOC provide an

estimate of the total amount of organic content, however cannot provide information regarding

NOM reactivity or structure. With knowledge that specific NOM fractions are more significant

in the formation of DBPs, it is postulated that methods which can characterize those fractions

will improve the accuracy of DBP formation models.

Analysis using fluorescence spectra has been gaining traction as a promising method for

determining information regarding organic matter structure and function in water (Zepp et al.,

2004; Bieroza et al., 2011). This type of analysis is completed through collection of

Fluorescence Excitation-Emission Matrices (FEEM). These matrices represent fluorescence

37

intensity at various excitation-emission pairs. When compared to other NOM characterization

techniques, including liquid chromatograph organic carbon detection (LC-OCD), FEEM analysis

is fast and provides consistent results with high sensitivity (Peiris et al., 2008). Several

components of NOM fractions fluoresce and therefore can be identified in a fluorescence

spectrum (Her et al., 2003). Most importantly, different compounds and compound groups

exhibit unique fluorescence signatures that allow them to be distinguished from each other

(Coble et al., 1990; Zepp et al., 2004). Traditional analysis of FEEMs involves identifying peaks

of fluorescence intensity. The position (excitation-emission region) of the peak can be correlated

with a specific compound and the maximum intensity of the peak with concentration (Bieroza et

al., 2011). This method, referred to as ‘peak picking,’ is limited in that it neglects to capture the

heterogeneity of NOM fractions (Peiris et al., 2010). The fluorescence intensity at specific

excitation/emission pairs is influenced by several variables including concentration, quantum

yield, and absorptivity. Molecular structure has a noted effect on some of these factors which

can be common among different molecules. By ‘peak picking’ the nature of overlapping

structural properties between distinct compounds and many of the potentially relevant

chromophores are neglected (Persson and Wedborg, 2001).


several samples, multivariate analysis techniques can be employed. Common advanced analysis

techniques involve using two-way principal component analysis (PCA) and multi-way parallel

factor (PARAFAC) analysis (Peiris et al., 2010). These multivariate techniques reduce the

number of variables needed to explain variance between samples in a given set (Persson and

Wedborg, 2001). Furthermore, this pattern recognition and data set simplification approach,

unlike ‘peak picking’, incorporates the entire fluorescence spectrum (Bieroza et al., 2011).

This work provides evidence regarding the application and robustness of principal

component analysis of FEEMs (FEEM-PCA) in the water treatment field. Specifically, FEEM-

PCA is compared to traditional NOM estimators including DOC, UVA, and SUVA for the

purpose of predicting the formation of THMs and HAAs from four different source waters to

water treatment facilities. In order to assess how FEEM-PCA may be related to other NOM

indicators, jar tests were conducted using a range of coagulant doses (5 – 70 mg/L), to provide a

large resulting range of NOM concentrations and associated DBP concentrations.

38

4.2 Method overview

4.2.1 Source waters

Four distinct surface water sources in Ontario, Canada were used in this study. They

were selected to cover a range of NOM concentrations (2.4 – 5.9 mg/L DOC) and represent both

lakes and rivers. Water quality parameters are reported in Table 4.1.

Table 4.1: Source water characteristicsa

Otonabee River Lake Simcoe Lake Ontario Ottawa River

DOC (mg/L) 5.6 4.1 2.4 5.9

Alkalinity (mg/L

as CaCO3) 101 121 87 35

pH 8.3 8.2 8.3 7.4

a All waters were collected between June and September of 2012.

4.2.2 Jar tests

To simulate the processes of coagulation, flocculation, sedimentation, and filtration for

each water source a bench-scale jar test approach was applied. All waters were coagulated with

aluminum sulphate (alum) (General Chemical, Parsipanny, NJ). To ensure a range of DOC an

alum dose range of 5 – 70 mg/L (5, 10, 20, 30, 40, 50, 60, 70 mg/L alum or 0.45, 0.89, 1.78,

2.67, 3.56, 4.45, 5.34, 6.23 mg/L as Al) was applied to each of the four waters. Jar tests were

conducted using a PB-700 Standard Jar Tester paddle stirrer with six square, acrylic 2-L jars

(Phipps & Bird, Richmond, VA). The protocol followed was adapted from the USEPA’s

Enhanced Coagulation Guidance Manual (USEPA, 1999). Coagulation was simulated through

rapid mix (100 RPM) for 1.5 minutes and flocculation by reducing the mixing speed to 30 RPM

for 15 minutes. The water was then allowed to stand (no mixing) for 30 minutes to simulate

sedimentation. Vacuum filtration was performed on the settled water using 1.2 micron glass

microfibre filters (Whatman 934-AH, Florham Park, NJ) to represent anthracite-sand media

filters (Wassink, 2011). This finished water was analyzed for organic material (TOC, FEEM)

and pH. For specific analyses including DOC and LC-OCD the water was further filtered

through 0.45 micron membrane filters (Pall Corporation Supor®-450, Port Washington, NY) to

ensure that all particulates had been removed (USEPA, 1999).

39

4.2.3 Disinfection by-product formation and analysis

Filtered water collected from the jar tests was chlorinated in order to form disinfection

by-products. To ensure equal conditions for DBP formation, the pH of the finished water was

adjusted to 7 ± 0.1 using sulphuric acid or sodium hydroxide prior to chlorination. Chlorine

dosages of 2.5 and 3.5 mg/L were applied to represent those used at water treatment plants in the

region. Chlorinated samples were sealed with no head space and incubated at 21 ± 1oC for 24

hours. Following 24 hours (± 0.5 hour) the free chlorine residual was measured (0.02 to 2.54

mg/L) and remaining chlorine quenched using ascorbic acid (100 mg/L ascorbic acid). Duplicate

25 mL samples of quenched solution were sealed with no head space and retained for THM and

HAA9 analysis. All waters were tested for THM formation and all except Simcoe was analyzed

for HAA9 formation.

Trihalomethane analysis was conducted using the EPA Method 551.1. The liquid-liquid

extraction method was carried out using MTBE (USEPA, 1990). This method allowed for

quantification of four THM species: trichloromethane (TCM), bromodichloromethane (BDCM),

chlorodibromomethane (CDBM), and tribromomethane (TBM). For HAA9, the EPA Method

552.3 was used. The nine HAA species analyzed using this method included: monochloroacetic

acid (MCAA), monobromoacetic acid (MBAA), dichloroacetic acid (DCAA), trichloroacetic

acid (TCAA), bromochloroacetic acid (BCAA), dibromoacetic acid (DBAA),

bromodichloroacetic acid (BDCAA), dibromochloroacetic acid (DBCAA), and tribromoacetic

acid (TBAA). For both analyses, stock standard solutions from Supelco Inc. (Bellefonte,

Pennsylvania, USA), were used for calibration and quality control. All standards were kept in

sealed glass vials at -4oC. Analyses were conducted using a Hewlett Packard 5890 Series II Plus

gas chromatograph (Mississauga, Ontario, CA) equipped with an electron capture detector and a

J&W Science DB-5.625 durabond column (length: 30m, inner diameter: 0.25 mm, film: 0.25

µm) (Agilent Technologies Canada Inc., Mississauga, Ontario, CA). Injections were run in

splitless mode, with helium as carrier gas and an argon/methane (95%/5%) mix as makeup gas.

4.2.4 DOC, TOC, and UVA measurements

Concentrations of DOC and TOC were obtained through heated persulfate oxidation

using an O.I. Analytical Aurora 1030 organic carbon analyzer (College Station, Texas, USA)

following Standard Method 5310 D (APHA, 2005). UVA at 254 nm was determined using a CE

3055 model spectrophotometer (Cecil Instruments, Cambridge, England) with a quartz cuvette

40

following Standard Method 5910 B (APHA, 2005). The instrument was first zeroed at 254 nm

using Milli-Q® water prior to analysis of samples.

4.2.5 Fluorescence spectra collection

FEEMs were collected using a Perkin Elmer Luminescence Spectrometer LS50B

(Waltham, Massachusetts, USA) with FLWinLab Version 3.0. No pre-treatment of the samples

was applied, except that all had been adjusted to a pH of 7. Maintaining a common pH between

samples ensured that fluorescence characteristics of the acidic functional groups in humic

molecules remained constant (Mobed et al., 1996). Collection of intensity values at 10 nm

increments within excitation-emission ranges of 250-380 nm and 300-600 nm, respectively.

Scan rate was set to 600 nm/min, slit width was set to 10 nm, and photomultiplier tube voltage

was set to 775 V. The instrument settings were determined based on ranges used in previous

studies (Peiris et al., 2010; Bieroza et al., 2011), protocols that increase resolution (Peiris et al.,

2009), and in-house testing to optimize FEEM collection. UV-grade polymethylmetacrylate

(PMMA) cuvettes with four optical windows were used. It has been shown that, while the

PMMA cuvettes used reduce the intensity of excitation wavelengths below 285 nm, this

approach is appropriate for purposes of distinguishing NOM elements using fluorescence (Peiris

et al., 2008). To account for background noise and scattering interference, spectra for Milli-Q®

water was obtained using the same instrument settings. Intensity values from the Milli-Q®

samples were subtracted from intensity values of sample spectra to reduce background noise

effects.

4.2.6 Fluorescence data analysis

Each sample produced a total of 4214 fluorescence intensity values at unique excitation-

emission wavelength pairs. In total, 35 unique samples were run in duplicate (70 FEEMs

collected in total). Prior to PCA, intensity data of each sample was arranged by row. All data

manipulation and organization of spectra was carried out using in-house data manipulation

scripts written in Python 2.6 (Python Software Foundation). This processing step generated

several unique data sets for PCA: one of all samples (XA: 70 x 4214 matrix), and one for each

water source (Otonabee XOE: 16 x 4214, Simcoe XSI: 18 x 4214, Ontario XON: 18 x 4214, Ottawa

XOT: 18 x 4214).

PCA is a multivariate data analysis technique that is used to approximate a large data

matrix through pattern detection. Common goals for PCA include the identification of

41

similarities between samples, data simplification, and detection of outliers (Wold et al., 1987).

Approximation of the dominant data patterns is achieved through new, mutually independent,

variables that are mathematically represented by linear combinations of the original variables

(Persson and Wedborg, 2001). In generalized terms, the original data matrix X is decomposed

into the product of two small matrices, ti and pi which are referred to as the scores and loading

matrix, respectively (equation 4.1). The residuals generated from the approximation are

represented in another matrix E. For multiple samples, n, in the matrix X, the product and

residuals are summed (Wold et al., 1987; Peiris et al., 2010).

∑

The loading matrix, pi, is a projection matrix that relates the PCA space to the original

spectral space. The information in the loading matrix can be used to identify the spectral areas

which a specific principal component represents. The scores matrix, ti, represents projections of

the samples, or object, into the principal component (PC) space. They serve as an analog for

concentrations or relative importance of a PC for a given sample (Persson and Wedborg, 2001).

PCA was applied independently, creating 5 PCA models of the variance in each data set.

PCA was performed using the PLS Toolbox V6.7.1 (Eigenvector Research Inc., Manson, WA) in

MATLAB 7.12.0 (MathWorks, Natick, MA).

To track any potential instrumental changes, Milli-Q® samples that were collected over

the experimental period were compared using the uncorrected matrix correlation (UMC) method.

This method compares two entire matrices and indicates the similarity using a value from 0 to 1

(1 representing a perfect correlation). Using this technique the realtive mean square error

(RMSE) between the matrices can be estimated (Burdick and Tu, 1989). All matrix correlations

had RMSE values below 0.06 indicating a high degree of similarity between Milli-Q© spectra

and implying instrument and hardware stability during the experimental time period.

4.3 Results and discussion

4.3.1 Conventional parameter results from jar tests

Based on traditional NOM indicators (DOC, UVA, SUVA), it was observed that

increasing alum dose caused a decreasing concentration of organic material (Figure 4.1). Within

(4.1)

42

the context of this work, these results are being used as reference data for determining the

strength of fluorescence results, presented later.

Figure 4.1: DOC, UVA, and SUVA results for all waters over the range of coagulant doses

Although the raw Ottawa River water was highest in DOC, it showed increased

sensitivity to coagulation in the range of 10 to 30 mg/L alum and DOC was reduced at a greater

rate when compared to the other waters (46% DOC reduction compared to 10 – 20% DOC

reduction between dosages of 10 to 30 mg/L alum). This effect was also observed in UVA

results. Furthermore, SUVA for Ottawa River water was not lower than that of the Otonabee

River at any alum dose, as was the case for DOC and UVA measurements. When considering

SUVA, the results suggest that while the Ottawa DOC was reduced the most, the reactive portion

of DOC was not as well removed at higher alum doses. This result was likely due to the low

alkalinity, 35 mg/L as CaCO3 for the Ottawa River, compared to 100 mg/L or greater for the

other water sources. The pH of Ottawa River water was reduced by the greatest amount over the

range of doses (7.3 to 5.2) due to its limited buffering capacity. This reduced pH improves the

efficiency of alum coagulation and ultimately results in greater NOM removal (Hu et al., 2006).

0.00

0.05

0.10

0.15

0.20

0.25

0 20 40 60 80

Alum dose (mg/L)

Lake Simcoe Otonabee River Lake Ontario Ottawa River

1.0

2.0

3.0

4.0

5.0

6.0

0 20 40 60 80

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 20 40 60 80

DOC (mg/L) UVA (cm-1

) SUVA (cm-1

/ mg/L)

Error bars represent standard deviation of duplicate samples

43

4.3.2 Fluorescence results from jar tests

Fluorescence intensity values for each coordinate pair were plotted to display the spectra

for a given sample. It has been previously established by others that fluorescence in distinct

spectral regions is associated with functional groups and molecular structure, while intensity is

proportional to concentration (Mobed et al., 1996; Her et al., 2003; Zepp et al., 2004). Based on

the location of the two main intensity peaks (Figure 4.2) and comparison to the literature, peak a

represents humic-acid type matter (Ex/Em: 270nm/450nm) while peak b represents fulvic-acid

type material common to fresh waters (Ex/Em: 340nm/440nm) (Stedmon and Markager, 2005;

Murphy et al., 2008). Peaks of high intensity at Ex/Em: 250-300 nm / 500 – 600 nm a and

Ex/Em: 300 – 380 nm / 300 – 380 nm are representative of second and first order Rayleigh

Scattering, respectively, which are both related to the concentration of particulates in the sample

(Peiris et al., 2010). Each of the four water sources differed in overall intensity of these two

peaks; however their presence and location was approximately consistent (Figure 4.2 to Figure

4.5). The excitation/emission location of peaks a and b (highest measured intensity) are reported

in Table 4.3. The similarity of peak locations between the four source waters speaks to the

similarity of their organic character, even when comparing lake and river sources.

Table 4.2: Excitation/emission peak location for each water source

Peak Excitation/Emission of peak (nm/nm)


a 280/430 280/437 280/428 280/437

b 340/434 340/431 340/429 340/443

Figure 4.2: Example fluorescence spectra (Otonabee River raw water)

a a

b

b

Note: 3D and 2D views of identical spectra

44

Figure 4.3: Fluorescence spectra of Ottawa River raw water

Figure 4.4: Fluorescence spectra of Lake Simcoe raw water

Figure 4.5: Flourescence specctra of Lake Ontario raw water

Note: 3D and 2D views of identical spectra, intensities truncated at 200 a.u.

Note: 3D and 2D views of identical spectra, intensities truncated at 200 a.u.

a a

b

b

a

a b

b

a a

b b

Note: 3D and 2D views of identical spectra

45

Five PCA models were created, one which included samples from all water sources and

four source-specific models. For each model the first 3 principal components explained the

majority of variance in the sample set (> 75% variance explained). A cut-off of 5% variance

explained by a PC was used to determine the extent of the PCA models. At rates below 5%, the

importance of the PC was deemed negligible and considered to be noise. A summary of the

variance explained by each principal component is shown in Table 4.3.

Table 4.3: Variance explained by each PC for each sample set

Principal

Component

Variance explained (%)

All Samples

(XA)

Otonabee

(XOE)

Simcoe (XSI) Ontario (XON) Ottawa (XOT)

1 74.5 87.4 85.5 56.0 76.5

2 13.8 6.2 8.3 9.6 14.6

3 6.3 NA NA 6.4 NA

Total Variance

Explained (%) 94.5 93.7 93.8 75.1 91.1

NA = Not applicable; variance explained by PC below 5%.

With the exception of Lake Ontario, the 3 PCs described > 91% of the variance for each

sample set. Since Lake Ontario has a low concentration of organic material (TOC ~ 2.5 mg/L)

and did not have a pronounced response to coagulation, the overall variance was lower. As such,

the signal (change due to coagulation) was small when compared to other water sources.

By plotting loading values and their coordinates (excitation/emission), we can discern

what each of the PCs represents in the original spectral space. Loading plots for a model

comprised of all samples is shown in Figure 4.6. Similar plots were produced for each of the

individual waters (not shown). Loading plots for PC 1 show that it represented humic material,

however there were no pronounced peaks which further reinforces the heterogeneity of the

organic material. Loading results for PC 2 show peaks in low excitation/emission regions

(Ex/Em: 250-290 nm / 300-350 nm), representative of protein-like substances (Stedmon and

Markager, 2005; Chen et al., 2003; Peiris et al., 2010). PC 3 was found to be similar to PC 2

with high loading values in the low excitation/emission range; however scattering regions were

more pronounced (Figure 4.6). Therefore, it is thought that PC 3 is mostly representative of

scattering and to a lesser degree, protein-like material.

46

Figure 4.6: Loading plots for three principal components of the ‘all samples’ model from

sample set XA.

Scores are analogous to concentration of the spectral elements represented by each PC for

a given sample, and were plotted to show the changes relative to alum dose (Figure 4.7). PC 1

scores for humic-like substances mimic the DOC and UVA results well. The exception is the

more pronounced increase in the Ottawa River water score following a dose of 50 mg/L alum. .

PC 2 results show limited changes as a function of coagulant dose. This follows from the known

mechanisms of coagulation with alum; protein-like substances are not significantly impacted.

PC 1

PC 2

PC 3

47

Ottawa River results differed from the other waters showing a pronounced increase in protein-

like concentration between alum doses of 30 and 50 mg/L.

Figure 4.7: Scores from all sample PCA model (XA)

4.3.3 Relationships between NOM indicators

One of the primary objectives of this work was to demonstrate the viability of using

FEEM-PCA to estimate NOM concentrations and character. As such, there is a need to compare

the results of FEEM-PCA with accepted and established measures of NOM.

Linear relationship strength between PC scores and traditional NOM indicators are

reported in Table 4.4 as R2 values. Overall, relationships can be considered strong to very strong

(R2 > 0.8). Patterns regarding which parameter is most correlated with FEEM-PCA are not

consistent between waters. With the exception of the Ottawa River, DOC is most correlated with

PC 1 score, followed by UVA and SUVA, respectively.

In an effort to improve the correlations, PC 2 score (protein-like) was added to that of PC

1 with the intention of producing a value more representative of ‘total’ NOM. The effect of

combining PC 1 and PC 2 scores on the correlations was not consistent between waters. In

comparison to correlations using only PC 1 scores, correlations with all samples decreased in

-100

-80

-60

-40

-20

0

20

40

60

80

0 20 40 60 80

Alum dose (mg/L)


Note: Error bars represent standard deviation of duplicate samples

-150

-100

-50

0

50

100

150

200

0 20 40 60 80

-150

-100

-50

0

50

100

150

200

250

0 20 40 60 80

PC 1 Score PC 2 Score PC 3 Score

48

strength for DOC (0.90 to 0.87) and UVA (0.90 to 0.66), while increasing with SUVA (0.79 to

0.87). A similar effect was observed for Lake Ontario samples; decreasing for DOC (0.87 to

0.69) and UVA (0.72 to 0.54), increasing for SUVA (0.03 to 0.47). However, with Lake Ontario

the correlation with SUVA was very poor in both cases and the observed change should not be

considered significant. Correlations did not change significantly for Otonabee River and Lake

Simcoe. Correlations for the Ottawa River sample most notably increased through the addition

of PC 1 and PC 2 scores. Correlations with all three parameters are increased; DOC: 0.87 to 0.97,

UVA: 0.89 to 0.98, and SUVA 0.90 to 0.98. It is hypothesized that the increase observed for the

Ottawa River results is due to pronounced variability in the protein-like score when compared to

the other waters (Figure 4.7). While there appears to be advantages to using a combined PC

score (protein-like and humic-like) for NOM concentration estimation, it is greatly dependent on

the source water.

Table 4.4: Linear relationship strength (R2) between FEEM-PCA and traditional NOM

indicators

NOM

indicator

All samples Otonabee

River

Lake Simcoe Lake

Ontario

Ottawa

River

Linear relationship strength (R2) with PC 1 (humic-like)

DOC (mg/L) 0.90 0.99 0.95 0.87 0.87

UVA (cm-1

) 0.90 0.99 0.99 0.72 0.89

SUVA

(cm-1

/mg/L) 0.79 0.96 0.94 0.03 0.90

Linear relationship strength (R2) with PC 1 + PC 2 (humic-like + protein-like)

DOC (mg/L) 0.87 0.99 0.94 0.69 0.97

UVA (cm-1

) 0.66 0.98 0.97 0.54 0.98

SUVA

(cm-1

/mg/L) 0.87 0.95 0.93 0.05 0.98

4.3.4 Disinfection by-product formation prediction

Results from FEEM-PCA analyses were compared to traditional NOM indicators for the

purpose of predicting the formation of disinfection by-products (DBPs). Corresponding with the

observed decrease in NOM concentration (Figure 4.1), THM and HAA concentrations decreased

with increasing coagulant dose for all waters (Figure 4.8 and Figure 4.9). Total concentrations of

49

THMs varied between 9.2 and 112 µg/L and total HAAs between 11.7 and 128 µg/L for all

waters and chlorine doses. As with organic content, the reduction in DBP concentrations with

increasing coagulant dose was more pronounced for the two river sources. Results from other

studies have shown similar results of decreasing DBP concentrations with increasing NOM

removal through coagulation (Teixeira et al., 2011; Sadiq and Rodriguez, 2004).

For THMs, only TCM and BDCM were observed above the method detection limits

(MDL) reported in section 3.2.9. For HAAs, only DCAA and TCAA were found above the

MDL (section 3.2.10). Speciation graphs for THMs are shown in the appendix as Figure 8.1 and

Figure 8.2. For both chlorine doses, in river source waters BDCM represented approximately

20-30% of total THMs, while in lake sources it represented 35-40% of the total. HAA speciation

graphs are shown in as Figure 8.3 and Figure 8.4. For river sources, TCAA represented

approximately 40-50% of the total HAAs formed, while it represented 30-40% at all chlorine

doses for lake sources. Others have presented similar speciation and total THM and HAA

concentration ranges for waters with low bromide ion concentrations (< 0.01 mg/L) and similar

organic content (2 – 7 mg/L TOC) (William et al., 1996; Ates et al., 2007). Results also

compared well to results reported in previous disinfection by-product studies performed on

Otonabee River water (Wassink, 2011).

Water quality parameters typically included in empirical models for the prediction of

disinfection by-product formation consist of reaction time, pH, water temperature, chlorine dose,

bromide ion concentration, and an indicator of NOM concentration (Chowdhury et al., 2007;

Sadiq and Rodriguez, 2004). In these experiments, reaction time, chlorination pH, temperature,

and dose were controlled to be equal for all waters and samples. Bromide ion concentrations

were not explicitly controlled, although the only brominated DBP observed above its MDL was

BDCM. Furthermore, under the assumption that bromide concentration would not be affected by

coagulation, source specific models would not require the inclusion of bromide. In future work,

it is suggested that bromide ion is measured for the purposes of creating a more accurate model

which can account for more than one source water.

50

Figure 4.8: Total THMs concentration vs. alum dose.

Figure 4.9: Total HAA9 concentration vs. alum dose

0

20

40

60

80

100

120

0 20 40 60 80

Tota

l TH

M c

on

cen

trat

ion

(µ

g/L

)

Alum dose (mg/L)


0

20

40

60

80

100

120

140

0 20 40 60 80

Tota

l HA

A c

on

cen

trat

ion

(µ

g/L

)

Alum dose (mg/L)

Otonabee River Lake Ontario Ottawa River

Note: Error bars represent standard deviation of duplicates

Note: Error bars represent standard deviation of duplicates

0

20

40

60

80

100

120

0 20 40 60 80

0

20

40

60

80

100

120

140

0 20 40 60 80

Chlorine dose: 2.5 mg/L Chlorine dose: 3.5 mg/L

Chlorine dose: 2.5 mg/L Chlorine dose: 3.5 mg/L

51

4.3.4.1 Non-linear models

Based on DBP models reported in literature, a non-linear power based model was

proposed to fit the data compiled in this work (Sadiq and Rodriguez, 2004). Since only the

organic concentration was not controlled at two chlorine doses, the model was simplified to only

these variables (equation 4.2).

where, c1 through c4 are constants determined via regression.

Using MATLAB, the coefficient of correlation (R2) for the non-linear model as well as

the mean absolute error (MAE) was determined. These two parameters were used to elucidate

differences in model strength and to compare various NOM indicators. MAE was used in place

of the more commonly calculated mean square error (MSE) since it is less affected by outliers in

the data. MSE’s tendency to skew results heavily due to outliers results from the term being

squared, when compared to MAE (Bermejo and Cabestany, 2001). The MAE was calculated by

summing the absolute value of residuals divided by the number of samples (equation 4.3).

∑ | |

⁄

where, fi is the predicted value corresponding with the measured value yi and n is the

number of samples.

Example plots of measured values versus predicted values for the regressions of all

samples (XA) are shown in Figure 4.10. Results of the non-linear models are reported in Table

4.5 and Table 4.6 for THMs and HAAs, respectively. Results from the non-linear models

generally showed very high correlations (R2 > 0.8), especially for river water sources. Lower

correlations for lake waters were likely associated with their lower organic content and smaller

range of DBP formation potential. The relatively low DBP concentrations formed in lake waters

likely increased the relative influence of noise and therefore reduced correlation strength.

Visually it can be seen that using DOC as the predictor (or SUVA which incorporates DOC), the

nature of Lake Ontario (low organic content) water was not captured by the model and results

fall well outside the 95% confidence intervals (Figure 4.10).

(4.2)

(4.3)

52

Figure 4.10: Measured values vs. predicted values of THMs from non-linear regressions of

source-specific models

0

20

40

60

80

100

120

0 20 40 60 80 100 120

0

20

40

60

80

100

120

0 20 40 60 80 100 120

0

20

40

60

80

100

120

0 20 40 60 80 100 120


(a) DOC (b)

Note: Solid line represents line of best fit, dashed lines represent 95%

confidence intervals. n = 70.

0

20

40

60

80

100

120

0 20 40 60 80 100 120

0

20

40

60

80

100

120

0 20 40 60 80 100 120

Pre

dic

ted

To

tal T

HM

s (µ

g/L)

Pre

dic

ted

To

tal T

HM

s (µ

g/L)

Pre

dic

ted

To

tal T

HM

s (µ

g/L)

Pre

dic

ted

To

tal T

HM

s (µ

g/L)

Pre

dic

ted

To

tal T

HM

s (µ

g/L)

Measured Total THMs (µg/L)

Measured Total THMs (µg/L) Measured Total THMs (µg/L)



(b) UVA

(c) SUVA (d) PC1

(e) PC1 + PC2

53

Table 4.5: Results of non-linear regression for various NOM indicators and total THM concentrations

NOM Indicator All samples Otonabee River Lake Simcoe Lake Ontario Ottawa River

R2 MAE R

2 MAE R

2 MAE R

2 MAE R

2 MAE

DOC (mg/L) 0.88 6.40 0.94 2.36 0.75 4.10 0.90 0.36 0.96 3.32

UVA (cm-1

) 0.94 3.85 0.94 2.62 0.75 3.99 0.81 0.56 0.96 3.42

SUVA (cm-1

/mg/L) 0.88 5.61 0.92 3.14 0.75 4.00 0.75 0.80 0.94 3.60

PC 1 0.82 7.36 0.95 2.42 0.75 4.20 0.77 0.66 0.94 4.00

PC 1 + PC 2 0.76 9.58 0.94 2.49 0.77 4.38 0.75 0.86 0.87 6.39

Table 4.6: Results of non-linear regression for various NOM indicators and total HAA concentrations

NOM Indicator All samples Otonabee River Lake Ontario Ottawa River

R2 MAE R

2 MAE R

2 MAE R

2 MAE

DOC (mg/L) 0.88 11.77 0.99 1.85 0.94 0.62 0.92 12.58

UVA (cm-1

) 0.94 7.05 0.98 1.95 0.81 1.24 0.92 12.64

SUVA (cm-1

/mg/L) 0.89 12.18 0.97 3.05 0.78 1.94 0.91 13.21

PC 1 0.78 17.54 0.99 2.27 0.77 1.60 0.94 11.07

PC 1 + PC 2 0.75 23.43 0.98 2.42 0.81 2.02 0.80 23.04

54

The MAE for each water source and DBP type was used to rank and compare the strength

of each NOM indicator as a predictor for DBP formation. The order of the indicators from

strongest to weakest was DOC > UVA > PC 1 Score > SUVA > PC 1 + PC 2 score. It should be

noted that, especially for river sources, the differences of both MAE and R2 values are very small

when comparing organic indicators (less than 0.09 and 3.07, respectively). In these cases it may

be concluded that all indicators performed to an equivalent degree, except for the PC 1 + PC 2

score. Poor correlations and model strength when using the PC 1 + PC 2 (humic-like + protein-

like) combined scores suggests that the protein fraction had no influence on DBP formation.

This follows from trends reported in literature, which identifies humic-like substances as being

the principal component involved in DBP formation (Barrett et al., 2000).

For the model comprising all samples (XA), results showed that UVA better predicted the

formation of DBPs analyzed in this study than the other NOM indicators. For these same models

SUVA and DOC performed similarly followed by PC 1 score.

4.4 Conclusions

The purpose of this work was to investigate the application of principal component

analysis of fluorescence excitation-emission matrices as a predictor for disinfection by-products

resulting from chlorination. The FEEM-PCA technique was compared to traditional organic

concentration indicators: DOC, UVA, and SUVA. FEEM-PCA was a strong indicator of organic

content and had good correlation with both THM and HAA formation. Consistent with other

NOM indicators the FEEM-PCA technique was less successful at modelling DBP formation

from lake water sources when compared to river sources. Furthermore, the results of modelling

using pooled data from all water sources showed the FEEM-PCA technique performed worse

than other traditional indicators, implicating that this technique is best applied to singular water

sources. Furthermore, a reduction in correlation strength when protein-like results were included

supports the assertion that protein content does not strongly influence DBP formation.

55

5 Contributions of spatial, temporal, and treatment impacts on natural organic matter

character using principal component analysis of fluorescence spectra

5.1 Introduction

The monitoring of Natural Organic Matter (NOM) has proven to be of great importance

in drinking water treatment. NOM has been shown to be directly related to disinfection by-

product formation (Sadiq and Rodriguez, 2004), negatively affects water treatment processes

including ultrafiltration (Peiris et al., 2009), and promotes biological growth in distribution

systems (Baghoth et al., 2011). In both surface and ground waters, NOM consists of a wide

array of compounds including humic acids, carbohydrates, and proteins. This diversity of

compounds within the definition of NOM is problematic when considering removal techniques

(Pifer and Fairey, 2012) since there is evidence that both functional groups and molecular weight

distributions of compounds are integral to determining treatability (Her et al., 2003).

Unfortunately, traditional NOM quantification techniques, such as total organic carbon (TOC),

provide only gross measurements and limited information regarding NOM character (Gone et al.,

2009). Two analysis techniques which have been shown to provide information on multiple

NOM fractions include liquid chromatography-organic carbon detection (LC-OCD) (Huber et

al., 2011) and multivariate analysis of fluorescence excitation-emission matrices (FEEM)

(Persson and Wedborg, 2001; Zepp et al., 2004; Peiris et al., 2010; Bieroza et al, 2011).

LC-OCD allows simultaneous quantification of several fractions of organic matter. It

consists of a DOC analyzer preceded by chromatographic separation on the basis of molecule

size and into hydrophilic and hydrophobic fractions (Huber and Frimmel, 1992). Hydrophilic

NOM is divided into five subcategories: biopolymers, humic substances, building blocks of

humic substances, low molecular weight (LMW) acids and LMW neutrals.

The use of fluorescence measurements has been gaining traction as a promising method

for determining information on organic matter structure and function in water (Bieroza et al.,

2011). This method recognizes that several components of NOM fractions have unique

fluorescence signatures and therefore can be identified in a fluorescence spectrum. The

fluorescence spectrum is a representation of fluorescence intensity values collected at multiple

iterations of excitation and emission wavelengths (Her et al., 2003). The position (excitation-

emission region) of a given peak can be correlated with a specific compound and the maximum

intensity of the peak correlated with concentration (Bieroza et al., 2011). This method, referred

56

to as ‘peak picking,’ is limited in that it neglects to capture the heterogeneity of NOM fractions

(Peiris et al., 2010). The fluorescence intensity at specific excitation/emission wavelength pairs

is influenced by several variables including concentration, quantum yield, and absorptivity.

Molecular structure has a noted effect on some of these factors, which can be common among

several different molecules. By ‘peak picking’ the nature of overlapping structural properties

between distinct compounds and many of the potentially relevant compounds are neglected

(Persson and Wedborg, 2001). When compared to other NOM characterization techniques, such

as LC-OCD, the multivariate analysis of FEEMs is much faster and lower cost, while still

providing consistent results with high sensitivity (Peiris et al., 2008).


several samples, multivariate analysis techniques have been used. Common techniques applied

include two-way principal component analysis (PCA) and multi-way parallel factor analysis

(PARAFAC) (Peiris et al., 2010). These multivariate techniques reduce the number of variables

needed to explain variance between samples in a set (Persson and Wedborg, 2001). Furthermore,

this pattern recognition and data set simplification approach, unlike ‘peak picking’, incorporates

the entire fluorescence spectrum (Bieroza et al., 2011).

The purpose of this study was to investigate the spatial, temporal, and treatment impacts

on NOM character at four City of Toronto water treatment plants (F.J. Horgan, Island, R.C.

Harris, and R.L. Clark). Furthermore, for the secondary purpose of comparing and identifying

applications of NOM characterization techniques, both LC-OCD and PCA of FEEM were used.

All four of the treatment facilities draw from the same source, Lake Ontario, Canada, but differ

in treatment processes applied. In total the four plants serve a population of greater than 3.5

million people. Raw water from the four water intakes was analyzed to identify if NOM

character varied temporally and spatially. With the intention of informing treatment optimization

work, NOM character of the treated water was also monitored to quantify the performance of

different treatments (e.g., coagulants, filtration media).

5.2 Method overview

5.2.1 Water treatment plant characteristics

A summary of treatment processes used and water sources at each of the four plants is

shown in Table 5.1 and Table 5.2. Intake locations and depths are also illustrated in Figure 5.1.

Process flow diagrams of each plant are presented in section 8.3.

57

Table 5.1: Intake descriptions (CTC Source Protection Committee, 2012)

Treatment Facility Intake location Intake depth Intake length

F.J. Horgan 1a 18 m (59 ft.) 2925 (9596 ft.)

R.C. Harris Northeast 15 (49 ft.) 2232 (7323 ft.)

Southwest 15 (49 ft.) 2125 (6972 ft.)

Island East 83 (272 ft.) 4848 (15905 ft.)

Middle 83 (272 ft.) 4662 (15295 ft.)

West 83 (272 ft.) 4696 (15407 ft.)

R.L. Clark 1a 11 (36 ft.) 1610 (5282 ft.)

a only one intake.

Table 5.2: Summary of treatment processes

Treatment facility Coagulation/Flocculation Coagulant Filter Media

F.J. Horgan Inline PACl in winter:

1.2 - 2 mg/L

Alum in summer

(above 10o

C): 4 -

5 mg/L in addition

to 0.4-0.5 mg/L

polyelectrolyte

1.5 - 2.2 m GAC

above 0.25 m sand

R.C. Harris Hydraulic flocculation,

sedimentation Alum: 3 - 8.25

mg/L

0.25 - 0.30 m GAC

or anthracite over

0.25 m sand Island Inline PACl: 1.8 mg/L 0.91 m anthracite

over 0.25 m sand R.L. Clark Mechanical flocculation,

sedimentation Alum: 5 - 8 mg/L 0.46 m anthracite

over 0.31 m sand

5.2.2 Total organic carbon

Organic carbon measurements were based on the wet oxidation method as described in

Standard Method 5310 D (APHA, 2005). Samples were acidified at the time of collection to pH

2 using sulphuric acid (VWR International, Mississauga, ON) for preservation. Concentrations

of total organic carbon were obtained through heated persulfate oxidation using an O.I.

Analytical Aurora 1030 organic carbon analyzer (College Station, Texas, USA).

58

Figure 5.1: Toronto WTP intake locations.

.

N

Note: Intake locations interpreted from Source Water Protection Plan (CTC Source Protection Committee, 2012), intake

lines interpreted based on treatment facility location. Map prepared in Google Earth V 7.0.2.8415 (Image © 2013

TerraMetrics, © 2012 Google, Image NOAA, Image © 2013 DigitalGlobe).

R.L. Clark intake (11 m depth)

Island intakes (83 m depth)

R.C. Harris intakes (15 m depth)

F.J. Horgan intake (18 m depth)

9.06 km

59


LC-OCD analyses were conducted using the method described by Huber et al. (2010).

Samples were passed through a 0.45 µm filter to remove any particulates prior to analyses. A

weak cation exchange column (250 mm x 20mm, Toso, Japan) provided chromatographic

separation of the NOM fractions. The mobile phase used was a phosphate buffer (MLE,

Dresden, Germany) exposed to UV at a flow rate of 1.1 mL/min. The samples first passed

through a UVA detector (254 nm) followed by an organic carbon detector (OCD). Prior to

entering the OCD, the solution was acidified to convert carbonates to carbonic acid. During the

acidification process, bound nitrogen (organic and inorganic) was converted to nitrate which

absorbs light in the UV region (Huber et al., 2011). This allows for the simultaneous

quantification of bound nitrogen in each separated fraction using the UVA detector. Calibration

was performed on-site using potassium hydrogen phthalate. Data acquisition and processing was

conducted using a customized software program (ChromCALC, DOC-LABOR, Karlsruhe,

Germany).

5.2.4 Fluorescence spectra collection

Fluorescence excitation-emission matrices were collected using a Perkin Elmer

Luminescence Spectrometer LS50B (Waltham, Massachusetts, USA) with FLWinLab Version

3.0. Collection of intensity values at 10 nm increments within excitation-emission ranges of

250-380 nm and 300-600 nm, respectively. Scan rate was set to 600 nm/min, slit width was set

to 10 nm, and photomultiplier tube voltage was set to 775 V. The instrument settings were

determined based on ranges used in previous studies (Bieroza et al., 2011), protocols that

increase resolution (Peiris et al., 2009), and in-house testing to optimize FEEM collection. UV-

grade polymethylmetacrylate (PMMA) cuvettes with four optical windows were used. It has

been shown that, while the PMMA cuvettes used reduce the intensity of excitation wavelengths

below 285 nm, this approach is acceptable for the purposes of distinguishing NOM elements

using fluorescence (Peiris et al., 2008). To account scattering interference, blank subtraction was

performed using spectra collected of Milli-Q© water using the same instrument settings (Her et

al., 2003).

5.2.4.1 Fluorescence data analysis

PCA is a multivariate data analysis technique that approximates a large data matrix

through observed patterns. Common goals for PCA include identification of similarities between

60

samples, data simplification, and detection of outliers (Abdi and Williams, 2010).

Approximation of dominant data patterns is achieved by obtaining new mutually independent

variables that are mathematically represented by linear combinations of the original variables

(Persson and Wedborg, 2001). In generalized terms, the original data matrix X is decomposed

into the product of two small matrices, ti and pi which are referred to as the scores and loading

matrix, respectively (equation 5.1).

∑

The residuals generated from the approximation are represented in another matrix, E. For

multiple samples, n, in the matrix X, the product and residuals are summed (Peiris et al., 2010).

The loading matrix, pi, is a projection matrix that relates the PCA space to the original spectral

space. Information in the loading matrix can be used to identify the spectral areas representing a

specific principal component. The scores matrix, ti, represents projections of the samples, or

object, into the principal component (PC) space. Scores are analogous to concentrations and

represent the relative importance of a PC for a given sample (Persson and Wedborg, 2001).

Each sample produced a total of 4214 fluorescence intensity values at unique excitation-

emission wavelength pairs. Prior to PCA, intensity data of each sample was arranged by row.

All data manipulation and organization of the spectrum was carried out using in-house data

manipulation scripts written in Python 2.6 (Python Software Foundation). Using the PLS

Toolbox (Eigenvector Research Inc.) for MATLAB 7.12.0, principal component analysis (PCA)

was applied to the fluorescent excitation-emission matrices. During the study period a total of

174 unique FEEMs were collected from all four WTPs.

The model input data was pre-processed using the PLS toolbox. Autoscaling was applied

to the intensity values to reduce bias. Autoscaling centers the data on the mean and scales each

variable to a unit standard deviation. Without autoscaling, the PCA technique would favour

higher intensity values and skew the components identified to those with higher fluorescence


subsets (subset size of 10 samples) was selected.

(5.1)

61

5.2.5 Statistical analysis

Tukey’s method allows simultaneous pairwise comparisons of multiple sample means

(Dean and Voss, 1999). The method calculates the minimum significance difference (MSD) for

each possible pair of means within the group. The MSD can be thought of as a confidence

interval for the difference between two sample means which, when spans 0, indicates no

significant difference between the sample sets. In generalized terms, assume Tukey’s method is

applied to identify significance between sample means in a group of four sample sets. For any

two sample sets within the group, i and j, the MSD can be expressed as follows:

(

√ )√ (

)

∑ ∑ ( )

where, µi and µj are the means of sample sets i and j, qv,n-1,α is the value of the studentized range

distribution for: v sample sets = 4, n observations, r replicates, α confidence level (95%) =

0.05, and y is a measured value.


All four treatment facilities utilize Lake Ontario as their source water. However, their

intake locations and depths differ considerably. It was of interest to understand raw and treated

water NOM fraction differences among the four treatment plants. NOM was quantified using

total organic carbon (TOC) measurements and was further characterized using both FEEM-PCA

and LC-OCD techniques.

5.3.1 Analysis of raw water

TOC results at all four water treatment facilities varied between 1.85 and 2.55 mg/L

during the study period; and periods of increasing or decreasing trends in TOC levels were

generally experienced simultaneously by all four sources. Using ANOVA coupled with Tukey’s

method for identifying significant differences, it was found that the raw water TOC from the

Island intake was significantly lower than the other three treatment plants (95% confidence).

Furthermore, R.C. Harris raw water TOC was significantly higher than both R.L. Clark and the

(5.2)

62

Island treatment plants. The maximum absolute difference (between the Island and R.C.

Harris) was 0.13 mg/L TOC and considered to be low.

Differences in character were first examined using LC-OCD. Results for raw water

samples are shown as Figure 5.2. Using one-way ANOVA, concentrations of all fractions were

found to be significantly equal, including overall DOC, at a 95% confidence level. The largest

NOM fraction was found to be humic substances followed by building blocks for all locations.

The relative distribution of NOM fractions is considered typical for lake sources (Huber et al.,

2011).

Figure 5.2: Raw water LC-OCD results

The fluorescence spectra for each of the four water sources exhibited similar

characteristics. Two main peaks, a and b, were identified from the spectral plots of fluorescence

intensities as shown in Figure 5.3 (example spectrums of raw water). Through comparison with

published work, peak a represents humic-acid type matter (Ex/Em: 270nm/450nm) whereas peak

b is consistent with fulvic-acid type material common to fresh waters (Ex/Em: 340nm/440nm)

(Peiris et al., 2010; Murphy et al., 2008). Peaks of high intensity were observed in both first and

second order Rayleigh scattering regions (Ex/Em: 300 – 380 nm / 300 – 380 nm and Ex/Em:

250-300 nm / 500 – 600 nm) which have been shown by to be related to sub-incident wavelength

sized particulate concentrations (Aslan et al., 2005; Peiris et al., 2010).

0.0

0.5

1.0

1.5

2.0

2.5

DOC Bio-Polymers HumicSubstances

Building Blocks LMW Neutrals LMW Acids

Co

nce

ntr

atio

n (

mg/

L)

F.J. Horgan Island R.C. Harris R.L. Clark

Note: Error bars represent one standard deviation (n = 6)

63

Figure 5.3: Example raw water spectra from all four water treatment plants

250300

350

300

400500

6000

10

20

Inte

nsity (

a.u

.)

Excitation (nm)Emission (nm)

Inte

nsity (

a.u

.)

0

5

10

15

20

Excitation (nm)

Em

issio

n (

nm

)

250 300 350300

350

400

450

500

550

600

Inte

nsity (

a.u

.)

0

5

10

15

20

250300

350

300

400500

6000

10

20

Inte

nsity (

a.u

.)


Inte

nsity (

a.u

.)

0

5

10

15

20

Excitation (nm)

Em

issio

n (

nm

)

250 300 350300

350

400

450

500

550

600

Inte

nsity (

a.u

.)

0

5

10

15

20

250300

350

300

400500

6000

10

20

Inte

nsity (

a.u

.)


Inte

nsity (

a.u

.)

0

5

10

15

20

Excitation (nm)

Em

issio

n (

nm

)

250 300 350300

350

400

450

500

550

600

Inte

nsity (

a.u

.)

0

5

10

15

20

(a)

(b)

(c)

(d)

(a) F.J. Horgan plant, (b) Island plant, (c) R.C. Harris plant, (d) R.L. Clark plant.

Note: Intensity values truncated at 20 a.u. (arbitrary units)

a b

a

b

a b

a

b

a b

a

b

a b

a

b

64

The excitation/emission location of peaks a and b (highest measured intensity) are shown

for each intake in Table 5.3. Peak locations were used to compare the similarity of organic

character among the four intakes. The most pronounced difference in spectrums was the lower

intensity of Rayleigh scattering peaks for the Island plant.

Table 5.3: Excitation/emission location of peaks for each water source

Peak Excitation/Emission of peak (nm/nm)

F.J. Horgan Island R.C. Harris R.L. Clark

a 280/443 280/443 280/436 280/442

b 340/433 340/428 340/433 340/430

Principal component analysis was applied to the full set of 174 samples from all four

plants; a cut off of 5% variance explained by each PC was applied. In total, the PCA model

explained 88.3% of the variance in the sample set when considering the first four PCs (Table

5.4).

Table 5.4: Variance explained by PCA model

Principal

Component Variance explained by PC (%) Cumulative variance explained (%)

1 64.62 64.62

2 13.42 78.04

3 5.50 83.54

4 4.80 88.34

The physical meaning of each principal component was determined through analysis of

loading plots (Figure 5.4). The loading plot for PC 1 displayed high values in areas (Ex/Em 260

– 380 / 350 – 550 nm) representing general humic-like substances as indicated in Figure 5.4 (a).

PC 2 loading values indicated that it predominantly represented scattering regions with high

loading values in both the first and second order Rayleigh scattering regions. The loading plot

for PC 3 showed a maximum at Ex/Em of 250/500 nm and a pronounced minimum at Ex/Em of

310/340 nm, neither of which were found to relate to a previously reported organic component

(Bagoth et al., 2011; Peiris et al., 2010). PC 4 represents protein-like substances as indicated by

high loading values within the low Ex/Em region (250-300/300-350 nm) (Stedmon and

Markager, 2004).

65

Figure 5.4: Loading plots for first 3 principal component

(a)

(b)

(c)

(d)

(a) PC 1, (b) PC 2, (c) PC 3, (d) PC 4

66

Average and MSD values for raw water FEEM-PCA scores (Figure 5.5) show the intake

water quality for each plant to be very similar in NOM character. Of note is the significantly

lower scattering score (PC 2) for the Island plant, evident from the non-overlapping MSD.

Furthermore, the MSD of each PC provides insight into the amount of variance during the

sampling time period. Scores varied more for humic-like (PC 1) than for the other identified

fractions (MSD PC1: 10.9 a.u., PC2: 7.2 a.u., PC3: 2.5 a.u.). In addition, the low MSD for the

protein-like fraction (PC 3) indicated low variability in protein-like material for all locations.

Figure 5.5: Averages and MSD for raw water FEEM-PCA scores

5.3.2 Analysis of treatment efficiency

The effectiveness of treatment at each of the four plants was illustrated by calculating the

difference between treated and raw water values (difference = raw - treated). Positive difference

values indicated that treatment reduced the concentration of the NOM fraction. One-way

ANOVA was carried out on the calculated differences from all four plants to observe significant

differences in treatment efficiency. Statistical analysis did not include data collected beyond

June 19th

, 2012 due to process and equipment changes at the F.J. Horgan plant. At this time,

ozonation commenced and the GAC filter media was replaced. Effects of these changes were

observed from the pronounced increase in TOC removal observed in July (Figure 5.6). The

one-way ANOVA coupled with Tukey’s method yielded results indicating significant effects on

PC 1 (humic-like) PC 2 (scattering) PC 4 (protein-like)

-30

-20

-10

0

10

20

30

40

50

Sco

re

F.J. Horgan WTP Island WTP R.C. Harris WTP R.L. Clark WTP

67

TOC due to treatment. The F.J. Horgan, R.C. Harris, and R.L. Clark plants reduced TOC by

significantly equal amounts (0.16 – 0.21 mg/L), while the Island plant had a significant but very

minor effect (0.04 mg/L TOC reduction) (95% confidence). The observed TOC reduction

(approx. 8 - 11%) at all plants except for the Island (2% reduction) was comparable to treatment

performances at plants using lake waters with low organic content (2 – 4 mg/L TOC) (Volk et

al., 2000; Uyak and Toroz, 2007). The 2% reduction in TOC at the Island plant was likely due to

a non-optimized coagulant dose.

Figure 5.6: Comparison of treatment performance for all plants: TOC

The results of statistical analysis on changes of NOM fractions due to treatment, as

analyzed by LC-OCD are shown in Table 5.5. Significant changes (95% confidence) to overall

DOC and humics were observed. At the R.L. Clark plant, bio-polymers were reduced; at the F.J

Horgan and R.L Clark plants building blocks were reduced. The Island plant was an exception

where no significant differences between raw and treated water were found. The results are

supported through evidence indication coagulation most significantly affects humic substances

(Chow et al., 2008; Baghoth et al., 2011) and biopolymers (Volk et al., 2000). Diemert et al.

(2012) reported similar trends of pronounced reduction of humic substances with minor changes

to biopolymer fractions from coagulation of lake and river waters using alum and PACl.

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Feb-12 Mar-12 Mar-12 Apr-12 May-12 May-12 Jun-12 Jul-12 Jul-12 Aug-12

Tota

l org

anic

car

bo

n d

iffe

ren

ce (

raw

- t

reat

ed

) (m

g/L)

Date (Month-Day)


GAC media replaced and

pre-ozonation applied at

F.J. Horgan WTP

68

Figure 5.7: Comparison of treatment performance for all plants: LC-OCD

Table 5.5: Significant changes in LC-OCD fractions between raw and treated water

Plant DOC Bio-

Polymers Humics

Building

Blocks

LMW

Neutrals

LMW

Acids

F.J. Horgan Yes No Yes Yes No No

Island No No No No No No

R.C. Harris Yes No Yes No No No

R.L. Clark Yes Yes Yes Yes No No Yes = significantly different (95% confidence); No = significantly similar (95% confidence)

Similar to TOC and LC-OCD results, the difference between raw and treated water scores

associated with FEEM-PCA data was calculated to observe changes to NOM fractions as a

function treatment. It is important to note that score values reported form PCA are not calibrated

to external standards. Score values, and changes to them, should be taken only in context of

score values from the same PCA model. The average and MSD of each PC for all treatment

plants is shown as Figure 5.8. PC 1 scores from the F.J. Horgan WTP showed that humic-like

substances were reduced throughout the entire study period. As was noted for TOC and LC-

OCD results, a marked increase in humic-like removal (approx. 79 a.u. to 196 a.u.) at the end of

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

Dif

fere

nce

in c

on

cen

trat

ion

(ra

w -

tre

ate

d)

(mg/

L)


Note: Error bars represent one standard deviation (n = 6)

DOC Bio-Polymers Humic Substances

Buliding Blocks

LMW Neutrals LMW Acids

69

June to early July occured when ozonation was applied and GAC filter media was replaced at the

F.J. Horgan WTP. All other treatment plants also showed significant reduction of PC 1 scores

due to treatment (interpreted by the MSD not overlapping 0 a.u.). Reductions of 11.4 a.u., 30.1

a.u., and 15.1 a.u. were observed for the Island, R.C. Harris, and R.L. Clark WTPs. In terms of

scattering (PC2), treatment at the F.J. Horgan WTP resulted in an increase following

implementation of process changes (-5.7 a.u. to 63 a.u.). Prior to this period no significant effect

in scattering due to treatment was observed. It is possible that ozonation potentially caused

breakdown of compounds resulting in increased concentration of small particulates which are

associated with scattering response as suggested by Peiris et al. (2010). Treatment at the Island

WTP produced no observable change to scattering. For both R.C. Harris WTP and R.L. Clark

WTP there was a significant decrease in particulate concentrations due to treatment. Throughout

the the study period protein-like scores (PC 4) were significantly reduced at the F.J. Horgan

WTP, while increased at the other three WTPs.

In order to make a full assessment on the apparent effects (TOC, LC-OCD, and FEEM-

PCA) of process changes at the F.J. Horgan plant, additional sampling is required. Trends

observed in the month of July indicated that treated water quality has not reached a steady state

and therefore further monitoring may help to determine the significance of apparent effects.

Figure 5.8: Average difference in PC score due to treatment

PC 1 (humic-like) PC 2 (scattering) PC 4 (protein-like)

-20

0

20

40

60

80

100

Sco

re d

iffe

ren

ce (

raw

- t

reat

ed

)


Note: Error bars represent standard error (Tukey’s method) n = 24

70

5.4 Conclusions

Analysis of raw water organic content (TOC, LC-OCD, and FEEM-PCA) at four Lake

Ontario water treatment plants by indicated strong similarities between the source waters from

different intake locations. Through FEEM-PCA data analysis it was observed that the Island

plant raw water had consistently lower scattering scores throughout the study period. Scattering

information from FEEM-PCA is known to be related to particulate concentrations (Peiris et al.,

2010), although more work is needed to determine what is being represented by scattering scores

within the context of water treatment. This intake is both deeper and farther from shore when

compared to the other plant intakes.

This work demonstrated the ability of FEEM-PCA as a rapid NOM characterization

technique for low organic content source waters. In comparison to LC-OCD and TOC, the

fluorescence method was also able to identify significant changes in overall

scattering/particulates. Furthermore, fluorescence results were well supported by both TOC and

LC-OCD.

Assessments of changes to the character of NOM via LC-OCD and FEEM-PCA showed

that primarily the humic fraction was influenced by treatment processes at all plants. The most

significant changes were observed to have resulted from process changes at the F.J Horgan plant.

Future work should include increased sample locations within the plant to determine whether the

ozonation process or replacement of GAC media caused the notable increase in NOM removal

effectiveness.

71

6 Pilot-scale coagulation optimization including NOM characterization with principal

component analysis of fluorescence spectra

6.1 Introduction

Implementing changes to water treatment processes has an inherent high degree of

uncertainty. Understanding the intricacies of the impacts of a process change at full-scale is not

necessarily conducive from bench scale tests where variables are often well controlled, water

volumes are vastly different, and there is difficulty in observing seasonal results caused by

changing source water quality. Pilot-scale studies have been shown as a means to address issues

associated with scale and reduce errors in process optimization (Knowles et al., 2012).

In order to take full advantage of pilot-scale studies, an appropriate experimental design

must be established that is able to identify when apparent process improvements are in fact, real.

Furthermore, the experimental design should show the reliability of scale-up from pilot to a full-

scale plant. At the core of pilot-based studies is the need to run two identical parallel trains

where one pilot train serves as a control, which may be constantly compared to the full-scale

performance, while the second acts as an experimental train. Fundamental to parallel pilot

studies is the validation of similarity between both pilot trains and the full-scale plant.

Validation involves establishing, with statistical significance, that with equal treatment applied,

both pilot trains and the full-scale train produce identical water quality at any given point in the

process. This allows for the conclusion that observed water quality differences in the

experimental train are due only to the controlled process changes. Furthermore, validation

permits for the conclusion that changes observed at pilot-scale will translate well if applied in a

full-scale scenario (Anderson et al., 1993).

Pilot-scale studies were conducted with the objective to identify impacts on water quality

resulting from use of aluminum sulphate (alum) or polyaluminum chloride (PACl) as coagulants.

The motivation for testing alternative coagulants was the need to limit sludge production

resulting from the coagulation process at the City of Peterborough, Ontario, Canada, water

treatment plant. Pilot-scale studies were aimed at determining the potential reduction of sludge

as well as to quantify changes to water quality and provide costs associated with implementing

PACl. As part of the water quality analysis, principal component analysis (PCA) of fluorescence

excitation-emission matrices (FEEM) was conducted to determine how NOM fractions were

affected by treatment processes and coagulant type.

72

6.2 Methods

The source water for both full and pilot-scale trains is the Otonabee River, Ontario,

Canada. A summary of typical source quality (March 2011 to May 2012) is shown in Table 6.1

Table 6.1: Peterborough raw water quality yearly ranges

Parameter Range (Yearly)

Temperature (oC) 0.0 – 27.7

Total organic carbon (TOC) (mg/L) 5.81 – 7.33

Ultraviolet absorbance (UVA) (cm-1

) 0.140 – 0.260

pH 7.43 – 8.62

Turbidity (NTU) 0.27 – 1.47

6.2.1 Pilot plant

One of the pilot-scale treatment trains was dosed with alum and operated to mimic the

current operation of the full-scale plant to ensure that pilot-scale tests accurately reflect full-scale

conditions. The second pilot treatment train was dosed with PACl such that differences in

treated water quality could be observed. Figure 6.1 shows a simplified process flow diagram,

which identifies major unit processes common to both full-scale and pilot-scale facilities.

Both treatment trains were outfitted with a wide array of instrumentation to monitor water

quality in real time with online data collection and tabulation completed through a sophisticated

supervisory control and data acquisition (SCADA) system. Measurements included pH,

Flocculation Sedimentation Dual Media Filtration

(anthracite and sand)

Chlorine Contact

Raw Water

Coagulant,

Flash Mix

Chlorine

(disinfection)

To Distribution

Chlorine

(ensure residual)

Sodium Silicate

(pH control)

Fluoride

Figure 6.1: Simplified process flow diagram of pilot and full-scale treatment train

73

temperature, total organic carbon (TOC), ultraviolet absorbance (UVA), turbidity, and particle

counts for raw water, settled water, and filtered water on all treatment trains. In addition, weekly

grab samples were collected at the pilot and full-scale plant including TOC, pH and FEEM for

raw water, settled water, and filtered water. Samples for THM and HAAs were collected post-

filtration for 4 days per week and chlorinated for 24 hours prior to being quenched.


FEEMs were collected using a Perkin Elmer Luminescence Spectrometer LS50B

(Waltham, Massachusetts, USA) with FLWinLab Version 3.0. No pre-treatment of the samples

was applied, however all had been adjusted to a pH of 7. Maintaining a common pH between

samples ensured that fluorescence characteristics of the acidic functional groups in humic

molecules remained constant (Mobed et al., 1996). Collection of intensity values at 10 nm

increments within excitation-emission ranges of 250-380 nm and 300-600 nm, respectively.

Scan rate was set to 600 nm/min, slit width was set to 10 nm, and photomultiplier tube voltage

was set to 775 V. The instrument settings were determined based on ranges used in previous

studies (Peiris et al., 2010; Bieroza et al., 2011), protocols that increase resolution (Peiris et al.,

2009), and in-house testing to optimize FEEM collection. UV-grade polymethylmetacrylate

(PMMA) cuvettes with four optical windows were used. It has been shown that, while the

PMMA cuvettes used reduce the intensity of excitation wavelengths below 285 nm, this

approach is appropriate for purposes of distinguishing NOM elements using fluorescence (Peiris

et al., 2008). To account for background noise and scattering interference, spectra for Milli-Q®

water was obtained using the same instrument settings. Intensity values from the Milli-Q®

samples were subtracted from intensity values of sample spectra to reduce background noise

effects.

Scattering/particulate regions of the FEEM were removed in an effort to increase model

sensitivity to humic and protein-like substances (Bahram et al., 2006). Rayleigh scattering is

characterized by emitted photons at equal or double the wavelength of excitation photons,

referred to as second order Rayleigh scattering (FORS) and second order Rayleigh scattering

(SORS), respectively (Mobed et al., 1996). A region surrounding Rayleigh scattering was

removed to fully exclude any influence from the spectra (Zepp et al., 2004). In this study, points

satisfied by equations 6.1 and 6.2 were removed from the spectrum (intensities set to 0).

Approximate FORS and SORS regions omitted are visualized in an example spectrum (Figure

6.2).

74

Figure 6.2: Example FEEM illustrating scattering regions (FORS and SORS)

6.2.3 Principal component analysis

Using the PLS Toolbox (Eigenvector Research Inc., Wenatchee, WA) for MATLAB,

principal component analysis (PCA) was applied to the fluorescent excitation-emission matrices.

Two models were constructed using different sample sets in order to increase sensitivity. Model

1 was created using all samples: raw water, settled water, and filtered water. Model 2 was

created using only treated water samples (settled and filtered water).

All data was pre-processed using the PLS toolbox prior to model creation. Autoscaling

was applied to the intensity values to reduce bias. Autoscaling centers the data on the mean and

scales each variable to a unit standard deviation. Without autoscaling, the PCA technique would

favour higher intensity values and skew the components to those with higher fluorescence


subsets was used.

6.2.4 Statistical assessment

Statistical assessments were made to determine similarity of full and pilot train treated

water as well as the treated water between the two pilots (alum vs. PACl). A difficulty

SORS

FORS

(6.1)

(6.2)

75

encountered with the analysis was the cyclic seasonal nature of water quality data collected for

over a year. Analysis of yearly averages would have incorrectly ignored natural fluctuations in

raw and treated water qualities. Therefore, the influence of natural changes in water such as

temperature and pH on the results was removed through the calculation of differences rather than

absolute values. With this approach the null hypothesis was a difference between of 0 would

indicate similarity between the two trains. This approach is reliant on the assumption that

external environmental conditions affect all treatment trains equally. This extends to imply that

the effect of source water quality on finished water quality is independent of the coagulant type.

Two sets of statistical tests were performed, one utilizing the baseline data to observe any

differences in the three treatment trains (one full-scale, two pilot-scale) operated at equal

conditions, and the second to compare changes in treated water quality between the two pilots to

illustrate differences associated with a coagulant change. During baseline testing both pilot

trains received alum doses equal to the full-scale plant. It was expected that baseline data should

demonstrate significantly similar water quality between the two pilot and full-scale trains. It was

possible, however, that differences in scale and other physical differences between the trains

would contribute to small, consistent offsets in an absolute value. These cases were identified

when the baseline sample standard deviation was 10% or less of the mean. This implied that the

offset was consistent and the error was assumed to be carried through into the experimental data.

As such, this baseline ‘offset’ mean was taken as the new assumed mean difference during the

experimental phase of pilot testing.

6.2.5 Tukey’s method








(6.3)

76

(

√ )√ (

)

∑ ∑ ( )



0.05, and y is a measured value.

Further details regarding statistical methods can be found in the appendix (section 8.2.3).


Statistical results presented in this work are classified as either during the ‘baseline,’ or

‘experimental’ period. During baseline testing both pilot trains received alum doses equal to the

full-scale plant. The baseline period was used to establish the validity of pilot trials through

analyzing the similarity in water quality between the pilot trains and to the full-scale plant. In

the experimental period, PACl was used in one of the two pilot trains while the other remained

on alum. In order to directly compare performance of the two coagulants, dosages were selected

which resulted in equal filtered water TOC, determined through periodic jar testing (once per

month). Daily averages of TOC and UVA from April 2011 to May 2012 are shown in Figure

6.3.

Using paired t-tests, filtered water UVA and TOC for the alum and PACl pilot were

found to be significantly equal during baseline testing (95% confidence). While the difference in

UVA was found insignificant between pilots during the experimental phase, difference in TOC

was significant (0.03 mg/L) but, less than one standard deviation of quality control standards (0.1

mg/L) (Section 3.2.1). During both testing periods a significant, but slight, difference in UVA

between the alum pilot and full-scale was identified (≤ 0.01 a.u.). Comparison of alum pilot and

full-scale TOC demonstrated significant differences of 0.14 and 0.05 mg/L for baseline and

experimental periods, respectively.

Due to the large sample sizes (baseline n ~ 25; experimental n ~ 374), small differences

in absolute values between trains were found to be significant. Therefore, significance was also

assessed on the basis of the absolute difference in terms of ultimate impact on the process. For

77

example, differences of ≤ 0.1 pH units (as seen during baseline testing) were not considered to

have a significant impact on water quality.

Figure 6.3: TOC and UVA of pilot plant settled water

The results of statistical tests performed on all monitored parameters are presented in the

appendix, section 8.1.3. Water treated with PACl exhibited a pH difference of 0.72 ± 0.12 in

settled water. The addition of alum depressed the pH of the water while the pre-hydrolyzed

PACl did not exhibit the same effect. Furthermore, it was shown that in comparison to PACl, the

alum treated settled water turbidity by 0.23 ± 0.23 NTU. Following filtration, no substantial

difference was observed between pilots (0.1 ± 0.1 NTU). A significant increase in total THM

concentrations in PACl treated water was observed (~20% increase), while there was no

significant change to HAA9 concentrations. Studies by others suggest that the elevated pH of the

PACl treated water caused the observed increase in THM formation (Singer, 1994; Sadiq and

Rodriguez, 2004). To test this hypothesis, a series of jar tests with both alum and PACl,

followed by chlorination at a controlled pH, was completed. The results of these tests showed

that, when alum or PACl treated water was chlorinated at an equal pH (7 and 8), total THMs

formed following 24 hours of contact time with free chlorine were equal. Additional details

showing the methodology and results from the pH controlled DBP formation tests are presented

in section 8.4.

0.04

0.06

0.08

0.10

0.12

0.14

0.16

2.30

2.50

2.70

2.90

3.10

3.30

3.50

3.70

3.90

4.10

4.30

UV

Ab

sorb

ance

25

4 n

m

TO

C (

mg/

L)

Month - YY

Alum Pilot Filtered Water TOC PACl Pilot Filtered Water TOC

Alum Pilot Filtered Water UVA PACl Pilot Filtered Water UVA

78

Both the alum and PACl pilot filters were backwashed simultaneously and therefore had

equal filter run times. The monthly average total loss of head from a full filter run is presented

as Figure 6.4. Consistently, it was observed that alum pilot filters had greater loss of head when

compared to the PACl pilot. Average differences for individual months were statistically

confirmed using Tukey’s method at 95% confidence. It was thought this was a result from with

the increased turbidity observed in alum settled water. This implies that for equal TOC removal,

PACl provided more efficient particle removal.

Figure 6.4 Average monthly total loss of head for pilot plant filter

Overall, comparison of the “control” pilot and the full-scale plant showed that statistically

they are not considered equal, however, in most cases, the absolute differences were small. A

complete table indicating statistical results and the absolute difference for all measured

parameters is presented as Table 8.15 in the appendix (section 8.1.3).

6.4 Performance evaluation using fluorescence spectra

6.4.1 Spectral features of Peterborough water

Intensities from the acquired FEEMs showed two prominent peaks at approximately

Excitation/Emission (Ex/Em) = 340 nm/430 nm (peak α) and Ex/Em = 280 nm/430 nm (peak β).

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

Loss

of

He

ad (

m·h

-1)

Month - YY

Alum Pilot Filtered Water PACl Pilot Filtered Water

Note: Error bars represent one standard deviation

79

The literature indicate that fluorophores at these excitation/emission pairs correspond with

fulvic-like humic substances (peak α) (Coble et al., 1990) and humic acid type material (peak β)

(Sierra et al., 2005; Peiris et al., 2010). Several pronounced peaks were also observed between

Ex 320 and 380 nm and Em of 300 and 400 nm and are considered to be First Order Raleigh

Scattering (FORS) resulting from particulate and colloidal matter in the water (Peiris et al.,

2010) (Figure 6.5 and Figure 6.6).

Figure 6.5: Raw water FEEM – 2D view

Figure 6.6: Raw water FEEM - 3D view

Peak β,

humic

acid

Peak ,

fulvic acid

Peak β,

humic acid

Peak ,

fulvic acid

Inte

nsi

ty (

a.u

.)

Inte

nsi

ty (

a.u

.)

80

6.4.2 Results of principal component analysis and model selection

Principal component analysis was completed using a set of 77 samples which included

raw, settled, and filtered water from the full-scale and pilot trains. In the context of this study

two types of comparisons were to be made; changes to PC scores between raw and treated water

as well as differences of PC scores between treated water samples. It was hypothesized that by

limiting the organic concentration range for model inputs, sensitivity of the output would be

increased for that same range.

Model 1 included all collected samples in order to assess changes between raw and

treated water.

Model 2 included only treated water samples (settled and filtered water) to examine

differences between alum or PACl treated water.

A summary of the variance explained by each PC for Models 1 and 2 is shown in Table

6.2. Cumulative variance explained by the first two components of Models 1 and Model 2 were

91.27% and 84.60%, respectively. Model 1 results show that more variance was explained by

PC 1 (83.13%), when compared to Model 2 (70.88%); PC 2 in Model 2 explained more variance

than in Model 1 (13.72% vs. 8.13%).

Table 6.2: Variance (%) explained by PCs for Models 1 and 2

Model # Samples Used for

Model Creation

Principal

Component #

Percent Variance

Explained by

This PC (%)

Cumulative

Percent Variance

Explained (%)

1 All Samples 1 83.13 83.13

2 8.13 91.27

2

Treated Water

Samples (Settled

and Filtered Water)

1 70.88 70.88

2 13.72 84.60

High loading values in specific excitation/emission regions indicate that the PC is

representative of the spectral data in the same excitation/emission region. For instance, it has

been previously established that fluorescence intensity at 340 nm/430 nm (excitation/emission)

indicates fulvic acid (Murphy et al., 2008). High loading values in this region would indicate

81

that the PC is also representative of fulvic acid. For PCs 1 to 3, Figure 6.7 to Figure 6.10 show

loading values plotted with their corresponding excitation/emission coordinates. Loading plots

for each Model were included to demonstrate the similarity of loading values between Models 1

and 2.

There was a high degree of commonality of PC loading values for each model. PC 1 in

all models had pronounced loading peaks in regions associated with humic-like material. The

humic-like region of 270 nm excitation / 360 nm emission to 380 nm excitation / 600 nm

emission was determined through reference to literature (Peiris et al., 2010; Her et al., 2003;

Coble et al., 1990). For Model 2, there appeared to be some division of peaks within the large

humic zone which was not present in Model 1. Two localized peaks are visible at approximately

320 nm excitation / 500 nm emission and 360 nm excitation / 430 nm emission (Figure 6.9).

From both models, PC 2 results exhibited loading peaks in the 260-300 nm excitation / 300-400

nm emission region. This region is known to be associated with protein-like substances (Peiris et

al., 2010; Her et al., 2003). In addition to the protein-like region, both models exhibited

shoulder peaks which continued into emission values up to 475 – 500 nm. It is unknown at this

time what this region represents.

6.4.3 Performance of coagulation on NOM fraction removal

Changes to humic-like and protein-like material identifiable by FEEM-PCA were tracked

by calculating the difference between treated and raw water scores (raw – treated). Scores are

reported in arbritrary units (a.u.) and it should be noted that the score values are only significant

in relation to other scores calculated by the same model. Positive differences indicate a decrease

of scores and illustrate the effectiveness of treatment (Figure 6.11). Scores for PC 1 differences

showed a significant effect of coagulation on humic-like material (difference of 139 – 152 a.u.).

This degree of humic-like removal was also shown to be significantly equal between the full-

scale and two pilot trains based on their MDLs. For PC 2 scores, both the full-scale plant and the

alum pilot showed increased protein-like scores (-20.3 a.u. and -10.7 a.u., respectively) while the

PACl pilot showed a slight decrease (11.3 a.u.), due to treatment. These findings are consistent

with the literature that indicates humic-like material is effectively removed through coagulation

and that proteins are not significantly affected (Gone et al., 2009).

82

Figure 6.7: Raw and treated water (Model 1), PC 1 loading plot

Figure 6.8: Raw and treated water (Model 1), PC 2 loading plot

Figure 6.9: Treated water (Model 2), PC 1 loading plot

83

Figure 6.10: Treated water (Model 2), PC 2 loading plot

Figure 6.11: Impact of coagulation on PC scores. Averages and MDL (bars) for each treatment

train (n = 9 – 10).

6.4.4 Performance of filtration on NOM fraction removal

FEEM-PCA was also used to track changes to NOM fraction removal associated with

filtration (Figure 6.12). For both alum treatment trains (full and pilot-scale) there was a general

trend of a minor reduction in humic-like score (15.6 a.u. and 11.4 a.u.). The PACl pilot did not

show any significant decrease of humic-like score due to filtration. Trends for PC 2, the protein-

PC 1 PC 2

-40

-20

0

20

40

60

80

100

120

140

160

180

Dif

fere

nce

in P

C S

core

(ra

w -

tre

ate

d)

Full-scale Alum Pilot PACl Pilot

84

like fraction were similar to PC 1. The full-scale plant and alum pilot filtration was found to

significantly decrease PC 2 score (25.1 a.u. and 11.3 a.u., respectively) while no significant

decrease was observed from the PACl pilot. These results were not unexpected as dual media

filtration (anthracite and sand) is not intended to remove the dissolved organic fractions.

Figure 6.12: Impact of filtration on PC scores. Averages and MDL (bars) for each treatment

train (n = 9 – 10).

6.4.5 Performance of FEEM-PCA scores related to common NOM indicators

FEEM-PCA was directly compared to total organic carbon (TOC), ultraviolet absorbance

(UVA), and specific ultraviolet absorbance (SUVA). Based on raw water and filtered water

samples (due to availability of online TOC and UVA data for these sampling points) PC 1 and

PC 2 scores were compared both independently as well as summed together as Total PC

(equation 6.4).

-10

0

10

20

30

40

Dif

fere

nce

in P

C S

core

(raw

- t

reat

ed

)

Full-scale Alum Pilot PACl Pilot

(6.4)

PC 1 PC 2

85

The strength of the linear relationship between FEEM-PCA score and the traditional

NOM indicators was assessed using the coefficient of determination (R2). Two iterations of

correlations for Model 1 (containing both raw and treated water data) were performed, one that

included both raw and treated water samples and the other with only results for the treated water

subset. Linear correlations show that FEEM-PCA scores were most correlated with TOC (Table

6.3). The strongest linear correlations were observed between PC 1 and TOC using Model 1 and

including all samples (R2

= 0.94, 0.83). Including both raw and treated water samples increases

the linear correlation strength for each PC or TPC (e.g. PC 1 R2 = 0.52 to 0.94 when including

raw water samples). This is likely due to the wider TOC range (3 – 7 mg/L) when compared to

the range that included only treated water samples (3 – 4 mg/L TOC). A lack of correlation was

observed with any traditional NOM indicator and PC 2 (all R2 < 0.27). Furthermore, inclusion of

PC 2 scores (TPC) resulted in a loss of correlation.

Table 6.3: Strength of linear correlation between FEEM-PCA and common NOM indicators

Model Samples

Used PC #

Correlation

with TOC

(R2)

Correlation

with UVA

(R2)

Correlation

with SUVA

(R2)

Model 1

(all

samples)

Raw and

Treated

Water

1(humic-like

material) 0.94 0.82 0.14

2 (protein-like

material) 0.01 0.00 0.02

TPC 0.83 0.75 0.16

Model 1

(all

samples)

Treated

Water

Samples

1 (humic-like

material) 0.52 0.10 0.32

2 (protein-like

material) 0.04 0.01 0.04

TPC 0.13 0.02 0.06

Model 2

(treated

samples)

Treated

Water

Samples

1 (humic-like

material) 0.61 0.14 0.40

2 (protein-like

material) 0.24 0.12 0.27

TPC 0.29 0.04 0.15

TPC = Total PC Score

The results also show that Model 2 (containing only treated water data) was better

correlated with TOC, UVA, and SUVA, than Model 1 when using results for the same treated

water samples (R2 = 0.61 vs 0.52, 0.14 vs 0.1, 0.40 vs 0.32). The motivation behind creating two

models was the hypothesis that model sensitivity would be increased through limiting the organic

86

concentration range of model inputs to be comparable to the samples of interest. The results

support the hypothesis and point to the necessity of selective model regression for increasing the

capabilities of FEEM-PCA.

In general, the identified high correlation strength of PC 1 with TOC is promising for

future work when using FEEM-PCA as an indicator in lieu of traditional measurement

techniques. To assess the utility of FEEM-PCA, an endpoint should be predicted with FEEM-

PCA as opposed to traditional NOM indicators. While correlation with TOC is useful, the

increased prediction strength of endpoints such as disinfection by-product formation could help

solidify the FEEM-PCA technique as a promising new NOM indicator.

6.5 Conclusions

Characterization of NOM changes due to coagulation showed that humic-like substances

were reduced. No effect on protein-like material was observed. The character of NOM in settled

water was equal between both alum and PACl treated water indicating no difference in removal

efficiencies for specific fractions. Filtration was shown only to have a minor and non-consistent

effect on humic-like substances, although it was not expected to reduce DOC. Results from

FEEM-PCA correlated well with other NOM measures including TOC, UVA, and SUVA. The

importance of sample selection for model regression was demonstrated through increased

sensitivity and correlations.

87

7 Summary, Conclusions and Recommendations

7.1 Summary

The application of FEEM-PCA was investigated for DBP formation prediction from

chlorination, assessment of temporal and spatial differences in raw water, and assessment of

treatment process efficiencies. In all applications, the FEEM-PCA technique was compared to

commonly used NOM surrogates including DOC, UVA, and SUVA as well as using LC-OCD.

Water from the Ottawa and Otonabee Rivers, as well as Lake Ontario and Simcoe were

coagulated and chlorinated over a range of dosages. Results indicated that FEEM-PCA

correlated well with DOC and both measures provided excellent predictive strength in DBP

modelling. UVA and SUVA were also shown to be strong surrogates in this application.

Spatially and temporal changes to NOM character was investigated in Lake Ontario using

TOC, FEEM-PCA, and LC-OCD. In a five month study, the raw and treated water at four City

of Toronto water treatment plants was monitored. Results indicated that Lake Ontario NOM did

not vary significantly among the four treatment plants as well as over the study period. Using

FEEM-PCA, it was noted that the Island WTP had decreased scattering or particulate

composition in the raw water; an observation not possible when using other NOM

characterization techniques. Only small differences in treatment efficiency were observed,

especially at the Island plant which showed no impact on overall NOM quantity or character asa

function of treatment. The application of ozonation and replacement of GAC filter media at the

F.J. Horgan WTP was shown to significantly increasing the removal of humics, while at the

same time increasing overall protein and particulate content.

Pilot studies at the City of Peterborough WTP examined water quality differences

resulting from the use of two different coagulants; PACl and alum. FEEM-PCA was applied to

identify differences in NOM following individual treatment steps. Both coagulants were shown

to have equal impacts on NOM removal, with no differences in specific fraction removal

efficiencies.

7.2 Conclusions

The conclusions of the research can be summarized as follows:

1. FEEM-PCA is a viable NOM surrogate for DBP formation modelling. It was

strongly correlated with DOC and provided similar predictive strength during

modelling.

88

2. Raw water was not found to vary spatially between the four City of Toronto water

treatment plants. Due to the low organic content of Lake Ontario (~2.0 mg/L TOC),

treatment provided little to no NOM removal.

3. FEEM-PCA was able to identify several notable differences between City of Toronto

raw and treated waters not observed by other NOM surrogates or characterization

methods.

a. Island WTP raw water was found to consistently have lower

scattering/particulates.

b. Application of ozonation and GAC was found to increase both scattering and

protein-like content in treated water at the F.J. Horgan WTP.

4. Alum and PACl showed equal efficiencies at removal of NOM fractions at pilot-scale

at the Peterborough WTP.

5. In comparison to alum, PACl caused no depression of pH prior to chlorination which

lead to ~20% increased THM formation.

6. In comparison to alum, application of PACl resulted in increased particulate removal

efficiency which translated into significantly lower loss of head build-up on media

filters.

7.3 Recommendations

Based on this work, the following recommendations are made for future study:

1. Directly compare NOM fractions identified through LC-OCD and FEEM-PCA.

While some LC-OCD and FEEM-PCA data was collected as part of this work, it was

limited to only Lake Ontario. Additional sampling from multiple water sources is

needed to obtain adequate concentration variations.

2. Further investigate multivariate model optimization through scattering region removal

or smoothing techniques.

3. Additional should be conducted following individual unit processes at the City of

Toronto treatment plants to better quantify NOM removal efficiencies.

4. Assess the ability of FEEM-PCA to identify low levels of wastewater influence in

drinking water sources.

89

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98

8 Appendices

8.1 Raw Data

8.1.1 Disinfection by-product modelling (Section 4)

99

Table 8.1: PC 1 Scores


Coagulant Dose (mg/L) Average STD Average STD Average STD Average STD

0 -1.96 0.98 134.31 9.16 -70.27 2.61 157.61 2.35

5 -1.30 0.39 NR NR -69.41 1.08 154.69 0.50

10 -7.09 0.15 130.59 9.47 -76.93 0.09 151.48 1.31

20 -22.66 0.22 101.81 1.99 -89.96 0.40 76.86 0.87

30 -34.49 0.26 79.63 2.03 -90.06 6.84 -20.51 0.73

40 -39.63 0.66 57.79 3.40 -88.78 0.87 -43.64 0.29

50 -46.45 0.30 32.32 4.50 -92.97 0.07 -46.42 1.83

60 -49.54 0.64 16.24 1.26 -92.25 1.13 -13.80 2.26

70 -53.26 0.32 8.92 3.38 -89.39 2.09 38.50 7.67

NR = Not Recorded

STD = Standard deviation

Table 8.2: PC 2 Scores



0 31.49 0.17 53.36 7.23 -10.61 0.74 -40.55 1.92

5 8.58 0.51 NR NR -21.81 0.34 -56.11 0.08

10 9.70 0.65 51.82 12.01 -16.57 0.11 -75.12 1.23

20 16.35 1.29 47.43 3.72 -7.53 0.24 -59.77 0.37

30 15.66 0.44 44.33 4.01 -2.07 8.12 -6.53 0.97

40 14.57 0.30 44.03 3.89 -7.04 1.90 -9.78 1.29

50 12.85 0.58 39.02 5.13 -10.12 1.52 -13.57 0.32

60 9.67 0.51 33.68 2.54 -9.77 1.56 -43.25 0.28

70 7.80 1.13 30.92 3.96 -12.04 2.89 -69.01 2.60

NR = Not Recorded


100

Table 8.3: TOC



0 3.87 0.02 5.59 0.00 2.47 0.01 5.85 0.02

5 3.75 0.01 NR NR 2.31 0.00 5.76 0.11

10 3.61 0.01 5.33 0.05 2.19 0.00 5.32 0.01

20 3.53 0.12 4.90 0.03 1.97 0.00 4.11 0.02

30 3.09 0.04 4.55 0.02 1.66 0.23 3.09 0.05

40 2.93 0.04 4.12 0.00 1.68 0.02 2.74 0.04

50 2.69 0.00 3.74 0.10 1.60 0.02 2.18 0.01

60 2.54 0.00 3.37 0.02 1.53 0.01 2.17 0.00

70 2.31 0.00 3.15 0.00 1.54 0.02 2.34 0.01 NR = Not Recorded


Table 8.4: UVA



0 0.065 NA 0.128 0.000 0.017 0.000 0.200 0.000

5 0.061 NA NR NR 0.015 0.000 0.198 0.006

10 0.058 NA 0.115 0.000 0.013 0.000 0.173 0.001

20 0.051 NA 0.103 0.001 0.012 0.001 0.111 0.001

30 0.045 NA 0.092 0.001 0.012 0.001 0.068 0.000

40 0.042 NA 0.078 0.000 0.010 0.001 0.055 0.000

50 0.037 NA 0.067 0.001 0.010 0.000 0.045 0.000

60 0.034 NA 0.059 0.000 0.012 0.000 0.043 0.001

70 0.032 NA 0.056 0.003 0.013 0.000 0.048 0.001 NR = Not recorded

NA = Not applicable (duplicates not collected)


101

Table 8.5: SUVA



0 1.68 0.01 2.29 0.00 0.69 0.00 3.42 0.01

5 1.63 0.01 NR NR 0.65 0.00 3.44 0.16

10 1.61 0.00 2.16 0.02 0.59 0.00 3.25 0.03

20 1.45 0.05 2.10 0.04 0.58 0.04 2.70 0.04

30 1.46 0.02 2.01 0.03 0.69 0.14 2.20 0.04

40 1.44 0.02 1.90 0.00 0.57 0.05 2.00 0.03

50 1.38 0.00 1.78 0.06 0.63 0.01 2.06 0.01

60 1.34 0.00 1.75 0.01 0.78 0.01 1.96 0.04

70 1.39 0.00 1.77 0.09 0.85 0.01 2.03 0.04 NR = Not Recorded


102

Table 8.6: THMs

Coagulant

Dose

(mg/L)


TCM BDCM TCM BDCM TCM BDCM TCM BDCM

avg std avg std avg std avg std avg std avg std avg std avg std

Chlorine dose = 2.5 mg/L

0 23.0 0.8 12.8 0.8 56.1 5.0 14.2 0.0 7.7 1.1 4.1 0.8 84.8 2.5 19.1 0.8

5 31.4 0.7 16.3 0.7 NR NR NR NR 7.1 0.1 4.0 0.2 74.6 2.5 20.2 0.8

10 30.6 0.1 16.6 0.2 49.3 9.1 13.6 0.4 7.5 0.4 4.2 0.2 73.2 2.5 15.7 0.8

20 19.5 2.5 12.0 0.8 58.3 11.5 15.7 0.9 7.1 0.1 4.1 0.0 56.6 4.2 16.0 1.0

30 21.1 2.3 12.7 1.9 56.7 5.7 14.3 1.1 6.8 0.2 4.0 0.3 40.9 14.1 11.7 2.5

40 25.5 0.6 14.6 0.3 37.4 7.7 11.1 0.9 5.5 0.3 3.8 0.5 32.6 2.4 12.2 0.9

50 19.2 4.9 11.9 2.3 34.0 7.0 11.4 1.0 5.4 1.2 3.9 0.5 33.5 1.6 12.7 0.5

60 15.5 0.4 10.5 0.1 35.1 0.2 11.3 0.7 5.1 0.3 3.8 0.5 31.2 5.2 12.9 2.9

70 20.5 0.3 12.4 0.1 24.8 1.7 9.1 0.4 5.4 0.0 4.0 0.0 35.9 2.5 13.8 1.3


0 30.0 5.0 16 1.2 65.9 10.7 17.2 1.2 8.5 1.2 5.0 0.6 71.2 2.5 23.3 2.5

5 27.0 5.3 15.1 2.2 NR NR NR 2.2 7.3 0.5 4.9 0.8 86.4 2.5 24.6 2.5

10 32.7 0.4 17.5 0.1 60.6 7.8 17.4 0.1 7.9 0.1 5.0 0.3 63.3 7.5 20.1 3.5

20 25.3 5.7 15.3 2.7 61.1 10.4 17.4 2.7 7.7 0.2 5.1 0.1 41.9 2.5 17.2 2.5

30 19.6 0.5 11.9 0.6 51.1 6.7 15 0.6 7.0 0.3 4.7 0.2 44.8 3.6 17.1 1.6

40 19.8 0.1 12.3 0.3 45.9 0.3 12.9 0.3 7.0 0.2 4.8 0.1 32.6 8.3 13.1 2.7

50 23.6 1.3 13.9 0.9 39.9 7.3 12.8 0.9 6.7 0.9 4.5 0.4 38.0 1.7 14.8 0.9

60 22.8 0.2 13.5 0.2 31.3 1.4 11.1 0.2 6.9 0.1 4.7 0.0 28.0 5.3 12.3 1.0

70 18.5 3.2 12.0 1.1 30.4 5.2 10.5 1.1 7.0 0.2 4.8 0.3 32.2 10.9 13.2 3.4 NR = Not Recorded

std = Standard deviation

avg = average

103

Table 8.7: HAAs

Coagulant

Dose

(mg/L)


DCAA TCAA DCAA TCAA DCAA TCAA DCAA TCAA

avg std avg std avg std avg std avg std avg std avg std avg std


0 4.0 0.2 4.6 0.1 52.6 5.8 52.7 3.7 21.5 1.2 12.0 0.8 71.7 3.8 64.5 3.5

5 0.7 0.7 6.8 0.7 NR NR NR NR 20.0 0.9 10.3 0.3 73.6 2.4 59.2 2.1

10 4.4 0.3 6.4 0.3 54.8 0.7 48.8 0.7 18.7 0.7 10.0 0.0 77.5 1.0 69.8 1.0

20 0.9 0.7 6.8 0.7 49.3 1.0 45.9 0.7 18.4 0.0 9.5 0.2 68.9 0.4 65.9 0.8

30 0.7 0.1 5.0 0.1 45.7 1.1 42.0 1.0 17.2 0.7 7.3 0.0 41.0 0.9 33.9 1.2

40 3.5 0.7 4.3 0.7 38.1 4.7 38.2 3.3 17.2 0.5 7.8 0.2 29.8 1.0 18.7 0.4

50 4.3 0.2 4.9 0.0 35.4 1.3 29.5 0.4 17.7 0.7 7.4 0.7 28.9 0.9 17.9 0.4

60 4.3 0.7 3.5 0.7 32.6 0.8 26.2 0.0 17.0 0.2 7.8 0.1 33.1 0.8 21.1 0.1

70 4.9 0.7 3.0 0.7 28.5 0.4 22.6 0.3 16.5 0.1 6.6 0.4 37.7 1.8 27.1 0.6


0 -0.3 0.5 4.6 0.0 59.3 3.0 66.3 4.0 21.9 1.1 13.6 1.2 106.9 3.9 111.6 4.8

5 -0.2 0.7 6.8 0.7 NR NR NR NR 21.9 0.4 12.7 0.2 111.0 1.4 111.4 0.3

10 1.9 2.2 5.9 0.4 58.3 0.1 62.3 0.8 20.9 0.6 11.4 0.3 103.0 2.8 112.9 2.9

20 5.3 0.7 5.7 0.7 56.1 0.4 59.2 1.6 20.9 0.1 10.5 0.1 89.2 0.7 99.7 0.7

30 2.5 2.5 4.7 0.1 49.4 2.7 48.8 2.7 20.7 0.5 9.8 0.2 45.4 2.1 36.5 1.2

40 0.0 0.7 4.7 0.7 42.6 3.5 41.6 2.0 19.0 0.4 8.7 0.3 32.2 0.8 21.8 0.6

50 4.3 0.1 4.6 0.0 38.1 1.8 33.9 1.2 19.3 0.4 9.7 0.1 31.4 0.7 21.8 0.1

60 4.5 0.7 3.3 0.7 34.5 1.4 30.5 1.2 19.4 1.0 9.4 0.6 36.3 0.5 25.3 0.8

70 4.4 0.7 3.3 0.7 32.2 0.8 28.4 0.5 19.3 0.3 8.9 0.2 41.1 1.8 31.8 0.5 NR = Not Recorded

std = Standard deviation

avg = average

104

Figure 8.1: THM speciation graphs, chlorine = 2.5 mg/L

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

0 5 10 20 30 40 50 60 70

Co

nce

ntr

atio

n (

µg/

L)

Coagulant Dose (mg/L) BDCM TCM

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0 5 10 20 30 40 50 60 70

0.0

10.0

20.0

30.0

40.0

50.0

60.0

0 5 10 20 30 40 50 60 70

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

0 5 10 20 30 40 50 60 70

Otonabee River Lake Simcoe

Lake Ontario Ottawa River

105

Figure 8.2: THM speciation graphs, chlorine = 3.5 mg/L

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

0 5 10 20 30 40 50 60 70

Co

nce

ntr

atio

n (

µg/

L)

Coagulant Dose (mg/L) BDCM TCM

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

0 5 10 20 30 40 50 60 70

0.0

10.0

20.0

30.0

40.0

50.0

60.0

0 5 10 20 30 40 50 60 70

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

0 5 10 20 30 40 50 60 70

Otonabee River Lake Simcoe

Lake Ontario Ottawa River

106

Figure 8.3: HAA speciation graphs, chlorine = 2.5 mg/L

0.0

20.0

40.0

60.0

80.0

100.0

120.0

0 5 10 20 30 40 50 60 70

Co

nce

ntr

atio

n (

µg/

L)

Coagulant Dose (mg/L) TCAA DCAA

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

0 5 10 20 30 40 50 60 70

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

0 5 10 20 30 40 50 60 70

Otonabee River Ottawa River

Lake Ontario

107

Figure 8.4: HAA speciation graphs, chlorine = 3.5 mg/L

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

0 5 10 20 30 40 50 60 70

Co

nce

ntr

atio

n (

µg/

L)

Coagulant Dose (mg/L) TCAA DCAA

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

0 5 10 20 30 40 50 60 70

0.0

50.0

100.0

150.0

200.0

250.0

0 5 10 20 30 40 50 60 70

Otonabee River Ottawa River

Lake Ontario

108

8.1.2 Toronto NOM project data tables

Table 8.8: Raw water LC-OCD results

Raw Water (mg/L)

FJ Horgan

Date DOC

Bio-

Polymers

Humic

Substances

Building

Blocks

LMW

Neutrals

LMW

Acids

10-Apr 2.00 0.23 0.79 0.48 0.23 0.11

25-Apr 2.03 0.18 0.82 0.47 0.32 0.09

10-May 2.07 0.22 0.81 0.44 0.22 0.09

05/23/12 2.11 0.22 0.80 0.43 0.28 0.09

06/06/12 2.06 0.31 0.83 0.44 0.18 0.09

Island

Date DOC

Bio-

Polymers

Humic

Substances

Building

Blocks

LMW

Neutrals

LMW

Acids

10-Apr 2.00 0.21 0.80 0.45 0.30 0.13

25-Apr 2.00 0.17 0.77 0.43 0.25 0.10

10-May 2.03 0.23 0.79 0.41 0.30 0.10

05/23/12 1.82 0.18 0.72 0.43 0.21 0.10

06/06/12 1.94 0.22 0.75 0.42 0.20 0.09

RC Harris

Date DOC

Bio-

Polymers

Humic

Substances

Building

Blocks

LMW

Neutrals

LMW

Acids

10-Apr 1.82 0.18 0.64 0.48 0.24 0.13

25-Apr 1.96 0.16 0.64 0.53 0.28 0.10

10-May 1.95 0.20 0.71 0.42 0.30 0.10

05/23/12 1.89 0.20 0.74 0.38 0.24 0.10

06/06/12 2.01 0.24 0.67 0.45 0.25 0.11

RL Clark

Date DOC

Bio-

Polymers

Humic

Substances

Building

Blocks

LMW

Neutrals

LMW

Acids

10-Apr 1.75 0.16 0.71 0.42 0.25 0.11

25-Apr 1.96 0.15 0.69 0.46 0.22 0.11

10-May 1.85 0.17 0.72 0.40 0.27 0.10

05/23/12 1.84 0.17 0.73 0.31 0.25 0.10

06/06/12 1.93 0.23 0.69 0.42 0.29 0.10

109

Table 8.9: Treated water LC-OCD results

Treated Water (mg/L)

FJ Horgan

Date DOC

Bio-

Polymers

Humic

Substances

Building

Blocks

LMW

Neutrals

LMW

Acids

10-Apr 1.92 0.22 0.77 0.44 0.21 0.11

25-Apr 1.84 0.15 0.72 0.41 0.24 0.10

10-May 2.01 0.19 0.75 0.42 0.23 0.11

05/23/12 NR NR NR NR NR NR

06/06/12 1.94 0.27 0.77 0.35 0.21 0.09

Island

Date DOC

Bio-

Polymers

Humic

Substances

Building

Blocks

LMW

Neutrals

LMW

Acids

10-Apr 1.82 0.21 0.77 0.51 0.23 0.12

25-Apr 2.10 0.17 0.80 0.44 0.22 0.09

10-May 2.06 0.23 0.82 0.42 0.25 0.08

23-May 1.82 0.17 0.76 0.40 0.26 0.08

06-June 2.07 0.22 0.79 0.42 0.24 0.08

RC Harris

Date DOC

Bio-

Polymers

Humic

Substances

Building

Blocks

LMW

Neutrals

LMW

Acids

10-Apr 2.10 0.21 0.75 0.54 0.22 0.14

25-Apr 2.00 0.20 0.81 0.46 0.25 0.09

10-May 1.95 0.23 0.84 0.45 0.24 0.10

23-May 2.13 0.25 0.79 0.44 0.23 0.09

06-June 2.29 0.32 0.86 0.38 0.24 0.09

RL Clark

Date DOC

Bio-

Polymers

Humic

Substances

Building

Blocks

LMW

Neutrals

LMW

Acids

10-Apr 2.26 0.22 0.84 0.58 0.30 0.21

25-Apr 2.03 0.21 0.74 0.47 0.25 0.09

10-May 2.05 0.25 0.80 0.51 0.25 0.10

23-May 2.14 0.23 0.81 0.45 0.25 0.08

06-June 2.05 0.30 0.81 0.46 0.23 0.09

110

Table 8.10: Treated water PC scores


FJ Horgan

Date PC1 PC2 PC3 PC4 PC5

04-Apr -1.39E+02 -3.82E+00 1.02E+01 2.37E+01 1.14E+01

10-Apr -8.71E+00 9.29E+01 -2.64E+01 1.97E+01 4.18E+00

12-Apr -2.81E+01 7.88E+01 -8.14E+00 -2.71E+01 -1.59E+01

17-Apr -1.28E+02 3.11E+01 1.26E+01 -2.06E+01 9.39E+00

19-Apr -1.28E+02 3.11E+01 1.26E+01 -2.06E+01 9.39E+00

25-Apr -1.28E+02 3.11E+01 1.26E+01 -2.06E+01 9.39E+00

02-May -8.38E+01 7.90E+00 -7.55E+00 2.05E+01 7.40E+00

04-May -7.74E+00 6.74E+01 -4.12E+01 2.75E+01 -8.29E+00

08-May -2.63E+01 1.85E+01 -1.33E+01 3.56E+01 -2.38E+00

10-May -5.59E+01 2.72E+01 -6.45E+00 1.82E+01 1.82E+00

15-May -5.81E+01 3.33E+01 -7.07E+00 1.51E+01 5.48E+00

Island

04-Apr 3.80E+01 -2.87E+01 -1.57E+01 4.26E+00 1.25E+01

10-Apr 1.46E+02 7.08E+00 6.81E+00 -6.34E+00 1.33E+01

12-Apr 2.46E+02 4.13E+01 3.44E+01 2.11E+01 5.93E+01

17-Apr 6.33E+00 -2.54E+01 7.81E+00 -3.99E+01 -2.76E+00

19-Apr 6.33E+00 -2.54E+01 7.81E+00 -3.99E+01 -2.76E+00

25-Apr 2.01E+01 -2.22E+00 1.93E+00 -1.53E+01 4.80E+00

02-May 7.76E+00 -4.17E+01 -1.39E+01 2.96E+00 -6.26E+00

04-May 1.60E+01 -3.84E+01 -1.67E+01 1.10E+01 -5.89E+00

08-May 9.44E+01 -6.95E+01 -2.06E+01 1.63E+01 -1.97E+01

10-May 2.80E+01 -3.77E+01 -5.15E+00 -9.20E+00 -5.42E+00

15-May 3.60E+01 -4.00E+01 -5.11E+00 -2.20E+01 5.41E-01

RC Harris

04-Apr -3.02E+01 -1.76E+01 5.46E+00 -9.46E+00 7.09E+00

10-Apr 9.56E+01 5.22E+01 9.06E+01 2.02E+01 -4.01E+01

12-Apr 1.37E+02 1.45E+01 -2.64E+01 -1.94E+01 -9.36E+00

17-Apr -3.20E+01 -1.02E+01 6.67E+01 2.50E+01 -1.75E+01

19-Apr -2.25E+01 -1.50E+01 4.38E+00 -3.90E+00 1.14E+01

25-Apr -1.91E+01 2.20E+00 -6.80E+00 -1.80E+00 3.87E+00

02-May -2.19E+01 -2.26E+01 -6.65E+00 1.39E+01 -2.50E+00

04-May 2.14E-01 -8.96E+00 -1.79E+01 1.94E+01 -5.10E+00

08-May 3.00E+01 -1.92E+01 -1.29E+01 3.09E+01 -1.21E+01

10-May -2.99E+01 4.05E+00 -1.77E+00 1.28E+01 2.18E+00

15-May -1.88E+01 -2.62E+00 -2.43E+00 7.93E-01 5.42E+00

111

Table 8.10: Treated water PC scores (continued)

RL Clark

04-Apr -3.97E+01 -3.45E+01 1.87E+01 -2.16E+01 4.81E+00

10-Apr 8.32E+01 6.11E+01 -2.34E+01 -3.03E+01 -1.77E+01

12-Apr 9.05E+01 5.49E+01 -1.69E+01 -3.45E+01 -1.57E+01

17-Apr -3.56E+01 -2.33E+01 1.66E+01 -2.21E+01 -2.27E+00

19-Apr 2.23E+00 -2.92E+01 1.18E+00 -3.49E+00 1.20E+01

25-Apr 2.23E+00 -2.92E+01 1.18E+00 -3.49E+00 1.20E+01

02-May -4.79E+01 -1.26E+01 -3.35E+00 1.75E+01 -4.14E-01

04-May 3.80E+00 -2.35E+01 -1.15E+01 7.81E+00 -6.58E+00

08-May 3.69E+01 -4.72E+01 -7.05E+00 2.02E+01 -1.54E+01

10-May -2.25E+01 -2.89E+01 1.01E+01 -2.58E+00 6.91E-01

15-May -1.55E+01 -1.92E+01 2.68E+00 -1.01E+01 5.84E+00

Table 8.11: Raw water PC scores

Raw Water (mg/L)

FJ Horgan

Date PC1 PC2 PC3 PC4 PC5

04-Apr -4.50E+01 8.58E+00 1.39E+01 1.50E+00 -1.17E+01

10-Apr 2.87E+02 -6.59E+01 -7.47E-02 5.74E+01 8.05E+00

12-Apr -3.68E+00 -1.33E+01 9.15E-01 -1.28E+01 -1.34E+01

17-Apr -1.02E+02 6.00E+00 1.43E+01 1.60E+01 -1.31E+01

19-Apr -1.02E+02 6.00E+00 1.43E+01 1.60E+01 -1.31E+01

25-Apr -1.02E+02 6.00E+00 1.43E+01 1.60E+01 -1.31E+01

02-May 7.72E+01 8.00E+01 -3.04E+01 -1.49E+00 6.09E+01

04-May -8.11E-01 -1.49E+01 1.30E+01 -2.36E+01 3.11E+00

08-May -2.14E+01 -1.64E+00 4.29E+01 -1.83E+01 5.12E+00

10-May -2.08E+01 -1.62E+01 1.23E+01 -8.50E+00 3.38E+00

15-May -4.37E+01 -1.94E+01 8.57E+00 -1.65E+00 -3.49E+00

Island

04-Apr 1.32E+01 1.66E+01 -3.03E+01 7.52E+00 -1.17E+01

10-Apr 5.68E+01 8.17E+00 -4.32E+01 -1.01E+01 -1.68E+01

12-Apr 2.43E+02 1.30E+00 4.16E+01 -2.51E+01 -1.12E+01

17-Apr -1.50E+01 2.94E+01 -2.51E+01 1.45E+01 -8.32E+00

19-Apr -1.50E+01 2.94E+01 -2.51E+01 1.45E+01 -8.32E+00

25-Apr 3.31E+01 -5.31E+00 -4.23E+01 5.09E+00 -1.33E+01

02-May -3.16E+00 4.27E+01 -2.71E+00 -2.60E+00 -1.11E+01

04-May 2.46E+01 4.60E+01 -5.15E+00 -4.94E+00 -9.22E+00

08-May 4.45E+01 6.40E+01 6.16E+00 -7.45E+00 -8.54E+00

10-May 3.89E-02 3.16E+01 -1.38E+01 5.71E+00 -8.97E+00

15-May 6.57E+00 2.95E+01 -2.50E+01 1.07E+01 -1.45E+01

Table 8.11: Raw water PC scores (continued)

112

RC Harris

04-Apr -8.48E+01 -2.36E+01 -8.73E+00 1.34E+01 -3.49E+00

10-Apr 5.24E+01 -3.68E+01 -6.78E+00 -2.67E+01 -7.64E+00

12-Apr 8.43E+01 -4.00E+01 -1.86E+01 -1.14E+01 -1.55E+01

17-Apr 3.24E+01 1.31E+01 3.89E+01 7.04E+01 5.82E+00

19-Apr -5.55E+01 -1.86E+01 -5.80E+00 4.06E+00 2.46E+00

25-Apr 7.75E+00 -1.08E+01 2.81E+01 1.47E+01 -7.24E-01

02-May -2.94E+01 1.39E+01 1.83E+01 -5.33E+00 -1.56E+00

04-May -1.76E+00 1.70E+01 1.79E+01 -7.64E+00 -1.05E+00

08-May 2.89E+00 4.35E+00 4.54E+01 -1.73E+01 6.22E+00

10-May -3.77E+01 -1.60E+01 2.27E+01 -3.80E+00 -1.24E-02

15-May -2.95E+01 -7.53E+00 -2.36E-01 7.28E+00 -2.81E+00

RL Clark

04-Apr -5.67E+01 -2.18E+01 -2.89E+01 6.84E+00 1.53E+01

10-Apr 2.29E+01 -5.72E+01 -2.39E+01 -2.78E+01 4.62E+00

12-Apr 5.07E+00 -5.32E+01 -1.93E+01 -2.21E+01 8.32E-01

17-Apr -9.05E+01 -2.67E+01 -4.17E+00 2.91E+00 2.63E+01

19-Apr -7.01E+01 -4.12E+01 -2.37E+00 2.60E+00 1.93E+01

25-Apr -7.01E+01 -4.12E+01 -2.37E+00 2.60E+00 1.93E+01

02-May -3.34E+01 9.69E+00 8.09E+00 -1.28E+01 7.00E+00

04-May 4.06E+01 3.37E+01 -6.60E+00 -1.48E+01 5.31E+00

08-May 6.41E+01 3.46E+01 2.18E+01 -2.44E+01 1.14E+01

10-May -2.20E+01 6.01E+00 -3.57E+00 -2.39E+00 1.28E+01

15-May -4.23E+01 -6.18E+00 -8.71E+00 3.10E+00 5.27E+00

113

Table 8.12: Raw water TOC

Raw water (mg/L)

Date

F. J. Horgan

WTP

R. C. Harris

WTP Island WTP R. L. Clark WTP

28-Feb 2.09 2.10 2.07 2.14

1-Mar 2.07 2.09 2.06 2.06

6-Mar 2.11 2.13 2.11 2.02

8-Mar 2.10 2.12 NR 2.06

13-Mar 2.19 2.20 2.10 2.06

15-Mar 2.13 2.14 2.07 2.00

20-Mar 2.16 2.19 2.08 2.08

22-Mar 2.19 2.15 2.03 1.84

27-Mar 2.00 2.06 1.98 1.97

29-Mar 2.05 2.17 1.97 2.02

2-Apr NR 2.24 2.15 2.21

4-Apr 2.07 2.07 1.99 2.06

10-Apr 2.17 2.18 2.12 2.33

12-Apr 2.10 2.10 2.24 2.07

17-Apr NR 2.14 NR 2.08

19-Apr NR 2.10 2.01 2.04

24-Apr NR 2.27 2.14 NR

26-Apr 2.24 2.38 2.24 NR

1-May 2.54 2.35 2.28 2.37

3-May 2.24 2.33 2.29 2.26

8-May 2.32 2.41 2.28 2.46

15-May 2.01 2.06 2.00 2.10

17-May 2.21 2.25 2.20 2.29

22-May 2.14 2.20 2.01 2.27

24-May 2.06 2.25 2.06 2.27

29-May 2.40 2.52 2.24 2.41

31-May 2.14 2.21 2.04 2.08

5-Jun 2.10 2.15 1.98 2.08

7-Jun 2.19 2.21 2.05 2.17

12-Jun 2.19 2.17 2.10 2.03

14-Jun 2.01 2.07 1.93 1.93

19-Jun 2.21 2.31 2.05 2.19

21-Jun 2.34 2.36 2.14 2.34

26-Jun 2.27 2.37 2.23 2.33

28-Jun 2.14 2.22 2.06 2.12

3-Jul 2.00 2.00 1.85 1.84

5-Jul NR 1.97 1.88 1.83

10-Jul 2.09 2.29 1.87 2.16

12-Jul 2.47 2.26 2.29 2.01

17-Jul 2.18 2.21 1.90 2.12

31-Jul 2.26 2.28 1.95 2.27

NR = not recorded

114

Table 8.13: Treated water TOC


Date

F. J. Horgan

WTP

R. C. Harris

WTP Island WTP R. L. Clark WTP

28-Feb 2.02 2.01 2.08 1.92

1-Mar 1.91 1.88 2.03 1.86

6-Mar 1.94 1.96 2.05 1.86

8-Mar 1.96 1.95 NR 1.96

13-Mar 1.87 1.93 2.09 2.07

15-Mar 1.94 1.93 1.95 1.95

20-Mar 1.90 1.96 2.04 1.95

22-Mar 1.90 1.99 2.05 2.02

27-Mar 1.73 1.84 1.92 1.78

29-Mar 1.75 1.86 1.96 1.86

2-Apr NR 2.06 2.09 2.05

4-Apr 1.83 1.85 1.96 1.86

10-Apr 1.99 1.98 2.09 1.98

12-Apr 1.88 1.87 1.98 1.90

17-Apr NR 2.09 NR 1.95

19-Apr NR 1.90 2.05 2.00

24-Apr NR 2.11 2.23 NR

26-Apr 2.10 2.09 2.20 NR

1-May 2.14 2.19 2.23 2.11

3-May 2.22 2.17 2.24 2.18

8-May 2.18 2.21 2.25 2.21

15-May 1.85 1.91 1.97 1.89

17-May 2.08 2.12 2.21 2.16

22-May 2.01 2.04 2.04 1.94

24-May 2.04 2.16 2.05 2.17

29-May 2.14 2.21 2.17 2.19

31-May 2.10 2.04 2.04 2.00

5-Jun 1.93 1.95 1.95 1.90

7-Jun 1.95 1.94 2.00 1.95

12-Jun 2.00 1.90 2.12 1.82

14-Jun 1.75 1.77 1.80 1.72

19-Jun 1.98 2.02 1.98 1.98

21-Jun 2.11 2.14 2.22 2.13

26-Jun 2.27 2.42 2.28 2.23

28-Jun 1.83 2.19 2.09 2.05

3-Jul 1.52 1.77 1.81 1.73

5-Jul NR 1.81 1.84 1.76

10-Jul 1.66 2.15 1.81 2.03

12-Jul 1.81 1.99 1.92 2.15

17-Jul 1.65 2.05 1.94 1.96

31-Jul 1.81 2.17 1.97 2.07

NR = not recorded

115

8.1.3 Peterborough pilot coagulant optimization data tables

Table 8.14: FEEM-PCA scores

Model 1

Pilot plant raw

water

Full-scale plant

raw water

Full-scale plant

settled water

Full-scale plant

filtered water

Date PC1 PC2 PC1 PC2 PC1 PC2 PC1 PC2

1/13/2012 122.16 -4.69 123.16 -4.62 -32.79 -7.67 -38.81 -11.27

1/19/2012 148.43 1.00 137.09 -2.95 5.25 28.63 -17.77 -7.55

1/26/2012 144.68 -11.33 137.33 -19.17 0.55 20.56 -20.59 -16.38

2/2/2012 176.47 -2.17 148.21 4.78 1.77 26.04 -24.49 1.62

2/9/2012 122.88 -18.82 131.46 -9.81 -18.67 4.55 -38.51 -19.57

2/16/2012 115.86 -31.30 107.23 -37.32 -35.63 -18.70 NR NR

3/22/2012 53.60 -44.28 54.44 -19.48 -76.47 -33.95 -72.93 -29.11

3/29/2012 98.84 39.57 97.27 40.58 -39.22 67.78 -60.27 12.00

4/4/2012 85.37 -3.17 84.05 -1.96 -37.16 45.10 -55.38 4.67

4/16/2012 82.54 34.82 NR NR NR NR -49.20 17.04

PACl pilot

settled water

Alum pilot settled

water

PACl pilot

filtered water

Alum pilot

filtered water

1/13/2012 -33.48 1.19 -29.62 -1.83 -34.66 3.59 29.62 0.64

1/19/2012 16.15 46.79 -3.03 12.16 -11.37 13.37 -15.57 4.46

1/26/2012 -4.96 15.86 -6.85 8.51 -23.57 -4.85 -16.08 0.13

2/2/2012 -25.49 2.27 -22.11 -4.40 -30.49 -7.66 -23.39 -6.74

2/9/2012 -39.36 -6.53 -37.45 0.13 -41.80 -4.98 -37.34 1.18

2/16/2012 -50.70 -32.68 -47.24 -31.55 -53.99 -32.34 -43.73 -3.94

3/22/2012 -54.71 -7.29 -74.49 -26.82 -79.01 -29.03 -76.79 -40.57

3/29/2012 -47.25 29.36 -39.92 28.33 -60.65 13.58 -51.87 27.52

4/4/2012 -59.46 -6.25 -33.03 36.29 -65.88 -10.71 -49.02 10.27

4/16/2012 -65.42 -16.95 -53.10 4.13 -59.16 1.65 -48.37 20.25 NR = not recorded

116

Table 8.15: Statistical comparison of pilot and full-scale treatment trains: water quality parameters

Parameter

Alum and PACl Pilot1

Alum Pilot and Full-scale Plant2

Significantly

Different?3

Average

Difference

Standard

Deviation

Significantly

Different?3

Average

Difference

Standard

Deviation

Settled Water

Turbidity (NTU)

Baseline No -0.03 0.06 Yes 0.12 0.13

Experimental Yes 0.23 0.23 Yes 0.12 0.13

Filtered Water

Turbidity (NTU)

Baseline Yes 0.00 0.00 Yes -0.03 0.14

Experimental Yes 0.01 0.01 Yes -0.01 0.02

Settled Water pH Baseline Yes 0.01 0.01 No 0.03 0.07

Experimental Yes -.72 0.12 Yes 0.02 0.08

Filtered Water pH Baseline No 0.00 0.03 Yes -0.29 0.03

Experimental Yes -0.73 0.13 Yes 0.07 0.55

Total Organic Carbon

(mg/L)

Baseline No 0.00 0.02 Yes -0.14 0.05

Experimental Yes -0.03 0.22 Yes -0.05 0.30

Ultraviolet

Absorbance

Baseline No 0.00 0.00 Yes -0.01 0.00

Experimental No 0.00 0.00 Yes 0.00 0.01

Particle Counts (2 – 3

µm) (counts/mL)

Baseline No -0.34 0.72 Yes -2.83 2.08



µm) (counts/mL)

Baseline Yes 3.34 1.67 Yes -13.79 12.72



µm) (counts/mL)

Baseline Yes 5.08 2.52 Yes -8.18 6.69



µm) (counts/mL)

Baseline Yes 0.54 0.51 Yes -6.56 4.16


Particle Counts (10 –

15 µm) (counts/mL)

Baseline Yes 0.16 0.06 Yes -1.81 0.92


Particle Counts ( > 15

µm) (counts/mL)

Baseline Yes 0.04 0.03 Yes -0.66 0.30


Total Flow (L/min) Baseline No 0.00 0.00 NA

NA NA

Experimental No 0.00 0.01 NA

NA NA

Filter Flow (L/min) Baseline No 0.00 0.00 Yes -0.15 0.14

Experimental No 0.00 0.01 Yes -0.05 0.24 1 difference = control pilot – experimental pilot

2 difference = control pilot – full-scale plant

3 Assessments made with 95% confidence

NA: not applicable

Particle counts are from filtered water

117

Table 8.16: Statistical comparison of pilot and full-scale treatment trains: chlorine residual and disinfection by-products

Parameter

Control and Experimental Pilot1

Control Pilot to Full-scale Plant2

Significantly

Different?3

Average

Difference

Standard

Deviation

Significantly

Different?3

Average

Difference

Standard

Deviation

Free Chlorine Residual

(SDS) (mg/L)

Baseline No 0.01 0.07 No -0.05 0.10


Total THMs (µg/L) Baseline No 2.3 8.9 No -0.3 6.6

Experimental Yes -7.6 10.2 Yes 2.7 8.0

Total HAAs (µg/L) Baseline No -3.0 5.6 No -3.9 7.2

Experimental No 0.8 21.0 No -3.2 10.5 1 difference = control pilot – experimental pilot

2 difference = control pilot – full-scale plant

3 Assessments made with 95% confidence

118

8.2 Calculations

8.2.1 Analysis of variance

Analysis of variance (ANOVA) was used to identify statistically significant differences

between the four treatment facilities. One-way ANOVA was used to test the null hypothesis that

two or more sample sets were from the same population through application of the F-statistic

(Dean and Voss, 1999). The F statistic was calculated as follows (Montgomery and Runger,

2003):

∑

∑ ( )

⁄

where, K is the total number of groups, N is the total number of observations, ni is the number of

observation within the i’th group, is the sample mean of the i’th group, is the mean of all

observations, i denotes the group, j denotes the observation within the i’th group

If the calculated F statistic is greater than the critical F value (from a standardized

distribution) at a specified confidence level, the null hypothesis that all sample sets are derived

from the same population is rejected. The drawback to ANOVA is it does not specify the sample

sets within the group which are significantly different (Montgomery and Runger, 2003).

8.2.2 Tukey’s method








(8.1)

(8.2)

119

(

√ )√ (

)



0.05.

∑ ∑ ( )

Where, y is a measured value

8.2.3 Statistics on water quality differences at Peterborough Pilot

The example calculation attempts to determine if there is a significant difference in TOC

between the control and experimental pilot trains.

Baseline:

To first remove the effects of source water quality, difference between the two parameters is

calculated.

where, Dn is the calculated difference on a given day (n), and n is the sample size (i.e. number of

days)

The average ( and standard deviation (S) of the sample set D is calculated. For

baseline TOC (n = 18) data the following values were found:

It is assumed that the ‘population’ mean or the expected difference between TOC for the

two pilots is 0. This leads to the postulation of the null and alternative hypotheses.

(8.3)

(8.4)

(8.5)

120

where, Ho is the null hypotheis, H1 is the alternative hypothesis,

Depending on the circumstance, either Student’s t statistic or Tukey’s method was used

to test the null hypothesis.

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121

8.3 Process flow diagrams of City of Toronto water treatment plants

Figure 8.5: F.J Horgan process flow diagram

Figure 8.6: Island process flow diagram

Figure 8.7: R.C. Harris process flow diagram

Figure 8.8: R.L. Clark process flow diagram

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122

Figure 8.5: F.J Horgan process flow diagram

123

123

Figure 8.6: Island process flow diagram

124

124

Figure 8.7: R.C. Harris process flow diagram

125

125

Figure 8.8: R.L. Clark process flow diagram

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126

8.4 Program code

8.4.1 Real RLS files (Python 2.6)

Compiles data from the fluorescence spectrophotometer into one data file. Output from

the software is emission wavelengths for every excitation wavelength.

import glob

import xlwt

outbook = xlwt.Workbook()

while True:

prefix = raw_input("sample prefix: ")

osheet = outbook.add_sheet("%s" % prefix)

filenames = []

rows = []

q =1

#determine number of files

for name in glob.glob('%s0*.RLS' % prefix):

q = q + 1

#print q

#put files in correct order

for i in range(1,q,1):

#puts files with just 1 digit first

for name in glob.glob('%s00%i.RLS' % (prefix, i)):

# print name

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127

filenames.append(name)

#then double digits

for name in glob.glob('%s0%i.RLS' % (prefix, i)):

# print name

filenames.append(name)

# print filenames

# go through an get values

for d in range(0,(q-1),1):

aFile = open(filenames[d], "rU")

f = 0

print aFile

for line in aFile.readlines():

rows = line.rstrip('\n').split('\t')

#print rows[1]

osheet.write(f, d, float(rows[1]))

f += 1

#columns = zip(*rows)

#print columns

aFile.close()

cont = raw_input("Continue? (0 to save file, 1 to continue): ")

if cont == '0':

break

outputname = raw_input("Output file name?: ")

outbook.save("%s.xls" % outputname)

print "saved as %s.xls" % outputname

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8.4.2 Manipulate for PCA input (Python 2.6)

Subtracts the Milli-Q® intensities (first worksheet for every day) and compiles all

samples in columns for PCA input. Iterates for all *.xls files in a folder.

import xlwt

import xlrd

import glob

import os


namebook = xlwt.Workbook()

osheet = outbook.add_sheet("Output")

namesheet = namebook.add_sheet("Names")

osheetcol = -1

onsheetrow = 0

for name in glob.glob('*.XLS'):

title, ext = os.path.splitext(name)

book = xlrd.open_workbook("%s.xls" % title)

msheet = book.sheet_by_index(0)

numrows = msheet.nrows

numcols = msheet.ncols

numdata = int(numrows*numcols)

print title

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for i in range(1,book.nsheets,1):

asheet = book.sheet_by_index(i)

namesheet.write(onsheetrow,0,title)

namesheet.write(onsheetrow,1,asheet.name)

#osheet = outbook.add_sheet(asheet.name)

osheetrow = 0

osheetcol = osheetcol + 1

print(".")

#subtract milliQ values and remove emission columns

#counting by 2 removes emission column values (not the actual columns)

for q in range(0,asheet.ncols,1):

for p in range(asheet.nrows):

#USED FOR ELIMINATION OF SCATTERING REGIONS

#if q <= 10 and p >= ((24*q)+361):

# osheet.write(osheetrow, osheetcol, "0")

#elif q >= 5 and p <= (25*(q-4)):

# osheet.write(osheetrow, osheetcol, "0")

#else:

# osheet.write(osheetrow, osheetcol, (float(asheet.cell(p,q).value) -

float(msheet.cell(p,q).value)))

osheet.write(osheetrow, osheetcol, (float(asheet.cell(p,q).value) -

float(msheet.cell(p,q).value)))

osheetrow = osheetrow + 1

onsheetrow = onsheetrow + 1

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130

outbook.save("Output RS Elim ALL.xls")

namebook.save("Output Names.xls")

8.4.3 Deconvolute loadings (Python 2.6)

Takes loadings in column format and reorganizes into spectrum format.

import xlwt

import xlrd

bopen = raw_input('open file: ')

book = xlrd.open_workbook("%s.xls" % bopen)


for i in range(book.sheet_by_index(0).ncols):

osheet = outbook.add_sheet('PC %i' % i)

rownum = 0

for p in range (0,13,1):

for q in range (0,601,1):

osheet.write(q, p, float(book.sheet_by_index(0).cell(rownum, i).value))

rownum = rownum + 1

outbook.save('%s results.xls' % bopen)

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8.5 Increased THM formation in PACl treated water

8.5.1 Background

Currently the Peterborough pilot plant is running two identical parallel treatment trains to

observe water quality differences caused by using either alum or poly-aluminum chloride (PACl)

as coagulants. The control scheme between the two pilot trains is to dose the respective

coagulants to match filtered water total organic carbon (TOC). While filtered water TOC levels

between alum and PACl treated water are matching, it has been observed that PACl treated water

produces higher concentrations of THMs (trihalomethanes) after contact with 2-3 mg/L of

sodium hypochlorite for 24 hours. Due to PACl being ‘pre-hydrolized’, it does not depress the

pH to a significant degree when added to water, unlike alum. This results in alum treated water

being approximately 0.6 pH units lower than PACl treated water. Literature indicates that more

basic waters are more favourable to THM production, therefore the observed difference in THM

formation could be due to the difference in treated water pH. Another possible explanation for

the observed THM concentrations is that alum and PACl have differing removal efficiencies for

specific NOM fractions which contribute to DBP formation.

8.5.2 Method: pH adjusted DBP Formation Tests

To address differences in pH, both alum and PACl treated water was chlorinated at a

common pH as well as without pH adjustment (baseline case). THM and HAA concentrations

samples (after 24 hours of chlorination) were measured and normalized by dividing by the initial

TOC concentration (measured prior to chlorination). The average normalized THM and HAA

concentrations of pH adjusted and non-adjusted samples were statistically compared using t-tests

to identify if there is a significant difference in concentrations. If pH is to explain the difference

in DBP concentrations the average normalized THM and HAA concentrations of alum and PACl

treated water was equal when chlorinated at an equal pH. Statistical comparison of normalized

DBP concentrations in the un-adjusted case was used to confirm the pilot observations that DBP

formation is not equal between alum and PACl when chlorinated at dissimilar pH values.

8.5.2.1 Jar Tests

To determine if differences in pH can account for the observed increased THM formation

in PACl treated water, bench-scale jar tests followed by 24 hour DBP formation potential tests

will be conducted. Jar tests will consist of coagulation, flocculation, sedimentation, and vacuum

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132

filtration. The jar tests will be conducted using a PB-700 Standard Jar Tester paddle stirrer with

six square, acrylic 2-L jars (Phipps & Bird, Richmond, VA). In addition to the raw water, both

alum and PACl will be collected from the pilot plant to ensure consistency of chemicals used.

Alum and PACl dosages for the jar tests will be equal to the dosages for the pilot plant at the

time of raw water collection. It is expected that these doses will be approximately 50 mg/L alum

and 25 mg/L HI705 PACl. Triplicates of each coagulant dose for the jar tests will be included to

give an indication of the precision of the results and to allow for paired t-tests to check for

statistical significance.

8.5.2.1.1 Peterborough Pilot Plant Samples

In conjunction with the jar tests, samples will be collected form the Peterborough pilot

plant. There will be no alterations to the operation of the pilot plant. Duplicate samples for

TOC, UV254, and FEEM will be collected from both pilot plant trains. TOC and UV254 samples

will be collected in a 40 mL vial with 3 drops of concentrated sulphuric acid for preservation.

FEEM samples will be collected in a separate 40 mL vial with no additives.

8.5.2.1.2 Analysis

Filtered samples from each jar and pilot plant samples will be subjected to TOC, UV254,

pH, and FEEM analysis. Analysis for these parameters will be completed at the University of

Toronto to ensure consistency of measurement. TOC and UV254 will indicate the amount of

organic material remaining in the treated water. To provide reference and allow for the

calculation of TOC reduction from each coagulant and dose, raw water TOC, UV254, and FEEM

will also be measured. These values will be ultimately used to normalize DBP concentrations and

provide a reference for FEEM results when comparing the two coagulants in terms of relative

NOM fraction removal.

8.5.2.2 DBP Formation Tests

Filtered samples from the jar tests will be chlorinated and allowed to stand at room

temperature (23oC) for 24 hours for DBP formation potential tests. Chlorine dose will be defined

by the dose used at the pilot plant for the day raw water is sampled. It is expected that this dose

will be approximately 3 mg/L. A solution of sodium hypochlorite at 600 mg/L is prepared to

dose the 250 mL sample bottles.

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133

Based on results from the pilot plant, it is expected that after filtration water treated with

alum will have a pH of approximately 7.1; while PACl treated water will match raw water pH

fairly closely with a pH of approximately 7.9. The hypothesis is that PACl treated water will

have a greater concentration of THMs than alum treated water, after 24 hours of contact time

with chlorine, due to the higher pH of PACl treated water. To test the validity of this claim, the

pH of a set of PACl and alum treated water samples will be reduced a target pH of 7 while

another set will be adjusted to a target pH of 8, prior to the DBP formation test. It is expected

that the concentration of DBPs will be equal between the two coagulants when they are

chlorinated at the same pH. Furthermore, the set of samples chlorinated at a pH of 8 will result

in higher THM concentrations and lower HAA concentrations compared to the set adjusted to a

pH of 7. Since the intention is to see the difference in DBP formation at different pH values,

non-pH adjusted samples of both alum and PACl treated water will be subjected to DBP

formation tests as a control measure and to provide baseline results.

For the jar tests, three 250 mL samples of filtered water will be collected from every jar.

Two of these 250 mL samples will be pH adjusted to 7 and 8 respectively and the final 250 mL

sample will be left un-adjusted. Chlorine residual of the samples will be measured after 24 hours

to determine the chlorine demand of each water sample. After quenching, samples from each

250 mL bottle will be collected for THM, HAA, TOC, UV254, and FEEM analysis. As with jar

test samples, TOC and UV254 will allow for tracking the changes to organic content due to

chlorination. These will primarily be used for comparison to FEEM results.

Similar to the jar tests, at the pilot plant three samples from each source will be collected

for pH adjustment and DBP formation tests (two for pH adjustment to 7 and 8, one for un-

adjusted). After pH adjustment chlorine will be spiked into the samples and capped. After 24

hours the chlorine residual will be measured and the remaining chlorine will be quenched. A set

of 25 mL vials will be prepared at UofT and shipped to Peterborough for the storage of

THM/HAA samples. Each 25 mL vial will have an appropriate amount of ammonium chloride

for quenching. These samples, once collected will be sent back to UofT for analysis.

Furthermore, from each of these adjusted samples, duplicates for TOC, UV254, and FEEM

analysis will be collected in 40 mL amber vials (1 for TOC/UV254 and 1 for FEEM).

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134

8.5.2.2.1 Analysis

The significance of the DBP results was determined through a series of paired t-tests.

The measured values, DBP concentrations, will be normalized with TOC by dividing DBP

concentration (µg/L) by TOC concentration (mg/L). Normalization of the total DBP

concentrations by a gross measure of organic content allows for comparison of the two

coagulant’s performance by the amount of available reactants. This will also allow for the

results from different doses for each coagulant to be included in a general analysis.

To test the hypothesis (that at a common pH DBP production will be equal between alum

and PACl treated water) a series of t-tests will be performed. Two pieces of evidence are needed

to prove the role of pH:

1. That normalized DBP concentrations between alum and PACl treated water are

statistically the same when chlorinated at a common pH (both pH 7 and 8).

2. That normalized DBP concentrations between alum and PACl treated water are

statistically different when chlorinated at dissimilar pH (unadjusted samples).

Another set of t-tests was set up to test the difference between jar test and pilot samples.

This will indicate how closely jar tests relate to the pilot plant. The set will attempt to show

statistical similarity between average normalized DBP concentrations of jar test and pilot plant

samples that underwent the same treatment (same coagulant, chlorinated at the same pH).

8.5.3 Results and Discussion

8.5.3.1 Jar test water quality

Water quality of the filtered water both from jar tests and pilot was characterized in order

to correlate initial conditions with DBP formation potential. As discussed in the methods

section, water quality was quantified through measurements of TOC, UVA254, pH, PCA of

FEEM, and LC-OCD. This section focuses on results for TOC, UVA, and pH while NOM

fractionation results are discussed in a later section.

Results were as expected for all parameters measured based on historical data from the

pilot plant. The coagulant doses were chosen to match the doses at the pilot plant on March 15th

,

2012 (50.7 mg/L Alum, 27.7 mg/L PACl). This was also when raw water was collected and sent

to UofT for the jar tests. The current operational scheme of the pilot was to maintain TOC levels

equal between the two trains. As expected, this was reflected in the results of the jar test. With

90% confidence, TOC levels between the alum and PACl treated water were found to be

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135

statistically equal. This was further reflected in t-tests showing that UVA and SUVA are also

statistically similar. The results also showed that the pH of alum and PACl treated water were not

similar. T-test results are shown in Table 8.17.

Table 8.17: Jar Test Water Quality T-Test Results

T-test parameters Critical Value

(90% confidence) T value

Significantly

Equal?

TOC of Alum vs. PACl

treated water 3.18 0.171 Yes

UVA of Alum vs.

PACl treated water 3.18 0.000 Yes

SUVA of Alum vs.

PACl treated water 3.18 0.494 Yes

pH of Alum vs. PACl

treated water 3.18 12.954 No

Water quality results between the triplicate jars showed good agreement and indicated

high precision in the jar test method. Standard deviations of TOC measurements between

triplicates were approximately 0.1 mg/L which was considered to be very low. UVA results

showed very low standard deviations which was anticipated for this measurement method. UVA

was less sensitive to NOM concentration and therefore will not reflect small changes in NOM

between samples.

To see how well the two measured parameters for NOM estimation related (TOC and

UVA), they were plotted as Figure 8.9 and Figure 8.10.

Figure 8.9: UVA vs. TOC for Jar Test Results: Full Range

R² = 0.958

0.000

1.000

2.000

3.000

4.000

5.000

6.000

7.000

0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 0.160 0.180

TOC

(m

g/L)

UVA

136

136

Figure 8.10: UVA vs. TOC for jar test results: filtered water samples

The entire range of results is shown in Figure 8.9, including raw water and the jar tests

blanks. In Figure 8.10 only filtered water samples from the jar test, without the Milli-Q blanks

and raw water are shown. The limited range is shown to demonstrate that there was a strong

relationship between UVA and TOC even when the value range was greatly limited. RSQ for

both ranges were very strong which implied a good linear correlation between TOC and UVA254

measurements.

8.5.4 Controls and Blanks

The TOC blanks (untreated Milli-Q) were found to have an average concentration of

0.363 mg/L TOC. This low amount of background interference was potentially from the

sulphuric acid used in the TOC analysis method. Alternatively, the background TOC levels

could have resulted from contaminated reagents used by the instrument. With this subtraction,

jar test blanks (AM50 1 and PM27 1) were found to have TOC concentrations of 0.590 and 0.577

mg/L, respectively. It is possible that the presence of low levels of TOC in these blanks came

from the filtration step of the jar tests. Some amount of the filter paper or substance on the filter

paper could have been introduced into the sample during vacuum filtration. The similarity of

TOC between the two jar test blanks indicates that all samples would contain a certain amount of

extra TOC introduced from the jar test method. Through the assumption that interference levels

were similar between all samples still allows for comparison, although blank results imply some

R² = 0.9317

3.450

3.500

3.550

3.600

3.650

3.700

0.073 0.074 0.075 0.076 0.077 0.078 0.079 0.080

TOC

(m

g/L)

UVA

137

137

error in overall accuracy of the results. For jar test blanks, UVA254 showed similar trends in

background NOM.

A series of 6 TOC quality control samples made to 3.0 mg/L were run with the samples

to check the validity of the calibration. The average measured concentration of the 6 quality

control was 3.01 ± 0.06 mg/L which indicated a high level of accuracy and precision.

8.5.5 Water quality

Pilot plant water quality results were obtained through averaging online data collected

during March 15th

, 2012. TOC and UVA measurements were made a 2 minute intervals

throughout the day. Each water source did not have a dedicated instrument therefore

approximately every 2 hours the source being analyzed by the instrument was switched as to

allow for multiple sources to be monitored from one instrument. This worked out to

approximately 160 – 200 measurements for each water source every day. pH of each water

source was measured continuously throughout the day resulting in 720 measurements at 2 minute

intervals.

Statistical tests were performed on pilot plant data to demonstrate the similarity of NOM

concentrations between both pilot plant trains. Results are shown in Table 8.18.

Table 8.18: Pilot Plant Water Quality T-Test Results

T-test parameters Critical Value

(90% confidence) T value

Significantly

Equal?

TOC of Alum vs. PACl


UVA of Alum vs.

PACl treated water 1.96 9.50 No

SUVA of Alum vs.

PACl treated water 1.96 7.98 No

pH of Alum vs. PACl


The t-test results for pilot plant data showed that no parameter was significantly equal

between trains (Alum vs. PACl). This was mainly due to the very large sample size of relatively

stable parameters. As discussed the sample numbers for these test were between 160 and 720.

This created an expected accuracy that exceeded the instrument’s capability. It is suggested that

in this case the values be accepted as equal since the differences between measured values is

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138

below the expected accuracy of the instrument. For example the TOC analyzer was accurate to

within 0.1 mg/L TOC. The average value for pilot plant 1 and 2 for March 15th

was 3.61 and

3.69, respectively, which was within the margin of error for the TOC analyzer. Using this

alternative way of looking at the data, similar conclusions to those found with jar test results can

be formed. This was further illustrated in the UVA254 measurements. There was a difference in

average absorbance of 0.001, which was far below the accuracy expected from the spectrometer.

Therefore, it was concluded TOC, UVA, and SUVA are equal between Alum and PACl treated

water at pilot-scale. Furthermore, the pH was not similar and was approximately 0.6 pH units

higher for the PACl treated water.

8.5.6 Comparison of pilot and jar test results

Results from the jar tests showed some discrepancies when compared to the pilot plant

data. An underlying effort of this study was to maintain variables as similar as possible between

the jar tests and the operation of the pilot plant.

The TOC of coagulated water from the jar tests compared to the pilot plant were similar

at approximately 3.6 mg/L TOC. As with the pilot plant samples, statistical significance using t-

tests had limited meaning since the number of samples for pilot plant data misrepresented the

accuracy of the instrument. However, all values for alum and PACl treated water for both jar

tests and pilot plant samples were within 0.1 mg/L TOC.

Similarity between pilot and jar tests did not extend past TOC of treated water samples.

Of particular note, was the difference in TOC for raw water. Based on 172 measurements, TOC

at the pilot plant was found to be 6.52 mg/L while duplicate samples at UofT were found to have

a TOC of 5.74 mg/L. Under an assumption that the TOC of treated water samples were the

same, as discussed above, a difference of 0.75 mg/L could not be attributed to instrument error.

If the discrepancy was due to instrument bias it would have been expected that all values would

be off by a similar amount. The implication was also that at the pilot plant, the same coagulant

dosages are responsible for the removal of an extra 0.75 mg/L TOC. One possible explanation

was the pilot plant filters were more effective than the 1.4 micron glass fibre filters used for jar

tests.

UVA measurements were found to be consistently higher at the pilot plant when

compared to jar test results. At pilot-scale, both trains were found to have a UVA254 of

approximately 0.11 while jar test results indicated an absorbance of 0.08 for both sample types.

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139

This result was unexpected based on the fact that TOC levels match between jar tests and pilot.

This was attributed to instrumental bias from one or both of the instruments.

Another significant between the jar tests and pilot results was the pH of all water

samples. It appears that pH measurements at UofT were higher by approximately 0.15 pH units.

The spread between Alum and PACl treated water was similar (0.68 for Jar Tests, 0.63 for pilot

samples). The similarity in spread would have indicated that the experiment is still comparable

between pilot and jar tests and that the error was due to instrumental bias. That being said there

is a larger implication in that the pH of raw water at UofT was measured to be above 8 while at

pilot it was measured to be below 8. During the pH adjustment prior to chlorination, PACl jar

tests water was adjusted down to a pH of 8 while PACl pilot plant samples were adjusted up to a

pH of 8. Since this instrumental bias was not accounted for in the experiment the water from

pilot and jar tests were very likely at dissimilar pH values when chlorinated.

8.5.7 DBP Formation Results

In sections 8.5.3.1, it was shown that NOM concentrations from both Alum and PACl

treated water were equal with doses of 50.7 mg/L and 27.7 mg/L, respectively. To perform the

DBP formation test, water samples were spiked with 2.6 mg/L of sodium hypochlorite and 24

hours of contact time was allowed. By maintaining the two concentrations of reactants for DBP

formation equal, the effect of pH and NOM fractions was observed.

8.5.7.1 Trihalomethane Formation

THM analysis showed that only chloroform (trichloromethane: TCM) and

bromodichloromethane (BDCM) were above detection limit in all samples. Total THMs were

calculated based on the sum of both TCM and BDCM concentrations. pH vs. Total THM

concentration is plotted to show the relationship between pH and THM formation. Figure 8.11

shows each replicate TTHM concentration and pH since pH did differ slightly between samples

even with adjustment. A second plot shows the average and standard deviation (as error bars) for

samples chlorinated at similar pH. Indications of sample groups are also shown on Figure 8.11.

A similar plot for pilot plant data is shown as Figure 8.12.

For both jar test and pilot-scale samples Total THM concentrations increased with

increasing pH. This conformed to expectations based on literature as well as pilot plant

historical data showing increased THM formation in PACl treated water. For jar tests and pilot

samples, average TTHM concentrations of un-adjusted PACl samples are 23% and 22% higher

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140

than un-adjusted Alum samples. This followed well with the historical data which shows 20-

30% higher TTHM concentrations in PACl treated water.

For jar test results, there was a relatively strong linear relationship between TTHM

concentration and pH. The RSQ for Alum and PACl treated water is 0.84 and 0.96, respectively.

A linear relationship was not as pronounced for pilot results with RSQ values of 0.70 and 0.81

for Alum and PACl treated water respectively. The reduced linearity of pilot results was likely

due to the large discrepancy between duplicates of samples adjusted to pH 7. It was not apparent

what this large deviation between duplicates at pH 7 was caused by, but it was consistent

between Alum and PACl treated water. It was concluded that the relationship between TTHM

concentration and pH appeared to be fairly linear between 7 and 8; however samples at more

varied pH values would be needed to confirm this assertion.

Figure 8.11: pH vs. Total THM Concentration for Jar Test Samples

35.00

40.00

45.00

50.00

55.00

60.00

6.8 7 7.2 7.4 7.6 7.8 8 8.2

Tota

l TH

M C

on

cen

trat

ion

(µ

g/L)

pH

Alum Jar Tests

PACl Jar Tests

Adjusted to pH 7

Un-adjusted

Alum Samples

Adjusted to pH

8 and un-

adjusted PACl

samples

141

141

Figure 8.12: pH vs Total THM Concentration for Pilot Plant Samples

To account for small variances in NOM concentrations between samples, TTHM values

were normalized with respect to TOC. This was performed by dividing TTHM concentration

(µg/L) by pre-chlorination TOC (mg/L), which yields a normalized value of µg TTHM/mg TOC.

In a sense this can be regarded as a measure of formation potential, as in for every mg of TOC in

the water sample so many µg of THMs form.

T-tests were performed on the normalized TTHM values to investigate if formation

potential matches between Alum and PACl treated water at an equal pH. The results of these t-

tests are shown in Table 8.19 and Table 8.20.

.

Table 8.19: T-Tests for Normalized TTHM Jar Test Results

T-Test Critical Value

(90% confidence) T Value Significant Equal?

Alum vs. PACl at pH 7 2.78 1.894 Yes


Alum UA vs. PACl UA 2.78 7.081 No

20.00

25.00

30.00

35.00

40.00

45.00

6.8 7 7.2 7.4 7.6 7.8 8

Tota

l TH

M C

on

cen

trat

ion

(µ

g/L)

pH

Pilot Plant 1 - Alum

Pilot Plant 2 - PACl

Adjusted to pH 7

Un-adjusted

Alum Samples

Adjusted to pH 8

Un-adjusted

PACl samples

142

142

Table 8.20: T-Tests for Normalized TTHM Pilot Plant Results

T-Test Critical Value

(90% confidence) T Value Significant Equal?



Alum UA vs. PACl UA 4.3 12.3524 No

The statistical tests showed that when chlorinated at a common pH, water treated either

by Alum or PACl had the same potential for THM formation. Furthermore, the t-tests confirmed

that un-adjusted samples have differing THM formation potentials. These two pieces of

information together support the hypothesis that the observed higher THM concentrations in

PACl treated water were due to a difference in pH.

8.5.7.2 Haloacetic Acid Formation

All samples were analyzed for all nine species of HAAs although only bromoacetic acid

(MBAA), trichloroacetic acid (TCAA), dibromoacetic acid (DBAA), and chlorodibromoacetic

acid (CDBAA) were detected. As with THMs, plots of pH vs. Total HAA Concentration were

prepared.

Figure 8.13: pH vs. total HAA concentration jar tests

55.00

60.00

65.00

70.00

75.00

80.00

85.00

6.8 7 7.2 7.4 7.6 7.8 8 8.2

Tota

l HA

A C

on

cen

trat

ion

(µ

g/L)

pH

Alum Jar Test

PACl Jar Test

Adjusted to pH 7

Un-adjusted

Alum Samples

Adjusted to pH

8 and un-

adjusted PACl

samples

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143

Figure 8.14: pH vs. total HAA concentration pilot samples

Results from the jar tests showed an apparent decrease in Total HAA concentration when

chlorinated at pH 8 compared to at pH 7. This agreed with literature in that HAA formation is

favoured in more acidic conditions. For jar tests, there was little difference between un-adjusted

and pH 8 PACl samples since the filtered water was found to be approximately pH 8. Pilot plant

results do not show the same drop in HAA concentration at a higher pH, except for Alum treated

water. Historical data from the pilot plant showed only small differences (±10%) between HAA

concentrations from both trains. Furthermore, one train was not observed to be consistently

higher than the other.

Jar test results showed a stronger negative linear correlation between total HAA

concentration and pH. RSQ was found to be 0.836 and 0.775 for Alum and PACl samples,

respectively. This indicated some degree of linear relationship. For pilot results, RSQ was

found to be 0.796 and 0.443 for Alum and PACl samples, respectively. This further supported

the observation that pilot samples do not show the negative correlation between pH and total

HAA concentration. From these results, it was concluded that the effect of pH on HAA

concentration was not as pronounced as seen with THMs.

As with TTHMs, THAAs were normalized to account for small changes in TOC between

samples. HAA formation potential was then subjected to several t-tests in order to show if

35.00

40.00

45.00

50.00

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60.00

6.8 7 7.2 7.4 7.6 7.8 8

Tota

l HA

A C

on

cen

trat

ion

(µ

g/L)

pH

Pilot Plant 1 - Alum

Pilot Plant 2 - PACl

Adjusted to pH 7 Un-adjusted

Alum Samples

Adjusted to pH 8

Adjusted to pH 8

Un-adjusted

PACl Samples

144

144

samples treated at equal pH have equal potential for HAA formation. Results from the t-tests are

shown in Table 8.21 and Table 8.22.

Table 8.21: T-Tests for Normalized THAA Jar Test Results

T-Test

Critical Value

(90%

confidence)

T Value Significant Equal?

Alum vs. PACl at pH 7 2.78 4.3274 no


Alum UA vs. PACl UA 2.78 3.5685 no

Table 8.22: T-Tests for Normalized THAA Pilot Plant Results

T-Test

Critical Value

(90%

confidence)

T Value Significant Equal?

Alum vs. PACl at pH 7 4.3 1.9540 yes


Alum UA vs. PACl UA 4.3 0.9843 yes

8.5.8 Conclusion

In conclusion, this work demonstrated that the observed increased THM formation in

PACl treated water in comparison to alum was caused by the pH. Since PACl does not depress

the pH, THM formation is favoured. When chlorinated at equal pH, PACl and alum treated

water showed no statistical difference in THM formation. HAAs were not found to be

significantly affected by any pH changes and furthermore, were found to be significantly equal at

unadjusted pH values (alum: ~7.1, PACl: ~7.8).

APPLICATIONS OF PRINCIPAL COMPONENT ANALYSIS OF ... · EXCITATION-EMISSION MATRICES FOR CHARACTERIZATION OF NATURAL ORGANIC MATTER IN WATER TREATMENT Nicolás Miguel Peleato Master

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