CHARACTERIZATION OF DISINFECTION BY-PRODUCT
PRECURSORS FROM MISSOURI STREAMS
………………………………………………………………………………………………………………………………………………
A Thesis presented to the Faculty of the Graduate School
University of Missouri-Columbia
………………………………………………………………………………………………………………………………
In Partial Fulfillment
Of the Requirements for the Degree
Master of Science
………………………………………………………………………………………………………………………………………………
By
ERIC GENTIL MBONIMPA
Dr. Tom E. Clevenger, Thesis Supervisor
May 2007 The undersigned, appointed by the Dean of the Graduate School, have examined the thesis entitled
CHARACTERIZATION OF DISINFECTION BY-PRODUCT PRECURSORS FROM MISSOURI STREAMS
Presented by
Eric Gentil Mbonimpa
A candidate for the degree of
Master of Science
And hereby certify that in their opinion it is worthy of acceptance
Dr. Tom E. Clevenger
Dr. Kathleen Trauth
Dr. Stephen H. Anderson
Acknowledgements
I would like to express my sincere thanks to Dr. Tom Clevenger and the Missouri Water
Resources Research Center for the financial support that enabled me to realize my master’s degree goals. His guidance and advice throughout my degree and this work is appreciated.
I would like also to extend my thanks to my thesis committee, Dr. Kathleen Trauth, and
Dr. Stephen H. Anderson. Their guidance, encouragement and enthusiasm meant a lot to me.
I also express my gratitude to the staff of the Missouri Water Resources Research Center,
Ed Hinderberger and Dan Crosby, for their help in lab work.
I am grateful to fellow graduate students and postdoctoral fellows, especially Jing Cheng and Dr. Bin Hua who introduced me to new technologies for my research.
Special thanks go to God and my Mum who took care of me, since I was a child until now.
ii Table of Contents
Page
Acknowledgements………………………………………………………………..ii Table of Contents…………………………………………………………………iii List of Tables.……………………………………………………………..……...vii List of Figures...………………………………………………………………….viii Abstract………...………………………………………………………………….xi CHAPTER 1: INTRODUCTION…………………………………………………1 1.1 Background……………………………………………………………………1 1.2 Motivation of the study………………………………………………………..2 1.3 Objectives of the study………………………………………………………...3 CHAPTER 2: LITERATURE REVIEW………………………………………….3 2.1 Natural Organic matter (NOM)………………………………………….…….4 2.1.1 Overview and sources……………………………………………………4 2.1.2 Characterization………………………………………………………….5 2.1.2.1 Bulk characterization: Total organic carbon (TOC) /Dissolved organic carbon (DOC)……………………………...5 2.1.2.2 Elemental Analysis……………………………………………...6 2.1.2.3 Characterization by “Fractionation” and “Isolation” using resins………………………………….…7 2.1.2.4 Characterization using spectroscopy…………………………….8 2.1.2.4.1 Ultraviolet wave absorbance (UVA) ………………….8 2.1.2.4.2 Fluorescence spectroscopy…………………………….9 2.1.2.4.2.1 Fluorescence Regional Integration (FRI)…10 2.1.2.4.2.2 Fluorescence Index (FI)………………...…11 2.1.2.4.2.3 Humification Index………………………..13 2.1.2.4.3 Carbon-13 Nuclear Magnetic Resonance (13C NMR)…13 2.1.2.4.4 Fourier Transform Infrared spectroscopy (FTIR)……...14
iii 2.1.2.5 Characterization using chromatography…………………………15 2.1.2.6 Characterization by physical methods…………………………...16 2.1.2.6.1 Thermal degradation: Pyrolysis Gas Chromatography-Mass Spectrometry (GC/MS)……………16 2.1.2.6.2 Ultra-filtration………………………………………...17 2.1.2.7 Characterization by Disinfection by-products formation potential (DBPFP)…………………………………....18 2.2 Disinfection byproducts ……………………………………………………...18 2.2.1 Types and formation of DBPs………………………………………….18 2.2.1.1 Chlorine chemistry……………………………………………...18 2.2.1.2 Types of DBPs………………………………………………….19 2.2.1.3 Formation mechanisms…………………………………………20 2.2.2 Health Effects…………………………………………………………...24 2.2.3 Regulations……………………………………………………………...25 CHAPTER 3: DESCRIPTION OF WATERSHEDS CONTAING SAMPLING SITES……………………………………………………………………………..27 3.1 Hydrologic units/subwatershed of sampling sites……………………………27 3.1.1 South Fork Salt sub-basin……………………….……………...... 27 3.1.2 North Fork Salt sub-basin…………………………...... 29 3.1.3 Little Chariton sub-basin………………………………………………..29 3.1.4 Lower Chariton sub-basin………………………………………………30 3.1.5 Lower Grand sub-basin…………………………………………………31 3.1.6 Upper Grand sub-basin………………………………………………….32 3.1.7 One hundred and Two sub-basin………………………………………..32 3.1.8 Platte sub-basin………………………………………………………….33 3.1.9 Moreau sub-basin………………………………………………………..33 CHAPTER 4: MATERIALS AND METHODS…………………………………34 4.1 Sampling……………………………………………………………………...34 4.2 Chlorination…………………………………………………………………..36
iv 4.2.1 Chlorine demand………………………………………………………..36 4.2.2 Reagents and Procedure………………………………………………...36 4.3 Trihalomethanes (THMs) analysis……………………………………………38 4.3.1 Procedure……………………………………………………………….38 4.3.2 Reagents, standards and quality assurance……………………………..39 4.4 Haloacetic acids (HAAs) analysis……………………………………………42 4.4.1 Procedure……………………………………………………………….42 4.4.2 Reagents, standards and quality assurance……………………………..43 4.5 Ultraviolet absorbance (UVA) analysis………………………………………44 4.6 Fluorescence Analysis………………………………………………………..45 CHAPTER 5: RESULTS AND DISCUSSION…………………………………..48 5.1 UV absorbance characteristics of NOM ……………..………………………...48 5.2 Disinfection by-products formation potential of NOM ……………...………...57 5.2.1 THMs formation potential of NOM………………………………………..57 5.2.2 HAAs formation potential of NOM………………………………...……...59 5.3 Relationship between UVA and DBPs formation potentials……………...…….61 5.3.1 Relationship between UVA and THMs formation potentials………..……61 5.3.2 Relationship between UVA and HAAs formation potentials ……….……63
5.4 Speciation of THMs and HAAs ...... 65 5.4.1 Trihalomethanes (THMs)…………………………..……….….……….….65 5.4.2 Haloacetic acids (HAAs)………………………………………….………..68 5.5. Fluorescence Signature of natural organic matter and relationship with THMFP,HAAFP, Aromatic and Source……………...71 5.5.1 Visual interpretation of Fluorophore Centers………………………….71 5.5.1.1 Scans for March samples………………………………………73 5.5.1.2 Scans for June samples………………………………………...77 5.5.1.3 Scans for September-October samples………………………...81 5.6 Approximation using Parallel factor analysis (PARAFAC)………………….85 5.6.1 Parallel factor analysis (PARAFAC) components……………………...85
v 5.6.2 Relationship between PARAFAC Components, DBPs Concentration and UVA 254…………………………………..………..89 5.7 Fluorescence Index………………………………………………………………91 5.8 Fluorescence Regional integration (RFI) results……………………………..94 5.8.1 Relationship between fluorescence regions and THMs………………..95 5.8.2 Relationship between fluorescence regions and HAAs………………..98 5.8.3 Relationship between fluorescence regions and UVA254…………...101 5.8.4 Relationship between fluorescence regions and Fluorescence index……………………………………………….104 CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS………………107 6.1 Conclusions………………………………………………………………..107 6.2 Recommendations…………………………………………………………109 REFERENCES………………………………………………………………….111 APPENDIX………………………………………………...……………………116 LIST OF ABBREVIATIONS AND ACRONYMS……………………………………124
vi
List of Tables
Page
Table 1: Predominant pyrolysis fragments from aquatic NOM……………….…………17
Table 2: Survey of Disinfectant use in US systems 1997……………………………..…19
Table 3: List of DBPs and Disinfectant Residuals ……………………………………....20
Table 4: Status of health information for THMs and Some HAAs……………………...25
Table 5: Sample number and collection sites.…….……………………………………..35
Table 6: Influence of FI on UVA254, THMs, and HAAs……………………………….91
Table 7: Correlation (+/-) and Coefficient of Determination (R2) of
the relationship between Fluorescence Regions and TTHMs…………………..97
Table 8: Correlation (+/-) and Coefficient of Determination (R2) of
the relationship between Fluorescence Regions and HAAs…………………..100
Table 9: Correlation (+/-) and Coefficient of Determination (R2) of
the relationship between Fluorescence Regions and UVA254…………………103
Table 10: Correlation (+/-) and Coefficient of Determination (R2) of
the relationship between Fluorescence Regions and FI……………………….106
Appendix
A.1: THMs formation potentials for March samples……...……………………………116 A.2: HAAs formation potentials for March samples………...…………………………117 A.3: THMs formation potentials for June samples…….…..…………………………...118 A.4: HAAs formation potentials for June samples……..………………………………119 A.5: THMs formation potentials for September samples……..………………………..120 A.6: HAAs formation potentials for September samples…..…………………………..121
vii
List of Figures
Page
Figure 1: Classification of DOC by fractionation…………………………………………8
Figure 2: Location of EEM peaks and EEM regions used in FRI……………………….11
Figure 3: Differences in carbon-specific fluorescence emission scans (CSF, Ex: 370 nm) between allochthonous and autochthonous DOC………..12 Figure 4: Examples of reaction models for formation of some DBPs species a) Chlorination of 1, 1, 1-Trichloroacetone b) Chlorination of purvate c) Chlorination of beta-diketone structure…………………………………….21
Figure 5: Missouri State Hydrologic Units………………………….……………….27
Figure 6: Graphical representation of a two-component PARAFAC model of the data array X……………………………………………………………………47
Figure 7: Variation of UVA254 during spring…………………………………………….48
Figure 8: Variation of UVA254 during summer ………………………………………….49
Figures 9: Variation of UVA254 during fall….... …………………...……………………49
Figure 10: Precipitation and river flow variations during sampling periods at the watersheds of sampling locations …..……………..51 Figure 11: Location of sampling sites ………………..……………………………….55 Figure 12: Soil type at sampling sites ………………………………………………...56 Figure 13: Variation of THMFP during spring ……………………………….……….57 Figure 14: Variation of THMFP during summer …………………..………………….58 Figure 15: Variation of THMFP during fall…..……………………………………….58 Figure 16: Variation of HAAs during spring ……………………………………….…...59 Figure 17: Variation of HAAs during summer ………………………………………..60 Figure 18: Variation of HAAs during fall…. ……………………………………………60
Figure 19: Relationship between UVA254 and THMFP in different seasons …….…….62
viii Figure 20: Relationship between UVA254 and THMFP for all seasons………… ……...62 Figure 21: Relationship between UVA254 and HAAs in different seasons …………….64 Figure 22: Relationship between UVA254 and HAAs for all seasons ……….....………64
Figure 23: THMs speciation for March samples…………..…………………………….66 Figure 24: THMs speciation for June samples…...……...………………………………66 Figure 25: THMs speciation for Sept-Oct samples…………….………………………67 Figure 26: HAAs speciation for March samples...…………………………………...….68 Figure 27: HAAs speciation for June samples……...…………………………………...69 Figure 28: HAAs speciation for Sept-Oct samples………………………………...…….69 Figure 29: Typical fluorescence EEMs observed at different locations. Fulvic acid (F), Humic acid (H) and Tryptophan (T) peaks…………………..71
Figure 30: Typical fluorescence excitation-emission matrix showing the locations of the fluorescence peaks……………………………………………………..72
Figure 31: Fluorescence EEMs of a) dextran b) albumin c) Sunawee River Humic Acid d) Sunawee River Fulvic Acid e) Algal organic matter f) BL-SW (sewage)……………………………………………………….……72
Figure 32: Excitation –Emission Matrix scans for March samples...……………….…..73
Figure 33: Excitation –Emission Matrix scans for June samples...………………….…..77
Figure 34: Excitation –Emission Matrix scans for September samples...…………….…81
Figure 35: Excitation –Emission Matrix for components fit by PARAFAC model……..85
Figure 36: Distribution of the two components in March samples………………………86 Figure 37: Distribution of the two components in June sample……...……………….…87 Figure 38: Distribution of the two components in Sept-Oct samples……………………87 Figure 39: Relationship between the two components and THMs………………………89 Figure 40: Relationship between the two components and HAAs………………………90
Figure 41: Relationship between the two components and UVA254……………………..90
Figure 42: Linear relationship between UVA254 and FI………………………...……….92
ix Figure 43: Linear relationship between THMs and FI…………………………………...93
Figure 44: Linear relationship between HAAs and FI…………………………………...93
Figure 45: Relationship between RGs and THMs……………………………………….96 Figure 46: Relationship between RGs and HAAs……………………………………….99
Figure 47: Relationship between RGs and UVA254…………………………………….102 Figure 48: Relationship between RGs and FI…………………………………………..105 Appendix A 7: Legend of the Geologic Map (Figure 12)……………………………...122
Appendix A 8: Map of Missouri Hydrography………………………………………...123
x
Abstract
Surface and ground water, used as sources of drinking water, contain natural organic matter that reacts with chlorine to generate carcinogenic organic compounds. Many water treatment systems, which use chlorine in the disinfection process, face stringent regulations regarding two types of disinfection by-products (DBPs): trihalomethanes (THMs) and haloacetic acids (HAAs). Due to the challenge of understanding the complex mixture of organic compounds in natural organic matter, DBPs removal is still a research issue. UV and fluorescence spectroscopy methods of characterization were used in this study to characterize organic matter contained in Missouri streams. The relationship with DBPs formation potentials observed in water was also investigated. THMs and HAAs formation potentials, UV- absorbance at 254 nm wavelength, and Excitation and Emission Matrix parameters were taken for samples collected at 38 streams and creeks during the months of March, June, and September 2006. Fluorescence index, PARAFAC model and Fluorescence Regional Integration were used to interpret information obtained from fluorescent compounds in water. Organic matter scanned originated mainly from terrestrial source (allochthonous) rather than within water body (autochthonous) as shown by fluorescence index. Three fluorophores: humic- like, fulvic-like and protein-like were recognized from fluorescence scans. The variation in organic matter was mainly due to weather and the watershed source. The rainfall in the month of March carried terrestrial organic matter of high aromatic rings (high UVA254) into streams, which in turn generated high amounts of THMs and HAAs. The summer increased THMs but reduced HAAs. High amount of DBPs were observed in samples from northeastern Missouri Watershed. Chloroform was the dominant of THMs species (average: 90%) and, DCAA and TCAA were dominant HAAs species (average: 87%). Finally, fluorescence regional integration showed that algal and microbial derived compounds (protein-like), as well as humic substances, generate THMs and HAAs.
xi CHAPTER 1: INTRODUCTION
1.1 Background
The disinfection of drinking water is one of the major public health advances in the
20th century. One hundred years ago, typhoid and cholera epidemics were common
throughout American cities and caused many deaths. Since the early 1900’s, chlorine, a
major disinfectant, has played the largest role in reducing the number of waterborne
disease outbreaks, increasing human productivity and longevity. However, in 1974 Rook
(1994) proved the formation of dangerous disinfection byproducts (DBPs) from the
chlorination of water. Some DBPs have been shown to cause cancer and reproductive and
developmental effects in laboratory animals (Boorman et al., 1999).
DBPs are of great concern because in the USA more than 200 million people consume
water that has been disinfected, mostly with chlorine (USEPA 2001, Table 2). Natural
organic matter, a complex mixture of organic compounds that occurs universally in
ground and surface waters, is believed to react with disinfectants to generate disinfection
byproducts (Singer, 1999).
Chlorine still remains the most widely used disinfectant for municipal water because it is a well established technology, is effective in removing pathogens and is cost effective compared to other disinfectants such as ozone or ultraviolet radiation. In addition to that, it has the advantage over other methods of providing residual disinfectant capabilities that protect the water distribution system against microbial growth (Rossman et al., 1994).
Because of the large population exposed, and the health risks associated with DBPs, it is
a challenge to drinking water suppliers to balance the risks from microbial pathogens and
1 disinfection byproducts. To ensure a reduction in health risk to the population, the 1996
Safe Drinking Water Act (SDWA) Amendments require the USEPA to develop rules to
achieve safe drinking water goals. Since then, the USEPA has promulgated standards for
two types of disinfection byproducts (trihalomethanes and haloacetic acids) that are of known potential risk. However, it is believed that more than one hundred disinfection
byproducts exist (Christman et al., 1983).
The stringent regulations require water utilities to consider modifications in the treatment processes to meet the standards. The USEPA proposed enhanced coagulation be used by water treatment plants to significantly reduce the total organic carbon (TOC)
as a currently affordable technology to meet Stage I compliance (USEPA, 1999).
However, several utilities still do not meet the standards. The reason being that unknown
specific organic compounds react with chlorine and even with significant removal of
TOC, enough of those compounds still remained to form disinfection by-products
(McCool, 2005).
1.2 Motivation of the study
As a good approach for solving the problem of DBPs, it is obvious that there is a need
to understand the nature of specific organic compounds in natural organic matter that
react with chlorine to form specific by-products. It is also necessary to understand the
source, the environmental factors and the watershed characteristics that affect the fate of
DBP precursors in order that effective and affordable solutions can be developed.
2 1.3 Objectives of the study
The goal of the study is to characterize the natural organic matter (NOM) in water
from selected streams in northern Missouri and to find the relationship with DBP
generation. Mainly, first order streams will be selected. To capture the effect of weather,
samples will be collected during the rise and fall of the storm runoff hydrograph. To
obtain undisturbed characteristics of the NOM, spectroscopy methods will be performed.
Fluorescence spectroscopy and ultraviolet radiation absorbance technologies will be used.
The proclivity of NOM from the selected sites to generate DBPs will be investigated by
adding chlorine and determining the formation potentials of THMs and HAAs. The study
will determine the effect of seasonal factors on the fate of trihalomethanes (THMs) and
haloacetic acids (HAAs) precursors. It will also be determined whether some techniques
such as spectrofluorometry that are used to characterize natural organic matter might be
used to predict and monitor the formation of DBPs. With these techniques, it will also be
determined whether the precursors are formed within the water body or from a terrestrial origin. Each site will be classified according to its drainage basins to determine the spatial variation effect on THM and HAA precursors.
3 CHAPTER 2: LITERATURE REVIEW
2.1 Natural Organic matter (NOM)
2.1.1 Overview and sources
Aquatic organic matter is a complex mixture of aromatic and aliphatic hydrocarbon
structures that have attached amide, carboxyl, hydroxyl, ketone, and various minor functional groups. It has a wide range of molecular masses and chemical structures that give it a multifunctional role in the natural environment. Its molecular weight can range from a few hundred to 100,000 Daltons (Da) and it can be classified in the colloidal size range. In most cases the complexity of describing dissolved organic matter (DOM) is overcome by characterization at the compound class level. Humic substances are the main component of DOM, their amount ranges between 50% and 70% of total dissolved organic matter (Baker et al., 2004).
Amino sugars, condensed tannins, terpenoids, lignins and proteins are suggested to be
major precursors for DOM components (Leenheer et al., 2003). The natural organic
matter components of dissolved organic matter in water are derived from two sources.
The allochthonous source is the organic matter produced by terrestrial decaying plants and is found in water in dissolved or colloidal form as humic substances that are derived from the partial microbial degradation of lignin-cellulose based carbon compounds of higher plants (Christman et al., 1998). The autochthonous source is the organic matter produced in reservoirs as a result of microbial activity and is largely produced by algal and cynobacterial photosynthesis. Autochthonous compounds are generally low in molecular weight (less than 500 Da), rich in carboxylic acid functional groups and more hydrophilic. Allochthonous organic matter generally has a high molecular weight
4 (greater than 1,000 Da) and is hydrophobic (Carlson, 2002). Autochthonous organic matter is also known to easily degrade, producing low molecular weight compounds
(Lepane et al., 2004). The occurrence of allochthonous organic matter is governed by factors such as hydrology, geomorphology, and vegetation distribution. However, DOM concentration, composition, and chemistry are highly variable and depend on: the source of organic matter (allochthonous versus autochthonous), the temperature, the ionic strength, the pH, the major cation composition of the water, the surface chemistry of sediment sorbents that act as solubility controls, and the presence of photolytic and microbiological degradation processes (Leenheer et al., 2003). Organic matter derived from higher plants, for instance, has been found to contain relatively large amounts of aromatic carbon, be high in phenolic content, and low in nitrogen content. Microbial- derived organic matter (from algae and bacteria), on the other hand, has a greater nitrogen content, and lower aromatic carbon and lower phenolic content (Aiken, 2002).
2.1.2 Characterization
2.1.2.1 Bulk characterization: Total organic carbon (TOC)/Dissolved organic carbon (DOC)
Total organic carbon (TOC) is the most comprehensive parameter used to quantify organic matter in aquatic systems. TOC is a composite of two fractions known as dissolved organic carbon (DOC) and particulate organic carbon (POC). DOC is conventionally organic matter smaller than 0.45 micrometers whereas POC is the fraction larger than 0.45micrometers. Generally, POC represents a minor fraction of TOC (less than 10%) and varies with river characteristics. The DOC concentration depends on the environmental parameters such as seasonal river flow variations, watershed
5 characteristics, runoff quantity and algal bloom. Also, a difference between DOC derived
from groundwater and surface water due to the alteration of organic matter by the microbial community in the subsurface environment has been reported (Fram et al.,
2005). Even though a major part of DOC is refractory, autochthonous NOM produced
from macrophytes, algae and bacteria is more biodegradable than allochthonous NOM
(Leenheer et al., 2003). The DOC or TOC concentration is analytically determined using a TOC analyzer. It is equipped with an ultraviolet lamp submerged in a continuous gas purged reactor filled with a constant-feed persulfate solution. When the sample is injected, it is oxidized to CO2 and carried in the gas stream to be detected by an infrared
detector.
2.1.2.2 Elemental Analysis
Elemental analysis involves the analytical determination of the amounts of carbon,
oxygen, hydrogen, nitrogen and sulfur in the water. Reckhow et al. (1985) analyzed 10
different aquatic humic substances and found that carbon ranged between 52 and 56%,
oxygen between 35 and 40%, nitrogen between 1 and 2%, sulfur between 1 and 2%.
Phosphorous and halides were minor constituents. It was also indicated that humics are generally richer in nitrogen than fulvics. The ratio of different chemical elements such as oxygen to carbon is related to polarity, hydrogen to carbon is related to saturation, and nitrogen to carbon is related to the source of the organic matter (microbial derived or vegetation) (Gang, 2001).
6 2.1.2.3 Characterization by “Fractionation” and “Isolation” using resins
This technique uses Amberlite XAD-resins to adsorb organic compounds with respect
to their chemical properties. The absorption to resins is governed by polarity of solutes
functional groups (carboxyl, phenolic, ketone, hydroxyl...) and solution pH. At low pH,
weak acids are protonated and adsorb on the resins. Two types of resins, XAD-8 and
XAD-4, of styrene and divinylbenzene polymeric properties, adsorb humic substance with respect to their hydrophobicity-hydrophilicity properties. Hydrophobic, humic large
molecules are retained by XAD-8 due to its small surface area and large pore size, while
XAD-4 has a large surface area and small pore size, retaining non-humic, hydrophilic
molecules. A base solution (pH 11-13) is used to ionize and to desorb the separated
fraction from resins for further measurements. The solution eluted from XAD-8 is
composed of humic acids mostly with carboxylic and phenolic functional groups. On the
other hand, the solution eluted from XAD-4 contains hydrophilic fulvic acids (Leenheer,
1981; Leenheer et al., 2003; Check, 2005).
Figure 1 elaborates different fractions obtained during fractionation using resins. The
fractionation using resins enabled the classification of some natural organic compounds;
fulvic acids are classified as hydrophobic acids, tannins are hydrophobic neutrals, and
aromatic amines belong to hydrophobic bases. Polyuronic acids belong to hydrophilic
acids, many organic sugars are classified as hydrophilic neutrals and proteins are
hydrophilic bases.
7
Figure 1. Classification of DOC by fractionation (adapted from Leenheer et al., 2003).
2.1.2.4 Characterization using spectroscopy
2.1.2.4.1 Ultraviolet wave absorbance (UVA)
The aromatic chromophores and functional groups containing unsaturated bonds, present in humic substances, are known to absorb both visible and UV light (Nikolaou et al., 2001; Reckhow et al., 2004). A UV-visible spectrum is broad with numerous chromophores features. The absorbance at 254 nm has been considered by many researches as a rough indicator of aromaticity of NOM and it has also been correlated with humic and fulvic acids (Reckhow et al., 2004). Specific UV absorbance (SUVA) at
254 nm, defined as UV absorbance at 254 nm divided by the DOC concentration, has been used as a measurement of aromatic carbon in NOM. High SUVA waters are rich in hydrophobic NOM such as humic substances. SUVA has also been used by many water
8 industries as a surrogate for disinfection byproducts precursors (Leenheer et al., 2003;
Gang, 2001).
2.1.2.4.2 Fluorescence spectroscopy
Fluorescence spectroscopy has been introduced to characterize aquatic and soil NOM, and recently has been extended to other applications such as tracking the origin of the oil spills in marine waters and tracking the origin of wastewater discharges in rivers. The molecular size of natural organic matter affects the intensity of fluorescence; an increase in molecular size causes a decrease in fluorescence intensity. Larger molecular weight aquatic humic fractions have a greater absorbance but lower fluorescence than smaller molecular weight fractions (McKnight et al., 2001). Reduction in fluorescence can also be caused by the closed position of hydrophobic subunits (aromatic moieties) inside the macromolecular structural skeleton masked by hydrophilic groups (Nikolaou et al.,
2001). The intensity of fluorescence decreases with the presence of electron-withdrawing groups, whereas it increases with the presence of electron-donating groups (Nikolaou et al., 2001).
Fluorescence can detect fluorescence centers attributed to aromatic proteins, humic and fulvic-like substances at concentrations down to parts per billion (ppb) levels
(Baker, 2002). Conventional fluorescence spectra are collected using mostly one of two modes. In the first mode the emission spectrum variation is recorded for a fixed excitation wavelength, or the excitation spectrum is recorded by measuring the emission intensity at a fixed wavelength while varying the excitation. In the other mode, a fluorescence signature is recorded as a matrix of fluorescence intensities with a coordinated variation of excitation and emission wavelength in a specific spectral
9 window. The signature is particular to the nature of the compound present (Marhaba et
al., 2000). The interpretation of a fluorescence excitation-emission matrix (EEM) that
has thousands of wavelength-dependent intensity data points has posed a significant
challenge to research. Some of the approximations developed to define and quantify
fluorophores from EEMs are discussed below.
2.1.2.4.2.1 Fluorescence Regional Integration (FRI)
Chen et al. (2003) divided an EEM into five regions. The peaks in each region have
been associated to already known fluorophores: humic-like, tyrosine-like, tryptophan-
like, and phenol-like organic compounds (Figure 2). Peaks at shorter excitation
wavelengths (<250 nm) and shorter emission wavelengths (<380 nm) were associated to
simple aromatic proteins such as tyrosine (Region I and Region II). Peaks at intermediate
excitation wavelengths (250-~280 nm) and shorter emission wavelengths (<380 nm) were
associated with soluble microbial byproducts-like material (Region IV). Peaks at longer
excitation wavelengths (>280 nm) and longer emission wavelengths (>380 nm) were
associated with humic acid-like organics (Region V). Peaks at shorter excitation wavelengths (<250 nm) and longer emission wavelengths (>350 nm) were associated with fulvic acid–like materials (Region III).
10
Figure 2. Location of EEM peaks and EEM regions used in FRI (Adapted from Wen Chen et al., 2003).
2.1.2.4.2.2 Fluorescence Index (FI)
Donahue et al. (1998) and McKnight et al. (2001) reported that the spectrofluorometric
analysis could measure qualitatively whether DOC is derived within a body of water
(autochthonous) or from the terrestrial catchments as result of decomposition of organic
litter (allochthonous). Autochthonous and allochthonous DOC have very different
optical properties; allochthonous DOC absorbs more visible and ultraviolet radiation than
autochthonous. The reason for the difference might be that dissolved organic matter
compounds derived from terrestrial vegetation and soils contain a significant content of
aromatic carbon, whereas microbially derived dissolved organic matter compounds are
lower in aromaticity (Battin, 1998).
McKnight et al. (2001) also introduced the fluorescence index, the ratio between fluorescence intensity at 450 nm emission wavelength and at 500 nm emission
wavelength with an excitation wavelength of 370 nm.
11 In a study conducted on the Sunawee River and Lake Fryxell, for allochthonous DOC, carbon specific fluorescence is higher than that of autochthonous DOC, and the peak fluorescence is also shifted to a higher wavelength (Figure 3). The ratio of emissions at
450 and 500 nm indicates the quality of DOC. The Suwanee River DOC has a ratio of approximately 1.4 that was linked to a predominance of terrestrial organic matter, and
Lake Fryxell DOC has a ratio of approximately 1.9 that was linked to a predominance of microbially derived components.
Battin (1998) reported the fluorescence indices for the Orinoco River as 1.24±0.04 and for the Surumoni River as ~1.15, and he indicated that for these two rivers, aquatic organic matter is dominated by allochthonous organic compounds.
Figure 3. Differences in carbon-specific fluorescence (CSF) emission scans (Ex: 370 nm) between allochthonous and autochthonous DOC (adapted from Donahue et al., 1998).
12
2.1.2.4.2.3 Humification Index
Vogt et al. (2004) defined the humification index as the ratio between the relative
fluorescence emission in the blue region (330-345 nm) and relative fluorescence emission in the red region (435-480 nm) when the fluorescence emission spectra of samples are scanned at a 254 nm excitation wavelength. The emission in the blue region is presumed to represent less condensed organic material in the samples, while the emission in the red
region is presumed to represent more condensed organic material.
2.1.2.4.3 Carbon-13 Nuclear Magnetic Resonance (13C NMR)
The nuclear magnetic resonance (NMR) is a common method used to quantitatively
and qualitatively characterize functional groups of organic matter molecules and gives information on the chemical bonding of carbon, hydrogen, nitrogen, phosphorous and oxygen atoms. NMR spectroscopy using carbon-13 nucleus has also been extensively used to determine physical, chemical and structural properties of humic substances molecules (Lu et al., 2000).
Nuclear magnetic resonance is a physical phenomenon based upon the magnetic
properties of an atom's nucleus. The technique requires sample preparation because high
resolution is achieved for solid state samples. Solid state magnetic resonance is
performed with Magic Angle Spinning, whereby an irradiated sample placed in a
magnetic field is spun at several kilohertz. Then a computational mathematical
transformation such as a Fourier Transform is applied to the collected signal to produce a
13 recognizable spectrum (http://en.wikipedia.org/wiki/Nuclear_magnetic_resonance(visited
January, 2007)).
Lu et al. (2000) applied the method to humic substances isolated from swamp water and recognized a very complex mixture of organic compounds. The spectra obtained were divided into four molecular structure types: the aliphatic carbons, oxygenated aliphatic carbons, aromatic carbon, and carboxylic carbons. In the aliphatic carbon region, the peaks were attributed to methyl, methylene, and methine carbons. In the oxygenated aliphatic carbon region, the peaks were associated with lignin and polysaccharides, such as cellulose. Other peaks were associated with non-protonated aromatic carbons in tannins. In the aromatic carbon region, the peaks were attributed to aromatic and unsaturated carbon or aryl-carbon with protonated aromatic carbon bonded to phenolics (with OH groups). The other peaks were associated with carboxylic acid, amide, ester, carbonyl, aldehyde and ketone. Although the method is time consuming due the sample preparation and enormous data set required, it provides an appreciable amount of information on the complex mixture of humic substances.
2.1.2.4.4 Fourier Transform Infrared spectroscopy (FTIR)
Fourier Transform Infrared spectroscopy (FTIR) is a measurement technique for collecting infrared spectra. As with all spectroscopic techniques, it can be used to
investigate the composition of a sample by identifying individual organic compounds,
because chemical bonds have specific frequencies at which they vibrate corresponding to
different energy levels. Different functional groups have known absorption ranges and
their specific intensities can be easily identified.
14 Salomon et al., 2005 used FTIR to study the long-term impact of land use changes on
the amount, composition, and structural stability of the different organic functional forms
present in the humic substances in samples collected in the Rift valley of Munessa,
Ethiopia. They found that easily degradable compounds such as polysaccharide carbons and labile compounds were prominent in native forest soils while aromatic carbons and some aliphatic carbons dominated in humic substances from plantations and continuously cultivated fields (Dawit et al., 2005). Information on the functionality of humic substances can be characterized using FTIR, but the characterization is qualitative and only some specific bands can be clearly assigned (Abbt-Braun et al., 2004).
2.1.2.5 Characterization using chromatography
Size exclusion chromatography (SEC)
Size exclusion chromatography (SEC) is a method in which particles are separated
based on their molecular or particle size, or in more technical terms, their hydrodynamic
volume, whereby the largest molecules have the shortest retention times. It is usually applied to large molecules or macromolecular complexes. Rolf et al. (2003) indicated
that SEC has been used to fractionate the DOC into 4 groups: polysaccharides, humic
substances, low molecular weight carboxylic acids and low weight substances. The
method uses a UV detector but has a disadvantage of a low response for NOM with low
UV absorbance; for instance, proteins, sugars, amino-sugars, and aliphatic acids that are
common components of aquatic NOM. However, as both aromatic and non-aromatic
NOM moieties are detected, this method can provide a better understanding of the
qualitative and quantitative NOM properties in natural and treated water samples without
preconcentration (Leenheer et al., 2003, Rolf et al., 2003).
15 2.1.2.6 Characterization by physical methods
2.1.2.6.1 Thermal degradation: Pyrolysis Gas Chromatography-Mass Spectrometry (GC/MS)
Analytical pyrolysis is a thermal degradation method, ans is very useful for chemical
characterization of materials through decomposition into their low molecular weight products. This method has been used to characterize NOM because natural biopolymers can be clearly identified. They yield very specific fragments with little interference among the biopolymers. Table 1 lists major pyrolysis fragments most commonly encountered in pyrochromatograms for aquatic NOM (Leenheer et al., 2003).
Saiz-Jimenez (1994) indicated that pyrolysis is not an ideal method to characterize the
structural features of complex macromolecular materials. Analytical pyrolysis may cause
modification of the original building blocks that can lead to inaccurate conclusions on the
molecular structure. Misleading inferences have been made mostly in the pyrolysis of
humic substances and biomacromolecules such as polysaccharides, proteins, lignins, etc.
16 Table 1. Predominant pyrolysis fragments from aquatic NOM (Adapted from Leenheer et al., 2003).
2.1.2.6.2 Ultra-filtration
This method involves passing water into a cascade of ultrafiltration membranes. The
membranes act as cut-offs depending on the molecular weight of organic compounds
relative to the pore size of the membrane. Ultra-filtration is capable of separating
6 particles across a wide range of nominal molecular weight (500 -10 Da). However, the
method does not provide exact characterization because the distribution of molecular
weight is influenced by many factors such as pH, ionic strength, membrane type,
pressure, and solute interactions. The solute rejection may be due to the charge of the
solutes, hydrophobicity, molecular weight, and interaction with the geometric properties
of the pores (Check, 2005; Leenheer et al., 2003).
17 Different materials are used to manufacture the membranes including, cellulose,
cellulose acetate, polyamide thin film composite (TFC), and polyethersulfone. Each
interacts differently with the solutes.
2.1.2.7 Characterization by disinfection by-products formation potential (DBPFP)
The disinfection byproducts formation potential is defined as the amount of DBPs
formed from a water sample under standardized conditions of temperature, pH, contact
time with chlorine and residual free-chlorine concentration at the end of the incubation
period under laboratory conditions (Fram et al., 2005).
Because two samples can have the same DOC concentrations and different DBPs when
chlorinated, this parameter reflects the propensity or the reactivity of a certain organic
matter to generate DBPs. When the parameter is normalized by dividing the
concentration of DBPs formed by the concentration of DOC, it is then referred as specific
DBPs formation potential (Chow et al., 2006).
2.2 Disinfection byproducts
2.2.1 Types and formation of DBPs
2.2.1.1 Chlorine chemistry
Table 2 shows that chlorine is used in more than 90% of all systems that perform disinfection of water (USEPA, 2001).
18 Table 2: Survey of Disinfectant use in US systems 1997 (adapted from USEPA, 2001).
When chlorine is added, as gas or solid hypochlorite, it is hydrolyzed and dissociates to hypochlorous and hydrochloric acids (Equation1). The hypochloric acid dissociates further to hypochlorite and hydrogen ions
- + + - Cl2 + H20 Ù HOCl + Cl + H and HOCl Ù H + 0Cl . (Equation 1)
Studies have shown that OCl- and HOCl are the two chlorinating agents that react with organic matter to generate DBPs (Gang, 2001).
2.2.1.2 Types of DBPs
There exist thousands of DBPs. Table 3 lists the most commonly studied and representative types of DBPs, and they were categorized into three classes USEPA
(1999). The list includes DBPs and disinfectants left in water after disinfection process, known as disinfectant residuals.
19
Table 3. DBPs and Disinfectant Residuals (adapted from USEPA, 2001).
2.2.1.3 Formation mechanisms
The mechanism by which chlorine reacts with organic matter is not well understood
because numerous possible oxidation, addition and substitutions reaction pathways exist
(Reckhow et al., 1985). Due to the complexity of DBPs formation, some models were developed to explain some possible reactions between organic matter compounds and chlorine (Figure 4).
20 a) Chlorination of 1, 1, 1-Trichloroacetone
b) Chlorination of purvate
Figure 4. Examples of reaction models for formation of some DBPs species (Reckhow and Singer, 1985).
21 c) Chlorination of beta-diketone structure
Figure 4. Examples of reaction models for formation of some DBPs species (Reckhow and Singer, 1985) (Concluded).
Reckhow et al. (1985) indicated that the reaction of organic matter and chlorine passes
through intermediate stages and the path of reactions depends on pH. Figure 4a shows that the reaction between trichloroacetone (TCAC) and chlorine has two pathways where
60% of the chloroform is formed at the same time with dichloroacetic acid (DCAA) at
the faster rate and 40% of the remaining chloroform is formed at via the slower reaction.
Figure 4b shows the effect of pH on the formation of different species. It was observed
that there is a propensity in the formation of absorbable organic halides (AOX) species at
22 pH 7. Figure 4c shows that the oxidation of a diketone with a quick substitution by
chlorine can generate chloroform, trichloroacetic acid (TCAA), and DCAA. It suggests
that chloroform, DCAA and TCAA might come from the same precursor and that the
reaction is also affected by pH.
Aromatic compounds with an electron donating group such as -OH, -OR, -NH2 or -R
are believed to be more reactive with chlorine than compounds with electron withdrawing
groups such as -NO2, -COOH, -COOR or -X. The electron donating group directs the
halogen to “ortho” and “para” positions on the molecules which increases the electron
density on the aromatic ring. The electron withdrawing groups are believed to direct the
halogen to the “Meta” position only. (Gang, 2003)
In other studies (Hwang et al., 2001; Leenheer, 2001a), it was indicated that amino
sugars generate a low yield of DBPs, even though they cause a high chlorine demand due
to amino groups. Plant organic compounds such as tannins and lignins are known to produce high yields of DBPs with chlorination, whereas, terpenoids have low yield of
DBPs (Croué et al., 2000, Hwang et al., 2001).
The algal biomass and extra-cellular products (ECPs) in organic matter from water
bodies have been also linked to trihalomethanes formation (Veum, 2006).
Humic acids have been found to be significantly reactive with chlorine and phenolic
functional groups in humic substances have been correlated to the formation potential of
THMs (Gang, 2001).
Nissinen at al. (1999) indicated that the raw water bromide concentration affected the
concentrations of individual THMs compounds. When the bromide concentration in raw
water exceeded 100 µg/l, 83 % of the total THMs produced were brominated. When
23 bromide concentration decreases, the percentage of brominated THMs decreases, and when the bromide concentration was below 100 µg/l, the formation of chloroform was the greatest.
2.2.2 Health Effects
Of the four THMs, chloroform has been the most studied and linked to renal tumors in rodents. Bromodischloromethane (BDCM) and bromoform have been associated with colon tumor in mice and rats. Chlorodibromomethane (CDBM) has caused increased tumor growth in rats and liver tumors in female mice.
For HAAs, DCAA caused liver cancer in mice and rats, and was found to cause testicular toxicity. TCAA caused liver tumors in mice alone, and has also been correlated with developmental defects (Boorman et al., 1999, C.J. Moudgal et al., 2000). The EPA has also classified health effects related to disinfectants and DBPs. Table 4 shows the effects of four THMs species and three HAAs species.
24 Table 4. Status of health information for THMs and Some HAAs (adapted from USEPA, 2001).
2.2.3 Regulations
In regulations promulgated under the authority of the Safe Drinking Water Act, the
EPA established two stages of rules for maximum contaminant level for DBPs. Stage 1, promulgated in 1994, set a maximum contaminant level of 100 µg/l for total trihalomethanes (TTHMs) and 80 µg/l for HAA5 that was later reduced to 80 µg/l and 60
µg/l for THMs and HAA5 respectively. Stage 2 set a maximum contaminant level of 40
µg/l and 30 µg/l for TTHMs and HAA5, respectively.
Facilities that serve more than 10,000 people were required to comply with stage 1 by
December 16, 2001 and smaller facilities needed to comply by December 16, 2002.
THMs, all species (chloroform, bromodichloromethane, dibromochloromethane and
25 bromoform) are included while for HAAs, facilities are required to comply on a total of five species (monochloroacetic acid, monobromoacetic acid, dichloroacetic acid, trichloroacetic acid and dibromoacetic acid). For Stage 2, depending on system size and the extent of needed construction, systems will begin the first year of compliance monitoring between 2012 and 2016 (USEPA, 1994, 2006).
26
CHAPTER 3: DESCRIPTION OF WATERSHEDS CONTAINING SAMPLING SITES
Sampling sites in this study are classified according to the hydrologic unit (drainage basins) where their sampling water bodies are located. Figure 5 shows the map of all
Missouri hydrologic units that subdivide the state major watersheds.
Figure 5. Missouri State Hydrologic Units.
27 3.1 Hydrologic units/subwatershed of sampling sites
3.1.1 South Fork Salt sub-basin
The South Fork Salt sub-basin (Figure 5, No7, USGS Cat. Unit 07110006) covers 42%
of the Salt River basin that drains 2,914 square miles of northeastern Missouri. It lies in
the dissected till plains physiographic region. There are about 165 third-order and larger
streams in the basin. Similar to many other basins in northeastern Missouri, 70% of the
land is used for agricultural purposes, 14% of the land is forested, and the rest includes
grassland, urban, open water, mining fields, etc. The major water quality concern is soil
erosion from cultivated lands and deposition of sediment into stream channels. Overall,
point source pollution has a minor impact on basin streams compared to non-point
sources. Only five municipal waste water treatment facilities discharge in the basin in an amount of 0.5 million gallons per day.
In the upper portion of the basin, where local relief is low, glacial till is overlain by
loess deposits in most areas. In the valleys of the Middle and South Fork sub-basins,
streams have eroded to expose limestone bedrock and shale. In the central part of the
basin around Mark Twain Lake, relief increases and exposed limestone and shales in the
valley walls and streambeds are more prevalent. Till quickly shallows in the lower Salt
River sub-basin as valleys become more abrupt with high relief. A relief of 440 feet is
attained at the lower end of the Salt River basin. Soils throughout most of the sub-basin
are typical of the Central Claypan Region, except in the extreme lower portion that is
located in the Central Mississippi Valley Wooded Slopes region.
(http://mdc.mo.gov./fish/watershed/usgs8.htm (Visited January, 2007)
28 3.1.2 North Fork Salt sub-basin
The North Fork Salt sub-basin (Figure 5, No8, USGS Cat. Unit 07110005) covers 32% of the Salt River basin. The North Fork Salt River is the longest stream in the sub-basin, flowing about 119 miles. Clarence Cannon Dam, located on the Salt River approximately
63 miles upstream of its confluence with Mississippi River, forms the 18,600-acre Mark
Twain lake. The land use characteristics are detailed above for the entire Salt River basin
(http://mdc.mo.gov./fish/watershed/usgs8.htm (Visited January, 2007).
3.1.3 Little Chariton sub-basin
The Little Chariton sub-basin (Figure 5, No36, USGS Cat. Unit 10280203) is part of the main watershed known as the Chariton River watershed. The soil types in the basin developed from loess and till parent material. These soils are classified as loams with differing clay and silt content. Soils with silt content are predominantly alluvial in origin.
The relatively low permeability of the soil and till coupled with the presence of shale and coal greatly inhibits the percolation of surface water to ground water sources. Because of this, most water movement occurs through the stream network.
The land use of the Little Chariton River sub-basin is 43% covered with hay or pasture,
38% covered by cropland, 15% covered by forest and woodland, and 4% used for other proposes such as municipalities, roads, impounded water, etc. Floodplains and ridge tops are used to grow crops and hay, while pasture occurs on the hill sides as well as ridge tops. The Mussel Fork Creek sub-basin is more heavily forested that the remainder of the basin. In 15% of forested area, as much as 66% of it is grazed.
29 There are large farms such as Whitetail and Valley View that raise more than 200,000
head of cattle and discharge a couple of hundreds of millions gallons of waste annually
from 22 lagoons. The facilities also drain into either first to third order streams or
directly to the fourth order Mussel Fork Creek.
As far as non-point source pollution is concerned, the primary pollutant is sediment delivered by the processes of sheet, rill, gully and stream banks erosion throughout the watershed. Drainage from abandoned mines causes receiving water streams to be
mineralized, with an increase in total dissolved solids, conductance, metals and variation
in pH. There are several thousands acres of strip mined lands within the basins of East
Fork and Middle Fork of the Little Chariton River.
Some of the examples of point sources that impact water quality in the watershed
include oil and petroleum pipelines (belonging to Amoco, Arco, and Mapco), wastewater
treatment facilities like Moberly’s west’s wastewater treatment facility ( that impact 2.5
miles of the East Fork Little Chariton River, the Thomas Hill power plant ash pond that
discharges into the Middle Fork of the Little Chariton River, and concentrated animal feeding operations and farms discharges (http://mdc.mo.gov./fish/watershed/usgs8.htm
(Visited January, 2007).
3.1.4 Lower Chariton sub-basin
The Lower Chariton sub-basin (Figure 5, No35, USGS Cat. Unit 10280202) is part of
the main watershed know as Chariton River watershed. The description is as same as
above for the whole Chariton River watershed.
30
3.1.5 Lower Grand sub-basin
The Lower Grand sub-basin (Figure 5, No33, USGS Cat. Unit 10280103) is part of the main Grand River basin that is located in northwest Missouri and part of southwest
Iowa. The watershed covers 7,900 square miles and over three-fourths of this area is in
Missouri. The basin is entirely rural and the land use is predominantly agricultural and cropland is the largest component. Ninety two percent is estimated to be agricultural and
5% is forest.
The geology of area is characterized by the soil that was glaciated and later subjected to loessial deposits. The predominant soils are derived from glacial drift and loess.
Loessial silt loam covers the greater part of the broad divides and gentle slopes. Glacial silt loams and silty clay loams, usually highly eroded, occur on the slopes. Generally, the soils are fine-grained and easily erodible.
The basin contains more than 1,000 third order and larger streams, and most streams with a drainage area of less than 50 square miles will stop flowing sometimes
(approximately seven days every two years or more).
Streams are frequently turbid and the water quality standards for iron, manganese and fecal coliforms bacteria are frequently exceeded. Major water quality problems are associated with non-point source pollutants such as soil erosion and manure runoff.
Channelization and excessive levee construction are the main management practices.
The combination of channel alteration and inadequate corridors results in erosion with tall stream banks.
31 The basin has precipitation that ranges from 32 inches in the northeast part to 36 inches in the southeast portion. The greatest amount of rainfall occurs in May and June. Most of the rainfall runs off the surface of the land rather than soaking into the soil. Streams show rapid flow increases in conjunction with rains but quickly return to low flow conditions when runoff stops (http://mdc.mo.gov./fish/watershed/usgs8.htm (Visited January, 2007).
3.1.6 Upper Grand sub-basin
The Upper Grand sub-basin (Figure 5, No31, USGS Cat. Unit 10280101) is part of the main Grand River basin. The description is same as above for the whole Grand River basin.
3.1.7 One Hundred and Two sub-basin
The One Hundred and Two sub-basin (Figure 5, No29, USGS Cat. Unit 10240013) is part of the main Platte River watershed. The Platte River watershed covers 2,149 square miles with 67.5% of area located in Missouri and the remainder in Iowa. There 435 third order and large prairie streams within the basin that have flows varying in conjunction with rains. The streams are turbid due to silty and sandy suspended matter.
The basin is covered primary in glacial till, and due to clay content, movement of water to the subsurface is greatly diminished.
Land use is dominated by agriculture and is composed of about 60% row crops, 17% pasture and 11% forest.
Notable point source pollutants concerns are those associated with municipal wastewater near urban areas.
32 Non-point sources are major problems because of excessive runoff and extended low flows. Major non-point sources include channelization, intensive row cropping and livestock. Numerous draws and gullies ensure thorough drainage but soil erosion is often high (http://mdc.mo.gov./fish/watershed/usgs8.htm (Visited January, 2007).
3.1.8 Platte sub-basin
The Platte sub-basin (Figure 5, No28, USGS Cat. Unit 10240012) is part of the main
Platte River watershed. The description is same as above for the whole Platte River watershed.
3.1.9 Moreau sub-basin
The Moreau River basin (Figure 5, No51, USGS Cat. Unit 10300102) is a sub-basin
of the Missouri River Basin. The basin is underlain with Ordovician age dolomite, thin
beds of shale and minor deposits of sandstone. Water penetration is poor and most runs
off to surface streams. The land use in the basin is 2.6% urban, 5.8% woodland, 18.4%
forest, 32.4% grassland, and 40.5% crop land.
The major pollution to streams in the basin is due to contamination by human and
animal wastes, soil erosion, and in-stream erosion.
Non-point source of pollution in the watershed involves soil eroded from crop land
and pasture land. Other sources include waste from livestock and residential septic
fields (http://mdc.mo.gov./fish/watershed/usgs8.htm (Visited January, 2007))
33
CHAPTER 4: MATERIALS AND METHODS
4.1 Sampling
Samples were collected from 38 creeks in northern Missouri. Table 5 shows the names of the sampling streams, the latitude and longitude, the lake where water is drained to, and the nearby highway. Samples were collected into two rounds: the first 18 samples were collected on March 14th, June 5th and September 17th of 2006. The second round of samples was collected on March 28th, June 20th and October 3rd of 2006. Samples were transported using two-liter amber bottles and were stored below 4oC before analysis.
34
Table 5. Sample number and collection sites. # NAME COUNTY HIGHWAY LAKE LATITUDE LONGITUDE 1 Rocky Fork Creek Boone US 63 39.0382 -92.3312 2 Silver Fork Boone US 63 39.1242 -92.3338 3 Reese Fork Monroe MO 151 Mark Twain 39.3864 -92.2112 4 Elk Fork Salt River Monroe MO 151 Mark Twain 39.4507 -92.2103 Middle Fork Salt 5 River Monroe MO 151 Mark Twain 39.5253 -92.1278 Daniel 6 Unnamed Creek Macon Olympic Avenue Boone 39.7816 -92.3117 Daniel 7 Ten Mile Creek Macon/Shelby CR 301 Oregon Rd Boone 39.7967 -92.2924 8 North Fork Salt River Shelby MO 151 Mark Twain 39.8300 -92.2298 East Fork Little 9 Chariton River Macon Rt. J Longbranch 39.9196 -92.5171 10 Long Branch Macon Rt. J Longbranch 39.9188 -92.4974 11 Duck Creek Macon CR 606 or 613 Macon Lake 39.7813 -92.4953 Middle Fork Little US 36 or old HWY 12 Chariton River Macon 36 Thomas Hill 39.7529 -92.5922 13 Brush Creek Macon US 36 39.7602 -92.8028 14 Mussel Fork Macon US 36 39.7590 -92.8570 15 East Yellow Creek Linn Kayak Rd 39.7975 -92.9660 16 Long Branch Linn Ira Dr 39.7960 -93.0600 17 Muddy Creek Linn US 36 39.7778 -93.2137 18 Parson Creek Linn US 36 39.7775 -93.3247 19 Grindstone Creek Dekalb US 36 Grindstone 39.7548 -94.3165 20 Little Platte River Clinton Rt. C Smithville 39.5546 -94.4393 21 Roberts Branch Clinton Rt. O Smithville 39.5079 -94.5280 22 Little Third Fork Dekalb MO 6 39.8928 -94.5023 23 Third Fork Dekalb MO 169 39.8475 -94.5897 24 Lost Creek Gentry/Dekalb Co. Line Rd King Lake 40.0390 -94.4476 25 White Cloud Creek Nodaway US 71 40.1409 -94.8697 26 Mozingo Creek Nodaway 210th St Mozingo 40.4166 -94.7763 27 Long Branch Nodaway MO 136 40.3441 -94.7453 28 Wildcat Creek Nodaway MO 136 40.2423 -94.6185 29 White Oak Creek Harrison MO 136 40.2521 -94.1389 30 Little Creek Harrison W 200th St Harrison Co. 40.4257 -94.0752 31 Polecat Creek Harrison MO 13 40.2203 -94.0234 32 Hickory Creek Daviess MO 13 40.0564 -93.9979 33 Muddy Creek Daviess 240th off MO 13 39.9800 -93.9476 34 Marrowbone Daviess MO 13 39.8671 -93.9384 35 Shoal Creek Livingston US 65 39.7317 -93.5557 36 Horse Fork Clinton MO 116 Smithville 39.5643 -94.4436 37 Little Platte River Clinton MO 116 Smithville 39.5678 -94.4074 38 Roberts Branch Clinton CR 287 Smithville 39.5209 -94.5138
35
4.2 Chlorination
The samples were first filtered using 1.2 µm and 0.45 µm filters, fixed on an air pump
(UL085E3), and stored at 4°C in amber bottles.
4.2.1 Chlorine demand
Chlorine demand is the difference between the amount of chlorine applied to the
sample and the amount of free, combined, or total chlorine residual remaining at the end
of a contact period. The chlorine demand was determined using the Uniform Formation
Conditions (UFC) method. The demand is caused by interaction with dissolved or
suspended organic matter and/or with inorganic reductants such as ferrous, manganous,
nitrite, sulfide, and sulfide ions, ammonia and cyanide. The chlorine demand helps to
determine the dose to be applied so that the reaction with organic matter can be complete
in order to allow determination of disinfection byproducts formation potential.
4.2.2 Reagents and Procedure
The free chlorine residual. The free chlorine residual analysis was conducted using a
spectrophotometer (HACH DR/2000) using the UFC method.The UFC method consists of running the test at pH of 8.0±0.2, a temperature of 20±1.0°C and incubating for
24h±1hr in the dark to produce a free chlorine residual of 1.0±0.4mg/l.
A sample is placed in 25 ml spectrophotometer cell and a free chlorine powder pillow is
added. The absorbance is then measured at 530 nm. The method reads a maximum free
chlorine concentration of 2.0 mg/l. If the spectrometer warns for greater concentrations,
the sample must be diluted using deionized (DI) water to a concentration below 2 mg/l
36 and the value obtained is multiplied by the dilution factor. For accuracy, each sample is
analyzed three times and the results are averaged.
pH 8 borate buffer. A 1.0 M boric acid, 0.26 M NaOH buffer is added to the test
solution to keep the samples at pH of 8.0±0.2. If necessary, sulfuric acid can be used to
adjust the pH to a value of approximately 8.0.
Hypochlorite solution (about 2000mg/l): 18 ml of sodium hypochlorite and 13%
active chlorite (ACROS 21925-5000) diluted to 500 ml using DI water.
Chlorine addition. The samples are chlorinated using a pH8 combined hypochlorite- buffer dosing solution (1000-3000 mg/l chlorine solution). The solution is prepared using a hypochlorite solution and pH of 6.7 borate buffer created by combining 1.0 M boric acid, and 0.11 M NaOH.
N, N-diethyl-p-phenylene diamine (DPD) free chlorite reagent (HACH 14070-99).
DPD is a colorant for free chlorine concentration determination.
Ammonium Chloride solution (88 g/l) is added to stop the reaction between the
chlorine and the organic matter.
37 4.3 Trihalomethanes (THMs) analysis
4.3.1 Procedure
Trihalomethanes analysis was conducted using the gas chromatography (Varian model
3800)/mass spectrometer (Varian Saturn 3800) (GC/MS) with a purge a trap concentrator
(Model Tekmer 300). The analysis was perfomed with referrence to EPA method 524.2
(Munch, 1995). Volatile organic compounds (such as trihalomethanes) and surrogates
with low water solubility are extracted (purged) from the sample matrix by bubbling an inert gas through the aqueous sample. Purged sample components are trapped in a tube containing suitable sorbent materials. When purging is complete, the sorbent tube is heated and backflushed with helium to desorb the trapped sample components into a capillary gas chromatography (GC) column interfaced to a mass spectrometer (MS). The column is temperature programmed to facilitate the separation of the method analytes which are then detected with the MS. Compounds eluting from the GC column are identified by comparing their measured mass spectra and retention times to reference spectra and retention times in a data base. Reference spectra and retention times for analytes are obtained by the measurement of calibration standards under the same conditions used for samples. Analytes are quantitated using procedural standard calibration. The concentration of each identified component is measured by relating the
MS response of the quantitation ion produced by that compound to the MS response of
the quantitation ion produced by a compound that is used as an internal standard.
Surrogate analytes, whose concentrations are known in every sample, are measured with
the same internal standard calibration procedure.
38
4.3.2 Reagents, standards and quality assurance
Stock standard solution-- A concentrated solution containing one or more method analytes prepared in the laboratory using assayed reference materials or purchased from a reputable commercial source.
THMs standard solution-- The THM standard solution (TSS) contains a concentration of 2000 µg/ml for each THM species (CHCl3, CHCl2Br, CHClBr2, and
o CHBr3) in methanol purchased from Supelco (Cat. N 48140U). A certificate of analysis yielded 1889 µg/ml for CHBrCl2, 1973 µg/ml for CHBr3, 1965 µg/ml for CHCl3, and
1952 µg/ml for CHBr2Cl.
Primary Dilution Standard Solution (PDS)-- A solution of several analytes prepared in the laboratory from stock standard solutions and diluted as needed to prepare calibration solutions and other needed analyte solutions. A dilution of standard solution using methanol was prepared to make the Primary Dilution THM standard (PDTS) of a concentration of 200 µg/ml.
Internal Standard (IS)-- A pure analyte(s) added to a sample, extract, or standard solution in known amount(s) and used to measure the relative responses of other method analytes that are components of the same sample or solution. The internal standard must be an analyte that is not a sample component. The internal standard for USEPA method
524.2 for TTHMs analysis is carbon tetrachloride (CCl4), used as a pure liquid from
Sigma Aldrich (Cat. 39, 580-3). The primary dilution internal standard (PDIS) of 1460
µg/ml CCl4 in methanol was prepared by diluting the internal standard solution.
Procedural Standard Calibration -- A calibration method, where aqueous calibration standards are prepared and processed (e.g., purged, extracted, and/or derived) in exactly
39 the same manner as a sample. All steps in the process from the addition of sampling
preservatives through instrumental analyses are included in the calibration. Using
procedural standard calibration compensates for any inefficiencies in the processing procedure. USEPA method 524.2 indicates that a minimum of five calibration standards
are required.
Quality Control Sample (QCS) -- A solution of method analytes of known
concentrations which is used to fortify an aliquot of lab reagent blank (LRB) or a sample
matrix. The QCS is obtained from a source external to the laboratory and different from
the source of calibration standards. It is used to check laboratory performance with
externally prepared test materials. The quality control THMs standards were obtained
from Environmental Resources Associates (Cat. No 702). The certified values for species
when 5 µl are added in 100 ml of deionized ultrafiltered (DIUF) water are; 20.1 µg/ml for
CHBrCl2,42.3 µg/ml for CHBr3, 11.8 µg/ml for CHCl3, 38.1 µg/ml for CHBr2Cl, and 112
µg/ml for TTHMs. The quality control performance acceptance limits are 15.7-25.5
µg/ml for CHBrCl2, 30.9-54.6 µg/ml for CHBr3, 9.15-14.7 µg/ml for CHCl3, 29.2-47.5
µg/ml for CHBr2Cl, and 89.7-137 µg/ml for TTHMs.
Surrogate Analyte (SA) -- A pure analyte(s), which is extremely unlikely to be found
in any sample, and which is added to a sample aliquot in known amount(s) before
extraction or other processing and is measured with the same procedures used to measure
other sample components. The purpose of the SA is to monitor method performance with
each sample.
Laboratory Duplicates (LD1 and LD2) -- Two aliquots of the same sample taken in
the laboratory and analyzed separately with identical procedures. An analysis of LD1
40 and LD2 indicates the precision associated with laboratory procedures, but not with sample collection, preservation, or storage procedures.
Field Duplicates (FD1 and FD2) -- Two separate samples collected at the same time and place under identical circumstances and treated exactly the same throughout field and laboratory procedures. Analyses of FD1 and FD2 give a measure of the precision associated with sample collection, preservation and storage, as well as with laboratory procedures.
Laboratory Reagent Blank (LRB) -- An aliquot of reagent water or other blank matrix that is treated exactly as a sample is including exposure to all glassware, equipment, solvents, reagents, internal standards, and surrogates that are used with other samples. The LRB is used to determine if method analytes or other interferences are present in the laboratory environment, the reagents, or the apparatus.
Field Reagent Blank (FRB) -- An aliquot of reagent water or other blank matrix that is placed in a sample container in the laboratory and treated as a sample in all respects, including shipment to the sampling site, exposure to sampling site conditions, storage, preservation, and all analytical procedures. The purpose of the FRB is to determine if method analytes or other interferences are present in the field environment.
Laboratory Performance Check Solution (LPC) -- A solution of one or more compounds (analytes, surrogates, and internal standard or other test compounds) used to evaluate the performance of the instrument system with respect to a defined set of method criteria.
Laboratory Fortified Blank (LFB) -- An aliquot of reagent water or other blank matrix to which known quantities of the method analytes are added in the laboratory.
41 The LFB is analyzed exactly like a sample, and its purpose is to determine whether the
methodology is reward, and whether the laboratory is capable of making accurate and
precise measurements.
Laboratory Fortified Sample Matrix (LFM) -- An aliquot of an environmental sample to which known quantities of the method analytes are added in the laboratory.
The LFM is analyzed exactly like a sample, and its purpose is to determine whether the sample matrix contributes bias to the analytical results. The background concentrations of the analytes in the sample matrix must be determined in a separate aliquot and the measured values in the LFM corrected for background concentrations.
Calibration Standard (CAL) -- A solution prepared from the primary dilution
standard solution or stock standard solutions and the internal standards and surrogate
analytes. The CAL solutions are used to calibrate the instrument response with respect to
analyte concentration.
4.4 Haloacetic acids (HAAs) analysis
4.4.1 Procedure
The HAAs analysis was conducted referring to USEPA Method 552.2 (Munch et al.,
1995). A 40 ml volume of sample was adjusted to pH<0.5 using a 2 ml dose of sulfuric acid. A salting agent (sodium sulfate) was added to increase the extraction efficiency.
The sample was extracted with 4 mL of methyl-tert-butyl-ether (MTBE). Haloacetic acids that have been partitioned into the organic phase are then converted to their methyl esters by the addition of acidic methanol followed by slight heating. The acidic extract is neutralized by a back extraction with a saturated solution of sodium bicarbonate and the
target analytes are identified and measured by capillary column gas chromatography
42 using an electron capture detector (GC/ECD). Analytes are quantitated using a
procedural standard calibration. The analysis provides the concentration of nine
haloacetic acid species (monochloroacetic acid (MCAA), monobromoacetic acid
(MBAA), dichloroacetic acid (DCAA), trichloroacetic acid (TCAA), bromochloroacetic
acid (BCAA), dibromoacetic acid (DBAA), bromodichloroacetic acid (BDCAA),
chlorodibromoacetic acid (CDBAA), and tribromoacetic acid (TBAA).
4.4.2 Reagents, standards and quality assurance
Stock Standard Solution --The HAA9 standard solution contains nine species
having a concentration range from 200 to 2000 µg/ml each species (MCAA, MBAA,
DCAA, TCAA, BCAA, DBAA, DBCAA, CDBAA, and TBAA) in methanol. The
solution was provided by Superlco (cat. 47781). A certificate of analysis yielded 590.2
µg/ml for MCAA, 386.7 µg/ml for MBAA, 590.7 µg/ml for DCAA, 183.5 µg/ml for
TCAA, 385.3 µg/ml for BCAA, 192.3 µg/ml for DBAA, 384.1 µg/ml for BDCAA, 987
µg/ml for CDBAA, and 1,977 µg/ml for TBAA.
Primary Dilution HAA9 Standard (PDHS)--The 5-50 µg/ml PDHS was prepared by
diluting HAA9 standard solution using MTBE.
Internal Standard (IS) – Internal standard for EPA method 552.2 (Munch et al.
(1995)) for HAA analysis is 1, 2, 3-trichloropropane having a concentration of 1000
µg/ml in MTBE (Supelco cat. 47789). A certificate of analysis yielded 1013 µg/ml. The primary dilution internal standard (PDIS) for HAA analysis contains 40 µg/ml of 1, 2, 3- trichloropropane in MTBE.
Surrogate Analyte (SA): a SA of 2-bromopropionic acid with a concentration of
1,000 µg/ml in MTBE is used for HAA analysis. The solution was supplied by Supelco
43 (Cat. 47645). A certificate of analysis yielded a concentration of 932µg/ml. The primary
dilution surrogate (PDS) contains the 2-bromopropionic acid at a concentration of 100
µg/ml in MTBE.
Laboratory Duplicates (LD1 and LD2), field duplicates (FD1 and FD2), the laboratory
reagent blank (LRB), the field reagent blank (FRB), the laboratory performance check
solution (LPC), the laboratory fortified sample matrix (LFM), the procedural standard
calibration, and the calibration standard (CAL) are all defined.
Quality Control Sample (QCS) -- A solution of method analytes of known
concentrations which is used to fortify an aliquot of LRB or a sample matrix. The QCS is
obtained from a source external to the laboratory and different from the source of
calibration standards. It is used to check laboratory performance with externally prepared
test materials. The quality control HAAs standards were obtained from Environmental
Resources Associates (Cat. No. 684). The Certified values for the various species when
100 µl are added in 100 ml of DIUF water are 40.0 µg/ml for MBAA, 41.7 µg/ml for
BCAA, 30.2 µg/ml for MCAA, 35.9 µg/ml for DBAA, 33.2 µg/ml for DCAA, and 34.6
µg/ml for TCAA. The quality control performance acceptance limits are; 26.5-54.3
µg/ml for MBAA, 26.7-54.3 µg/ml for BCAA, 18.2-41.4 µg/ml for MCAA, 20.6-47.7
µg/ml for DBAA, 21.7-41.4 µg/ml for DCAA, and 20.2-47.5 µg/ml for TCAA.
4.5 Ultraviolet absorbance (UVA) analysis
UV scans were conducted using a UV-visible spectrophotometer (CARY 50 CONC)
with a clean and dry 1-cm quartz cell. The UV absorbance was scanned in a wavelength
range of 550 nm-250 nm and the absorbance at 254 nm was recorded. Each sample was
44 recorded in duplicate for quality purposes. An additional sample scan was performed
when the difference between the samples and its duplicate was greater than 0.001/cm,
and the scanned values of each sample were averaged. DI water was scanned prior to
sample analysis and utilized as a baseline or blank.
4.6 Fluorescence Analysis
Fluorescence analysis was conducted using a fluorescence spectrophotometer (Model
Hitachi F-4500 Spectrograph, Hitachi Co.). Samples were introduced in a standard 1-cm
quartz cuvette cleaned using acetone and the internal lamp is set at 700 volts for the experiments. All the samples producing a UVA reading greater than 0.12 cm-1 were
diluted using DI water to eliminate inner filter effect. The excitation wavelength range
was set between 220 nm and 540 nm at 4 nm intervals and the emission scan was
collected between 250 nm and 600 nm at 3 nm intervals. The experiment was performed at a standard room temperature (21±2°C). The excitation-emission matrices (EEMs) were corrected for Raleigh scatters problem, the first order scatter appears at an emission wavelength that is equal to the excitation wavelength and the second order scatter appears at en emission wavelength twice the excitation wavelength. Raleigh scatter is removed because it does not contain any fluorophore information. The EEM for DI water was considered as the baseline and was subtracted from sample’s EEMs. Then, N
fluorescence spectra are arranged into a three-way array. A three-way tensor A was
generated to be decomposed by parallel factor analysis, using PARAFAC model.
45 Parallel factor analysis (PARAFAC)
PARAFAC is a multivariate analysis technique useful in investigating organic material
derived from diverse environmental systems and it is suggested that it can be used for
watershed management decisions (Holbrook et al., 2006).
PARAFAC is a decomposition of multi-way data, whereby EEM is decomposed into
trilinear terms and a residual (equation 2).
F X ijk = ∑ kfjfif + ecba ijk , (Equation 2) f = 1
th th where, for EEM data, X ijk is the fluorescence intensity of the i sample at the k
th excitation and j emission wavelength. The term aif is a quantitative approximation of
the concentration of the fth fluorophore in the ith sample, is also termed as score. The
th terms b jf and ckf are estimates of the emission and excitation spectrum of the f
fluorophore, also defined as loadings, respectively. The variable F is the number of
components (fluorophore moieties or analytes) and eijk are the residual elements of the
model. The graphical representation of the PARAFAC model is presented on Figure 6.
PARAFAC helps to identify individual fluorophores in EEM data and their relative
concentrations quantified in complex mixtures, if the correct number of components are
chosen when executing the computer model and outliers are removed from the dataset.
The program is executed using MATLAB (mathworks) and the N-way Toolbox
(Holbrook et al., 2006; Bro, 1997, http://www.models.kvl.dk (visited July 2006)).
46
Figure 6: Graphical representation of a two-component PARAFAC model of the data array X (adapted from Bro, 1997).
47
CHAPTER 5: RESULTS AND DISCUSSION
5.1 UV absorbance characteristics of NOM
UVA254 of NOM increases with aromaticity and conjugated double bonds of organic
chemical compounds. In this study, the UVA254 values vary with location and time.
Samples that were collected in the spring, during the month of March, have higher
UVA254 mean values compared to samples that were collected in the summer, during the month of June, and in fall, during the months of September and October. (Figures 7, 8, 9)
1.0 March 14, 2006 March 28, 2006
0.8 Max = 0.7800 -1 0.6 in cm in 254 0.4 St.Dev = 0.2055 UVA Mean = 0.3053
0.2
Min = 0.1000 0.0 1234567891011121314151617181920212325262728293031323334353738 Site
Figure 7. Variation of UVA254 during spring.
48 1.0 June 5, 2006 June 20, 2006
0.8
-1 0.6
in cm in Max = 0.4600 254 0.4 UVA
Mean = 0.2270 st.dev = 0.1092 0.2
Min = 0.0600 0.0 1234567891011121314151617181920212223242526272829303132333435363738 Site Figure 8. Variation of UVA254 during summer.
1.0 September 17, 2006 October 3, 2006
0.8
-1 0.6 in cm in Max = 0.4430 254 0.4 UVA
St. dev = 0.0793
0.2 Mean = 0.1695
Min = 0.0700 0.0 01234567891011121314151617181920212223242526272829303132333435363738 Site
Figure 9. Variation of UVA254 during fall.
49 From Figures 7, 8, and 9, the UVA254 mean value is the highest in the spring
(0.3053), second highest in the summer (0.227) and the lowest in the fall (0.1695). In
addition, it was also noticed that the UVA254 standard deviation variation has the same
trend as the seasonal mean values variation. The spring season has the largest standard deviation (0.2055), while the second highest occurs in the summer (0.1092) and the lowest occurs in the fall (0.0793). If the UVA254 variation is compared to river flows, there is a similarity in the pattern between UVA254 and river flows: The highest flows were observed in the spring, and the lowest in the fall (Figure 10). Figure 10 shows
USGS flows recorded by the gauges near the sampling basins of Salt River basin, Platte
River basin, Grand River basin, and Chariton River basin.
50
Figure 10. Precipitation and river flow variations during sampling periods at the watersheds of sampling locations (USGS, http://mo.water.usgs.gov/ (visited February 2007)).
51
Figure 10. Precipitation and river flow variations during sampling periods at the watersheds of sampling locations (USGS, http://mo.water.usgs.gov/ (visited February 2007)) (continued).
52
Figure 10. Precipitation and river flow variations during sampling periods at the watersheds of sampling locations (USGS, http://mo.water.usgs.gov/ (visited February 2007)) (concluded).
UVA254 value varied with the location from which samples were collected. Figures 7,
8, and 9 are each divided into two parts. The first part is for samples number 1 to 18 and the other part is for samples numbered 19 to 28. The first part represents sites located in northeastern Missouri while the second part represents sites in northwestern Missouri
53 (Figure 11). Northeastern Missouri UVA254 values vary more than those from
northwestern Missouri, which also contains the UVA254 maxima.
Reckhow et al. (2004) state that rainfall events can lead to increased hydraulic connectivity among isolated wetlands and streams channels, which in turn affects the type of NOM in the rivers. They also indicated that depending on the type of soils, flushing of upper soil horizons can lead to transport of recently deposited organic compounds to surface waters.
Figure 10 and 12 suggest that two reasons might be the cause of the variability observed
on UVA254 based on the river flows and the geology. The river flow increase in the
spring and reduces in the fall (Figure 10). Figure 12 shows that the northeastern part of
the state has different soils, labeled as Desmonisian series whereas Missourian and
Virgilian series soils occur in the northwest. Desmonisian series allows more runoff
because they are more clayey than Missourian and Virgilian series soils.
Topsoil horizons contain plant-derived organic compounds such as degraded tannins and
lignins that have aromatic rings which absorb UV light. Thus, the increase in runoff can
contribute humic substances generated from terrestrial organic matter into streams.
54
Figure 11. Location of sampling sites.
Legend; Part 1; A: Moreau, B: South-Fork Salt, C: North-Fork Salt, D: Little Chariton, E: Lower Chariton, F: Lower Grand.
Part 2: G: Thompson, H: Upper Grand, I: Platte, J: One hundred and two
55
Figure 12. Soil type at sampling sites (see Appendix for Details).
56 5.2 Disinfection by-products formation potential of NOM
5.2.1 THMs formation potential of NOM
The THM formation potential is an indicator of the proclivity of NOM to form THMs
or a surrogate of THM precursors. In this study, the THM formation potential varied with
location and time. During the summer period, the mean value of THMs generated
(558ppb) is greater that the mean values of THMs generated in the spring (468ppb) or the
fall (275ppb) (Figure 13, 14, and 15).
1400 March 14, 2006 March 28, 2006
1200
1000 Max = 965
800
600 St. dev = 280 THMFP in ppb THMFP Mean = 468 400
200
Min = 118 0 01234567891011121314151617181920212223242526272829303132333435363738 Site Figure 13. Variation of THMFP during spring.
57 1400 June 5, 2006 Max = 1389 June 20, 2006
1200
1000
800 St. dev = 425 600 Mean = 558 TTHMFP in ppbTTHMFP in
400
200
Min = 53 0 0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738 Site
Figure 14. Variation of THMFP during summer.
1400 1300 September 17, 2006 October 3, 2006 1200 1100 1000 900 800 700 Max = 752 600 TTHMFP in ppb in TTHMFP 500 St. dev = 148 400 300 Mean = 275 200 100 Min = 63 0 1234567891011121314151617181920212223242526272829303132333435363738 Site
58 Figure 15. Variation of THMFP during fall.
Figures 13, 14, and 15 also show a higher standard deviation in the summer than in the
spring or the fall. Spatially, northeastern Missouri sites numbers (1-18) have higher THM
formation potentials in the spring and the fall,as compared to northwestern Missouri sites.
In the fall, the variation appeared to vary on both sides.
5.2.2 HAAs formation potential of NOM
The HAA formation potential is the proclivity of NOM to form HAAs or a surrogate of
HAAs precursors. HAA formation potential varied with location and time as did THMs.
Unlike THMs, the average HAA formation potential is higher in spring than in the
summer and or fall (Figure 16, 17, and 18).
1200 March 14, 2006 March 28, 2006 1100
1000
900
800 Max = 742 700
600
500 St. dev = 172 THAAFP in ppb THAAFP 400 Mean = 294 300
200
100 Min = 90 0 1234567891011121314151617181920212223242526272829303132333435363738 Site
Figure 16. Variation of HAAs during spring
59 1200 June 5, 2006 June 20, 2006
1000
800
600 THAAFP in ppb THAAFP 400 Max = 331
St. dev = 70 200 Mean = 149
Min = 44 0 1234567891011121314151617181920212223242526272829303132333435363738 Site Figure 17. Variation of HAAs during summer.
1200 September 17, 2006 October 3, 2006
Max = 1054 1000
800
600 THAAFP in ppb THAAFP
400 St. dev = 189 Mean = 223 200
Min = 92 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 Site Figure 18. Variation of HAAs during fall.
60 Models developed by Reckhow et al.(1985) indicate that a portion of the THM and
HAA might be originating from the same organic compounds and the amount of either
type formed depends on factors such as pH, the structure of the organic compounds, temperature, ionic concentrations, chlorine concentration and contact time, as well as
some that may be unknown. Thus, one can suggest that the increase in THMs from spring
to summer and the reduction of HAAs can be related to precursors with characteristics of
models such as 1,1,1 trichloroacetone where chloroform (a THM specie) was favored
more than DCAA (a HAA specie).
5.3 Relationship between UVA and DBPs formation potentials
5.3.1 Relationship between UVA and THMs formation potentials
The seasonal variation in UVA254 indicated in Figures 7, 8, and 9 is similar to the
THMs formation potentials,where both showed maxima in the northeastern watersheds.
The linear relationships in Figures 19 and 20 show that UVA254 is correlated with THM
generation.
61
THMs variation with repect to aromaticity(UVA254) for the all sampling periods
1600 March june sept-oct
1400 June R2 = 0.7969 1200
1000
R2 = 0.6213 800 March THMs in µg/l 600 R2 = 0.5161
400
sept-oct 200
0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90
UVA254
Figure 19. Relationship between UVA254 and THMFP in different seasons.
THMs variation with aromaticity in overall
1600
R2 = 0.5006
1400
1200
1000
800 TTHMs in µg/l µg/l in TTHMs 600
400
200
0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90
-1 UVA254 in cm
Figure 20. Relationship between UVA254 and THMFP for all seasons.
62
The linear relationship is in agreement with many studies that suggest that chlorine
reacts with organic compounds with high concentrations of aromatic rings such as
phenolic compounds, conjugated dienes and carbonyls that absorb UV light. However,
the correlation obtained varies between 0.5-0.8, which means that there are also aromatic
materials that are unreactive. Fram et al. (1999) indicated that non-aromatic compounds
such as enolizable beta-di-ketones, that do not absorb UV light, can react with chlorine to
generate THMs.
The correlation charts between degree of aromaticity (UVA254) and THM formation
potential show a higher correlation for samples taken in hot season in the month of June
(R2~0.7969) when compared with values collected at the end of September (R2~0.5161)
when temperatures begin to decrease. Figure 19 also shows a higher slope for linear
fitting in the month of June and the lowest for the end of September readings. The reason
for this difference might be due the fact that high temperatures lead to increased
biotransformation of organic matter and higher dissolution of it in water. It has also been
suggested that fast biodegradation occurs more with the labile material that are mostly
non-aromatic. Thus, in June there is a high correlation probably because reactive, non-
aromatic compounds (that don’t absorb UV) have been reduced by biodegradation. Also,
the increase in humification of plant material that generates more precursors to THMS is
known to increase with temperature.
5.3.2 Relationship between UVA and HAAs formation potentials
HAA formation potentials have a linear correlation with UVA254 and it varies with
seasons as shown in Figures 21 and 22.
63
HAAs variation with repect to aromaticity(UVA254) for the all sampling periods
1200 March June Sept-Oct
1000
800 R2 = 0.7082
Sept-Oct 600 March R2 = 0.4734 HAAs inHAAs µg/l
400
R2 = 0.6343
200
June
0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90
UVA254
Figure 21. Relationship between UVA254 and HAAs in different seasons.
HAAs concentration variation with respect to aromaticity of NOM in overall
1200
R2 = 0.4349
1000
800
600 HAAs in µg/l µg/l in HAAs
400
200
0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90
-1 UVA254 in cm
Figure 22. Relationship between UVA254 and HAAs for all seasons.
64 The correlation varies between 0.4 and 0.7 with the lowest correlation in March
(R2~0.4734). the correlation increased in June (R2~ 0.6343) and the highest correlation
was observed at the end of September (R2~0.7082). This trend follows the rivers flow
curve (USGS), whereby the flow is high in March and reaches the lowest flow values in
September. This suggests that at low flows in the rivers that are more aromatic
compounds that are precursors to HAAs. It was suggested by Reckhow et al. (2004) that
high flows due to high precipitation carry freshly deposited materials such as plant leaves
that might contain high aromatic compounds, but this fresh organic matter is not very
reactive. This might be the reason why with low flows, high aromatic compounds react with chlorine to generate HAAs.
5.4 Speciation of THMs and HAAs
5.4.1 Trihalomethanes (THMs)
Trihalomethanes involve four species: trichloromethanes also known as chloroform
(CHCl3), bromodichloromethanes (CHCl2Br), chlorodibromomethanes (CHClBr2), and tribromomethanes also known as bromoform (CHBr3). The speciation plots in figures
23,24 and 25 show that chloroform is the dominant specie. It was on average 90% of all
species and little variation was observed between seasons. Spring had 90%, the summer
had 89% and the fall had 92% of THMs as chloroform. Bromodichloromethanes was the
second most abundant specie with an average of 9% and the reminder was
chlorodibromomethane. Bromoform was absent in all seasons. However, Figures 23, 24,
and 25 show a variation between individual sampling sites.
65
Distribution of THMs Species per site in March CHCl3 CHCl2Br CHClBr2 CHBr3
100%
90%
80%
70%
60%
50%
40% Percent of THMs species 30%
20%
10%
0% 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435 Sample number
Figure 23.THMs speciation for March samples.
Distribution of THMs Species per site in June CHCl3 CHCl2Br CHClBr2 CHBr3
100%
90%
80%
70%
60%
50%
40% Percent of THMs speciesPercent of THMs 30%
20%
10%
0% 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536 Sample number
Figure 24. THMs speciation for June samples.
66 Distribution of THMs Species per site in Sept-Oct
CHCl3 CHCl2Br CHClBr2 CHBr3 100%
90%
80%
70%
60%
50%
40% Percent of THMs species 30%
20%
10%
0% 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435 Sample number
Figure 25. THMs speciation for Sept-Oct samples.
Samples collected in northwestern Missouri (19-38) have more brominated species
than northeastern Missouri. The very low concentrations of brominated species suggest
that all samples contained very low levels of bromide. Also, the brominated compounds
fraction increased from the spring to the beginning of the fall.
Nikolaou et al. (2003) indicated that, at low concentrations of bromide, there is the
formation of species with fewer atoms of bromide. This might explain the presence of
CHCl2Br and the absence of CHBr3. Chlorine dose favor chloroform over other species.
The pH also affects the speciation of THMs as shown by models developed Reckhow and
Singer (1985), but the exact path of the chlorination reaction of organic matter is not well
understood.
67 5.4.2 Haloacetic acids (HAAs)
The speciation of HAAs includes the concentrations of nine species (monochloroacetic
acid (MCAA), monobromoacetic acid (MBAA), dichloroacetic acid (DCAA),
trichloroacetic acid (TCAA), bromochloroacetic acid (BCAA), dibromoacetic acid
(DBAA), bromodichloroacetic acid (BDCAA), chlorodibromoacetic acid (CDBAA), and
tribromoacetic acid (TBAA). The formation of HAAs favor the non-brominated HAAs
species, TCAA and DCAA, for all samples analyzed as shown in Figures 26, 27 and 28.
This suggests low levels of bromide concentration in water.
Distribution of HAAs Species per site in March
100%
90%
80%
70% TBAA CDBAA 60% DBAA BDCAA 50% BCAA TCAA
40% DCAA MBAA Percent of HAAs species MCAA 30%
20%
10%
0% 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435 Sample number
Figure 26. HAAs speciation for March samples.
68
Distribution of HAAs Species per site in June
100%
90%
80%
70% TBAA CDBAA 60% DBAA BDCAA 50% BCAA TCAA
40% DCAA MBAA Percent species of HAAs MCAA 30%
20%
10%
0% 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435 Sample number
Figure 27. HAAs speciation for June samples
Distribution of HAAs Species per site in Sept-Oct
100%
90%
80%
70% TBAA CDBAA 60% DBAA BDCAA 50% BCAA TCAA
40% DCAA MBAA Percentof species HAAs MCAA 30%
20%
10%
0% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Sample number
Figure 28: HAAs speciation for Sept-Oct samples
69 Two species, DCAA and TCAA, were the dominant species. DCAA and TCAA together constituted an average of 87 percent of all species (80% in March, 91% in June and 90% in September). TCAA had the highest concentration with 47% of all species while DCAA had 40%. The presence of fewer brominated species suggests low levels of bromide in the waters.
Nikolaou et al. (2003) indicated that with a high enough chlorine concentration to complete the reaction, species with more chlorine atoms will be formed, i.e., TCAA and
DCAA. In this study, because the chlorine demand was satisfied, the formation of larger amounts of TCAA and DCAA and smaller amounts of MCAA were observed. The
Nikolaou study indicated that with low concentrations of bromide, the species with fewer atoms of bromide are formed. This explains the presence of monobromoacetic acid
(MBAA) and the absence of tribromoacetic acid (TBAA).
From the Figures 26, 27, and 28, it can be seen that some brominated HAAs (BDCAA) were formed in greater concentrations in March, than in June or September for northwestern watershed samples. One can conclude that there is a higher concentration of bromide in that area compared to the northeastern watersheds. Because the brominated species concentrations were higher in the month of March, it might be due to the increase in connectedness of streams, ground water channels and wetlands caused by the increase in rainfalls that might have in turn contributed to an increase in bromide concentrations.
70
5.5 Fluorescence Signature of natural organic matter and relationship with THMFP, HAAFP, Aromaticity and Source
5.5.1 Visual interpretation of fluorophore centers
In many studies (Baker, 2001; 2002a, b; Holbrook et al., 2006) natural streams present mainly three fluorophore centers known as the humic-like center, the fulvic-like center and the tryptophan-like (protein-like) center. Figure 29 and 30 display these centers.
Her et al. (2003) also used different isolates and found the fluorophore’s centers for humic acid, fulvic acid, algal organic matter, albumin, and dextran (Figure 31).
Figures 29, 30, and 31 serve as guidance in order to recognize and name some of the
components in the samples fluorescence scans in Figures 32, 33 and 34.
Figure 29. Typical fluorescence EEMs observed at different locations. Fulvic acid (F), Humic acid (H) and Tryptophan (T) peaks (adapted from Baker 2001).
71
Figure 30. Typical fluorescence excitation-emission matrix showing the locations of the fluorescence peaks (adapted from Holbrook et al., 2006).
Figure 31. Fluorescence EEMs of a) dextran, b) albumin, c) Sunawee River Humic Acid, d) Sunawee River Fulvic Acid, e) Algal organic matter, f) BL-SW (sewage).
72
5.5.1.1 Scans for March samples
Figure 32. Excitation –Emission Matrix scans for March samples.
73
Figure 32: Excitation –Emission Matrix scans for March samples (Continued).
74
Figure 32: Excitation –Emission Matrix scans for March samples (Concluded).
75
The first EEM in Figure 32 is a scan for DI water which was used as a blank. The
blank shows two inclined lines, the same as in the samples. These lines are called first
and second Raleigh scatter. The information for the peaks is contained in between these
lines. Referring to peaks presented by Baker and others, the sites sampled in March show
mainly three major peaks. humic-like, fulvic-like, and protein-like. All samples presented
the fulvic-like peak that is located at an emission wavelength (Em) between 410 and 440
nm with an Excitation wavelength (Ex) between 275 and 310 nm. They also show the
peak that was identified in Baker (2002) as a humic-like peak (Em: 450-500 nm with Ex:
320-360 nm). The protein-like peaks were only seen in a few samples (1, 14, 20, 22, 23,
27, 29, and 31). The protein-like peaks were found in two locations: the first at Em: 320-
360 nm with an Ex: 250-300 nm and the second at an Em: 320 nm-360 nm with an Ex:
200-250 nm. The peaks can be matched with proteins peaks (Figure 31), such as
tryptophan and albumin (for samples 14 and 29).
The fulvic acid and humic acid peaks occurred in all samples because humic
substances are abundant in water with a terrestrial origin. Humic substances generated through degradation of dead plants and other organisms are carried to rivers through runoff and leachated from peat soils. The samples that showed protein peaks might have resulted from a point source discharge of sewage or other industrial waste or from generation by microbial activities and algae when water is stagnant in swamps and bogs.
76 5.5.1.2 Scans for June samples
Figure 33. Excitation –Emission Matrix scans for June samples.
77
Figure 33. Excitation –Emission Matrix scans for June samples (continued).
78
Figure 33. Excitation –Emission Matrix scans for June samples (Concluded).
79
In summer (June), samples in general showed an increase in the intensity of the fulvic
acid peak. Samples 14, 22, 23, 27, 29, 31 and 33 lost their protein-like peak which might
be due to low 2006 summer flows reducing the interconnectedness of streams,
representing sampling sites that had lost the tributaries that were bringing in protein- derived compounds. On the other hand, samples 37 and 38 had a protein-like peak during summer. The reason for this occurrence might be due to a source that was previously too dilute at high flows (in March) or from the stagnation of water due to low flows that created microbial activity.
The change in the fulvic acid-like fluorophore also might be the cause of the observed
increase in THM formation potential of some samples (2, 3, 16, 17, 18, 23, and 17).
However, the increase in THM formation resulted in a reduction in HAAs formation
potential which indicates a change in the reaction path of chlorine and NOM with a change in fluorophores.
The increase in fulvic-like fluorophores occurred while there was a decrease of THM
and HAAs formation potentials for some samples (31, 33, 34, and 35). The increase in
fulvic acid-like fluorophore does not necessarily imply increase in either THMs or
HAAs. The reason might be the source of the material and the land use of the watershed
at sampling sites. Samples 31, 33, 34, and 35 showed a decrease in both THMs and
HAAs and originated from Upper Grand River watersheds.
80
5.5.1.3 Scans for September-October samples
Figure 34. Excitation –Emission Matrix scans for September samples.
81
Figure 34. Excitation –Emission Matrix scans for September samples (Continued).
82
Figure 34. Excitation –Emission Matrix scans for September samples (Concluded). .
83 The scans for September samples (Figure 34) are characterized by a negligible or
absent protein-like peak that was observed in the March samples. The fulvic-like and
humic-like peaks remained in all samples but changed in intensity or shape compared to
the June sampling. In September, the temperatures started cooling and low flows were
expected in the rivers, hence bacterial activitiy levels were reduced. That might be the
reason why there was an absence of tryptophan-like peaks which indicate the presence of aromatic proteinaceous compounds that are the byproducts of microbial activities in water. However, one cannot confirm that the absence of aromatic protein-like compounds absolutely excludes microbial derived compounds because there was a higher fluorescence index in September than in March or June. The lower the fluorescence index, the less autochthonous natural organic matter in the samples. The change in peaks can be linked to changes in THMs and HAAs formation potential. Samples from the
Upper Grand River watershed sites (27, 28, 30, 31, and 33) and the Platte River
watershed sites (37 and 38) had higher THM and HAAs formation potentials in
September samples than in March and June samples. On the Contrary, northeastern
watersheds such as the Moreau and Salt River watershed sites (1, 2, 3, 4, 5 and 8) showed
an increase in HAAs and a decrease in THMs from June to September. The Chariton
River watershed samples sites (9, 11, 12, 13, and 14) and the Moreau River watershed
sample site (1) showed a decrease in both THMs and HAAs. These DBPs changes cannot
be predicted by visual interpretation of fluorescence scans, probably because there are
compounds that form THMs and HAAs but that are not fluorescent or fluorescent in
different Excitation and Emission ranges.
84
5.6 Approximation using Parallel factor analysis (PARAFAC)
5.6.1 Parallel factor analysis (PARAFAC) components
The PARAFAC model generated a best fit of two major fluorophore components for
all samples (see Figure 35). The model evaluates all scanned samples and looks for major
common components in all samples. The models also estimates the amount of each
component in arbitrary units.
Figure 35. Excitation –Emission Matrix for components fit by PARAFAC model.
Component 1, located at an emission wavelength between 420-450 nm with a 250 nm excitation wavelength can be compared to the humic-like material peak identified by
Holbrook et al. (2006). The protein-like peak identified in the same study also can be compared to the primary peak of component 2 located between an emission wavelength of 330-350 nm at a 250 nm excitation wavelength.
Coble (1996) indicated that the protein-like peak arises due to fluorescence of aromatic amino acids, either free or as protein constituents. It is mainly observed as a tyrosine-like peak at an emission wavelengths of 300-305 nm and a tryptophan-like peak at an emission wavelength of 340-350 nm, with an excitation wavelength lying between 220
85 and 275 nm. Hence, one can suggest that component 2 observed in this study can be caused by tryptophan moieties in water.
Figures 36, 37, and 38 represent the results of the amount of each of the two
components. The arbitrary units used to express the amount of each component are also
known as “normalized scores of PARAFAC model.” Figures 36, 37, and 38 are bars
charts showing the fraction of each component in individual samples for three seasons.
Fractions of Components fit by PARAFAC for March samples
1st Component 2nd Component 1.00
0.90
0.80
0.70
0.60
0.50
0.40 Fraction of components of Fraction 0.30
0.20
0.10
0.00 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738 Sample number
Figure 36. Distribution of the two components in March samples.
86 Fractions of Components fit by PARAFAC for June samples
1st Component 2nd Component
1.00
0.90
0.80
0.70
0.60
0.50
0.40 Fraction of components Fraction 0.30
0.20
0.10
0.00 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738 Sample number
Figure 37. Distribution of the two components in June samples.
Fractions of Components fit by PARAFAC for Sept-oct samples
1st Component 2nd Component
1.00
0.90
0.80
0.70
0.60
0.50
0.40 Fraction ofcomponents 0.30
0.20
0.10
0.00 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738 Sample number
Figure 38. Distribution of the two components in Sept-Oct samples.
87
The proportions of components fit by PARAFAC model do not have a clear pattern that might explain the change of components with location sampling sites and hydrological properties of the sites. However, overall, the component 1 that was compared to humic-like fluorophore has higher proportions in many samples compared to component 2.
For samples collected in March, 23 out of the 32 samples had component 1 as the dominant component, while of those samples collected in June, 23 out of 33 samples had component 1 as the dominant component, and in September, all except 3 samples had component 1 as the dominant component. Humic-like was seen as the dominant component due the presence of terrestrial organic matter, originating from dead plant cells. The higher amount of component 2 observed during spring and the beginning of summer compared to fall might be related to higher amount of rainfall that increase runoff and non-point source contributions, which in turn contributed compounds from anthropogenic activities in the watershed to the river. Protein-like fluorophores are known to originate from either human activity (e.g. sewage) or microbial activities.
Another observation from the scans is that northeastern Missouri watersheds (samples 1 through 18) had more protein-like fluorophores in March than the northwestern watershed. This could imply the presence of a higher amount of non-point source anthropogenic compounds transported during the first flush of spring rainfalls.
88 5.6.2 Relationship between PARAFAC Components, DBPs Concentration and UVA 254
The linear correlation between components and parameters showed low coefficients
of determination (below 0.5), as shown in Figure 39, 40, and 41.
The correlation between component 1 and THMs, HAAs, and UVA254 was higher
when compared to component 2. The coefficients of determination were 0.32, 0.47, 0.47, respectively for component 1 and 0.12, 0.18, and 0.27, respectively for component 2.
This is in agreement with many studies (Singer (1999), Hwang et al. (2001), Leenheer et al. (2001a), Croué et al. (2000)) which suggest that protein- derived organic matter
generates fewer DBPs and are less aromatic compared to humic-derived organic matter.
Relationship between THMs and PARAFAC Components
TTHMs versus 1st Component TTHMs versus 2nd Component 1600
1400
1200 R2 = 0.1234
2 1000 R = 0.3211
800 TTHMs in µg/l
600
400
200
0 0.00 20.00 40.00 60.00 80.00 100.00 120.00 Scores in Arbitrary units
Figure 39. Relationship between the two components and THMs.
89
Relationship between HAAs and PARAFAC Components
HAAs Versus 1st component HAAs versus 2nd Component 1200
1000
800
R2 = 0.4199
600
2 HAAs in µg/l in HAAs R = 0.1755
400
200
0 0.00 20.00 40.00 60.00 80.00 100.00 120.00 Scores in Arbitrary units
Figure 40. Relationship between the two components and HAAs.
Relationship between PARAFAC components and UVA254
1st component versus UVA254 2nd component versus UVA254 120.00
100.00
R2 = 0.4683 80.00
60.00
R2 = 0.2656
40.00 Scores of components inArbitrary units
20.00
0.00 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 UVA254 in cm-1
Figure 41. Relationship between the two components and UVA254.
90 5.7 Fluorescence Index
Natural organic matter originates from two sources; an allochthonous source (from
terrestrial materials) and an autochthonous source (generated within the water body by
microbes and algae). When the FI values are low (~<1.45), it is suggested that the water
contains higher amounts of terrestrial organic matter than microbial-derived organic
matter. Terrestrial organic matter is believed to have higher aromaticity (i.e., high
UVA254) and DBP formation potential compared to autochthonous NOM. Table 6
contains the average values for FI, UVA254, and DBPs that were observed in the spring,
the summer, and the fall. From summer to fall, there was an increase in FI but a reduction
UVA254 and THMs.
Table 6: Influence of FI on UVA254, THMs, and HAAs
Season Location FI UVA254 THMs HAAs All All All All Spring Northeastern 1.289 0.438 685 411 Northwesten 1.295 1.29 0.134 0.305 189 468 144 294 Summer Northeastern 1.269 0.298 893 200 Northwesten 1.284 1.28 0.149 0.226 202 558 94 149 Fall Northeastern 1.344 0.192 268 266 Northwesten 1.339 1.34 0.141 0.169 284 270 155 223
Organic matter of terrestrial origin has a high proportion of compounds having many
aromatic structures within the molecules. Terrestrial humic substances contain fulvic
acids and humic acids that have aromatic rings which absorb Ultraviolet light. However,
due to the complexity of the mixture of organic matter, i.e., a mixture of aromatic and
non-aromatic compounds of both terrestrial sources and microbial sources, the linear regression curve fitting shows only a very small negative correlation between UVA254
and FI (R2=0.103) in Figure 42.
91
UVA254 Vs FI UVA254 Vs FI Linear (UVA254 Vs FI) 0.8 R2 = 0.1025 0.7
0.6
0.5
0.4
0.3 UVA254 i n cm-1 0.2
0.1
0 1.20 1.25 1.30 1.35 1.40 1.45 1.50 FI
Figure 42. Linear relationship between UVA254 and FI.
Table 6 shows that the FI of organic matter also has impact on the formation potential
of THMs and HAAs. Allochthonous material (terrestrial natural organic matter) contains
more THM and HAA precursors. However, the linear correlation between DBPs and FI
was very low and changes drastically with time periods. Samples collected in September and October show a higher linear correlation compared to samples collected in March and June (R2 ~0.3802, R2~0.0232, R2~0.0827, respectively, for THMs and R2 ~0.2, R2
~0.02, R2 ~0.01,respectively for HAAs) as shown in Figure 43 and 44.
The variation in correlation might be due the low river flows in September caused by
decreased flows in the rivers compared to previous months. The fluorescence index
indicates the source of major components in the sample, but, high flows transport a
higher mixture of reactive (THM and HAA precursors) and unreactive materials than
when the rivers have low flows. A good linear relationship between DBPs and FI would
not be expected because of this.
92 Correlation between THMs and FI
1600 March June Sept-Oct
1400
1200
1000
June 800 THMs in µg/l
600
March
400 R2 = 0.0232
Sept-Oct 200
R2 = 0.0827 R2 = 0.3802
0 1.18 1.23 1.28 1.33 1.38 1.43 1.48 1.53 Fluorescence Index
Figure 43. Linear relationship between THMs and FI.
Correlation between HAAs and FI
1200
March June Sept-Oct
1000
800
600 HAAs in µg/l
400 March R2 = 0.0182 June 200 Sept-Oct
R2 = 0.0106 R2 = 0.1926
0 1.18 1.23 1.28 1.33 1.38 1.43 1.48 1.53 Fluorescence Index
Figure 44. Linear relationship between HAAs and FI.
93
5.8 Fluorescence Regional Integration (FRI) Results
Chen et al. (2003) located different types of organic matter compounds in five regions of an EEM, as seen earlier in Figure 2. The fluorescent aromatic protein tyrosine was allocated to region 1 (RG1) of the EEM. Other aromatic proteins that contributed to
BOD5 in water were located in region 2 (RG2). Fulvic acid-like compounds such as hydrophobic acids, fulvic acids, and Sunawee river fulvic acids were allocated into region
3(RG3) of the EEM. Region 4 (RG4) consisted of soluble microbial by-product-like compounds, protein-like containing tryptophan compounds, and some tyrosine compounds. Humic acid-like, marine humic acids, model acid polymers, and hydrophobic acids were found in region 5 (RG5) of the EEM.
To quantify the fluorescent compound’s intensities in each region, integration beneath the EEM, which represents the cumulative fluorescence response of dissolved organic matter with similar properties, was performed as follows (Equation 3):
= ( ) ddIRG λλλλ . (Equation 3) i ∫∫ emexemex ex em
To simplify the integration, the volumes beneath the EEMs were expressed as discrete data (Equation 4):
RG i = ∑∑ I emex )( ΔΔ λλλλ emex , (Equation 4) ex em
where I(λ λemex ) is the fluorescence intensity at each excitation-emission wavelength pair,
Δλex is the excitation wavelength interval (taken as 4 nm in this study), and Δλem is the emission wavelength interval (taken as 3 nm in this study).
94 The following relationships were examined to investigate whether THM and HAA precursors were fluorescent in the defined regions.
5.8.1 Relationship between fluorescence regions and THMs
The relationship between fluorescence regions and THMs was investigated. THMs values and the corresponding fluorescent regions are plotted in Figure 45 and the coefficients of variation for the linear fit are summarized in Table 7.
95 TTHMs and RGs relationship(March14 samples) TTHMs and RGs relationship(Sept.19 samples) 1200 800 1000 700 RG1 800 600 RG1 RG2 500 RG2 600 RG4 400 RG3 400 RG3 300
TTHMs inµg/l RG4
200 RG5 THMs in µg/l 200 RG5 0 100 0204060 0 01020 RGs in AU RGs in AU
TTHMs and RGs relationship(June 5 samples) TTHMs and RGs relationship(March 28 samples) 1600 300 1400 250 RG1 1200 RG1 RG2 200 RG2 1000 RG3 RG3 800 150 600 RG4 RG4
100 inTTHMs µg/l 400 RG5 RG5 TTHMs in µg/l 200 50 0 0 0102030 0 5 10 15 RGs in AU RGs in AU
TTHMs and RGs relationship(October 3 samples) TTHMs and RGs relationship(June 20 samples)
450 600 400 500 RG1 350 RG1 300 RG2 400 RG2 250 RG3 300 RG3 200 RG4 RG4 150
THMs in µg/l 200 THMs in µg/l 100 RG5 100 RG5 50 0 0 01020 0246810 RGs in AU RGs in AU
Figure 45. Relationship between RGs and THMs.
96
Table 7. Correlation (+/-) and Coefficient of Determination (R2) of the relationship between Fluorescence Regions and TTHMs.
Date of DBPs samples Fluorescence Regions RG1 RG2 RG3 RG4 RG5 14-Mar 0.3937(+) 0.4698(+) 0.5596(+) 0.1291(+) 0.4908(+) 28-Mar 0.0029(-) 0.0227(+) 0.00004(+) 0.0445(-) 0.0223(-) TTHMs 5-Jun 0.397(+) 0.5214(+) 0.5663(+) 0.6402(+) 0.7563(+) 20-Jun 0.4287(+) 0.0741(+) 0.0101(+) 0.0614(+) 0.0012(+) 19-Sep 0.3888(+) 0.2597(+) 0.2744(+) 0.2143(+) 0.3752(+) 3-Oct 0.0995(-) 0.0494(+) 0.0894(+) 0.0333(+) 0.0796(+)
For samples that were collected on March14, region 3 and region 5 had higher positive linear correlations with the formation potential of THMs. This implies that some humic acid-like and fulvic acid-like material reacts with chlorine to generate THMs. The trend completely change for samples collected on March 28. Because these samples are from different sites, it suggests that the reactivity is site specific. Overall, samples collected from northeastern watersheds (March 14, June 5, and Sept 19) had higher linear correlations with THMs than northwestern watershed sites. Protein-like and microbial by-product-like fluorescent moieties had a correlation with the formation of THMs
(R2~0.6402). It was reported in some studies (Veum, 2006) that algal biomass and extra- cellular products (ECPs) have been linked to trihalomethanes formation. The highest correlation was observed between region 5 (humic-like) and THMs .This correlation is in agreement with many studies (Gang, 2001) which indicated that humic acids were found to be THM precursors.
97 5.8.2 Relationship between fluorescence regions and HAAs
The relationship between fluorescence regions and HAAs was investigated. HAAs values and the corresponding fluorescent regions are plotted in Figure 46 and the coefficients of variation for the linear fit are summarized in Table 8.
98 HAAs a nd RGs re la tionship(Ma rch 14 sa mple s) HAAs and RGs relationship(June 20 samples)
800 160 700 140 RG1 600 RG1 120 RG2 500 RG2 100 RG3 80 400 RG3 RG4 60 300 RG4 HAAs inµg/l RG5 HAAs in µg/l 40 200 RG5 100 20 0 0 051015 0 102030405060 RGs in AU RGs in AU
HAAs and RGs relationship(March 28 samples) HAAs a nd RGs re la tionship(se pt.19 sa mple s) 250 1200 200 RG1 1000 RG2 RG1 150 RG3 800 RG2
100 RG4 600 RG3
HAAs in µg/l µg/l in HAAs RG5 RG4 400
50 µg/l in HAAs RG5 200 0 0 5 10 15 20 0 RGs in AU 051015 RGs in AU
HAAs and RGs relationship(October 3 samples) HAAs and RGs relationship(June 5 samples)
350 250
300 200 RG1 RG1 250 RG2 RG2 150 200 RG3 RG3 150 RG4 100 RG4 HAAs in µg/l µg/l in HAAs
HAAs in µg/l µg/l in HAAs RG5 100 RG5 50 50 0 0 0246810 0 5 10 15 20 25 30 RGs in AU RGs in AU
Figure 46. Relationship between RGs and HAAs.
99 Table 8. Correlation (+/-) and Coefficient of Determination (R2) of the relationship between Fluorescence Regions and HAAs.
Date of DBPs samples Fluorescence Regions RG1 RG2 RG3 RG4 RG5 14-Mar 0.1874(+) 0.1899(+) 0.396(+) 0.004(+) 0.2832(+) 28-Mar 0.2574(+) 0.3531(+) 0.3374(+) 0.2347(+) 0.258(+) HAAs 5-Jun 0.2531(+) 0.3377(+) 0.4593(+) 0.5669(+) 0.6766(+) 20-Jun 0.003(-) 0.0361(-) 0.0194(-) 0.015(-) 0.0057(-) 19-Sep 0.3912(+) 0.31(+) 0.3914(+) 0.3331(+) 0.5326(+) 3-Oct 0.1612(-) 0.0416(-) 0.0054(-) 0.032(-) 0.0131(-)
Region 5 of the EEM can be also linked to the generation of HAAs. The highest region
5 (humic-like) and HAA precursor was observed for the summer samples (June 5) with a positive correlation (R2~0.6766). Similar to THMs, all regions for the June 5 samples contained compounds that are related to an increase in HAAs. As reported in the literature review, some organic compounds such as ketones and purvates react with chlorine to generate chloroform, DCAA and TCA, the main THM and HAA species. This suggests that HAAs and THMs have some similar precursors. The EEM regions of the samples collected on March 28, which originated mostly in northwestern Missouri watersheds, did not have a correlation with both THMs and HAAs. Different sites have different natural organic matter transformation processes that can be linked to the nature of land cover, weather, pH of soil and water, geomorphology, etc…Thus, the lack of correlation of humic-like and fulvic-like compounds that originally was believed to be
THMs and HAAs precursors, can be attributed to the difference in functional groups of organic matter compounds. For example, it was seen in the literature review that humic substances with electron donating groups are more reactive with chlorine than compounds with many electron withdrawing groups.
100
5.8.3 Relationship between fluorescence regions and UVA254
The relationship between fluorescence regions and aromaticity surrogate (UVA254) was investigated. UVA254 values and the corresponding fluorescent regions are plotted in Figure 47 and the coefficients of variation for the linear fit are summarized in Table 9.
101 UV A 254 and RGs relationship(March 14 UV A 254 and RGs relationship(June 5 samples) samples) 60 30
50 25 RG1 40 RG2 20 RG1 RG2 30 RG3 15 RG4 RG3 20 RG5 10 RG4 RG5 10 5
0 0 0.00 0.20 0.40 0.60 0.80 1.00 0.00 0.10 0.20 0.30 0.40 0.50 -1 UVA 254 in cm -1 UVA 254 in cm
UV A 254 and RGs relationship(March 28 samples) UV A 254 and RGs relationship(sept.19 samples)
16 16
14 14 12 RG1 12 RG1 10 RG2 10 RG2 8 RG3 8 RG3 6 RG4 RG4 6 RGs in AU RGs in 4 RG5 RG5 4 2 2 0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0
-1 0 0.1 0.2 0.3 0.4 0.5 UVA 254 in cm UVA in cm -1 254
UV A 254 and RGs relationship(June 20 samples) UV A and RGs relationship(Oct.3 samples) 14 254
10 12 9 10 8 RG1 RG2 7 8 RG1 RG3 6 RG2 6 RG4 5 RG3 RG5 4 RG4 4 3 RG5 2 2
0 1 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0 -1 UVA 254 in cm 0 0.05 0.1 0.15 0.2 0.25
-1 UVA 254 in cm
Figure 47. Relationship between RGs and UVA254.
102 Table 9. Correlation (+/-) and Coefficient of Determination (R2) of the relationship between Fluorescence Regions and UVA254.
Date of Parameter samples Fluorescence Regions RG1 RG2 RG3 RG4 RG5 14-Mar 0.4179(+) 0.2927(+) 0.4837(+) 0.0004(+) 0.5049(+) 28-Mar 0.0439(-) 0.0989(-) 0.049(-) 0.018(+) 0.0037(+)
UVA254 5-Jun 0.5092(+) 0.7143(+) 0.7944(+) 0.5881(+) 0.8815(+) 20-Jun 0.207(+) 0.6133(+) 0.6526(+) 0.6029(+) 0.6186(+) 19-Sep 0.6514(+) 0.6965(+) 0.7319(+) 0.671(+) 0.8219(+) 3-Oct 0.0086(+) 0.3877(+) 0.4225(+) 0.3587(+) 0.4255(+)
Table 9 shows a higher linear correlation between UVA254 and fluorescence regions
mostly for samples collected during the summer. The humic-like region has shown the
2 highest correlation with UVA254 (R ~0.8815). UVA254 has been known to be a surrogate
of organic matter compounds of high aromaticity and conjugated double bonds. During
the summer season, due to high temperatures or base flows in streams, there is an
increase in fluorescent moieties for all EEM regions defined above. Some studies
(McKnight et al. (2001), Nikolaou et al. (2001)) indicated that the increase in molecular weight increases the UV absorbance but decreases the fluorescence. During the summer, organic matter has been degraded by bacteria, producing low molecular weight compounds, whereas in March, waterways contain high amounts of fresh litter that are rich in high molecular weight plant-derived compounds such as lignins, tannins and terpenoids. In the literature review, it was mentioned that electron donating groups such as -OH and -NH2 increase the fluorescence. In the summer, the increase in aromaticity
with fluorescence might be due to the presence of high amounts of compounds having
aromatic rings containing donating groups, such as phenolics.
103 5.8.4 Relationship between fluorescence regions and fluorescence index
The source of organic matter is believed to have a big impact on fluorescence. Figure
48 and the linear fit coefficients of variations summary table (Table 10) were used to
assess the relationship between RGs and Fluorescence index.
The linear relationship between fluorescence regions and fluorescence index gave very
low values of coefficient of determination and poor correlation. Because the fluorescence
index is used to indicate the origin of organic natural organic matter (Allochthonous or autochthonous), this suggests that fluorescence regions quantification cannot determine
the origin of fluorescent moieties.
104 FI and RGs relationship(March 14 samples) FI and RGs relationship(June 20 samples)
60 14
50 12
RG1 10 40 RG1 RG2 RG2 8 RG3 30 RG3 RG4 6 RG4 20 RG5 RG5 4 10 2 0 1. 2 2 1. 2 4 1. 2 6 1. 2 8 1. 3 0 1. 3 2 1. 3 4 1. 3 6 1. 3 8 0 1.20 1.25 1.30 1.35 1.40 1.45 Fluorescence index( FI) Fluorescence index( FI)
FI and RGs relationship(March 28 samples) FI and RGs relationship(sept.19 samples)
16 16 14 14 12 RG1 12 10 RG2 RG1 10 8 RG3 RG2 RG4 8 RG3 6 RG5 RG4 6 4 RG5 4 2 2 0 1.20 1.25 1.30 1.35 1.40 1.45 0 1.20 1.25 1.30 1.35 1.40 1.45 1.50 Fluorescence index(FI) Fluorescence index(FI)
FI and RGs relationship(june 5 samples) FI and RGs relationship(Oct.3 samples) 30 10
25 9 8 20 RG1 7 RG1 RG2 6 RG2 15 RG3 5 RG3 RG4 10 4 RG4 RG5 3 RG5 5 2 1 0 0 1. 2 0 1. 2 5 1. 3 0 1. 3 5 1. 2 8 1. 3 0 1. 3 2 1. 3 4 1. 3 6 1. 3 8 1. 4 0 1. 4 2 1. 4 4 1. 4 6 Fluorescence index(FI) Fluorescence index( FI)
Figure 48. Relationship between RGs and FI.
105
Table 10: Correlation (+/-) and Coefficient of Determination (R2) of the relationship between Fluorescence Regions and FI
Date of Parameter samples Fluorescence Regions RG1 RG2 RG3 RG4 RG5 14-Mar 0.0219(-) 0.0688(-) 0.0762(-) 0.039(-) 0.0742(-) 28-Mar 0.3783(+) 0.2472(+) 0.3312(+) 0.08(+) 0.2883(+) FI 5-Jun 0.04(-) 0.064(-) 0.0451(-) 0.0015(-) 0.0233(-) 20-Jun 0.146(-) 0.02949(-) 0.0064(-) 0.0325(-) 0.0008(-) 19-Sep 0.2229(-) 0.3841(-) 0.4136(-) 0.3004(-) 0.4275(-) 3-Oct 0.0132(+) 0.0037(+) 0.00429+) 0.000005(+) 0.0076(+)
106 CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS
6.1 Conclusions
Many analytical methods can be used to characterize the complex mixture of organic compounds in natural waters, UV and fluorescence spectroscopy methods have been used in this study because they are quick, do not change the chemistry of water (non- destructive), and, with fluorescence, a great deal of information can be obtained with one single measurement of Excitation and Emission Matrix.
However, these methods were found to be qualitative and do not provide
concentrations of specific organic compounds in water. Additional methods, including
determing trihalomethanes and haloacetic acid formation potentials were used to identify
parameters that were related to THMs and HAAs.
The characteristics of natural organic matter compounds have been found to vary with
environmental factors such as weather (seasons, and rainfalls), soils and the flow in
streams. The determination and analysis of six parameters (THMFP, HAAFP, UVA254,
fluorescence EEMs, PARAFAC components and fluorescence regional integration zones)
provided a number of findings.
During the period of high rainfalls, in the spring season (around the beginning of
March) organic matter transported to streams contains compounds with aromatic ring and
non-saturated bonds, as was shown by UVA254. These compounds also appeared to be
precursors to THMs and HAAs because of higher amounts of DBPs, THMFP (average:
468 µg/l) and HAAFP (average: 294 µg/l). During the summer, due to low flows and less
connectedness of streams, there was a small reduction in HAAs (average: 149 µg/l) but
THMs increased (average: 558 µg/l). This is inconsistent with studies that reported that
107 HAAs and THMs precursors are higher in summer compared to other seasons. HAAs in
this study were higher in the spring than in summer. However, the maxima for TTHMs and HAAs were the highest in summer (~1720 µg/l).
Chloroform was the dominant THM specie with an average of 90% of the total THMs.
It was observed that North Missouri watersheds have low bromide concentrations and
thus low formation potential for brominated species. The HAAs of TCAA and DCAA
constituted an average of 87% of all HAAs species due to the low amount of bromide.
Few brominated species were observed having one atom of bromide.
There was also a significant variation of natural organic matter with location.
Northeastern Missouri watersheds (sites 1-18) samples showed compounds of higher
aromaticity than northwestern Missouri watersheds. They also had a higher average
amount of trihalomethane formation potential (THMFP) and haloacetic acids formation
potential (HAAFP).
Higher average amounts of disinfection by-products formation potentials (DBPFPs) were observed in samples containing a higher proportion of compounds of terrestrial origin as was shown by the florescence index. Terrestrial precursors are believed to come from humic acids and fulvic acids that originate mainly from decayed plant’s litter.
There was also a high linear relationship between various parameters. UVA254 was
correlated with DBPFP which confirm the reactivity of aromatic rings compounds.
Moreover, a high degree of linear fit between UVA254 and THMs during warmer temperature season showed a loss of labile organic matter and precursor to THM due to biodegradation.
108 The fluorescence scans were compared to scans from other studies and the presence of
humic-like, fulvic-like and protein-like organic matter was found. Humic-like matter, the
dominant fluorophore as was shown by the PARAFAC model, is believed to have come
from decayed plant liter brought into streams from terrestrial sources by runoff. Protein-
like matter is believed to have come from microbial activities in bogs and marshes where
water has stagnated. In addition, algal biomasses provided a protein-like signature and
generated THMs and HAAs. This might be the reason for the correlation between
protein-like fluorescent matter and THM and HAA formation potentials.
From fluorescence regional integration, Humic-like materials were highly correlated to the formation of THMs and HAAs compared to other fluorophores. However, it varied
with location and season, due to various molecular structures of humic substances
compounds that in turn caused variable fluorescence.
Finally, the fluorescence methods used in this study were not able to predict exactly the
formation of THMs and HAA. More research effort is required to find the best way to
interpret fluorescence results.
6.2 Recommendations
Fluorescence spectroscopy has many features that need to be studied in detail. For
example, the excitation-emission matrix window used is empirically chosen. Many previous studies have used different wavelength intervals and ranges of excitation and
emission. Because different organic compounds from different locations fluoresce at different specific wavelengths, in further research, other windows should be used to try to find the wavelengths that can best characterize Missouri stream’s waters. There was a
109 problem with the separation of compounds that fluoresce at the same excitation-emission
pair because they likely will overlap. Fluorescence scans of different fractions, obtained
using polarity properties (hydrophobic and hydrophilic) and ultrafiltration (different
molecular weights), should be performed to see whether the problem of overlaps in
fluorophore peaks can be overcome.
Finally, fluorescence spectroscopy can be used to see whether it can detect the removal of organic matter in different physical-chemical treatment processes such as coagulation,
adsorption, and filtration.
110 REFERENCES
Abbt-Braun, G., U. Lankes, F. H. Frimmel, “ Structural characterization of aquatic humic substances-The need for a multiple approach” Aquatic sciences 66 (2004): 151-170.
Aiken, G.R. ” Organic matter in groundwater” U.S. Geological Survey Artificial Recharge Workshop, April 2002, Sacramento, California.
Baker, A., R.G. M. Spencer, “ Characterization of dissolved organic matter from source to sea using fluorescence and absorbance spectroscopy” Science of the Total Environment 333 (2004): 217-232.
Baker, A. ” Fluorescence excitation-emission matrix characterization of river waters impacted by a tissue mill effluent” Environmental Science & Technology 36 / 7( 2002): 1377-1382.
Baker, A. ” Spectrophotometric discrimination of river dissolved organic matter” Hydrological Processes 16 (2002): 3203-3213.
Battin, T. M. “ Dissolved organic matter and its optical properties in black water tributary of the upper Orinoco river, Venezuela” Organic Geochemistry 28 (1998): 561-569.
Boorman, G. A., V. Dellarco, J. K. Dunnick, R. E. Chapin, S. Hunter, F. Hauchman, H. Gardner, M. Cox, R. C. Sills, ” Drinking water disinfection byproducts” Review and approach to toxicity evaluation; Environmental Health Perspectives Vol.107, Supplement 1: Reviews in Environmental health, Feb 1999, pp 207-217.
Bro, R. “ PARAFAC Tutorial and applications” Chemometrics and Intelligent Laboratory System 38 (1997): 149-171.
Check, J. K. “ Characterization and removal of NOM from raw waters in coastal environments” Master’s thesis, Georgia Institute of Technology, (2005).
Chen, W., P. Westerhoff, J. A. Leenheer, K. Booksh, “ Fluorescence excitation-emission regional integration to quantify spectra for dissolved organic matter” Environmental Science and Technology 37 (2003): 5701-5710.
Christman, R. F., D.L. Norwood, D.S. Millington, J.D. Johnson, ” Oxidative degradation of aquatic humic material” Advances in the identification and analysis of organic pollutants in water 2 (1983), Keith, L. H. editor, Ann arbor science publishers, Ann Arbor, MI
111 Chow, A. T., Fengmao Guo, Suduan Gao, R. S. Breuer, “ Trihalomethane reactivity of water and sodium hydroxide extractable organic carbon fractions from peat soils” Journal of Environmental Quality 35 (2006): 114-121.
Croué, J. P., G.V Korshin, M. Benjamin, “ Characterization of natural organic matter in drinking water” Report 90780 (2000), AWWA Research Foundation, Denver, Co
Coble, P. G. ” Characterization of marine and terrestrial DOM in seawater using excitation-emission matrix spectroscopy” Marine Chemistry 51 (1996): 325-346.
Dawit, S., J. Lehman, J. Kinyangi, B. Liang, T. Schafer, “ Carbon K-Edge NEXAFS and FTIR-ATR Spectroscopic Investigation of organic carbon speciation in soils” Soil Science Society of America Journal 69 (2005): 107-119.
Donahue, W. F., D.W. Schindler, J. P. Stephen, M.P. Stainton, ” Acid-Induced changes in DOC quality in an expermintal whole-lake manipulation” Environmental Science and Technology 32 (1998): 2954-2960.
Fram, M. S., D. K. Maurer, M. S. Lico, “ Potential for Formation of Disinfection By- Products from Storage of Chlorinated Surface Water in the Basalt Aquifer near Fallon, Nevada” Scientific Investigations Report 5142 (2005), U.S. Department of the Interior, U.S. Geological Survey.
Fram, M. S., R. Fujii, J. L. Weishaar, B. A. Bergamaschi, G. R. Aiken, “ How DOC composition may explain the poor correlation between specific trihalomethane formation potential and specific UV absorbance” US Geological Survey Toxic Substances Hydrology Program (1999).
Gang, D. “ Modeling of THM and HAA formation in Missouri Waters upon chlorination” PhD dissertation, University of Missouri-Columbia, (2001).
Her, N., G. Amy, D. McKnight, J. Sohn, Y. Yoon, “ Characterization of DOM as a function of MW by fluorescence EEM and HPLC-SEC using UVA, DOC, and fluorescence detection” Water Research 37 (2003): 4295-4303.
Holbrook, D. R., J. H. Yen, T. J. Grizzard,” Characterizing natural organic material from the Occoquan watershed (northern Virginia, US) using fluorescence spectroscopy and PARAFAC” Science of the Total Environment 361 (2006): 249-266.
Huang, C. J., S. W. Krasner, M. J. Sclimenti, G. L. Amy, E. Dickenson, A. Bruchet, C. Prompsy, G. Filippi, J. P. Croué, D. Violleau, “ Polar NOM: Characterization , DBPs, and Treatment” Report 90877 (2001), AWWA Research Foundation, Denver, Co.
Leenheer, J. A., Croué J. P. “ Characterizing aquatic dissolved organic matter”
112 Environmental Science and Technology 37 (Jan 1, 2003) (1):18A-26A
Lepane, V., A. Leeben, O. Malashenko, “ Characterization of sediment pore-water dissolved organic matter of lakes by high-performance size exclusion chromatography” Aquatic sciences 66 (2004): 185-194
Lu, X. Q., J. V. Hanna, W. D. Johnson, “ Source indicators of humic substances: an elemental composition, solid state 13C CP/MAS NMR and Py-GC/MS study” Applied Geochemistry 15 (2000): 1019-1033
Magnuson, L. M., T. E. Urbansky, M. K. Schenck, M. S. Elovitz, “ Disinfection By- Product (DBP) Chemistry: Formation and Determination” EPA Report Chapter 3 (2001).
Marhaba, T. F., H. K. Ishvinder, ” Rapid prediction of disinfection byproduct formation potential by fluorescence” Environmental engineering and Policy 2 (2000): 29-36.
McCool, P. “ The total organic carbon removal requirement: is it necessary? ” The Kansas lifeline. (November 2005).
McKnight, D. M., E. W. Boyer, P. K. Westerhoff, P. T. Doran, T. Kulbe, D.T Andersen, “ Spectrofluorometric characterization of dissolved organic matter for indication of precursor organic material and aromaticity” Limnology and Oceanography 46 (2001) : 38-48.
Missouri Department of Conservation. Missouri watersheds. http://mdc.mo.gov./fish/watershed/usgs8.htm (Visited January, 2007)
Moudgal, C. J., J. C. Lipscomb, R. M. Bruce, “ Potential health effects of drinking water disinfection by-products using quantitative structural toxicity relationship” Toxicology 147 (2000): 109-131.
Munch, J. W. “ Measurement of purgeable organic compounds in water by capillary column gas chromatography/mass spectrometry” National exposure research laboratory office of research and development. United States Environmental Protection Agency Cincinnati, Ohio 45268 (1995).
Munch, D. J., J. W. Munch, A. M. Pawlecki, “ Determination of haloacetic acids and dalapon in drinking water by liquid-liquid extraction, derivatization and gas chromatography with electron capture detection” National exposure research laboratory office of research and development. United States Environmental Protection Agency Cincinnati, Ohio 45268 (1995).
Nikolaou, A. D., D. L. Themistokies, “ The role of natural organic matter during formation of chlorination by-products” A Review, Acta hydrochim. Hydrobiol. 29/ 2-3 (2001): 63-77
113 Nissinen, T., T. Myllykangas, P. Rantakokko, “ brominated disinfection by-products: formation and control during drinking water disinfection” Finnish research programme on environmental health. Consortium: Drinking water and health 1999 http://www.ktl.fi/sytty/abstracts/varti3.htm (Visited January, 2007)
Reckhow, D. A., P. L. S. Rees, D. Bryan, “ Watershed sources of disinfection byproduct precursors. Water Science and Technology” Water Supply 4/ 4 (2004): 61-69.
Reckhow, D. A., P. C. Singer, R. L. Malcom, “ Chlorination of humic materials: By- product formation and chemical interpretations” Environmental Science and Technology 24 (1990): 1655-1664.
Reckhow, D. A., P. C. Singer, “ Mechanisms of organic halide formation during fulvic acid chlorination and implication with respect to preozonation water chlorination” Chemistry, Environmental Impact and Health Effects 5 (1985): 1229-1257.
Rook, J. J. “ Formation of haloforms during chlorination of natural waters” Water Treatment Exam 23/2 (1974): 234-243.
Rossman, L. A., R. M. Clark, W. M. Grayman, “ Modeling chlorine residuals in drinking water Distribution Systems” Journal of Environmental Engineering 120/ 4 (July 1994): 803-820.
Saiz-Jimenez, C. “ Analytical pyrolysis of humic substances; Pitfalls, limitations, and possible solutions” Environmental Science and Technology 28/ 11 (1994): 1773-1780.
Singer, P.C. “ Humic substances as precursors for potential harmful disinfection by- products” Water Science and Technology 40/ 9 (1999): 25-30.
Unites States Environmental Protection Agency, National Primary Drinking Water Regulations: Stage 2 Disinfectants and Disinfection Byproducts Rule; Final Rule Federal Register / Vol. 71, No. 2 / Wednesday, January 4, 2006 / Rules and Regulations.
United States Environmental Protection Agency. Enhanced coagulation and softening guidance manual. (May 1999).
United States Environmental Protection Agency, Drinking water treatment for small communities focus on EPA’s research, EPA/640/K-94/003. (1994).
United States Geological survey. http://mo.water.usgs.gov/ (visited February 2007)).
Veum, K. S. “ Disinfection by-product precursors and formation potentials of Missouri Reservoirs”Master’s thesis, University of Missouri-Columbia, (2006).
Vogt, R. D., J. Akkanen, D. O. Andersen , R. Brüggemann, B. Chatterjee, E. Gjessing, J. V. K. Kukkonen, H. E. Larsen, J. Luster, A. Paul, S. Pflugmacher, M. Starr, C. E. W.
114 Steinberg, P. Schmitt-Kopplin, Á. Zsolnay, “ Key site variables governing the functional characteristics of Dissolved Natural Organic Matter (DNOM) in Nordic forested catchments” Aquatic Sciences 66 (2004): 195–210.
Wikipidia. Nuclear Magnetic Resonance. http://en.wikipedia.org/wiki/Nuclear_magnetic_resonance (Visited January 2007)
115 APPENDIX
A.1: THMs formation potentials for March samples
Site Number TCM BDCM DBCM TBM TTHMs s1 390.80 34.77 2.19 0.02 427.78 s2 549.80 16.56 0.29 0.02 566.67 s3 831.60 17.76 0.17 0.01 849.54 s4 528.00 9.45 0.15 0.18 537.78 s5 578.90 11.71 0.10 0.01 590.73 s6 806.10 34.65 0.80 0.02 841.57 s7 648.10 50.00 2.44 0.02 700.56 s8 522.60 13.82 0.19 0.01 536.62 s9 945.00 19.66 0.25 0.05 964.96 s10 566.70 17.78 0.33 0.01 584.82 s11 392.40 19.79 0.61 0.01 412.81 s12 695.30 18.33 0.24 0.01 713.88 s13 810.00 12.96 0.09 0.01 823.06 s14 632.40 9.78 0.06 0.00 642.25 s15 688.00 13.99 0.27 0.07 702.33 s16 938.40 13.73 0.10 0.01 952.24 s17 741.30 14.30 0.17 0.04 755.81 s18 709.60 13.85 0.14 0.00 723.59 s19 90.86 23.08 4.15 0.11 118.20 s20 98.92 16.71 2.00 0.03 117.66 s22 168.80 27.39 3.18 0.06 199.43 s23 114.70 5.84 3.70 0.07 124.30 s25 118.80 19.29 2.52 0.05 140.66 s26 s27 111.80 22.08 2.51 0.04 136.43 s28 140.00 22.81 2.50 0.04 165.34 s29 203.60 33.19 3.86 0.07 240.72 s30 145.60 12.51 0.76 0.01 158.88 s31 242.30 36.09 5.55 0.13 284.07 s32 s33 204.10 40.54 4.79 0.09 249.53 s34 168.70 28.46 3.45 0.07 200.68 s35 201.10 32.21 3.61 0.08 237.00 s37 s38 244.30 26.86 2.08 0.25 273.49
116
A.2: HAAs formation potentials for March samples
Site Number MCAA MBAA DCAA TCAA BCAA BDCAA DBAA CDBAA TBAA THAAs s1 2.82 0.67 56.69 53.10 0.00 1.74 3.07 0.23 0.00 118.32 s2 9.77 0.00 16.49 301.07 0.00 0.45 3.86 0.10 0.00 331.72 s3 3.37 0.00 191.17 334.16 0.00 0.31 2.57 0.19 0.00 531.77 s4 0.00 0.00 7.26 0.45 240.55 2.94 0.88 0.00 0.00 252.09 s5 0.00 0.00 0.00 1.16 212.61 2.77 0.67 0.00 0.00 217.21 s6 4.80 0.00 270.56 457.61 0.00 3.13 5.02 0.86 0.00 741.97 s7 3.51 0.00 184.21 245.72 0.00 4.68 7.64 0.63 4.28 450.66 s8 6.20 0.00 161.55 199.19 0.00 3.02 2.89 0.48 0.00 373.33 s9 0.00 0.00 7.91 563.72 0.00 3.47 1.75 0.54 0.00 577.38 s10 0.00 0.00 5.88 427.18 0.86 1.01 2.55 0.24 0.00 437.72 s11 26.20 0.00 2.91 0.00 271.52 1.13 2.05 0.43 0.00 304.23 s12 10.41 0.00 7.07 336.82 0.00 0.67 1.91 0.14 9.42 366.43 s13 0.00 2.76 128.72 300.02 0.00 0.00 1.30 0.00 0.00 432.80 s14 0.99 0.73 150.74 228.78 0.00 1.18 1.46 0.00 0.00 383.89 s15 2.78 3.71 194.53 253.98 0.00 0.41 2.55 0.12 0.00 458.08 s16 15.06 0.00 205.92 269.32 0.00 3.77 1.90 0.00 0.00 495.97 s17 7.72 0.00 159.95 166.72 0.00 4.10 2.25 46.91 132.49 520.14 s18 3.53 0.00 202.76 191.36 0.00 0.00 3.06 0.00 2.78 403.49 s19 6.31 0.00 58.16 27.85 12.61 6.18 4.91 1.47 0.00 117.49 s20 1.31 0.00 0.90 33.79 4.88 46.43 6.70 1.23 0.00 95.24 s22 1.32 0.00 124.21 23.79 3.11 37.03 8.43 1.59 1.17 200.64 s23 0.00 0.00 62.94 45.25 14.26 5.09 5.30 1.29 0.00 134.13 s25 1.80 0.00 124.55 15.70 3.23 42.74 6.79 1.33 0.00 196.13 s26 s27 1.12 0.00 67.50 0.00 3.88 40.27 0.75 0.00 0.00 113.52 s28 2.48 0.00 91.80 7.60 3.51 45.90 5.52 1.05 0.00 157.86 s29 5.41 17.31 77.32 46.38 14.16 4.18 4.99 0.89 0.00 170.64 s30 1.42 0.00 85.46 17.79 1.80 45.88 3.25 0.36 0.00 155.96 s31 0.35 56.10 68.25 6.96 6.42 31.05 6.01 1.55 0.00 176.68 s32 s33 0.00 0.00 11.57 47.26 5.72 51.96 9.97 1.88 0.28 128.64 s34 0.00 0.00 49.58 1.82 34.36 3.94 0.76 0.00 90.45 s35 3.63 0.00 77.04 7.07 2.50 21.10 11.74 0.93 0.00 124.01 s37 s38 0.94 0.00 96.03 18.79 0.00 35.65 5.66 0.52 1.71 159.30
117
A.3: THMs formation potentials for June samples
Site Number TCM BDCM DBCM TBM TTHMs s1 250.30 43.91 5.38 0.09 299.68 s2 548.60 28.14 0.95 0.08 577.77 s3 996.80 34.80 0.57 0.01 1032.18 s4 720.50 28.80 0.72 0.01 750.04 s5 1062.00 36.55 0.66 0.00 1099.21 s6 812.60 62.59 3.08 0.00 878.27 s7 913.80 41.19 1.09 0.00 956.08 s8 656.80 42.12 1.83 0.00 700.75 s9 1356.00 32.31 0.41 0.00 1388.72 s10 1130.00 36.82 0.72 0.12 1167.67 s11 s12 1026.00 38.58 1.37 0.72 1066.66 s13 1130.00 28.41 0.51 0.10 1159.03 s14 391.20 20.95 0.86 0.25 413.26 s15 380.70 26.95 1.37 0.06 409.07 s16 1065.00 45.25 1.38 0.17 1111.80 s17 1182.00 108.20 5.77 0.06 1296.03 s18 691.20 116.90 15.19 0.30 823.59 s19 90.91 18.64 2.56 0.11 112.23 s20 s22 48.80 3.97 0.37 0.05 53.19 s23 127.60 30.41 4.97 0.12 163.10 s25 78.22 15.55 2.13 0.00 95.90 s26 163.20 31.75 4.79 0.13 199.87 s27 130.30 25.05 3.10 0.09 158.54 s28 107.50 20.31 2.69 0.12 130.62 s29 278.00 55.52 7.86 0.19 341.57 s30 346.10 21.20 0.77 0.02 368.09 s31 125.50 26.93 4.99 0.17 157.59 s32 189.20 14.30 0.75 0.01 204.27 s33 193.30 22.37 2.10 0.02 217.79 s34 117.30 24.61 4.56 0.12 146.59 s35 96.85 24.96 6.36 0.23 128.40 s37 330.40 66.56 9.27 0.14 406.37 s38 145.30 21.37 2.56 0.06 169.29
118
A.4: HAAs formation potentials for June samples
Site Number MCAA MBAA DCAA TCAA BCAA BDCAA DBAA CDBAA TBAA THAAs s1 0.00 1.17 39.60 19.48 6.82 0.65 3.88 0.24 0.00 71.83 s2 0.00 0.15 106.97 105.90 0.00 0.81 7.64 0.13 0.00 221.60 s3 0.00 2.31 122.52 105.24 0.00 1.80 7.20 0.00 0.00 239.07 s4 0.00 4.08 107.56 98.87 0.00 2.09 6.03 0.00 0.00 218.63 s5 0.00 1.23 109.78 106.39 0.00 1.61 6.25 0.05 0.00 225.31 s6 0.00 0.00 116.14 114.97 0.00 1.80 13.34 0.80 0.00 247.05 s7 0.00 0.75 108.34 96.08 0.00 1.48 7.56 0.00 0.00 214.21 s8 0.00 2.91 99.08 89.92 0.00 1.13 8.48 0.00 0.00 201.52 s9 0.00 0.00 127.01 90.22 0.00 0.41 3.12 0.00 0.00 220.75 s10 0.00 0.17 150.56 170.23 0.00 1.50 8.32 0.13 0.00 330.91 s11 s12 0.00 1.18 101.16 77.97 0.00 1.16 5.44 0.00 0.00 186.90 s13 0.00 0.00 111.74 83.65 0.00 1.27 4.63 0.00 0.00 201.28 s14 0.00 1.40 63.98 34.17 0.00 0.28 2.51 0.00 0.00 102.33 s15 0.00 1.91 74.87 49.75 0.00 0.35 3.98 0.00 0.00 130.86 s16 0.00 0.17 99.44 90.52 0.00 1.21 6.25 0.00 0.00 197.58 s17 0.00 0.00 107.85 82.83 0.00 1.66 12.17 0.35 0.00 204.86 s18 0.00 3.08 85.10 61.92 15.36 2.46 16.17 1.73 0.00 185.82 s19 0.00 0.00 61.59 39.92 8.77 1.39 10.28 1.08 0.00 123.04 s20 s22 0.00 1.17 28.60 12.66 0.00 0.00 1.54 0.00 0.00 43.98 s23 0.00 0.74 67.22 46.27 12.24 1.89 13.55 1.78 0.00 143.69 s25 0.00 0.00 43.39 32.36 8.07 0.95 6.70 0.83 0.00 92.30 s26 0.00 1.74 46.55 26.02 1.60 1.82 7.65 0.66 0.00 86.04 s27 0.00 1.75 58.70 41.46 10.95 1.47 9.96 0.93 0.00 125.22 s28 0.00 1.37 32.10 15.10 5.10 0.58 3.03 0.22 0.00 57.50 s29 0.00 0.00 53.44 32.47 7.33 1.37 10.28 0.98 0.00 105.86 s30 0.00 0.00 58.11 43.41 0.00 0.31 3.58 0.00 0.00 105.40 s31 0.00 0.00 38.50 26.33 8.55 1.12 7.30 0.92 0.00 82.72 s32 0.00 0.00 66.00 53.00 0.00 0.54 5.69 0.00 0.00 125.23 s33 0.00 0.19 35.57 29.03 0.00 0.26 3.41 0.00 0.00 68.47 s34 0.00 3.11 33.90 14.77 4.35 0.83 4.98 0.49 0.00 62.42 s35 0.00 2.93 36.65 18.24 8.03 1.73 8.27 1.24 0.00 77.10 s37 0.00 0.67 44.90 31.39 8.81 1.15 9.09 1.20 0.00 97.21 s38 0.00 0.00 34.94 27.41 0.00 0.59 4.77 0.38 0.00 68.10
119 A.5: THMs formation potentials for September samples
Site Number TCM BDCM DBCM TBM TTHMs s1 77.61 22.88 5.74 0.23 106.46 s2 184.60 16.13 1.02 0.03 201.78 s3 739.10 12.54 0.14 0.01 751.78 s4 377.70 9.40 0.19 0.01 387.30 s5 269.00 22.23 1.39 0.04 292.66 s6 s7 s8 140.30 15.75 1.61 0.07 157.73 s9 248.00 16.89 0.92 0.02 265.83 s10 325.80 18.78 0.92 0.16 345.66 s11 s12 151.30 11.07 0.63 0.03 163.03 s13 248.10 14.22 1.14 0.01 263.46 s14 219.30 10.67 1.08 0.01 231.05 s15 s16 60.58 2.68 0.22 0.02 63.50 s17 197.40 20.66 1.81 0.05 219.92 s18 271.50 26.12 4.41 0.07 302.10 s19 110.50 23.18 4.15 0.09 137.92 s20 s22 s23 285.90 29.11 5.00 0.08 320.09 s25 s26 s27 229.80 31.19 3.44 0.07 264.50 s28 157.20 22.08 2.56 0.04 181.88 s29 119.30 28.29 5.15 0.17 152.91 s30 545.00 15.88 0.38 0.05 561.30 s31 260.70 25.80 2.95 0.04 289.50 s32 s33 259.70 21.74 1.41 0.03 282.88 s34 200.30 12.36 2.33 0.03 215.02 s35 169.40 37.90 6.29 0.15 213.74 s37 397.90 19.88 0.63 0.00 418.41 s38 380.60 27.87 1.33 0.05 409.84
120
A.6: HAAs formation potentials for September samples
Site Number MCAA MBAA DCAA TCAA BCAA BDCAA DBAA CDBAA TBAA THAAs s1 2.71 22.46 30.98 44.03 11.68 3.57 11.89 1.93 0.00 129.24 s2 1.19 4.72 86.01 132.20 0.87 0.82 9.99 0.80 0.00 236.60 s3 0.00 40.72 243.09 760.18 0.00 1.77 8.08 0.23 0.00 1054.07 s4 1.69 1.54 107.99 146.44 0.00 0.57 3.41 0.78 0.00 262.42 s5 2.93 1.47 90.21 103.11 0.00 1.17 10.77 0.66 0.00 210.32 s6 s7 s8 3.86 18.24 91.95 98.21 0.00 1.81 11.41 1.17 0.00 226.64 s9 0.00 11.73 93.18 97.85 0.00 1.07 7.01 0.12 0.00 210.95 s10 3.64 1.24 110.19 150.85 0.00 0.91 9.15 0.21 0.00 276.19 s11 s12 3.31 4.04 77.42 83.77 0.00 0.54 5.31 0.00 0.00 174.40 s13 2.45 17.88 53.73 74.23 0.72 0.58 4.62 0.05 0.00 154.27 s14 0.19 4.36 57.96 37.03 0.00 0.47 6.38 0.10 0.00 106.49 s15 s16 3.17 2.19 100.28 72.97 0.00 0.89 6.45 0.32 0.00 186.27 s17 4.23 0.00 93.56 181.50 0.00 1.16 12.04 0.46 0.00 292.95 s18 0.00 6.98 78.25 97.20 0.00 3.22 1.24 11.55 0.94 199.38 s19 0.00 13.14 50.24 14.05 1.93 1.72 9.87 0.59 0.00 91.55 s20 s22 s23 0.84 14.63 62.26 93.64 2.46 1.74 11.00 1.01 0.12 187.69 s25 s26 s27 0.00 3.77 82.21 96.18 3.37 1.40 11.32 0.52 0.00 198.77 s28 2.58 17.45 51.09 72.50 2.05 1.27 8.73 0.49 0.00 156.16 s29 2.94 0.74 55.05 50.38 13.63 1.92 9.47 0.79 0.00 134.93 s30 0.00 s31 1.76 7.08 81.28 83.94 1.17 1.15 8.70 0.56 0.00 185.63 s32 s33 s34 3.49 19.91 59.23 83.01 0.00 0.87 9.69 0.64 0.00 176.85 s35 2.50 12.36 37.25 46.95 4.37 1.73 7.32 0.68 0.00 113.16 s37 0.00 5.64 101.04 106.38 0.00 0.44 5.63 0.21 0.00 219.34 s38 0.00 1.10 118.24 32.15 0.00 0.85 10.93 0.49 0.00 163.76 Note: Units are in microgram per liter or ppb (parts per billion)
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A.7 Legend of the Geologic Map (Figure 12)
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A.8 Map of Missouri Hydrography
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LIST OF ABBREVIATIONS AND ACRONYMS
AWWA American Water Works Association AOX Absorbable Organic halides BCAA Bromochloroacetic acid BDCAA Bromodichloroacetic acids BDCM Bromodichloromethanes CDBAA Chlorodibromoacetic acid CDBM Chlorodibromomethane C13NMR Carbon-13 Nuclear Magnetic Resonance
CO2 Carbon dioxide -COOH Carboxyl group -COOR Ester
Cl2 Chlorine Da Daltons DBCAA Dibromochloroacetic acids DBCM Dibromochloromethanes DBPs Disinfection By-Products DBPFP Disinfection By-Products Formation Potential DCAA Dichloroacetic acid DI Deionized DIUF Deionized Ultrafiltered DOC Dissolved Organic Carbon DOM Dissolved Organic Matter DNA Deoxyribonucleic acid ECP Extra-Cellular Products EEM Excitation Emission Matrix Em Emission Ex Excitation FI Fluorescence Index
124 FRI Fluorescence Regional Integration FTIR Fourier Transform Infra-Red GC Gas Chromatography GC/MS Gas Chromatography/Mass Spectrometry H+ Hydrogen Ion HAAs Halogenated Acetic Acids
HAA5 Halogenated Acetic Acids (Five species) HAAFP Halogenated Acetic Acids Formation Potential THAAs Total haloacetic acids
H2O Water HOCl Hypochloric Acid HCl Hydrochloric Acid µg/l Micrograms per liter µg/ml Micrograms per mililiter MBAA Monobromoacetic acids MCAA Monochloroacetic acids MCL Maximum Contamination Level MTBE Metyl Tertialry Butyl Ether NOM Natural Organic matter
-NH2 Amine group nm nanometer
-NO2 Amide group NaOH Sodium hydroxide OCl- Hypochlorite ion -OH Alcohol group -OR Ether % Percent PARAFAC Parallel Factor Analysis pH Potential of Hydrogen POC Particulate Organic Carbon ppb Parts per billion
125 -R Alkyl group R2 Coefficient of Determination RG Region SEC Size Exclusion Chromatography SDWA Safe Drinking Water Act SUVA Specific Ultra-Violet Absorbance TBAA Tribromoacetic acids TBM Tribromomethanes TCAA Trichloroacetic Acids TCAC Trichloroacetone TCM Trichloromethanes TFC Thin film composite THMs Trihalomethanes THMFP Trihalomethanes Formation Potential TOC Total Organic Carbon TOX Total Organohalides TTHM Total Trihalomethanes TTHMFP Total Trihalomethanes Formation Potential UFC Uniform Formation Conditions US United States USGS United States Geological Survey USEPA United States Environmental Protection Agency UV Ultra-Violet
UVA254 Ultra-Violet Absorbance at 254 nanometer wavelength -X Halogen group XAD Brand of Resins ∫ Integral ∑ Sum ≈ Approximately equal to ≥ Greater than or equal to ≤ Less than or equal to
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